From 316430f805acba0ab9ebf5b8256b93ee74417f2c Mon Sep 17 00:00:00 2001 From: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:58:08 +0800 Subject: [PATCH 001/119] [None] [waive] Waive the failed step3p7 test case due to ckpt update (#14997) Signed-off-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 347ed4313ee6..454e249fbb16 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -322,6 +322,7 @@ triton_server/test_triton_llm.py::test_llmapi_backend[4-0-disableDecoupleMode-te triton_server/test_triton_llm.py::test_mistral_v1_multi_models[False-1---False-True-False-0-128-enableDecoupleMode-inflight_fused_batching-disableTrtOverlap--max_utilization-4096--1-1-1-False-ensemble] SKIP triton_server/test_triton_rcca.py::test_mistral_beam_search[rcca_4714407-True-10---False-True-False-0-128-disableDecoupleMode-inflight_fused_batching-disableTrtOverlap--guaranteed_no_evict---1-1-1-False-ensemble] SKIP (https://nvbugs/5240060) triton_server/test_triton_rcca.py::test_rcca_bug_4934893[Temperature:0.5-TOP_P:0.95-TOP_K:10-False-1---False-True-False-0-2048-enableDecoupleMode-inflight_fused_batching-disableTrtOverlap--max_utilization---1-1-1-False-ensemble] SKIP (https://nvbugs/5619369) +unittest/_torch/modeling/test_modeling_step3p7.py::TestStep3p7Checkpoint::test_config_and_weight_accounting_1_nvfp4 SKIP unittest/_torch/modules/moe/test_moe_backend.py::test_moe_backend[act=Relu2-e60_k4_h2048_i1408-seq=8-dtype=torch.bfloat16-backend=TRTLLM-quant=NVFP4-routing=Renormalize] SKIP (https://nvbugs/5989912) unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_multi_gpu[parallel=DEP-comm=IGNORE-e8_k1_h512_i512-seq=8-dtype=torch.bfloat16-backend=MEGAMOE_DEEPGEMM-quant=W4A8_MXFP4_MXFP8-routing=DeepSeekV3] SKIP (https://nvbugs/6175060) unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_multi_gpu_eplb -k "MEGAMOE_DEEPGEMM" SKIP (https://nvbugs/6175060) From d5de55ea4440727cb2ee13a8f6316a8a67d17c0f Mon Sep 17 00:00:00 2001 From: xiweny <13230610+VALLIS-NERIA@users.noreply.github.com> Date: Fri, 5 Jun 2026 15:42:41 +0800 Subject: [PATCH 002/119] [https://nvbugs/6210714][fix] Fix mamba block calculation (#14524) Signed-off-by: Xiwen Yu <13230610+VALLIS-NERIA@users.noreply.github.com> Signed-off-by: xiweny <13230610+VALLIS-NERIA@users.noreply.github.com> --- .../_torch/pyexecutor/mamba_cache_manager.py | 2 - .../_torch/pyexecutor/resource_manager.py | 20 ++++---- tests/integration/test_lists/waives.txt | 1 - .../executor/test_mamba_cache_manager.py | 46 +++++++++++++++++-- 4 files changed, 51 insertions(+), 18 deletions(-) diff --git a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py index 58435ce36516..7f1af74d4c83 100644 --- a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py @@ -1712,8 +1712,6 @@ def _prepare_resources(self, scheduled_batch: ScheduledRequests): # we skip refresh_blocks entirely when nothing was scheduled. self._pending_state_transfers = self.impl.copy_linear_attention_block_batch( self.requests) - if self._pending_state_transfers: - logger.info(f"Need to transfer mamba state blocks") self._setup_state_indices() num_contexts = len(scheduled_batch.context_requests) if num_contexts > 0: diff --git a/tensorrt_llm/_torch/pyexecutor/resource_manager.py b/tensorrt_llm/_torch/pyexecutor/resource_manager.py index 86fbbcc87da3..ed829639a908 100644 --- a/tensorrt_llm/_torch/pyexecutor/resource_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/resource_manager.py @@ -734,10 +734,9 @@ def append_to_kv_heads_per_layer(num_kv_heads_per_layer: List[int], live_state_slots = self.max_batch_size * pp_size max_snapshots = live_state_slots if kv_cache_config.enable_block_reuse: - max_snapshots = max( + max_snapshots += ( kv_cache_config.max_tokens // - linear_attention_metadata.states_snapshot_interval, - live_state_slots) + linear_attention_metadata.states_snapshot_interval) blocks_per_window[LinearCacheType.RECURRENT_STATES.value] = ( int(max_snapshots), 0) @@ -2035,16 +2034,13 @@ def _calculate_max_num_blocks_for_linear_attention( pp_size = self.mapping.pp_size if self.mapping is not None else 1 intercept = self.max_batch_size * pp_size * state_bytes_local - # heuristic: When block reuse is enabled, we assume the mamba snapshots are dominant instead of active states, - # otherwise we may run out of kv cache blocks prior to mamba blocks due to the large number of max_batch_size. - # So we ignore intercept and only calculate max_tokens based on slope - # This can be improved by a more accurate max_batch_size and ISL/OSL estimation in the future. - if mamba_slope > 0: - max_tokens = max((primary_budget) // slope, 0) - else: - max_tokens = max((primary_budget - intercept) // slope, 0) + max_tokens = max((primary_budget - intercept) // slope, 0) if kv_cache_config.max_tokens is not None: max_tokens = min(kv_cache_config.max_tokens, max_tokens) + if max_tokens < kv_cache_config.max_tokens: + logger.warning( + f'The memory budget for Mamba + KV cache cannot fit the user-specified max_tokens of {kv_cache_config.max_tokens}. The calculated max_tokens based on the memory budget is {max_tokens}. Please consider adjusting max_batch_size/max_tokens/mamba_state_cache_interval.' + ) kv_blocks_in_primary_pool = int(max_tokens // self.tokens_per_block) @@ -2067,7 +2063,7 @@ def _calculate_max_num_blocks_for_linear_attention( max_snapshots += self.spec_config.max_draft_len if (kv_cache_config.enable_block_reuse and interval is not None and interval > 0): - max_snapshots = max(max_tokens // interval, max_snapshots) + max_snapshots += max_tokens // interval secondary_snapshots = int(max_snapshots * (self._secondary_pool_memory_bytes / diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 454e249fbb16..bea7163e19f4 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -135,7 +135,6 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_sof accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.5-fp8kv=True] SKIP (https://nvbugs/6248783) accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=False] SKIP (https://nvbugs/6260915) accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_fp8[enable_block_reuse=False] SKIP (https://nvbugs/6212252) -accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_fp8[enable_block_reuse=True] SKIP (https://nvbugs/6210714) accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl] SKIP (https://nvbugs/6255417) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_off] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_on] SKIP (https://nvbugs/6212250) diff --git a/tests/unittest/_torch/executor/test_mamba_cache_manager.py b/tests/unittest/_torch/executor/test_mamba_cache_manager.py index 85bbc5e0d68b..030f5d77f721 100644 --- a/tests/unittest/_torch/executor/test_mamba_cache_manager.py +++ b/tests/unittest/_torch/executor/test_mamba_cache_manager.py @@ -290,7 +290,13 @@ def test_cpp_get_state_indices_resolves_sentinel_to_reserved_slot(): # --------------------------------------------------------------------------- -def _build_hybrid_with_mamba_layer(spec_config=None, max_batch_size=4, enable_block_reuse=False): +def _build_hybrid_with_mamba_layer( + spec_config=None, + max_batch_size=4, + enable_block_reuse=False, + mamba_state_cache_interval=256, + is_estimating_kv_cache=False, +): """Construct a real CppMambaHybridCacheManager with one mamba layer + one full-attention layer so the parent KVCacheManager goes through the linear-attention pool sizing path.""" @@ -299,7 +305,11 @@ def _build_hybrid_with_mamba_layer(spec_config=None, max_batch_size=4, enable_bl attn_mask = [False, True] mapping = Mapping(world_size=1, rank=0, tp_size=1, pp_size=1) # Cap max_tokens to keep the real C++ pool allocation tiny. - kv_cache_config = KvCacheConfig(max_tokens=512, enable_block_reuse=enable_block_reuse) + kv_cache_config = KvCacheConfig( + max_tokens=512, + enable_block_reuse=enable_block_reuse, + mamba_state_cache_interval=mamba_state_cache_interval, + ) return CppMambaHybridCacheManager( mamba_d_state=8, mamba_d_conv=4, @@ -321,6 +331,7 @@ def _build_hybrid_with_mamba_layer(spec_config=None, max_batch_size=4, enable_bl mapping=mapping, spec_config=spec_config, layer_mask=attn_mask, + is_estimating_kv_cache=is_estimating_kv_cache, ) @@ -449,7 +460,10 @@ def test_cpp_hybrid_recurrent_pool_floor_with_block_reuse(): """ max_batch_size = 4 mgr = _build_hybrid_with_mamba_layer( - spec_config=None, max_batch_size=max_batch_size, enable_block_reuse=True + spec_config=None, + max_batch_size=max_batch_size, + enable_block_reuse=True, + mamba_state_cache_interval=256, ) recurrent_primary, _ = mgr.blocks_per_window[LinearCacheType.RECURRENT_STATES.value] assert recurrent_primary >= max_batch_size + 1, ( @@ -459,6 +473,32 @@ def test_cpp_hybrid_recurrent_pool_floor_with_block_reuse(): ) +@skip_no_cuda +def test_cpp_hybrid_dry_run_recurrent_pool_additive_with_block_reuse(): + """Dry-run path (is_estimating_kv_cache=True) under block reuse must + keep the live-state floor *plus* room for snapshots, not collapse to + max(snapshots, live). With max_batch_size=4, interval=256, max_tokens=512: + old: max_snapshots = max(512//256, 4) = 4 (no headroom for snapshots) + new: max_snapshots = 4 + 512//256 = 6 (live + snapshots) + """ + max_batch_size = 4 + mgr = _build_hybrid_with_mamba_layer( + spec_config=None, + max_batch_size=max_batch_size, + enable_block_reuse=True, + mamba_state_cache_interval=256, + is_estimating_kv_cache=True, + ) + recurrent_primary, _ = mgr.blocks_per_window[LinearCacheType.RECURRENT_STATES.value] + # 4 live state slots + 2 reuse snapshots = 6. + expected_min = max_batch_size + (512 // 256) + assert recurrent_primary >= expected_min, ( + f"dry-run recurrent-state pool has {recurrent_primary} slots, " + f"need >= live_state + reuse_snapshots = {expected_min}; the old " + f"max(reuse, live) formula dropped reuse headroom" + ) + + # --------------------------------------------------------------------------- # CppMambaHybridCacheManager: rank with zero local mamba layers # From 681823375294270bdc681ebff01cd5a9a15f5d08 Mon Sep 17 00:00:00 2001 From: xinhe-nv <200704525+xinhe-nv@users.noreply.github.com> Date: Fri, 5 Jun 2026 16:01:44 +0800 Subject: [PATCH 003/119] =?UTF-8?q?[https://nvbugs/5546507][https://nvbugs?= =?UTF-8?q?/5612313][test]=20Remove=20obsolet=E2=80=A6=20(#14995)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Xin He (SW-GPU) <200704525+xinhe-nv@users.noreply.github.com> --- tests/integration/defs/examples/test_eagle.py | 53 +-------- tests/integration/defs/examples/test_phi.py | 110 +----------------- .../test_lists/test-db/l0_h100.yml | 1 - tests/integration/test_lists/waives.txt | 1 - 4 files changed, 5 insertions(+), 160 deletions(-) diff --git a/tests/integration/defs/examples/test_eagle.py b/tests/integration/defs/examples/test_eagle.py index 0d03d9af5f76..0385edaff354 100644 --- a/tests/integration/defs/examples/test_eagle.py +++ b/tests/integration/defs/examples/test_eagle.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -15,7 +15,7 @@ import pytest from defs.common import convert_weights, venv_check_call -from defs.conftest import get_sm_version, skip_post_blackwell, skip_pre_ada +from defs.conftest import get_sm_version, skip_post_blackwell from defs.trt_test_alternative import check_call # skip trt flow cases on post-Blackwell-Ultra @@ -94,52 +94,3 @@ def test_llm_eagle_1gpu(batch_size, data_type, use_dynamic_tree, [f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"]) venv_check_call(llm_venv, summary_cmd) - - -# TODO: remove skip_post_blackwell after Speculative decoding is supported. -@skip_post_blackwell -@skip_pre_ada -@pytest.mark.parametrize("batch_size", [8], ids=['bs8']) -@pytest.mark.parametrize("data_type", ['float16']) -@pytest.mark.parametrize("eagle_model_roots", ["llama3.1-eagle-8b-hf_v0.5"], - indirect=True) -def test_llm_eagle_1gpu_modelopt_ckpt(batch_size, data_type, eagle_model_roots, - eagle_example_root, llm_datasets_root, - llm_rouge_root, llm_venv, cmodel_dir, - engine_dir): - print("Build engines...") - model_name = "eagle" - - # Although the datatype is float16, the actual weights are FP8. - # The datatype in the convert stage is used for the input and output of the plugin. - - model_dir = convert_weights(llm_venv=llm_venv, - example_root=eagle_example_root, - cmodel_dir=cmodel_dir, - model=model_name, - model_path=eagle_model_roots, - data_type=data_type) - - build_cmd = [ - "trtllm-build", - f"--checkpoint_dir={model_dir}", - f"--output_dir={engine_dir}", - f"--max_beam_width=1", - "--use_paged_context_fmha=enable", - f"--max_batch_size={batch_size}", - "--speculative_decoding_mode=eagle", - "--multiple_profiles=enable" # also test multiple_profiles - ] - - check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) - - print("Run run...") - - run_cmd = [ - f"{eagle_example_root}/../run.py", f"--engine_dir={engine_dir}", - f"--tokenizer_dir={eagle_model_roots}", - "--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]", - "--max_output_len=100" - ] - - venv_check_call(llm_venv, run_cmd) diff --git a/tests/integration/defs/examples/test_phi.py b/tests/integration/defs/examples/test_phi.py index 8a414137c1bd..62f67e4bae65 100644 --- a/tests/integration/defs/examples/test_phi.py +++ b/tests/integration/defs/examples/test_phi.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,15 +12,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import csv import os import defs.ci_profiler import pytest -from defs.common import (convert_weights, quantize_data, - test_llm_torch_multi_lora_support, venv_check_call) -from defs.conftest import (get_sm_version, skip_fp8_pre_ada, - skip_post_blackwell, skip_pre_ada) +from defs.common import test_llm_torch_multi_lora_support, venv_check_call +from defs.conftest import get_sm_version, skip_post_blackwell, skip_pre_ada from defs.trt_test_alternative import check_call # skip trt flow cases on post-Blackwell-Ultra @@ -42,107 +39,6 @@ def phi_example_root(llm_root, llm_venv): return example_root -@pytest.mark.parametrize("data_type", ["float16", "fp8"], - ids=["base_fp16", "base_fp8"]) -@pytest.mark.parametrize("lora_data_type", ["float16"], ids=["lora_fp16"]) -@pytest.mark.parametrize("llm_phi_model_root", ["Phi-3-mini-4k-instruct"], - indirect=True) -@pytest.mark.parametrize("llm_lora_model_root", - ["Phi-3-mini-4k-instruct-ru-lora"], - indirect=True) -def test_llm_phi_lora_1gpu(data_type, lora_data_type, phi_example_root, - llm_phi_model_root, llm_datasets_root, llm_venv, - cmodel_dir, engine_dir, llm_lora_model_root, - qcache_dir_without_install_package): - "run phi lora test on 1gpu" - print("Converting checkpoint...") - model_name = 'phi-3-lora' - if data_type == 'fp8': - skip_fp8_pre_ada(use_fp8=True) - if get_sm_version() >= 100: - pytest.skip("FP8 is not supported on post-Blackwell architectures") - model_dir = quantize_data( - llm_venv, - phi_example_root, - model_dir=llm_phi_model_root, - calib_dataset=f"{llm_datasets_root}/cnn_dailymail", - dtype="float16", - qformat="fp8", - kv_cache_dtype="fp8", - quantize_dir=qcache_dir_without_install_package, - calib_size=512) - else: - model_dir = convert_weights(llm_venv=llm_venv, - example_root=phi_example_root, - cmodel_dir=cmodel_dir, - model=model_name, - model_path=llm_phi_model_root) - - print("Build engines...") - build_cmd = [ - "trtllm-build", - f"--checkpoint_dir={model_dir}", - f"--output_dir={engine_dir}", - "--lora_plugin=auto", - "--gemm_plugin=auto", - "--max_batch_size=8", - f"--lora_dir={llm_lora_model_root}", - ] - check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) - - ref_1 = [ - 1, 1815, 366, 3867, 5837, 304, 17545, 18240, 310, 9892, 16397, 322, - 8338, 265, 29888, 21211, 29973, 306, 29915, 29885, 3063, 363, 907, 1230, - 322, 9045, 29891, 9522, 5547, 393, 11039, 403, 1716, 285, 21211, 29889, - 29871 - ] - - ref_2 = [ - 1815, 366, 3867, 5837, 304, 17545, 18240, 310, 9892, 16397, 322, 8338, - 265, 29888, 21211, 29973, 13, 13, 7900, 22137, 29901, 315, 13946, 368, - 29991, 2266, 526, 777, 907, 1230, 5837, 304, 13389, 9892, 16397, 322 - ] - - input_text = "Can you provide ways to eat combinations of bananas and dragonfruits?" - - print(f"Run inference with lora id 0...") - venv_check_call(llm_venv, [ - f"{phi_example_root}/../../../run.py", - "--max_output_len=20", - f"--input_text={input_text}", - "--lora_task_uids=0", - f"--tokenizer_dir={llm_lora_model_root}", - f"--engine_dir={engine_dir}", - f"--output_csv={llm_venv.get_working_directory()}/use_lora.csv", - "--use_py_session", - ]) - - with open(f"{llm_venv.get_working_directory()}/use_lora.csv") as f: - predict = csv.reader(f) - predict = next(predict) - predict = [int(p) for p in predict] - assert ref_1 == predict or data_type != "float16" - - print(f"Run inference with lora id -1...") - venv_check_call(llm_venv, [ - f"{phi_example_root}/../../../run.py", - "--max_output_len=20", - f"--input_text={input_text}", - "--lora_task_uids=-1", - f"--tokenizer_dir={llm_phi_model_root}", - f"--engine_dir={engine_dir}", - f"--output_csv={llm_venv.get_working_directory()}/no_lora.csv", - "--use_py_session", - ]) - - with open(f"{llm_venv.get_working_directory()}/no_lora.csv") as f: - predict = csv.reader(f) - predict = next(predict) - predict = [int(p) for p in predict] - - assert ref_2 == predict or data_type != "float16" - - @skip_pre_ada @pytest.mark.parametrize("data_type", ['float16', 'bfloat16']) @pytest.mark.parametrize("qformat", ['fp8']) diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index f2dfacf22389..ff45caf899f7 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -391,7 +391,6 @@ l0_h100: backend: tensorrt tests: # ------------- TRT tests --------------- - - examples/test_eagle.py::test_llm_eagle_1gpu_modelopt_ckpt[llama3.1-eagle-8b-hf_v0.5-float16-bs8] # 9 mins - examples/test_eagle.py::test_llm_eagle_1gpu[EAGLE-Vicuna-7B-v1.3-float16-bs1-eagle1] - examples/test_eagle.py::test_llm_eagle_1gpu[EAGLE-Vicuna-7B-v1.3-float16-bs1-eagle2] # 5 mins - accuracy/test_llm_api.py::TestMistral_NeMo_Minitron_8B_Instruct::test_fp8 diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index bea7163e19f4..3008547e0013 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -191,7 +191,6 @@ examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padd examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padding-use_attention_plugin-enable_context_fmha-tp:2-pp:1-float16-RobertaForQuestionAnswering-bert/roberta-base-squad2] SKIP (https://nvbugs/5234058) examples/test_eagle.py::test_llm_eagle_1gpu[EAGLE-Vicuna-7B-v1.3-float16-bs1-eagle1] SKIP (https://nvbugs/5546507) examples/test_eagle.py::test_llm_eagle_1gpu[EAGLE-Vicuna-7B-v1.3-float16-bs1-eagle2] SKIP (https://nvbugs/5546507) -examples/test_eagle.py::test_llm_eagle_1gpu_modelopt_ckpt[llama3.1-eagle-8b-hf_v0.5-float16-bs8] SKIP (https://nvbugs/5546507) examples/test_gpt.py::test_llm_minitron_fp8_with_pseudo_loras[4b] SKIP (https://nvbugs/5606233) examples/test_granite.py::test_granite_bf16_lora[granite-3.0-1b-a400m-instruct] SKIP (https://nvbugs/5431132) examples/test_granite.py::test_llm_granite[granite-3.0-1b-a400m-instruct-bfloat16] SKIP (https://nvbugs/5608979) From fdcdcb3605adb7c84ec3d06163dd67dac48d475a Mon Sep 17 00:00:00 2001 From: Bala Marimuthu <246387390+bmarimuthu-nv@users.noreply.github.com> Date: Fri, 5 Jun 2026 01:09:37 -0700 Subject: [PATCH 004/119] [None][fix] AutoDeploy: Move hf_id_to_local_model_dir to function for GLM4.7 Flash test (#14999) Signed-off-by: Balamurugan Marimuthu <246387390+bmarimuthu-nv@users.noreply.github.com> --- .../defs/accuracy/test_llm_api_autodeploy.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py index d266e0467265..3c76f81ebe4e 100644 --- a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py +++ b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py @@ -876,8 +876,8 @@ class TestGLM4Flash(LlmapiAccuracyTestHarness): """Accuracy regression tests for GLM-4.7-Flash variants.""" MODEL_NAME = "GLM-4.7-Flash" - MODEL_PATH_BF16 = hf_id_to_local_model_dir("zai-org/GLM-4.7-Flash") - MODEL_PATH_NVFP4 = hf_id_to_local_model_dir("DeepInfra/GLM-4.7-Flash-NVFP4") + MODEL_HF_ID_BF16 = "zai-org/GLM-4.7-Flash" + MODEL_HF_ID_NVFP4 = "DeepInfra/GLM-4.7-Flash-NVFP4" # Set minimum possible seq len + small buffer, for test speed & memory usage MAX_SEQ_LEN = max(MMLU.MAX_INPUT_LEN + MMLU.MAX_OUTPUT_LEN, @@ -939,8 +939,8 @@ def get_default_sampling_params(self): def test_auto_dtype(self, enable_chunked_prefill, attn_backend): kwargs = self.get_default_kwargs(enable_chunked_prefill, attn_backend) sampling_params = self.get_default_sampling_params() - with AutoDeployLLM(model=self.MODEL_PATH_BF16, - tokenizer=self.MODEL_PATH_BF16, + model_path = hf_id_to_local_model_dir(self.MODEL_HF_ID_BF16) + with AutoDeployLLM(model=model_path, tokenizer=model_path, **kwargs) as llm: task = MMLU(self.MODEL_NAME) task.evaluate(llm, sampling_params=sampling_params) @@ -953,8 +953,8 @@ def test_auto_dtype(self, enable_chunked_prefill, attn_backend): def test_nvfp4(self, enable_chunked_prefill): kwargs = self.get_default_kwargs(enable_chunked_prefill) sampling_params = self.get_default_sampling_params() - with AutoDeployLLM(model=self.MODEL_PATH_NVFP4, - tokenizer=self.MODEL_PATH_NVFP4, + model_path = hf_id_to_local_model_dir(self.MODEL_HF_ID_NVFP4) + with AutoDeployLLM(model=model_path, tokenizer=model_path, **kwargs) as llm: # Manually set quant_config for NVFP4 model to get the accuracy threshold llm.args.quant_config.quant_algo = QuantAlgo.NVFP4 From 6387eac1553267b2e6e01721f1232239a72d6c81 Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Fri, 5 Jun 2026 18:40:53 +0800 Subject: [PATCH 005/119] [TRTLLM-12893][infra] Parallelize post stages: Rerun Report, Test Coverage, and AI Failure Analysis (#14528) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) --- jenkins/L0_MergeRequest.groovy | 338 ++++++++++++++++++--------------- 1 file changed, 182 insertions(+), 156 deletions(-) diff --git a/jenkins/L0_MergeRequest.groovy b/jenkins/L0_MergeRequest.groovy index 27d2e1bef8e6..8c845ba32b2a 100644 --- a/jenkins/L0_MergeRequest.groovy +++ b/jenkins/L0_MergeRequest.groovy @@ -1096,10 +1096,11 @@ def getOnlyOneGroupChanged(pipeline, testFilter, globalVars) { return "" } -def collectTestResults(pipeline, testFilter) +def collectTestResults(pipeline, testFilter, globalVars) { collectResultPodSpec = createKubernetesPodConfig("", "agent") trtllm_utils.launchKubernetesPod(pipeline, collectResultPodSpec, "alpine", { + // 1. Serial: download tarballs, extract, and run junit stage ("Collect Test Result") { sh "rm -rf **/*.xml *.tar.gz" @@ -1129,80 +1130,192 @@ def collectTestResults(pipeline, testFilter) } junit(testResults: '**/results*.xml', allowEmptyResults : true) - } // Collect test result stage - stage("Rerun Report") { - sh "rm -rf rerun && mkdir -p rerun" - sh "find . -type f -wholename '*/rerun_results.xml' -exec sh -c 'mv \"{}\" \"rerun/\$(basename \$(dirname \"{}\"))_rerun_results.xml\"' \\; || true" - sh "find rerun -type f" - def rerunFileCount = sh(returnStdout: true, script: 'find rerun -type f | wc -l').replaceAll("\\s","").toInteger() - if (rerunFileCount == 0) { - echo "Rerun report is skipped because there is no rerun test data file." - return - } - def xmlFiles = findFiles(glob: 'rerun/**/*.xml') - def xmlFileList = xmlFiles.collect { it.path } - def inputfiles = xmlFileList.join(',') - echo "inputfiles: ${inputfiles}" - trtllm_utils.llmExecStepWithRetry(pipeline, script: "apk add python3") + + // Pre-install shared dependencies for parallel tasks trtllm_utils.llmExecStepWithRetry(pipeline, script: "apk add py3-pip") trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 config set global.break-system-packages true") - sh """ - python3 llm/jenkins/scripts/test_rerun.py \ - generate_rerun_report \ - --output-file=rerun/rerun_report.xml \ - --input-files=${inputfiles} - """ - trtllm_utils.uploadArtifacts("rerun/rerun_report.html", "${UPLOAD_PATH}/test-results/") - echo "Rerun report: https://urm.nvidia.com/artifactory/${UPLOAD_PATH}/test-results/rerun_report.html" - catchError( - buildResult: 'SUCCESS', - stageResult: 'UNSTABLE') { - error "Some failed tests were reruned, please check the rerun report." - } - } // Rerun report stage - try { - stage("Test Coverage") { - sh "ls" - def CUR_PATH = sh(returnStdout: true, script: 'pwd').replaceAll("\\s","") - sh "echo ${CUR_PATH}" - sh "rm -rf cov && mkdir -p cov" - sh "find . -type f -wholename '*/.coverage.*' -exec mv {} cov/ \\; || true" - sh "cd cov && find . -type f" - def fileCount = sh(returnStdout: true, script: 'find cov -type f | wc -l').replaceAll("\\s","").toInteger() - if (fileCount == 0) { - echo "Test coverage is skipped because there is no test data file." + } // Collect test result stage + + // 2. Parallel: Rerun Report, Test Coverage, and AI Failure Analysis + def parallelTasks = [:] + parallelTasks["Rerun Report"] = { + try { + timeout(time: 10, unit: 'MINUTES') { + stage("Rerun Report") { + sh "rm -rf rerun && mkdir -p rerun" + sh "find . -type f -wholename '*/rerun_results.xml' -exec sh -c 'mv \"{}\" \"rerun/\$(basename \$(dirname \"{}\"))_rerun_results.xml\"' \\; || true" + sh "find rerun -type f" + def rerunFileCount = sh(returnStdout: true, script: 'find rerun -type f | wc -l').replaceAll("\\s","").toInteger() + if (rerunFileCount == 0) { + echo "Rerun report is skipped because there is no rerun test data file." return } - trtllm_utils.llmExecStepWithRetry(pipeline, script: "apk add py3-pip") - trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 config set global.break-system-packages true") - trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install coverage") - sh "coverage --version" - - sh "cp llm/examples/openai_triton/manual_plugin/fmha_triton.py llm/examples/openai_triton/plugin_autogen/" - def coverageConfigFile = "cov/.coveragerc" + def xmlFiles = findFiles(glob: 'rerun/**/*.xml') + def xmlFileList = xmlFiles.collect { it.path } + def inputfiles = xmlFileList.join(',') + echo "inputfiles: ${inputfiles}" sh """ - echo '[paths]' > ${coverageConfigFile} - echo 'source1=\n ${CUR_PATH}/llm/examples/\n */TensorRT-LLM/src/examples/' >> ${coverageConfigFile} - echo 'source2=\n ${CUR_PATH}/llm/tensorrt_llm/\n */tensorrt_llm/' >> ${coverageConfigFile} - cat ${coverageConfigFile} + python3 llm/jenkins/scripts/test_rerun.py \ + generate_rerun_report \ + --output-file=rerun/rerun_report.xml \ + --input-files=${inputfiles} """ - - sh "cd cov && coverage combine" - sh "cd cov && find . -type f" - sh "cd cov && coverage report -i" // -i: ignore errors. Ignore the error that the source code file cannot be found. - sh "cd cov && coverage html -d test_coverage_html -i" - trtllm_utils.uploadArtifacts("cov/test_coverage_html/*", "${UPLOAD_PATH}/test-results/coverage-report/") - echo "Test coverage report: https://urm.nvidia.com/artifactory/${UPLOAD_PATH}/test-results/coverage-report/index.html" - } // Test coverage + trtllm_utils.uploadArtifacts("rerun/rerun_report.html", "${UPLOAD_PATH}/test-results/") + echo "Rerun report: https://urm.nvidia.com/artifactory/${UPLOAD_PATH}/test-results/rerun_report.html" + catchError( + buildResult: 'SUCCESS', + stageResult: 'UNSTABLE') { + error "Some failed tests were reruned, please check the rerun report." + } + } // Rerun report stage + } // timeout 10 min + } catch (Exception e) { + echo "Rerun Report failed or timed out: ${e.toString()}" + } } - catch (InterruptedException e) - { - throw e + parallelTasks["Test Coverage"] = { + try { + timeout(time: 10, unit: 'MINUTES') { + try { + stage("Test Coverage") { + sh "ls" + def CUR_PATH = sh(returnStdout: true, script: 'pwd').replaceAll("\\s","") + sh "echo ${CUR_PATH}" + sh "rm -rf cov && mkdir -p cov" + sh "find . -type f -wholename '*/.coverage.*' -exec mv {} cov/ \\; || true" + sh "cd cov && find . -type f" + def fileCount = sh(returnStdout: true, script: 'find cov -type f | wc -l').replaceAll("\\s","").toInteger() + if (fileCount == 0) { + echo "Test coverage is skipped because there is no test data file." + return + } + trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install coverage") + sh "coverage --version" + + sh "cp llm/examples/openai_triton/manual_plugin/fmha_triton.py llm/examples/openai_triton/plugin_autogen/" + def coverageConfigFile = "cov/.coveragerc" + sh """ + echo '[paths]' > ${coverageConfigFile} + echo 'source1=\n ${CUR_PATH}/llm/examples/\n */TensorRT-LLM/src/examples/' >> ${coverageConfigFile} + echo 'source2=\n ${CUR_PATH}/llm/tensorrt_llm/\n */tensorrt_llm/' >> ${coverageConfigFile} + cat ${coverageConfigFile} + """ + + sh "cd cov && coverage combine" + sh "cd cov && find . -type f" + sh "cd cov && coverage report -i" // -i: ignore errors. Ignore the error that the source code file cannot be found. + sh "cd cov && coverage html -d test_coverage_html -i" + trtllm_utils.uploadArtifacts("cov/test_coverage_html/*", "${UPLOAD_PATH}/test-results/coverage-report/") + echo "Test coverage report: https://urm.nvidia.com/artifactory/${UPLOAD_PATH}/test-results/coverage-report/index.html" + } // Test coverage + } + catch (InterruptedException e) + { + throw e + } + catch (Exception e) + { + pipeline.echo("Test coverage failed execution.") + } + } // timeout 10 min + } catch (Exception e) { + echo "Test Coverage failed or timed out: ${e.toString()}" + } } - catch (Exception e) - { - pipeline.echo("Test coverage failed execution.") + if (currentBuild.currentResult == 'FAILURE') { + parallelTasks["AI Failure Analysis"] = { + try { + timeout(time: 10, unit: 'MINUTES') { + stage("AI Failure Analysis") { + try { + def prNumber = null + if (globalVars[GITHUB_PR_API_URL]) { + def prMatch = (globalVars[GITHUB_PR_API_URL] =~ /\/pulls?\/(\d+)/) + if (prMatch) { + prNumber = prMatch[0][1] + } + } + def analysis = trtllm_utils.analyzePipelineFailureWithAgent( + pipeline, env.JOB_NAME, env.BUILD_NUMBER, prNumber) + if (analysis) { + def bucket = 'sw-tensorrt-ci-analysis' + def key = "${env.JOB_NAME}/${env.BUILD_NUMBER}/failure_analysis.html" + def htmlUrl = "https://pbss.s8k.io/v1/AUTH_svc_tensorrt/${bucket}/${key}" + // Self-rendering HTML page: marked.js parses the analysis at page load + // and DOMPurify sanitises the result before injection into the DOM. The + // analysis text comes from the CI agent which consumes build logs (which + // can include attacker-controlled PR content), so we treat it as untrusted. + // Hardening: + // 1. CDN scripts pinned to specific versions and protected with SRI. + // 2. Analysis embedded in a `` + // and break out of the data block. + // 3. marked output is run through DOMPurify before innerHTML assignment + // to strip event-handler attributes and other XSS vectors. + def jsonAnalysis = groovy.json.JsonOutput.toJson(analysis).replace("<", "\\u003c") + def htmlDoc = """ + +CI Failure Analysis · ${env.JOB_NAME} #${env.BUILD_NUMBER} + + + + +
${env.JOB_NAME} #${env.BUILD_NUMBER}
+
+ + + +""" + writeFile file: 'failure_analysis.html', text: htmlDoc + trtllm_utils.llmExecStepWithRetry(pipeline, script: 'apk add --no-cache aws-cli') + // Alpine's musl libc fires A and AAAA queries in parallel; pbss.s8k.io's AAAA + // returns SERVFAIL and musl treats that as a fatal lookup failure (glibc would + // not). Pin the A-record IP in /etc/hosts so getaddrinfo resolves from files. + trtllm_utils.llmExecStepWithRetry(pipeline, script: ''' + if ! grep -q 'pbss.s8k.io' /etc/hosts; then + ip=$(nslookup -type=A pbss.s8k.io 2>/dev/null | awk '/^Address[: ]/ && $NF !~ /:53$/ && $NF !~ /#53$/ { print $NF; exit }') + if [ -n "$ip" ]; then + printf '%s\\n' "$ip pbss.s8k.io" >> /etc/hosts + fi + fi + ''') + withCredentials([string( + credentialsId: 'svc_tensorrt-swift-stack-key', + variable: 'AWS_SECRET_ACCESS_KEY')]) { + trtllm_utils.llmExecStepWithRetry(pipeline, script: + "AWS_ACCESS_KEY_ID=svc_tensorrt aws s3 cp failure_analysis.html" + + " 's3://${bucket}/${key}' --endpoint-url https://pbss.s8k.io" + + " --content-type text/html") + } + // Surface the URL via currentBuild.description so the upstream PR_Github + // wrapper can extract it and include it in the GitHub PR comment. + def existingDesc = currentBuild.description ?: "" + currentBuild.description = existingDesc + + (existingDesc ? "
" : "") + + "CI Agent Failure Analysis" + echo "CI Agent Failure Analysis: ${htmlUrl}" + } + } catch (Exception e) { + // Analysis is best-effort; do not fail the pipeline + } + } + } // timeout 10 min + } catch (Exception e) { + echo "AI Failure Analysis failed or timed out: ${e.toString()}" + } + } } + parallel parallelTasks }) } @@ -1613,98 +1726,8 @@ pipeline { } } } - failure { - script { - try { - def prNumber = null - if (globalVars[GITHUB_PR_API_URL]) { - def prMatch = (globalVars[GITHUB_PR_API_URL] =~ /\/pulls?\/(\d+)/) - if (prMatch) { - prNumber = prMatch[0][1] - } - } - def analysis = trtllm_utils.analyzePipelineFailureWithAgent( - this, env.JOB_NAME, env.BUILD_NUMBER, prNumber) - if (analysis) { - def bucket = 'sw-tensorrt-ci-analysis' - def key = "${env.JOB_NAME}/${env.BUILD_NUMBER}/failure_analysis.html" - def htmlUrl = "https://pbss.s8k.io/v1/AUTH_svc_tensorrt/${bucket}/${key}" - // Self-rendering HTML page: marked.js parses the analysis at page load - // and DOMPurify sanitises the result before injection into the DOM. The - // analysis text comes from the CI agent which consumes build logs (which - // can include attacker-controlled PR content), so we treat it as untrusted. - // Hardening: - // 1. CDN scripts pinned to specific versions and protected with SRI. - // 2. Analysis embedded in a `` - // and break out of the data block. - // 3. marked output is run through DOMPurify before innerHTML assignment - // to strip event-handler attributes and other XSS vectors. - def jsonAnalysis = groovy.json.JsonOutput.toJson(analysis).replace("<", "\\u003c") - def htmlDoc = """ - -CI Failure Analysis · ${env.JOB_NAME} #${env.BUILD_NUMBER} - - - - -
${env.JOB_NAME} #${env.BUILD_NUMBER}
-
- - - -""" - writeFile file: 'failure_analysis.html', text: htmlDoc - container("alpine") { - trtllm_utils.llmExecStepWithRetry(this, script: 'apk add --no-cache aws-cli') - // Alpine's musl libc fires A and AAAA queries in parallel; pbss.s8k.io's AAAA - // returns SERVFAIL and musl treats that as a fatal lookup failure (glibc would - // not). Pin the A-record IP in /etc/hosts so getaddrinfo resolves from files. - trtllm_utils.llmExecStepWithRetry(this, script: ''' - if ! grep -q 'pbss.s8k.io' /etc/hosts; then - ip=$(nslookup -type=A pbss.s8k.io 2>/dev/null | awk '/^Address[: ]/ && $NF !~ /:53$/ && $NF !~ /#53$/ { print $NF; exit }') - if [ -n "$ip" ]; then - printf '%s\\n' "$ip pbss.s8k.io" >> /etc/hosts - fi - fi - ''') - withCredentials([string( - credentialsId: 'svc_tensorrt-swift-stack-key', - variable: 'AWS_SECRET_ACCESS_KEY')]) { - trtllm_utils.llmExecStepWithRetry(this, script: - "AWS_ACCESS_KEY_ID=svc_tensorrt aws s3 cp failure_analysis.html" + - " 's3://${bucket}/${key}' --endpoint-url https://pbss.s8k.io" + - " --content-type text/html") - } - } - // Surface the URL via currentBuild.description so the upstream PR_Github - // wrapper can extract it and include it in the GitHub PR comment. - def existingDesc = currentBuild.description ?: "" - currentBuild.description = existingDesc + - (existingDesc ? "
" : "") + - "CI Agent Failure Analysis" - echo "CI Agent Failure Analysis: ${htmlUrl}" - } - } catch (Exception e) { - // Analysis is best-effort; do not fail the pipeline - } - } - } always { script { - if (!isReleaseCheckMode && !GEN_POST_MERGE_BUILDS_ONLY) { - collectTestResults(this, testFilter) - } stage("Upload Build Info") { try { def branch = env.gitlabBranch ? env.gitlabBranch : "main" @@ -1722,6 +1745,9 @@ pipeline { echo "Upload Build Info failed: ${e.toString()}" } } + if (!isReleaseCheckMode && !GEN_POST_MERGE_BUILDS_ONLY) { + collectTestResults(this, testFilter, globalVars) + } } } } From 2336e476569640493a97f1897e3326368b7baf8b Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Fri, 5 Jun 2026 20:53:28 +0800 Subject: [PATCH 006/119] [None][infra] Waive 11 failed cases for main in post-merge 2760 (#15003) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> Signed-off-by: Emma Qiao Co-authored-by: Emma Qiao --- tests/integration/test_lists/waives.txt | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 3008547e0013..b067d84fbc95 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -13,12 +13,14 @@ accuracy/test_llm_api.py::TestMistralNemo12B::test_fp8 SKIP (https://nvbugs/5413 accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (https://nvbugs/6194934) accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) +accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6272656) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-trtllm] SKIP (https://nvbugs/6185150) accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-trtllm] SKIP (https://nvbugs/6185150) accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-8] SKIP (https://nvbugs/6248757) accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) +accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6272668) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] SKIP (https://nvbugs/6191524) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput] SKIP (https://nvbugs/6084775) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_mtp] SKIP (https://nvbugs/6029882) @@ -60,6 +62,7 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_no_kv_cache_reuse[qua accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5945081) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6224637) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/6272673) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6245394) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_dflash SKIP (https://nvbugs/6156233) @@ -213,6 +216,10 @@ examples/test_visual_gen.py::test_ltx2_lpips_against_golden SKIP (https://nvbugs examples/test_visual_gen.py::test_wan21_t2v_lpips_against_golden SKIP (https://nvbugs/6215688) examples/test_visual_gen.py::test_wan22_t2v_lpips_against_golden SKIP (https://nvbugs/6215688) examples/test_visual_gen.py::test_wan_t2v_example SKIP (https://nvbugs/6215688) +examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[attn2d_2x2] SKIP (https://nvbugs/6272644) +examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[cfg2_ulysses2] SKIP (https://nvbugs/6272644) +examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses2_ring2] SKIP (https://nvbugs/6272644) +examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses4] SKIP (https://nvbugs/6272644) examples/test_whisper.py::test_llm_whisper_general[large-v3-disable_gemm_plugin-disable_attention_plugin-disable_weight_only-float16-nb:1-use_python_runtime] SKIP (https://nvbugs/5244570) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_tensor] SKIP (https://nvbugs/5161074) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_token_channel] SKIP (https://nvbugs/5161074) @@ -334,6 +341,7 @@ unittest/_torch/multi_gpu/test_user_buffers.py::test_user_buffers_pass[2-fp16-_t unittest/_torch/multi_gpu/test_user_buffers.py::test_user_buffers_pass[2-fp16-_tokens16-_hidden512] SKIP (https://nvbugs/6266259) unittest/_torch/multi_gpu/test_user_buffers.py::test_user_buffers_pass[2-fp16-_tokens256-_hidden32] SKIP (https://nvbugs/6266259) unittest/_torch/multi_gpu/test_user_buffers.py::test_user_buffers_pass[2-fp16-_tokens256-_hidden512] SKIP (https://nvbugs/6266259) +unittest/_torch/multimodal/test_mm_encoder_standalone.py::test_single_request_chat_multiple_images[pd_disagg-qwen3_30b_a3b_fp8] SKIP (https://nvbugs/6272573) unittest/_torch/thop/serial/test_moe.py::TestMoeFp4::test_no_autotune[use_score_as_input-RoutingDSv3-swiglu-1024-1024-1] SKIP (https://nvbugs/5908070) unittest/_torch/thop/serial/test_moe.py::TestMoeFp4::test_no_autotune[use_score_as_input-RoutingRenormalize_qwen_next-swiglu-1024-1024-150] SKIP (https://nvbugs/5908070) unittest/_torch/thop/serial/test_moe.py::TestMoeFp4::test_no_autotune[use_score_as_input-RoutingRenormalize_topk_4-swiglu-1024-1024-150] SKIP (https://nvbugs/5908070) @@ -353,3 +361,4 @@ unittest/llmapi/test_llm_pytorch.py::test_nemotron_nas_lora[cuda_graph_config0] unittest/llmapi/test_memory_profiling.py::test_profile_kvcache SKIP (https://nvbugs/5580781) unittest/tools/test_layer_wise_benchmarks.py::test_performance_alignment[1] SKIP (https://nvbugs/6127669) unittest/tools/test_layer_wise_benchmarks.py::test_qwen3_next_gen_tep[1] SKIP (https://nvbugs/6153575) +verl/test_verl_cases.py::test_trtllm_abort SKIP (https://nvbugs/6272653) From 58da60af295be5fed91cfcf85601e2642018ddcb Mon Sep 17 00:00:00 2001 From: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> Date: Fri, 5 Jun 2026 06:35:09 -0700 Subject: [PATCH 007/119] =?UTF-8?q?[None][fix]=20Uncomment=20Qwen3.5=20and?= =?UTF-8?q?=20DSR1=20from=20model=20registry=20so=20that=20they=20can=20ru?= =?UTF-8?q?n=20f=E2=80=A6=20(#15001)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> --- .../auto_deploy/model_registry/models.yaml | 21 ++++++++----------- 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/examples/auto_deploy/model_registry/models.yaml b/examples/auto_deploy/model_registry/models.yaml index 5ecfd108f746..2864c1e2427e 100644 --- a/examples/auto_deploy/model_registry/models.yaml +++ b/examples/auto_deploy/model_registry/models.yaml @@ -310,10 +310,9 @@ models: # - name: deepseek-ai/DeepSeek-R1 # config_id: deepseek_r1 # yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'deepseek-r1.yaml', 'enable_sharder_ir.yaml'] -# OOM during AutoDeploy run. -# - name: deepseek-ai/DeepSeek-R1-0528 -# config_id: deepseek_r1 -# yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'deepseek-r1.yaml', 'enable_sharder_ir.yaml'] +- name: deepseek-ai/DeepSeek-R1-0528 + config_id: deepseek_r1 + yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'deepseek-r1.yaml', 'enable_sharder_ir.yaml'] # OOM during AutoDeploy run. # - name: deepseek-ai/DeepSeek-Coder-V2-Instruct # config_id: deepseek_v2_ep @@ -374,14 +373,12 @@ models: # config_id: default_ws_4 # yaml_extra: ['dashboard_default.yaml', 'world_size_4.yaml'] # --- Qwen3.5 MoE (Feb 2026) --- -# tensorrt_llm.executor.utils.RequestError: Ran into a kwarg keyword mismatch. -# - name: Qwen/Qwen3.5-35B-A3B -# config_id: qwen3_5_moe_35b -# yaml_extra: ['dashboard_default.yaml', 'world_size_2.yaml', 'qwen3.5_moe_35b.yaml', 'enable_sharder_ir.yaml'] -# OOM during AutoDeploy run. -# - name: Qwen/Qwen3.5-397B-A17B -# config_id: qwen3_5_moe_400b -# yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'qwen3.5_moe_400b.yaml', 'enable_sharder_ir.yaml'] +- name: Qwen/Qwen3.5-35B-A3B + config_id: qwen3_5_moe_35b + yaml_extra: ['dashboard_default.yaml', 'world_size_2.yaml', 'qwen3.5_moe_35b.yaml', 'enable_sharder_ir.yaml'] +- name: Qwen/Qwen3.5-397B-A17B + config_id: qwen3_5_moe_400b + yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'qwen3.5_moe_400b.yaml', 'enable_sharder_ir.yaml'] # --- GLM-5 (Feb 2026) --- - name: zai-org/GLM-5 config_id: glm_5 From 73c824cc51a0d1a90c00f189050724f767c7e385 Mon Sep 17 00:00:00 2001 From: NVShreyas <158103197+NVShreyas@users.noreply.github.com> Date: Fri, 5 Jun 2026 10:16:34 -0500 Subject: [PATCH 008/119] [TRTLLM-11410][feat] Cosmos3 Support (#14824) Signed-off-by: Shreyas Misra --- docs/source/models/supported-models.md | 25 +- docs/source/models/visual-generation.md | 3 + .../visual_gen/configs/cosmos3-nano-1gpu.yaml | 32 ++ .../configs/cosmos3-super-4gpu.yaml | 31 ++ examples/visual_gen/models/cosmos3_ti2v.py | 134 +++++ .../models/cosmos3/pipeline_cosmos3.py | 16 +- .../models/cosmos3/transformer_cosmos3.py | 124 ++--- .../test_lists/test-db/l0_b200.yml | 2 + .../test_cosmos3_transformer_parallel.py | 519 ++++++++++++++++++ .../visual_gen/test_cosmos3_pipeline.py | 278 ++++++++++ .../visual_gen/test_cosmos3_transformer.py | 317 +++++++++++ 11 files changed, 1384 insertions(+), 97 deletions(-) create mode 100644 examples/visual_gen/configs/cosmos3-nano-1gpu.yaml create mode 100644 examples/visual_gen/configs/cosmos3-super-4gpu.yaml create mode 100644 examples/visual_gen/models/cosmos3_ti2v.py create mode 100644 tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py create mode 100644 tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py create mode 100644 tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 802d324b5405..8da40f0fa66e 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -128,16 +128,19 @@ For full documentation, see the [Visual Generation](./visual-generation.md) page | `Lightricks/LTX-2` | Text-to-Video (with Audio), Image-to-Video (with Audio) | | `Qwen/Qwen-Image` | Text-to-Image | | `Qwen/Qwen-Image-2512` | Text-to-Image | - -## Feature Matrix - -| Model | TeaCache | CFG Parallelism | Ulysses Parallelism | Parallel VAE | CUDA Graph | torch.compile | trtllm-serve | -|---|---|---|---|---|---|---|---| -| **FLUX.1** | Yes | No [^vg1] | Yes | No | Yes | Yes | Yes | -| **FLUX.2** | Yes | No [^vg1] | Yes | No | Yes | Yes | Yes | -| **Wan 2.1** | Yes | Yes | Yes | Yes | Yes | Yes | Yes | -| **Wan 2.2** | No | Yes | Yes | Yes | Yes | Yes | Yes | -| **LTX-2** | No | Yes | Yes | No | Yes | Yes | Yes | -| **Qwen-Image** | No | No | Yes | No | Yes | Yes | Yes | +| `nvidia/Cosmos3-Nano` | Text-to-Image, Text-to-Video, Image-to-Video | +| `nvidia/Cosmos3-Super` | Text-to-Image, Text-to-Video, Image-to-Video | + +### Feature Matrix + +| Model | FP8 blockwise | NVFP4 | TeaCache | CFG Parallelism | Ulysses Parallelism | Parallel VAE | CUDA Graph | torch.compile | trtllm-serve | Attention2D | Ring Attention | Tensor Parallelism | +|---|---|---|---|---|---|---|---|---|---|--|--|--| +| **FLUX.1** | Yes | Yes | Yes | No [^1] | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | +| **FLUX.2** | Yes | Yes | Yes | No [^1] | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | +| **Wan 2.1** | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | +| **Wan 2.2** | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | +| **LTX-2** | Yes | Yes | No | Yes | Yes | No | No | Yes | Yes | Yes | Yes | No | +| **Qwen-Image** [^2] | Yes | Yes | No | No | Yes | No | Yes | Yes | Yes | Yes | Yes | No | +| **Cosmos3** | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | No | No | Yes | [^vg1]: FLUX models use embedded guidance and do not have a separate negative prompt path, so CFG parallelism is not applicable. diff --git a/docs/source/models/visual-generation.md b/docs/source/models/visual-generation.md index db8c73912969..b6f125ed027c 100644 --- a/docs/source/models/visual-generation.md +++ b/docs/source/models/visual-generation.md @@ -35,6 +35,8 @@ TensorRT-LLM **VisualGen** provides a unified inference stack for diffusion mode | `Lightricks/LTX-2` | Text-to-Video (with Audio), Image-to-Video (with Audio) | | `Qwen/Qwen-Image` | Text-to-Image | | `Qwen/Qwen-Image-2512` | Text-to-Image | +| `nvidia/Cosmos3-Nano` | Text-to-Image, Text-to-Video, Image-to-Video | +| `nvidia/Cosmos3-Super` | Text-to-Image, Text-to-Video, Image-to-Video | Models are auto-detected from the checkpoint directory. Diffusers-format models are detected via `model_index.json`; LTX-2 monolithic safetensors checkpoints are detected via embedded metadata. The `AutoPipeline` registry selects the appropriate pipeline class automatically. @@ -48,6 +50,7 @@ Models are auto-detected from the checkpoint directory. Diffusers-format models | **Wan 2.2** | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | | **LTX-2** | Yes | Yes | No | Yes | Yes | No | No | Yes | Yes | Yes | Yes | No | | **Qwen-Image** [^2] | Yes | Yes | No | No | Yes | No | Yes | Yes | Yes | Yes | Yes | No | +| **Cosmos3** | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | No | No | Yes | [^1]: FLUX models use embedded guidance and do not have a separate negative prompt path, so CFG parallelism is not applicable. diff --git a/examples/visual_gen/configs/cosmos3-nano-1gpu.yaml b/examples/visual_gen/configs/cosmos3-nano-1gpu.yaml new file mode 100644 index 000000000000..b67ab39e235b --- /dev/null +++ b/examples/visual_gen/configs/cosmos3-nano-1gpu.yaml @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU Cosmos3 (Nano / Super) with FP8 dynamic quantization. +# Model: nvidia/Cosmos3-Nano or nvidia/Cosmos3-Super +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +# +# Cosmos3 constraints: VANILLA attention only; +# no Attention2D / Ring. Use CFG + Ulysses for multi-GPU (see cosmos3-super-4gpu.yaml). +quant_config: + quant_algo: FP8 + dynamic: true + ignore: ["language_model.*", "vae2llm", "llm2vae", "time_embedder.*"] +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/configs/cosmos3-super-4gpu.yaml b/examples/visual_gen/configs/cosmos3-super-4gpu.yaml new file mode 100644 index 000000000000..34ddec38ceea --- /dev/null +++ b/examples/visual_gen/configs/cosmos3-super-4gpu.yaml @@ -0,0 +1,31 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 4-GPU Cosmos3-Super with FP8 dynamic quantization (CFG + Ulysses + parallel VAE). +# Launch with 4 processes, e.g. torchrun --nproc_per_node=4 ... +# Model: nvidia/Cosmos3-Super +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +# +# GPU layout: cfg_size=2 (positive | negative) x ulysses_size=2 (sequence split). +quant_config: + quant_algo: FP8 + dynamic: true + ignore: ["language_model.*", "vae2llm", "llm2vae", "time_embedder.*"] +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 2 + ulysses_size: 2 + parallel_vae_size: 4 diff --git a/examples/visual_gen/models/cosmos3_ti2v.py b/examples/visual_gen/models/cosmos3_ti2v.py new file mode 100644 index 000000000000..69e50e3b7c52 --- /dev/null +++ b/examples/visual_gen/models/cosmos3_ti2v.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +r"""Cosmos3 Text(+Image)-to-Video generation. + +Cosmos3 OmniMoT supports text-only (T2V) and image-conditioned (I2V/TI2V) +generation from the same checkpoint. Pass ``--image_path`` to condition on a +reference frame. + +Checkpoints (pass the Hub ID or local path via ``--model``): + +- `nvidia/Cosmos3-Nano `_ +- `nvidia/Cosmos3-Super `_ + +Guardrails are enabled by default (required by the +`NVIDIA Open Model License Agreement +`_). +Install and authenticate as follows:: + + pip install cosmos_guardrail==0.3.0 && pip uninstall opencv-python + +Accept the terms for the guardrail checkpoint at +https://huggingface.co/nvidia/Cosmos-1.0-Guardrail and set a valid ``HF_TOKEN`` +(the checkpoint is downloaded automatically on first run). + +To run without guardrails (you are responsible for safe deployment):: + + export TRTLLM_DISABLE_COSMOS3_GUARDRAILS=1 + +Deployment configs (``examples/visual_gen/configs/``): + +- ``cosmos3-nano-1gpu.yaml`` — 1 GPU, FP8 dynamic quant +- ``cosmos3-super-4gpu.yaml`` — 4 GPU, CFG + Ulysses + parallel VAE + +Usage: + python cosmos3_ti2v.py --model nvidia/Cosmos3-Nano \\ + --prompt "The video opens with a view of a well-lit indoor space featuring a " \\ + "wooden display case with compartments filled with various fruits, " \\ + "including bananas, apples, pears, oranges, and carambolas. " \\ + "The bananas are neatly arranged in the middle compartment, while apples " \\ + "are in the left and a mix of pears, oranges, and carambolas are in the " \\ + "right. " \\ + "Two robotic arms with grippers are positioned at the bottom of the frame, " \\ + "with the one on the left remaining stationary, partially obscuring the " \\ + "apples. " \\ + "The robotic arm on the right begins its action, extending towards the " \\ + "right side of the display case. " \\ + "It carefully picks up a pear from the fruit section, placing it into a " \\ + "plastic bag in the shopping cart nearby, which has red handles. " \\ + "After securing the pear, the arm retracts back to its original position. " \\ + "The process repeats as the robotic arm picks up an orange and places it " \\ + "in the bag, followed by a carambola. " \\ + "The final frame captures the robotic arm returning to its initial " \\ + "position, leaving the display case and surrounding area unchanged. " \\ + "The video showcases a seamless and efficient automated fruit-picking " \\ + "process, highlighting the precision and efficiency of modern robotics " \\ + "in a retail setting." \\ + --visual_gen_args ../configs/cosmos3-nano-1gpu.yaml +""" + +import argparse + +from tensorrt_llm import VisualGen, VisualGenArgs + + +def main(): + parser = argparse.ArgumentParser(description="Cosmos3 Text(+Image)-to-Video example") + parser.add_argument( + "--model", + type=str, + default="nvidia/Cosmos3-Nano", + help="Model path or HuggingFace Hub ID (nvidia/Cosmos3-Nano, nvidia/Cosmos3-Super)", + ) + parser.add_argument( + "--visual_gen_args", + "--extra_visual_gen_options", + dest="visual_gen_args", + type=str, + default=None, + help="Path to YAML config (same as trtllm-serve --visual_gen_args)", + ) + parser.add_argument( + "--prompt", + type=str, + required=True, + help="Text prompt for generation", + ) + parser.add_argument( + "--image_path", + type=str, + default=None, + help="Optional conditioning image path for I2V/TI2V", + ) + parser.add_argument( + "--output_path", + type=str, + default="cosmos3_ti2v_output.mp4", + help="Path to save the output video", + ) + args = parser.parse_args() + + # Engine config from shared YAML (optional); model-specific defaults apply otherwise. + extra_args = VisualGenArgs.from_yaml(args.visual_gen_args) if args.visual_gen_args else None + visual_gen = VisualGen(model=args.model, args=extra_args) + + # --- Model-specific: T2V / TI2V request construction --- + # Query per-model defaults (resolution, steps, guidance, seed, etc.). + params = visual_gen.default_params + if args.image_path is not None: + params.image = args.image_path + + output = visual_gen.generate( + inputs=args.prompt, + params=params, + ) + + output.save(args.output_path) + print(f"Saved: {args.output_path}") + + +if __name__ == "__main__": + main() diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py index 800006216bf2..d01e71d998ec 100644 --- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py +++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py @@ -52,8 +52,16 @@ TRTLLM_DISABLE_COSMOS3_GUARDRAILS = os.environ.get("TRTLLM_DISABLE_COSMOS3_GUARDRAILS", "0") == "1" -# TODO: add hf_ids -@register_pipeline("Cosmos3OmniMoTPipeline") +@register_pipeline( + "Cosmos3OmniMoTPipeline", + hf_ids=[ + "nvidia/Cosmos3-Nano", + "nvidia/Cosmos3-Super", + "nvidia/Cosmos3-Super-Text2Image", + "nvidia/Cosmos3-Super-Image2Video", + ], + doc="Cosmos3 Omnimodal world models.", +) class Cosmos3OmniMoTPipeline(BasePipeline): def __init__(self, model_config): super().__init__(model_config) @@ -107,6 +115,10 @@ def load_standard_components( subfolder=PipelineComponent.SCHEDULER, ) + # Re-check the env var in case it was changed after initialization like in unit tests. + guardrails_disabled = os.environ.get("TRTLLM_DISABLE_COSMOS3_GUARDRAILS", "0") == "1" + global TRTLLM_DISABLE_COSMOS3_GUARDRAILS + TRTLLM_DISABLE_COSMOS3_GUARDRAILS = guardrails_disabled if not TRTLLM_DISABLE_COSMOS3_GUARDRAILS: # lazy import try: diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py index 015534cfab02..10de6fc87235 100644 --- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py +++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py @@ -17,7 +17,6 @@ from typing import Tuple import torch -import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F from diffusers.models.embeddings import TimestepEmbedding @@ -29,6 +28,7 @@ from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader +from tensorrt_llm._torch.visual_gen.utils import SequenceSharder from tensorrt_llm.logger import logger from tensorrt_llm.models.modeling_utils import QuantConfig @@ -221,7 +221,7 @@ def __init__( num_key_value_heads=num_key_value_heads, head_dim=head_dim, qkv_mode=QKVMode.SEPARATE_QKV, - qk_norm=True, + qk_norm=False, qk_norm_mode="per_head", bias=False, config=model_config, @@ -247,9 +247,9 @@ def forward_with_kv( q, k, v = self.get_qkv(hidden_states) - q = q.view(batch_size, seq_len, self.num_attention_heads, self.head_dim) - k = k.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim) - v = v.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim) + q = q.view(batch_size, seq_len, self.local_num_attention_heads, self.head_dim) + k = k.view(batch_size, seq_len, self.local_num_key_value_heads, self.head_dim) + v = v.view(batch_size, seq_len, self.local_num_key_value_heads, self.head_dim) q, k = self.apply_qk_norm(q, k) q, k = qwen3_apply_rotary_pos_emb(q, k, freqs_cos, freqs_sin) @@ -300,7 +300,7 @@ def __init__( num_key_value_heads=num_key_value_heads, head_dim=head_dim, qkv_mode=QKVMode.FUSE_QKV, - qk_norm=True, + qk_norm=False, qk_norm_mode="per_head", bias=False, config=model_config, @@ -341,9 +341,9 @@ def forward( q, k, v = self.get_qkv(hidden_states) - q = q.view(batch_size, seq_len_gen, self.num_attention_heads, self.head_dim) - k = k.view(batch_size, seq_len_gen, self.num_key_value_heads, self.head_dim) - v = v.view(batch_size, seq_len_gen, self.num_key_value_heads, self.head_dim) + q = q.view(batch_size, seq_len_gen, self.local_num_attention_heads, self.head_dim) + k = k.view(batch_size, seq_len_gen, self.local_num_key_value_heads, self.head_dim) + v = v.view(batch_size, seq_len_gen, self.local_num_key_value_heads, self.head_dim) q, k = self.apply_qk_norm(q, k) q, k = qwen3_apply_rotary_pos_emb(q, k, freqs_cos, freqs_sin) @@ -395,6 +395,7 @@ def __init__(self, model_config: DiffusionModelConfig, layer_idx: int): dtype=torch.bfloat16, config=model_config, layer_idx=layer_idx, + reduce_output=model_config.mapping.tp_size > 1, ) def forward( @@ -457,6 +458,7 @@ def __init__(self, model_config: DiffusionModelConfig, layer_idx: int): dtype=torch.bfloat16, config=model_config, layer_idx=layer_idx, + reduce_output=model_config.mapping.tp_size > 1, ) def forward( @@ -669,44 +671,31 @@ def __init__(self, model_config: DiffusionModelConfig): ) vgm = model_config.visual_gen_mapping - attn2d_row_size = vgm.attn2d_row_size if vgm else 1 - attn2d_col_size = vgm.attn2d_col_size if vgm else 1 - attn2d_mesh_size = attn2d_row_size * attn2d_col_size - ulysses_size = vgm.ulysses_size if vgm else 1 - use_attn2d = attn2d_mesh_size > 1 - use_ulysses = ulysses_size > 1 - if vgm is not None and vgm.tp_size > 1: - raise ValueError( - f"Cosmos3 does not support tensor parallelism. Got tp_size={vgm.tp_size}" - ) + self.sharder = SequenceSharder.from_vgm( + vgm, + num_attention_heads=self.num_attention_heads, + num_kv_heads=self.num_kv_heads, + ) + tp_size = vgm.tp_size if vgm else 1 + ulysses_size = vgm.ulysses_size if vgm else 1 + cp_size = vgm.cp_size if vgm else 1 + head_divisibility_factor = tp_size * ulysses_size - if use_ulysses and ( - self.num_attention_heads % ulysses_size != 0 or self.num_kv_heads % ulysses_size != 0 + if (ulysses_size > 1 or tp_size > 1) and ( + self.num_attention_heads % head_divisibility_factor != 0 + or self.num_kv_heads % head_divisibility_factor != 0 ): raise ValueError( f"num_attention_heads ({self.num_attention_heads}) and " f"num_kv_heads ({self.num_kv_heads}) must be divisible by " - f"ulysses_size ({ulysses_size})" + f"TP * Ulysses size ({tp_size} * {ulysses_size})" ) - if use_attn2d: - # Attention2D is not compatible with Cosmos3 cross-attention: its forward() + if cp_size > 1: + # Context parallelism is not compatible with Cosmos3 cross-attention: its forward() # TODO: Re-enable once Ring/Attn2D PRs with cross-attention support have landed. - raise NotImplementedError( - "Attention2D (Ring attention) is not supported for Cosmos3. " - "Use Ulysses sequence parallelism instead." - ) - elif use_ulysses: - self.use_seq_parallel = True - self.seq_parallel_size = ulysses_size - self.seq_parallel_pg = vgm.ulysses_group - self.seq_parallel_rank = vgm.ulysses_rank - else: - self.use_seq_parallel = False - self.seq_parallel_size = 1 - self.seq_parallel_pg = None - self.seq_parallel_rank = 0 + raise NotImplementedError("Context parallelism is not supported for Cosmos3. ") self.language_model = Cosmos3LanguageModel(model_config) @@ -920,73 +909,40 @@ def forward( cached_kv_full = self.language_model(text_ids, text_mask, freqs_und) self.cached_freqs_gen = freqs_gen - if self.use_seq_parallel: - rank = self.seq_parallel_rank - # Round max_real_len up to next multiple of ulysses_size. - # At most seq_parallel_size-1 extra positions, negligible softmax dilution. - val = ( - self.seq_parallel_size - max_real_len % self.seq_parallel_size - ) % self.seq_parallel_size + if self.sharder.is_active: + # Round max_real_len up to next multiple of sharder.size. + # At most size-1 extra positions, negligible softmax dilution. + val = (self.sharder.size - max_real_len % self.sharder.size) % self.sharder.size S_text_shard_total = int(max_real_len) + val - S_text_shard = S_text_shard_total // self.seq_parallel_size self.cached_kv = [] for k, v in cached_kv_full: - # Slice to S_text_shard_total; zero out the val padding positions k = k[:, :S_text_shard_total].clone() v = v[:, :S_text_shard_total].clone() if val > 0: k[:, int(max_real_len) :] = 0 v[:, int(max_real_len) :] = 0 self.cached_kv.append( - ( - k[:, rank * S_text_shard : (rank + 1) * S_text_shard], - v[:, rank * S_text_shard : (rank + 1) * S_text_shard], - ) + (self.sharder.shard(k, dim=1), self.sharder.shard(v, dim=1)) ) else: self.cached_kv = cached_kv_full - if self.use_seq_parallel: - S_gen = hidden_gen.shape[1] - pad = (self.seq_parallel_size - S_gen % self.seq_parallel_size) % self.seq_parallel_size - if pad > 0: - # This will cause minor noise in softmax due to padding. - hidden_gen = F.pad(hidden_gen, (0, 0, 0, pad)) - cos, sin = self.cached_freqs_gen - cos_padded = F.pad(cos, (0, 0, 0, 0, 0, pad)) - sin_padded = F.pad(sin, (0, 0, 0, 0, 0, pad)) - else: - cos_padded, sin_padded = self.cached_freqs_gen - padded_s_gen = S_gen + pad - S_shard = padded_s_gen // self.seq_parallel_size - hidden_gen = hidden_gen[ - :, self.seq_parallel_rank * S_shard : (self.seq_parallel_rank + 1) * S_shard - ] - # Shard freqs_gen to match - freqs_gen = ( - cos_padded[ - :, self.seq_parallel_rank * S_shard : (self.seq_parallel_rank + 1) * S_shard - ], - sin_padded[ - :, self.seq_parallel_rank * S_shard : (self.seq_parallel_rank + 1) * S_shard - ], - ) - else: - freqs_gen = self.cached_freqs_gen + S_gen = hidden_gen.shape[1] + hidden_gen = self.sharder.shard(hidden_gen, dim=1, pad_to_multiple=True) + cos, sin = self.cached_freqs_gen + cos = self.sharder.shard(cos, dim=1, pad_to_multiple=True) + sin = self.sharder.shard(sin, dim=1, pad_to_multiple=True) + freqs_gen = (cos, sin) for i, layer in enumerate(self.gen_layers): k_und, v_und = self.cached_kv[i] - if self.seq_parallel_size <= 1: + if not self.sharder.is_active: k_und = k_und[:, :max_real_len] v_und = v_und[:, :max_real_len] hidden_gen = layer(hidden_gen, k_und, v_und, freqs_gen) - if self.use_seq_parallel: - hidden_gen = hidden_gen.contiguous() - parts = [torch.empty_like(hidden_gen) for _ in range(self.seq_parallel_size)] - dist.all_gather(parts, hidden_gen, group=self.seq_parallel_pg) - hidden_gen = torch.cat(parts, dim=1)[:, :S_gen] # [B, S_gen, patch_latent_dim] + hidden_gen = self.sharder.gather(hidden_gen, dim=1, unpad_to=S_gen) hidden_gen = self.norm_moe_gen(hidden_gen) return self.unpatchify(self.llm2vae(hidden_gen), T, H, W) diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index e553fdc88c06..0d3d5f4932e9 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -208,6 +208,8 @@ l0_b200: - unittest/_torch/visual_gen/test_wan21_i2v_teacache.py - unittest/_torch/visual_gen/test_wan21_t2v_teacache.py - unittest/_torch/visual_gen/test_wan_transformer.py + - unittest/_torch/visual_gen/test_cosmos3_transformer.py + - unittest/_torch/visual_gen/test_cosmos3_pipeline.py - examples/test_visual_gen.py::test_wan_t2v_example # - examples/test_visual_gen.py # ------------- Host perf module regression tests (6 representative scenarios) --------------- diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py b/tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py new file mode 100644 index 000000000000..a12b2015b2db --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py @@ -0,0 +1,519 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Unit tests for Cosmos3VFMTransformer parallelism (TP, Ulysses, CFG). + +Synthetic config + stabilized random weights; compares distributed forwards against +a single-GPU reference on the same rank (same pattern as test_wan_tp / test_flux_tp). +No checkpoint loading. + +Run with: + pytest tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py -v -s +""" + +import os +from types import SimpleNamespace +from typing import Callable, Tuple + +os.environ["TLLM_DISABLE_MPI"] = "1" +os.environ["TRTLLM_DISABLE_COSMOS3_GUARDRAILS"] = "1" + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +try: + from tensorrt_llm._torch.visual_gen.config import ( + AttentionConfig, + DiffusionModelConfig, + TorchCompileConfig, + ) + from tensorrt_llm._torch.visual_gen.mapping import VisualGenMapping + from tensorrt_llm._torch.visual_gen.models.cosmos3.transformer_cosmos3 import ( + Cosmos3VFMTransformer, + ) + from tensorrt_llm._utils import get_free_port + from tensorrt_llm.models.modeling_utils import QuantConfig + + MODULES_AVAILABLE = True +except ImportError: + MODULES_AVAILABLE = False + +pytestmark = pytest.mark.cosmos3 + +# Small Cosmos3 config: 8 Q / 4 KV (GQA), hidden_size = 8 * 64 = 512. +# Divisible by TP=2 and TP×Ulysses=4 for both Q and KV head counts. +_COSMOS3_TEST_CONFIG = dict( + hidden_size=512, + intermediate_size=512, + num_hidden_layers=4, + latent_patch_size=2, + latent_channel=4, + position_embedding_type="unified_3d_mrope", + num_attention_heads=8, + num_key_value_heads=4, + head_dim=64, + rope_scaling={"rope_type": "default", "mrope_section": [16, 12, 12]}, + rms_norm_eps=1e-6, + vocab_size=1024, + rope_theta=1_000_000.0, + max_position_embeddings=4096, + timestep_scale=1.0, + base_fps=24.0, + unified_3d_mrope_temporal_modality_margin=100, + enable_fps_modulation=True, +) + +# Video: [B, C, T, H, W]. patch_size=2 → seq_len = T * (H/2) * (W/2). +# T=2, H=W=4 → seq_len=8 (divisible by Ulysses=2). T>1 exercises fps modulation. +_LATENT_T = 2 +_LATENT_H = 4 +_LATENT_W = 4 +_TEXT_LEN = 8 +_MAX_TEXT_LEN = 16 +_TIMESTEP = 500.0 +_FPS = 24.0 + +SEED_WEIGHTS = 123 +SEED_INPUT = 456 +SEED_COND_TEXT = 42 +SEED_UNCOND_TEXT = 123 + +RTOL = 1e-2 +ATOL = 1e-2 + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +# ============================================================================= +# Distributed helpers (same pattern as test_wan_tp.py) +# ============================================================================= + + +def init_distributed_worker(rank: int, world_size: int, backend: str = "nccl", port: int = 29500): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + torch.cuda.set_device(rank % torch.cuda.device_count()) + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + + +def cleanup_distributed(): + try: + from tensorrt_llm._torch.device_mesh import DeviceMeshTopologyImpl + + DeviceMeshTopologyImpl.device_mesh = None + DeviceMeshTopologyImpl.tp_mesh = None + VisualGenMapping.seq_mesh = None + except ImportError: + pass + if dist.is_initialized(): + dist.barrier() + dist.destroy_process_group() + + +def _distributed_worker(rank, world_size, backend, test_fn, port): + try: + init_distributed_worker(rank, world_size, backend, port) + test_fn(rank, world_size) + except Exception as e: + print(f"Rank {rank} failed with error: {e}", flush=True) + raise + finally: + cleanup_distributed() + + +def run_test_in_distributed(world_size: int, test_fn: Callable, use_cuda: bool = True): + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + if use_cuda and torch.cuda.device_count() < world_size: + pytest.skip(f"Test requires {world_size} GPUs, only {torch.cuda.device_count()} available") + backend = "nccl" if use_cuda else "gloo" + port = get_free_port() + mp.spawn( + _distributed_worker, args=(world_size, backend, test_fn, port), nprocs=world_size, join=True + ) + + +# ============================================================================= +# Model config + weight helpers +# ============================================================================= + + +def _make_model_config( + pretrained_dict, + *, + cfg_size=1, + tp_size=1, + ulysses_size=1, + backend="VANILLA", +): + pretrained_config = SimpleNamespace(**pretrained_dict) + ws = cfg_size * tp_size * ulysses_size + if ws > 1 and dist.is_initialized(): + ws = dist.get_world_size() + rk = dist.get_rank() + else: + rk = 0 + vgm = VisualGenMapping( + world_size=ws, + rank=rk, + cfg_size=cfg_size, + tp_size=tp_size, + ulysses_size=ulysses_size, + ) + config = DiffusionModelConfig( + pretrained_config=pretrained_config, + quant_config=QuantConfig(), + torch_compile=TorchCompileConfig(enable=False), + attention=AttentionConfig(backend=backend), + visual_gen_mapping=vgm, + skip_create_weights_in_init=False, + ) + config.mapping = vgm.to_llm_mapping() + return config + + +def _stabilize_model_weights(model: Cosmos3VFMTransformer) -> None: + """Small uniform init so BF16 forwards stay bounded through both pathways.""" + with torch.no_grad(): + for name, p in model.named_parameters(): + if "norm" in name and name.endswith(".weight"): + p.fill_(1.0) + elif p.ndim >= 2: + fan_in = p.shape[1] + std = 0.02 / max(1.0, fan_in**0.5) + p.data.uniform_(-std, std) + else: + p.data.uniform_(-0.01, 0.01) + + +def _shard_dim0(tensor, tp_rank, tp_size): + chunk = tensor.shape[0] // tp_size + return tensor[tp_rank * chunk : (tp_rank + 1) * chunk].contiguous() + + +def _shard_dim1(tensor, tp_rank, tp_size): + chunk = tensor.shape[1] // tp_size + return tensor[:, tp_rank * chunk : (tp_rank + 1) * chunk].contiguous() + + +def _shard_fused_qkv(tensor, tp_rank, tp_size, q_dim, kv_dim): + q, k, v = tensor.split([q_dim, kv_dim, kv_dim], dim=0) + return torch.cat( + [ + _shard_dim0(q, tp_rank, tp_size), + _shard_dim0(k, tp_rank, tp_size), + _shard_dim0(v, tp_rank, tp_size), + ], + dim=0, + ) + + +def _shard_fused_gate_up(tensor, tp_rank, tp_size): + half = tensor.shape[0] // 2 + gate, up = tensor.split([half, half], dim=0) + return torch.cat( + [ + _shard_dim0(gate, tp_rank, tp_size), + _shard_dim0(up, tp_rank, tp_size), + ], + dim=0, + ) + + +def _qkv_output_dims() -> Tuple[int, int]: + """Full (q_dim, kv_dim) for fused gen-path QKV (GQA-aware).""" + q_dim = _COSMOS3_TEST_CONFIG["num_attention_heads"] * _COSMOS3_TEST_CONFIG["head_dim"] + kv_dim = _COSMOS3_TEST_CONFIG["num_key_value_heads"] * _COSMOS3_TEST_CONFIG["head_dim"] + return q_dim, kv_dim + + +def _fused_qkv_dims(ref_param: torch.Tensor) -> Tuple[int, int]: + """Return (q_dim, kv_dim) for a fused QKV weight/bias stacked as [Q|K|V] on dim 0.""" + q_dim, kv_dim = _qkv_output_dims() + expected = q_dim + 2 * kv_dim + if ref_param.shape[0] == expected: + return q_dim, kv_dim + raise ValueError( + f"Cannot infer fused QKV dims from shape {ref_param.shape}; " + f"expected dim0={expected} for GQA config" + ) + + +def _copy_ref_weights_to_tp(ref_model, tp_model, tp_rank, tp_size): + """Copy TP=1 reference weights into a TP-sharded model (WAN/Flux pattern).""" + ref_params = dict(ref_model.named_parameters()) + + with torch.no_grad(): + for tp_name, tp_param in tp_model.named_parameters(): + if tp_name not in ref_params: + continue + + ref_param = ref_params[tp_name] + + if tp_param.shape == ref_param.shape: + tp_param.data.copy_(ref_param.data) + elif tp_param.ndim >= 2 and tp_param.shape[1] == ref_param.shape[1]: + if "qkv_proj" in tp_name: + q_dim, kv_dim = _fused_qkv_dims(ref_param) + tp_param.data.copy_( + _shard_fused_qkv(ref_param.data, tp_rank, tp_size, q_dim, kv_dim) + ) + elif "gate_up_proj" in tp_name: + tp_param.data.copy_(_shard_fused_gate_up(ref_param.data, tp_rank, tp_size)) + else: + tp_param.data.copy_(_shard_dim0(ref_param.data, tp_rank, tp_size)) + elif tp_param.ndim >= 2 and tp_param.shape[0] == ref_param.shape[0]: + tp_param.data.copy_(_shard_dim1(ref_param.data, tp_rank, tp_size)) + elif tp_param.ndim == 1 and tp_param.shape[0] < ref_param.shape[0]: + if "qkv_proj" in tp_name: + q_dim, kv_dim = _fused_qkv_dims(ref_param) + tp_param.data.copy_( + _shard_fused_qkv(ref_param.data, tp_rank, tp_size, q_dim, kv_dim) + ) + elif "gate_up_proj" in tp_name: + tp_param.data.copy_(_shard_fused_gate_up(ref_param.data, tp_rank, tp_size)) + else: + tp_param.data.copy_(_shard_dim0(ref_param.data, tp_rank, tp_size)) + else: + raise ValueError( + f"Cannot shard {tp_name}: ref={ref_param.shape}, tp={tp_param.shape}" + ) + + +def _cosmos3_inputs( + device: torch.device, + *, + channels: int, + text_seed: int, + batch: int = 1, + dtype: torch.dtype = torch.bfloat16, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[int, int, int]]: + torch.manual_seed(SEED_INPUT) + hidden_states = ( + torch.randn( + batch, + channels, + _LATENT_T, + _LATENT_H, + _LATENT_W, + device=device, + dtype=dtype, + ) + * 0.1 + ) + timestep = torch.full((batch,), _TIMESTEP, device=device, dtype=torch.float32) + torch.manual_seed(text_seed) + text_ids = torch.randint(1, 1000, (batch, _MAX_TEXT_LEN), device=device, dtype=torch.long) + text_mask = torch.zeros(batch, _MAX_TEXT_LEN, device=device, dtype=torch.long) + text_mask[:, :_TEXT_LEN] = 1 + return hidden_states, timestep, text_ids, text_mask, (_LATENT_T, _LATENT_H, _LATENT_W) + + +def _forward(model: Cosmos3VFMTransformer, device: torch.device, text_seed: int) -> torch.Tensor: + channels = _COSMOS3_TEST_CONFIG["latent_channel"] + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + device, channels=channels, text_seed=text_seed + ) + model.reset_cache() + with torch.inference_mode(): + return model( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + fps=_FPS, + ) + + +def _build_ref_and_parallel( + *, + tp_size: int = 1, + ulysses_size: int = 1, + cfg_size: int = 1, +) -> Tuple[Cosmos3VFMTransformer, Cosmos3VFMTransformer, VisualGenMapping, torch.device]: + device = torch.device(f"cuda:{dist.get_rank() % torch.cuda.device_count()}") + + torch.manual_seed(SEED_WEIGHTS) + ref_config = _make_model_config(_COSMOS3_TEST_CONFIG, tp_size=1, ulysses_size=1) + # Do not .to(bfloat16) the whole module — RoPE inv_freq must stay fp32 (see + # Qwen3VLTextRotaryEmbedding.forward). post_load_weights() sets Linear/MLP to bf16. + ref_model = Cosmos3VFMTransformer(ref_config).to(device).eval() + _stabilize_model_weights(ref_model) + ref_model.post_load_weights() + + torch.manual_seed(SEED_WEIGHTS) + parallel_config = _make_model_config( + _COSMOS3_TEST_CONFIG, + cfg_size=cfg_size, + tp_size=tp_size, + ulysses_size=ulysses_size, + ) + vgm = parallel_config.visual_gen_mapping + parallel_model = Cosmos3VFMTransformer(parallel_config).to(device).eval() + if tp_size > 1: + _copy_ref_weights_to_tp(ref_model, parallel_model, vgm.tp_rank, tp_size) + else: + parallel_model.load_state_dict(ref_model.state_dict()) + parallel_model.post_load_weights() + + return ref_model, parallel_model, vgm, device + + +def _cfg_text_seed(rank: int, *, tp_size: int, ulysses_size: int, cfg_size: int) -> int: + if cfg_size > 1: + cfg_rank = rank // (tp_size * ulysses_size) + return SEED_COND_TEXT if cfg_rank == 0 else SEED_UNCOND_TEXT + return SEED_COND_TEXT + + +def _assert_parity(actual: torch.Tensor, expected: torch.Tensor, *, msg: str) -> None: + assert actual.shape == expected.shape, f"{msg}: shape {actual.shape} vs {expected.shape}" + actual_f = actual.float() + expected_f = expected.float() + assert not torch.isnan(actual_f).any(), msg + assert not torch.isinf(actual_f).any(), msg + torch.testing.assert_close(actual_f, expected_f, rtol=RTOL, atol=ATOL, msg=msg) + + +# ============================================================================= +# Test logic (module-level for mp.spawn pickling) +# ============================================================================= + + +def _logic_cosmos3_tp_vs_single_gpu(rank, world_size): + ref_model, tp_model, _, device = _build_ref_and_parallel(tp_size=world_size) + text_seed = _cfg_text_seed(rank, tp_size=world_size, ulysses_size=1, cfg_size=1) + + ref_out = _forward(ref_model, device, text_seed) + tp_out = _forward(tp_model, device, text_seed) + + if rank == 0: + diff = (tp_out.float() - ref_out.float()).abs() + print( + f"[tp={world_size}] max_abs_diff={diff.max().item():.6e}, " + f"mean_abs_diff={diff.mean().item():.6e}", + flush=True, + ) + + _assert_parity(tp_out, ref_out, msg=f"Rank {rank}: TP output differs from single-GPU reference") + + +def _logic_cosmos3_ulysses_vs_single_gpu(rank, world_size): + ref_model, ulysses_model, _, device = _build_ref_and_parallel(ulysses_size=world_size) + text_seed = _cfg_text_seed(rank, tp_size=1, ulysses_size=world_size, cfg_size=1) + + ref_out = _forward(ref_model, device, text_seed) + ulysses_out = _forward(ulysses_model, device, text_seed) + + if rank == 0: + diff = (ulysses_out.float() - ref_out.float()).abs() + print( + f"[ulysses={world_size}] max_abs_diff={diff.max().item():.6e}, " + f"mean_abs_diff={diff.mean().item():.6e}", + flush=True, + ) + + _assert_parity( + ulysses_out, + ref_out, + msg=f"Rank {rank}: Ulysses output differs from single-GPU reference", + ) + + +def _logic_cosmos3_tp_ulysses_vs_single_gpu(rank, world_size): + tp_size = 2 + ulysses_size = 2 + ref_model, combined_model, _, device = _build_ref_and_parallel( + tp_size=tp_size, ulysses_size=ulysses_size + ) + text_seed = _cfg_text_seed(rank, tp_size=tp_size, ulysses_size=ulysses_size, cfg_size=1) + + ref_out = _forward(ref_model, device, text_seed) + combined_out = _forward(combined_model, device, text_seed) + + if rank == 0: + diff = (combined_out.float() - ref_out.float()).abs() + print( + f"[tp={tp_size},ulysses={ulysses_size}] max_abs_diff={diff.max().item():.6e}, " + f"mean_abs_diff={diff.mean().item():.6e}", + flush=True, + ) + + _assert_parity( + combined_out, + ref_out, + msg=f"Rank {rank}: TP+Ulysses output differs from single-GPU reference", + ) + + +def _logic_cosmos3_cfg_ulysses_vs_single_gpu(rank, world_size): + cfg_size = 2 + ulysses_size = 2 + ref_model, parallel_model, _, device = _build_ref_and_parallel( + cfg_size=cfg_size, ulysses_size=ulysses_size + ) + text_seed = _cfg_text_seed(rank, tp_size=1, ulysses_size=ulysses_size, cfg_size=cfg_size) + + ref_out = _forward(ref_model, device, text_seed) + parallel_out = _forward(parallel_model, device, text_seed) + + cfg_rank = rank // ulysses_size + if rank == 0: + diff = (parallel_out.float() - ref_out.float()).abs() + print( + f"[cfg={cfg_size},ulysses={ulysses_size},stream={cfg_rank}] " + f"max_abs_diff={diff.max().item():.6e}, " + f"mean_abs_diff={diff.mean().item():.6e}", + flush=True, + ) + + _assert_parity( + parallel_out, + ref_out, + msg=f"Rank {rank}: CFG+Ulysses output differs from single-GPU reference", + ) + + +# ============================================================================= +# Tests +# ============================================================================= + + +@pytest.mark.gpu2 +class TestCosmos3TransformerParallel: + """Cosmos3 TP / Ulysses / CFG parity vs single-GPU (synthetic weights, no checkpoint).""" + + def _skip_if_unavailable(self): + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + + def test_tp2_vs_single_gpu(self): + self._skip_if_unavailable() + run_test_in_distributed(world_size=2, test_fn=_logic_cosmos3_tp_vs_single_gpu) + + def test_ulysses2_vs_single_gpu(self): + self._skip_if_unavailable() + run_test_in_distributed(world_size=2, test_fn=_logic_cosmos3_ulysses_vs_single_gpu) + + @pytest.mark.gpu4 + def test_tp2_ulysses2_vs_single_gpu(self): + self._skip_if_unavailable() + run_test_in_distributed(world_size=4, test_fn=_logic_cosmos3_tp_ulysses_vs_single_gpu) + + @pytest.mark.gpu4 + def test_cfg2_ulysses2_vs_single_gpu(self): + self._skip_if_unavailable() + run_test_in_distributed(world_size=4, test_fn=_logic_cosmos3_cfg_ulysses_vs_single_gpu) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) diff --git a/tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py b/tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py new file mode 100644 index 000000000000..46899e799945 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py @@ -0,0 +1,278 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Smoke tests for Cosmos3OmniMoTPipeline. + +Loads Cosmos3-Nano when available, runs end-to-end generation, and asserts +valid uint8 video outputs. No diffusers reference comparison. + +Run all pipeline smoke tests: + pytest tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py -v -s -m cosmos3 + +Run single mode: + pytest tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py -v -s -m cosmos3_i2v + +Override checkpoint: + DIFFUSION_MODEL_PATH_COSMOS3=/path/to/Cosmos3-Nano \\ + pytest tests/unittest/_torch/visual_gen/test_cosmos3_pipeline.py -v -s +""" + +import gc +import os +from pathlib import Path + +os.environ["TLLM_DISABLE_MPI"] = "1" +os.environ["TRTLLM_DISABLE_COSMOS3_GUARDRAILS"] = "1" + +import PIL.Image +import pytest +import torch + +from tensorrt_llm._torch.visual_gen.models.cosmos3.pipeline_cosmos3 import Cosmos3OmniMoTPipeline +from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader +from tensorrt_llm.visual_gen.args import TorchCompileConfig, VisualGenArgs + +pytestmark = pytest.mark.cosmos3 + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +@pytest.fixture(autouse=True) +def _cleanup_gpu(): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + yield + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def _llm_models_root() -> str: + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch.trt_llm_data/llm-models/") + assert root.exists(), ( + "Set LLM_MODELS_ROOT or ensure /home/scratch.trt_llm_data_ci/llm-models/ is accessible." + ) + return str(root) + + +def _checkpoint(env_var: str, default_name: str) -> str: + return os.environ.get(env_var) or os.path.join(_llm_models_root(), default_name) + + +COSMOS3_NANO_PATH = _checkpoint("DIFFUSION_MODEL_PATH_COSMOS3", "Cosmos3-Nano") + +PROMPT = "A serene mountain lake at sunrise with mist rising from the water." +NUM_STEPS = 4 +SEED = 42 +HEIGHT = 720 +WIDTH = 1280 +NUM_FRAMES = 9 +GUIDANCE_SCALE = 6.0 +FRAME_RATE = 24.0 + +COSMOS3_FP8_QUANT_CONFIG = { + "quant_algo": "FP8", + "dynamic": True, + "ignore": ["language_model.*", "vae2llm", "llm2vae", "time_embedder.*"], +} + + +def _require_checkpoint() -> str: + if not COSMOS3_NANO_PATH or not os.path.exists(COSMOS3_NANO_PATH): + pytest.skip(f"Checkpoint not found: {COSMOS3_NANO_PATH}") + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + return COSMOS3_NANO_PATH + + +def _load_pipeline(checkpoint_path: str, **visual_gen_kwargs): + args = VisualGenArgs( + model=checkpoint_path, + torch_compile_config=TorchCompileConfig(enable=False), + **visual_gen_kwargs, + ) + return PipelineLoader(args).load(skip_warmup=True) + + +def _run_forward(pipeline, *, image=None, num_frames=NUM_FRAMES, **extra): + return pipeline.forward( + prompt=PROMPT, + image=image, + height=HEIGHT, + width=WIDTH, + num_frames=num_frames, + num_inference_steps=NUM_STEPS, + guidance_scale=GUIDANCE_SCALE, + seed=SEED, + frame_rate=FRAME_RATE, + use_guardrails=False, + **extra, + ) + + +def _assert_valid_video( + video: torch.Tensor, + *, + num_frames: int, + height: int = HEIGHT, + width: int = WIDTH, +): + """PipelineOutput.video is (B, T, H, W, C) uint8 per output.py.""" + assert video is not None + assert video.dtype == torch.uint8 + assert video.dim() == 5, f"Expected (B,T,H,W,C), got {video.shape}" + batch, t, h, w, c = video.shape + assert batch == 1 + assert t == num_frames + assert h == height and w == width + assert c == 3 + vf = video.float() + assert not torch.isnan(vf).any() + assert not torch.isinf(vf).any() + assert vf.min() >= 0 and vf.max() <= 255 + + +def _make_test_image() -> PIL.Image.Image: + image_path = os.environ.get("COSMOS3_TEST_IMAGE") + if image_path and os.path.exists(image_path): + return PIL.Image.open(image_path).convert("RGB") + return PIL.Image.new("RGB", (WIDTH, HEIGHT), color=(64, 128, 192)) + + +@pytest.fixture(scope="class") +def cosmos3_pipeline(): + checkpoint = _require_checkpoint() + pipeline = _load_pipeline(checkpoint) + yield pipeline + del pipeline + gc.collect() + torch.cuda.empty_cache() + + +@pytest.mark.integration +class TestCosmos3PipelineLoad: + def test_load_pipeline(self): + checkpoint = _require_checkpoint() + pipeline = _load_pipeline(checkpoint) + try: + assert isinstance(pipeline, Cosmos3OmniMoTPipeline) + assert pipeline.transformer is not None + finally: + del pipeline + gc.collect() + torch.cuda.empty_cache() + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2v +@pytest.mark.high_cuda_memory +class TestCosmos3T2V: + def test_t2v_smoke(self, cosmos3_pipeline): + result = _run_forward(cosmos3_pipeline, image=None, num_frames=NUM_FRAMES) + _assert_valid_video(result.video, num_frames=NUM_FRAMES) + assert result.frame_rate == FRAME_RATE + + +@pytest.mark.integration +@pytest.mark.cosmos3_i2v +@pytest.mark.high_cuda_memory +class TestCosmos3I2V: + def test_i2v_smoke(self, cosmos3_pipeline): + image = _make_test_image() + result = _run_forward(cosmos3_pipeline, image=image, num_frames=NUM_FRAMES) + _assert_valid_video(result.video, num_frames=NUM_FRAMES) + assert result.frame_rate == FRAME_RATE + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2i +@pytest.mark.high_cuda_memory +class TestCosmos3T2I: + def test_t2i_smoke(self, cosmos3_pipeline): + result = _run_forward(cosmos3_pipeline, image=None, num_frames=1) + _assert_valid_video(result.video, num_frames=1) + assert result.frame_rate == FRAME_RATE + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2v +@pytest.mark.high_cuda_memory +class TestCosmos3PromptTemplates: + @pytest.mark.parametrize( + "use_duration_template,use_resolution_template,use_system_prompt", + [ + (True, True, True), + (False, False, False), + (False, False, True), + ], + ids=["all-on", "all-off", "system-prompt-only"], + ) + def test_template_variants( + self, + cosmos3_pipeline, + use_duration_template, + use_resolution_template, + use_system_prompt, + ): + result = _run_forward( + cosmos3_pipeline, + use_duration_template=use_duration_template, + use_resolution_template=use_resolution_template, + use_system_prompt=use_system_prompt, + ) + _assert_valid_video(result.video, num_frames=NUM_FRAMES) + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2v +@pytest.mark.high_cuda_memory +class TestCosmos3NegativePrompt: + @pytest.mark.parametrize("negative_prompt", [None, ""], ids=["default", "empty"]) + def test_negative_prompt(self, cosmos3_pipeline, negative_prompt): + result = _run_forward(cosmos3_pipeline, negative_prompt=negative_prompt) + _assert_valid_video(result.video, num_frames=NUM_FRAMES) + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2v +class TestCosmos3BatchRejected: + def test_batch_prompt_raises(self, cosmos3_pipeline): + with pytest.raises(ValueError, match="Batch generation is not supported"): + cosmos3_pipeline.forward( + prompt=["first prompt", "second prompt"], + height=HEIGHT, + width=WIDTH, + num_frames=NUM_FRAMES, + num_inference_steps=NUM_STEPS, + guidance_scale=GUIDANCE_SCALE, + seed=SEED, + frame_rate=FRAME_RATE, + use_guardrails=False, + ) + + +@pytest.mark.integration +@pytest.mark.cosmos3_t2v +@pytest.mark.high_cuda_memory +class TestCosmos3FP8Load: + def test_fp8_load_and_t2v(self): + checkpoint = _require_checkpoint() + pipeline = _load_pipeline(checkpoint, quant_config=COSMOS3_FP8_QUANT_CONFIG) + try: + assert pipeline.model_config.quant_config.quant_algo is not None + result = _run_forward(pipeline, image=None, num_frames=NUM_FRAMES) + _assert_valid_video(result.video, num_frames=NUM_FRAMES) + assert result.frame_rate == FRAME_RATE + finally: + del pipeline + gc.collect() + torch.cuda.empty_cache() diff --git a/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py b/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py new file mode 100644 index 000000000000..59c14656b25d --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py @@ -0,0 +1,317 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Smoke tests for Cosmos3VFMTransformer. + +Unit tests load architecture params from ``transformer/config.json`` in the +Cosmos3-Nano checkpoint (random weights). Integration tests load full weights. + +Run unit tests: + pytest tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py -v -s -k Unit + +Run all: + pytest tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py -v -s + +Override checkpoint: + DIFFUSION_MODEL_PATH_COSMOS3=/path/to/Cosmos3-Nano \\ + pytest tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py -v -s +""" + +import gc +import os +from pathlib import Path + +os.environ["TLLM_DISABLE_MPI"] = "1" +os.environ["TRTLLM_DISABLE_COSMOS3_GUARDRAILS"] = "1" + +import pytest +import torch + +from tensorrt_llm._torch.modules.linear import Linear +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig, VisualGenArgs +from tensorrt_llm._torch.visual_gen.models.cosmos3.transformer_cosmos3 import Cosmos3VFMTransformer +from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineComponent, PipelineLoader +from tensorrt_llm.visual_gen.args import TorchCompileConfig + +pytestmark = pytest.mark.cosmos3 + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +@pytest.fixture(autouse=True) +def _cleanup_gpu(): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + yield + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def _llm_models_root() -> str: + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch/trt_llm_data/llm-models/") + assert root.exists(), ( + "Set LLM_MODELS_ROOT or ensure /home/scratch.trt_llm_data_ci/llm-models/ is accessible." + ) + return str(root) + + +def _checkpoint(env_var: str, default_name: str) -> str: + return os.environ.get(env_var) or os.path.join(_llm_models_root(), default_name) + + +COSMOS3_NANO_PATH = _checkpoint("DIFFUSION_MODEL_PATH_COSMOS3", "Cosmos3-Nano") + +DEVICE = "cuda" +DTYPE = torch.bfloat16 + +COSMOS3_FP8_QUANT_CONFIG = { + "quant_algo": "FP8", + "dynamic": True, + "ignore": ["language_model.*", "vae2llm", "llm2vae", "time_embedder.*"], +} + +_SKIP_AUX = [ + PipelineComponent.VAE, + PipelineComponent.SCHEDULER, + PipelineComponent.TOKENIZER, +] + + +def _transformer_config_path(checkpoint_dir: str) -> str: + return os.path.join(checkpoint_dir, "transformer", "config.json") + + +def _require_checkpoint() -> str: + if not COSMOS3_NANO_PATH or not os.path.isdir(COSMOS3_NANO_PATH): + pytest.skip(f"Checkpoint not found: {COSMOS3_NANO_PATH}") + config_path = _transformer_config_path(COSMOS3_NANO_PATH) + if not os.path.isfile(config_path): + pytest.skip(f"Transformer config not found: {config_path}") + return COSMOS3_NANO_PATH + + +def _load_model_config(checkpoint_dir: str) -> DiffusionModelConfig: + """Build DiffusionModelConfig from ``checkpoint_dir/transformer/config.json``.""" + args = VisualGenArgs( + model=checkpoint_dir, + torch_compile_config=TorchCompileConfig(enable=False), + ) + return DiffusionModelConfig.from_pretrained(checkpoint_dir, args=args) + + +def _init_all_weights(model: torch.nn.Module, std: float = 0.02) -> None: + with torch.no_grad(): + for name, param in model.named_parameters(): + if "norm" in name and name.endswith(".weight"): + param.fill_(1.0) + elif param.numel() > 0: + torch.nn.init.normal_(param, mean=0.0, std=std) + + +def _build_random_weight_model(model_config: DiffusionModelConfig) -> Cosmos3VFMTransformer: + """Instantiate on CUDA with random weights; keep fp32 RoPE/time embed buffers.""" + model = Cosmos3VFMTransformer(model_config=model_config).to(DEVICE).eval() + _init_all_weights(model) + model.post_load_weights() + return model + + +def _cosmos3_inputs( + device: str, + *, + batch: int = 1, + channels: int = 16, + t: int = 1, + h: int = 8, + w: int = 8, + text_len: int = 32, + max_text_len: int = 64, + dtype: torch.dtype = DTYPE, +): + torch.manual_seed(42) + hidden_states = torch.randn(batch, channels, t, h, w, device=device, dtype=dtype) + timestep = torch.tensor([500.0], device=device, dtype=torch.float32) + text_ids = torch.randint(1, 1000, (batch, max_text_len), device=device, dtype=torch.long) + text_mask = torch.zeros(batch, max_text_len, device=device, dtype=torch.long) + text_mask[:, :text_len] = 1 + video_shape = (t, h, w) + return hidden_states, timestep, text_ids, text_mask, video_shape + + +def _assert_finite_output(out: torch.Tensor, expected_shape: torch.Size) -> None: + assert out.shape == expected_shape, f"Expected {expected_shape}, got {out.shape}" + out_f = out.float() + assert not torch.isnan(out_f).any() + assert not torch.isinf(out_f).any() + + +@pytest.mark.integration +class TestCosmos3Unit: + """Unit tests — Nano architecture from checkpoint config, random weights.""" + + @pytest.fixture(autouse=True) + def _require_cuda(self): + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + @pytest.fixture(scope="class") + def cosmos3_model_config(self): + checkpoint_dir = _require_checkpoint() + return _load_model_config(checkpoint_dir) + + def test_model_structure(self, cosmos3_model_config): + cfg = cosmos3_model_config.pretrained_config + model = Cosmos3VFMTransformer(model_config=cosmos3_model_config) + assert hasattr(model, "language_model") + assert hasattr(model, "gen_layers") + assert len(model.language_model.layers) == cfg.num_hidden_layers + assert len(model.gen_layers) == cfg.num_hidden_layers + assert hasattr(model, "vae2llm") + assert hasattr(model, "llm2vae") + assert hasattr(model, "time_embedder") + linear_names = [n for n, m in model.named_modules() if isinstance(m, Linear)] + assert any("to_q" in n or "qkv_proj" in n for n in linear_names) + + @pytest.mark.high_cuda_memory + def test_sanity_forward(self, cosmos3_model_config): + cfg = cosmos3_model_config.pretrained_config + model = _build_random_weight_model(cosmos3_model_config) + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + DEVICE, channels=cfg.latent_channel + ) + with torch.inference_mode(): + out = model( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + ) + _assert_finite_output(out, hs.shape) + + @pytest.mark.high_cuda_memory + def test_reset_cache(self, cosmos3_model_config): + cfg = cosmos3_model_config.pretrained_config + model = _build_random_weight_model(cosmos3_model_config) + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + DEVICE, channels=cfg.latent_channel + ) + with torch.inference_mode(): + out1 = model( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + ) + out2 = model( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + ) + _assert_finite_output(out1, hs.shape) + _assert_finite_output(out2, hs.shape) + + @pytest.mark.high_cuda_memory + def test_sanity_forward_i2v_mask(self, cosmos3_model_config): + cfg = cosmos3_model_config.pretrained_config + model = _build_random_weight_model(cosmos3_model_config) + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + DEVICE, channels=cfg.latent_channel, t=2 + ) + noisy_frame_mask = torch.zeros(1, 1, 2, 1, 1, device=DEVICE, dtype=DTYPE) + noisy_frame_mask[:, :, 0, :, :] = 0.0 + noisy_frame_mask[:, :, 1, :, :] = 1.0 + with torch.inference_mode(): + out = model( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + noisy_frame_mask=noisy_frame_mask, + ) + _assert_finite_output(out, hs.shape) + + +@pytest.mark.integration +class TestCosmos3TransformerCheckpoint: + """Load Cosmos3-Nano transformer weights and run a single forward step.""" + + @pytest.fixture(scope="class") + def cosmos3_transformer(self): + checkpoint_dir = _require_checkpoint() + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + args = VisualGenArgs( + model=checkpoint_dir, + torch_compile_config=TorchCompileConfig(enable=False), + ) + pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=_SKIP_AUX) + transformer = pipeline.transformer + yield transformer + del pipeline + gc.collect() + torch.cuda.empty_cache() + + def test_load_weights_and_forward(self, cosmos3_transformer): + transformer = cosmos3_transformer + c = transformer.latent_channel_size + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + DEVICE, channels=c, t=1, h=16, w=16 + ) + transformer.reset_cache() + with torch.inference_mode(): + out = transformer( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + ) + _assert_finite_output(out, hs.shape) + + @pytest.mark.parametrize("quant_algo", ["FP8"]) + def test_load_fp8_quantization(self, quant_algo: str): + checkpoint_dir = _require_checkpoint() + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + args = VisualGenArgs( + model=checkpoint_dir, + quant_config={**COSMOS3_FP8_QUANT_CONFIG, "quant_algo": quant_algo}, + torch_compile_config=TorchCompileConfig(enable=False), + ) + pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=_SKIP_AUX) + try: + assert pipeline.model_config.quant_config.quant_algo is not None + transformer = pipeline.transformer + c = transformer.latent_channel_size + hs, ts, text_ids, text_mask, video_shape = _cosmos3_inputs( + DEVICE, channels=c, t=1, h=8, w=8 + ) + transformer.reset_cache() + with torch.inference_mode(): + out = transformer( + hidden_states=hs, + timestep=ts, + text_ids=text_ids, + text_mask=text_mask, + video_shape=video_shape, + ) + _assert_finite_output(out, hs.shape) + finally: + del pipeline + gc.collect() + torch.cuda.empty_cache() From fb5bd448f79e867a3103f53a6bd22a38cd1be0a1 Mon Sep 17 00:00:00 2001 From: Ziyi Xiong <219238287+ziyixiong-nv@users.noreply.github.com> Date: Sat, 6 Jun 2026 00:24:11 +0800 Subject: [PATCH 009/119] [https://nvbugs/5859886][fix] Remove the waiver (#14948) Signed-off-by: ziyixiong-nv <219238287+ziyixiong-nv@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index b067d84fbc95..78a3483143da 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -31,7 +31,6 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[h accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[host_cache_offload_mtp1] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[host_cache_offload_mtp3_no_adp] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[baseline] SKIP (https://nvbugs/6185196) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[disable_skip_indexer] SKIP (https://nvbugs/5859886) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[latency_default] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baseline] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baseline_mtp1] SKIP (https://nvbugs/6185196) From 501b5c223271ab92c361c1e68d98acae09971d89 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Sat, 6 Jun 2026 00:28:51 +0800 Subject: [PATCH 010/119] =?UTF-8?q?[https://nvbugs/6248744][fix]=20Added?= =?UTF-8?q?=20`trust=5Fremote=5Fcode=3DTrue`=20to=20the=20`LLM(...)`=20con?= =?UTF-8?q?structor=20and=20removed=20the=E2=80=A6=20(#14892)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> --- .../integration/defs/accuracy/test_llm_api_pytorch_multimodal.py | 1 + tests/integration/test_lists/waives.txt | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py b/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py index 4e248a59e1ff..4d9817281c4c 100644 --- a/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py +++ b/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py @@ -251,6 +251,7 @@ def test_auto_dtype(self, enable_chunked_prefill, max_num_tokens): enable_chunked_prefill=enable_chunked_prefill, max_num_tokens=max_num_tokens, kv_cache_config=self.kv_cache_config, + trust_remote_code=True, ) as llm: task = MMMU(self.MODEL_NAME) task.evaluate( diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 78a3483143da..9db38f2bc97b 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -145,7 +145,6 @@ accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[ accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[full_budget] SKIP (https://nvbugs/6211189) accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized SKIP (https://nvbugs/6215689) accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (https://nvbugs/6181383) -accuracy/test_llm_api_pytorch_multimodal.py::TestNemotron_Nano_12B_V2_VL::test_auto_dtype[forced_chunked_prefill] SKIP (https://nvbugs/6248744) accuracy/test_llm_api_pytorch_ray.py::TestLlama3_1_8BInstruct::test_pp2_ray SKIP (https://nvbugs/6094070) cpp/test_e2e.py::test_benchmarks[bart-90] SKIP (https://nvbugs/5550689) cpp/test_e2e.py::test_benchmarks[gpt-80] SKIP (https://nvbugs/5550689) From 37ece3f9227e132215bf0d3d5bee2dac3deda764 Mon Sep 17 00:00:00 2001 From: Gal Hubara-Agam <96368689+galagam@users.noreply.github.com> Date: Fri, 5 Jun 2026 19:54:43 +0300 Subject: [PATCH 011/119] [https://nvbugs/6160629][fix] AutoDeploy: Fix manual seed setting for standalone tests (#14954) Signed-off-by: Gal Hubara Agam <96368689+galagam@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 - .../custom_ops/rope/test_rope_op_variants.py | 13 ++++++++++++- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 9db38f2bc97b..21c18d75ef37 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -347,7 +347,6 @@ unittest/_torch/visual_gen/test_flux_pipeline.py::TestFluxCombinedOptimizations: unittest/auto_deploy/singlegpu/models/test_qwen3_5_moe.py::test_vision_attention_matches_reference SKIP (https://nvbugs/6189450) unittest/auto_deploy/singlegpu/models/test_qwen3_5_moe.py::test_vision_block_matches_reference SKIP (https://nvbugs/6189450) unittest/auto_deploy/singlegpu/models/test_qwen3_5_moe.py::test_vlm_wrapper_delta_is_request_scoped_no_cross_call_leakage SKIP (https://nvbugs/6189450) -unittest/auto_deploy/standalone/test_standalone_package.py::TestStandalonePackage::test_run_unit_tests SKIP (https://nvbugs/6160629) unittest/bindings/test_transfer_agent_bindings.py::TestNixlFunctionalTransfer::test_nixl_wait_in_progress_on_zero_timeout SKIP (https://nvbugs/6260897) unittest/executor/test_rpc.py::TestRpcCorrectness::test_incremental_task_async SKIP (https://nvbugs/5741476) unittest/executor/test_rpc_proxy.py SKIP (https://nvbugs/5605741) diff --git a/tests/unittest/auto_deploy/singlegpu/custom_ops/rope/test_rope_op_variants.py b/tests/unittest/auto_deploy/singlegpu/custom_ops/rope/test_rope_op_variants.py index a7fdb7db9d4e..8f36ebab9a99 100644 --- a/tests/unittest/auto_deploy/singlegpu/custom_ops/rope/test_rope_op_variants.py +++ b/tests/unittest/auto_deploy/singlegpu/custom_ops/rope/test_rope_op_variants.py @@ -23,7 +23,12 @@ import tensorrt_llm._torch.auto_deploy # noqa: F401 -torch.manual_seed(1234) +# NOTE: the existing tests below seed the RNG explicitly (torch.manual_seed) at +# their start. They use tight (near-ULP) half-precision tolerances that only +# hold for specific inputs, so without a fixed seed the standalone-package run +# (pytest-xdist, dynamic test distribution) used order-dependent inputs and +# failed intermittently. New tests should NOT copy this seeding crutch: choose +# tolerances that reflect the dtype error floor so the test holds for any input. @pytest.mark.parametrize("head_dim", [64, 256]) # head_dim must be a multiple of 64 @@ -42,6 +47,7 @@ def test_flashinfer_custom_op_and_hf_impl(dtype, atol, rtol, head_dim): - cos_sin_cache: [S, D] = [cos||sin] concatenated. - HF path: Q/K -> [B, N, S, D], cos_new/sin_new: [S, D] duplicated, then broadcast to [B, S, D]. """ + torch.manual_seed(1234) device = "cuda" batch = 2 seq_len = 4 @@ -121,6 +127,7 @@ def test_flashinfer_custom_op_and_complex_impl(dtype, atol, rtol, head_dim): - freqs_cis: [B, S, D/2] complex polar values. - flashinfer uses cos_sin_cache: [S, D] interleaved from real/imag of freqs_cis. """ + torch.manual_seed(1234) device = "cuda" batch = 2 seq_len = 4 @@ -193,6 +200,7 @@ def test_triton_custom_op_and_hf_impl(layout, head_dim, dtype, atol, rtol): - cosin_cache: [S, D/2, 2] interleaved [cos,sin]. - HF path: cos_full/sin_full: [S, D] then expanded to [B, S, D]. """ + torch.manual_seed(1234) device = "cuda" batch, seq_len, n_head = 2, 4, 3 @@ -254,6 +262,7 @@ def test_ds_impl_and_hf_impl(dtype, head_dim, atol, rtol): - cos_new/sin_new: [S, D] duplicated real values. - HF path: Q/K -> [B,N,S,D], cos_expand/sin_expand: [B,S,D], unsqueezed at dim=1. """ + torch.manual_seed(1234) device = "cuda" batch = 2 seq_len = 4 @@ -322,6 +331,7 @@ def test_flashinfer_custom_op_strided_interleaved(dtype, atol, rtol, head_dim): Tests is_neox=False (interleaved mode), matching contiguous-input results and complex-multiplication reference. """ + torch.manual_seed(1234) device = "cuda" batch = 2 seq_len = 4 @@ -388,6 +398,7 @@ def test_rope_deinterleave_load_hook(has_bias): - kv_a_proj: first kv_lora_rank rows unchanged, last qk_rope_head_dim rows permuted. - bias (when present): same split+permute pattern as kv_a_proj weight. """ + torch.manual_seed(1234) from tensorrt_llm._torch.auto_deploy.models.custom.mla_rope_utils import ( _rope_deinterleave_load_hook, ) From 86f9602057ac3c76df44419248c0f65daf1f79e6 Mon Sep 17 00:00:00 2001 From: Thor Johnsen <41591019+thorjohnsen@users.noreply.github.com> Date: Fri, 5 Jun 2026 13:35:58 -0500 Subject: [PATCH 012/119] [TRTLLM-12714][feat] KVCacheManagerV2: wire PyExecutor rebalance hook (single GPU, aggregated for now) (#14578) Signed-off-by: Thor Johnsen <41591019+thorjohnsen@users.noreply.github.com> --- tensorrt_llm/_torch/pyexecutor/_util.py | 2 + tensorrt_llm/_torch/pyexecutor/py_executor.py | 93 ++++++- .../_torch/pyexecutor/resource_manager.py | 12 + tensorrt_llm/llmapi/llm_args.py | 13 + .../kv_cache_manager_v2/_storage/_core.py | 16 +- .../kv_cache_manager_v2/_storage_manager.py | 10 +- .../test_kv_pool_rebalance_accuracy.py | 153 +++++++++++ .../integration/test_lists/test-db/l0_a10.yml | 1 + .../test_lists/test-db/l0_h100.yml | 2 + .../_torch/executor/test_kv_pool_rebalance.py | 249 ++++++++++++++++++ .../test_kv_cache_manager_v2.py | 44 +++- 11 files changed, 583 insertions(+), 12 deletions(-) create mode 100644 tests/integration/defs/accuracy/test_kv_pool_rebalance_accuracy.py create mode 100644 tests/unittest/_torch/executor/test_kv_pool_rebalance.py diff --git a/tensorrt_llm/_torch/pyexecutor/_util.py b/tensorrt_llm/_torch/pyexecutor/_util.py index addf98611597..31b0c8d28328 100644 --- a/tensorrt_llm/_torch/pyexecutor/_util.py +++ b/tensorrt_llm/_torch/pyexecutor/_util.py @@ -1761,6 +1761,8 @@ def create_py_executor_instance( execution_stream=execution_stream, waiting_queue_policy=waiting_queue_policy, dwdp_manager=dwdp_manager, + enable_kv_pool_rebalance=llm_args.kv_cache_config. + enable_kv_pool_rebalance, ) diff --git a/tensorrt_llm/_torch/pyexecutor/py_executor.py b/tensorrt_llm/_torch/pyexecutor/py_executor.py index 4d4de1a5d03c..b438a31e2719 100644 --- a/tensorrt_llm/_torch/pyexecutor/py_executor.py +++ b/tensorrt_llm/_torch/pyexecutor/py_executor.py @@ -37,6 +37,7 @@ from tensorrt_llm.logger import logger from tensorrt_llm.mapping import CpType from tensorrt_llm.runtime.generation import CUASSERT +from tensorrt_llm.runtime.kv_cache_manager_v2._exceptions import OutOfPagesError from tensorrt_llm.tools.layer_wise_benchmarks import get_calibrator from tensorrt_llm.tools.profiler.host_profile_tools.host_profiler import ( get_global_profiler, host_profiler_context) @@ -313,7 +314,8 @@ def __init__( execution_stream: Optional[torch.cuda.Stream] = None, waiting_queue_policy: WaitingQueuePolicy = WaitingQueuePolicy.FCFS, adp_router: Optional[ADPRouter] = None, - dwdp_manager: Optional[DwdpManager] = None): + dwdp_manager: Optional[DwdpManager] = None, + enable_kv_pool_rebalance: bool = False): super(PyExecutor, self).__init__() self.device_id = torch.cuda.current_device() self.global_rank = dist.rank @@ -346,6 +348,7 @@ def __init__( None) self.guided_decoder = guided_decoder self.disable_overlap_scheduler = disable_overlap_scheduler + self.enable_kv_pool_rebalance = enable_kv_pool_rebalance self.enable_early_first_token_response = enable_early_first_token_response self.virtual_memory_pools = virtual_memory_pools @@ -2702,6 +2705,9 @@ def _executor_loop(self): if self._resource_governor_enabled: self._sync_and_process_resource_governor_queue() + if self._is_kv_manager_v2 and self._can_pause_for_rebalance(): + self._maybe_rebalance_kv_pools() + scheduled_batch, iter_stats = self._prepare_and_schedule_batch() self._handle_control_request() @@ -2936,6 +2942,88 @@ def _sync_and_process_resource_governor_queue(self): else: raise ValueError(f"Invalid request type: {type(request)}.") + def _can_pause_for_rebalance(self) -> bool: + """Gate KV pool rebalance to the cases the v1 hook supports. + + MVP scope: single-GPU aggregated, no in-flight disagg transfer, + no beam search, no drafter, not during warmup or shutdown. + Honors the ``enable_kv_pool_rebalance`` opt-in flag (default off). + """ + if not self.enable_kv_pool_rebalance: + return False + if self.dist.pp_size > 1: + return False + if self.kv_cache_transceiver is not None: + return False + if self.is_warmup: + return False + if self.is_shutdown: + return False + if self.kv_cache_manager.max_beam_width > 1: + return False + if self.drafter is not None: + return False + return True + + def _consume_previous_batch_for_rebalance(self) -> None: + """Drain ``previous_batch`` so its _KVCache instances are quiescent. + + No-op when ``previous_batch is None`` -- i.e., always a no-op in + the non-overlap loop, since that loop never sets previous_batch. + In the overlap loop this fires when the rebalance hook catches a + pending in-flight iteration; we consume it inline so suspend can + safely run. + + Mirrors the inline sequence in ``_executor_loop_overlap`` that + handles ``previous_batch``. Unlike the inline code we are not + guarded by ``should_process_previous_batch``: the rebalance gate + already excludes the multi-rank-divergence cases that flag exists + to handle. + """ + if self.previous_batch is None: + return + self._update_requests(self.previous_batch.sample_state) + self._send_kv_async( + self.previous_batch.scheduled_requests.all_requests()) + self._flush_pending_transfer_responses() + self._process_previous_batch() + self.perf_manager.compute_batch_gpu_times( + self.previous_batch.scheduled_requests.all_requests()) + self.previous_batch = None + + def _maybe_rebalance_kv_pools(self) -> None: + """Rebalance KV pool ratios when the V2 auto-tuner asks for it. + + Fast path: ``need_adjustment`` checks the sample counter and the + 120s cooldown before doing any real work. On the slow path we + drain pending GPU work, consume any in-flight ``previous_batch`` + (overlap loop only), suspend every active request, call + ``adjust()``, and resume. Resume failures stay suspended; the + scheduler reactivates them through prepare_context / + try_allocate_generation on the next iteration, the same path it + uses today after eviction. + """ + mgr = self.kv_cache_manager + if not mgr.impl.need_adjustment: + return + + torch.cuda.current_stream().synchronize() + self._consume_previous_batch_for_rebalance() + + paused: List[LlmRequest] = [] + for req in self.active_requests: + if mgr.is_request_active(req.py_request_id): + mgr.suspend_request(req) + paused.append(req) + + try: + mgr.impl.adjust() + except OutOfPagesError as e: + logger.warning(f"KV pool adjust() failed: {e!r}") + + for req in paused: + mgr.resume_request(req) + @contextmanager def control_action(self): """ @@ -2982,6 +3070,9 @@ def _executor_loop_overlap(self): if self._resource_governor_enabled: self._sync_and_process_resource_governor_queue() + if self._is_kv_manager_v2 and self._can_pause_for_rebalance(): + self._maybe_rebalance_kv_pools() + scheduled_batch, iter_stats = self._prepare_and_schedule_batch() self._handle_control_request() diff --git a/tensorrt_llm/_torch/pyexecutor/resource_manager.py b/tensorrt_llm/_torch/pyexecutor/resource_manager.py index ed829639a908..84070dd0864c 100644 --- a/tensorrt_llm/_torch/pyexecutor/resource_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/resource_manager.py @@ -3051,6 +3051,18 @@ def suspend_request(self, req: LlmRequest) -> None: if kv_cache is not None and kv_cache.is_active: kv_cache.suspend() + def resume_request(self, req: LlmRequest) -> bool: + """Resume a previously-suspended KV cache for *req*. + + Returns True if the cache is (or becomes) active on GPU, False if + resume was refused (e.g. GPU pressure above max_util_for_resume) + or no cache exists for the request. + """ + kv_cache = self.kv_cache_map.get(req.py_request_id) + if kv_cache is None: + return False + return self._resume_and_restore(req.py_request_id, kv_cache) + # ---- prepare_resources ---- @nvtx_range("prepare_resources_kv_cache_manager_v2") diff --git a/tensorrt_llm/llmapi/llm_args.py b/tensorrt_llm/llmapi/llm_args.py index 673a139372f9..0bac2d7ee253 100644 --- a/tensorrt_llm/llmapi/llm_args.py +++ b/tensorrt_llm/llmapi/llm_args.py @@ -2833,6 +2833,19 @@ class KvCacheConfig(StrictBaseModel, PybindMirror): "The maximum utilization of the KV cache for resume. Default is 95%. Only used when using KV cache manager v2 (experimental)." ) + enable_kv_pool_rebalance: bool = Field( + default=False, + status="prototype", + description= + "Opt in to the KVCacheManagerV2 auto-tuner (``adjust()``) for " + "rebalancing pool-group ratios between iterations. When True the " + "PyExecutor calls ``adjust()`` opportunistically; the auto-tuner " + "itself remains gated by V2's internal 2000-sample / 120s cooldown. " + "When False (default) the rebalance hook is skipped entirely and " + "pool ratios remain at their warmup-derived values. Beta: enable at " + "your own risk. Only used when using KV cache manager v2 " + "(experimental).") + def _to_pybind(self): config = _KvCacheConfig( enable_block_reuse=self.enable_block_reuse, diff --git a/tensorrt_llm/runtime/kv_cache_manager_v2/_storage/_core.py b/tensorrt_llm/runtime/kv_cache_manager_v2/_storage/_core.py index 1db403131ba8..ab33a0eb5dac 100644 --- a/tensorrt_llm/runtime/kv_cache_manager_v2/_storage/_core.py +++ b/tensorrt_llm/runtime/kv_cache_manager_v2/_storage/_core.py @@ -429,14 +429,14 @@ def shrink_in_progress(self) -> bool: def finish_shrink(self) -> bool: assert NDEBUG or self._check() - if ( - self.shrink_in_progress - and self._target_capacity + len(self._overflow_slots) == self._num_active_slots - ): - assert ( - len(set(s.slot_id for s in self._overflow_slots)) == len(self._overflow_slots) - and len(self._overflow_slots) == self._num_active_slots - self._target_capacity - ), "Some slots are still in use." + # Overflow-range IDs that were ever issued are exactly + # max(0, _num_active_slots - _target_capacity); the underused case + # (_num_active_slots <= _target_capacity) collapses to zero. + expected_overflow = max(0, self._num_active_slots - self._target_capacity) + if self.shrink_in_progress and len(self._overflow_slots) == expected_overflow: + assert len(set(s.slot_id for s in self._overflow_slots)) == len(self._overflow_slots), ( + "Some slots are still in use." + ) for ev in set(s.ready_event for s in self._overflow_slots): ev.synchronize() for slot in self._overflow_slots: diff --git a/tensorrt_llm/runtime/kv_cache_manager_v2/_storage_manager.py b/tensorrt_llm/runtime/kv_cache_manager_v2/_storage_manager.py index 90f822ba5539..9ade62d5dc99 100644 --- a/tensorrt_llm/runtime/kv_cache_manager_v2/_storage_manager.py +++ b/tensorrt_llm/runtime/kv_cache_manager_v2/_storage_manager.py @@ -637,6 +637,15 @@ def shrink_pool_group( ), "Not enough slots" pool_group = self._levels[level].storage._pool_groups[pg_idx] assert new_num_slots < pool_group.num_slots, "Not required for expansion of pools" + allocator = pool_group._slot_allocator + # Fast path: when no slot id has ever been issued in the to-be-removed + # range [new_num_slots, _capacity), there is nothing to migrate. + # _num_active_slots is a monotone high-water mark of issued ids. + if allocator._num_active_slots <= new_num_slots: + allocator.prepare_for_shrink(new_num_slots) + allocator.finish_shrink() + pool_group.resize_pools(new_num_slots) + return ctrl = self._levels[level].controller # pages with overflow slots and their indices in the eviction queue. overflow_slots = deque[tuple[int, Page]]() @@ -647,7 +656,6 @@ def shrink_pool_group( num_overflow_persistent = len(overflow_persistent_pages) if num_overflow_persistent > new_num_slots: raise OutOfPagesError("Not enough slots to hold all persistent pages") - allocator = pool_group._slot_allocator # prevent allocating slots with id >= new_num_slots allocator.prepare_for_shrink(new_num_slots) min_num_evicted = 0 diff --git a/tests/integration/defs/accuracy/test_kv_pool_rebalance_accuracy.py b/tests/integration/defs/accuracy/test_kv_pool_rebalance_accuracy.py new file mode 100644 index 000000000000..121a2bc65bac --- /dev/null +++ b/tests/integration/defs/accuracy/test_kv_pool_rebalance_accuracy.py @@ -0,0 +1,153 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +r"""Accuracy test for the KVCacheManagerV2 rebalance hook. + +Verifies that forcing the V2 auto-tuner to fire mid-generation does not +change greedy-decode outputs. Uses Gemma-3-1B with explicit VSWA so the +KV cache lands in >=2 pool groups and ``adjust()`` has real work to do +(a single pool group would make rebalance a no-op). + +Run as: + LLM_MODELS_ROOT=/path pytest \ + tests/integration/defs/accuracy/test_kv_pool_rebalance_accuracy.py +""" + +import pytest + +from tensorrt_llm import LLM +from tensorrt_llm.llmapi import KvCacheConfig, SamplingParams + +from ..conftest import llm_models_root, skip_pre_hopper + +# --------------------------------------------------------------------------- # +# Ratio injection +# --------------------------------------------------------------------------- # + + +def _inject_pool_ratio_mismatch(llm: LLM, *, skew: float = 2.0) -> None: + """Force the V2 auto-tuner to do real pool-resize work on the next rebalance call. + + Bypasses the 2000-sample / 120s cooldown gates by stomping counters, + then perturbs ``_target_ratio_list_gpu`` so it differs from + ``_current_gpu_ratio`` by more than the 1.25x threshold inside + ``_need_adjustment``. + + Requires a model with >=2 pool groups (e.g. Gemma-3-1B with VSWA). + Asserts the precondition so a future model change can't silently + turn this test into a no-op. + """ + executor = llm._executor.engine + kv_cache_manager = executor.kv_cache_manager + impl = kv_cache_manager.impl + + impl._num_sampled_kv_caches = 2001 + impl._last_adjustment_time = 0.0 + + current = list(impl._current_gpu_ratio) + assert len(current) >= 2, ( + f"Ratio injection requires >=2 pool groups; got {len(current)}. " + "Check that VSWA is actually configured for this model." + ) + + skewed = [current[0] * skew] + list(current[1:]) + total = sum(skewed) + impl._target_ratio_list_gpu = [x / total for x in skewed] + + +# --------------------------------------------------------------------------- # +# Test +# --------------------------------------------------------------------------- # + +# A handful of prompts spanning short, medium, and long context lengths. +# The long prompt is intentionally repetitive so it occupies multiple KV +# blocks and creates enough pool pressure for rebalance to matter. +_PROMPTS = [ + "The capital of France is", + "Write one sentence about transformers.", + "List three prime numbers greater than 100:", + "The quick brown fox jumps over the lazy dog. " * 40, +] + +_SAMPLING = SamplingParams(max_tokens=64, temperature=0.0, top_k=1) + + +def _vswa_kv_cache_config(*, enable_rebalance: bool) -> KvCacheConfig: + """V2 manager + explicit VSWA pattern that yields multiple pool groups. + + Gemma-3-1B has 5 sliding-window layers : 1 full-attention layer. + """ + return KvCacheConfig( + use_kv_cache_manager_v2=True, + enable_kv_pool_rebalance=enable_rebalance, + max_attention_window=[512, 512, 512, 512, 512, 32768], + # Block reuse disabled per the standing Gemma3 WAR for non- + # inclusive sliding window kernel support. + enable_block_reuse=False, + enable_partial_reuse=False, + tokens_per_block=32, + free_gpu_memory_fraction=0.6, + ) + + +def _generate_tokens(*, model_path: str, disable_overlap: bool, enable_rebalance: bool): + """Run one LLM, return list[list[int]] of generated token ids. + + Note: the ratio-injection helper requires direct access to the + in-process PyExecutor, so the test runs in single-process worker + mode (``TLLM_WORKER_USE_SINGLE_PROCESS=1``). The caller is + responsible for setting that env var (via monkeypatch or otherwise) + before invoking this helper. + """ + with LLM( + model_path, + disable_overlap_scheduler=disable_overlap, + kv_cache_config=_vswa_kv_cache_config(enable_rebalance=enable_rebalance), + ) as llm: + if enable_rebalance: + _inject_pool_ratio_mismatch(llm) + outputs = llm.generate(_PROMPTS, _SAMPLING) + return [list(o.outputs[0].token_ids) for o in outputs] + + +@skip_pre_hopper +class TestKvPoolRebalanceAccuracy: + """Token-exact greedy-decode equivalence under rebalance. + + Compares rebalance=off and rebalance=on with a forced mid-generation + adjust(). + """ + + MODEL_PATH = f"{llm_models_root()}/gemma/gemma-3-1b-it/" + + @pytest.mark.parametrize("disable_overlap", [True, False], ids=["no_overlap", "overlap"]) + def test_rebalance_matches_baseline(self, disable_overlap, monkeypatch): + # Keep the PyExecutor in-process so the ratio-injection helper + # can reach .engine on the client side. + monkeypatch.setenv("TLLM_WORKER_USE_SINGLE_PROCESS", "1") + + baseline = _generate_tokens( + model_path=self.MODEL_PATH, disable_overlap=disable_overlap, enable_rebalance=False + ) + + treated = _generate_tokens( + model_path=self.MODEL_PATH, disable_overlap=disable_overlap, enable_rebalance=True + ) + + assert len(baseline) == len(treated) == len(_PROMPTS) + for i, (b, t) in enumerate(zip(baseline, treated)): + assert b == t, ( + f"prompt {i}: rebalance changed greedy-decode output\n" + f" baseline: {b[:16]}...\n" + f" treated: {t[:16]}..." + ) diff --git a/tests/integration/test_lists/test-db/l0_a10.yml b/tests/integration/test_lists/test-db/l0_a10.yml index 7007503f3e1b..e78984886eee 100644 --- a/tests/integration/test_lists/test-db/l0_a10.yml +++ b/tests/integration/test_lists/test-db/l0_a10.yml @@ -32,6 +32,7 @@ l0_a10: - unittest/_torch/executor/test_scheduler_serializable_output.py - unittest/_torch/executor/test_kv_cache_estimation.py - unittest/_torch/executor/test_kv_cache_budget_split.py + - unittest/_torch/executor/test_kv_pool_rebalance.py - unittest/_torch/executor/test_disagg_index_mapper_early_release.py - unittest/_torch/modules/dwdp/test_dwdp_fixup_moe_backends.py - unittest/_torch/modules/dwdp/test_dwdp_manager.py diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index ff45caf899f7..3462d75ff1a9 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -94,6 +94,8 @@ l0_h100: - accuracy/test_llm_api_pytorch.py::TestGemma3_1BInstruct::test_auto_dtype_vswa_reuse_low_memory_available_partial_reuse - accuracy/test_llm_api_pytorch.py::TestGemma3_1BInstruct::test_auto_dtype_vswa_without_reuse_disable_overlap_scheduler - accuracy/test_llm_api_pytorch.py::TestGemma3_1BInstruct::test_auto_dtype_vswa_reuse_disable_overlap_scheduler + - accuracy/test_kv_pool_rebalance_accuracy.py::TestKvPoolRebalanceAccuracy::test_rebalance_matches_baseline[no_overlap] + - accuracy/test_kv_pool_rebalance_accuracy.py::TestKvPoolRebalanceAccuracy::test_rebalance_matches_baseline[overlap] - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_chunked_prefill[use_temperature=False-attn_backend=TRTLLM] TIMEOUT (90) - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_chunked_prefill[use_temperature=True-attn_backend=TRTLLM] TIMEOUT (90) - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_dummy_load_format diff --git a/tests/unittest/_torch/executor/test_kv_pool_rebalance.py b/tests/unittest/_torch/executor/test_kv_pool_rebalance.py new file mode 100644 index 000000000000..59f4f3eb39ea --- /dev/null +++ b/tests/unittest/_torch/executor/test_kv_pool_rebalance.py @@ -0,0 +1,249 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Functional unit tests for the KVCacheManagerV2 rebalance hook in +PyExecutor (``_can_pause_for_rebalance``, ``_maybe_rebalance_kv_pools``, +``_consume_previous_batch_for_rebalance``). + +These tests intentionally do not spin up a real PyExecutor: PyExecutor's +constructor pulls in the model engine, sampler, scheduler, KV cache +manager, distributed, etc. Instead we follow the same pattern as +``test_py_executor.py`` and call the methods under test as unbound +attribute lookups on a ``MagicMock(spec=PyExecutor)`` with just the +fields each method reads. + +The accuracy of pool rebalancing itself (i.e., that suspend/adjust/resume +preserves generated tokens) is covered by the integration accuracy test; +here we only verify the call chain and gate logic. +""" + +from unittest.mock import MagicMock + +import pytest + +from tensorrt_llm._torch.pyexecutor.py_executor import PyExecutor +from tensorrt_llm.runtime.kv_cache_manager_v2._exceptions import OutOfPagesError + +# --------------------------------------------------------------------------- # +# Helpers +# --------------------------------------------------------------------------- # + + +def _make_executor( + *, + enable_kv_pool_rebalance: bool = True, + pp_size: int = 1, + kv_cache_transceiver=None, + is_warmup: bool = False, + is_shutdown: bool = False, + max_beam_width: int = 1, + drafter=None, + need_adjustment: bool = True, + active_requests=None, + previous_batch=None, +) -> MagicMock: + """Construct a MagicMock shaped like PyExecutor with exactly the + attributes the rebalance code path reads. + """ + exe = MagicMock(spec=PyExecutor) + + # Gate inputs. + exe.enable_kv_pool_rebalance = enable_kv_pool_rebalance + exe.dist = MagicMock(pp_size=pp_size) + exe.kv_cache_transceiver = kv_cache_transceiver + exe.is_warmup = is_warmup + exe.is_shutdown = is_shutdown + exe.drafter = drafter + + # KV cache manager (resource-manager wrapper). + exe.kv_cache_manager = MagicMock() + exe.kv_cache_manager.max_beam_width = max_beam_width + exe.kv_cache_manager.impl = MagicMock() + exe.kv_cache_manager.impl.need_adjustment = need_adjustment + + # is_request_active returns True for every id we tracked, False for + # everything else. Tests set active_requests to a list of mocks with + # py_request_id attributes. + exe.active_requests = active_requests or [] + active_ids = {r.py_request_id for r in exe.active_requests} + exe.kv_cache_manager.is_request_active.side_effect = lambda rid: rid in active_ids + + # Previous batch (overlap loop). + exe.previous_batch = previous_batch + + return exe + + +def _make_request(req_id: int) -> MagicMock: + req = MagicMock() + req.py_request_id = req_id + return req + + +# --------------------------------------------------------------------------- # +# Gate tests +# --------------------------------------------------------------------------- # + + +class TestCanPauseForRebalance: + """Cover every short-circuit branch of ``_can_pause_for_rebalance``.""" + + def test_default_setup_returns_true(self): + exe = _make_executor() + assert PyExecutor._can_pause_for_rebalance(exe) is True + + def test_flag_off_returns_false(self): + exe = _make_executor(enable_kv_pool_rebalance=False) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_pp_size_gt_one_returns_false(self): + exe = _make_executor(pp_size=2) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_transceiver_present_returns_false(self): + exe = _make_executor(kv_cache_transceiver=MagicMock()) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_warmup_returns_false(self): + exe = _make_executor(is_warmup=True) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_shutdown_returns_false(self): + exe = _make_executor(is_shutdown=True) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_beam_width_gt_one_returns_false(self): + exe = _make_executor(max_beam_width=2) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + def test_drafter_present_returns_false(self): + exe = _make_executor(drafter=MagicMock()) + assert PyExecutor._can_pause_for_rebalance(exe) is False + + +# --------------------------------------------------------------------------- # +# _maybe_rebalance_kv_pools +# --------------------------------------------------------------------------- # + + +class TestMaybeRebalanceKvPools: + """The hook body: synchronize -> drain -> suspend -> adjust -> resume.""" + + def test_no_op_when_need_adjustment_false(self, monkeypatch): + exe = _make_executor(need_adjustment=False, active_requests=[_make_request(1)]) + monkeypatch.setattr("torch.cuda.current_stream", MagicMock()) + + PyExecutor._maybe_rebalance_kv_pools(exe) + + exe.kv_cache_manager.impl.adjust.assert_not_called() + exe.kv_cache_manager.suspend_request.assert_not_called() + exe.kv_cache_manager.resume_request.assert_not_called() + + def test_fires_full_cycle(self, monkeypatch): + reqs = [_make_request(1), _make_request(2)] + exe = _make_executor(active_requests=reqs) + # Stub the consume helper (its own behavior is covered below). + exe._consume_previous_batch_for_rebalance = MagicMock() + monkeypatch.setattr("torch.cuda.current_stream", MagicMock()) + + PyExecutor._maybe_rebalance_kv_pools(exe) + + exe._consume_previous_batch_for_rebalance.assert_called_once() + exe.kv_cache_manager.impl.adjust.assert_called_once() + assert exe.kv_cache_manager.suspend_request.call_count == 2 + assert exe.kv_cache_manager.resume_request.call_count == 2 + + def test_skips_already_suspended_requests(self, monkeypatch): + active = _make_request(1) + suspended = _make_request(2) + exe = _make_executor(active_requests=[active]) + exe.active_requests = [active, suspended] + # Override side_effect: only req 1 is active on GPU. + exe.kv_cache_manager.is_request_active.side_effect = lambda rid: rid == 1 + exe._consume_previous_batch_for_rebalance = MagicMock() + monkeypatch.setattr("torch.cuda.current_stream", MagicMock()) + + PyExecutor._maybe_rebalance_kv_pools(exe) + + # Only the active request was suspended and resumed. + exe.kv_cache_manager.suspend_request.assert_called_once_with(active) + exe.kv_cache_manager.resume_request.assert_called_once_with(active) + + def test_expected_adjust_failure_does_not_skip_resume(self, monkeypatch, caplog): + """OutOfPagesError from adjust() is the one expected runtime failure. + + It must be swallowed so paused requests are still resumed. + """ + reqs = [_make_request(1)] + exe = _make_executor(active_requests=reqs) + exe._consume_previous_batch_for_rebalance = MagicMock() + exe.kv_cache_manager.impl.adjust.side_effect = OutOfPagesError("boom") + monkeypatch.setattr("torch.cuda.current_stream", MagicMock()) + + # Should not raise. + PyExecutor._maybe_rebalance_kv_pools(exe) + + exe.kv_cache_manager.suspend_request.assert_called_once() + exe.kv_cache_manager.resume_request.assert_called_once() + + def test_unexpected_adjust_failure_propagates(self, monkeypatch): + """Any non-OutOfPagesError (programmer bug) must propagate. + + Such errors fail fast rather than being downgraded to a warning. + """ + reqs = [_make_request(1)] + exe = _make_executor(active_requests=reqs) + exe._consume_previous_batch_for_rebalance = MagicMock() + exe.kv_cache_manager.impl.adjust.side_effect = RuntimeError("boom") + monkeypatch.setattr("torch.cuda.current_stream", MagicMock()) + + with pytest.raises(RuntimeError, match="boom"): + PyExecutor._maybe_rebalance_kv_pools(exe) + + +# --------------------------------------------------------------------------- # +# _consume_previous_batch_for_rebalance +# --------------------------------------------------------------------------- # + + +class TestConsumePreviousBatch: + """Overlap-mode drain helper.""" + + def test_no_op_when_previous_batch_none(self): + exe = _make_executor(previous_batch=None) + PyExecutor._consume_previous_batch_for_rebalance(exe) + exe._update_requests.assert_not_called() + exe._send_kv_async.assert_not_called() + exe._flush_pending_transfer_responses.assert_not_called() + exe._process_previous_batch.assert_not_called() + + def test_consumes_and_clears(self): + prev = MagicMock() + prev.sample_state = MagicMock() + prev.scheduled_requests.all_requests.return_value = [_make_request(1)] + exe = _make_executor(previous_batch=prev) + # perf_manager needs compute_batch_gpu_times. + exe.perf_manager = MagicMock() + + PyExecutor._consume_previous_batch_for_rebalance(exe) + + exe._update_requests.assert_called_once_with(prev.sample_state) + exe._send_kv_async.assert_called_once() + exe._flush_pending_transfer_responses.assert_called_once() + exe._process_previous_batch.assert_called_once() + exe.perf_manager.compute_batch_gpu_times.assert_called_once() + assert exe.previous_batch is None + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py b/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py index 65deac29381c..7e84c0ba75c2 100755 --- a/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py +++ b/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py @@ -65,7 +65,7 @@ from kv_cache_manager_v2._copy_engine import CopyTask, batched_copy from kv_cache_manager_v2._exceptions import OutOfPagesError from kv_cache_manager_v2._life_cycle_registry import SsmLifeCycle - from kv_cache_manager_v2._storage._core import CacheLevelStorage + from kv_cache_manager_v2._storage._core import CacheLevelStorage, SlotAllocator from kv_cache_manager_v2._utils import ( CachedCudaStream, HalfOpenRange, @@ -118,7 +118,10 @@ from tensorrt_llm.runtime.kv_cache_manager_v2._copy_engine import CopyTask, batched_copy from tensorrt_llm.runtime.kv_cache_manager_v2._exceptions import OutOfPagesError from tensorrt_llm.runtime.kv_cache_manager_v2._life_cycle_registry import SsmLifeCycle - from tensorrt_llm.runtime.kv_cache_manager_v2._storage._core import CacheLevelStorage + from tensorrt_llm.runtime.kv_cache_manager_v2._storage._core import ( + CacheLevelStorage, + SlotAllocator, + ) from tensorrt_llm.runtime.kv_cache_manager_v2._utils import ( CachedCudaStream, HalfOpenRange, @@ -2516,5 +2519,42 @@ def test_scratch_chunk_size_variation( self.manager.clear_reusable_blocks() +class TestSlotAllocatorShrink(unittest.TestCase): + def test_shrink_underused_pool(self) -> None: + # Regression for NVBug 6225866: shrinking a pool whose new size is + # still above the slot-ID high-water mark used to assert because + # _num_active_slots - _target_capacity went negative. + allocator = SlotAllocator(capacity=184064) + slots = [allocator.allocate() for _ in range(2048)] + for s in slots: + allocator.release(s) + self.assertEqual(allocator._num_active_slots, 2048) + + allocator.prepare_for_shrink(122624) + self.assertEqual(len(allocator._overflow_slots), 0) + self.assertTrue(allocator.finish_shrink()) + self.assertEqual(allocator._capacity, 122624) + self.assertEqual(allocator._num_active_slots, 2048) + self.assertFalse(allocator.shrink_in_progress) + + def test_shrink_touched_pool(self) -> None: + # Sanity-check that the non-trivial migration path still works: + # all ids are issued, half released, shrink to half. + allocator = SlotAllocator(capacity=16) + slots = [allocator.allocate() for _ in range(16)] + for s in slots[8:]: + allocator.release(s) + self.assertEqual(allocator._num_active_slots, 16) + + allocator.prepare_for_shrink(8) + self.assertEqual(len(allocator._overflow_slots), 8) + self.assertTrue(allocator.finish_shrink()) + self.assertEqual(allocator._capacity, 8) + self.assertEqual(allocator._num_active_slots, 8) + + for s in slots[:8]: + allocator.release(s) + + if __name__ == "__main__": unittest.main() From 3e17560fe3a998a79a8d7f97b10ec4f5dfe3175e Mon Sep 17 00:00:00 2001 From: o-stoner <245287810+o-stoner@users.noreply.github.com> Date: Fri, 5 Jun 2026 16:42:08 -0700 Subject: [PATCH 013/119] [None][feat] add Wan I2V generation example (#14981) Signed-off-by: Olivia Stoner <245287810+o-stoner@users.noreply.github.com> --- examples/visual_gen/README.md | 2 + .../configs/wan2.2-i2v-fp4-1gpu.yaml | 27 +++++++ examples/visual_gen/models/wan_i2v.py | 80 +++++++++++++++++++ 3 files changed, 109 insertions(+) create mode 100644 examples/visual_gen/configs/wan2.2-i2v-fp4-1gpu.yaml create mode 100644 examples/visual_gen/models/wan_i2v.py diff --git a/examples/visual_gen/README.md b/examples/visual_gen/README.md index 0991276b3c2c..11460505db33 100644 --- a/examples/visual_gen/README.md +++ b/examples/visual_gen/README.md @@ -18,9 +18,11 @@ for feature details. # Defaults python quickstart_example.py python models/wan_t2v.py +python models/wan_i2v.py # With engine config (quant, parallelism, etc.) python models/wan_t2v.py --visual_gen_args configs/wan2.2-t2v-fp4-1gpu.yaml +python models/wan_i2v.py --visual_gen_args configs/wan2.2-i2v-fp4-1gpu.yaml --image /path/to/image.png ``` Install deps from the repo root: `pip install -r requirements-dev.txt`. diff --git a/examples/visual_gen/configs/wan2.2-i2v-fp4-1gpu.yaml b/examples/visual_gen/configs/wan2.2-i2v-fp4-1gpu.yaml new file mode 100644 index 000000000000..8fbda2c55b64 --- /dev/null +++ b/examples/visual_gen/configs/wan2.2-i2v-fp4-1gpu.yaml @@ -0,0 +1,27 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU Wan 2.2 I2V with NVFP4 dynamic quantization. +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +quant_config: + quant_algo: NVFP4 + dynamic: true +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/models/wan_i2v.py b/examples/visual_gen/models/wan_i2v.py new file mode 100644 index 000000000000..854ed43784b3 --- /dev/null +++ b/examples/visual_gen/models/wan_i2v.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Wan Image-to-Video generation. + +Usage: + python wan_i2v.py + python wan_i2v.py --visual_gen_args ../configs/wan2.2-i2v-fp4-1gpu.yaml +""" + +import argparse +import os + +from tensorrt_llm import VisualGen, VisualGenArgs + +_DEFAULT_IMAGE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "cat_piano.png") + + +def main(): + parser = argparse.ArgumentParser(description="Wan Image-to-Video example") + parser.add_argument( + "--model", + type=str, + default="Wan-AI/Wan2.2-I2V-A14B-Diffusers", + help="Model path or HuggingFace Hub ID", + ) + parser.add_argument( + "--visual_gen_args", + "--extra_visual_gen_options", + dest="visual_gen_args", + type=str, + default=None, + help="Path to YAML config (same as trtllm-serve --visual_gen_args)", + ) + parser.add_argument( + "--image", + type=str, + default=_DEFAULT_IMAGE, + help="Path to input image for I2V conditioning", + ) + parser.add_argument( + "--output_path", + type=str, + default="wan_i2v_output.mp4", + help="Path to save the output video", + ) + args = parser.parse_args() + + # Engine config from shared YAML (optional); model-specific defaults apply otherwise. + extra_args = VisualGenArgs.from_yaml(args.visual_gen_args) if args.visual_gen_args else None + visual_gen = VisualGen(model=args.model, args=extra_args) + + # --- Model-specific: I2V request construction --- + # Start from per-model defaults (steps, guidance, seed, etc.) and set the input image. + params = visual_gen.default_params + params.image = args.image + + output = visual_gen.generate( + inputs="A cat presses the piano keys with its paws, soft notes filling the quiet room.", + params=params, + ) + + output.save(args.output_path) + print(f"Saved: {args.output_path}") + + +if __name__ == "__main__": + main() From 52ba2bbfe13db9bdc760dd0ea9dd7a2ace2d12b5 Mon Sep 17 00:00:00 2001 From: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com> Date: Fri, 5 Jun 2026 19:57:06 -0400 Subject: [PATCH 014/119] [TRTLLM-12527][feat] Parallelize multi-shard visual-gen checkpoint loading and pre-fetch checking (#14021) Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com> --- .../_torch/visual_gen/checkpoints/prefetch.py | 165 ++++++++++++++++++ .../visual_gen/checkpoints/weight_loader.py | 41 ++++- .../visual_gen/models/ltx2/pipeline_ltx2.py | 23 ++- .../models/ltx2/pipeline_ltx2_two_stages.py | 9 +- 4 files changed, 230 insertions(+), 8 deletions(-) create mode 100644 tensorrt_llm/_torch/visual_gen/checkpoints/prefetch.py diff --git a/tensorrt_llm/_torch/visual_gen/checkpoints/prefetch.py b/tensorrt_llm/_torch/visual_gen/checkpoints/prefetch.py new file mode 100644 index 000000000000..7e4f7a34e5a0 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/checkpoints/prefetch.py @@ -0,0 +1,165 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Host page-cache prefetch helpers for visual generation checkpoints.""" + +import multiprocessing +import os +from concurrent.futures import ThreadPoolExecutor +from typing import Iterable, Optional, Set + +import psutil +import torch.distributed as dist + +from tensorrt_llm.logger import logger + +_PREFETCH_CHUNK_SIZE = 16 * 1024 * 1024 + + +def _dist_initialized() -> bool: + return dist.is_available() and dist.is_initialized() + + +def _dist_barrier() -> None: + if _dist_initialized(): + dist.barrier() + + +def _get_int_env(name: str, default: int) -> int: + try: + return int(os.environ.get(name, "")) + except ValueError: + return default + + +def _local_rank_and_size() -> tuple[int, int]: + if not _dist_initialized(): + return 0, 1 + + rank = dist.get_rank() + world_size = dist.get_world_size() + local_rank = _get_int_env("LOCAL_RANK", rank) + local_size = _get_int_env("LOCAL_WORLD_SIZE", world_size) + + if local_size < 1 or local_rank < 0 or local_rank >= local_size: + return rank, world_size + return local_rank, local_size + + +def _get_local_available_host_memory() -> int: + """Return the minimum available host memory observed by local ranks. + + The prefetch/skip decision must be the same for all ranks that synchronize + at the post-prefetch barrier. Use torch.distributed to collect per-rank + snapshots and reduce the local slice to its minimum. + """ + available_memory = psutil.virtual_memory().available + if not _dist_initialized() or dist.get_world_size() == 1: + return available_memory + + world_size = dist.get_world_size() + gathered_memory: list[int | None] = [None] * world_size + dist.all_gather_object(gathered_memory, int(available_memory)) + + local_rank, local_size = _local_rank_and_size() + local_start = dist.get_rank() - local_rank + local_end = min(local_start + local_size, world_size) + local_memory = [ + memory for memory in gathered_memory[local_start:local_end] if memory is not None + ] + if not local_memory: + return available_memory + return min(local_memory) + + +def _normalize_paths( + file_names: Iterable[str], + prefetched_paths: Optional[Set[str]], +) -> list[str]: + paths: list[str] = [] + seen: set[str] = set() + for file_name in file_names: + path = os.path.abspath(file_name) + if path in seen: + continue + seen.add(path) + if prefetched_paths is not None and path in prefetched_paths: + continue + paths.append(path) + return paths + + +def _prefetch_file(file_name: str, description: str) -> None: + if not os.path.exists(file_name): + return + + logger.info(f"Prefetching {description} file {file_name} to host page cache...") + with open(file_name, "rb") as f: + while f.read(_PREFETCH_CHUNK_SIZE): + pass + logger.info(f"Finished prefetching {description} file {file_name}.") + + +def prefetch_files_to_host_cache( + file_names: Iterable[str], + *, + description: str, + prefetched_paths: Optional[Set[str]] = None, + ignore_errors: bool = False, +) -> bool: + """Warm checkpoint files in host page cache across distributed local ranks. + + Returns True only when all selected files were already prefetched or were + prefetched successfully. If prefetch is skipped or fails, returns False + when ignore_errors=True and raises otherwise. + """ + paths = _normalize_paths(file_names, prefetched_paths) + success = False + try: + if not paths: + success = True + return True + + prefetch_size = sum(os.path.getsize(path) for path in paths if os.path.exists(path)) + available_memory = _get_local_available_host_memory() + if prefetch_size >= available_memory * 0.9: + logger.info( + f"Skipping {description} prefetch because files require " + f"{prefetch_size / (1024**3):.2f}GB and available host memory is " + f"{available_memory / (1024**3):.2f}GB." + ) + return False + + local_rank, local_size = _local_rank_and_size() + local_paths = paths[local_rank::local_size] + if local_paths: + logger.info( + f"Prefetching {prefetch_size / (1024**3):.2f}GB {description} " + "files across distributed local ranks." + ) + max_workers = min(multiprocessing.cpu_count() * 2, 16, len(local_paths)) + with ThreadPoolExecutor(max_workers=max_workers) as executor: + list(executor.map(lambda path: _prefetch_file(path, description), local_paths)) + + success = True + return True + except Exception as exc: + if not ignore_errors: + raise + logger.warning(f"{description} prefetch failed; continuing without prefetch: {exc}") + return False + finally: + if success and prefetched_paths is not None: + prefetched_paths.update(paths) + _dist_barrier() diff --git a/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py b/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py index f1b22553ba66..824120840131 100644 --- a/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py +++ b/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py @@ -1,6 +1,7 @@ """Weight loader for diffusion models.""" import json +from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path from typing import Any, Dict, List, Union @@ -8,6 +9,7 @@ import tqdm from tensorrt_llm._torch.models.checkpoints.base_weight_loader import BaseWeightLoader +from tensorrt_llm._torch.visual_gen.checkpoints.prefetch import prefetch_files_to_host_cache from tensorrt_llm._torch.visual_gen.pipeline_registry import PipelineComponent from tensorrt_llm.logger import logger from tensorrt_llm.mapping import Mapping @@ -18,7 +20,7 @@ class WeightLoader(BaseWeightLoader): Weight loader for diffusion models. Loads weights from safetensors/bin files, similar to HfWeightLoader - but simpler (no parallel loading optimization for now). + but tailored for diffusion checkpoint layouts. Supports loading multiple components (e.g., transformer and transformer_2): loader = WeightLoader(components=["transformer", "transformer_2"]) @@ -87,12 +89,13 @@ def load_weights( if not weight_files: raise ValueError(f"No weight files found in {weight_dir}") - # Load all weights with progress bar - component_weights = {} - desc = f"Loading {component}" if is_pipeline else "Loading checkpoint" - for wf in tqdm.tqdm(weight_files, desc=desc): - component_weights.update(self._load_file(wf)) + if all(wf.endswith(".safetensors") for wf in weight_files): + prefetch_files_to_host_cache( + weight_files, + description="visual-gen checkpoint", + ) + component_weights = self._load_weight_files(weight_files, component, is_pipeline) all_weights[component] = component_weights # Return flat dict for single component (backward compatibility) @@ -102,6 +105,32 @@ def load_weights( # Return nested dict for multiple components return all_weights + def _load_weight_files( + self, weight_files: List[str], component: str, is_pipeline: bool + ) -> Dict[str, Any]: + desc = f"Loading {component}" if is_pipeline else "Loading checkpoint" + if len(weight_files) <= 1: + component_weights = {} + for wf in tqdm.tqdm(weight_files, desc=desc): + component_weights.update(self._load_file(wf)) + return component_weights + + workers = min(4, len(weight_files)) + + logger.info(f"Loading {len(weight_files)} {component} shard files with {workers} workers") + component_weights = {} + with ThreadPoolExecutor(max_workers=workers) as executor: + futures = {executor.submit(self._load_file, wf): wf for wf in weight_files} + for future in tqdm.tqdm(as_completed(futures), total=len(futures), desc=desc): + wf = futures[future] + try: + loaded = future.result() + except Exception as exc: + raise RuntimeError(f"Failed to load weight file {wf}") from exc + component_weights.update(loaded) + + return component_weights + def _find_weight_files(self, weight_dir) -> List[str]: """Find safetensors or bin weight files. diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py index fc5a21758478..c66d795bc47f 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py @@ -8,7 +8,7 @@ import os import time from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Set, Tuple, Union import safetensors.torch import torch @@ -17,6 +17,7 @@ from tensorrt_llm._torch.utils import make_weak_ref from tensorrt_llm._torch.visual_gen.cache.teacache import CacheContext +from tensorrt_llm._torch.visual_gen.checkpoints.prefetch import prefetch_files_to_host_cache from tensorrt_llm._torch.visual_gen.cuda_graph_runner import CUDAGraphRunner, CUDAGraphRunnerConfig from tensorrt_llm._torch.visual_gen.output import CudaPhaseTimer, PipelineOutput from tensorrt_llm._torch.visual_gen.pipeline import BasePipeline, ExtraParamSchema @@ -151,6 +152,23 @@ def _assert_resolution(height: int, width: int, *, is_two_stage: bool = False) - ) +_LTX2_PREFETCHED_SAFETENSORS: Set[str] = set() + + +def _prefetch_ltx2_safetensors_files(file_names: List[str]) -> bool: + """Warm LTX-2 safetensors files in the host page cache. + + For distributed runs, local ranks split the file list and synchronize before + weight loading so ranks do not duplicate the prefetch work on the same node. + """ + return prefetch_files_to_host_cache( + file_names, + description="LTX-2 checkpoint", + prefetched_paths=_LTX2_PREFETCHED_SAFETENSORS, + ignore_errors=True, + ) + + def _load_ltx2_transformer_weights( checkpoint_dir: str, prefix: str, @@ -178,6 +196,8 @@ def _load_ltx2_transformer_weights( if not sft_paths: raise ValueError(f"No safetensors files found in {checkpoint_dir}") + _prefetch_ltx2_safetensors_files(sft_paths) + exclude_prefixes = tuple(exclude_prefixes) if exclude_prefixes else () weights: Dict[str, torch.Tensor] = {} @@ -860,6 +880,7 @@ def load_standard_components( # --- Resolve native config ---------------------------------------- native_config = self.model_config.extra_attrs.get("monolithic_safetensors_config") sft_paths = _find_safetensors_files(checkpoint_dir) + _prefetch_ltx2_safetensors_files(sft_paths) if native_config is None and sft_paths: native_config = _read_safetensors_config(sft_paths[0]) diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py index 6a8e365d41cf..4b65cb8332e0 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py @@ -35,7 +35,12 @@ ) from .ltx2_core.upsampler import LatentUpsamplerConfigurator, upsample_video from .ltx2_core.video_vae import TilingConfig -from .pipeline_ltx2 import LTX2Pipeline, _assert_resolution, _find_safetensors_files +from .pipeline_ltx2 import ( + LTX2Pipeline, + _assert_resolution, + _find_safetensors_files, + _prefetch_ltx2_safetensors_files, +) STAGE_2_DISTILLED_SIGMA_VALUES = [0.909375, 0.725, 0.421875, 0.0] _FP8_DTYPES = (torch.float8_e4m3fn, torch.float8_e5m2) @@ -97,6 +102,7 @@ def _load_lora_deltas( sft_paths = _find_safetensors_files(lora_path) if not sft_paths: raise ValueError(f"No safetensors files found at {lora_path}") + _prefetch_ltx2_safetensors_files(sft_paths) raw: Dict[str, torch.Tensor] = {} alpha_dict: Dict[str, float] = {} @@ -666,6 +672,7 @@ def load_standard_components( sft_paths = _find_safetensors_files(spatial_upsampler_path) if not sft_paths: raise ValueError(f"No safetensors files found at {spatial_upsampler_path}") + _prefetch_ltx2_safetensors_files(sft_paths) config: Dict[str, Any] = {} try: From d639c5793aee8cf4d8565596d6380f82cb09da6a Mon Sep 17 00:00:00 2001 From: dongfengy <99041270+dongfengy@users.noreply.github.com> Date: Fri, 5 Jun 2026 17:10:18 -0700 Subject: [PATCH 015/119] [https://nvbugs/6250866][fix] Fix deep ep partial warp sync for gptoss shapes (#14977) Signed-off-by: Dongfeng Yu --- 3rdparty/fetch_content.json | 3 +- .../deep_ep_intranode_combine_fix.patch | 35 +++++++++++++++++++ 2 files changed, 37 insertions(+), 1 deletion(-) create mode 100644 3rdparty/patches/deep_ep_intranode_combine_fix.patch diff --git a/3rdparty/fetch_content.json b/3rdparty/fetch_content.json index e6218c2629c2..6a4679db5262 100644 --- a/3rdparty/fetch_content.json +++ b/3rdparty/fetch_content.json @@ -26,7 +26,8 @@ "display_name": "deep_ep", "git_repository": "${github_base_url}/deepseek-ai/DeepEP", "git_tag": "5be51b228a7c82dbdb213ea58e77bffd12b38af8", - "use_url": true + "use_url": true, + "patch_file": "patches/deep_ep_intranode_combine_fix.patch" }, { "name": "deepgemm", diff --git a/3rdparty/patches/deep_ep_intranode_combine_fix.patch b/3rdparty/patches/deep_ep_intranode_combine_fix.patch new file mode 100644 index 000000000000..fbed0107f81b --- /dev/null +++ b/3rdparty/patches/deep_ep_intranode_combine_fix.patch @@ -0,0 +1,35 @@ +--- a/csrc/kernels/intranode.cu ++++ b/csrc/kernels/intranode.cu +@@ -844,9 +844,15 @@ + + #ifndef DISABLE_SM90_FEATURES + // Wait TMA arrival ++ // hidden_int4 is not always divisible by a warp. The final tile can have ++ // only a subset of lanes active, so synchronize only participating lanes. ++ auto const tile_start = i - lane_id; ++ auto const active_lanes = min(32, hidden_int4 - tile_start); ++ auto const sync_mask = active_lanes == 32 ? 0xffffffffu : ((1u << active_lanes) - 1u); ++ + if (lane_id == 0) + tma_store_wait(); +- __syncwarp(); ++ __syncwarp(sync_mask); + + // Write into TMA buffer + auto tma_stage_idx = (i / 32) % kNumStages; +@@ -854,13 +860,13 @@ + + // Issue TMA + tma_store_fence(); +- __syncwarp(); ++ __syncwarp(sync_mask); + if (lane_id == 0) { + auto tma_bytes = min(32, hidden_int4 - i) * static_cast(sizeof(int4)); + tma_store_1d(reinterpret_cast(tma_buffer) + tma_stage_idx * 32, + recv_int4 + token_idx * hidden_int4 + i, tma_bytes, false); + } +- __syncwarp(); ++ __syncwarp(sync_mask); + #else + recv_int4[token_idx * hidden_int4 + i] = out_int4; + #endif From 3b2109310d8453815a1065f5c4a6204c1cf51384 Mon Sep 17 00:00:00 2001 From: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> Date: Fri, 5 Jun 2026 17:44:09 -0700 Subject: [PATCH 016/119] [https://nvbugs/6272668][infra] Unwaive DSR1 and Qwen3.5 again (#15010) Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 21c18d75ef37..3866d63e2df4 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -13,14 +13,12 @@ accuracy/test_llm_api.py::TestMistralNemo12B::test_fp8 SKIP (https://nvbugs/5413 accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (https://nvbugs/6194934) accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) -accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6272656) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-trtllm] SKIP (https://nvbugs/6185150) accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-trtllm] SKIP (https://nvbugs/6185150) accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-8] SKIP (https://nvbugs/6248757) accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) -accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6272668) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] SKIP (https://nvbugs/6191524) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput] SKIP (https://nvbugs/6084775) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_mtp] SKIP (https://nvbugs/6029882) From d7a58725967a1fc19b05aff78cd8b30d11c062d2 Mon Sep 17 00:00:00 2001 From: Raghav Ravishankar <113712354+alyosha-swamy@users.noreply.github.com> Date: Sat, 6 Jun 2026 07:32:26 +0530 Subject: [PATCH 017/119] [None][feat] Afmoe trinity support (#13148) Signed-off-by: Alyosha-Swamy --- docs/source/models/supported-models.md | 1 + tensorrt_llm/_torch/models/__init__.py | 2 + .../_torch/models/checkpoints/__init__.py | 7 +- .../checkpoints/hf/afmoe_weight_mapper.py | 105 +++ tensorrt_llm/_torch/models/modeling_afmoe.py | 465 +++++++++++ .../test_lists/test-db/l0_b200.yml | 1 + .../_torch/modeling/test_modeling_afmoe.py | 731 ++++++++++++++++++ 7 files changed, 1309 insertions(+), 3 deletions(-) create mode 100644 tensorrt_llm/_torch/models/checkpoints/hf/afmoe_weight_mapper.py create mode 100644 tensorrt_llm/_torch/models/modeling_afmoe.py create mode 100644 tests/unittest/_torch/modeling/test_modeling_afmoe.py diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 8da40f0fa66e..546dfc10288b 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -5,6 +5,7 @@ The following is a table of supported models for the PyTorch backend: | Architecture | Model | HuggingFace Example | | ------------------------------------ | ---------------------------------- | -------------------------------------------- | +| `AfmoeForCausalLM` | Arcee Foundation MoE (Trinity) | `arcee-ai/Trinity-Mini` | | `BertForSequenceClassification` | BERT-based | `textattack/bert-base-uncased-yelp-polarity` | | `Cohere2ForCausalLM` | Command A | `CohereLabs/c4ai-command-a-03-2025` | | `DeciLMForCausalLM` | Nemotron | `nvidia/Llama-3_1-Nemotron-51B-Instruct` | diff --git a/tensorrt_llm/_torch/models/__init__.py b/tensorrt_llm/_torch/models/__init__.py index 16d9875083b4..11760e684361 100644 --- a/tensorrt_llm/_torch/models/__init__.py +++ b/tensorrt_llm/_torch/models/__init__.py @@ -5,6 +5,7 @@ # under transformers >= 5.5; see _torch/configs/__init__.py. import tensorrt_llm._torch.configs # noqa: F401 +from .modeling_afmoe import AfmoeForCausalLM from .modeling_auto import AutoModelForCausalLM from .modeling_bert import BertForSequenceClassification from .modeling_clip import CLIPVisionModel @@ -53,6 +54,7 @@ # Note: for better readiblity, this should have same order as imports above __all__ = [ + "AfmoeForCausalLM", "AutoModelForCausalLM", "BertForSequenceClassification", "CLIPVisionModel", diff --git a/tensorrt_llm/_torch/models/checkpoints/__init__.py b/tensorrt_llm/_torch/models/checkpoints/__init__.py index f4094417b3ca..b10a93132ca8 100644 --- a/tensorrt_llm/_torch/models/checkpoints/__init__.py +++ b/tensorrt_llm/_torch/models/checkpoints/__init__.py @@ -1,4 +1,5 @@ from .base_checkpoint_loader import BaseCheckpointLoader +from .hf.afmoe_weight_mapper import AfmoeHfWeightMapper from .hf.checkpoint_loader import HfCheckpointLoader from .hf.config_loader import HfConfigLoader from .hf.gemma3_weight_mapper import Gemma3HfWeightMapper @@ -24,9 +25,9 @@ from .mx.checkpoint_loader import MXCheckpointLoader __all__ = [ - "HfConfigLoader", "HfWeightLoader", "HfWeightMapper", "MistralConfigLoader", - "MistralWeightMapper", "MistralCheckpointLoader", "BaseCheckpointLoader", - "HfCheckpointLoader", "NemotronHHfWeightMapper", + "AfmoeHfWeightMapper", "HfConfigLoader", "HfWeightLoader", "HfWeightMapper", + "MistralConfigLoader", "MistralWeightMapper", "MistralCheckpointLoader", + "BaseCheckpointLoader", "HfCheckpointLoader", "NemotronHHfWeightMapper", "NemotronNasHfWeightMapper", "Gemma3HfWeightMapper", "MixtralHfWeightMapper", "Llama4HfWeightMapper", "Qwen2MoeHfWeightMapper", "Qwen3MoeHfWeightMapper", "Qwen2VLHfWeightMapper", diff --git a/tensorrt_llm/_torch/models/checkpoints/hf/afmoe_weight_mapper.py b/tensorrt_llm/_torch/models/checkpoints/hf/afmoe_weight_mapper.py new file mode 100644 index 000000000000..40bb97939175 --- /dev/null +++ b/tensorrt_llm/_torch/models/checkpoints/hf/afmoe_weight_mapper.py @@ -0,0 +1,105 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn + +from tensorrt_llm._torch.models.checkpoints.hf.weight_mapper import HfWeightMapper +from tensorrt_llm._torch.models.modeling_utils import register_mapper +from tensorrt_llm._torch.modules.fused_moe.interface import MoE + + +@register_mapper("HF", "AfmoeForCausalLM") +class AfmoeHfWeightMapper(HfWeightMapper): + def __init__(self): + super().__init__() + + self.params_map = { + # MoE expert weights: gate_proj->w1, up_proj->w3, down_proj->w2 + r"(.*experts\.\d+\.)gate_proj(.*)": r"\1w1\2", + r"(.*experts\.\d+\.)up_proj(.*)": r"\1w3\2", + r"(.*experts\.\d+\.)down_proj(.*)": r"\1w2\2", + # HF router weight path -> TRT-LLM gate path + r"(.*)\.router\.gate\.(.*)": r"\1.gate.\2", + # expert_bias -> gate.e_score_correction_bias + r"(.*)\.mlp\.expert_bias(.*)": r"\1.mlp.gate.e_score_correction_bias\2", + } + + def preprocess_weights(self, weights: dict) -> dict: + weights = self.rename_by_params_map(self.params_map, weights) + weights = self._fuse_attention_gate(weights) + return weights + + def _fuse_attention_gate(self, weights: dict) -> dict: + """Fuse the separate attention ``gate_proj`` into ``q_proj``. + + AfmoeAttention uses ``attn_output_gate=True``, so the gate weights are + interleaved with the query weights per head and loaded through the fused + QKV projection. The HF checkpoint stores ``q_proj`` and ``gate_proj`` as + two separate matrices of shape ``[num_heads * head_dim, hidden]``; the + fused QKV projection expects the query slot laid out per head as + ``[head0_q, head0_gate, head1_q, head1_gate, ...]`` (see + ``Attention.forward`` where ``q_gate`` is viewed as + ``[..., num_heads, 2 * head_dim]`` and chunked into q/gate). + """ + marker = ".self_attn.gate_proj." + gate_keys = [k for k in weights if marker in k] + if not gate_keys: + return weights + + num_heads = self.model.config.num_attention_heads + for gate_key in gate_keys: + prefix, suffix = gate_key.split(marker) + q_key = f"{prefix}.self_attn.q_proj.{suffix}" + if q_key not in weights: + continue + weights[q_key] = self._interleave_per_head(weights[q_key], weights[gate_key], num_heads) + del weights[gate_key] + return weights + + @staticmethod + def _interleave_per_head(q: torch.Tensor, gate: torch.Tensor, num_heads: int) -> torch.Tensor: + """Interleave q and gate rows per head: ``[h0_q, h0_gate, h1_q, ...]``. + + Works for 2D weights ``[num_heads * per_head, hidden]`` as well as 1D + biases and FP8 block scales, since the split is always taken along the + leading (output) dimension. + """ + assert q.shape[0] % num_heads == 0, ( + f"q_proj rows {q.shape[0]} not divisible by num_heads {num_heads}" + ) + assert gate.shape == q.shape, f"gate_proj shape {gate.shape} != q_proj shape {q.shape}" + per_head = q.shape[0] // num_heads + tail = q.shape[1:] + q = q.reshape(num_heads, per_head, *tail) + gate = gate.reshape(num_heads, per_head, *tail) + fused = torch.stack([q, gate], dim=1) + return fused.reshape(num_heads * 2 * per_head, *tail).contiguous() + + def is_special_instance_module(self, module: nn.Module) -> bool: + return isinstance(module, MoE) + + def handle_special_instance_module( + self, + module: nn.Module, + module_name: str, + module_weights: dict, + allow_partial_loading: bool = False, + ) -> None: + if isinstance(module, MoE): + module.load_weights( + weights=[module_weights], + allow_partial_loading=allow_partial_loading, + ) diff --git a/tensorrt_llm/_torch/models/modeling_afmoe.py b/tensorrt_llm/_torch/models/modeling_afmoe.py new file mode 100644 index 000000000000..e11ad402409e --- /dev/null +++ b/tensorrt_llm/_torch/models/modeling_afmoe.py @@ -0,0 +1,465 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Inference-only AFMoE (Arcee Foundation MoE) for TensorRT-LLM. + +Follows the HF implementation of AfmoeForCausalLM. + +Key architectural features: + - Per-layer attention type (sliding_attention vs global) + - Q/K RMSNorm in attention + - Gated attention output (sigmoid gate) + - RoPE only on local (sliding-window) attention layers + - Dense MLP for early layers, MoE with shared experts for later layers + - 4 layer norms per decoder block (pre/post attention, pre/post MLP) + - Optional muP embedding scaling +""" + +from typing import Optional + +import torch +from torch import nn +from transformers import PretrainedConfig +from transformers.models.auto.configuration_auto import CONFIG_MAPPING + +from tensorrt_llm.functional import PositionEmbeddingType + +from ...logger import logger +from ..attention_backend import AttentionMetadata +from ..attention_backend.interface import PositionalEmbeddingParams, RopeParams +from ..distributed import AllReduce +from ..model_config import ModelConfig +from ..modules.decoder_layer import DecoderLayer +from ..modules.embedding import Embedding +from ..modules.fused_moe import DeepSeekV3MoeRoutingMethod, create_moe +from ..modules.fused_moe.routing import Deepseekv3RoutingImpl +from ..modules.gated_mlp import GatedMLP +from ..modules.qk_norm_attention import QKNormRoPEAttention +from ..modules.rms_norm import RMSNorm +from ..utils import AuxStreamType +from .modeling_utils import DecoderModel, DecoderModelForCausalLM, register_auto_model + + +class AfmoeConfig(PretrainedConfig): + model_type = "afmoe" + + +if AfmoeConfig.model_type not in CONFIG_MAPPING: + logger.warning_once( + "transformers does not natively support 'AfmoeConfig'. " + "Registering AfmoeConfig so AutoConfig can load AFMoE checkpoints.", + key="AFMOE_REGISTER_WARNING", + ) + CONFIG_MAPPING.register(AfmoeConfig.model_type, AfmoeConfig, exist_ok=True) + + +def _validate_routing_config(config: PretrainedConfig) -> None: + """Validate that the routing config matches our Deepseekv3RoutingImpl assumptions.""" + score_func = getattr(config, "scoring_func", getattr(config, "score_func", "sigmoid")) + if score_func != "sigmoid": + raise ValueError( + f"AFMoE implementation uses sigmoid scoring via " + f"Deepseekv3RoutingImpl, but config has " + f"scoring_func={score_func!r}. Only 'sigmoid' is supported." + ) + + norm_topk = getattr(config, "norm_topk_prob", getattr(config, "route_norm", True)) + if not norm_topk: + raise ValueError( + "AFMoE implementation assumes normalized top-k probabilities " + "(norm_topk_prob=True / route_norm=True), but config disables it." + ) + + +class AfmoeGate(nn.Module): + """Router gate for AFMoE, following the DeepSeekV3 grouped top-k pattern.""" + + def __init__( + self, + hidden_size: int, + num_experts: int, + top_k: int, + n_group: int, + topk_group: int, + route_scale: float, + dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.weight = nn.Parameter( + torch.empty((num_experts, hidden_size), dtype=dtype), + requires_grad=False, + ) + self.e_score_correction_bias = nn.Parameter( + torch.empty(num_experts, dtype=torch.float32), + requires_grad=False, + ) + self.routing_impl = Deepseekv3RoutingImpl( + top_k=top_k, + n_group=n_group, + topk_group=topk_group, + routed_scaling_factor=route_scale, + is_fused=True, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + logits = torch.ops.trtllm.dsv3_router_gemm_op( + hidden_states, + self.weight.t(), + bias=None, + out_dtype=torch.float32, + ) + return logits + + def load_weights(self, weights: list[dict]): + assert len(weights) == 1 + self.weight.copy_(weights[0]["weight"][:]) + self.e_score_correction_bias.copy_( + weights[0]["e_score_correction_bias"][:].to(self.e_score_correction_bias.dtype) + ) + + @property + def routing_method(self) -> DeepSeekV3MoeRoutingMethod: + return DeepSeekV3MoeRoutingMethod( + top_k=self.routing_impl.top_k, + n_group=self.routing_impl.n_group, + topk_group=self.routing_impl.topk_group, + routed_scaling_factor=self.routing_impl.routed_scaling_factor, + is_fused=self.routing_impl.is_fused, + callable_e_score_correction_bias=lambda: self.e_score_correction_bias, + ) + + +class AfmoeMoE(nn.Module): + """MoE layer with shared experts for AFMoE. + + Both routed experts and shared experts produce TP-partial results + (reduce_results=False / reduce_output=False). After summing them + we perform a single AllReduce so that each rank holds the full + hidden-state, matching the DeepSeekV3 MoE pattern. + """ + + def __init__( + self, + model_config: ModelConfig[PretrainedConfig], + aux_stream: torch.cuda.Stream, + layer_idx: Optional[int] = None, + ): + super().__init__() + config = model_config.pretrained_config + + self.hidden_dim = config.hidden_size + self.num_experts = config.num_experts + self.top_k = config.num_experts_per_tok + self.num_shared_experts = getattr(config, "num_shared_experts", 0) + self.enable_attention_dp = model_config.mapping.enable_attention_dp + + self.gate = AfmoeGate( + hidden_size=self.hidden_dim, + num_experts=self.num_experts, + top_k=self.top_k, + n_group=config.n_group, + topk_group=config.topk_group, + route_scale=getattr(config, "route_scale", 1.0), + dtype=config.torch_dtype, + ) + + self.experts = create_moe( + num_experts=self.num_experts, + routing_method=self.gate.routing_method, + hidden_size=self.hidden_dim, + intermediate_size=config.moe_intermediate_size, + aux_stream_dict={AuxStreamType.MoeChunkingOverlap: aux_stream}, + dtype=config.torch_dtype, + reduce_results=False, + model_config=model_config, + layer_idx=layer_idx, + ) + + if self.num_shared_experts > 0: + shared_intermediate = config.moe_intermediate_size * self.num_shared_experts + self.shared_experts = GatedMLP( + hidden_size=self.hidden_dim, + intermediate_size=shared_intermediate, + bias=False, + dtype=config.torch_dtype, + config=model_config, + overridden_tp_size=1 if self.enable_attention_dp else None, + reduce_output=False, + layer_idx=layer_idx, + ) + else: + self.shared_experts = None + + self.mapping = model_config.mapping + + self.allreduce = None + if not self.enable_attention_dp and self.mapping.tp_size > 1: + self.allreduce = AllReduce( + mapping=model_config.mapping, + strategy=model_config.allreduce_strategy, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attn_metadata: AttentionMetadata, + ) -> torch.Tensor: + all_rank_num_tokens = attn_metadata.all_rank_num_tokens + router_logits = self.gate(hidden_states) + + routed_output = self.experts( + hidden_states, + router_logits, + all_rank_num_tokens=all_rank_num_tokens, + use_dp_padding=False, + ) + + if self.shared_experts is not None: + shared_output = self.shared_experts(hidden_states) + final_output = shared_output.add_(routed_output) + else: + final_output = routed_output + + if self.allreduce is not None: + final_output = self.allreduce(final_output) + + return final_output + + +class AfmoeAttention(QKNormRoPEAttention): + """Attention with Q/K norm, per-layer sliding window, and a sigmoid output gate. + + Inherits QK-norm + RoPE handling from ``QKNormRoPEAttention``. The output + gate is fused into the QKV projection (``attn_output_gate=True``), and RoPE + is applied only on local (sliding-window) layers, matching the HF + ``AfmoeAttention`` reference. + """ + + def __init__( + self, + model_config: ModelConfig[PretrainedConfig], + layer_idx: Optional[int] = None, + ): + config = model_config.pretrained_config + layer_types = getattr(config, "layer_types", []) + self.is_local_attention = ( + layer_idx is not None + and layer_idx < len(layer_types) + and layer_types[layer_idx] == "sliding_attention" + ) + self.attention_window_size = config.sliding_window if self.is_local_attention else None + + pos_embd_params = None + if self.is_local_attention: + pos_embd_params = PositionalEmbeddingParams( + type=PositionEmbeddingType.rope_gpt_neox, + rope=RopeParams.from_config(config), + ) + + super().__init__( + hidden_size=config.hidden_size, + num_attention_heads=config.num_attention_heads, + num_key_value_heads=config.num_key_value_heads, + max_position_embeddings=getattr(config, "max_position_embeddings", 131072), + bias=False, + pos_embd_params=pos_embd_params, + fuse_qk_norm_rope=False, + skip_rope=not self.is_local_attention, + attn_output_gate=True, + is_qk_norm=True, + layer_idx=layer_idx, + dtype=config.torch_dtype, + config=model_config, + ) + + def forward( + self, + position_ids: torch.IntTensor, + hidden_states: torch.Tensor, + attn_metadata: AttentionMetadata, + **kwargs, + ) -> torch.Tensor: + return super().forward( + position_ids=position_ids, + hidden_states=hidden_states, + attn_metadata=attn_metadata, + attention_window_size=self.attention_window_size, + **kwargs, + ) + + +class AfmoeDecoderLayer(DecoderLayer): + def __init__( + self, + model_config: ModelConfig[PretrainedConfig], + layer_idx: int, + aux_stream: torch.cuda.Stream, + ): + super().__init__() + config = model_config.pretrained_config + self.hidden_size = config.hidden_size + self.layer_idx = layer_idx + + self.self_attn = AfmoeAttention(model_config, layer_idx=layer_idx) + + num_dense_layers = getattr(config, "num_dense_layers", 0) + self.moe_enabled = layer_idx >= num_dense_layers + if self.moe_enabled: + self.mlp = AfmoeMoE(model_config, aux_stream, layer_idx=layer_idx) + else: + self.mlp = GatedMLP( + hidden_size=config.hidden_size, + intermediate_size=config.intermediate_size, + bias=False, + dtype=config.torch_dtype, + config=model_config, + overridden_tp_size=1 if model_config.mapping.enable_attention_dp else None, + layer_idx=layer_idx, + ) + + self.input_layernorm = RMSNorm( + hidden_size=config.hidden_size, + eps=config.rms_norm_eps, + dtype=config.torch_dtype, + ) + self.post_attention_layernorm = RMSNorm( + hidden_size=config.hidden_size, + eps=config.rms_norm_eps, + dtype=config.torch_dtype, + ) + self.pre_mlp_layernorm = RMSNorm( + hidden_size=config.hidden_size, + eps=config.rms_norm_eps, + dtype=config.torch_dtype, + ) + self.post_mlp_layernorm = RMSNorm( + hidden_size=config.hidden_size, + eps=config.rms_norm_eps, + dtype=config.torch_dtype, + ) + + def forward( + self, + position_ids: torch.IntTensor, + hidden_states: torch.Tensor, + attn_metadata: AttentionMetadata, + residual: Optional[torch.Tensor], + **kwargs, + ) -> torch.Tensor: + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm(hidden_states, residual) + + hidden_states = self.self_attn( + position_ids=position_ids, + hidden_states=hidden_states, + attn_metadata=attn_metadata, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + + hidden_states, residual = self.pre_mlp_layernorm(hidden_states, residual) + + if self.moe_enabled: + hidden_states = self.mlp(hidden_states, attn_metadata) + else: + hidden_states = self.mlp(hidden_states) + + hidden_states = self.post_mlp_layernorm(hidden_states) + + return hidden_states, residual + + +class AfmoeModel(DecoderModel): + def __init__(self, model_config: ModelConfig[PretrainedConfig]): + super().__init__(model_config) + config = model_config.pretrained_config + _validate_routing_config(config) + + self.vocab_size = config.vocab_size + self.mup_enabled = getattr(config, "mup_enabled", False) + self.hidden_size = config.hidden_size + self.aux_stream = torch.cuda.Stream() + + self.embed_tokens = Embedding( + config.vocab_size, + config.hidden_size, + dtype=config.torch_dtype, + enable_torch_compile_for_embedding=model_config.enable_torch_compile_for_embedding, + ) + + self.layers = nn.ModuleList( + [ + AfmoeDecoderLayer(model_config, layer_idx, self.aux_stream) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.norm = RMSNorm( + hidden_size=config.hidden_size, + eps=config.rms_norm_eps, + dtype=config.torch_dtype, + ) + + def forward( + self, + attn_metadata: AttentionMetadata, + input_ids: Optional[torch.IntTensor] = None, + position_ids: Optional[torch.IntTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + **kwargs, + ) -> torch.Tensor: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at " + "the same time, and must specify either one" + ) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self.mup_enabled: + inputs_embeds = inputs_embeds * (self.hidden_size**0.5) + + hidden_states = inputs_embeds + + residual = None + for decoder_layer in self.layers: + hidden_states, residual = decoder_layer( + position_ids=position_ids, + hidden_states=hidden_states, + attn_metadata=attn_metadata, + residual=residual, + **kwargs, + ) + + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + + +@register_auto_model("AfmoeForCausalLM") +class AfmoeForCausalLM(DecoderModelForCausalLM[AfmoeModel, PretrainedConfig]): + def __init__(self, model_config: ModelConfig[PretrainedConfig]): + super().__init__( + AfmoeModel(model_config), + config=model_config, + hidden_size=model_config.pretrained_config.hidden_size, + vocab_size=model_config.pretrained_config.vocab_size, + ) + + def load_weights(self, weights: dict, weight_mapper, **kwargs): + weights = weight_mapper.preprocess_weights(weights) + super().load_weights(weights=weights, weight_mapper=weight_mapper, **kwargs) diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index 0d3d5f4932e9..e1ee8e83bad9 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -134,6 +134,7 @@ l0_b200: - unittest/_torch/modeling -k "modeling_llama" - unittest/_torch/modeling -k "modeling_mixtral" - unittest/_torch/modeling -k "modeling_gpt_oss" + - unittest/_torch/modeling/test_modeling_afmoe.py - unittest/_torch/modeling/test_modeling_exaone_moe.py - unittest/_torch/modeling/test_modeling_gemma4.py - unittest/_torch/modeling/test_gemma4_multimodal.py diff --git a/tests/unittest/_torch/modeling/test_modeling_afmoe.py b/tests/unittest/_torch/modeling/test_modeling_afmoe.py new file mode 100644 index 000000000000..934fa59b650a --- /dev/null +++ b/tests/unittest/_torch/modeling/test_modeling_afmoe.py @@ -0,0 +1,731 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import tempfile +import unittest +from copy import deepcopy +from unittest.mock import Mock, patch + +import torch + +import tensorrt_llm +from tensorrt_llm import LLM, SamplingParams +from tensorrt_llm._torch.attention_backend.utils import get_attention_backend +from tensorrt_llm._torch.metadata import KVCacheParams +from tensorrt_llm._torch.model_config import ModelConfig +from tensorrt_llm._torch.models.modeling_afmoe import ( + AfmoeConfig, + AfmoeForCausalLM, + AfmoeMoE, + _validate_routing_config, +) +from tensorrt_llm._torch.models.modeling_utils import ( + MODEL_CLASS_MAPPER_MAPPING, + MODEL_CLASS_MAPPING, +) +from tensorrt_llm._torch.modules.linear import TensorParallelMode +from tensorrt_llm._torch.pyexecutor.resource_manager import KVCacheManager +from tensorrt_llm.bindings.executor import KvCacheConfig +from tensorrt_llm.llmapi import KvCacheConfig as LlmKvCacheConfig +from tensorrt_llm.llmapi import MoeConfig +from tensorrt_llm.mapping import Mapping +from tensorrt_llm.models.modeling_utils import QuantConfig + +# AFMoE is a recent addition to HF transformers; older installed versions may +# not ship it. Guard the reference-model imports (matching the exaone4 test +# pattern) so the whole module still collects when HF afmoe is unavailable and +# only the HF parity test is skipped. +SKIP_AFMOE_HF_ACCURACY_TEST = False +try: + from transformers import AfmoeConfig as HFAfmoeConfig + from transformers.models.afmoe.modeling_afmoe import AfmoeForCausalLM as HFAfmoeForCausalLM +except ImportError: + SKIP_AFMOE_HF_ACCURACY_TEST = True + +WINDOW_SIZE = 4 +NUM_HIDDEN_LAYERS = 4 +NUM_DENSE_LAYERS = 1 + +AFMOE_CONFIG = { + "architectures": ["AfmoeForCausalLM"], + "dtype": "bfloat16", + "hidden_size": 256, + "intermediate_size": 512, + "max_position_embeddings": 2048, + "model_type": "afmoe", + "moe_intermediate_size": 128, + "n_group": 1, + "norm_topk_prob": True, + "num_attention_heads": 8, + "num_dense_layers": NUM_DENSE_LAYERS, + "num_experts": 8, + "num_experts_per_tok": 2, + "num_hidden_layers": NUM_HIDDEN_LAYERS, + "num_key_value_heads": 2, + "num_shared_experts": 1, + "rms_norm_eps": 1e-05, + "rope_theta": 10000, + "route_scale": 1.0, + "scoring_func": "sigmoid", + "sliding_window": WINDOW_SIZE, + "layer_types": [ + "sliding_attention", + "sliding_attention", + "sliding_attention", + "full_attention", + ], + "tie_word_embeddings": False, + "topk_group": 1, + "vocab_size": 1024, + "hidden_act": "silu", + "mup_enabled": False, +} + + +def _force_mpi_topology_mapping(): + # These tests inspect module TP attributes in one pytest process. Force + # the lightweight MPI-topology Mapping even when TLLM_DISABLE_MPI=1 would + # otherwise require an initialized torch.distributed DeviceMesh. + return patch("tensorrt_llm.mapping.mpi_disabled", return_value=False) + + +def _force_mpi_collectives(): + return patch("tensorrt_llm._torch.distributed.ops.mpi_disabled", return_value=False) + + +def _shutdown_kv_cache_manager(kv_cache_manager: KVCacheManager) -> None: + if torch.cuda.is_available(): + torch.cuda.synchronize() + kv_cache_manager.shutdown() + + +class TestAfmoeRegistry(unittest.TestCase): + """Verify AfmoeForCausalLM resolves through _torch auto-model registration.""" + + def test_auto_model_registry(self): + self.assertIn("AfmoeForCausalLM", MODEL_CLASS_MAPPING) + self.assertIs(MODEL_CLASS_MAPPING["AfmoeForCausalLM"], AfmoeForCausalLM) + + def test_weight_mapper_registry(self): + self.assertIn("AfmoeForCausalLM_HF", MODEL_CLASS_MAPPER_MAPPING) + + def test_legacy_model_map_does_not_contain_afmoe(self): + from tensorrt_llm.models import MODEL_MAP + + self.assertNotIn("AfmoeForCausalLM", MODEL_MAP) + + +class TestAfmoeRoutingValidation(unittest.TestCase): + """Verify routing assumption guards.""" + + def test_valid_sigmoid_config(self): + config = AfmoeConfig.from_dict(deepcopy(AFMOE_CONFIG)) + _validate_routing_config(config) + + def test_rejects_softmax_scoring(self): + d = deepcopy(AFMOE_CONFIG) + d["scoring_func"] = "softmax" + config = AfmoeConfig.from_dict(d) + with self.assertRaisesRegex(ValueError, "Only 'sigmoid' is supported"): + _validate_routing_config(config) + + def test_rejects_disabled_norm_topk(self): + d = deepcopy(AFMOE_CONFIG) + d["norm_topk_prob"] = False + config = AfmoeConfig.from_dict(d) + with self.assertRaisesRegex(ValueError, "norm_topk_prob"): + _validate_routing_config(config) + + def test_model_init_rejects_invalid_routing(self): + d = deepcopy(AFMOE_CONFIG) + d["scoring_func"] = "softmax" + config = AfmoeConfig.from_dict(d) + model_config = ModelConfig(pretrained_config=config) + with self.assertRaisesRegex(ValueError, "Only 'sigmoid' is supported"): + AfmoeForCausalLM(model_config) + + +class TestAfmoeWeightMapper(unittest.TestCase): + """Verify AfmoeHfWeightMapper key transformations.""" + + def setUp(self): + from tensorrt_llm._torch.models.checkpoints.hf.afmoe_weight_mapper import ( + AfmoeHfWeightMapper, + ) + + self.mapper = AfmoeHfWeightMapper() + + def test_expert_key_remapping(self): + fake_weights = { + "model.layers.1.mlp.experts.0.gate_proj.weight": torch.zeros(1), + "model.layers.1.mlp.experts.0.up_proj.weight": torch.zeros(1), + "model.layers.1.mlp.experts.0.down_proj.weight": torch.zeros(1), + "model.layers.1.mlp.experts.3.gate_proj.weight": torch.zeros(1), + "model.layers.1.mlp.experts.3.up_proj.weight": torch.zeros(1), + "model.layers.1.mlp.experts.3.down_proj.weight": torch.zeros(1), + } + result = self.mapper.preprocess_weights(fake_weights) + for expert_id in [0, 3]: + prefix = f"model.layers.1.mlp.experts.{expert_id}" + self.assertIn(f"{prefix}.w1.weight", result) + self.assertIn(f"{prefix}.w3.weight", result) + self.assertIn(f"{prefix}.w2.weight", result) + self.assertNotIn(f"{prefix}.gate_proj.weight", result) + self.assertNotIn(f"{prefix}.up_proj.weight", result) + self.assertNotIn(f"{prefix}.down_proj.weight", result) + + def test_router_gate_rename(self): + fake_weights = { + "model.layers.2.mlp.router.gate.weight": torch.zeros(1), + } + result = self.mapper.preprocess_weights(fake_weights) + self.assertIn("model.layers.2.mlp.gate.weight", result) + self.assertNotIn("model.layers.2.mlp.router.gate.weight", result) + + def test_expert_bias_rename(self): + fake_weights = { + "model.layers.2.mlp.expert_bias": torch.zeros(1), + } + result = self.mapper.preprocess_weights(fake_weights) + self.assertIn("model.layers.2.mlp.gate.e_score_correction_bias", result) + self.assertNotIn("model.layers.2.mlp.expert_bias", result) + + def test_attention_gate_fused_into_q(self): + # AfmoeAttention uses attn_output_gate=True, so the separate gate_proj + # is interleaved per head into q_proj and the gate_proj key is dropped. + num_heads, head_dim, hidden = 8, 32, 256 + self.mapper._model = Mock() + self.mapper._model.config = Mock(num_attention_heads=num_heads) + + q = torch.arange(num_heads * head_dim * hidden, dtype=torch.float32).reshape( + num_heads * head_dim, hidden + ) + gate = q + 0.5 + fake_weights = { + "model.layers.0.self_attn.q_proj.weight": q, + "model.layers.0.self_attn.gate_proj.weight": gate, + } + result = self.mapper.preprocess_weights(fake_weights) + + self.assertNotIn("model.layers.0.self_attn.gate_proj.weight", result) + fused = result["model.layers.0.self_attn.q_proj.weight"] + self.assertEqual(fused.shape, (2 * num_heads * head_dim, hidden)) + # Per head the layout is [q_head, gate_head]: first head_dim rows are q, + # next head_dim rows are gate. + torch.testing.assert_close(fused[:head_dim], q[:head_dim]) + torch.testing.assert_close(fused[head_dim : 2 * head_dim], gate[:head_dim]) + + def test_qkv_keys_unchanged_without_gate(self): + # Without a gate_proj key (e.g. partial weight dicts), q/k/v are untouched. + fake_weights = { + "model.layers.0.self_attn.q_proj.weight": torch.zeros(1), + "model.layers.0.self_attn.k_proj.weight": torch.zeros(1), + "model.layers.0.self_attn.v_proj.weight": torch.zeros(1), + } + result = self.mapper.preprocess_weights(fake_weights) + self.assertIn("model.layers.0.self_attn.q_proj.weight", result) + self.assertIn("model.layers.0.self_attn.k_proj.weight", result) + self.assertIn("model.layers.0.self_attn.v_proj.weight", result) + + def test_dense_mlp_keys_unchanged_by_preprocess(self): + fake_weights = { + "model.layers.0.mlp.gate_proj.weight": torch.zeros(1), + "model.layers.0.mlp.up_proj.weight": torch.zeros(1), + "model.layers.0.mlp.down_proj.weight": torch.zeros(1), + } + result = self.mapper.preprocess_weights(fake_weights) + self.assertIn("model.layers.0.mlp.gate_proj.weight", result) + self.assertIn("model.layers.0.mlp.up_proj.weight", result) + self.assertIn("model.layers.0.mlp.down_proj.weight", result) + + def test_is_special_instance_module_for_moe(self): + from unittest.mock import MagicMock + + from tensorrt_llm._torch.modules.fused_moe.interface import MoE + + mock_moe = MagicMock(spec=MoE) + mock_moe.__class__ = MoE + self.assertTrue(self.mapper.is_special_instance_module(mock_moe)) + + mock_linear = MagicMock(spec=torch.nn.Linear) + self.assertFalse(self.mapper.is_special_instance_module(mock_linear)) + + +class TestAfmoeWeightLoading(unittest.TestCase): + """Verify AfmoeForCausalLM applies mapper preprocessing in the real load hook.""" + + def test_load_weights_preprocesses_mapper_weights(self): + from tensorrt_llm._torch.models.modeling_utils import DecoderModelForCausalLM + + model = object.__new__(AfmoeForCausalLM) + raw_weights = {"model.layers.1.mlp.router.gate.weight": torch.zeros(1)} + processed_weights = {"model.layers.1.mlp.gate.weight": torch.zeros(1)} + mapper = Mock() + mapper.preprocess_weights.return_value = processed_weights + + with patch.object(DecoderModelForCausalLM, "load_weights", autospec=True) as load_weights: + AfmoeForCausalLM.load_weights(model, raw_weights, mapper, allow_partial_loading=True) + + mapper.preprocess_weights.assert_called_once_with(raw_weights) + load_weights.assert_called_once() + args, kwargs = load_weights.call_args + self.assertIs(args[0], model) + self.assertIs(kwargs["weights"], processed_weights) + self.assertIs(kwargs["weight_mapper"], mapper) + self.assertTrue(kwargs["allow_partial_loading"]) + + +class TestAfmoeSanity(unittest.TestCase): + """Smoke test: build a tiny AFMoE and run a forward pass.""" + + def test_afmoe_sanity(self): + config_dict = deepcopy(AFMOE_CONFIG) + afmoe_config = AfmoeConfig.from_dict(config_dict) + + dtype = afmoe_config.torch_dtype + device = torch.device("cuda") + with _force_mpi_topology_mapping(): + mapping = Mapping(world_size=1, tp_size=1, rank=0) + # Keep this model-wiring smoke test off backend-native attention kernels. + model_config = ModelConfig( + pretrained_config=afmoe_config, + quant_config=QuantConfig(), + mapping=mapping, + attn_backend="VANILLA", + ) + model = AfmoeForCausalLM(model_config).to(device) + + input_ids = torch.tensor( + [100, 200, 300, 100, 200, 100, 400, 500], dtype=torch.int, device=device + ) + + context_sequence_lengths = [3, 2, 1] + sequence_lengths = context_sequence_lengths + [1, 1] + past_seen_tokens = [0, 0, 0, 62, 75] + request_ids = list(range(len(sequence_lengths))) + token_nums = (torch.tensor(past_seen_tokens) + torch.tensor(sequence_lengths)).tolist() + prompt_lens = token_nums[:3] + past_seen_tokens[3:] + + num_blocks = 100 + tokens_per_block = 128 + head_dim = afmoe_config.hidden_size // afmoe_config.num_attention_heads + num_layers = afmoe_config.num_hidden_layers + num_kv_heads = afmoe_config.num_key_value_heads + max_seq_len = num_blocks * tokens_per_block + batch_size = len(context_sequence_lengths) + 2 + + if dtype == torch.half: + kv_cache_dtype = tensorrt_llm.bindings.DataType.HALF + elif dtype == torch.bfloat16: + kv_cache_dtype = tensorrt_llm.bindings.DataType.BF16 + else: + raise ValueError("Invalid dtype") + + kv_cache_config = KvCacheConfig( + enable_block_reuse=False, + enable_partial_reuse=False, + copy_on_partial_reuse=False, + max_tokens=num_blocks * tokens_per_block, + ) + kv_cache_manager = KVCacheManager( + kv_cache_config, + tensorrt_llm.bindings.internal.batch_manager.CacheType.SELF, + num_layers=num_layers, + num_kv_heads=num_kv_heads, + head_dim=head_dim, + tokens_per_block=tokens_per_block, + max_seq_len=max_seq_len, + max_batch_size=batch_size, + mapping=mapping, + dtype=kv_cache_dtype, + ) + kv_cache_manager.add_dummy_requests(request_ids, token_nums) + + metadata_cls = get_attention_backend(model_config.attn_backend).Metadata + attn_metadata = metadata_cls( + seq_lens=torch.tensor(sequence_lengths, dtype=torch.int), + num_contexts=len(context_sequence_lengths), + kv_cache_params=KVCacheParams( + use_cache=True, + num_cached_tokens_per_seq=past_seen_tokens, + ), + kv_cache_manager=kv_cache_manager, + request_ids=request_ids, + prompt_lens=prompt_lens, + max_num_requests=len(context_sequence_lengths) + 2, + max_num_tokens=8192, + ) + + position_ids = [] + for i, tokens in enumerate(past_seen_tokens): + seq_len = context_sequence_lengths[i] if i < len(context_sequence_lengths) else 1 + position_id = torch.arange(tokens, tokens + seq_len, device=input_ids.device) + position_ids.append(position_id) + position_ids = torch.cat(position_ids).unsqueeze(0) + + try: + with torch.inference_mode(), _force_mpi_collectives(): + attn_metadata.prepare() + logits = model.forward( + input_ids=input_ids, position_ids=position_ids, attn_metadata=attn_metadata + ) + + self.assertEqual(len(past_seen_tokens), logits.shape[0]) + finally: + _shutdown_kv_cache_manager(kv_cache_manager) + + def test_moe_layer_config(self): + config_dict = deepcopy(AFMOE_CONFIG) + afmoe_config = AfmoeConfig.from_dict(config_dict) + + device = torch.device("cuda") + model_config = ModelConfig(pretrained_config=afmoe_config) + model = AfmoeForCausalLM(model_config).to(device) + + self.assertEqual(len(model.model.layers), NUM_HIDDEN_LAYERS) + + for i in range(NUM_HIDDEN_LAYERS): + layer = model.model.layers[i] + if i < NUM_DENSE_LAYERS: + self.assertFalse(layer.moe_enabled, f"Layer {i} should be dense") + self.assertNotIsInstance(layer.mlp, AfmoeMoE) + else: + self.assertTrue(layer.moe_enabled, f"Layer {i} should be MoE") + self.assertIsInstance(layer.mlp, AfmoeMoE) + + +class TestAfmoeEndToEnd(unittest.TestCase): + """Exercise AFMoE through the PyTorch LLM API with dummy weights.""" + + def test_llm_dummy_load_generates_from_token_ids(self): + if not torch.cuda.is_available(): + self.skipTest("AFMoE LLM API test requires CUDA") + + with tempfile.TemporaryDirectory() as tmp_model_dir: + with open(f"{tmp_model_dir}/config.json", "w", encoding="utf-8") as f: + json.dump(AFMOE_CONFIG, f, indent=2) + + prompts = [ + {"prompt_token_ids": [100, 200, 300]}, + {"prompt_token_ids": [101, 202]}, + ] + sampling_params = SamplingParams( + max_tokens=2, + end_id=AFMOE_CONFIG["vocab_size"] - 1, + pad_id=AFMOE_CONFIG["vocab_size"] - 1, + detokenize=False, + ignore_eos=True, + ) + + with LLM( + model=tmp_model_dir, + load_format="dummy", + tensor_parallel_size=1, + enable_chunked_prefill=False, + disable_overlap_scheduler=True, + attn_backend="TRTLLM", + max_batch_size=len(prompts), + max_num_tokens=16, + max_seq_len=64, + moe_config=MoeConfig(max_num_tokens=64), + moe_expert_parallel_size=-1, + moe_tensor_parallel_size=-1, + enable_attention_dp=False, + kv_cache_config=LlmKvCacheConfig(enable_block_reuse=False), + ) as llm: + outputs = llm.generate(prompts, sampling_params=sampling_params) + + self.assertEqual(len(outputs), len(prompts)) + for prompt, output in zip(prompts, outputs): + self.assertEqual(output.prompt_token_ids, prompt["prompt_token_ids"]) + self.assertEqual(len(output.outputs), 1) + self.assertEqual(len(output.outputs[0].token_ids), sampling_params.max_tokens) + + +class TestAfmoeTPAttributes(unittest.TestCase): + """Verify TP-related module attributes are wired correctly.""" + + def _build_model(self, tp_size): + config_dict = deepcopy(AFMOE_CONFIG) + afmoe_config = AfmoeConfig.from_dict(config_dict) + with _force_mpi_topology_mapping(): + mapping = Mapping(world_size=tp_size, tp_size=tp_size, rank=0) + model_config = ModelConfig( + pretrained_config=afmoe_config, mapping=mapping, allreduce_strategy="NCCL" + ) + return AfmoeForCausalLM(model_config) + + def _build_attention_dp_model(self, tp_size): + config_dict = deepcopy(AFMOE_CONFIG) + afmoe_config = AfmoeConfig.from_dict(config_dict) + with _force_mpi_topology_mapping(): + mapping = Mapping( + world_size=tp_size, + tp_size=tp_size, + rank=0, + enable_attention_dp=True, + ) + model_config = ModelConfig( + pretrained_config=afmoe_config, mapping=mapping, allreduce_strategy="NCCL" + ) + return AfmoeForCausalLM(model_config) + + def test_qkv_is_column_parallel_with_output_gate(self): + model = self._build_model(tp_size=1) + for layer in model.model.layers: + attn = layer.self_attn + self.assertTrue(attn.attn_output_gate) + self.assertEqual(attn.qkv_proj.tp_mode, TensorParallelMode.COLUMN) + + def test_moe_experts_no_reduce(self): + model = self._build_model(tp_size=1) + for layer in model.model.layers: + if layer.moe_enabled: + self.assertFalse(layer.mlp.experts.reduce_results) + + def test_allreduce_created_for_tp2(self): + model = self._build_model(tp_size=2) + for layer in model.model.layers: + if layer.moe_enabled: + self.assertIsNotNone( + layer.mlp.allreduce, "MoE layer should have allreduce for tp_size=2" + ) + + def test_no_allreduce_for_tp1(self): + model = self._build_model(tp_size=1) + for layer in model.model.layers: + if layer.moe_enabled: + self.assertIsNone( + layer.mlp.allreduce, "MoE layer should NOT have allreduce for tp_size=1" + ) + + def test_qkv_output_includes_fused_gate(self): + # With attn_output_gate=True the query slot is doubled (q + gate) and + # fused into qkv_proj, so its local output is 2*q_size + 2*kv_size. + model = self._build_model(tp_size=2) + for layer in model.model.layers: + attn = layer.self_attn + expected_out = attn.q_size * 2 + 2 * attn.kv_size + actual_out = attn.qkv_proj.weight.shape[0] + self.assertEqual( + actual_out, + expected_out, + f"qkv_proj local output should be 2*q_size + 2*kv_size = " + f"{expected_out}, got {actual_out}", + ) + + def test_attention_dp_uses_unsharded_qkv_and_mlp_modules(self): + model = self._build_attention_dp_model(tp_size=2) + + for layer in model.model.layers: + attn = layer.self_attn + self.assertEqual(attn.qkv_proj.tp_size, 1) + # Unsharded: full heads, q slot doubled by the output gate. + expected_out = attn.q_size * 2 + 2 * attn.kv_size + self.assertEqual(attn.qkv_proj.weight.shape[0], expected_out) + + if layer.moe_enabled: + self.assertIsNone(layer.mlp.allreduce) + if layer.mlp.shared_experts is not None: + self.assertEqual(layer.mlp.shared_experts.gate_up_proj.tp_size, 1) + self.assertEqual(layer.mlp.shared_experts.down_proj.tp_size, 1) + else: + self.assertEqual(layer.mlp.gate_up_proj.tp_size, 1) + self.assertEqual(layer.mlp.down_proj.tp_size, 1) + + def test_attention_layer_types(self): + model = self._build_model(tp_size=1) + layer_types = AFMOE_CONFIG["layer_types"] + for i, layer in enumerate(model.model.layers): + attn = layer.self_attn + if layer_types[i] == "sliding_attention": + self.assertTrue(attn.is_local_attention) + self.assertEqual(attn.attention_window_size, WINDOW_SIZE) + else: + self.assertFalse(attn.is_local_attention) + self.assertIsNone(attn.attention_window_size) + + +@unittest.skipUnless(torch.cuda.is_available(), "needs CUDA") +@unittest.skipIf( + SKIP_AFMOE_HF_ACCURACY_TEST, + "installed transformers does not provide the HF afmoe reference model", +) +class TestAfmoeAllCloseToHF(unittest.TestCase): + """Compare TRT-LLM AFMoE context-phase logits against the HF reference. + + Loads the HF model's weights into the TRT-LLM model via AfmoeHfWeightMapper, + exercising the per-head q/gate fusion (attn_output_gate) and the HF + fused-expert -> per-expert conversion, then checks logit parity. + """ + + # Field names follow the HF AfmoeConfig schema (released-checkpoint names). + HF_CONFIG = { + "hidden_size": 256, + "intermediate_size": 512, + "moe_intermediate_size": 128, + "head_dim": 32, + "num_attention_heads": 8, + "num_key_value_heads": 2, + "num_hidden_layers": NUM_HIDDEN_LAYERS, + "num_dense_layers": NUM_DENSE_LAYERS, + "num_experts": 8, + "num_experts_per_tok": 2, + "num_shared_experts": 1, + "global_attn_every_n_layers": 4, + "sliding_window": WINDOW_SIZE, + "max_position_embeddings": 2048, + "rms_norm_eps": 1e-5, + "rope_theta": 10000, + "route_scale": 1.0, + "route_norm": True, + "score_func": "sigmoid", + "vocab_size": 1024, + "hidden_act": "silu", + "tie_word_embeddings": False, + } + + @staticmethod + def _convert_hf_experts(state_dict, moe_intermediate_size): + """Split HF fused 3D expert params into per-expert gate/up/down weights. + + HF-native AFMoE stores experts as ``experts.gate_up_proj`` + ``[num_experts, 2 * moe_inter, hidden]`` and ``experts.down_proj`` + ``[num_experts, hidden, moe_inter]``; AfmoeHfWeightMapper expects the + released-checkpoint layout with separate per-expert matrices. + """ + converted = dict(state_dict) + gate_up_keys = [k for k in state_dict if k.endswith(".experts.gate_up_proj")] + for gate_up_key in gate_up_keys: + prefix = gate_up_key[: -len(".gate_up_proj")] + gate_up = converted.pop(gate_up_key) + down = converted.pop(prefix + ".down_proj") + for expert_idx in range(gate_up.shape[0]): + converted[f"{prefix}.{expert_idx}.gate_proj.weight"] = gate_up[expert_idx][ + :moe_intermediate_size + ].contiguous() + converted[f"{prefix}.{expert_idx}.up_proj.weight"] = gate_up[expert_idx][ + moe_intermediate_size: + ].contiguous() + converted[f"{prefix}.{expert_idx}.down_proj.weight"] = down[expert_idx].contiguous() + return converted + + @torch.no_grad() + def test_afmoe_allclose_to_hf(self): + from tensorrt_llm._torch.models.checkpoints.hf.afmoe_weight_mapper import ( + AfmoeHfWeightMapper, + ) + + torch.manual_seed(0) + device = torch.device("cuda") + dtype = torch.bfloat16 + + hf_config = HFAfmoeConfig(dtype="float32", **self.HF_CONFIG) + hf_model = HFAfmoeForCausalLM(hf_config).to(dtype).to(device).eval() + + # TRT-LLM needs a couple of routing fields that the HF schema names + # differently; provide both so AfmoeConfig validates and builds. + trt_config_dict = dict(self.HF_CONFIG) + trt_config_dict.update( + architectures=["AfmoeForCausalLM"], + model_type="afmoe", + dtype="bfloat16", + n_group=1, + topk_group=1, + scoring_func=self.HF_CONFIG["score_func"], + norm_topk_prob=self.HF_CONFIG["route_norm"], + ) + afmoe_config = AfmoeConfig.from_dict(trt_config_dict) + with _force_mpi_topology_mapping(): + mapping = Mapping(world_size=1, tp_size=1, rank=0) + # Keep HF parity focused on AFMoE weights/model math, not attention-kernel coverage. + model_config = ModelConfig( + pretrained_config=afmoe_config, + mapping=mapping, + attn_backend="VANILLA", + ) + model = AfmoeForCausalLM(model_config).to(dtype).to(device) + + weights = self._convert_hf_experts( + hf_model.state_dict(), self.HF_CONFIG["moe_intermediate_size"] + ) + weights = {k: v.to(dtype) for k, v in weights.items()} + + weight_mapper = AfmoeHfWeightMapper() + weight_mapper.init_model_and_config(model, model_config) + model.load_weights(weights, weight_mapper) + if hasattr(model, "post_load_weights"): + model.post_load_weights() + + # Short context: input_len < sliding_window so sliding == full attention. + input_len = WINDOW_SIZE - 1 + input_ids = torch.tensor([101, 202, 303][:input_len], dtype=torch.int32, device=device) + position_ids = torch.arange(input_len, dtype=torch.int32, device=device).unsqueeze(0) + + num_blocks, tokens_per_block = 4, 128 + max_seq_len = num_blocks * tokens_per_block + kv_cache_manager = KVCacheManager( + KvCacheConfig( + enable_block_reuse=False, + enable_partial_reuse=False, + copy_on_partial_reuse=False, + max_tokens=num_blocks * tokens_per_block, + ), + tensorrt_llm.bindings.internal.batch_manager.CacheType.SELF, + num_layers=hf_config.num_hidden_layers, + num_kv_heads=hf_config.num_key_value_heads, + head_dim=hf_config.head_dim, + tokens_per_block=tokens_per_block, + max_seq_len=max_seq_len, + max_batch_size=1, + mapping=mapping, + dtype=tensorrt_llm.bindings.DataType.BF16, + ) + kv_cache_manager.add_dummy_requests([0], [input_len]) + metadata_cls = get_attention_backend(model_config.attn_backend).Metadata + attn_metadata = metadata_cls( + seq_lens=torch.tensor([input_len], dtype=torch.int), + num_contexts=1, + kv_cache_params=KVCacheParams(use_cache=True, num_cached_tokens_per_seq=[0]), + kv_cache_manager=kv_cache_manager, + request_ids=[0], + prompt_lens=[input_len], + max_num_requests=1, + max_num_tokens=8192, + ) + + try: + hf_position_ids = position_ids.to(torch.long) + with torch.inference_mode(), _force_mpi_collectives(): + attn_metadata.prepare() + logits = model.forward( + input_ids=input_ids, position_ids=position_ids, attn_metadata=attn_metadata + ) + ref = hf_model.forward( + input_ids=input_ids.unsqueeze(0).long(), + position_ids=hf_position_ids, + use_cache=False, + ) + + # Loose tolerance: bf16 + token-choice MoE routing amplify per-logit + # noise (same rationale as the EXAONE-MoE parity test). + torch.testing.assert_close(logits, ref.logits[:, -1].float(), atol=1.0, rtol=0.5) + finally: + _shutdown_kv_cache_manager(kv_cache_manager) + + +if __name__ == "__main__": + unittest.main() From 4279e5b4d89d3a18db3e1c7016529d7c3d091a85 Mon Sep 17 00:00:00 2001 From: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> Date: Sat, 6 Jun 2026 03:13:00 +0000 Subject: [PATCH 018/119] [None][infra] Check in most recent lock file from nightly pipeline Signed-off-by: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> --- security_scanning/examples/apps/poetry.lock | 12 +-- .../examples/auto_deploy/poetry.lock | 52 ++++++------ .../examples/draft_target_model/poetry.lock | 18 ++-- security_scanning/examples/eagle/poetry.lock | 18 ++-- .../llm-eval/lm-eval-harness/poetry.lock | 46 +++++----- .../examples/lookahead/poetry.lock | 18 ++-- security_scanning/examples/medusa/poetry.lock | 18 ++-- .../models/contrib/baichuan/poetry.lock | 18 ++-- .../examples/models/contrib/bloom/poetry.lock | 18 ++-- .../models/contrib/chatglm-6b/poetry.lock | 18 ++-- .../models/contrib/chatglm2-6b/poetry.lock | 18 ++-- .../contrib/chatglm3-6b-32k/poetry.lock | 18 ++-- .../examples/models/contrib/dbrx/poetry.lock | 18 ++-- .../models/contrib/deepseek_v1/poetry.lock | 18 ++-- .../models/contrib/deepseek_v2/poetry.lock | 18 ++-- .../models/contrib/falcon/poetry.lock | 20 ++--- .../models/contrib/falcon/pyproject.toml | 2 +- .../examples/models/contrib/gptj/poetry.lock | 18 ++-- .../models/contrib/gptneox/poetry.lock | 18 ++-- .../examples/models/contrib/grok/poetry.lock | 18 ++-- .../models/contrib/hyperclovax/poetry.lock | 18 ++-- .../models/contrib/internlm/poetry.lock | 18 ++-- .../examples/models/contrib/jais/poetry.lock | 18 ++-- .../examples/models/contrib/mmdit/poetry.lock | 18 ++-- .../examples/models/contrib/mpt/poetry.lock | 18 ++-- .../examples/models/contrib/opt/poetry.lock | 18 ++-- .../models/contrib/skywork/poetry.lock | 18 ++-- .../examples/models/contrib/smaug/poetry.lock | 18 ++-- .../examples/models/contrib/stdit/poetry.lock | 24 +++--- .../examples/models/core/commandr/poetry.lock | 18 ++-- .../examples/models/core/gemma/poetry.lock | 18 ++-- .../examples/models/core/glm-4-9b/poetry.lock | 18 ++-- .../examples/models/core/gpt/poetry.lock | 18 ++-- .../examples/models/core/llama/poetry.lock | 18 ++-- .../examples/models/core/mamba/poetry.lock | 18 ++-- .../examples/models/core/mixtral/poetry.lock | 12 +-- .../examples/models/core/mllama/poetry.lock | 12 +-- .../examples/models/core/nemotron/poetry.lock | 18 ++-- .../examples/models/core/phi/poetry.lock | 18 ++-- .../examples/models/core/qwen/poetry.lock | 18 ++-- .../models/core/qwen2audio/poetry.lock | 18 ++-- .../examples/models/core/qwenvl/poetry.lock | 18 ++-- .../models/core/recurrentgemma/poetry.lock | 18 ++-- .../examples/models/core/whisper/poetry.lock | 24 +++--- security_scanning/examples/ngram/poetry.lock | 18 ++-- .../examples/quantization/poetry.lock | 18 ++-- .../examples/redrafter/poetry.lock | 18 ++-- security_scanning/examples/serve/poetry.lock | 46 +++++----- .../examples/trtllm-eval/poetry.lock | 46 +++++----- security_scanning/metadata.json | 4 +- security_scanning/poetry.lock | 84 +++++++++---------- security_scanning/triton_backend/poetry.lock | 18 ++-- 52 files changed, 547 insertions(+), 539 deletions(-) diff --git a/security_scanning/examples/apps/poetry.lock b/security_scanning/examples/apps/poetry.lock index 06cbf5b70b82..533961745a26 100644 --- a/security_scanning/examples/apps/poetry.lock +++ b/security_scanning/examples/apps/poetry.lock @@ -477,14 +477,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -492,10 +492,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typing-extensions" diff --git a/security_scanning/examples/auto_deploy/poetry.lock b/security_scanning/examples/auto_deploy/poetry.lock index 91da0e3677d4..29bca252de72 100644 --- a/security_scanning/examples/auto_deploy/poetry.lock +++ b/security_scanning/examples/auto_deploy/poetry.lock @@ -678,20 +678,20 @@ test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.6.2)", "tcolorpy (>=0.1.2)"] [[package]] name = "datasets" -version = "4.8.5" +version = "5.0.0" description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff"}, - {file = "datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772"}, + {file = "datasets-5.0.0-py3-none-any.whl", hash = "sha256:7dd34927a0fd7046e98aad5cb9430e699c373238a15befa7b9bf22b991a7fee6"}, + {file = "datasets-5.0.0.tar.gz", hash = "sha256:83dbbbdb07a33b82192b8c419deb18739b138ee2ce1a322d55ce6b100954ec1a"}, ] [package.dependencies] dill = ">=0.3.0,<0.4.2" filelock = "*" -fsspec = {version = ">=2023.1.0,<=2026.2.0", extras = ["http"]} +fsspec = {version = ">=2023.1.0,<=2026.4.0", extras = ["http"]} httpx = "<1.0.0" huggingface-hub = ">=0.25.0,<2.0" multiprocess = "<0.70.20" @@ -707,16 +707,18 @@ xxhash = "*" [package.extras] audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers", "transformers (>=4.42.0)", "zstandard"] +dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyiceberg[pyarrow,sql-sqlite]", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "sqlalchemy", "teich (==0.1.1a76)", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", 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@@ files = [ [[package]] name = "fsspec" -version = "2026.2.0" +version = "2026.4.0" description = "File-system specification" optional = false python-versions = ">=3.10" groups = ["main"] files = [ - {file = "fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437"}, - {file = "fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff"}, + {file = "fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2"}, + {file = "fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4"}, ] [package.dependencies] @@ -1137,14 +1139,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions 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optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/eagle/poetry.lock b/security_scanning/examples/eagle/poetry.lock index f7fa763eec34..d80a41791164 100644 --- a/security_scanning/examples/eagle/poetry.lock +++ b/security_scanning/examples/eagle/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock b/security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock index be865ce11852..6e52c45c2e85 100644 --- a/security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock +++ b/security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock @@ -584,20 +584,20 @@ test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.6.2)", "tcolorpy (>=0.1.2)"] [[package]] name = "datasets" -version = "4.8.5" +version = "5.0.0" description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff"}, - {file = "datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772"}, + {file = 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"lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyiceberg[pyarrow,sql-sqlite]", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "teich (==0.1.1a76)", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "trimesh (>=4.10.0)", "zstandard"] torch = ["torch"] vision = ["Pillow (>=9.4.0)"] @@ -851,14 +853,14 @@ files = [ [[package]] name = "fsspec" -version = "2026.2.0" +version = "2026.4.0" description = "File-system specification" optional = false python-versions = ">=3.10" groups = ["main"] files = [ - {file = "fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437"}, - {file = "fsspec-2026.2.0.tar.gz", hash = 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"sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -1672,15 +1674,15 @@ dill = ">=0.4.1" [[package]] name = "narwhals" -version = "2.22.0" +version = "2.22.1" description = "Extremely lightweight compatibility layer between dataframe libraries" optional = false python-versions = ">=3.10" groups = ["main"] markers = "python_version >= \"3.11\"" files = [ - {file = "narwhals-2.22.0-py3-none-any.whl", hash = "sha256:1421797ede01789cc1537619dbc3f36f840737240f748fdb24a60a0225fc80be"}, - {file = "narwhals-2.22.0.tar.gz", hash = "sha256:6486282bb7e4b4ab55963efbd8be1451b764cc4874b74d1fd625eba9dc60b86f"}, + {file = "narwhals-2.22.1-py3-none-any.whl", hash = "sha256:60567d774edf77db53906f89d9fbd164e66e56d66d388e1e6990f17ac33cfb53"}, + {file = "narwhals-2.22.1.tar.gz", hash = "sha256:d62920805a0a43b7ff8b54b0c0d3142d796f8a9301836ada37e573d6a33cbcd9"}, ] [package.extras] @@ -3756,14 +3758,14 @@ pyyaml = ["pyyaml"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3771,10 +3773,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "tqdm-multiprocess" diff --git a/security_scanning/examples/lookahead/poetry.lock b/security_scanning/examples/lookahead/poetry.lock index 4bcce5e406a4..c7ef3615275a 100644 --- a/security_scanning/examples/lookahead/poetry.lock +++ b/security_scanning/examples/lookahead/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/medusa/poetry.lock b/security_scanning/examples/medusa/poetry.lock index 4bcce5e406a4..c7ef3615275a 100644 --- a/security_scanning/examples/medusa/poetry.lock +++ b/security_scanning/examples/medusa/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/baichuan/poetry.lock b/security_scanning/examples/models/contrib/baichuan/poetry.lock index f10a9c28fa35..8aec3ee0d2df 100644 --- a/security_scanning/examples/models/contrib/baichuan/poetry.lock +++ b/security_scanning/examples/models/contrib/baichuan/poetry.lock @@ -839,14 +839,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2188,14 +2188,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2203,10 +2203,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/contrib/bloom/poetry.lock b/security_scanning/examples/models/contrib/bloom/poetry.lock index 6f143788d531..82771c901296 100644 --- a/security_scanning/examples/models/contrib/bloom/poetry.lock +++ b/security_scanning/examples/models/contrib/bloom/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/chatglm-6b/poetry.lock b/security_scanning/examples/models/contrib/chatglm-6b/poetry.lock index b7a0a8b9dcf3..db7e2bdb9186 100644 --- a/security_scanning/examples/models/contrib/chatglm-6b/poetry.lock +++ b/security_scanning/examples/models/contrib/chatglm-6b/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2180,14 +2180,14 @@ blobfile = ["blobfile (>=3)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2195,10 +2195,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/chatglm2-6b/poetry.lock b/security_scanning/examples/models/contrib/chatglm2-6b/poetry.lock index b7a0a8b9dcf3..db7e2bdb9186 100644 --- a/security_scanning/examples/models/contrib/chatglm2-6b/poetry.lock +++ b/security_scanning/examples/models/contrib/chatglm2-6b/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2180,14 +2180,14 @@ blobfile = ["blobfile (>=3)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2195,10 +2195,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/chatglm3-6b-32k/poetry.lock b/security_scanning/examples/models/contrib/chatglm3-6b-32k/poetry.lock index b7a0a8b9dcf3..db7e2bdb9186 100644 --- a/security_scanning/examples/models/contrib/chatglm3-6b-32k/poetry.lock +++ b/security_scanning/examples/models/contrib/chatglm3-6b-32k/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2180,14 +2180,14 @@ blobfile = ["blobfile (>=3)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2195,10 +2195,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/dbrx/poetry.lock b/security_scanning/examples/models/contrib/dbrx/poetry.lock index 947f62f6e2be..8fc5be8ff3f8 100644 --- a/security_scanning/examples/models/contrib/dbrx/poetry.lock +++ b/security_scanning/examples/models/contrib/dbrx/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2062,14 +2062,14 @@ blobfile = ["blobfile (>=2)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2077,10 +2077,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/deepseek_v1/poetry.lock b/security_scanning/examples/models/contrib/deepseek_v1/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/contrib/deepseek_v1/poetry.lock +++ b/security_scanning/examples/models/contrib/deepseek_v1/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/deepseek_v2/poetry.lock b/security_scanning/examples/models/contrib/deepseek_v2/poetry.lock index a9067d53486b..9778dfcfa864 100644 --- a/security_scanning/examples/models/contrib/deepseek_v2/poetry.lock +++ b/security_scanning/examples/models/contrib/deepseek_v2/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/falcon/poetry.lock b/security_scanning/examples/models/contrib/falcon/poetry.lock index 6de82aef06ed..399438819279 100644 --- a/security_scanning/examples/models/contrib/falcon/poetry.lock +++ b/security_scanning/examples/models/contrib/falcon/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2093,14 +2093,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2108,10 +2108,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" @@ -2547,4 +2547,4 @@ propcache = ">=0.2.1" [metadata] lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "9fdd64cff9a4ce97a8ed5b0fa4a6a529068b558aeb6e44fd72166bcbff224a21" +content-hash = "5de86f507db596040900b02825ab746f61f595a0e2157264185ed64bf67fc60a" diff --git a/security_scanning/examples/models/contrib/falcon/pyproject.toml b/security_scanning/examples/models/contrib/falcon/pyproject.toml index f3482d3e7d12..13b1fd619fdc 100644 --- a/security_scanning/examples/models/contrib/falcon/pyproject.toml +++ b/security_scanning/examples/models/contrib/falcon/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "evaluate (>=0.4.6,<0.5.0)", "rouge-score (>=0.1.2,<0.2.0)", "sentencepiece (>=0.1.99)", - "tqdm (>=4.67.3,<5.0.0)" + "tqdm (>=4.68.1,<5.0.0)" ] diff --git a/security_scanning/examples/models/contrib/gptj/poetry.lock b/security_scanning/examples/models/contrib/gptj/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/contrib/gptj/poetry.lock +++ b/security_scanning/examples/models/contrib/gptj/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/gptneox/poetry.lock b/security_scanning/examples/models/contrib/gptneox/poetry.lock index f7fa763eec34..d80a41791164 100644 --- a/security_scanning/examples/models/contrib/gptneox/poetry.lock +++ b/security_scanning/examples/models/contrib/gptneox/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/grok/poetry.lock b/security_scanning/examples/models/contrib/grok/poetry.lock index f0bff63e620f..ab4afea7a8f8 100644 --- a/security_scanning/examples/models/contrib/grok/poetry.lock +++ b/security_scanning/examples/models/contrib/grok/poetry.lock @@ -981,14 +981,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -3019,14 +3019,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3034,10 +3034,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "treescope" diff --git a/security_scanning/examples/models/contrib/hyperclovax/poetry.lock b/security_scanning/examples/models/contrib/hyperclovax/poetry.lock index 3fb1bd1e414e..dc9ca331171d 100644 --- a/security_scanning/examples/models/contrib/hyperclovax/poetry.lock +++ b/security_scanning/examples/models/contrib/hyperclovax/poetry.lock @@ -394,14 +394,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -1388,14 +1388,14 @@ scipy = ["scipy"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -1403,10 +1403,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "triton" diff --git a/security_scanning/examples/models/contrib/internlm/poetry.lock b/security_scanning/examples/models/contrib/internlm/poetry.lock index 4bcce5e406a4..c7ef3615275a 100644 --- a/security_scanning/examples/models/contrib/internlm/poetry.lock +++ b/security_scanning/examples/models/contrib/internlm/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/jais/poetry.lock b/security_scanning/examples/models/contrib/jais/poetry.lock index 6f143788d531..82771c901296 100644 --- a/security_scanning/examples/models/contrib/jais/poetry.lock +++ b/security_scanning/examples/models/contrib/jais/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/mmdit/poetry.lock b/security_scanning/examples/models/contrib/mmdit/poetry.lock index dc5df45fe55e..67e92b3c8716 100644 --- a/security_scanning/examples/models/contrib/mmdit/poetry.lock +++ b/security_scanning/examples/models/contrib/mmdit/poetry.lock @@ -420,14 +420,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -1077,14 +1077,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -1092,10 +1092,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/contrib/mpt/poetry.lock b/security_scanning/examples/models/contrib/mpt/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/contrib/mpt/poetry.lock +++ b/security_scanning/examples/models/contrib/mpt/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/opt/poetry.lock b/security_scanning/examples/models/contrib/opt/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/contrib/opt/poetry.lock +++ b/security_scanning/examples/models/contrib/opt/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/skywork/poetry.lock b/security_scanning/examples/models/contrib/skywork/poetry.lock index 6f143788d531..82771c901296 100644 --- a/security_scanning/examples/models/contrib/skywork/poetry.lock +++ b/security_scanning/examples/models/contrib/skywork/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = 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"sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/contrib/smaug/poetry.lock b/security_scanning/examples/models/contrib/smaug/poetry.lock index 6f143788d531..82771c901296 100644 --- a/security_scanning/examples/models/contrib/smaug/poetry.lock +++ b/security_scanning/examples/models/contrib/smaug/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = 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b/security_scanning/examples/models/contrib/stdit/poetry.lock index d8baae8c646b..124f08aca705 100644 --- a/security_scanning/examples/models/contrib/stdit/poetry.lock +++ b/security_scanning/examples/models/contrib/stdit/poetry.lock @@ -944,14 +944,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2412,14 +2412,14 @@ pyyaml = ["pyyaml"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2427,10 +2427,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" @@ -2566,14 +2566,14 @@ zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""] [[package]] name = "wcwidth" -version = "0.7.0" +version = "0.8.0" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = ">=3.8" groups = ["main"] files = [ - {file = "wcwidth-0.7.0-py3-none-any.whl", hash = "sha256:5d69154c429a82910e241c738cd0e2976fac8a2dd47a1a805f4afed1c0f136f2"}, - {file = "wcwidth-0.7.0.tar.gz", hash = "sha256:90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0"}, + {file = "wcwidth-0.8.0-py3-none-any.whl", hash = "sha256:8c75e6099cefd197c4bcc67a486f70b5dbc68f997c05f34a811d853910450d64"}, + {file = "wcwidth-0.8.0.tar.gz", hash = "sha256:68a882ff6d14e3d14e0cae590b96a0551be64ce4905408112a8254434a1bdf69"}, ] [[package]] diff --git a/security_scanning/examples/models/core/commandr/poetry.lock b/security_scanning/examples/models/core/commandr/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/core/commandr/poetry.lock +++ b/security_scanning/examples/models/core/commandr/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/gemma/poetry.lock b/security_scanning/examples/models/core/gemma/poetry.lock index 06be457d39a9..381fe7bd24bd 100644 --- a/security_scanning/examples/models/core/gemma/poetry.lock +++ b/security_scanning/examples/models/core/gemma/poetry.lock @@ -951,14 +951,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -3208,14 +3208,14 @@ numpy = ">=1.22.0" [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3223,10 +3223,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/glm-4-9b/poetry.lock b/security_scanning/examples/models/core/glm-4-9b/poetry.lock index b7a0a8b9dcf3..db7e2bdb9186 100644 --- a/security_scanning/examples/models/core/glm-4-9b/poetry.lock +++ b/security_scanning/examples/models/core/glm-4-9b/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2180,14 +2180,14 @@ blobfile = ["blobfile (>=3)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2195,10 +2195,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/gpt/poetry.lock b/security_scanning/examples/models/core/gpt/poetry.lock index 6f143788d531..82771c901296 100644 --- a/security_scanning/examples/models/core/gpt/poetry.lock +++ b/security_scanning/examples/models/core/gpt/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/llama/poetry.lock b/security_scanning/examples/models/core/llama/poetry.lock index 942ddfa480f5..dee5d856b386 100644 --- a/security_scanning/examples/models/core/llama/poetry.lock +++ b/security_scanning/examples/models/core/llama/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2093,14 +2093,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2108,10 +2108,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/mamba/poetry.lock b/security_scanning/examples/models/core/mamba/poetry.lock index 3bd3d78d3694..0c382d6b6380 100644 --- a/security_scanning/examples/models/core/mamba/poetry.lock +++ b/security_scanning/examples/models/core/mamba/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2093,14 +2093,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2108,10 +2108,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/mixtral/poetry.lock b/security_scanning/examples/models/core/mixtral/poetry.lock index 6d8811859d04..b9f8d5a51c22 100644 --- a/security_scanning/examples/models/core/mixtral/poetry.lock +++ b/security_scanning/examples/models/core/mixtral/poetry.lock @@ -1349,14 +1349,14 @@ pyyaml = ["pyyaml"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -1364,10 +1364,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/mllama/poetry.lock b/security_scanning/examples/models/core/mllama/poetry.lock index 388eb61ffe6e..fa67077bb069 100644 --- a/security_scanning/examples/models/core/mllama/poetry.lock +++ b/security_scanning/examples/models/core/mllama/poetry.lock @@ -1839,14 +1839,14 @@ scipy = ["scipy"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -1854,10 +1854,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/nemotron/poetry.lock b/security_scanning/examples/models/core/nemotron/poetry.lock index f7284c6ea434..01e83d08667f 100644 --- a/security_scanning/examples/models/core/nemotron/poetry.lock +++ b/security_scanning/examples/models/core/nemotron/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2009,14 +2009,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2024,10 +2024,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/phi/poetry.lock b/security_scanning/examples/models/core/phi/poetry.lock index 72f6163c769d..251f2fdb43d4 100644 --- a/security_scanning/examples/models/core/phi/poetry.lock +++ b/security_scanning/examples/models/core/phi/poetry.lock @@ -840,14 +840,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2074,14 +2074,14 @@ blobfile = ["blobfile (>=2)"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2089,10 +2089,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/models/core/qwen/poetry.lock b/security_scanning/examples/models/core/qwen/poetry.lock index ee9118e15444..4d0961ba39fc 100644 --- a/security_scanning/examples/models/core/qwen/poetry.lock +++ b/security_scanning/examples/models/core/qwen/poetry.lock @@ -996,14 +996,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -3127,14 +3127,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3142,10 +3142,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/qwen2audio/poetry.lock b/security_scanning/examples/models/core/qwen2audio/poetry.lock index 6f50520b3373..2dbc17c9d7f2 100644 --- a/security_scanning/examples/models/core/qwen2audio/poetry.lock +++ b/security_scanning/examples/models/core/qwen2audio/poetry.lock @@ -840,14 +840,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2263,14 +2263,14 @@ testing = ["datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff", [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2278,10 +2278,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/qwenvl/poetry.lock b/security_scanning/examples/models/core/qwenvl/poetry.lock index 31af16d6bd73..05a57f4ed1d2 100644 --- a/security_scanning/examples/models/core/qwenvl/poetry.lock +++ b/security_scanning/examples/models/core/qwenvl/poetry.lock @@ -1199,14 +1199,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -3502,14 +3502,14 @@ scipy = ["scipy"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3517,10 +3517,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/recurrentgemma/poetry.lock b/security_scanning/examples/models/core/recurrentgemma/poetry.lock index 6200020367eb..ddc8b39e2959 100644 --- a/security_scanning/examples/models/core/recurrentgemma/poetry.lock +++ b/security_scanning/examples/models/core/recurrentgemma/poetry.lock @@ -971,14 +971,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2834,14 +2834,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2849,10 +2849,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/models/core/whisper/poetry.lock b/security_scanning/examples/models/core/whisper/poetry.lock index b0b7a87b8264..354a81df59b4 100644 --- a/security_scanning/examples/models/core/whisper/poetry.lock +++ b/security_scanning/examples/models/core/whisper/poetry.lock @@ -1005,14 +1005,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -1641,15 +1641,15 @@ dill = ">=0.3.8" [[package]] name = "narwhals" -version = "2.22.0" +version = "2.22.1" description = "Extremely lightweight compatibility layer between dataframe libraries" optional = false python-versions = ">=3.10" groups = ["main"] markers = "python_version >= \"3.11\"" files = [ - {file = "narwhals-2.22.0-py3-none-any.whl", hash = "sha256:1421797ede01789cc1537619dbc3f36f840737240f748fdb24a60a0225fc80be"}, - {file = "narwhals-2.22.0.tar.gz", hash = "sha256:6486282bb7e4b4ab55963efbd8be1451b764cc4874b74d1fd625eba9dc60b86f"}, + {file = "narwhals-2.22.1-py3-none-any.whl", hash = "sha256:60567d774edf77db53906f89d9fbd164e66e56d66d388e1e6990f17ac33cfb53"}, + {file = "narwhals-2.22.1.tar.gz", hash = "sha256:d62920805a0a43b7ff8b54b0c0d3142d796f8a9301836ada37e573d6a33cbcd9"}, ] [package.extras] @@ -3500,14 +3500,14 @@ pyyaml = ["pyyaml"] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -3515,10 +3515,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/ngram/poetry.lock b/security_scanning/examples/ngram/poetry.lock index 804fe4b8db0c..c6995ec2bab5 100644 --- a/security_scanning/examples/ngram/poetry.lock +++ b/security_scanning/examples/ngram/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2024,14 +2024,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2039,10 +2039,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/quantization/poetry.lock b/security_scanning/examples/quantization/poetry.lock index 99f5e746b4d4..52686658cd8e 100644 --- a/security_scanning/examples/quantization/poetry.lock +++ b/security_scanning/examples/quantization/poetry.lock @@ -792,14 +792,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = 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"sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2169,10 +2169,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/redrafter/poetry.lock b/security_scanning/examples/redrafter/poetry.lock index 4bcce5e406a4..c7ef3615275a 100644 --- a/security_scanning/examples/redrafter/poetry.lock +++ b/security_scanning/examples/redrafter/poetry.lock @@ -828,14 +828,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = "sha256:f0c5ecd1ef8c6a60f86f61ee278f2c1570ba9e279c9f54de9094210723b3613b"}, ] [package.dependencies] @@ -2088,14 +2088,14 @@ files = [ [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -2103,10 +2103,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "typer" diff --git a/security_scanning/examples/serve/poetry.lock b/security_scanning/examples/serve/poetry.lock index 50b87aa9ad41..0f9a1057e5e3 100644 --- a/security_scanning/examples/serve/poetry.lock +++ b/security_scanning/examples/serve/poetry.lock @@ -1110,20 +1110,20 @@ pandas = ["numpy (>=2.0.2)", "pandas (>=2.2.3)"] [[package]] name = "datasets" -version = "4.8.5" +version = "5.0.0" description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff"}, - {file = "datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772"}, + {file = "datasets-5.0.0-py3-none-any.whl", hash = "sha256:7dd34927a0fd7046e98aad5cb9430e699c373238a15befa7b9bf22b991a7fee6"}, + {file = "datasets-5.0.0.tar.gz", hash = "sha256:83dbbbdb07a33b82192b8c419deb18739b138ee2ce1a322d55ce6b100954ec1a"}, ] [package.dependencies] dill = ">=0.3.0,<0.4.2" filelock = "*" -fsspec = {version = ">=2023.1.0,<=2026.2.0", extras = ["http"]} +fsspec = {version = ">=2023.1.0,<=2026.4.0", extras = ["http"]} httpx = "<1.0.0" huggingface-hub = ">=0.25.0,<2.0" multiprocess = "<0.70.20" @@ -1139,16 +1139,18 @@ xxhash = "*" [package.extras] audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", 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["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "zstandard"] -tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch 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"polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyiceberg[pyarrow,sql-sqlite]", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "teich (==0.1.1a76)", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "trimesh (>=4.10.0)", "zstandard"] torch = ["torch"] vision = ["Pillow (>=9.4.0)"] @@ -1497,14 +1499,14 @@ files = [ [[package]] name = "fsspec" -version = "2026.2.0" +version = "2026.4.0" description = "File-system specification" optional = false python-versions = ">=3.10" groups = ["main"] files = [ - {file = "fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437"}, - {file = "fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff"}, + {file = "fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2"}, + {file = "fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4"}, ] [package.dependencies] @@ -1804,14 +1806,14 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "1.17.0" +version = "1.18.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "huggingface_hub-1.17.0-py3-none-any.whl", hash = "sha256:3b8156d23118e87f6a587648bfbc04f04a12a757ccb4ed298b35c4ae638bf24c"}, - {file = "huggingface_hub-1.17.0.tar.gz", hash = "sha256:fad842b6763ef70ebc3919665b1b9273645203185400a7d6c5eddc2323cc3435"}, + {file = "huggingface_hub-1.18.0-py3-none-any.whl", hash = "sha256:729be4a976fb706dcc02d176bcda8a3f32bdf21a294e8f4b3dda6fbcbc9c1ab1"}, + {file = "huggingface_hub-1.18.0.tar.gz", hash = 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"Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] files = [ - {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"}, - {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"}, + {file = "tqdm-4.68.1-py3-none-any.whl", hash = "sha256:fea4a90e4023f764914569f7802a297277c5ab1a66be5144143e142e1a4031d8"}, + {file = "tqdm-4.68.1.tar.gz", hash = "sha256:fc163d96b287bd031e1aa24421ce4411b25559bd0a1be4fe649bdaa4d2c02bf5"}, ] [package.dependencies] @@ -5150,10 +5152,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"] -discord = ["requests"] +discord = ["envwrap", "requests"] notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] +slack = ["envwrap", "slack-sdk"] +telegram = ["envwrap", "requests"] [[package]] name = "transformers" diff --git a/security_scanning/examples/trtllm-eval/poetry.lock b/security_scanning/examples/trtllm-eval/poetry.lock index 8ee9a14e1903..ae5eaa6e259b 100644 --- a/security_scanning/examples/trtllm-eval/poetry.lock +++ b/security_scanning/examples/trtllm-eval/poetry.lock @@ -584,20 +584,20 @@ test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.6.2)", "tcolorpy (>=0.1.2)"] [[package]] name = "datasets" -version = "4.8.5" +version = "5.0.0" description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.10.0" groups = ["main"] files = [ - {file = "datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff"}, - {file = "datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772"}, + {file = "datasets-5.0.0-py3-none-any.whl", hash = "sha256:7dd34927a0fd7046e98aad5cb9430e699c373238a15befa7b9bf22b991a7fee6"}, + {file = "datasets-5.0.0.tar.gz", hash = "sha256:83dbbbdb07a33b82192b8c419deb18739b138ee2ce1a322d55ce6b100954ec1a"}, ] [package.dependencies] dill = ">=0.3.0,<0.4.2" filelock = "*" -fsspec = {version = ">=2023.1.0,<=2026.2.0", extras = ["http"]} +fsspec = {version = ">=2023.1.0,<=2026.4.0", extras = ["http"]} httpx = "<1.0.0" huggingface-hub = ">=0.25.0,<2.0" multiprocess = "<0.70.20" @@ -613,16 +613,18 @@ xxhash = "*" [package.extras] audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", 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jenkins/L0_Test.groovy | 1 + .../test_lists/test-db/l0_b200.yml | 11 +++++ .../test_lists/test-db/l0_dgx_b200.yml | 46 +++++++++++-------- 3 files changed, 38 insertions(+), 20 deletions(-) diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index 60f68382e988..b3269262dbe4 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -4053,6 +4053,7 @@ def launchTestJobs(pipeline, testFilter) "DGX_B200-Triton-Post-Merge-1": ["auto:dgx-b200-flex", "l0_b200", 1, 1, 1, 1, true], "DGX_B200-PyTorch-Post-Merge-1": ["auto:dgx-b200-flex", "l0_b200", 1, 2, 1, 1, true], "DGX_B200-PyTorch-Post-Merge-2": ["auto:dgx-b200-flex", "l0_b200", 2, 2, 1, 1, true], + "DGX_B200-2_GPUs-PyTorch-1": ["auto:dgx-b200-flex", "l0_dgx_b200", 1, 1, 2, 1, true], "DGX_B200-4_GPUs-PyTorch-1": ["auto:dgx-b200-flex", "l0_dgx_b200", 1, 3, 4, 1, true], "DGX_B200-4_GPUs-PyTorch-2": ["auto:dgx-b200-flex", "l0_dgx_b200", 2, 3, 4, 1, true], "DGX_B200-4_GPUs-PyTorch-3": ["auto:dgx-b200-flex", "l0_dgx_b200", 3, 3, 4, 1, true], diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index e1ee8e83bad9..308931c5d396 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -120,6 +120,7 @@ l0_b200: - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "CUTEDSL" - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "DEEPGEMM" - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "DENSEGEMM" + - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "MEGAMOE_DEEPGEMM" # ------------- MoE: FlashInfer & TRTLLM symbol collision tests --------------- - unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py # --- MoE end @@ -307,6 +308,16 @@ l0_b200: - accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-trtllm-auto] - accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-auto] - accuracy/test_llm_api_pytorch_multimodal.py::TestNanoV3Omni::test_auto_dtype[bf16] + # ------------- VisualGen single-GPU tests --------------- + - examples/test_visual_gen.py::test_visual_gen_quickstart + - examples/test_visual_gen.py::test_visual_gen_api_walkthrough + - examples/test_visual_gen.py::test_flux1_lpips_against_golden + - examples/test_visual_gen.py::test_flux2_lpips_against_golden + - examples/test_visual_gen.py::test_ltx2_lpips_against_golden + - examples/test_visual_gen.py::test_wan21_t2v_lpips_against_golden + - examples/test_visual_gen.py::test_wan22_t2v_lpips_against_golden + - visual_gen/test_visual_gen_benchmark.py::test_offline_benchmark + - visual_gen/test_visual_gen_benchmark.py::test_online_benchmark[openai-videos] # ------------- AutoDeploy Backend Stages --------------- - condition: ranges: diff --git a/tests/integration/test_lists/test-db/l0_dgx_b200.yml b/tests/integration/test_lists/test-db/l0_dgx_b200.yml index a22b8e2558b1..30bc403c054b 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b200.yml @@ -1,5 +1,30 @@ version: 0.0.1 l0_dgx_b200: +- condition: + ranges: + system_gpu_count: + gte: 2 + lte: 2 + wildcards: + gpu: + - '*b200*' + linux_distribution_name: ubuntu* + cpu: x86_64 + terms: + stage: pre_merge + backend: pytorch + orchestrator: mpi + tests: + - unittest/_torch/misc/test_autotuner.py::test_autotuner_distributed_strategy + - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp2-CUTLASS] + - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp2-TRTLLM] + # ------------- KV Cache V2 Scheduler IT (multi-GPU) --------------- + - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_draft_tokens + - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_chunked_draft_tokens + - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_eviction + # ------------- VisualGen multi-GPU tests --------------- + - unittest/_torch/visual_gen/test_flux_pipeline.py::TestFluxParallelism::test_ulysses_2gpu_correctness + - unittest/_torch/visual_gen/test_flux_pipeline.py::TestFluxCombinedOptimizations::test_all_optimizations_combined - condition: ranges: system_gpu_count: @@ -15,7 +40,6 @@ l0_dgx_b200: backend: pytorch orchestrator: mpi tests: - - unittest/_torch/misc/test_autotuner.py::test_autotuner_distributed_strategy - accuracy/test_llm_api_pytorch.py::TestNemotronV3Super::test_auto_dtype_4gpus[4-4-False-True-True] - accuracy/test_llm_api_pytorch.py::TestNemotronV3Super::test_auto_dtype_4gpus[4-4-True-True-True] - accuracy/test_llm_api_pytorch.py::TestNemotronV3Super::test_nvfp4_4gpu_mtp_ar TIMEOUT (60) @@ -30,8 +54,6 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_trtllm-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_cutlass-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_cutlass-torch_compile=True] - - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp2-CUTLASS] - - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp2-TRTLLM] - disaggregated/test_disaggregated.py::test_disaggregated_deepseek_v3_lite_fp8_ucx[DeepSeek-V3-Lite-fp8] - disaggregated/test_disaggregated.py::test_disaggregated_deepseek_v3_lite_fp8_nixl[DeepSeek-V3-Lite-fp8] - disaggregated/test_disaggregated.py::test_disaggregated_gpt_oss_120b_harmony[gpt_oss/gpt-oss-120b] @@ -42,10 +64,6 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=2] - accuracy/test_llm_api_pytorch.py::TestMiniMaxM2::test_4gpus[attention_dp=False-cuda_graph=True-overlap_scheduler=True-tp_size=4-ep_size=4] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] TIMEOUT (60) - # ------------- KV Cache V2 Scheduler IT (multi-GPU) --------------- - - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_draft_tokens - - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_chunked_draft_tokens - - kv_cache/test_kv_cache_v2_scheduler.py::TestKVCacheV2DSv3Lite::test_mtp_eviction # ------------- NVBug 6025177: trtllm-serve cross-request KV contamination (OpenAI) --------------- - test_e2e.py::test_openai_kv_cache_contamination TIMEOUT (120) - condition: @@ -81,7 +99,6 @@ l0_dgx_b200: - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_multi_gpu -k "DEEPGEMM and not MEGAMOE_DEEPGEMM" # --- MEGAMOE_DEEPGEMM (W4A8_MXFP4_MXFP8 only) --- - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_multi_gpu -k "MEGAMOE_DEEPGEMM" - - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "MEGAMOE_DEEPGEMM" # ------------- MoE: test_multi_gpu_eplb --------------- - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_multi_gpu_eplb - condition: @@ -165,8 +182,6 @@ l0_dgx_b200: - accuracy/test_disaggregated_serving.py::TestQwen3NextInstruct::test_auto_dtype[use_py_transceiver=False] TIMEOUT (60) # ------------- VisualGen multi-GPU tests --------------- - unittest/_torch/visual_gen/multi_gpu - - unittest/_torch/visual_gen/test_flux_pipeline.py::TestFluxParallelism::test_ulysses_2gpu_correctness - - unittest/_torch/visual_gen/test_flux_pipeline.py::TestFluxCombinedOptimizations::test_all_optimizations_combined - condition: ranges: system_gpu_count: @@ -192,7 +207,6 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[baseline_fp8kv] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[latency] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[disable_skip_indexer] TIMEOUT (60) - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_attn_multi_gpus TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baseline_fp8kv] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[latency] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[disable_skip_indexer] TIMEOUT (60) @@ -305,14 +319,7 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTEDSL-mtp_nextn=2-ep4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTEDSL-mtp_nextn=2-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp4_tp2pp2[torch_compile=False-enable_gemm_allreduce_fusion=False] - - examples/test_visual_gen.py::test_visual_gen_quickstart - - examples/test_visual_gen.py::test_visual_gen_api_walkthrough - examples/test_visual_gen.py::test_wan_t2v_example - - examples/test_visual_gen.py::test_flux1_lpips_against_golden - - examples/test_visual_gen.py::test_flux2_lpips_against_golden - - examples/test_visual_gen.py::test_ltx2_lpips_against_golden - - examples/test_visual_gen.py::test_wan21_t2v_lpips_against_golden - - examples/test_visual_gen.py::test_wan22_t2v_lpips_against_golden - examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses4] - examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[cfg2_ulysses2] - examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses2_ring2] @@ -320,8 +327,6 @@ l0_dgx_b200: - examples/test_visual_gen.py::test_vbench_dimension_score_wan - examples/test_visual_gen.py::test_vbench_dimension_score_wan22_a14b_fp8 - examples/test_visual_gen.py::test_vbench_dimension_score_wan22_a14b_nvfp4 - - visual_gen/test_visual_gen_benchmark.py::test_offline_benchmark - - visual_gen/test_visual_gen_benchmark.py::test_online_benchmark[openai-videos] - examples/test_visual_gen.py::test_vbench_dimension_score_ltx2_bf16 - examples/test_visual_gen.py::test_vbench_dimension_score_ltx2_fp8 - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.5-fp8kv=False] @@ -329,6 +334,7 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=False] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=True] - disaggregated/test_disaggregated.py::test_disaggregated_mamba_conc_greater_than_mbs[NVIDIA-Nemotron-3-Super-120B-A12B-FP8] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_attn_multi_gpus TIMEOUT (60) # ------------- AutoDeploy Backend Stages --------------- - condition: ranges: From 520262d8509e316ed42c5b64ad2b79b5ab51e0bb Mon Sep 17 00:00:00 2001 From: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com> Date: Sat, 6 Jun 2026 17:09:13 -0400 Subject: [PATCH 020/119] [None][feat] Add LTX-2 visual generation example (#14976) Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com> Signed-off-by: Chang Liu <9713593+chang-l@users.noreply.github.com> Co-authored-by: Chang Liu <9713593+chang-l@users.noreply.github.com> --- examples/visual_gen/README.md | 3 +- .../visual_gen/configs/ltx2-t2v-fp4-1gpu.yaml | 27 ++++++ .../visual_gen/configs/ltx2-t2v-fp8-1gpu.yaml | 27 ++++++ examples/visual_gen/models/ltx2.py | 97 +++++++++++++++++++ 4 files changed, 153 insertions(+), 1 deletion(-) create mode 100644 examples/visual_gen/configs/ltx2-t2v-fp4-1gpu.yaml create mode 100644 examples/visual_gen/configs/ltx2-t2v-fp8-1gpu.yaml create mode 100644 examples/visual_gen/models/ltx2.py diff --git a/examples/visual_gen/README.md b/examples/visual_gen/README.md index 11460505db33..a475538246ed 100644 --- a/examples/visual_gen/README.md +++ b/examples/visual_gen/README.md @@ -18,11 +18,12 @@ for feature details. # Defaults python quickstart_example.py python models/wan_t2v.py -python models/wan_i2v.py +python models/ltx2.py # With engine config (quant, parallelism, etc.) python models/wan_t2v.py --visual_gen_args configs/wan2.2-t2v-fp4-1gpu.yaml python models/wan_i2v.py --visual_gen_args configs/wan2.2-i2v-fp4-1gpu.yaml --image /path/to/image.png +python models/ltx2.py --visual_gen_args configs/ltx2-t2v-fp8-1-gpu.yaml ``` Install deps from the repo root: `pip install -r requirements-dev.txt`. diff --git a/examples/visual_gen/configs/ltx2-t2v-fp4-1gpu.yaml b/examples/visual_gen/configs/ltx2-t2v-fp4-1gpu.yaml new file mode 100644 index 000000000000..862d8a4fa640 --- /dev/null +++ b/examples/visual_gen/configs/ltx2-t2v-fp4-1gpu.yaml @@ -0,0 +1,27 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU LTX-2 text-to-video with audio. +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +quant_config: + quant_algo: NVFP4 + dynamic: true +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/configs/ltx2-t2v-fp8-1gpu.yaml b/examples/visual_gen/configs/ltx2-t2v-fp8-1gpu.yaml new file mode 100644 index 000000000000..0f4afcdb4384 --- /dev/null +++ b/examples/visual_gen/configs/ltx2-t2v-fp8-1gpu.yaml @@ -0,0 +1,27 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU LTX-2 text-to-video with audio. +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +quant_config: + quant_algo: FP8_BLOCK_SCALES + dynamic: true +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/models/ltx2.py b/examples/visual_gen/models/ltx2.py new file mode 100644 index 000000000000..c3d2616ff347 --- /dev/null +++ b/examples/visual_gen/models/ltx2.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""LTX-2 Text-to-Video generation with audio. + +Usage: + python ltx2.py + python ltx2.py --visual_gen_args ../configs/ltx2.yaml +""" + +import argparse + +from tensorrt_llm import VisualGen, VisualGenArgs + + +def main(): + parser = argparse.ArgumentParser(description="LTX-2 Text-to-Video example") + parser.add_argument( + "--model", + type=str, + default="Lightricks/LTX-2", + help="Model path or HuggingFace Hub ID", + ) + parser.add_argument( + "--visual_gen_args", + "--extra_visual_gen_options", + dest="visual_gen_args", + type=str, + default=None, + help="Path to YAML config (same as trtllm-serve --visual_gen_args)", + ) + parser.add_argument( + "--text_encoder_path", + type=str, + default=None, + help=( + "Gemma3 text encoder path. Overrides pipeline_config.text_encoder_path " + "from --visual_gen_args when set." + ), + ) + parser.add_argument( + "--output_path", + type=str, + default="ltx2_t2v_output.mp4", + help="Path to save the output video", + ) + args = parser.parse_args() + + # LTX-2 requires pipeline_config.text_encoder_path for the Gemma3 text + # encoder. The YAML path is preferred for production configs; the default + # below keeps this script runnable as a minimal offline example. + extra_args = ( + VisualGenArgs.from_yaml(args.visual_gen_args) if args.visual_gen_args else VisualGenArgs() + ) + text_encoder_path = args.text_encoder_path + if text_encoder_path is None and not args.visual_gen_args: + text_encoder_path = "google/gemma-3-12b-it" + if text_encoder_path is not None: + extra_args.pipeline_config = { + **extra_args.pipeline_config, + "text_encoder_path": text_encoder_path, + } + visual_gen = VisualGen(model=args.model, args=extra_args) + + # --- Model-specific: T2V request construction --- + # Start from LTX-2 defaults and override the main request shape explicitly. + params = visual_gen.default_params + params.height = 512 + params.width = 768 + params.num_frames = 121 + params.frame_rate = 24.0 + params.num_inference_steps = 40 + params.guidance_scale = 4.0 + + output = visual_gen.generate( + inputs="A cinematic shot of a cat walking through a field of flowers", + params=params, + ) + + output.save(args.output_path) + print(f"Saved: {args.output_path}") + + +if __name__ == "__main__": + main() From ec6b28406251d3f72385402f75bd429ea14c69c4 Mon Sep 17 00:00:00 2001 From: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> Date: Sun, 7 Jun 2026 03:16:34 +0000 Subject: [PATCH 021/119] [None][infra] Check in most recent lock file from nightly pipeline Signed-off-by: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> --- .../examples/models/core/whisper/poetry.lock | 24 ++++++++++--------- .../models/core/whisper/pyproject.toml | 2 +- security_scanning/metadata.json | 4 ++-- security_scanning/poetry.lock | 24 ++++++++++--------- security_scanning/pyproject.toml | 2 +- 5 files changed, 30 insertions(+), 26 deletions(-) diff --git a/security_scanning/examples/models/core/whisper/poetry.lock b/security_scanning/examples/models/core/whisper/poetry.lock index 354a81df59b4..3935f2c85af3 100644 --- a/security_scanning/examples/models/core/whisper/poetry.lock +++ b/security_scanning/examples/models/core/whisper/poetry.lock @@ -3229,25 +3229,27 @@ files = [ [[package]] name = "soundfile" -version = "0.13.1" +version = "0.14.0" description = "An audio library based on libsndfile, CFFI and NumPy" optional = false -python-versions = "*" +python-versions = ">=3.10" groups = ["main"] files = [ - {file = "soundfile-0.13.1-py2.py3-none-any.whl", hash = "sha256:a23c717560da2cf4c7b5ae1142514e0fd82d6bbd9dfc93a50423447142f2c445"}, - {file = "soundfile-0.13.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:82dc664d19831933fe59adad199bf3945ad06d84bc111a5b4c0d3089a5b9ec33"}, - {file = "soundfile-0.13.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:743f12c12c4054921e15736c6be09ac26b3b3d603aef6fd69f9dde68748f2593"}, - {file = "soundfile-0.13.1-py2.py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:9c9e855f5a4d06ce4213f31918653ab7de0c5a8d8107cd2427e44b42df547deb"}, - {file = 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"soundfile-0.14.0-py2.py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:e85724a90bc99a6e8062c0b4ddf725f53b2a3b70afd4da875e9d2cfc4e92f377"}, + {file = "soundfile-0.14.0-py2.py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:1e38bac1853412871318e82a1ba69a8be677619b56025bbfcccdb41b6cafe82d"}, + {file = "soundfile-0.14.0-py2.py3-none-win32.whl", hash = "sha256:0a6ae43c50c71b4e020cc55382925cb89451c1ed1a0c3d0f5d802da269226849"}, + {file = "soundfile-0.14.0-py2.py3-none-win_amd64.whl", hash = "sha256:299491d3499460fb1b74bb4bd78b57ffc2d243a5fafa7b6ec1b264875c78453e"}, + {file = "soundfile-0.14.0-py2.py3-none-win_arm64.whl", hash = "sha256:e090704718e124e7c844695236f1fce8d18a5e761eaf7c82dfcd124620805f98"}, + {file = "soundfile-0.14.0.tar.gz", hash = "sha256:ba1c1a2d618bca5c406647c83b89f07cc8810fa506a50622a6993ba130c1de11"}, ] [package.dependencies] cffi = ">=1.0" numpy = "*" +typing-extensions = "*" [[package]] name = "soxr" @@ -3984,4 +3986,4 @@ propcache = ">=0.2.1" [metadata] lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "6a6fef19c566d145183769645bdce5878b6b51916cc078a67f29f79cd068b015" +content-hash = "9053b63e71d2a4215a557ec805eff87fa2874651de2b7914cab9a3d341896786" diff --git a/security_scanning/examples/models/core/whisper/pyproject.toml b/security_scanning/examples/models/core/whisper/pyproject.toml index 9e374cb8fcbb..9cce21319216 100644 --- a/security_scanning/examples/models/core/whisper/pyproject.toml +++ b/security_scanning/examples/models/core/whisper/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "kaldialign (>=0.10.0,<0.11.0)", "openai-whisper (>=20250625,<20250626)", "librosa (>=0.11.0,<0.12.0)", - "soundfile (>=0.13.1,<0.14.0)", + "soundfile (>=0.14.0,<0.15.0)", "safetensors (>=0.7.0,<0.8.0)", "transformers (>=5.10.2,<6.0.0)", "janus (>=2.0.0,<3.0.0)" diff --git a/security_scanning/metadata.json b/security_scanning/metadata.json index 89ffa839488e..f3e3b3ce3b78 100644 --- a/security_scanning/metadata.json +++ b/security_scanning/metadata.json @@ -1,4 +1,4 @@ { - "commit_hash": "d7a58725967a1fc19b05aff78cd8b30d11c062d2", - "timestamp": "2026-06-06T02:48:18Z" + "commit_hash": "520262d8509e316ed42c5b64ad2b79b5ab51e0bb", + "timestamp": "2026-06-07T02:46:38Z" } diff --git a/security_scanning/poetry.lock b/security_scanning/poetry.lock index dfa084a5a48a..f2b32f2105c3 100644 --- a/security_scanning/poetry.lock +++ b/security_scanning/poetry.lock @@ -6268,25 +6268,27 @@ files = [ [[package]] name = "soundfile" -version = "0.13.1" +version = "0.14.0" description = "An audio library based on libsndfile, CFFI and NumPy" optional = false -python-versions = "*" +python-versions = ">=3.10" groups = ["main"] files = [ - {file = "soundfile-0.13.1-py2.py3-none-any.whl", hash = "sha256:a23c717560da2cf4c7b5ae1142514e0fd82d6bbd9dfc93a50423447142f2c445"}, - {file = "soundfile-0.13.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:82dc664d19831933fe59adad199bf3945ad06d84bc111a5b4c0d3089a5b9ec33"}, - {file 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"sha256:e090704718e124e7c844695236f1fce8d18a5e761eaf7c82dfcd124620805f98"}, + {file = "soundfile-0.14.0.tar.gz", hash = "sha256:ba1c1a2d618bca5c406647c83b89f07cc8810fa506a50622a6993ba130c1de11"}, ] [package.dependencies] cffi = ">=1.0" numpy = "*" +typing-extensions = "*" [[package]] name = "sse-starlette" @@ -7440,4 +7442,4 @@ type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""] [metadata] lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "dbf44c1b9de822c3a40358221a3d959baaeb2f456fdc87cc0f486572cc5166ad" +content-hash = "e62d24c062db206e1ca96b2c5688cffbd579d7207c1f3f2340e42ca256657c65" diff --git a/security_scanning/pyproject.toml b/security_scanning/pyproject.toml index 34e0dc04d865..40988bcbe97d 100644 --- a/security_scanning/pyproject.toml +++ b/security_scanning/pyproject.toml @@ -64,7 +64,7 @@ dependencies = [ "meson (>=1.11.1,<2.0.0)", "ninja (>=1.13.0,<2.0.0)", "blake3 (>=1.0.8,<2.0.0)", - "soundfile (>=0.13.1,<0.14.0)", + "soundfile (>=0.14.0,<0.15.0)", "xdsl (>=0.59.0)", "tiktoken (>=0.13.0,<0.14.0)", "blobfile (>=3.2.0,<4.0.0)", From bedad859d7117799bb7998d0dd091d4a60151b56 Mon Sep 17 00:00:00 2001 From: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> Date: Sat, 6 Jun 2026 21:47:19 -0700 Subject: [PATCH 022/119] [None][feat] AutoDeploy: Fix hardcoded configs (#14943) Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com> --- .../_torch/auto_deploy/shim/ad_executor.py | 15 ++-- .../auto_deploy/singlegpu/shim/test_engine.py | 78 +++++++++++++++++++ 2 files changed, 88 insertions(+), 5 deletions(-) diff --git a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py index 053d28a014e9..cf3c8bbd0e6f 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py @@ -394,11 +394,6 @@ def __init__( self.llm_args.print_iter_log = reporting_info.print_log self.llm_args.enable_iter_perf_stats = reporting_info.enable_iter_perf_stats self.llm_args.enable_iter_req_stats = reporting_info.enable_iter_req_stats - self.llm_args.stream_interval = 1 - self.llm_args.attention_dp_config = None - self.llm_args.batch_wait_timeout_ms = 0 - self.llm_args.batch_wait_timeout_iters = 0 - self.llm_args.batch_wait_max_tokens_ratio = 0.0 self.llm_args.max_num_tokens = cache_seq_interface.info.max_num_tokens self.llm_args.max_seq_len = cache_seq_interface.info.max_seq_len self.iter_counter = 0 @@ -408,12 +403,22 @@ def __init__( self.enable_attention_dp = dist_config.enable_attention_dp if dist_config else False if ad_config is not None: + self.llm_args.stream_interval = ad_config.stream_interval + self.llm_args.attention_dp_config = ad_config.attention_dp_config + self.llm_args.batch_wait_timeout_ms = ad_config.batch_wait_timeout_ms + self.llm_args.batch_wait_timeout_iters = ad_config.batch_wait_timeout_iters + self.llm_args.batch_wait_max_tokens_ratio = ad_config.batch_wait_max_tokens_ratio self.max_beam_width = ad_config.max_beam_width self.spec_config = ad_config.speculative_config self._disable_overlap_scheduler = ad_config.disable_overlap_scheduler self.llm_args.max_stats_len = ad_config.max_stats_len self._enable_chunked_prefill = getattr(ad_config, "enable_chunked_prefill", False) else: + self.llm_args.stream_interval = 1 + self.llm_args.attention_dp_config = None + self.llm_args.batch_wait_timeout_ms = 0 + self.llm_args.batch_wait_timeout_iters = 0 + self.llm_args.batch_wait_max_tokens_ratio = 0.0 self.max_beam_width = 1 self.spec_config = None self._disable_overlap_scheduler = False diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_engine.py b/tests/unittest/auto_deploy/singlegpu/shim/test_engine.py index e68233861935..05eae181d395 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_engine.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_engine.py @@ -21,10 +21,12 @@ from tensorrt_llm import SamplingParams from tensorrt_llm._torch.auto_deploy._compat import KvCacheConfig +from tensorrt_llm._torch.auto_deploy.llm_args import LlmArgs from tensorrt_llm._torch.auto_deploy.shim.ad_executor import ADEngine from tensorrt_llm._torch.auto_deploy.shim.demollm import DemoEngine from tensorrt_llm._torch.auto_deploy.shim.interface import CachedSequenceInterface from tensorrt_llm._torch.pyexecutor.scheduler import ScheduledRequests +from tensorrt_llm.llmapi import AttentionDpConfig class TransformerLikeModelwithFakeCachePool(nn.Module): @@ -107,6 +109,82 @@ def test_engine(engine_cls: Type[ADEngine], tokens_per_block: int): cache_seq_interface.shutdown() +def _make_cache_seq_interface(device, max_seq_len=64, max_batch_size=8): + return CachedSequenceInterface( + max_seq_len=max_seq_len, + max_batch_size=max_batch_size, + max_num_tokens=default_max_num_tokens(max_seq_len, max_batch_size), + device=device, + kv_cache_config=KvCacheConfig(tokens_per_block=max_seq_len), + ) + + +def test_ad_engine_propagates_pyexecutor_scheduling_config(): + """ADEngine must copy PyExecutor scheduling/streaming knobs from ad_config. + + Regression: these were hardcoded stubs (stream_interval=1, + attention_dp_config=None, batch_wait_*=0), silently dropping the yaml settings. + """ + if not torch.cuda.is_available(): + pytest.skip("ADEngine construction builds the model on CUDA") + + device = torch.device("cuda") + max_seq_len, max_batch_size = 64, 8 + cache_seq_interface = _make_cache_seq_interface(device, max_seq_len, max_batch_size) + cache_seq_interface.to(device) + + ad_config = LlmArgs( + model="test-model", + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + max_input_len=32, + backend="_autodeploy", + cuda_graph_config={"max_batch_size": max_batch_size}, + stream_interval=20, + attention_dp_config=AttentionDpConfig( + enable_balance=True, batching_wait_iters=50, timeout_iters=1 + ), + batch_wait_timeout_ms=5.0, + batch_wait_timeout_iters=3, + batch_wait_max_tokens_ratio=0.5, + ) + + try: + engine = ADEngine(get_inference_model, cache_seq_interface, ad_config=ad_config) + + assert engine.llm_args.stream_interval == 20 + assert engine.llm_args.attention_dp_config is not None + assert engine.llm_args.attention_dp_config.enable_balance is True + assert engine.llm_args.attention_dp_config.batching_wait_iters == 50 + assert engine.llm_args.attention_dp_config.timeout_iters == 1 + assert engine.llm_args.batch_wait_timeout_ms == 5.0 + assert engine.llm_args.batch_wait_timeout_iters == 3 + assert engine.llm_args.batch_wait_max_tokens_ratio == 0.5 + finally: + cache_seq_interface.shutdown() + + +def test_ad_engine_scheduling_config_defaults_without_ad_config(): + """Without ad_config, ADEngine falls back to the previous stub defaults.""" + if not torch.cuda.is_available(): + pytest.skip("ADEngine construction builds the model on CUDA") + + device = torch.device("cuda") + cache_seq_interface = _make_cache_seq_interface(device) + cache_seq_interface.to(device) + + try: + engine = ADEngine(get_inference_model, cache_seq_interface) + + assert engine.llm_args.stream_interval == 1 + assert engine.llm_args.attention_dp_config is None + assert engine.llm_args.batch_wait_timeout_ms == 0 + assert engine.llm_args.batch_wait_timeout_iters == 0 + assert engine.llm_args.batch_wait_max_tokens_ratio == 0.0 + finally: + cache_seq_interface.shutdown() + + @pytest.mark.parametrize("tokens_per_block", [0, 2]) def test_demo_engine_sampling(tokens_per_block: int): """Test sampling logic specific to DemoEngine.""" From 47666de06c81ea0dca29928b85835de202b5e409 Mon Sep 17 00:00:00 2001 From: Grzegorz Kwasniewski <213329731+greg-kwasniewski1@users.noreply.github.com> Date: Sun, 7 Jun 2026 13:10:10 +0200 Subject: [PATCH 023/119] [#13718][feat] AutoDeploy MoE all-to-all: cache + runtime max-tokens (#13723) Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com> --- .../compile/backends/torch_cudagraph.py | 16 +- .../custom_ops/attention_interface.py | 36 ++- .../custom_ops/fused_moe/torch_moe.py | 10 +- .../custom_ops/fused_moe/trtllm_moe.py | 196 +++++++++++++--- .../_torch/auto_deploy/shim/ad_executor.py | 30 ++- .../_torch/auto_deploy/transform/interface.py | 15 +- .../transform/library/fuse_quant.py | 25 ++- .../auto_deploy/transform/library/sharding.py | 26 ++- .../transform/library/sharding_ir.py | 18 +- .../_torch/auto_deploy/utils/cuda_graph.py | 40 ++++ .../defs/accuracy/test_llm_api_autodeploy.py | 17 +- .../test_lists/qa/llm_function_core.txt | 12 +- .../test_lists/test-db/l0_b200.yml | 4 +- .../test_lists/test-db/l0_dgx_b200.yml | 4 +- .../test_lists/test-db/l0_dgx_h100.yml | 5 +- .../test_lists/test-db/l0_h100.yml | 4 +- .../test_lists/test-db/l0_l40s.yml | 2 +- tests/integration/test_lists/waives.txt | 2 - .../compile/test_bypass_captured_graphs.py | 212 ++++++++++++++++++ 19 files changed, 602 insertions(+), 72 deletions(-) create mode 100644 tests/unittest/auto_deploy/multigpu/compile/test_bypass_captured_graphs.py diff --git a/tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py b/tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py index 80226aca6dff..7ca4402a04ba 100644 --- a/tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py +++ b/tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py @@ -45,7 +45,7 @@ def autotune(*args, **kwargs): yield # no-op in standalone mode -from ...utils.cuda_graph import CudaGraphWarmUpPhase +from ...utils.cuda_graph import CudaGraphWarmUpPhase, cuda_graph_state from ...utils.logger import ad_logger from ...utils.multi_stream_utils import disable_multi_stream from ..compiler import CompileBackendRegistry, CompilerBackend, GetArgsKwargsForBatchSize @@ -350,6 +350,11 @@ def refresh_args_static(_bs: int = bs) -> None: def forward(self, *args, **kwargs) -> Any: """Run the compiled graph.""" + # Bypass replay (attn-DP mixed-mode); see BypassCapturedGraphs() in + # tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py for rationale. + if cuda_graph_state.in_bypass(): + return self.model(*args, **kwargs) + args, kwargs = self._normalize_args_kwargs(args, kwargs) assert self.num_batched_inputs is not None, "Graphs must be captured before replay." @@ -814,6 +819,10 @@ def forward( **kwargs, ) -> Any: """Forward pass: static segments replay graphs, dynamic segments run eagerly.""" + # Bypass replay (attn-DP mixed-mode); see BypassCapturedGraphs() in + # tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py for rationale. + if cuda_graph_state.in_bypass(): + return self.original_model(*args, **kwargs) if self.split_gm is not None: self._copy_to_static_buffers(kwargs) ADPiecewiseRunner.set_current_num_tokens(num_tokens) @@ -962,6 +971,11 @@ def _narrow(v): def forward(self, *args, **kwargs) -> Any: # NOTE: AD calls model(**named_args) so everything is in kwargs, args is empty + # Bypass replay (attn-DP mixed-mode); see BypassCapturedGraphs() in + # tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py for rationale. + if cuda_graph_state.in_bypass(): + ADPiecewiseRunner.set_current_num_tokens(None) + return self.piecewise.original_model(*args, **kwargs) if self._is_decode_only(**kwargs): ADPiecewiseRunner.set_current_num_tokens(None) return self.monolithic(*args, **kwargs) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py index cf907ee08e39..dfbfdfef8bbe 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py @@ -402,7 +402,7 @@ class BatchInfo: Args: batch_info_host: The batch info tensor on the host. - The information is stored in a 14-element batch_info_host tensor as follows: + The information is stored in a 15-element batch_info_host tensor as follows: Slots 0-5 (batch composition): - [0] num_prefill: number of prefill requests @@ -428,10 +428,17 @@ class BatchInfo: Slot 13 (replay mode flag, set once at runtime init): - [13] use_replay: 1 if SSM replay state-update path is active, 0 otherwise + Slot 14 (DP-aware token info, updated per forward when attention-DP is on): + - [14] max_dp_num_tokens: max(total_num_tokens) across all DP ranks for this + forward step. Equals local total_num_tokens when attention-DP is off. + Used by MoE all-to-all to size dispatch padding without over-padding to the + static config max_num_tokens. Mirrors base TRT-LLM's + ``runtime_max_tokens_per_rank`` from ``model_engine._get_all_rank_num_tokens``. + All fields can be accessed and updated with the convenience functions below. """ - _NUM_ELEMENTS = 14 + _NUM_ELEMENTS = 15 def __init__(self, batch_info_host: Optional[torch.Tensor] = None): if batch_info_host is None: @@ -564,6 +571,21 @@ def update_use_replay(self, use_replay: bool) -> None: def is_use_replay(self) -> bool: return bool(self._batch_info[13]) + # --- DP-aware token info (slot 14) writer --- + + def update_max_dp_num_tokens(self, max_dp_num_tokens: int) -> None: + """Set the max-across-DP-ranks total token count for this forward. + + When attention-DP is off, callers should write the local total_num_tokens + so consumers can read this slot uniformly without checking attn-DP state. + """ + self._batch_info[14] = max_dp_num_tokens + + # --- DP-aware token info (slot 14) reader --- + + def get_max_dp_num_tokens(self) -> int: + return int(self._batch_info[14]) + class SequenceInfo: """An interface to hold information about how the sequence is laid out and stored in cache. @@ -607,7 +629,8 @@ class SequenceInfo: ### BATCH INFO OBJECT ######################################################################## - batch_info_host: a single host tensor managed by the ``BatchInfo`` class. It consolidates - batch composition, max sequence info, and tokens gather info into one 12-element int tensor. + batch composition, max sequence info, tokens gather info, spec-dec info, and DP-aware + token info into one 14-element int tensor. See the ``BatchInfo`` docstring for the full layout. Custom ops receive this tensor as a graph input and should wrap it via ``BatchInfo(batch_info_host)`` to extract fields. @@ -1325,6 +1348,10 @@ def nest_sequences( num_prefill_tokens = int(sl_host.sum()) - num_decode batch_info = [num_prefill, num_prefill_tokens, 0, 0, num_decode, num_decode] self.batch_info.update(batch_info) + # Default slot 14 (max_dp_num_tokens) to local total tokens; the executor + # overrides this with the cross-rank max via update_max_dp_num_tokens() + # when attention-DP is enabled. + self.batch_info.update_max_dp_num_tokens(self.batch_info.get_total_num_tokens()) # check for updated input_pos (i.e. cache start position) if isinstance(input_pos, int): @@ -1747,6 +1774,9 @@ def switch_to_generate_(self) -> None: # update batch_info self.batch_info.update([0, 0, 0, 0, num_seq, num_seq]) self.batch_info.update_tokens_gather_info(num_seq, False) + # Default slot 14 (max_dp_num_tokens) to local total tokens; the executor + # overrides this when attention-DP is on. + self.batch_info.update_max_dp_num_tokens(num_seq) # check if we need a d2h sync _REQUIRES_UPDATE = { diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py index a94863fab303..bf648f0a0526 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py @@ -14,7 +14,7 @@ # limitations under the License. from functools import partial -from typing import Callable, List +from typing import Callable, List, Optional import torch import torch.nn.functional as F @@ -287,6 +287,7 @@ def torch_moe( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Unified Mixture-of-Experts (MoE) operator that uses a Mixtral-style dispatch @@ -364,6 +365,7 @@ def torch_moe_fake( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: return torch.empty_like(x) @@ -454,6 +456,7 @@ def torch_quant_fp8_moe( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ FP8 MoE op using quantized linear operations. Computes a Mixture-of-Experts layer similar to the reference @@ -574,6 +577,7 @@ def torch_quant_fp8_moe_fake( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: return torch.empty_like(x) @@ -601,6 +605,7 @@ def torch_quant_nvfp4_moe( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ FP4 MoE op using quantized linear operations. @@ -737,6 +742,7 @@ def torch_quant_nvfp4_moe_fake( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: return torch.empty_like(x) @@ -808,6 +814,7 @@ def torch_quant_finegrained_fp8_moe( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ FineGrainedFP8 MoE op using block-wise FP8 quantized linear operations. @@ -922,5 +929,6 @@ def torch_quant_finegrained_fp8_moe_fake( max_num_tokens: int = 0, apply_routing_on_input: bool = False, layer_type: str = "moe", + batch_info_host: Optional[torch.Tensor] = None, ) -> torch.Tensor: return torch.empty_like(x) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py index 41c4d0abbd03..215653ffddd1 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Tuple +from typing import List, Optional, Tuple import torch @@ -39,6 +39,116 @@ def _get_cached_f32_scale(scale: torch.Tensor) -> torch.Tensor: return f32 +# ============================================================================= +# Module-level cache for MoeAlltoAll state +# ============================================================================= +# +# Custom ops are stateless functions, but `MoeAlltoAll` is fundamentally +# stateful: base TensorRT-LLM constructs it once per fused-MoE module and +# reuses `self.moe_a2a` across forward steps. Auto-deploy previously +# re-instantiated it on every layer call and re-parsed the serialized +# `DistConfig` JSON, paying that overhead per-layer per-step. +# +# This module-level cache mirrors the `_GlobalTrtllmPlanner` pattern in +# `trtllm_attention.py` (and `_GlobalFlashInferPlanner` in +# `flashinfer_attention.py`): a Python-level singleton that lives across +# custom-op invocations, lazily constructed on first use, keyed by +# parameters that determine workspace identity. The class-level +# `MoeAlltoAll._WORKSPACE` already deduplicates the GPU workspace and the +# C++ `moe_a2a_initialize` call across instances; this cache additionally +# eliminates the redundant Python ctor and JSON deserialization. + + +class _MoeAll2AllCache: + """Per-process singleton cache for `MoeAlltoAll` instances and parsed `DistConfig` mappings. + + A separate cache instance lives in each MPI worker process — there is no + cross-rank shared state here (that lives in `MoeAlltoAll._WORKSPACE`, which is + initialized via the C++ `moe_a2a_initialize` collective). Within a rank, all + fused-MoE layers share this cache so the Python `MoeAlltoAll` ctor and + `DistConfig` JSON deserialization run at most once per (workspace) configuration + per process, regardless of how many MoE-DP layers the model has. + + Note: EPLB (Expert Parallelism Load Balancing — `MoeAlltoAll(num_experts=…)` + selects an EPLB-aware codepath) is not used by auto-deploy today; + `eplb_num_experts` is always `None` from the call sites. The cache still keys + on it explicitly so the helper composes correctly if EPLB is wired later. + """ + + def __init__(self) -> None: + self._all2all: dict[tuple, MoeAlltoAll] = {} + self._mapping: dict[str, Tuple[Optional[Mapping], bool]] = {} + + def get_mapping(self, mapping_config: str) -> Tuple[Optional[Mapping], bool]: + """Return cached `(Mapping, enable_alltoall)` parsed from `mapping_config`. + + All-to-all is used when attention-DP is enabled and experts are sharded + (EP > 1). The returned `mapping` is `None` when `mapping_config` is empty. + + Note: `max_num_tokens` is a per-call argument and is therefore not part + of the cache key. Callers must validate it themselves (see `_check_moe_alltoall`). + """ + cached = self._mapping.get(mapping_config) + if cached is not None: + return cached + if not mapping_config: + cached = (None, False) + else: + dc = DistConfig.deserialize(mapping_config) + mapping = dc.to_mapping() + enable = dc.enable_attention_dp and dc.moe_ep_size > 1 + cached = (mapping, enable) + self._mapping[mapping_config] = cached + return cached + + def get_alltoall( + self, + mapping: Mapping, + max_num_tokens: int, + top_k: int, + num_slots: int, + hidden_size: int, + dtype: torch.dtype, + eplb_num_experts: Optional[int] = None, + ) -> MoeAlltoAll: + """Return a `MoeAlltoAll` instance, constructing on first use. + + Keyed on the tuple of ctor parameters that determine workspace identity. + In practice `MoeAlltoAll._WORKSPACE` is process-wide and only one + configuration is valid per process, but a tuple key keeps semantics + correct if the runner ever changes shapes between calls. + """ + workspace_size = MoeAlltoAll.calculate_required_workspace_size( + mapping.moe_ep_size, top_k, max_num_tokens, hidden_size, dtype, eplb_num_experts + ) + key = ( + mapping.moe_ep_size, + mapping.moe_ep_rank, + top_k, + num_slots, + max_num_tokens, + workspace_size, + eplb_num_experts, + ) + inst = self._all2all.get(key) + if inst is None: + inst = MoeAlltoAll( + mapping=mapping, + max_num_tokens=max_num_tokens, + top_k=top_k, + num_slots=num_slots, + workspace_size_per_rank=workspace_size, + num_experts=eplb_num_experts, + ) + self._all2all[key] = inst + return inst + + +# Per-process singleton: instantiated once at module import; each MPI rank +# has its own instance because each rank is a separate Python process. +_GlobalMoeAll2AllCache = _MoeAll2AllCache() + + def _check_moe_alltoall(mapping_config: str, max_num_tokens: int) -> Tuple[Mapping | None, bool]: """Check if MoE all-to-all mode should be used and validate parameters. @@ -47,11 +157,7 @@ def _check_moe_alltoall(mapping_config: str, max_num_tokens: int) -> Tuple[Mappi Returns: (mapping, enable_alltoall) — mapping is None when mapping_config is empty. """ - if not mapping_config: - return None, False - dc = DistConfig.deserialize(mapping_config) - mapping = dc.to_mapping() - enable_alltoall = dc.enable_attention_dp and dc.moe_ep_size > 1 + mapping, enable_alltoall = _GlobalMoeAll2AllCache.get_mapping(mapping_config) if enable_alltoall and max_num_tokens <= 0: raise ValueError("max_num_tokens must be > 0 when enable_alltoall is True") return mapping, enable_alltoall @@ -74,6 +180,7 @@ def _run_moe_with_alltoall( use_deepseek_fp8_block_scale: bool = False, finegrained_fp8_block_scales: Tuple[torch.Tensor, torch.Tensor] | None = None, is_gated_mlp: bool = True, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: """ Execute MoE with all-to-all dispatch/combine pattern. @@ -117,6 +224,11 @@ def _run_moe_with_alltoall( ``use_deepseek_fp8_block_scale`` internally. is_gated_mlp: Whether gated MLP is used. Needed by the Blackwell finegrained FP8 path to compute ``intermediate_size``. + batch_info_host: Pinned-host int tensor managed by ``BatchInfo``. When + provided, slot 14 (``max_dp_num_tokens``) is read at capture time as + ``runtime_max_tokens_per_rank`` (the cross-rank max of + ``total_num_tokens``, computed pre-forward by the AD shim via + ``tp_allgather``). When ``None``, falls back to ``max_num_tokens``. Returns: 2-D output tensor ``(num_tokens, hidden_size)`` — the caller reshapes to the @@ -129,28 +241,32 @@ def _run_moe_with_alltoall( local_num_experts = fc1_expert_weights.shape[0] global_num_experts = local_num_experts * mapping.moe_ep_size + # runtime_max_tokens_per_rank: max(total_num_tokens) across DP ranks. + # Mirrors base TRT-LLM's `runtime_max_tokens_per_rank = max(all_rank_num_tokens)` + # in fused_moe_cutlass.py / fused_moe_trtllm_gen.py. AD's shim writes slot 14 + # pre-forward via `tp_allgather` of `total_num_tokens` (when attention-DP is on); + # nest_sequences seeds slot 14 with the local total as a safe default. See + # ``BatchInfo`` in attention_interface.py for the full slot layout. + if batch_info_host is not None: + runtime_max_tokens_per_rank = int(batch_info_host[14].item()) + if runtime_max_tokens_per_rank <= 0: + runtime_max_tokens_per_rank = max_num_tokens + else: + runtime_max_tokens_per_rank = max_num_tokens + # Workspace must be sized for the LARGEST element type used by dispatch or combine. # The input x may be quantized (fp8/fp4), but combine outputs in the model dtype # (bf16/fp16). We always pass the model dtype so the combine buffer is large enough. - workspace_size = MoeAlltoAll.calculate_required_workspace_size( - mapping.moe_ep_size, top_k, max_num_tokens, hidden_size, output_dtype - ) - - # We need runtime_max_tokens_per_rank = max(tokens across all EP ranks). - # An NCCL all_reduce cannot run inside CUDA-graph capture, so we conservatively - # use max_num_tokens (the config-level upper bound) as an over-approximation. - # This causes the dispatch to allocate larger recv buffers (padded with invalid - # tokens that are skipped by the kernel), trading memory for correctness. - runtime_max_tokens_per_rank = max_num_tokens - - # Build MoeAlltoAll (num_slots = num_experts without EPLB load balancing) - moe_a2a = MoeAlltoAll( + # The instance is cached across calls (see `_MoeAll2AllCache`), so the Python ctor and + # workspace-size computation happen once per (workspace) configuration per process. + moe_a2a = _GlobalMoeAll2AllCache.get_alltoall( mapping=mapping, max_num_tokens=max_num_tokens, top_k=top_k, - num_slots=global_num_experts, # No EPLB: num_slots == num_experts - workspace_size_per_rank=workspace_size, - num_experts=None, # None = EPLB disabled + num_slots=global_num_experts, + hidden_size=hidden_size, + dtype=output_dtype, + eplb_num_experts=None, ) invalid_expert_id = global_num_experts @@ -299,6 +415,7 @@ def _run_trtllm_gen_nvfp4_moe_with_alltoall( mapping: Mapping, max_num_tokens: int, act_type: int, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: """Run TRTLLM-Gen NVFP4 MoE through the all-to-all dispatch/combine path.""" @@ -306,18 +423,22 @@ def _run_trtllm_gen_nvfp4_moe_with_alltoall( hidden_size = x.shape[-1] local_num_experts = int(fc1_expert_weights_fp4.shape[0]) global_num_experts = local_num_experts * mapping.moe_ep_size - workspace_size = MoeAlltoAll.calculate_required_workspace_size( - mapping.moe_ep_size, top_k, max_num_tokens, hidden_size, x.dtype - ) - runtime_max_tokens_per_rank = max_num_tokens + # See _run_moe_with_alltoall above for the slot-14 contract. + if batch_info_host is not None: + runtime_max_tokens_per_rank = int(batch_info_host[14].item()) + if runtime_max_tokens_per_rank <= 0: + runtime_max_tokens_per_rank = max_num_tokens + else: + runtime_max_tokens_per_rank = max_num_tokens - moe_a2a = MoeAlltoAll( + moe_a2a = _GlobalMoeAll2AllCache.get_alltoall( mapping=mapping, max_num_tokens=max_num_tokens, top_k=top_k, num_slots=global_num_experts, - workspace_size_per_rank=workspace_size, - num_experts=None, + hidden_size=hidden_size, + dtype=x.dtype, + eplb_num_experts=None, ) invalid_expert_id = global_num_experts @@ -400,6 +521,7 @@ def trtllm_moe_fused( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: x_shape = x.shape x = x.view(-1, x_shape[-1]) @@ -445,6 +567,7 @@ def trtllm_moe_fused( activation_type=activation_type, mapping=mapping, max_num_tokens=max_num_tokens, + batch_info_host=batch_info_host, ).view(x_shape) # EP WITH ALL-REDUCE PATH: Expert IDs are in LOCAL coordinates (from sharding.py), @@ -475,6 +598,7 @@ def trtllm_moe_fused_fake( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: return torch.empty_like(x) @@ -514,6 +638,7 @@ def trtllm_quant_fp8_moe_fused( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: """TensorRT-LLM Cutlass FP8 (W8A8) MoE for gated and non-gated MLP. @@ -591,6 +716,7 @@ def trtllm_quant_fp8_moe_fused( activation_type=act_fn, mapping=mapping, max_num_tokens=max_num_tokens, + batch_info_host=batch_info_host, ).view(x_shape) # EP WITH ALL-REDUCE PATH: Expert IDs are in LOCAL coordinates. @@ -628,6 +754,7 @@ def trtllm_quant_fp8_moe_fused_fake( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: _validate_mlp_style_and_act_fn(is_gated_mlp, act_fn) return torch.empty_like(x) @@ -651,6 +778,7 @@ def trtllm_quant_nvfp4_moe_fused( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: """TensorRT-LLM Cutlass NVFP4 W8A8 MoE for gated and non-gated MLP. @@ -720,6 +848,7 @@ def trtllm_quant_nvfp4_moe_fused( mapping=mapping, max_num_tokens=max_num_tokens, nvfp4_act_global_scale=fc1_act_global_scale, + batch_info_host=batch_info_host, ).view(x.shape) # EP WITH ALL-REDUCE PATH: Expert IDs are in LOCAL coordinates. @@ -766,6 +895,7 @@ def trtllm_quant_nvfp4_moe_fused_fake( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: return torch.empty_like(x) @@ -784,6 +914,7 @@ def trtllm_quant_finegrained_fp8_moe_fused( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: """TensorRT-LLM Cutlass FP8 Block Scale MoE for FineGrainedFP8 format. @@ -846,6 +977,7 @@ def trtllm_quant_finegrained_fp8_moe_fused( max_num_tokens=max_num_tokens, finegrained_fp8_block_scales=(fc1_weight_scale, fc2_weight_scale), is_gated_mlp=is_gated_mlp, + batch_info_host=batch_info_host, ).view(x_shape) # EP WITH ALL-REDUCE PATH: Expert IDs are in LOCAL coordinates (from sharding.py), @@ -932,6 +1064,7 @@ def trtllm_quant_finegrained_fp8_moe_fused_fake( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: _validate_mlp_style_and_act_fn(is_gated_mlp, act_fn) return torch.empty_like(x) @@ -960,6 +1093,7 @@ def _trtllm_nvfp4_trtllm_gen_moe_impl( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: _validate_mlp_style_and_act_fn(is_gated_mlp, act_fn) if act_fn in (ActivationType.Gelu, ActivationType.Geglu): @@ -1036,6 +1170,7 @@ def _trtllm_nvfp4_trtllm_gen_moe_impl( mapping=mapping, max_num_tokens=max_num_tokens, act_type=act_type, + batch_info_host=batch_info_host, ) if final_hidden_states.shape[1] > x_shape[-1]: final_hidden_states = final_hidden_states[:, : x_shape[-1]].contiguous() @@ -1108,6 +1243,7 @@ def trtllm_nvfp4_trtllm_gen_moe_fused( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: return _trtllm_nvfp4_trtllm_gen_moe_impl( x, @@ -1132,6 +1268,7 @@ def trtllm_nvfp4_trtllm_gen_moe_fused( mapping_config=mapping_config, max_num_tokens=max_num_tokens, apply_routing_on_input=apply_routing_on_input, + batch_info_host=batch_info_host, ) @@ -1159,5 +1296,6 @@ def trtllm_nvfp4_trtllm_gen_moe_fused_fake( mapping_config: str = "", max_num_tokens: int = 0, apply_routing_on_input: bool = False, + batch_info_host: torch.Tensor | None = None, ) -> torch.Tensor: return torch.empty_like(x) diff --git a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py index cf3c8bbd0e6f..b3bc4e1dca49 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py @@ -54,6 +54,7 @@ from ..distributed.common import initialize_or_skip from ..llm_args import LlmArgs from ..transform.optimizer import InferenceOptimizer +from ..utils.cuda_graph import BypassCapturedGraphs from ..utils.dist_config import DistConfig from ..utils.logger import ad_logger from .interface import CachedSequenceInterface, GetInferenceModel @@ -127,6 +128,15 @@ def wrapper( def _call_func(): return func(self, scheduled_requests, resource_manager, *args, **kwargs) + def _call_func_eager(): + # When this wrapper has decided that all ranks must run eager, also force + # the inner cudagraph backend to bypass captured graphs. Otherwise, ranks + # whose shapes happen to match a captured graph would still replay and + # use stale capture-time scalar kernel args (e.g. runtime_max_tokens_per_rank + # baked from local total at capture, vs cross-rank max read fresh in eager). + with BypassCapturedGraphs(): + return _call_func() + # check conditions for current rank can_run_cuda_graph = self.cuda_graph_used and scheduled_requests.can_run_cuda_graph batch_size = scheduled_requests.batch_size @@ -158,7 +168,7 @@ def _call_func(): can_run_cuda_graph_all = all(r_info[0] for r_info in all_rank_info) if not can_run_cuda_graph_all: - return _call_func() + return _call_func_eager() # get closest cudagraph batch size based on max_batch_size across ALL ranks # NOTE: we assume uniform cudagraph batch sizes across all ranks ensuring all ranks get the @@ -167,14 +177,14 @@ def _call_func(): cg_batch_size = _round_up_to_closest(self.cuda_graph_batch_sizes, max_batch_size) if cg_batch_size is None: - return _call_func() + return _call_func_eager() # let's check if all ranks can pad the batch if they need to can_pad_all = all(r_info[1] or (r_info[2] == cg_batch_size) for r_info in all_rank_info) # fall back if we cannot run cudagraph due to padding issues if not can_pad_all: - return _call_func() + return _call_func_eager() # check actual amount of padding needed num_padding = cg_batch_size - batch_size @@ -948,6 +958,20 @@ def forward( ) self.iter_counter += 1 + # Compute DP-aware max(total_num_tokens) and write to BatchInfo slot 14 + # (``max_dp_num_tokens``). Mirrors base TRT-LLM's pattern in + # ``model_engine._get_all_rank_num_tokens``: MoE all-to-all needs the + # cross-rank max to size dispatch padding without over-padding to the + # static config ``max_num_tokens``. ``nest_sequences`` already + # initialized slot 14 to the local ``total_num_tokens``; this overrides + # with the cross-rank max only when attention-DP requires it. + if self.enable_attention_dp and self.dist_config.tp_size > 1: + assert self.dist is not None, "Distributed object is required for attention DP mode" + info = self.cache_seq_interface.info + local_total_num_tokens = info.batch_info.get_total_num_tokens() + all_rank_num_tokens = list(self.dist.tp_allgather(local_total_num_tokens)) + info.batch_info.update_max_dp_num_tokens(max(all_rank_num_tokens)) + # compute outputs outputs = self._run_forward() diff --git a/tensorrt_llm/_torch/auto_deploy/transform/interface.py b/tensorrt_llm/_torch/auto_deploy/transform/interface.py index b7fb5f80367e..8f3c135710e0 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/interface.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/interface.py @@ -795,13 +795,24 @@ def _apply_to_full_model( ) def _add_or_retrieve_input( - self, gm: GraphModule, cm: CachedSequenceInterface, name: str + self, gm: GraphModule, cm: CachedSequenceInterface, name: str, init_val: bool = False ) -> Node: """Add or retrieve an input node from the graph.""" input_nodes = gm.graph.find_nodes(op="placeholder", target=name) if len(input_nodes) == 0: cm.info.activate_arg(name) - return add_graph_input(gm, name) + if init_val: + # Pass the runtime tensor so add_graph_input populates the + # placeholder's meta["val"] with a proper FakeTensor (shape + + # dtype propagated via fake_mode.from_tensor). Leaving meta empty + # (the default _NO_VAL path) causes downstream transforms that + # look up node.meta["val"] (FX export, fuse_fp8_linear's pattern + # matcher) to silently misbehave -- e.g. an adjacent + # fc2_latent_proj ends up with None in its input slot and the FP8 + # fake impl crashes on `input.dtype`. + return add_graph_input(gm, name, val=cm.info.get_arg(name)) + else: + return add_graph_input(gm, name) elif len(input_nodes) == 1: return input_nodes[0] else: diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py b/tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py index 39816f0cc279..393f45933eee 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py @@ -102,8 +102,8 @@ def _fp4_ref_repl_1( alpha: torch.Tensor, ): return torch.ops.auto_deploy.torch_quant_nvfp4_linear( - x, - w_fp4, + input=x, + weight_fp4=w_fp4, bias=None, input_scale=input_scale, weight_scale=weight_scale, @@ -140,8 +140,8 @@ def _fp4_ref_repl_2( alpha: torch.Tensor, ): return torch.ops.auto_deploy.torch_quant_nvfp4_linear( - x, - w_fp4, + input=x, + weight_fp4=w_fp4, bias=bias, input_scale=input_scale, weight_scale=weight_scale, @@ -154,7 +154,10 @@ def _register_quant_fp8_linear_patterns(patterns: ADPatternMatcherPass, op) -> N Register FP8 linear patterns with robust dummy args and minimal ignores. """ - # Define replacement functions that use the provided op + # Define replacement functions that use the provided op. + # Use keyword-only binding for input/weight/bias so the call stays robust + # against any FX-state perturbation that affects positional arg layout + # (e.g., sharding placeholder insertion in this PR). def _fp8_ref_repl_1( x: torch.Tensor, w_fp8: torch.Tensor, @@ -162,9 +165,9 @@ def _fp8_ref_repl_1( weight_scale: torch.Tensor, ): return op( - x, - w_fp8, - None, + input=x, + weight_fp8=w_fp8, + bias=None, input_scale=input_scale, weight_scale=weight_scale, ) @@ -177,9 +180,9 @@ def _fp8_ref_repl_2( weight_scale: torch.Tensor, ): return op( - x, - w_fp8, - bias, + input=x, + weight_fp8=w_fp8, + bias=bias, input_scale=input_scale, weight_scale=weight_scale, ) diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py b/tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py index 14b5526999bb..954d2d6168bd 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py @@ -1333,6 +1333,18 @@ def check_and_apply(transform: ShardingTransformInfo) -> bool: f"BMM={len(transforms.bmm_transforms)}, " f"RMSNorm={len(transforms.rmsnorm_transforms)}" ) + + # If there are EP transforms and we have a CachedSequenceInterface, ensure + # batch_info_host is added to the graph as a placeholder and activated on + # the SequenceInfo so the runtime DP-aware max_num_tokens (slot 14) flows + # into the MoE all-to-all op as a kwarg. _add_or_retrieve_input is + # idempotent — safe even if another transform (e.g. + # gather_logits_before_lm_head) already added the placeholder. + # When cm is None (e.g., unit tests that drive the sharding transform + # standalone), skip — the MoE op falls back to max_num_tokens. + if transforms.ep_transforms and cm is not None: + self._add_or_retrieve_input(gm, cm, "batch_info_host", init_val=True) + with WeightBiasInfoCache(): for tp_transform in transforms.weight_sharding_transforms: if check_and_apply(tp_transform): @@ -2138,11 +2150,23 @@ def get_partition(lst, world_size, rank): # (Will be used inside the op to determine enable_alltoall and workspace size) mapping_config = config.dist_config.serialize() + # Look up batch_info_host placeholder if present. ShardingTransformExecutor + # ensures it's added/activated when there are EP transforms; if it's missing + # (e.g., a different code path bypasses the executor), the MoE op falls back + # to max_num_tokens for runtime padding. + batch_info_host_nodes = gm.graph.find_nodes(op="placeholder", target="batch_info_host") + batch_info_host_node = batch_info_host_nodes[0] if batch_info_host_nodes else None + # Write back weight/scale list updates (applied above) and inject mapping args. # set_op_args uses the op schema to place values into kwargs or the correct # positional slot, avoiding manual index arithmetic. node.args = tuple(args) - set_op_args(node, mapping_config=mapping_config, max_num_tokens=config.max_num_tokens) + set_op_args( + node, + mapping_config=mapping_config, + max_num_tokens=config.max_num_tokens, + batch_info_host=batch_info_host_node, + ) if not enable_alltoall: # ===================================================================================== diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/sharding_ir.py b/tensorrt_llm/_torch/auto_deploy/transform/library/sharding_ir.py index cbb4bfd8c692..46cc733c0e68 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/sharding_ir.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/sharding_ir.py @@ -734,9 +734,15 @@ def get_partition(lst, world_size, rank): self.node.args = tuple(args) if enable_alltoall: - # mapping and max_num_tokens are needed downstream for MoE all-to-all dispatcher mapping_config = dc.serialize() - set_op_args(self.node, mapping_config=mapping_config, max_num_tokens=max_num_tokens) + batch_info_host_nodes = gm.graph.find_nodes(op="placeholder", target="batch_info_host") + batch_info_host_node = batch_info_host_nodes[0] if batch_info_host_nodes else None + set_op_args( + self.node, + mapping_config=mapping_config, + max_num_tokens=max_num_tokens, + batch_info_host=batch_info_host_node, + ) else: # with pure EP/TP parallelism, global expert indices must be localized self._localize_expert_indices( @@ -1092,6 +1098,14 @@ def _apply( max_num_tokens = cm.info.max_num_tokens if (cm and cm.info) else 0 + # When attention-DP is active with EP, the MoE all-to-all ops need + # runtime token counts (batch_info_host slot 14, ``max_dp_num_tokens``) + # to avoid over-padding. + # Add the placeholder before the node loop so MoEShardableNode.apply() + # can find and wire it into each MoE node. + if dc.enable_attention_dp and dc.moe_ep_size > 1 and cm is not None: + self._add_or_retrieve_input(gm, cm, "batch_info_host", init_val=True) + num_updates = 0 if self.config.simple_shard_only: num_updates = _apply_simple_shard(gm, dc) diff --git a/tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py b/tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py index d31cb39aca1b..d9f4337bb15c 100644 --- a/tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py +++ b/tensorrt_llm/_torch/auto_deploy/utils/cuda_graph.py @@ -30,6 +30,13 @@ def __new__(cls, *args, **kwargs): # the graph is executed with representative inputs. WARM_UP: bool = False + # Indicates that captured-graph wrappers must short-circuit to eager. + # Set by ad_executor.maybe_pad_for_cuda_graph under attention-DP mixed mode + # so all ranks read kwargs (e.g. batch_info_host slot 14) consistently + # instead of using stale capture-time scalar kernel args. See + # BypassCapturedGraphs() below. + BYPASS: bool = False + def begin_warm_up(): if CudaGraphState.WARM_UP: raise ValueError("Already in a warm-up state") @@ -43,6 +50,19 @@ def end_warm_up(): def in_warm_up() -> bool: return CudaGraphState.WARM_UP + def begin_bypass(): + if CudaGraphState.BYPASS: + raise ValueError("Already in a bypass state") + CudaGraphState.BYPASS = True + + def end_bypass(): + if not CudaGraphState.BYPASS: + raise ValueError("Not in bypass state") + CudaGraphState.BYPASS = False + + def in_bypass() -> bool: + return CudaGraphState.BYPASS + cuda_graph_state = CudaGraphState @@ -54,3 +74,23 @@ def CudaGraphWarmUpPhase(): yield finally: cuda_graph_state.end_warm_up() + + +@contextmanager +def BypassCapturedGraphs(): + """Force every CapturedGraph wrapper inside this scope to short-circuit to eager. + + Used by ``ad_executor.maybe_pad_for_cuda_graph`` under attention-DP mixed mode: + when the cross-rank ``tp_allgather`` vote says some ranks must run eager (e.g. + one rank is in prefill while others are in decode), all ranks enter this + context for the call so captured graphs whose shapes happen to match are + bypassed too. Otherwise the captured kernel-launch args (notably the + ``int(batch_info_host[14].item())`` baked at capture time, where slot 14 + holds ``max_dp_num_tokens`` per ``BatchInfo``) would diverge from the eager + ranks' fresh reads, corrupting the ``MoeAlltoAll`` collective. + """ + cuda_graph_state.begin_bypass() + try: + yield + finally: + cuda_graph_state.end_bypass() diff --git a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py index 3c76f81ebe4e..366da2f629e7 100644 --- a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py +++ b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py @@ -564,6 +564,8 @@ def get_default_sampling_params(self): @pytest.mark.skip_less_device_memory(32000) @pytest.mark.parametrize("attn_backend", ["flashinfer", "trtllm"]) + @pytest.mark.parametrize("enable_attention_dp", [False, True], + ids=["attn_dp_off", "attn_dp_on"]) @pytest.mark.parametrize("world_size", [1, 2, 4]) @pytest.mark.parametrize( "model_id", @@ -574,18 +576,29 @@ def get_default_sampling_params(self): pytest.param("nvfp4", marks=skip_pre_blackwell), ], ) - def test_accuracy(self, model_id, world_size, attn_backend): + def test_accuracy(self, model_id, world_size, enable_attention_dp, + attn_backend): if world_size > get_device_count(): pytest.skip(f"Not enough devices for world_size={world_size}") + # attention-DP requires at least 2 ranks to exercise the cross-rank + # max_dp_num_tokens path; on world_size=1 it's a no-op. + if enable_attention_dp and world_size < 2: + pytest.skip("attention_dp requires world_size >= 2") model_path = self.MODEL_PATHS[model_id] kwargs = {} device_memory_mib = get_device_memory() # bf16 always needs low-memory overrides; below H100-class total # memory, the quantized variants do too, since the 30B FP8 / NVFP4 # weights leave too little headroom for the nano_v3.yaml defaults. - if model_id == "bf16" or device_memory_mib < 80000: + # attention_dp adds non-trivial overhead from MoE all-to-all dispatch + # buffers and per-rank expert allocations, so the quantized variants + # also need low-memory overrides on H100-class hardware when it's on. + if (model_id == "bf16" or device_memory_mib < 80000 + or enable_attention_dp): low_memory_overrides(kwargs) kwargs["attn_backend"] = attn_backend + kwargs.setdefault("transforms", {}).setdefault( + "detect_sharding", {})["enable_attention_dp"] = enable_attention_dp with AutoDeployLLM(model=model_path, tokenizer=model_path, diff --git a/tests/integration/test_lists/qa/llm_function_core.txt b/tests/integration/test_lists/qa/llm_function_core.txt index 5526145cae72..be5a78639f9f 100644 --- a/tests/integration/test_lists/qa/llm_function_core.txt +++ b/tests/integration/test_lists/qa/llm_function_core.txt @@ -123,12 +123,12 @@ accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_ accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[trtllm-flashinfer_ssm-False] accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[trtllm-triton_ssm-False] accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[trtllm-triton_ssm-True] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-1-trtllm] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-trtllm] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-trtllm] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-trtllm] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-1-trtllm] -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-1-attn_dp_off-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-attn_dp_off-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-attn_dp_off-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-attn_dp_off-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-1-attn_dp_off-trtllm] +accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-attn_dp_off-trtllm] accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_off-trtllm] accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_on-trtllm] accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_off-trtllm] diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index 308931c5d396..5ac2ad5f115f 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -335,7 +335,7 @@ l0_b200: tests: - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[trtllm-False-1] - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[nvfp4-1-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_functional_small[bf16] # ------------- AutoDeploy unit tests --------------- @@ -377,7 +377,7 @@ l0_b200: - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_auto_dtype[False] - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_auto_dtype[True] - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_nvfp4[True] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-1-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-1-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_functional_small[fp8] # ------------- AutoDeploy Perf Sanity --------------- - perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws1_1k1k] TIMEOUT (120) diff --git a/tests/integration/test_lists/test-db/l0_dgx_b200.yml b/tests/integration/test_lists/test-db/l0_dgx_b200.yml index 30bc403c054b..a7587d7d9b4b 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b200.yml @@ -361,7 +361,7 @@ l0_dgx_b200: - unittest/auto_deploy/multigpu/transformations/library/test_tp_sharding.py::test_moe_tp_shard_nvfp4 - unittest/auto_deploy/multigpu/transformations/library/test_allreduce_residual_rmsnorm_fusion.py -k "strategy_auto" - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[trtllm-False-4] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-4] - accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] @@ -385,7 +385,7 @@ l0_dgx_b200: # Move to post-merge due to https://nvbugspro.nvidia.com/bug/6221483 - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_mtp[nvfp4_ws4_80gb-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-4] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_on-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[nvfp4-4-attn_dp_on-trtllm] diff --git a/tests/integration/test_lists/test-db/l0_dgx_h100.yml b/tests/integration/test_lists/test-db/l0_dgx_h100.yml index 34110b214d1d..7a516247f1d2 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_h100.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_h100.yml @@ -370,7 +370,8 @@ l0_dgx_h100: - unittest/auto_deploy/multigpu/transformations - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[trtllm-False-4] - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B_Instruct_Eagle3::test_eagle3_one_model[trtllm] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-attn_dp_off-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-4-attn_dp_on-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_mtp[fp8_ws4_80gb-trtllm] - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_attention_dp[4] @@ -401,7 +402,7 @@ l0_dgx_h100: - accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[meta-llama_Llama-3.3-70B-Instruct-False] - accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-FP8-True] - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B_Instruct_Eagle3::test_eagle3_one_model[flashinfer] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_on-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_on-trtllm] diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index 3462d75ff1a9..4f568c96fbb5 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -495,7 +495,7 @@ l0_h100: - accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma3n_e2b_it - accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[triton-False-1] - examples/test_ad_speculative_decoding.py::test_autodeploy_eagle3_one_model_acceptance_rate[trtllm-torch-cudagraph] - examples/test_ad_speculative_decoding.py::test_autodeploy_eagle3_one_model_acceptance_rate[flashinfer-torch-simple] @@ -527,4 +527,4 @@ l0_h100: - accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[trtllm-triton_ssm-True] - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_auto_dtype[False] - accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_auto_dtype[True] - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-1-trtllm] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-1-attn_dp_off-trtllm] diff --git a/tests/integration/test_lists/test-db/l0_l40s.yml b/tests/integration/test_lists/test-db/l0_l40s.yml index b76f207dd9c1..af84c6e13384 100644 --- a/tests/integration/test_lists/test-db/l0_l40s.yml +++ b/tests/integration/test_lists/test-db/l0_l40s.yml @@ -32,7 +32,7 @@ l0_l40s: - accuracy/test_llm_api_pytorch_multimodal.py::TestVILA1_5_3B::test_auto_dtype # AutoDeploy: Nemotron-Nano-V3 on Ada uses flashinfer; trtllm has no # (E4M3 input, BF16 output, paged_kv, head_dim=128, sm_89) FMHA cubin. - - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-flashinfer] + - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[fp8-1-attn_dp_off-flashinfer] - condition: ranges: system_gpu_count: diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 3866d63e2df4..c63b9e64c914 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -14,8 +14,6 @@ accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 SKIP (https://nvbu accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (https://nvbugs/6194934) accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[bf16-4-trtllm] SKIP (https://nvbugs/6185150) -accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-trtllm] SKIP (https://nvbugs/6185150) accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-8] SKIP (https://nvbugs/6248757) accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) diff --git a/tests/unittest/auto_deploy/multigpu/compile/test_bypass_captured_graphs.py b/tests/unittest/auto_deploy/multigpu/compile/test_bypass_captured_graphs.py new file mode 100644 index 000000000000..313b29783880 --- /dev/null +++ b/tests/unittest/auto_deploy/multigpu/compile/test_bypass_captured_graphs.py @@ -0,0 +1,212 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +"""Multi-GPU regression test for the captured-graph bypass mechanism. + +Guards both layers of the fix that landed in PR #13723 for the conc=32 NaN +regression on MoE all-to-all under attention-DP: + + Level 1 (leaf): ``CapturedGraph.forward`` honours the + ``BypassCapturedGraphs()`` context manager. A Python int read inside the + captured graph (``int(host_tensor[0].item())``, mirroring + ``int(batch_info_host[14].item())`` in ``trtllm_moe.py``, where slot 14 of + ``BatchInfo`` is ``max_dp_num_tokens``) is baked into the captured + kernel-launch as a scalar argument. Replay reuses it regardless of + post-capture host updates. While inside ``BypassCapturedGraphs()`` (i.e. + ``cuda_graph_state.in_bypass() == True``), the wrapper short-circuits to + eager and reads the host fresh. + + Level 2 (wrapper): ``maybe_pad_for_cuda_graph`` correctly detects cross-rank + state mismatch via ``tp_allgather`` and enters ``BypassCapturedGraphs()`` + around the call when ANY rank reports ``can_run_cuda_graph == False``. + Captured graphs whose shapes happen to match are bypassed too — this is the + actual cross-rank divergence the conc=32 NaN bug exposed when one rank ran + prefill (eager) while another ran decode (replay) with a stale capture-time + ``max_dp_num_tokens``. + +The cross-rank decision is propagated via the process-wide +``cuda_graph_state.BYPASS`` flag toggled by ``BypassCapturedGraphs()``, NOT by +traversing ``model.modules()`` and toggling a per-instance flag (the previous +approach was both fragile to renames/subclasses and an unpythonic instance-state +mutation). End-to-end MoE-A2A coverage is provided by +``TestNemotronSuperV3::test_accuracy[*-4-attn_dp_on-trtllm]`` (post-merge). +""" + +from types import SimpleNamespace +from unittest.mock import MagicMock + +import pytest +import torch +import torch.distributed as dist +import torch.nn as nn +from _dist_test_utils import get_device_counts + +from tensorrt_llm._torch.auto_deploy.compile.backends.torch_cudagraph import CapturedGraph +from tensorrt_llm._torch.auto_deploy.distributed.common import spawn_multiprocess_job +from tensorrt_llm._torch.auto_deploy.shim.ad_executor import maybe_pad_for_cuda_graph +from tensorrt_llm._torch.auto_deploy.utils.cuda_graph import BypassCapturedGraphs, cuda_graph_state + + +class _SlotReadingModel(nn.Module): + """Mimics the ``int(batch_info_host[14].item())`` scalar read in the MoE A2A op. + + The Python int returned by ``.item()`` is consumed as a kernel-launch + argument by ``torch.full``; under cuda graph capture that argument value is + recorded into the graph and reused at replay irrespective of the current + host tensor value. The eager path (uncaptured shape / under + ``BypassCapturedGraphs()``) re-runs the Python body and reads the host fresh. + """ + + def __init__(self, host: torch.Tensor): + super().__init__() + self._host = host + + def forward(self, x: torch.Tensor) -> torch.Tensor: + v = int(self._host[0].item()) + return torch.full((x.shape[0],), float(v), device="cuda") + + +def _run_bypass_test(rank: int, world_size: int) -> None: + device = "cuda" + + # ----- Level 1 setup: tiny CapturedGraph over the slot-reading model ----- + host = torch.zeros(1, dtype=torch.int32, pin_memory=True) + inner = _SlotReadingModel(host) + cg = CapturedGraph(inner, num_batched_inputs=1) + + capture_bs = [1, 2, 4] + + def get_args_kwargs(bs: int): + # Mimic ``SequenceInfo.set_capture_batch`` seeding ``max_dp_num_tokens`` + # with the local total at capture time. Each captured graph thus bakes + # its own value. + host[0] = bs + return (torch.zeros(bs, device=device),), {} + + cg.capture_graph(get_args_kwargs, capture_bs) + + # ----- Level 2 setup: minimal fake ADEngine for maybe_pad_for_cuda_graph -- + # The wrapper accesses: cuda_graph_used, enable_attention_dp, dist_config.tp_size, + # dist.tp_allgather, padding_dummy_request, cuda_graph_batch_sizes. + # We fake all of them with MagicMock and assign concrete values where the + # wrapper's conditionals depend on the value. + fake_engine = MagicMock() + fake_engine.cuda_graph_used = True + fake_engine.enable_attention_dp = True + fake_engine.dist_config = SimpleNamespace(tp_size=world_size) + # Non-None so the wrapper's "create dummy" branch is skipped on first call. + fake_engine.padding_dummy_request = MagicMock() + fake_engine.cuda_graph_batch_sizes = capture_bs + fake_engine.max_total_draft_tokens = 0 + fake_engine.max_beam_width = 1 + + def real_tp_allgather(local_obj): + gathered = [None] * world_size + dist.all_gather_object(gathered, local_obj) + return gathered + + fake_engine.dist = SimpleNamespace(tp_allgather=real_tp_allgather) + + def fake_forward(self, scheduled_requests, resource_manager, *args, **kwargs): + # Mimic ADEngine.forward: gather cross-rank totals, write to slot, run model. + # The captured graph's `int(host[0].item())` happens INSIDE cg(...) — so + # under replay it returns the baked value, under bypass it reads fresh. + all_locals = list(self.dist.tp_allgather(scheduled_requests.local_total)) + host[0] = max(all_locals) + return cg(torch.zeros(scheduled_requests.batch_size, device=device)) + + wrapped = maybe_pad_for_cuda_graph(fake_forward) + + def mk_scheduled(can_cg: bool, bs: int, local_total: int): + sr = MagicMock() + sr.can_run_cuda_graph = can_cg + sr.batch_size = bs + sr.local_total = local_total + # Real list — the wrapper's padding branch calls .extend / [:-num_padding] + sr.generation_requests = [] + return sr + + # ===== Scenario A (Level 1): all ranks can run cuda graph -> REPLAY ===== + # All ranks have can_cg=True, bs=1, local_total=1; cross-rank max also = 1. + # Wrapper allows replay; captured graph for bs=1 returns its baked value (= 1). + sr_a = mk_scheduled(can_cg=True, bs=1, local_total=1) + out_a = wrapped(fake_engine, sr_a, MagicMock()) + assert int(out_a[0].item()) == 1, ( + f"rank={rank} scenario A (REPLAY): expected baked capture-time value 1, " + f"got {out_a.tolist()}" + ) + assert cuda_graph_state.in_bypass() is False, ( + f"rank={rank} scenario A: bypass state should remain False after replay" + ) + + # ===== Scenario B (Level 2): rank 0 prefill, rank 1 decode -> BYPASS ===== + # Cross-rank state diverges; wrapper must allgather, see at least one False, + # enter BypassCapturedGraphs() around the call, and run eager. Eager reads + # host fresh -> value = max(local_total across ranks). + bypass_state_seen_during_call: list[bool] = [] + + def fake_forward_probed(self, scheduled_requests, resource_manager, *args, **kwargs): + # Records the global bypass state at call time. Captured graphs honour + # this same state via cuda_graph_state.in_bypass() in their forward(). + bypass_state_seen_during_call.append(cuda_graph_state.in_bypass()) + all_locals = list(self.dist.tp_allgather(scheduled_requests.local_total)) + host[0] = max(all_locals) + return cg(torch.zeros(scheduled_requests.batch_size, device=device)) + + wrapped_probed = maybe_pad_for_cuda_graph(fake_forward_probed) + + sr_b = mk_scheduled( + can_cg=(rank == 1), # only rank 1 says yes + bs=1, + local_total=(99 if rank == 0 else 1), # cross-rank max should be 99 + ) + out_b = wrapped_probed(fake_engine, sr_b, MagicMock()) + assert int(out_b[0].item()) == 99, ( + f"rank={rank} scenario B (BYPASS-EAGER): expected fresh cross-rank max 99, " + f"got {out_b.tolist()}" + ) + assert bypass_state_seen_during_call == [True], ( + f"rank={rank} scenario B: cuda_graph_state.in_bypass() must be True during the " + f"wrapped call (so CapturedGraph submodules skip replay), got history " + f"{bypass_state_seen_during_call}" + ) + assert cuda_graph_state.in_bypass() is False, ( + f"rank={rank} scenario B: bypass state must be RESTORED to False after the " + f"BypassCapturedGraphs() context exits (try/finally), got " + f"{cuda_graph_state.in_bypass()}" + ) + + # ===== Scenario C: after bypass cleared, replay resumes -> baked value ==== + sr_c = mk_scheduled(can_cg=True, bs=1, local_total=1) + out_c = wrapped(fake_engine, sr_c, MagicMock()) + assert int(out_c[0].item()) == 1, ( + f"rank={rank} scenario C: replay should resume baked behaviour after bypass cleared, " + f"got {out_c.tolist()}" + ) + + # ===== Scenario D: direct context-manager smoke test (no wrapper) ========= + # Confirms BypassCapturedGraphs() alone is sufficient to short-circuit a + # CapturedGraph instance to eager — independent of maybe_pad_for_cuda_graph. + host[0] = 7919 + out_d_no_bypass = cg(torch.zeros(1, device=device)) + assert int(out_d_no_bypass[0].item()) == 1, ( + f"rank={rank} scenario D pre-context: baked replay should yield 1, got {out_d_no_bypass}" + ) + with BypassCapturedGraphs(): + assert cuda_graph_state.in_bypass() is True + out_d_bypass = cg(torch.zeros(1, device=device)) + assert cuda_graph_state.in_bypass() is False + assert int(out_d_bypass[0].item()) == 7919, ( + f"rank={rank} scenario D inside-context: eager should read fresh slot 7919, " + f"got {out_d_bypass}" + ) + + +@pytest.mark.parametrize("device_count", get_device_counts([2])) +def test_bypass_captured_graphs(device_count): + spawn_multiprocess_job(job=_run_bypass_test, size=device_count) From 428cc3eb68acd7f5311a9595220fb9a737be7d6b Mon Sep 17 00:00:00 2001 From: Guoming Zhang <137257613+nv-guomingz@users.noreply.github.com> Date: Sun, 7 Jun 2026 22:40:30 +0800 Subject: [PATCH 024/119] [TRTLLM-13177][doc] Add Nemotron 3 Ultra doc (#14964) Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- docs/source/_static/config_db.json | 16 ++++ ...loyment-guide-for-nemotron-3-on-trtllm.md} | 95 ++++++++++++++++--- docs/source/deployment-guide/index.rst | 2 +- docs/source/models/supported-models.md | 3 +- examples/configs/curated/lookup.yaml | 5 + .../curated/nemotron-3-ultra-throughput.yaml | 19 ++++ .../core/nemotron/README_nemotron_super_v3.md | 2 +- scripts/generate_config_table.py | 4 + 8 files changed, 128 insertions(+), 18 deletions(-) rename docs/source/deployment-guide/{deployment-guide-for-nemotron-3-super-on-trtllm.md => deployment-guide-for-nemotron-3-on-trtllm.md} (67%) create mode 100644 examples/configs/curated/nemotron-3-ultra-throughput.yaml diff --git a/docs/source/_static/config_db.json b/docs/source/_static/config_db.json index 2856a32e5c0f..8b104ce02dbd 100644 --- a/docs/source/_static/config_db.json +++ b/docs/source/_static/config_db.json @@ -12,6 +12,18 @@ "model_url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "scenario": "Max Throughput" }, + { + "command": "trtllm-serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --config ${TRTLLM_DIR}/examples/configs/curated/nemotron-3-ultra-throughput.yaml", + "config_filename": "nemotron-3-ultra-throughput.yaml", + "config_github_url": "https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/configs/curated/nemotron-3-ultra-throughput.yaml", + "config_path": "examples/configs/curated/nemotron-3-ultra-throughput.yaml", + "config_raw_url": "https://raw.githubusercontent.com/NVIDIA/TensorRT-LLM/main/examples/configs/curated/nemotron-3-ultra-throughput.yaml", + "gpu_compatibility": "B200, B300, GB200, GB300, H100, H200", + "model": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", + "model_display_name": "Nemotron v3 Ultra (NVFP4)", + "model_url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", + "scenario": "Max Throughput" + }, { "command": "trtllm-serve Qwen/Qwen3-Next-80B-A3B-Thinking --config ${TRTLLM_DIR}/examples/configs/curated/qwen3-next.yaml", "config_filename": "qwen3-next.yaml", @@ -3516,6 +3528,10 @@ "display_name": "Nemotron v3 Super (NVFP4)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" }, + "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4": { + "display_name": "Nemotron v3 Ultra (NVFP4)", + "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4" + }, "openai/gpt-oss-120b": { "display_name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b" diff --git a/docs/source/deployment-guide/deployment-guide-for-nemotron-3-super-on-trtllm.md b/docs/source/deployment-guide/deployment-guide-for-nemotron-3-on-trtllm.md similarity index 67% rename from docs/source/deployment-guide/deployment-guide-for-nemotron-3-super-on-trtllm.md rename to docs/source/deployment-guide/deployment-guide-for-nemotron-3-on-trtllm.md index ac0ddcf279d5..da286825f438 100644 --- a/docs/source/deployment-guide/deployment-guide-for-nemotron-3-super-on-trtllm.md +++ b/docs/source/deployment-guide/deployment-guide-for-nemotron-3-on-trtllm.md @@ -1,8 +1,13 @@ -# Deployment Guide for Nemotron v3 Super on TensorRT LLM - Blackwell & Hopper Hardware +# Deployment Guide for Nemotron v3 (Ultra & Super) on TensorRT LLM - Blackwell & Hopper Hardware ## Introduction -This deployment guide provides step-by-step instructions for running the NVIDIA Nemotron v3 Super 120B-A12B model using TensorRT LLM. Nemotron v3 Super is a hybrid architecture model combining Mixture-of-Experts (MoE) with SSM (Mamba) and attention layers, delivering 120B total parameters with only 12B active parameters per token for efficient inference. This guide covers model access, environment setup, server configuration, and inference validation. +This deployment guide provides step-by-step instructions for running the NVIDIA Nemotron v3 family of models using TensorRT LLM. It covers two models: + +* **Nemotron v3 Ultra (550B-A55B)** — 550B total parameters with 55B active per token. +* **Nemotron v3 Super (120B-A12B)** — 120B total parameters with 12B active per token. + +Both models share a hybrid architecture (`NemotronHForCausalLM`) that interleaves Mamba-2 (SSM), Mixture-of-Experts (MoE), and attention layers for efficient inference. Nemotron v3 Ultra additionally uses a Latent Mixture-of-Experts (LatentMoE) design and ships with built-in Multi-Token Prediction (MTP) layers. On TensorRT LLM, Nemotron v3 Ultra supports MTP, prefix caching (KV cache reuse), and disaggregated serving. This guide covers model access, environment setup, server configuration, and inference validation for both models. ## Prerequisites @@ -14,6 +19,13 @@ This deployment guide provides step-by-step instructions for running the NVIDIA ## Models +### Nemotron v3 Ultra (550B-A55B) + +* [NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16) +* [NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4) + +### Nemotron v3 Super (120B-A12B) + * [NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16) * [NVIDIA-Nemotron-3-Super-120B-A12B-FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8) * [NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4) @@ -22,7 +34,27 @@ All models are available under the [nvidia/nvidia-nemotron-v3](https://huggingfa ## GPU Requirements -Nemotron v3 Super 120B-A12B has 120B total parameters. The minimum GPU memory required depends on the precision: +The minimum GPU memory required depends on the model size and precision. + +### Nemotron v3 Ultra (550B-A55B) + +The NVFP4 checkpoint is the recommended (and minimum-footprint) deployment precision for Ultra. The published minimum GPU requirements for the NVFP4 checkpoint are: + +| Platform | Minimum GPUs | +|----------|--------------| +| B200 | 4x B200 | +| B300 | 4x B300 | +| GB200 | 4x GB200 | +| GB300 | 4x GB300 | +| H100 | 8x H100 \* | + +The NVFP4 checkpoint uses an FP8 KV cache. On Blackwell (B200/B300) and Grace Blackwell (GB200/GB300), a single node of 4 GPUs fits the NVFP4 weights plus the KV cache with headroom. + +\* The same NVFP4 checkpoint can also be served on Hopper. Because Hopper lacks a native NVFP4 tensor-core GEMM, NVFP4 weights are run through a W4A16 fallback path that dequantizes them on the fly; this requires a minimum of 8x H100 (fewer may suffice on the higher-memory H200) and delivers somewhat lower throughput than Blackwell. No checkpoint conversion or command change is needed — the runtime selects the fallback automatically. + +The `Base-BF16` checkpoint is the pre-training checkpoint and is primarily intended for research and fine-tuning rather than serving. + +### Nemotron v3 Super (120B-A12B) | Checkpoint | Minimum GPUs (H100/H200 80GB) | Minimum GPUs (B200/GB200 192GB) | |------------|-------------------------------|---------------------------------| @@ -61,12 +93,36 @@ We maintain YAML configuration files with recommended performance settings in th ```shell TRTLLM_DIR=/app/tensorrt_llm # change as needed to match your environment +``` + +Select the config file that matches the model you are deploying: + +```shell +# Nemotron v3 Ultra +EXTRA_LLM_API_FILE=${TRTLLM_DIR}/examples/configs/curated/nemotron-3-ultra-throughput.yaml + +# Nemotron v3 Super EXTRA_LLM_API_FILE=${TRTLLM_DIR}/examples/configs/curated/nemotron-3-super-throughput.yaml ``` -Note: if you don't have access to the source code locally, you can manually create the YAML config file using the code in the dropdown below. +Note: if you don't have access to the source code locally, you can manually create the YAML config file using the code in the dropdowns below. -````{admonition} Show code +````{admonition} Show Nemotron v3 Ultra config +:class: dropdown + +```{literalinclude} ../../../examples/configs/curated/nemotron-3-ultra-throughput.yaml +--- +language: shell +prepend: | + EXTRA_LLM_API_FILE=/tmp/config.yml + + cat << EOF > ${EXTRA_LLM_API_FILE} +append: EOF +--- +``` +```` + +````{admonition} Show Nemotron v3 Super config :class: dropdown ```{literalinclude} ../../../examples/configs/curated/nemotron-3-super-throughput.yaml @@ -81,16 +137,25 @@ append: EOF ``` ```` +The Ultra config is a starting point tuned for max throughput on 4x B200; adjust the parallelism, batch sizes, and KV cache fraction to match your hardware and traffic pattern. + ### Launch the TensorRT LLM Server -Below are example commands to launch the TensorRT LLM server with the Nemotron v3 Super model from within the container. +Below are example commands to launch the TensorRT LLM server from within the container. Make sure `EXTRA_LLM_API_FILE` points to the config that matches your model (see above). + +**Nemotron v3 Ultra — NVFP4 model (recommended):** + +```shell +trtllm-serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --host 0.0.0.0 --port 8000 --reasoning_parser nemotron-v3 --tool_parser qwen3_coder --config ${EXTRA_LLM_API_FILE} +``` -**NVFP4 model (recommended, lowest memory footprint):** +**Nemotron v3 Super — NVFP4 model (recommended, lowest memory footprint):** ```shell trtllm-serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --host 0.0.0.0 --port 8000 --reasoning_parser nano-v3 --tool_parser qwen3_coder --config ${EXTRA_LLM_API_FILE} ``` +The `nemotron-v3` and `nano-v3` reasoning parsers are aliases for the same Nemotron v3 parser and are interchangeable. Reasoning can be toggled per request by passing `enable_thinking` through `chat_template_kwargs` in the request body, for example `{"chat_template_kwargs": {"enable_thinking": true}}` (set it to `false` to disable reasoning). After the server is set up, the client can now send prompt requests to the server and receive results. @@ -102,7 +167,7 @@ These options provide control over TensorRT LLM's behavior and are set within th #### `tensor_parallel_size` -* **Description:** Sets the **tensor-parallel size**. This should typically match the number of GPUs you intend to use for a single model instance. For BF16, use 4 or more GPUs on H100/H200. For NVFP4, 2 GPUs on H100/H200 may suffice. +* **Description:** Sets the **tensor-parallel size**. This should typically match the number of GPUs you intend to use for a single model instance. For Super BF16, use 4 or more GPUs on H100/H200; for Super NVFP4, 2 GPUs on H100/H200 may suffice. For Ultra NVFP4, use 4 GPUs (single node on B200). #### `moe_expert_parallel_size` @@ -158,11 +223,11 @@ curl -s -o /dev/null -w "Status: %{http_code}\n" "http://localhost:8000/health" When the `Status: 200` code is returned, the server is ready for queries. Note that the very first query may take longer due to initialization and compilation. -After the TensorRT LLM server is set up and shows Application startup complete, you can send requests to the server. +After the TensorRT LLM server is set up and shows Application startup complete, you can send requests to the server. The example below uses Nemotron v3 Ultra; replace the `model` field with the model you launched (for example `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4`). ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ - "model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", + "model": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "messages": [ { "role": "user", @@ -182,7 +247,7 @@ Here is an example response: "id": "chatcmpl-abc123def456", "object": "chat.completion", "created": 1759022940, - "model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", + "model": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "choices": [ { "index": 0, @@ -209,7 +274,7 @@ Here is an example response: * For performance issues, check GPU utilization with `nvidia-smi` while the server is running. * If the container fails to start, verify that the NVIDIA Container Toolkit is properly installed. * For connection issues, make sure the server port (`8000` in this guide) is not being used by another application. -* Nemotron v3 Super is a hybrid SSM/attention model with MoE — ensure you have sufficient GPU memory for the full 120B parameter weights even though only 12B parameters are active per token. +* Nemotron v3 is a hybrid SSM/attention model with MoE — ensure you have sufficient GPU memory for the full parameter weights even though only a fraction of parameters are active per token (12B for Super, 55B for Ultra). ## Benchmarking Performance @@ -220,14 +285,14 @@ cat <<'EOF' > bench.sh #!/usr/bin/env bash set -euo pipefail -# Adjust the model name based on which Nemotron v3 Super variant you're benchmarking -MODEL_NAME="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" +# Adjust the model name based on which Nemotron v3 variant you're benchmarking +MODEL_NAME="nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4" concurrency_list="1 2 4 8 16 32 64 128" multi_round=5 isl=1024 osl=1024 -result_dir=/tmp/nemotron_super_output +result_dir=/tmp/nemotron_v3_output for concurrency in ${concurrency_list}; do num_prompts=$((concurrency * multi_round)) diff --git a/docs/source/deployment-guide/index.rst b/docs/source/deployment-guide/index.rst index 9c30c72f8698..20788d0fc93c 100644 --- a/docs/source/deployment-guide/index.rst +++ b/docs/source/deployment-guide/index.rst @@ -28,7 +28,7 @@ The deployment guides below provide more detailed instructions for serving speci :maxdepth: 1 :name: Deployment Guides - deployment-guide-for-nemotron-3-super-on-trtllm.md + deployment-guide-for-nemotron-3-on-trtllm.md deployment-guide-for-deepseek-r1-on-trtllm.md deployment-guide-for-llama3.3-70b-on-trtllm.md deployment-guide-for-llama4-scout-on-trtllm.md diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 546dfc10288b..fbcd3b96cc92 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -36,7 +36,7 @@ The following is a table of supported models for the PyTorch backend: | `MixtralForCausalLM` | Mixtral | `mistralai/Mixtral-8x7B-v0.1` | | `MllamaForConditionalGeneration` | Llama 3.2 | `meta-llama/Llama-3.2-11B-Vision` | | `NemotronForCausalLM` | Nemotron-3, Nemotron-4, Minitron | `nvidia/Minitron-8B-Base` | -| `NemotronHForCausalLM` | Nemotron-3-Nano, Nemotron-3-Super | `nvidia/nvidia-nemotron-v3` | +| `NemotronHForCausalLM` | Nemotron-3-Nano, Nemotron-3-Super, Nemotron-3-Ultra | `nvidia/nvidia-nemotron-v3` | | `NemotronNASForCausalLM` | NemotronNAS | `nvidia/Llama-3_3-Nemotron-Super-49B-v1` | | `Olmo3ForCausalLM` [^5] | OLMo 3, OLMo 3.1 | `allenai/Olmo-3.1-32B-Instruct` | | `OpenELMForCausalLM` [^5] | OpenELM | `apple/OpenELM-270M-Instruct` | @@ -70,6 +70,7 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl | `Qwen3_5MoeForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Yes | N/A | Untested | Untested | | `Glm4MoeLiteForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Untested | N/A | Untested | Untested | | `NemotronHForCausalLM` (Super) | Yes | Yes | Untested | Untested | Yes | Yes | No | No | No | Yes | Yes | Untested | N/A | Untested | Untested | +| `NemotronHForCausalLM` (Ultra) | Yes | Yes | Untested | Yes | Yes | Yes | No | No | No | Yes | Yes | Yes | N/A | Untested | Untested | | `Gemma4ForConditionalGeneration` | Untested | Yes | Untested | No | Yes | No | No | No | No | Yes | Untested | No | Yes | Untested | Untested | | `Step3p7ForConditionalGeneration`| Yes | Yes | Yes | Untested | Untested | Yes | No | No | No | Yes | Untested | Untested | Yes | Untested | Untested | diff --git a/examples/configs/curated/lookup.yaml b/examples/configs/curated/lookup.yaml index 8ada2085a063..8a642a47b846 100644 --- a/examples/configs/curated/lookup.yaml +++ b/examples/configs/curated/lookup.yaml @@ -4,6 +4,11 @@ config_path: examples/configs/curated/nemotron-3-super-throughput.yaml scenario: Max Throughput gpu_compatibility: "B200, GB200" +- model: nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 + arch: NemotronHForCausalLM + config_path: examples/configs/curated/nemotron-3-ultra-throughput.yaml + scenario: Max Throughput + gpu_compatibility: "B200, B300, GB200, GB300, H100, H200" - model: Qwen/Qwen3-Next-80B-A3B-Thinking arch: Qwen3NextForCausalLM config_path: examples/configs/curated/qwen3-next.yaml diff --git a/examples/configs/curated/nemotron-3-ultra-throughput.yaml b/examples/configs/curated/nemotron-3-ultra-throughput.yaml new file mode 100644 index 000000000000..72dcc2fe888a --- /dev/null +++ b/examples/configs/curated/nemotron-3-ultra-throughput.yaml @@ -0,0 +1,19 @@ +max_batch_size: 256 +max_num_tokens: 2048 +tensor_parallel_size: 4 +moe_expert_parallel_size: 4 +trust_remote_code: true +enable_attention_dp: true +cuda_graph_config: + enable_padding: true + max_batch_size: 256 +kv_cache_config: + free_gpu_memory_fraction: 0.8 + enable_block_reuse: false + mamba_ssm_cache_dtype: float16 + mamba_ssm_philox_rounds: 5 + mamba_ssm_stochastic_rounding: true +moe_config: + backend: CUTEDSL +num_postprocess_workers: 4 +stream_interval: 10 diff --git a/examples/models/core/nemotron/README_nemotron_super_v3.md b/examples/models/core/nemotron/README_nemotron_super_v3.md index 0c9637a42256..e78992359c19 100644 --- a/examples/models/core/nemotron/README_nemotron_super_v3.md +++ b/examples/models/core/nemotron/README_nemotron_super_v3.md @@ -200,4 +200,4 @@ Key options: # Notes * prefix-cache is not supported for Nemotron Super V3 yet, so please set `enable_block_reuse: false` when launching a server. -* For detailed deployment instructions, see the [deployment guide](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/deployment-guide/deployment-guide-for-nemotron-3-super-on-trtllm.md). +* For detailed deployment instructions, see the [deployment guide](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/deployment-guide/deployment-guide-for-nemotron-3-on-trtllm.md). diff --git a/scripts/generate_config_table.py b/scripts/generate_config_table.py index 7f21932bf868..af422d40a74d 100644 --- a/scripts/generate_config_table.py +++ b/scripts/generate_config_table.py @@ -42,6 +42,10 @@ "display_name": "Nemotron v3 Super (NVFP4)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", }, + "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4": { + "display_name": "Nemotron v3 Ultra (NVFP4)", + "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", + }, "deepseek-ai/DeepSeek-R1-0528": { "display_name": "DeepSeek-R1", "url": "https://huggingface.co/deepseek-ai/DeepSeek-R1-0528", From dcd4e903e34ce551dbb4d3f072dc2cbce6b6b915 Mon Sep 17 00:00:00 2001 From: Marina Yanovskiy <256585945+marinayanov@users.noreply.github.com> Date: Sun, 7 Jun 2026 19:05:58 +0300 Subject: [PATCH 025/119] [#10710][feat] Make explicit CLI flags take precedence over --config / --extra_llm_api_options YAML (#14812) Signed-off-by: marinayanov <256585945+marinayanov@users.noreply.github.com> --- .../commands/trtllm-serve/trtllm-serve.rst | 2 +- docs/source/release-notes.md | 2 + tensorrt_llm/bench/benchmark/__init__.py | 28 +- tensorrt_llm/bench/benchmark/low_latency.py | 10 +- tensorrt_llm/bench/benchmark/throughput.py | 8 +- .../bench/dataclasses/configuration.py | 7 +- tensorrt_llm/commands/eval.py | 31 +- tensorrt_llm/commands/serve.py | 111 ++++-- tensorrt_llm/commands/utils.py | 27 ++ tensorrt_llm/llmapi/llm_args.py | 113 +++++- .../_test_trtllm_serve_duplicated_args.py | 8 +- tests/unittest/llmapi/test_llm_args.py | 372 ++++++++++++++++-- 12 files changed, 604 insertions(+), 115 deletions(-) diff --git a/docs/source/commands/trtllm-serve/trtllm-serve.rst b/docs/source/commands/trtllm-serve/trtllm-serve.rst index d4b335472a65..4008b234b23e 100644 --- a/docs/source/commands/trtllm-serve/trtllm-serve.rst +++ b/docs/source/commands/trtllm-serve/trtllm-serve.rst @@ -317,7 +317,7 @@ Example output: Configuring with YAML Files ---------------------------- -You can configure various options of ``trtllm-serve`` using YAML files by setting the ``--config`` option to the path of a YAML file. The arguments in the file override the corresponding command line arguments. +You can configure various options of ``trtllm-serve`` using YAML files by setting the ``--config`` option to the path of a YAML file. Explicit CLI flags take precedence over values in the YAML; un-set CLI flags fall back to the YAML. .. include:: ../../_includes/note_sections.rst :start-after: .. start-note-config-flag-alias diff --git a/docs/source/release-notes.md b/docs/source/release-notes.md index b5aee8cf0c0e..9f0ed17d4f76 100644 --- a/docs/source/release-notes.md +++ b/docs/source/release-notes.md @@ -26,6 +26,8 @@ All published functionality in the Release Notes has been fully tested and verif ### API Changes +- `trtllm-serve`, `trtllm-eval`, `trtllm-bench`: explicit CLI flags now take precedence over values in `--config` / `--extra_llm_api_options` YAML files (was: YAML overrode CLI). Un-set CLI flags continue to fall back to the YAML, then to model-specific and built-in defaults. + ### Fixed Issues ### Known Issues diff --git a/tensorrt_llm/bench/benchmark/__init__.py b/tensorrt_llm/bench/benchmark/__init__.py index 83fd3e066614..53fa8b337d66 100644 --- a/tensorrt_llm/bench/benchmark/__init__.py +++ b/tensorrt_llm/bench/benchmark/__init__.py @@ -1,6 +1,6 @@ import json from pathlib import Path -from typing import Callable, Dict, Optional +from typing import Callable, Dict, Optional, Set from pydantic import AliasChoices, BaseModel, Field @@ -10,8 +10,34 @@ from tensorrt_llm.bench.build.build import get_model_config from tensorrt_llm.bench.dataclasses.configuration import RuntimeConfig from tensorrt_llm.bench.dataclasses.general import BenchmarkEnvironment +from tensorrt_llm.commands.utils import \ + collect_explicit_cli_keys as _collect_explicit_cli_keys from tensorrt_llm.logger import logger +# Map trtllm-bench Click parameter names to the LlmArgs field name (or +# merge-function CLI scalar name) used by `update_llm_args_with_extra_options`. +# `--beam_width` is intentionally absent: it feeds SamplingParams, not +# llm_args, so it must not participate in the CLI-vs-YAML precedence. +_BENCH_CLICK_TO_LLM_ARG = { + "tp": "tensor_parallel_size", + "pp": "pipeline_parallel_size", + "ep": "moe_expert_parallel_size", + "cluster_size": "moe_cluster_parallel_size", + "kv_cache_free_gpu_mem_fraction": "free_gpu_memory_fraction", + "enable_chunked_context": "enable_chunked_prefill", +} + + +def collect_explicit_cli_keys() -> Set[str]: + """Return CLI flag names the user typed, translated to LlmArgs field names. + + Thin trtllm-bench-specific wrapper around the shared + `tensorrt_llm.commands.utils.collect_explicit_cli_keys` helper. + """ + return _collect_explicit_cli_keys(exclude=("extra_llm_api_options", + "config"), + translate=_BENCH_CLICK_TO_LLM_ARG) + class GeneralExecSettings(BaseModel): model_config = { diff --git a/tensorrt_llm/bench/benchmark/low_latency.py b/tensorrt_llm/bench/benchmark/low_latency.py index c2c66d9e37d8..127b1bcdebe9 100644 --- a/tensorrt_llm/bench/benchmark/low_latency.py +++ b/tensorrt_llm/bench/benchmark/low_latency.py @@ -24,7 +24,8 @@ optgroup) from huggingface_hub import snapshot_download -from tensorrt_llm.bench.benchmark import (generate_json_report, +from tensorrt_llm.bench.benchmark import (collect_explicit_cli_keys, + generate_json_report, get_general_cli_options, get_llm) from tensorrt_llm.bench.benchmark.utils.asynchronous import async_benchmark from tensorrt_llm.bench.benchmark.utils.general import generate_warmup_dataset @@ -65,9 +66,9 @@ "extra_llm_api_options", type=str, default=None, - help= - "Path to a YAML file that overwrites the parameters specified by trtllm-bench. " - "Can be specified as either --config or --extra_llm_api_options.") + help="Path to a YAML configuration file. Explicit CLI flags take precedence " + "over values in this file. Can be specified as either --config or " + "--extra_llm_api_options.") @optgroup.option( "--backend", type=click.Choice(ALL_SUPPORTED_BACKENDS), @@ -297,6 +298,7 @@ def latency_command( exec_settings["performance_options"]["multi_block_mode"] = True exec_settings["extra_llm_api_options"] = params.get("extra_llm_api_options") + exec_settings["explicit_cli_keys"] = collect_explicit_cli_keys() # Decoding Options if medusa_choices is not None: diff --git a/tensorrt_llm/bench/benchmark/throughput.py b/tensorrt_llm/bench/benchmark/throughput.py index 2e9df91deae1..4e2fd04b9a13 100755 --- a/tensorrt_llm/bench/benchmark/throughput.py +++ b/tensorrt_llm/bench/benchmark/throughput.py @@ -25,6 +25,7 @@ from huggingface_hub import snapshot_download from tensorrt_llm.bench.benchmark import (GeneralExecSettings, + collect_explicit_cli_keys, generate_json_report, get_general_cli_options, get_llm) from tensorrt_llm.bench.benchmark.utils.asynchronous import async_benchmark @@ -81,9 +82,9 @@ "extra_llm_api_options", type=str, default=None, - help= - "Path to a YAML file that overwrites the parameters specified by trtllm-bench. " - "Can be specified as either --config or --extra_llm_api_options.") + help="Path to a YAML configuration file. Explicit CLI flags take precedence " + "over values in this file. Can be specified as either --config or " + "--extra_llm_api_options.") @optgroup.option("--sampler_options", type=click.Path(exists=True, readable=True, @@ -437,6 +438,7 @@ def throughput_command( # LlmArgs exec_settings["extra_llm_api_options"] = params.pop("extra_llm_api_options") exec_settings["iteration_log"] = options.iteration_log + exec_settings["explicit_cli_keys"] = collect_explicit_cli_keys() # Construct the runtime configuration dataclass. runtime_config = RuntimeConfig(**exec_settings) diff --git a/tensorrt_llm/bench/dataclasses/configuration.py b/tensorrt_llm/bench/dataclasses/configuration.py index 45d27b557677..d88bd2b722fe 100755 --- a/tensorrt_llm/bench/dataclasses/configuration.py +++ b/tensorrt_llm/bench/dataclasses/configuration.py @@ -2,7 +2,7 @@ from dataclasses import dataclass from pathlib import Path -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Any, Dict, List, Literal, Optional, Set, Union from pydantic import (BaseModel, Field, PositiveFloat, field_validator, model_validator) @@ -36,6 +36,7 @@ class RuntimeConfig(BaseModel): backend: Literal["pytorch", "_autodeploy", None] = None extra_llm_api_options: Optional[str] = None iteration_log: Optional[Path] = None + explicit_cli_keys: Optional[Set[str]] = None def get_llm_args(self) -> Dict: model = self.engine_dir or self.model_path or self.model @@ -86,7 +87,9 @@ def get_llm_args(self) -> Dict: llm_args["kv_cache_config"] = backend_cache_config | kv_cache_config updated_llm_args = update_llm_args_with_extra_options( - llm_args, self.extra_llm_api_options) + llm_args, + self.extra_llm_api_options, + explicit_cli_keys=self.explicit_cli_keys) if self.backend == "pytorch": cuda_graph_config = updated_llm_args.pop( diff --git a/tensorrt_llm/commands/eval.py b/tensorrt_llm/commands/eval.py index 024821737ce8..82553c2545f7 100644 --- a/tensorrt_llm/commands/eval.py +++ b/tensorrt_llm/commands/eval.py @@ -27,6 +27,17 @@ from ..llmapi.llm_utils import update_llm_args_with_extra_options from ..logger import logger, severity_map from ..usage import config as _telemetry_config +from .utils import collect_explicit_cli_keys + +# Map Click parameter names to the LlmArgs field name (or merge-function CLI +# scalar name) used by `update_llm_args_with_extra_options`. +_CLICK_TO_LLM_ARG = { + "tp_size": "tensor_parallel_size", + "pp_size": "pipeline_parallel_size", + "ep_size": "moe_expert_parallel_size", + "kv_cache_free_gpu_memory_fraction": "free_gpu_memory_fraction", + "disable_kv_cache_reuse": "enable_block_reuse", +} @click.group() @@ -112,8 +123,9 @@ "extra_llm_api_options", type=str, default=None, - help="Path to a YAML file that overwrites the parameters. " - "Can be specified as either --config or --extra_llm_api_options.") + help="Path to a YAML configuration file. Explicit CLI flags " + "take precedence over values in this file. Can be specified " + "as either --config or --extra_llm_api_options.") @click.option("--disable_kv_cache_reuse", is_flag=True, default=False, @@ -132,6 +144,10 @@ def main(ctx, model: str, tokenizer: Optional[str], telemetry: bool): logger.set_level(log_level) + explicit_cli_keys = collect_explicit_cli_keys( + exclude=("extra_llm_api_options", "config"), + translate=_CLICK_TO_LLM_ARG) + kv_cache_config = KvCacheConfig( free_gpu_memory_fraction=kv_cache_free_gpu_memory_fraction, enable_block_reuse=not disable_kv_cache_reuse) @@ -182,13 +198,10 @@ def main(ctx, model: str, tokenizer: Optional[str], param_hint="backend") if extra_llm_api_options is not None: - llm_args = update_llm_args_with_extra_options(llm_args, - extra_llm_api_options) - - # CLI --no-telemetry always wins over YAML config - if not telemetry: - llm_args["telemetry_config"] = llm_args["telemetry_config"].model_copy( - update={"disabled": True}) + llm_args = update_llm_args_with_extra_options( + llm_args, + extra_llm_api_options, + explicit_cli_keys=explicit_cli_keys) profiler.start("trtllm init") llm = llm_cls(**llm_args) diff --git a/tensorrt_llm/commands/serve.py b/tensorrt_llm/commands/serve.py index 5608872da2dd..629dfa2d6a5a 100644 --- a/tensorrt_llm/commands/serve.py +++ b/tensorrt_llm/commands/serve.py @@ -11,7 +11,7 @@ import sys import uuid from pathlib import Path -from typing import Any, Dict, Literal, Mapping, Optional, Sequence +from typing import Any, Dict, Literal, Mapping, Optional, Sequence, Set import click import torch @@ -23,7 +23,8 @@ from tensorrt_llm import MultimodalEncoder from tensorrt_llm._tensorrt_engine import LLM from tensorrt_llm._utils import mpi_rank -from tensorrt_llm.commands.utils import get_is_diffusion_model +from tensorrt_llm.commands.utils import (collect_explicit_cli_keys, + get_is_diffusion_model) from tensorrt_llm.executor.utils import LlmLauncherEnvs from tensorrt_llm.inputs.multimodal import MultimodalServerConfig from tensorrt_llm.llmapi import (BuildConfig, CapacitySchedulerPolicy, @@ -119,13 +120,15 @@ def _signal_handler_cleanup_child(signum, frame): sys.exit(128 + signum) -def is_non_default_or_required(param_name, value, backend): +def is_non_default_or_required(param_name, value, backend, explicit_cli_keys): """ Check if a parameter should be explicitly included in llm_args. Returns True if parameter is either: 1. Always required (core params that must be present), OR - 2. Different from its default value in the backend's LlmArgs class + 2. Set explicitly on the CLI (its name or one of its constructor + scalars is in `explicit_cli_keys`), OR + 3. Different from its default value in the backend's LlmArgs class """ always_include = { "model", "backend", "tokenizer", "custom_tokenizer", @@ -138,6 +141,20 @@ def is_non_default_or_required(param_name, value, backend): if value is None: return False + if param_name in explicit_cli_keys: + return True + + # LlmArgs fields built from CLI scalars whose names differ from the field + # name (e.g. `--free_gpu_memory_fraction` constructs `kv_cache_config`). + cli_derived_fields = { + "kv_cache_config": ("free_gpu_memory_fraction", "kv_cache_dtype"), + "build_config": + ("max_batch_size", "max_num_tokens", "max_beam_width", "max_seq_len"), + } + if any(s in explicit_cli_keys + for s in cli_derived_fields.get(param_name, ())): + return True + if backend == "tensorrt": llm_args_class = TrtLlmArgs elif backend == "_autodeploy": @@ -192,8 +209,11 @@ def get_llm_args( telemetry: bool = True, agent_percentage: float = 0.0, agent_types: Optional[str] = None, + explicit_cli_keys: Optional[Set[str]] = None, **llm_args_extra_dict: Any): + explicit_cli_keys = explicit_cli_keys or set() + if gpus_per_node is None: gpus_per_node = device_count() if gpus_per_node == 0: @@ -209,9 +229,6 @@ def get_llm_args( raise ValueError(f"Invalid cp_type: {cp_config['cp_type']}. " \ f"Must be one of: {', '.join([t.name for t in CpType])}") - kv_cache_default_fraction = KvCacheConfig.model_fields[ - 'free_gpu_memory_fraction'].default - cli_maybe_overrides = { "model": model, @@ -225,8 +242,7 @@ def get_llm_args( tokenizer or model, "kv_cache_config": KvCacheConfig(free_gpu_memory_fraction=free_gpu_memory_fraction, - dtype=kv_cache_dtype) if free_gpu_memory_fraction - != kv_cache_default_fraction or kv_cache_dtype != "auto" else None, + dtype=kv_cache_dtype), "cp_config": cp_config, "build_config": @@ -291,12 +307,35 @@ def get_llm_args( llm_args = { param: value for param, value in cli_maybe_overrides.items() - if is_non_default_or_required(param, value, backend) + if is_non_default_or_required(param, value, backend, explicit_cli_keys) } return llm_args, llm_args_extra_dict +def _build_llm_args_from_disagg_server_cfg(other_args: Dict) -> Dict: + """Construct llm_args from a disaggregated server config's `other_args`. + + `other_args` is a single source — there is no separate CLI / YAML + distinction here. Every key is user-set, so we pass all keys as + `explicit_cli_keys` to `get_llm_args` to bypass the value-based filter + that would otherwise drop fields equal to their LlmArgs class defaults + (e.g. `tensor_parallel_size: 1`). + + Do NOT pass `explicit_cli_keys` to `update_llm_args_with_extra_dict`: + `llm_args_extra_dict` here is just the catch-all for kwargs that didn't + match `get_llm_args`'s named signature (e.g. `quant_config`, + `lora_config`, `pytorch_backend_config`) — it isn't a separate YAML + being overridden. Passing the explicit set would trigger the merge + function's "drop YAML keys claimed by explicit CLI" filter and + silently lose those kwargs. + """ + disagg_explicit_keys = set(other_args) + llm_args, llm_args_extra_dict = get_llm_args( + **other_args, explicit_cli_keys=disagg_explicit_keys) + return update_llm_args_with_extra_dict(llm_args, llm_args_extra_dict) + + def launch_server( host: str, port: int, @@ -717,9 +756,9 @@ def convert(self, value: Any, param: Optional["click.Parameter"], type=str, default=None, help=help_info_with_stability_tag( - "Path to a YAML file that overwrites the parameters specified by trtllm-serve. " - "Can be specified as either --config or --extra_llm_api_options.", - "prototype")) + "Path to a YAML configuration file. Explicit CLI flags take precedence " + "over values in this file. Can be specified as either --config or " + "--extra_llm_api_options.", "prototype")) @click.option( "--reasoning_parser", type=click.Choice(["auto"] + list(ReasoningParserFactory.keys())), @@ -933,6 +972,9 @@ def serve( f"Failed to import custom module from {custom_module_dir}: {e}") raise e + explicit_cli_keys = collect_explicit_cli_keys( + exclude=("extra_llm_api_options", "config")) + def _serve_llm(): nonlocal server_role llm_args, _ = get_llm_args( @@ -964,19 +1006,15 @@ def _serve_llm(): video_pruning_rate=video_pruning_rate, telemetry=telemetry, agent_percentage=agent_percentage, - agent_types=agent_types) + agent_types=agent_types, + explicit_cli_keys=explicit_cli_keys) llm_args_extra_dict = {} if extra_llm_api_options is not None: with open(extra_llm_api_options, 'r') as f: llm_args_extra_dict = yaml.safe_load(f) - llm_args = update_llm_args_with_extra_dict(llm_args, - llm_args_extra_dict) - - # CLI --no-telemetry always wins over YAML config - if not telemetry: - llm_args["telemetry_config"] = llm_args[ - "telemetry_config"].model_copy(update={"disabled": True}) + llm_args = update_llm_args_with_extra_dict( + llm_args, llm_args_extra_dict, explicit_cli_keys=explicit_cli_keys) metadata_server_cfg = parse_metadata_server_config_file( metadata_server_config_file) @@ -1100,9 +1138,8 @@ def _serve_visual_gen(): "extra_encoder_options", type=str, default=None, - help= - "Path to a YAML file that overwrites the parameters specified by trtllm-serve. " - "Prefer --config over --extra_encoder_options.") + help="Path to a YAML configuration file. Explicit CLI flags take precedence " + "over values in this file. Prefer --config over --extra_encoder_options.") @click.option("--hf_revision", "--revision", "revision", @@ -1143,6 +1180,9 @@ def serve_encoder(model: str, host: str, port: int, log_level: str, logger.warning( "--extra_encoder_options is deprecated, use --config instead.") + explicit_cli_keys = collect_explicit_cli_keys( + exclude=("extra_encoder_options", "config")) + llm_args, _ = get_llm_args( model=model, max_batch_size=max_batch_size, @@ -1152,19 +1192,15 @@ def serve_encoder(model: str, host: str, port: int, log_level: str, revision=revision, free_gpu_memory_fraction=free_gpu_memory_fraction, tensor_parallel_size=tensor_parallel_size, - telemetry=telemetry) + telemetry=telemetry, + explicit_cli_keys=explicit_cli_keys) encoder_args_extra_dict = {} if extra_encoder_options is not None: with open(extra_encoder_options, 'r') as f: encoder_args_extra_dict = yaml.safe_load(f) - encoder_args = update_llm_args_with_extra_dict(llm_args, - encoder_args_extra_dict) - - # CLI --no-telemetry always wins over YAML config - if not telemetry: - encoder_args["telemetry_config"] = encoder_args[ - "telemetry_config"].model_copy(update={"disabled": True}) + encoder_args = update_llm_args_with_extra_dict( + llm_args, encoder_args_extra_dict, explicit_cli_keys=explicit_cli_keys) metadata_server_cfg = parse_metadata_server_config_file( metadata_server_config_file) @@ -1324,9 +1360,7 @@ def disaggregated_mpi_worker(config_file: Optional[str], log_level: str): DisaggLauncherEnvs.TLLM_DISAGG_INSTANCE_IDX) server_cfg = disagg_cfg.server_configs[int(instance_idx)] - llm_args, llm_args_extra_dict = get_llm_args(**server_cfg.other_args) - llm_args = update_llm_args_with_extra_dict(llm_args, - llm_args_extra_dict) + llm_args = _build_llm_args_from_disagg_server_cfg(server_cfg.other_args) # Ignore the non-LLM args llm_args.pop("router", None) @@ -1349,9 +1383,10 @@ def disaggregated_mpi_worker(config_file: Optional[str], log_level: str): instance_idx) server_cfg = disagg_cfg.server_configs[instance_idx] - llm_args, llm_args_extra_dict = get_llm_args(**server_cfg.other_args) - llm_args = update_llm_args_with_extra_dict(llm_args, - llm_args_extra_dict) + # NOTE: the resulting llm_args is currently unused; _launch_disaggregated_leader + # does not take it. Keeping the call symmetric with the client branch above + # so any future use of llm_args here behaves the same way. + _build_llm_args_from_disagg_server_cfg(server_cfg.other_args) _launch_disaggregated_leader(sub_comm, instance_idx, config_file, log_level) diff --git a/tensorrt_llm/commands/utils.py b/tensorrt_llm/commands/utils.py index 46a87bbc2da1..b968e30cbe3a 100644 --- a/tensorrt_llm/commands/utils.py +++ b/tensorrt_llm/commands/utils.py @@ -2,6 +2,10 @@ import json import logging import os +from typing import Iterable, Mapping, Optional, Set + +import click +from click.core import ParameterSource from tensorrt_llm._torch.visual_gen.config import ParallelConfig from tensorrt_llm.llmapi.utils import download_hf_partial @@ -134,6 +138,29 @@ def get_visual_gen_model_type(model_path: str): ) +def collect_explicit_cli_keys( + *, + exclude: Iterable[str] = (), + translate: Optional[Mapping[str, str]] = None, +) -> Set[str]: + """Return CLI flag names the user typed on the command line. + + Reads the active Click context and selects parameters whose source is + `ParameterSource.COMMANDLINE`. `exclude` removes meta flags that aren't + config keys (e.g. `extra_llm_api_options`, `config`). `translate` maps + each Click parameter name to the LlmArgs field name (or merge-function + CLI scalar name) used by `update_llm_args_with_extra_options`; entries + not present in the map pass through unchanged. + """ + ctx = click.get_current_context() + explicit = { + name for name in ctx.params if ctx.get_parameter_source(name) == ParameterSource.COMMANDLINE + } - set(exclude) + if translate is None: + return explicit + return {translate.get(name, name) for name in explicit} + + def get_visual_gen_num_gpus(diffusion_config: dict) -> int: """Compute the number of GPUs from a visual_gen config. diff --git a/tensorrt_llm/llmapi/llm_args.py b/tensorrt_llm/llmapi/llm_args.py index 0bac2d7ee253..3ad7c5dc7c28 100644 --- a/tensorrt_llm/llmapi/llm_args.py +++ b/tensorrt_llm/llmapi/llm_args.py @@ -4803,23 +4803,56 @@ def get_executor_config( def update_llm_args_with_extra_dict( llm_args: Dict, llm_args_dict: Dict, - extra_llm_api_options: Optional[str] = None) -> Dict: + extra_llm_api_options: Optional[str] = None, + explicit_cli_keys: Optional[Set[str]] = None) -> Dict: + """Merge YAML overrides into a CLI-derived llm_args dict. + + If `explicit_cli_keys` is provided, those CLI flag names override any + conflicting YAML values. CLI flags whose name does not match the + LlmArgs field name (e.g. `--free_gpu_memory_fraction` constructs + `kv_cache_config.free_gpu_memory_fraction`) are mapped to the nested + field they target. + + If `explicit_cli_keys` is None, YAML wins on conflicts. + """ + # CLI scalar -> nested KvCacheConfig field. Callers add the CLI scalar + # name to `explicit_cli_keys` to make it win over YAML's same-named + # field inside `kv_cache_config:`. + cli_to_kv_cache_field = { + "free_gpu_memory_fraction": "free_gpu_memory_fraction", + "kv_cache_dtype": "dtype", + "enable_block_reuse": "enable_block_reuse", + } + # Scalars that live both at the top level of LlmArgs and inside + # `build_config`. The build_config patch propagates the winning source + # to the nested location. + build_config_dual_loc_keys = ( + "max_batch_size", + "max_num_tokens", + "max_beam_width", + "max_seq_len", + ) + + explicit_cli_keys = explicit_cli_keys or set() if 'hf_revision' in llm_args_dict: llm_args_dict.setdefault('revision', llm_args_dict.pop('hf_revision')) # Deep merge kv_cache_config to prevent partial YAML kv_cache_config from replacing the complete kv_cache_config if 'kv_cache_config' in llm_args and 'kv_cache_config' in llm_args_dict: - # Convert KvCacheConfig object to dict if necessary base_kv_config = llm_args['kv_cache_config'] if isinstance(base_kv_config, KvCacheConfig): base_kv_config = base_kv_config.model_dump(exclude_unset=True) - llm_args_dict['kv_cache_config'] = base_kv_config | llm_args_dict[ - 'kv_cache_config'] + merged = base_kv_config | llm_args_dict['kv_cache_config'] + for cli_name, kv_field in cli_to_kv_cache_field.items(): + if cli_name in explicit_cli_keys and kv_field in base_kv_config: + merged[kv_field] = base_kv_config[kv_field] + llm_args_dict['kv_cache_config'] = merged # Deep merge telemetry_config: YAML can override fields like `disabled`, # but `usage_context` is determined by the CLI entry point and must not - # be overridden by user config. + # be overridden by user config. When `--telemetry/--no-telemetry` was + # typed explicitly, CLI's `disabled` wins over YAML. if 'telemetry_config' in llm_args and 'telemetry_config' in llm_args_dict: yaml_tc = llm_args_dict['telemetry_config'] if not isinstance(yaml_tc, (dict, TelemetryConfig)): @@ -4833,7 +4866,27 @@ def update_llm_args_with_extra_dict( if isinstance(yaml_tc, TelemetryConfig): yaml_tc = yaml_tc.model_dump(exclude_unset=True) yaml_tc.pop('usage_context', None) - llm_args_dict['telemetry_config'] = base_tc | yaml_tc + merged = base_tc | yaml_tc + if "telemetry" in explicit_cli_keys and 'disabled' in base_tc: + merged['disabled'] = base_tc['disabled'] + llm_args_dict['telemetry_config'] = merged + + # Drop YAML keys claimed by explicit CLI flags so the outer merge below + # cannot overwrite them. Warn only when the CLI value actually differs from + # the YAML value, so users who relied on the previous "YAML wins" behavior + # are notified that CLI now takes precedence. + if explicit_cli_keys: + overridden = sorted( + k for k in llm_args_dict + if k in explicit_cli_keys and llm_args.get(k) != llm_args_dict[k]) + if overridden: + logger.warning( + f"Explicit CLI flag(s) {overridden} override the value(s) set " + f"in the YAML config; CLI takes precedence.") + llm_args_dict = { + k: v + for k, v in llm_args_dict.items() if k not in explicit_cli_keys + } field_mapping = { "quant_config": QuantConfig, @@ -4853,8 +4906,9 @@ def update_llm_args_with_extra_dict( for field_name, field_type in field_mapping.items(): if field_name in llm_args_dict: llm_args_dict[field_name] = field_type(**llm_args_dict[field_name]) - extra_llm_str = f"because it's specified in {extra_llm_api_options}" if extra_llm_api_options else "" - logger.warning(f"Overriding {field_name} {extra_llm_str}") + if field_name in llm_args: + extra_llm_str = f" because it's specified in {extra_llm_api_options}" if extra_llm_api_options else "" + logger.info(f"YAML overrides {field_name}{extra_llm_str}") llm_args = llm_args | llm_args_dict @@ -4864,28 +4918,45 @@ def update_llm_args_with_extra_dict( if isinstance(llm_args["build_config"], dict): llm_args["build_config"] = BuildConfig(**llm_args["build_config"]) - for key in [ - "max_batch_size", - "max_num_tokens", - "max_beam_width", - "max_seq_len", - ]: - if key in llm_args_dict: + # Propagate dual-location scalars into build_config: explicit CLI flag + # wins; otherwise YAML's top-level scalar; otherwise leave alone. Warn + # only when the explicit CLI value actually differs from the YAML + # build_config value being replaced (a genuine override). + for key in build_config_dual_loc_keys: + if key in explicit_cli_keys and key in llm_args: + # Warn only on a genuine override of a YAML build_config value; + # otherwise just record where the value came from. + if getattr(llm_args["build_config"], key) != llm_args[key]: + logger.warning( + f"Explicit CLI flag --{key}={llm_args[key]} overrides " + f"the value set in the YAML build_config; CLI takes " + f"precedence.") + else: + logger.info( + f"build_config.{key} set to {llm_args[key]} from explicit CLI flag" + ) + setattr(llm_args["build_config"], key, llm_args[key]) + elif key in llm_args_dict: + setattr(llm_args["build_config"], key, llm_args_dict[key]) logger.info( - f"Overriding {key} from build_config to {llm_args_dict[key]}" + f"build_config.{key} set to {llm_args_dict[key]} from YAML top-level scalar" ) - setattr(llm_args["build_config"], key, llm_args_dict[key]) return llm_args -def update_llm_args_with_extra_options(llm_args: Dict, - extra_llm_api_options: str) -> Dict: +def update_llm_args_with_extra_options( + llm_args: Dict, + extra_llm_api_options: str, + explicit_cli_keys: Optional[Set[str]] = None) -> Dict: if extra_llm_api_options is not None: with open(extra_llm_api_options, 'r') as f: llm_args_dict = yaml.safe_load(f) - llm_args = update_llm_args_with_extra_dict(llm_args, llm_args_dict, - extra_llm_api_options) + llm_args = update_llm_args_with_extra_dict( + llm_args, + llm_args_dict, + extra_llm_api_options, + explicit_cli_keys=explicit_cli_keys) return llm_args diff --git a/tests/unittest/llmapi/apps/_test_trtllm_serve_duplicated_args.py b/tests/unittest/llmapi/apps/_test_trtllm_serve_duplicated_args.py index 32c81948d88e..d271cda11a9f 100644 --- a/tests/unittest/llmapi/apps/_test_trtllm_serve_duplicated_args.py +++ b/tests/unittest/llmapi/apps/_test_trtllm_serve_duplicated_args.py @@ -22,8 +22,10 @@ def temp_extra_llm_api_options_file(): temp_dir = tempfile.gettempdir() temp_file_path = os.path.join(temp_dir, "extra_llm_api_options.yaml") try: + # YAML attempts to override tensor_parallel_size; the CLI's --tp_size 1 + # must win. max_num_tokens is YAML-only and should still be applied. extra_llm_api_options_dict = { - "tensor_parallel_size": 1, + "tensor_parallel_size": 99, "max_num_tokens": 16384, } @@ -45,8 +47,10 @@ def example_root(): @pytest.fixture(scope="module") def server(model_name: str, temp_extra_llm_api_options_file: str): model_path = get_model_path(model_name) + # If YAML's tensor_parallel_size: 99 won over the CLI, the server would + # fail to start; the server starting at TP=1 is the assertion. args = [ - "--tp_size", "99", "--extra_llm_api_options", + "--tp_size", "1", "--extra_llm_api_options", temp_extra_llm_api_options_file ] with RemoteOpenAIServer(model_path, port=8000, diff --git a/tests/unittest/llmapi/test_llm_args.py b/tests/unittest/llmapi/test_llm_args.py index 53fa03306f96..6ba201ad4805 100644 --- a/tests/unittest/llmapi/test_llm_args.py +++ b/tests/unittest/llmapi/test_llm_args.py @@ -618,7 +618,14 @@ def check_nested_dict_equality(dict1, dict2, path=""): class TestTelemetryConfigPrecedence: - """Test that telemetry config follows: default < YAML < CLI precedence.""" + """Telemetry-config precedence in the merge helper. + + Two modes are exercised: + - `explicit_cli_keys is None` (legacy / programmatic): YAML wins on + conflicts; `usage_context` carve-out still applies. + - `explicit_cli_keys` provided (CLI mode): explicit keys win on + conflicts; see `TestExplicitCliKeysPrecedence` for that path. + """ def test_default_telemetry_config_preserved_when_no_yaml(self): """Default telemetry_config survives YAML merge when YAML has none.""" @@ -679,8 +686,15 @@ def test_yaml_cannot_override_usage_context(self): assert tc.usage_context == UsageContext.CLI_SERVE assert tc.disabled is True - def test_cli_disabled_overrides_yaml_enabled(self): - """CLI --telemetry-disabled wins over YAML disabled=false.""" + def test_cli_disabled_overrides_yaml_enabled_legacy_fixup(self): + """Legacy post-merge fixup pattern (preserved for back-compat). + + This exercises the pre-`explicit_cli_keys` flow where the CLI entry + point overrode `disabled` after the merge by hand. The CLI tools no + longer use this pattern (they pass `explicit_cli_keys={"telemetry"}` + instead — see `TestExplicitCliKeysPrecedence`), but third-party + callers may still build the merge this way. + """ from tensorrt_llm.usage.config import TelemetryConfig, UsageContext base = { "model": @@ -691,7 +705,6 @@ def test_cli_disabled_overrides_yaml_enabled(self): } yaml_dict = {"telemetry_config": {"disabled": False}} merged = update_llm_args_with_extra_dict(base, yaml_dict) - # Simulate CLI --no-telemetry (as done in eval.py / serve.py) telemetry = False if not telemetry: merged["telemetry_config"] = merged["telemetry_config"].model_copy( @@ -701,7 +714,7 @@ def test_cli_disabled_overrides_yaml_enabled(self): assert tc.usage_context == UsageContext.CLI_EVAL def test_yaml_disabled_respected_when_cli_not_set(self): - """When CLI doesn't set --no-telemetry, YAML disabled=true is kept.""" + """YAML disabled=true is honored when explicit_cli_keys is None.""" from tensorrt_llm.usage.config import TelemetryConfig, UsageContext base = { "model": @@ -712,11 +725,6 @@ def test_yaml_disabled_respected_when_cli_not_set(self): } yaml_dict = {"telemetry_config": {"disabled": True}} merged = update_llm_args_with_extra_dict(base, yaml_dict) - # CLI flag not set (--telemetry is default True) — no override - telemetry = True - if not telemetry: - merged["telemetry_config"] = merged["telemetry_config"].model_copy( - update={"disabled": True}) tc = merged["telemetry_config"] assert tc.disabled is True assert tc.usage_context == UsageContext.CLI_SERVE @@ -740,6 +748,270 @@ def test_yaml_null_telemetry_config_preserves_default(self, yaml_value): assert tc.disabled is False +class TestExplicitCliKeysPrecedence: + """`explicit_cli_keys` makes the CLI side win over YAML on conflicts.""" + + def test_explicit_cli_key_wins_over_yaml_scalar(self): + base = {"model": "dummy", "tensor_parallel_size": 4} + yaml_dict = {"tensor_parallel_size": 8} + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"tensor_parallel_size"}) + assert merged["tensor_parallel_size"] == 4 + + def test_non_explicit_value_loses_to_yaml_scalar(self): + # Backward-compat: when explicit_cli_keys is None, today's "YAML wins" + # behavior is preserved. + base = {"model": "dummy", "tensor_parallel_size": 4} + yaml_dict = {"tensor_parallel_size": 8} + merged = update_llm_args_with_extra_dict(base, yaml_dict) + assert merged["tensor_parallel_size"] == 8 + + def test_kv_cache_config_explicit_field_wins_yaml_siblings_preserved(self): + # CLI builds a KvCacheConfig from --free_gpu_memory_fraction; YAML + # provides a partial kv_cache_config with sibling fields that should + # survive the merge. + base = { + "model": "dummy", + "kv_cache_config": KvCacheConfig(free_gpu_memory_fraction=0.85), + } + yaml_dict = { + "kv_cache_config": { + "free_gpu_memory_fraction": 0.5, + "enable_block_reuse": False, + } + } + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"free_gpu_memory_fraction"}) + kv = merged["kv_cache_config"] + assert kv.free_gpu_memory_fraction == 0.85 + assert kv.enable_block_reuse is False + + def test_build_config_tier_cli_wins(self): + # Tier 1: explicit CLI scalar wins over both top-level YAML and nested. + base = { + "model": "dummy", + "max_batch_size": 64, + "build_config": BuildConfig(max_batch_size=64), + } + yaml_dict = { + "max_batch_size": 256, + "build_config": { + "max_batch_size": 300 + }, + } + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"max_batch_size"}) + assert merged["max_batch_size"] == 64 + assert merged["build_config"].max_batch_size == 64 + + def test_build_config_tier_yaml_top_level_wins(self): + # Tier 2: no explicit CLI, but YAML top-level scalar -> propagate to + # build_config (legacy behavior). + base = { + "model": "dummy", + "build_config": BuildConfig(max_batch_size=8), + } + yaml_dict = {"max_batch_size": 256} + merged = update_llm_args_with_extra_dict(base, yaml_dict) + assert merged["max_batch_size"] == 256 + assert merged["build_config"].max_batch_size == 256 + + def test_build_config_tier_yaml_nested_only_leaves_alone(self): + # Tier 3: no explicit CLI, no top-level YAML scalar; nested YAML + # build_config is imported by the outer merge. + base = { + "model": "dummy", + "build_config": BuildConfig(max_batch_size=8), + } + yaml_dict = {"build_config": {"max_batch_size": 256}} + merged = update_llm_args_with_extra_dict(base, yaml_dict) + assert merged["build_config"].max_batch_size == 256 + + def test_telemetry_explicit_disabled_wins_over_yaml(self): + from tensorrt_llm.usage.config import TelemetryConfig, UsageContext + base = { + "model": + "dummy", + "telemetry_config": + TelemetryConfig(disabled=True, + usage_context=UsageContext.CLI_SERVE), + } + yaml_dict = {"telemetry_config": {"disabled": False}} + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"telemetry"}) + assert merged["telemetry_config"].disabled is True + + def test_kv_cache_dtype_explicit_wins_over_yaml(self): + # Mirrors the kv_cache_config tier-2 path for the second mapped CLI + # scalar (`--kv_cache_dtype` -> `kv_cache_config.dtype`). + base = { + "model": "dummy", + "kv_cache_config": KvCacheConfig(dtype="fp8"), + } + yaml_dict = {"kv_cache_config": {"dtype": "auto"}} + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"kv_cache_dtype"}) + assert merged["kv_cache_config"].dtype == "fp8" + + def test_enable_block_reuse_explicit_wins_over_yaml(self): + # Mirrors the kv_cache_config tier-2 path for `--disable_kv_cache_reuse`, + # which translates to `enable_block_reuse` in explicit_cli_keys. + base = { + "model": "dummy", + "kv_cache_config": KvCacheConfig(enable_block_reuse=False), + } + yaml_dict = {"kv_cache_config": {"enable_block_reuse": True}} + merged = update_llm_args_with_extra_dict( + base, yaml_dict, explicit_cli_keys={"enable_block_reuse"}) + assert merged["kv_cache_config"].enable_block_reuse is False + + +class TestEvalTranslationMap: + """eval's _CLICK_TO_LLM_ARG via the shared helper.""" + + def _collect(self, click_param_names): + """Simulate a Click ctx with the given params explicitly set.""" + import click as _click + + from tensorrt_llm.commands import eval as eval_mod + from tensorrt_llm.commands.utils import collect_explicit_cli_keys + + class _FakeCtx: + params = {name: object() for name in click_param_names} + + @staticmethod + def get_parameter_source(name): + from click.core import ParameterSource + return ParameterSource.COMMANDLINE + + original = _click.get_current_context + _click.get_current_context = lambda: _FakeCtx + try: + return collect_explicit_cli_keys( + exclude=("extra_llm_api_options", "config"), + translate=eval_mod._CLICK_TO_LLM_ARG) + finally: + _click.get_current_context = original + + @pytest.mark.parametrize( + "click_name,expected", + [ + ("tp_size", "tensor_parallel_size"), + ("pp_size", "pipeline_parallel_size"), + ("ep_size", "moe_expert_parallel_size"), + ("kv_cache_free_gpu_memory_fraction", "free_gpu_memory_fraction"), + ("disable_kv_cache_reuse", "enable_block_reuse"), + ("max_batch_size", "max_batch_size"), # unmapped: identity + ]) + def test_translation(self, click_name, expected): + assert expected in self._collect({click_name}) + + def test_meta_flags_excluded(self): + assert self._collect({"extra_llm_api_options", "config"}) == set() + + +class TestBenchTranslationMap: + """`collect_explicit_cli_keys` in bench.benchmark rewrites Click param names.""" + + def _collect(self, click_param_names): + import click as _click + + from tensorrt_llm.bench import benchmark as bench_mod + + class _FakeCtx: + params = {name: object() for name in click_param_names} + + @staticmethod + def get_parameter_source(name): + from click.core import ParameterSource + return ParameterSource.COMMANDLINE + + # `bench_mod.collect_explicit_cli_keys()` calls `click.get_current_context()`. + original = _click.get_current_context + _click.get_current_context = lambda: _FakeCtx + try: + return bench_mod.collect_explicit_cli_keys() + finally: + _click.get_current_context = original + + @pytest.mark.parametrize( + "click_name,expected", + [ + ("tp", "tensor_parallel_size"), + ("pp", "pipeline_parallel_size"), + ("ep", "moe_expert_parallel_size"), + ("cluster_size", "moe_cluster_parallel_size"), + ("kv_cache_free_gpu_mem_fraction", "free_gpu_memory_fraction"), + ("enable_chunked_context", "enable_chunked_prefill"), + ("max_batch_size", "max_batch_size"), # unmapped: identity + ]) + def test_translation(self, click_name, expected): + assert expected in self._collect({click_name}) + + def test_beam_width_does_not_participate(self): + # `--beam_width` is a SamplingParams flag, not an llm_args field, so + # it must be left out of the translation map. Otherwise an explicit + # `--beam_width N` would silently drop YAML's `max_beam_width` + # without anything in llm_args to replace it. + explicit = self._collect({"beam_width"}) + assert "max_beam_width" not in explicit + assert "beam_width" in explicit + + def test_meta_flags_excluded(self): + assert self._collect({"extra_llm_api_options", "config"}) == set() + + +class TestDisaggLauncherKwargsPreservation: + """Regression tests for `_build_llm_args_from_disagg_server_cfg`. + + The disagg launcher takes a single `server_cfg.other_args` dict and + must produce an llm_args dict that contains every user-set field — + including kwargs that fell through `get_llm_args`'s named signature + into its `**llm_args_extra_dict` catch-all (e.g. `quant_config`, + `lora_config`, `pytorch_backend_config`). + """ + + def test_extra_kwargs_survive(self): + from tensorrt_llm.commands.serve import \ + _build_llm_args_from_disagg_server_cfg + + other_args = { + "model": llama_model_path, + "backend": "pytorch", + "tensor_parallel_size": 1, + "gpus_per_node": 1, + # These do not match get_llm_args's named params; they go into + # **llm_args_extra_dict and must reach the LLM constructor. + "quant_config": { + "quant_algo": "FP8" + }, + "lora_config": { + "lora_dir": ["/tmp/lora-test"] + }, + } + + final = _build_llm_args_from_disagg_server_cfg(other_args) + + assert "quant_config" in final + assert "lora_config" in final + assert isinstance(final["quant_config"], QuantConfig) + assert isinstance(final["lora_config"], LoraConfig) + + def test_default_valued_named_params_survive(self): + """A disagg-YAML field equal to its LlmArgs class default survives.""" + from tensorrt_llm.commands.serve import \ + _build_llm_args_from_disagg_server_cfg + + other_args = { + "model": llama_model_path, + "backend": "pytorch", + "tensor_parallel_size": 1, # equals LlmArgs class default + "gpus_per_node": 1, + } + final = _build_llm_args_from_disagg_server_cfg(other_args) + assert final.get("tensor_parallel_size") == 1 + + class TestTorchLlmArgsCudaGraphSettings: def test_cuda_graph_batch_sizes_case_0(self): @@ -1485,89 +1757,121 @@ class TestConfig(StrictBaseModel): class TestServeDefaults: def test_serve_get_llm_args_preserves_model_defaults(self): - # Get llm_args with default values (simulating serve.py behavior) + # No explicit CLI flags: only required params and serve-side defaults + # reach the constructor; everything else is left for YAML / model + # defaults to provide. llm_args, _ = get_llm_args( model=llama_model_path, backend="pytorch", - # Don't pass parameters to test default behavior ) - # Verify that required params are present assert "model" in llm_args assert "backend" in llm_args assert "postprocess_tokenizer_dir" in llm_args - # For PyTorch backend, build_config and scheduler_config should NOT be included + # PyTorch backend: build_config / scheduler_config stay None and are + # filtered out. assert "build_config" not in llm_args assert "scheduler_config" not in llm_args - # Test that when we DO pass values, they're included appropriately + # Explicit CLI flags survive the filter. llm_args_with_values, _ = get_llm_args( model=llama_model_path, backend="pytorch", - max_batch_size=128, # Non-default value - tensor_parallel_size=4, # Non-default value + max_batch_size=128, + tensor_parallel_size=4, + explicit_cli_keys={"max_batch_size", "tensor_parallel_size"}, ) assert llm_args_with_values.get("max_batch_size") == 128 assert llm_args_with_values.get("tensor_parallel_size") == 4 def test_serve_filters_default_values(self): - # Test with all defaults for PyTorch backend + # All defaults, no explicit CLI flags. llm_args, _ = get_llm_args(model=llama_model_path, backend="pytorch") - # Should only include required params assert "model" in llm_args assert "backend" in llm_args assert "postprocess_tokenizer_dir" in llm_args - # Should NOT include build_config or scheduler_config for PyTorch assert "build_config" not in llm_args assert "scheduler_config" not in llm_args - # Test with custom values + # Custom values survive only when listed in explicit_cli_keys. llm_args, _ = get_llm_args( model=llama_model_path, backend="pytorch", - max_batch_size=128, # Non-default value - tensor_parallel_size=4, # Non-default value + max_batch_size=128, + tensor_parallel_size=4, + explicit_cli_keys={"max_batch_size", "tensor_parallel_size"}, ) - # Custom values should be included assert llm_args.get("max_batch_size") == 128 assert llm_args.get("tensor_parallel_size") == 4 def test_serve_backend_specific_configs(self): - # Test PyTorch backend + # PyTorch backend: build_config / scheduler_config stay None and are + # filtered out. llm_args_pytorch, _ = get_llm_args(model=llama_model_path, backend="pytorch") assert "build_config" not in llm_args_pytorch assert "scheduler_config" not in llm_args_pytorch - # Test TensorRT backend + # TensorRT backend: both are non-None and differ from the LlmArgs + # class default, so the value-based filter keeps them. llm_args_trt, _ = get_llm_args(model=llama_model_path, backend="tensorrt") assert "build_config" in llm_args_trt assert "scheduler_config" in llm_args_trt + def test_serve_explicit_cli_default_value_wins_over_yaml(self): + """Typing --tensor_parallel_size 1 (the default) must beat YAML.""" + llm_args, _ = get_llm_args( + model=llama_model_path, + backend="pytorch", + tensor_parallel_size=1, + explicit_cli_keys={"tensor_parallel_size"}, + ) + # The CLI value lands in llm_args because it is explicit. + assert llm_args["tensor_parallel_size"] == 1 + merged = update_llm_args_with_extra_dict( + llm_args, + {"tensor_parallel_size": 8}, + explicit_cli_keys={"tensor_parallel_size"}, + ) + assert merged["tensor_parallel_size"] == 1 + def test_serve_is_non_default_or_required_helper(self): # Test always_include parameters - assert is_non_default_or_required("model", "test-model", "pytorch") - assert is_non_default_or_required("backend", "pytorch", "pytorch") + assert is_non_default_or_required("model", "test-model", "pytorch", + set()) + assert is_non_default_or_required("backend", "pytorch", "pytorch", + set()) assert is_non_default_or_required("tokenizer", "test-tokenizer", - "pytorch") + "pytorch", set()) # Test None values - assert not is_non_default_or_required("max_batch_size", None, "pytorch") + assert not is_non_default_or_required("max_batch_size", None, "pytorch", + set()) # Test default values (should return False) assert not is_non_default_or_required("tensor_parallel_size", 1, - "pytorch") + "pytorch", set()) assert not is_non_default_or_required("pipeline_parallel_size", 1, - "pytorch") + "pytorch", set()) # Test non-default values (should return True) - assert is_non_default_or_required("tensor_parallel_size", 4, "pytorch") - assert is_non_default_or_required("max_batch_size", 128, "pytorch") + assert is_non_default_or_required("tensor_parallel_size", 4, "pytorch", + set()) + assert is_non_default_or_required("max_batch_size", 128, "pytorch", + set()) + + # Test explicit CLI source overrides the default-equals-value check + assert is_non_default_or_required("tensor_parallel_size", 1, "pytorch", + {"tensor_parallel_size"}) + # Test CLI-derived field (--free_gpu_memory_fraction -> kv_cache_config) + assert is_non_default_or_required("kv_cache_config", KvCacheConfig(), + "pytorch", + {"free_gpu_memory_fraction"}) class TestPyTorchBackendModelDefaults: From 8be182d2d36e0f437603677db9daae077963ad3c Mon Sep 17 00:00:00 2001 From: Bo Deng Date: Mon, 8 Jun 2026 09:22:48 +0800 Subject: [PATCH 026/119] [https://nvbugs/6260907][fix] unwaive test (#15058) Signed-off-by: Bo Deng --- tests/integration/test_lists/waives.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index c63b9e64c914..01cd980257dc 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -5,7 +5,6 @@ accuracy/test_disaggregated_serving.py::TestDeepSeekV32Exp::test_auto_dtype_with accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=2] SKIP (https://nvbugs/6075533) accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_kv_cache_v2_nixl_python SKIP (https://nvbugs/6184575) accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ngram SKIP (https://nvbugs/6245651) -accuracy/test_disaggregated_serving.py::TestQwen3NextInstruct::test_auto_dtype[use_py_transceiver=True] SKIP (https://nvbugs/6260907) accuracy/test_disaggregated_serving.py::TestQwen3_30B_A3B::test_mixed_ctx_gen_model[ctxpp2gentp2] SKIP (https://nvbugs/5748664) accuracy/test_llm_api.py::TestLlama3_1_8BInstruct::test_gather_generation_logits_cuda_graph SKIP (https://nvbugs/5772995) accuracy/test_llm_api.py::TestLlama3_1_8BInstruct::test_guided_decoding_4gpus[xgrammar] SKIP (https://nvbugs/5346443) From b8d17d708c319e75b3037d87904684e5f569668b Mon Sep 17 00:00:00 2001 From: tburt-nv <195370667+tburt-nv@users.noreply.github.com> Date: Sun, 7 Jun 2026 22:35:02 -0400 Subject: [PATCH 027/119] [None][chore] Increase GB200-4_GPUs-PyTorch shards (#14836) Signed-off-by: Tyler Burt <195370667+tburt-nv@users.noreply.github.com> --- jenkins/L0_Test.groovy | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index b3269262dbe4..89d5c1c421b3 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -4128,8 +4128,10 @@ def launchTestJobs(pipeline, testFilter) fullSet += SBSATestConfigs.keySet() SBSASlurmTestConfigs = [ - "GB200-4_GPUs-PyTorch-1": ["auto:gb200-x4", "l0_gb200_multi_gpus", 1, 2, 4], - "GB200-4_GPUs-PyTorch-2": ["auto:gb200-x4", "l0_gb200_multi_gpus", 2, 2, 4], + "GB200-4_GPUs-PyTorch-1": ["auto:gb200-x4", "l0_gb200_multi_gpus", 1, 4, 4], + "GB200-4_GPUs-PyTorch-2": ["auto:gb200-x4", "l0_gb200_multi_gpus", 2, 4, 4], + "GB200-4_GPUs-PyTorch-3": ["auto:gb200-x4", "l0_gb200_multi_gpus", 3, 4, 4], + "GB200-4_GPUs-PyTorch-4": ["auto:gb200-x4", "l0_gb200_multi_gpus", 4, 4, 4], "GB200-4_GPUs-PyTorch-Post-Merge-1": ["auto:gb200-x4", "l0_gb200_multi_gpus", 1, 1, 4], "GB10-PyTorch-Post-Merge-1": ["gb10x-single", "l0_gb10", 1, 1], "GB300-PyTorch-1": ["auto:gb300-x4", "l0_gb300", 1, 1], From 71debd51a382fb2e0b79266b6bb0b570b9b388b6 Mon Sep 17 00:00:00 2001 From: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> Date: Mon, 8 Jun 2026 03:11:06 +0000 Subject: [PATCH 028/119] [None][infra] Check in most recent lock file from nightly pipeline Signed-off-by: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> --- security_scanning/docs/poetry.lock | 6 +- .../examples/auto_deploy/poetry.lock | 240 +++++++++--------- .../examples/draft_target_model/poetry.lock | 240 +++++++++--------- security_scanning/examples/eagle/poetry.lock | 240 +++++++++--------- .../llm-eval/lm-eval-harness/poetry.lock | 240 +++++++++--------- .../examples/lookahead/poetry.lock | 240 +++++++++--------- security_scanning/examples/medusa/poetry.lock | 240 +++++++++--------- .../models/contrib/baichuan/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/bloom/poetry.lock | 240 +++++++++--------- .../models/contrib/chatglm-6b/poetry.lock | 240 +++++++++--------- .../models/contrib/chatglm2-6b/poetry.lock | 240 +++++++++--------- .../contrib/chatglm3-6b-32k/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/dbrx/poetry.lock | 240 +++++++++--------- .../models/contrib/deepseek_v1/poetry.lock | 240 +++++++++--------- .../models/contrib/deepseek_v2/poetry.lock | 240 +++++++++--------- .../models/contrib/falcon/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/gptj/poetry.lock | 240 +++++++++--------- .../models/contrib/gptneox/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/grok/poetry.lock | 240 +++++++++--------- .../models/contrib/hyperclovax/poetry.lock | 58 +++-- .../models/contrib/hyperclovax/pyproject.toml | 2 +- .../models/contrib/internlm/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/jais/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/mpt/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/opt/poetry.lock | 240 +++++++++--------- .../models/contrib/skywork/poetry.lock | 240 +++++++++--------- .../examples/models/contrib/smaug/poetry.lock | 240 +++++++++--------- .../examples/models/core/commandr/poetry.lock | 240 +++++++++--------- .../examples/models/core/gemma/poetry.lock | 240 +++++++++--------- .../examples/models/core/glm-4-9b/poetry.lock | 240 +++++++++--------- .../examples/models/core/gpt/poetry.lock | 240 +++++++++--------- .../examples/models/core/llama/poetry.lock | 240 +++++++++--------- .../examples/models/core/mamba/poetry.lock | 240 +++++++++--------- .../examples/models/core/nemotron/poetry.lock | 240 +++++++++--------- .../examples/models/core/phi/poetry.lock | 240 +++++++++--------- .../examples/models/core/qwen/poetry.lock | 240 +++++++++--------- .../models/core/qwen2audio/poetry.lock | 240 +++++++++--------- .../examples/models/core/qwenvl/poetry.lock | 240 +++++++++--------- .../models/core/recurrentgemma/poetry.lock | 240 +++++++++--------- .../examples/models/core/whisper/poetry.lock | 240 +++++++++--------- security_scanning/examples/ngram/poetry.lock | 240 +++++++++--------- .../examples/quantization/poetry.lock | 240 +++++++++--------- .../examples/ray_orchestrator/poetry.lock | 240 +++++++++--------- .../examples/redrafter/poetry.lock | 240 +++++++++--------- .../examples/trtllm-eval/poetry.lock | 240 +++++++++--------- security_scanning/metadata.json | 4 +- security_scanning/poetry.lock | 240 +++++++++--------- security_scanning/triton_backend/poetry.lock | 240 +++++++++--------- 48 files changed, 5318 insertions(+), 5312 deletions(-) diff --git a/security_scanning/docs/poetry.lock b/security_scanning/docs/poetry.lock index 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b/tests/integration/defs/stress_test/disagg_cancel/README.md index 8be4c893a469..a0b4415f13f0 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/README.md +++ b/tests/integration/defs/stress_test/disagg_cancel/README.md @@ -19,14 +19,15 @@ land incrementally: | Thread | Status | |--------|--------| | `log_scanner_thread` | Implemented — hard-zero log fail-fast | -| `metrics_thread` | Stub (Step 2) | +| `metrics_thread` | Implemented — `trtllm_kv_cache_utilization` scraper | | `injector_thread` | Implemented — SIGSTOP/SIGCONT/SIGKILL + respawn | -| `canary_thread` | Stub | +| `canary_thread` | Implemented — greedy canaries + token-equivalence | | `load_thread` | Stub | -Component-level coverage: `test_log_scanner.py`, `test_injector.py`. -The parametrized marathon pytest still runs a lifecycle smoke until -`setup()` launches a real cluster and the remaining threads are wired. +Component-level coverage: `test_log_scanner.py`, `test_metrics_thread.py`, +`test_injector.py`, `test_canary.py`. The parametrized marathon pytest +still runs a lifecycle smoke until `setup()` launches a real cluster +and the remaining thread (`load`) is wired. ## File layout @@ -37,7 +38,9 @@ tests/integration/defs/stress_test/disagg_cancel/ ├── harness.py (DisaggCancellationStressHarness) ├── test_disagg_cancel_stress.py (pytest entry point) ├── test_log_scanner.py (log_scanner unit tests) +├── test_metrics_thread.py (metrics_thread unit tests) ├── test_injector.py (injector unit tests) +├── test_canary.py (canary_thread unit tests) └── configs/ ├── README.md (YAML schema + how to add a config) ├── marathon_cpp_v1_deepseek.yaml @@ -74,15 +77,21 @@ cd /path/to/TensorRT-LLM export PYTHONPATH=tests/integration/defs:tests/integration/defs/disaggregated +# Step 4 — canary thread (greedy canaries + token-equivalence) +python3 -m pytest -c /dev/null -o addopts= \ + --confcutdir=tests/integration/defs/stress_test \ + tests/integration/defs/stress_test/disagg_cancel/test_canary.py -v + # Step 3 — injector thread (SIGSTOP / SIGCONT / SIGKILL + respawn) python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ tests/integration/defs/stress_test/disagg_cancel/test_injector.py -v -# Step 1 — log scanner (optional sanity alongside injector PR) +# Steps 1-2 — log scanner + metrics (optional sanity) python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ - tests/integration/defs/stress_test/disagg_cancel/test_log_scanner.py -v + tests/integration/defs/stress_test/disagg_cancel/test_log_scanner.py \ + tests/integration/defs/stress_test/disagg_cancel/test_metrics_thread.py -v # Marathon YAML parse/validate (includes stress_config.injections schedule) python3 -m pytest -c /dev/null -o addopts= \ @@ -95,9 +104,7 @@ All three together: ```bash python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ - tests/integration/defs/stress_test/disagg_cancel/test_injector.py \ - tests/integration/defs/stress_test/disagg_cancel/test_log_scanner.py \ - tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py::test_all_marathon_yamls_parse_and_validate -q + tests/integration/defs/stress_test/disagg_cancel/ -q ``` In a full TRT-LLM dev container/venv (with `transformers` installed), diff --git a/tests/integration/defs/stress_test/disagg_cancel/harness.py b/tests/integration/defs/stress_test/disagg_cancel/harness.py index f98be691a415..6b925f26c932 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/harness.py +++ b/tests/integration/defs/stress_test/disagg_cancel/harness.py @@ -28,6 +28,7 @@ from __future__ import annotations +import json import logging import os import random @@ -586,6 +587,122 @@ def _fetch_kv_cache_utilization( return util, None +# --------------------------------------------------------------------------- +# Canary helpers +# --------------------------------------------------------------------------- + + +def _load_canary_prompts(path: Path) -> list[dict[str, Any]]: + """Load and validate the canary prompts JSON. + + Schema (Step 6 reference generator): + {"prompts": [{"prompt": str, + "reference_token_ids": [int, ...]?, + "reference_text": str?}, ...]} + + Strict validation here keeps a malformed reference from raising + `TypeError` inside the daemon canary thread (which would + silently freeze `_canary_records`). + + Raises: + OSError: If the file cannot be opened. + ValueError: On malformed JSON, top-level not a mapping, + missing/non-list `prompts`, entry without a string + `prompt`, or `reference_token_ids` present but not a + list of ints. + """ + with path.open("r", encoding="utf-8") as f: + try: + doc = json.load(f) + except json.JSONDecodeError as exc: + raise ValueError(f"canary prompts file {path} is not valid JSON: {exc}") from exc + if not isinstance(doc, dict) or "prompts" not in doc: + raise ValueError(f"canary prompts file {path} must be an object with a 'prompts' list") + prompts = doc["prompts"] + if not isinstance(prompts, list): + raise ValueError(f"canary prompts file {path}: 'prompts' must be a list") + for i, entry in enumerate(prompts): + if not isinstance(entry, dict) or not isinstance(entry.get("prompt"), str): + raise ValueError( + f"canary prompts file {path}: prompts[{i}] must be an object with a string 'prompt'" + ) + ref = entry.get("reference_token_ids") + if ref is not None and ( + not isinstance(ref, list) or not all(isinstance(t, int) for t in ref) + ): + raise ValueError( + f"canary prompts file {path}: prompts[{i}].reference_token_ids " + "must be a list of ints (or omitted)" + ) + return prompts + + +def _send_canary_request( + server_url: str, + model: str, + prompt: str, + max_tokens: int, + seed: int, + timeout_s: float, +) -> tuple[Optional[list[int]], Optional[str], Optional[str]]: + """POST a greedy, deterministic completion to `/v1/completions`. + + Requests `detokenize=False` so the response carries generated + `token_ids` on `choices[0]` (see + `CompletionResponseChoice.token_ids` in + `tensorrt_llm/serve/openai_protocol.py`); `temperature=0.0` and + a fixed `seed` request greedy determinism. + + Returns: + `(token_ids, text, error)`. On success `error is None`. Any + failure — HTTP error, connection refused, timeout, malformed + body, or a 200 response that omits `token_ids` — folds into + a short `error` string so the canary thread records and + continues (only the log scanner is fail-fast). + """ + url = f"{server_url.rstrip('/')}/v1/completions" + payload = { + "model": model, + "prompt": prompt, + "max_tokens": max_tokens, + "temperature": 0.0, + "seed": seed, + "stream": False, + "detokenize": False, + } + data = json.dumps(payload).encode("utf-8") + req = urllib.request.Request( + url, data=data, headers={"Content-Type": "application/json"}, method="POST" + ) + try: + with urllib.request.urlopen(req, timeout=timeout_s) as response: + body = response.read().decode("utf-8", errors="replace") + except urllib.error.HTTPError as exc: + # HTTPError subclasses URLError — catch it first. + return None, None, f"http_error: {exc.code}" + except urllib.error.URLError as exc: + return None, None, f"url_error: {exc.reason}" + except (TimeoutError, OSError) as exc: + return None, None, f"io_error: {exc}" + try: + obj = json.loads(body) + choice = obj["choices"][0] + except (json.JSONDecodeError, KeyError, IndexError, TypeError) as exc: + return None, None, f"parse_error: {exc}" + token_ids = choice.get("token_ids") if isinstance(choice, dict) else None + text = choice.get("text") if isinstance(choice, dict) else None + if token_ids is None: + return None, text, "missing_token_ids" + return token_ids, text, None + + +def _tokens_equivalent(returned: Optional[list[int]], reference: Optional[list[int]]) -> bool: + """True iff `returned` exactly matches `reference`; `None` on either side is non-equivalent.""" + if returned is None or reference is None: + return False + return list(returned) == list(reference) + + # --------------------------------------------------------------------------- # Harness # --------------------------------------------------------------------------- @@ -607,10 +724,12 @@ class DisaggCancellationStressHarness: wind down promptly. Thread-based composition (rather than asyncio) keeps the - subprocess-control injector, the file-tailing log scanner, and - the HTTP/Prometheus metrics scraper failure-isolated and debugged - independently. The load and canary threads each run their own - asyncio event loops internally for HTTP I/O. + subprocess-control injector, the file-tailing log scanner, the + HTTP/Prometheus metrics scraper, and the HTTP canary client + failure-isolated and debugged independently. The metrics and + canary threads use blocking `urllib` for their low-rate request + streams; the load thread runs its own asyncio event loop + internally (wrapping `run_cancel_stress_test`). """ def __init__( @@ -621,6 +740,8 @@ def __init__( metrics_scrape_interval_s: float = 30.0, metrics_scrape_timeout_s: float = 5.0, injector_poll_interval_s: float = 1.0, + canary_request_timeout_s: float = 10.0, + canary_interval_s: Optional[float] = None, ) -> None: """Construct a marathon harness. @@ -645,6 +766,13 @@ def __init__( injector thread while waiting for the next scheduled event. Tests pass a smaller value to keep wall-clock latency bounded. + canary_request_timeout_s: Per-request HTTP timeout for + one canary completion; a slow/hung request becomes + an error rather than blocking the canary stream. + canary_interval_s: Optional override (seconds) for the + gap between requests. `None` derives from + `canary.rate_per_min` (`60 / rate_per_min`); tests + pass a small value. Raises: ValueError: If the YAML is malformed or its @@ -664,6 +792,8 @@ def __init__( self._metrics_scrape_interval_s: float = metrics_scrape_interval_s self._metrics_scrape_timeout_s: float = metrics_scrape_timeout_s self._injector_poll_interval_s: float = injector_poll_interval_s + self._canary_request_timeout_s: float = canary_request_timeout_s + self._canary_interval_s: Optional[float] = canary_interval_s # Cluster + worker tracking (populated by setup()). self._cluster: Any = None # tuple returned by setup_disagg_cluster @@ -671,6 +801,12 @@ def __init__( self._tracked_workers: list[_TrackedWorker] = [] self._marathon_start_monotonic: float = 0.0 + # Disagg-server front-end the canary targets; populated by + # setup() or bind_server_endpoint(). None until then — the + # canary thread warns and exits. + self._server_url: Optional[str] = None + self._model_name: Optional[str] = None + # Thread handles (populated by start()). self._load_thread: Optional[threading.Thread] = None self._canary_thread: Optional[threading.Thread] = None @@ -722,6 +858,17 @@ def bind_tracked_workers( ] self._worker_specs = list(ctx_specs) + list(gen_specs) + def bind_server_endpoint(self, server_url: str, model_name: str) -> None: + """Register the disagg server front-end for the canary client. + + Called by `setup()` (or tests). Until called, the canary + thread warns and exits — the lifecycle smoke has no live + server. `model_name` goes in the OpenAI envelope only; the + disagg server routes regardless. + """ + self._server_url = server_url + self._model_name = model_name + def start(self) -> None: """Spawn the five worker threads. Returns immediately. @@ -896,13 +1043,100 @@ def _load_thread_body(self) -> None: self.stop_event.set() def _canary_thread_body(self) -> None: - """Send greedy-decode canaries, check token-equivalence. - - Stub: no-op. Real implementation loads - ``stress_canary_prompts.json``, sends 5 reqs/min, asserts - token IDs match the recorded reference. + """Send greedy canaries and append per-request records to `_canary_records`. + + Each record: `{timestamp, elapsed_s, prompt_index, success, + token_equivalent, latency_s, error}`. `token_equivalent` is + True/False when a reference is recorded and the check is + enabled, else None. Failures are recorded — not fail-fast — + because errors during bursts/injections are expected; the + end-of-marathon gates (error rate, recovery time) are + computed from these records later. + + Exits on `stop_event` or `failed_event`. The between-request + wait only observes `stop_event`, so `failed_event` is acted + on at the next request boundary (max lag = one interval); + the metrics thread has the same gap, a shared `wait_for_any` + helper is deferred to a follow-up PR. Warns and exits if the + server endpoint or prompts file is absent. """ - logger.debug("[canary_thread] stub — exiting immediately") + canary_cfg = self.config.raw.get("canary") or {} + if not self._server_url: + logger.warning("[canary] no server endpoint bound (setup() not wired); exiting") + return + prompts_file = canary_cfg.get("prompts_file") + if not prompts_file: + logger.warning("[canary] no canary.prompts_file in config; exiting") + return + prompts_path = Path(prompts_file) + if not prompts_path.is_absolute(): + prompts_path = self.yaml_path.parent / prompts_path + try: + prompts = _load_canary_prompts(prompts_path) + except (OSError, ValueError) as exc: + logger.warning("[canary] cannot load prompts %s: %s; exiting", prompts_path, exc) + return + if not prompts: + logger.warning("[canary] prompts file %s has no prompts; exiting", prompts_path) + return + + if self._canary_interval_s is not None: + interval_s = self._canary_interval_s + else: + rate_per_min = float(canary_cfg.get("rate_per_min") or 0) + if rate_per_min <= 0: + logger.warning( + "[canary] canary.rate_per_min is missing/zero/negative; defaulting to 5/min" + ) + rate_per_min = 5.0 + interval_s = 60.0 / rate_per_min + max_tokens = int(canary_cfg.get("max_tokens", 128)) + seed = int(canary_cfg.get("seed", 42)) + check_token_equiv = bool(canary_cfg.get("check_token_equivalent", True)) + model = self._model_name or "canary" + + logger.info( + "[canary] interval %.2fs over %d prompt(s) to %s", + interval_s, + len(prompts), + self._server_url, + ) + + idx = 0 + while not self.stop_event.is_set() and not self.failed_event.is_set(): + send_start = time.monotonic() + prompt_index = idx % len(prompts) + entry = prompts[prompt_index] + idx += 1 + reference = entry.get("reference_token_ids") + token_ids, _, err = _send_canary_request( + self._server_url, + model, + entry["prompt"], + max_tokens, + seed, + self._canary_request_timeout_s, + ) + success = err is None + token_equivalent: Optional[bool] = None + if success and check_token_equiv and reference is not None: + token_equivalent = _tokens_equivalent(token_ids, reference) + self._canary_records.append( + { + "timestamp": time.time(), + "elapsed_s": time.monotonic() - self._marathon_start_monotonic, + "prompt_index": prompt_index, + "success": success, + "token_equivalent": token_equivalent, + "latency_s": time.monotonic() - send_start, + "error": err, + } + ) + remaining = interval_s - (time.monotonic() - send_start) + if remaining > 0.0: + self.stop_event.wait(timeout=remaining) + + logger.debug("[canary] exiting; %d record(s)", len(self._canary_records)) def _injector_thread_body(self) -> None: """Fire SIGSTOP / SIGCONT / SIGKILL+respawn on the configured schedule. diff --git a/tests/integration/defs/stress_test/disagg_cancel/test_canary.py b/tests/integration/defs/stress_test/disagg_cancel/test_canary.py new file mode 100644 index 000000000000..8cd3f0b803f5 --- /dev/null +++ b/tests/integration/defs/stress_test/disagg_cancel/test_canary.py @@ -0,0 +1,494 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Unit tests for `DisaggCancellationStressHarness._canary_thread_body`. + +Loader and token-equivalence helpers run directly. Transport and +thread behavior run against an in-process HTTP server that answers +`POST /v1/completions` with configurable payloads. +""" + +from __future__ import annotations + +import json +import socket +import textwrap +import threading +import time +from http.server import BaseHTTPRequestHandler, HTTPServer +from pathlib import Path + +import pytest + +from .harness import ( + DisaggCancellationStressHarness, + _load_canary_prompts, + _send_canary_request, + _tokens_equivalent, +) + +# --------------------------------------------------------------------------- +# Fixtures and helpers +# --------------------------------------------------------------------------- + + +def _pick_port() -> int: + """Bind-and-release to return an OS-allocated free TCP port.""" + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("127.0.0.1", 0)) + return s.getsockname()[1] + + +class _CompletionsHandler(BaseHTTPRequestHandler): + """Answers `POST /v1/completions`. + + Tests mutate `server.token_ids` / `text` / `status` / `raw_body` + to drive outcomes and read `server.request_bodies` for + wire-format assertions. + """ + + def do_POST(self) -> None: # noqa: N802 — fixed by BaseHTTPRequestHandler + if self.path != "/v1/completions": + self.send_response(404) + self.end_headers() + return + length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(length) if length else b"" + try: + self.server.request_bodies.append(json.loads(body)) # type: ignore[attr-defined] + except (json.JSONDecodeError, AttributeError): + pass + + status = getattr(self.server, "status", 200) + if status != 200: + self.send_response(status) + self.end_headers() + return + + raw_body = getattr(self.server, "raw_body", None) + if raw_body is not None: + encoded = raw_body.encode("utf-8") + else: + payload = { + "choices": [ + { + "token_ids": getattr(self.server, "token_ids", None), + "text": getattr(self.server, "text", ""), + } + ] + } + encoded = json.dumps(payload).encode("utf-8") + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(encoded))) + self.end_headers() + self.wfile.write(encoded) + + def log_message(self, *_args, **_kwargs) -> None: # silence test noise + pass + + +@pytest.fixture +def completions_server(): + """In-process HTTP server answering `POST /v1/completions`; yields `(server, port)`.""" + port = _pick_port() + server = HTTPServer(("127.0.0.1", port), _CompletionsHandler) + server.token_ids = [1, 2, 3] # type: ignore[attr-defined] + server.text = "ok" # type: ignore[attr-defined] + server.status = 200 # type: ignore[attr-defined] + server.raw_body = None # type: ignore[attr-defined] + server.request_bodies = [] # type: ignore[attr-defined] + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + yield server, port + finally: + server.shutdown() + server.server_close() + thread.join(timeout=2.0) + + +def _write_prompts(tmp_path: Path, entries: list[dict]) -> Path: + path = tmp_path / "stress_canary_prompts.json" + path.write_text(json.dumps({"prompts": entries}), encoding="utf-8") + return path + + +_CANARY_YAML = textwrap.dedent( + """\ + hostname: localhost + model: dummy + backend: pytorch + context_servers: {{}} + generation_servers: {{}} + stress_config: + duration_min: 1 + kv_cache_manager: v1 + transceiver: cpp + canary: + prompts_file: {prompts_file} + rate_per_min: 5 + max_tokens: 8 + seed: 42 + check_token_equivalent: {check_equiv} + """ +) + + +def _make_harness( + tmp_path: Path, + *, + prompts_file: str = "stress_canary_prompts.json", + check_equiv: bool = True, + server_url: str | None = "http://127.0.0.1:1", +) -> DisaggCancellationStressHarness: + """Construct a canary harness with a small interval and bound endpoint.""" + yaml_path = tmp_path / "marathon.yaml" + yaml_path.write_text( + _CANARY_YAML.format( + prompts_file=prompts_file, + check_equiv="true" if check_equiv else "false", + ) + ) + h = DisaggCancellationStressHarness( + yaml_path, + canary_interval_s=0.02, + canary_request_timeout_s=1.0, + ) + if server_url is not None: + h.bind_server_endpoint(server_url, "test-model") + return h + + +def _run_canary_thread_briefly(h: DisaggCancellationStressHarness, duration_s: float) -> None: + """Drive `_canary_thread_body` for `duration_s` then stop.""" + thread = threading.Thread(target=h._canary_thread_body, daemon=True) + thread.start() + time.sleep(duration_s) + h.stop_event.set() + thread.join(timeout=2.0) + assert not thread.is_alive(), "canary thread failed to exit after stop_event" + + +def _run_until_self_exit(h: DisaggCancellationStressHarness) -> None: + """Spawn the thread and wait for it to exit on its own (warn-and-return paths).""" + thread = threading.Thread(target=h._canary_thread_body, daemon=True) + thread.start() + thread.join(timeout=2.0) + assert not thread.is_alive() + + +def _wait_until(predicate, *, timeout_s: float, poll_s: float = 0.01) -> None: + deadline = time.monotonic() + timeout_s + while time.monotonic() < deadline: + if predicate(): + return + time.sleep(poll_s) + raise AssertionError(f"predicate did not become true within {timeout_s}s") + + +# --------------------------------------------------------------------------- +# Prompt-loader tests +# --------------------------------------------------------------------------- + + +def test_load_prompts_valid(tmp_path: Path) -> None: + path = _write_prompts( + tmp_path, + [ + {"prompt": "hello", "reference_token_ids": [1, 2]}, + {"prompt": "world", "reference_token_ids": [3, 4], "reference_text": "w"}, + ], + ) + prompts = _load_canary_prompts(path) + assert len(prompts) == 2 + assert prompts[0]["prompt"] == "hello" + assert prompts[1]["reference_token_ids"] == [3, 4] + + +def test_load_prompts_reference_token_ids_omitted_is_allowed(tmp_path: Path) -> None: + path = _write_prompts(tmp_path, [{"prompt": "p"}]) + assert _load_canary_prompts(path) == [{"prompt": "p"}] + + +@pytest.mark.parametrize( + "raw_content,match", + [ + pytest.param("{not valid json", "not valid JSON", id="malformed-json"), + pytest.param(json.dumps({"items": []}), "'prompts' list", id="missing-prompts-key"), + pytest.param( + json.dumps({"prompts": {"prompt": "x"}}), "must be a list", id="prompts-not-list" + ), + pytest.param( + json.dumps({"prompts": [{"reference_token_ids": [1]}]}), + "string 'prompt'", + id="entry-missing-prompt", + ), + pytest.param( + json.dumps({"prompts": [{"prompt": "p", "reference_token_ids": 123}]}), + "reference_token_ids", + id="reftokens-not-list", + ), + pytest.param( + json.dumps({"prompts": [{"prompt": "p", "reference_token_ids": [1, "two"]}]}), + "reference_token_ids", + id="reftokens-non-int-element", + ), + ], +) +def test_load_prompts_invalid_raises(tmp_path: Path, raw_content: str, match: str) -> None: + path = tmp_path / "p.json" + path.write_text(raw_content, encoding="utf-8") + with pytest.raises(ValueError, match=match): + _load_canary_prompts(path) + + +# --------------------------------------------------------------------------- +# Token-equivalence helper tests +# --------------------------------------------------------------------------- + + +def test_tokens_equivalent_exact_match() -> None: + assert _tokens_equivalent([1, 2, 3], [1, 2, 3]) is True + + +def test_tokens_equivalent_mismatch() -> None: + assert _tokens_equivalent([1, 2, 3], [1, 2, 4]) is False + assert _tokens_equivalent([1, 2], [1, 2, 3]) is False + + +def test_tokens_equivalent_none_is_false() -> None: + assert _tokens_equivalent(None, [1, 2]) is False + assert _tokens_equivalent([1, 2], None) is False + assert _tokens_equivalent(None, None) is False + + +# --------------------------------------------------------------------------- +# Send-request tests (in-process HTTP server) +# --------------------------------------------------------------------------- + + +def test_send_success_returns_token_ids(completions_server) -> None: + server, port = completions_server + server.token_ids = [10, 20, 30] + server.text = "hi" + token_ids, text, err = _send_canary_request( + f"http://127.0.0.1:{port}", "m", "prompt", max_tokens=8, seed=42, timeout_s=1.0 + ) + assert err is None + assert token_ids == [10, 20, 30] + assert text == "hi" + + +def test_send_http_503_returns_http_error(completions_server) -> None: + server, port = completions_server + server.status = 503 + token_ids, text, err = _send_canary_request( + f"http://127.0.0.1:{port}", "m", "prompt", max_tokens=8, seed=42, timeout_s=1.0 + ) + assert token_ids is None and text is None + assert err is not None and err.startswith("http_error: 503") + + +def test_send_connection_refused_returns_url_error() -> None: + port = _pick_port() # nobody listening + token_ids, _text, err = _send_canary_request( + f"http://127.0.0.1:{port}", "m", "prompt", max_tokens=8, seed=42, timeout_s=0.5 + ) + assert token_ids is None + assert err is not None and err.startswith("url_error") + + +def test_send_malformed_body_returns_parse_error(completions_server) -> None: + server, port = completions_server + server.raw_body = "{not json" + token_ids, _text, err = _send_canary_request( + f"http://127.0.0.1:{port}", "m", "prompt", max_tokens=8, seed=42, timeout_s=1.0 + ) + assert token_ids is None + assert err is not None and err.startswith("parse_error") + + +def test_send_missing_token_ids_returns_error(completions_server) -> None: + # 200 with `choices[0]` lacking `token_ids` is a server problem, + # not a token-equivalence mismatch downstream. + server, port = completions_server + server.token_ids = None + server.text = "hi" + token_ids, text, err = _send_canary_request( + f"http://127.0.0.1:{port}", "m", "prompt", max_tokens=8, seed=42, timeout_s=1.0 + ) + assert token_ids is None + assert text == "hi" + assert err == "missing_token_ids" + + +def test_send_wire_format_includes_greedy_determinism_knobs(completions_server) -> None: + # Pin the wire shape so a future change can't silently drop + # `temperature=0.0` / `seed` / `detokenize=False`. + server, port = completions_server + _send_canary_request( + f"http://127.0.0.1:{port}", + "test-model", + "the prompt", + max_tokens=11, + seed=7, + timeout_s=1.0, + ) + assert len(server.request_bodies) == 1 + body = server.request_bodies[0] + assert body["model"] == "test-model" + assert body["prompt"] == "the prompt" + assert body["max_tokens"] == 11 + assert body["temperature"] == 0.0 + assert body["seed"] == 7 + assert body["stream"] is False + assert body["detokenize"] is False + + +# --------------------------------------------------------------------------- +# Thread-body integration tests +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "server_tokens,ref_tokens,check_equiv,expected", + [ + pytest.param([1, 2, 3], [1, 2, 3], True, True, id="match"), + pytest.param([9, 9, 9], [1, 2, 3], True, False, id="mismatch"), + pytest.param([1, 2, 3], None, True, None, id="no-reference"), + pytest.param([1, 2, 3], [1, 2, 3], False, None, id="check-disabled"), + ], +) +def test_thread_records_token_equivalent( + tmp_path, completions_server, server_tokens, ref_tokens, check_equiv, expected +) -> None: + server, port = completions_server + server.token_ids = server_tokens + entry: dict = {"prompt": "p"} + if ref_tokens is not None: + entry["reference_token_ids"] = ref_tokens + _write_prompts(tmp_path, [entry]) + h = _make_harness(tmp_path, check_equiv=check_equiv, server_url=f"http://127.0.0.1:{port}") + + _run_canary_thread_briefly(h, duration_s=0.15) + + assert len(h._canary_records) >= 1 + for rec in h._canary_records: + assert rec["success"] is True + assert rec["error"] is None + assert rec["token_equivalent"] is expected + + +def test_thread_records_error_when_server_down(tmp_path) -> None: + port = _pick_port() # nobody listening + _write_prompts(tmp_path, [{"prompt": "p", "reference_token_ids": [1]}]) + h = _make_harness(tmp_path, server_url=f"http://127.0.0.1:{port}") + + _run_canary_thread_briefly(h, duration_s=0.15) + + assert len(h._canary_records) >= 1 + for rec in h._canary_records: + assert rec["success"] is False + assert rec["token_equivalent"] is None + assert rec["error"] is not None + + +def test_thread_round_robins_prompts(tmp_path, completions_server) -> None: + server, port = completions_server + server.token_ids = [1] + _write_prompts( + tmp_path, + [ + {"prompt": "p0", "reference_token_ids": [1]}, + {"prompt": "p1", "reference_token_ids": [1]}, + {"prompt": "p2", "reference_token_ids": [1]}, + ], + ) + h = _make_harness(tmp_path, server_url=f"http://127.0.0.1:{port}") + + thread = threading.Thread(target=h._canary_thread_body, daemon=True) + thread.start() + try: + _wait_until(lambda: len(h._canary_records) >= 4, timeout_s=2.0) + finally: + h.stop_event.set() + thread.join(timeout=2.0) + + seen = {rec["prompt_index"] for rec in h._canary_records} + assert {0, 1, 2}.issubset(seen) + + +def test_thread_exits_when_no_server_url(tmp_path) -> None: + _write_prompts(tmp_path, [{"prompt": "p", "reference_token_ids": [1]}]) + h = _make_harness(tmp_path, server_url=None) + _run_until_self_exit(h) + assert h._canary_records == [] + + +def test_thread_exits_when_prompts_file_missing(tmp_path, completions_server) -> None: + _, port = completions_server + h = _make_harness( + tmp_path, prompts_file="does_not_exist.json", server_url=f"http://127.0.0.1:{port}" + ) + _run_until_self_exit(h) + assert h._canary_records == [] + + +def test_thread_exits_when_prompts_list_empty(tmp_path, completions_server) -> None: + _, port = completions_server + _write_prompts(tmp_path, []) + h = _make_harness(tmp_path, server_url=f"http://127.0.0.1:{port}") + _run_until_self_exit(h) + assert h._canary_records == [] + + +def test_thread_exits_promptly_on_failed_event(tmp_path, completions_server) -> None: + # The between-request wait only observes `stop_event`; verify + # `failed_event` is acted on within one canary interval (records + # grow by at most one after the event fires). + _, port = completions_server + _write_prompts(tmp_path, [{"prompt": "p", "reference_token_ids": [1, 2, 3]}]) + h = _make_harness(tmp_path, server_url=f"http://127.0.0.1:{port}") + + thread = threading.Thread(target=h._canary_thread_body, daemon=True) + thread.start() + _wait_until(lambda: len(h._canary_records) >= 1, timeout_s=2.0) + pre = len(h._canary_records) + h.failed_event.set() + thread.join(timeout=2.0) + assert not thread.is_alive() + assert len(h._canary_records) - pre <= 1 + + +def test_thread_resolves_absolute_prompts_path(tmp_path, completions_server) -> None: + # Absolute `prompts_file` must NOT be joined against `yaml_path.parent`. + server, port = completions_server + server.token_ids = [1, 2, 3] + outside_dir = tmp_path / "elsewhere" + outside_dir.mkdir() + prompts_path = outside_dir / "abs_prompts.json" + prompts_path.write_text( + json.dumps({"prompts": [{"prompt": "p", "reference_token_ids": [1, 2, 3]}]}), + encoding="utf-8", + ) + h = _make_harness( + tmp_path, prompts_file=str(prompts_path), server_url=f"http://127.0.0.1:{port}" + ) + + _run_canary_thread_briefly(h, duration_s=0.15) + + assert len(h._canary_records) >= 1 + assert all(rec["success"] is True for rec in h._canary_records) + assert all(rec["token_equivalent"] is True for rec in h._canary_records) From 98a88f7f49082c934eecf902858e9d7a472307c6 Mon Sep 17 00:00:00 2001 From: Balaram Buddharaju <169953907+brb-nv@users.noreply.github.com> Date: Sun, 7 Jun 2026 20:37:04 -0700 Subject: [PATCH 030/119] [TRTLLM-12507][feat] Cudagraph support for routed-expert MoE LoRA with Cutlass backend - Part 1 (#14923) Signed-off-by: Balaram Buddharaju <169953907+brb-nv@users.noreply.github.com> --- .../cutlass_kernels/include/moe_kernels.h | 9 + .../include/moe_lora_device_path.h | 149 +++++ .../include/moe_lora_pointer_expand.h | 81 +++ .../include/moe_lora_problem_builder.h | 118 ++++ .../cutlass_kernels/moe_gemm/moe_kernels.cu | 173 ++++- .../moe_gemm/moe_lora_pointer_expand.cu | 178 +++++ .../moe_gemm/moe_lora_problem_builder.cu | 152 +++++ cpp/tensorrt_llm/thop/moeOp.cpp | 622 ++++++++++++++++-- cpp/tests/unit_tests/kernels/CMakeLists.txt | 5 + .../kernels/moeLoraPointerExpandTest.cu | 420 ++++++++++++ .../kernels/moeLoraProblemBuilderTest.cu | 364 ++++++++++ .../_torch/lora/test_moe_lora_device_path.py | 184 ++++++ .../unittest/_torch/lora/test_moe_lora_op.py | 48 ++ 13 files changed, 2443 insertions(+), 60 deletions(-) create mode 100644 cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h create mode 100644 cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h create mode 100644 cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h create mode 100644 cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_pointer_expand.cu create mode 100644 cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_problem_builder.cu create mode 100644 cpp/tests/unit_tests/kernels/moeLoraPointerExpandTest.cu create mode 100644 cpp/tests/unit_tests/kernels/moeLoraProblemBuilderTest.cu create mode 100644 tests/unittest/_torch/lora/test_moe_lora_device_path.py diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_kernels.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_kernels.h index 4e76d2be5614..216877a4ffc7 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_kernels.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_kernels.h @@ -21,6 +21,7 @@ #include "tensorrt_llm/common/cudaUtils.h" #include "tensorrt_llm/common/quantization.h" #include "tensorrt_llm/kernels/cutlass_kernels/fp8_blockscale_gemm/fp8_blockscale_gemm.h" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h" #include #ifdef ENABLE_FP4 #include @@ -68,6 +69,14 @@ struct LoraParams cudaEvent_t* memcpy_event_ptr; + // Device-side capture-safe LoRA path scratch. When device_path.enabled is + // true, the kernel uses launchMoeLoraPointerExpand, launchMoeLoraProblemBuilder, + // and cudaGraph(SplitK)GroupedGemm instead of the legacy host-pointer + // LoraImpl::run path. The pointers refer to persistent allocations owned by + // the calling FusedMoeRunner, so their addresses are stable across + // CUDA-graph captures and replays. + ::tensorrt_llm::kernels::cutlass_kernels::MoeLoraDevicePath device_path; + LoraParams() = default; LoraParams(int num_reqs, int32_t const* fc1_lora_ranks, void const* const* fc1_lora_weight_ptrs, diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h new file mode 100644 index 000000000000..2d2c133255c4 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h @@ -0,0 +1,149 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "tensorrt_llm/common/config.h" + +#include + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels::cutlass_kernels +{ + +// Forward declaration; the typedef below references it by name. +struct MoeLoraDevicePathModule; + +// Function-pointer dispatch for the libtorch-dependent GEMM stage of the MoE +// LoRA device path. The implementation lives in th_common (moeOp.cpp) because +// the cudaGraph(SplitK)GroupedGemm wrappers allocate workspace via at::Tensor, +// which cannot be linked from libmoe_gemm_src.a (that archive is also linked +// into the TensorRT plugin, which must not depend on libtorch). +// +// It repacks mod into a MoeLoraGemmGroupArrays, runs the problem builder, and +// dispatches the in/out GEMMs, accumulating into output_base (which the caller +// must initialize). data_type is the scalar dtype (fp16/bf16/fp32). +using MoeLoraDeviceRunFn = void (*)(MoeLoraDevicePathModule const& mod, int64_t num_permuted_tokens, + int64_t in_hidden_size, int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, void const* input_base, + void* output_base, nvinfer1::DataType data_type, cudaStream_t stream); + +// Per-module device-resident scratch for the MoE LoRA capture-safe path. +// Pointers refer to device memory unless noted. +// +// The struct is typed with void* rather than the concrete +// cutlass::gemm::GemmCoord* / int64_t* types so this header can be included +// from moe_kernels.h without dragging in cutlass headers. The concrete types +// are recovered at the call site (matching the contract documented in +// moe_lora_problem_builder.h): +// +// problem_sizes_* -> cutlass::gemm::GemmCoord* (device, [P_max]) +// a_ptrs_*/b/d -> void** (device, [P_max]) +// lda/ldb/ldd_* -> int64_t* (device, [P_max]) +// splitk_offsets -> int64_t* (device, [P_max + 1]) +// lowrank_ws_dev -> void* (device, [P_max, max_lora_rank, dtype_bytes]) +// host_max_* -> cutlass::gemm::GemmCoord* (pinned host, [1]) +// +// The split-K in-GEMM's partial-sum scratch is allocated internally by the +// cuda_graph_split_k_grouped_gemm wrapper (sized from the host max-problem +// hint); only the per-problem splitk_offsets are produced here. +// +// out_hidden_size is the trailing dimension of the module's output buffer; it +// is inter_size for fc1/gated and hidden_size for fc2. The output base address +// itself is passed directly to runMoeLoraDeviceModule at the call site. +struct MoeLoraDevicePathModule +{ + // Per-source-token (rank, A_ptr, B_ptr) device mirrors, staged via a + // pinned-host to device async H2D in FusedMoeRunner::buildMoeLoraParams. + // These feed launchMoeLoraPointerExpand as ranks_src / ptrs_src. + int32_t const* ranks_src_dev = nullptr; + int64_t const* ptrs_src_dev = nullptr; + + // Inner (A) and outer (B) dimensions for this module, fed to the + // pointer-expand kernel as dim_a / dim_b so it can compute the per-expert + // offset weight_index * dim * lora_rank. For fc1/gated this is + // (hidden_size, inter_size); for fc2 it is (inter_size, hidden_size). + int64_t dim_a = 0; + int64_t dim_b = 0; + + // Per-permuted-row (rank, A_ptr + offset, B_ptr + offset). + int32_t* permuted_ranks_dev = nullptr; + int64_t* permuted_ptrs_dev = nullptr; + + // cuda_graph_(split_k_)grouped_gemm-ready bundle. + void* problem_sizes_in_dev = nullptr; + void* problem_sizes_out_dev = nullptr; + void** a_ptrs_in_dev = nullptr; + void** b_ptrs_in_dev = nullptr; + void** d_ptrs_in_dev = nullptr; + void** b_ptrs_out_dev = nullptr; + void** d_ptrs_out_dev = nullptr; + int64_t* lda_in_dev = nullptr; + int64_t* ldb_in_dev = nullptr; + int64_t* ldd_in_dev = nullptr; + int64_t* ldb_out_dev = nullptr; + int64_t* ldd_out_dev = nullptr; + int64_t* splitk_offsets_dev = nullptr; + + // Low-rank intermediate workspace shared between the in- and out-GEMM. The + // split-K partial-sum scratch is owned by the GEMM wrapper, not here. + void* lowrank_workspace_dev = nullptr; + + // Host (pinned) per-call max problem size hints, required by the + // cuda_graph_*_grouped_gemm wrappers for kernel selection. The + // values are upper bounds (max_M, max_N, max_K) safe to fix at + // warmup time. + void* host_max_problem_in_pinned = nullptr; + void* host_max_problem_out_pinned = nullptr; + + // Trailing dimension of the module's output buffer (inter_size for + // fc1/gated, hidden_size for fc2). The output base address is supplied + // directly to runMoeLoraDeviceModule at the call site. + int64_t out_hidden_size = 0; +}; + +// Top-level device-path bundle attached to LoraParams when the device LoRA +// path is active. enabled == false means the FusedMoeRunner runs the legacy +// host path. +struct MoeLoraDevicePath +{ + bool enabled = false; + + // Scalars common to all three modules. Fixed for the lifetime of the + // FusedMoeRunner once the scratch is allocated. + int64_t in_hidden_size = 0; + int64_t max_lora_rank = 0; + int64_t dtype_bytes = 0; + int64_t splitk_slices = 0; + + bool has_gated = false; + + // libtorch-bound GEMM dispatch entry point, populated by moeOp.cpp when the + // device path is enabled. nullptr means the device path is unavailable from + // this consumer (for example, the TensorRT plugin). + MoeLoraDeviceRunFn run = nullptr; + + MoeLoraDevicePathModule fc1; + MoeLoraDevicePathModule fc2; + MoeLoraDevicePathModule gated; +}; + +} // namespace kernels::cutlass_kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h new file mode 100644 index 000000000000..c2f509c4ed9c --- /dev/null +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "tensorrt_llm/common/config.h" + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels::cutlass_kernels +{ + +// Device-side description of one LoRA module (fc1, fc2, or gated) for the +// MoE per-token A/B pointer-table expansion. All pointers refer to device +// memory. +// +// Inputs (per source token, indexed by the pre-permutation source row index): +// ranks_src: int32 [num_rows], per-source-token LoRA rank. +// ptrs_src: int64 [num_rows * 2], pointer bits laid out as (A_ptr, B_ptr) +// per source token. +// +// Outputs (per permuted row, sized expanded_num_rows == num_rows * top_k): +// ranks_out: int32 [expanded_num_rows], per-permuted-row LoRA rank. +// ptrs_out: int64 [expanded_num_rows * 2], per-permuted-row +// (A_ptr + offset, B_ptr + offset). The per-expert offset is +// weight_index * dim * rank * lora_dtype_bytes, so the consumer +// can reinterpret directly as the LoRA scalar type. +// +// dim_a and dim_b are the non-rank dimension of A and B respectively: +// fc1/gated use (hidden_size, inter_size); fc2 uses (inter_size, hidden_size). +struct MoeLoraExpandModule +{ + int32_t const* ranks_src = nullptr; + int64_t const* ptrs_src = nullptr; + int64_t dim_a = 0; + int64_t dim_b = 0; + int32_t* ranks_out = nullptr; + int64_t* ptrs_out = nullptr; +}; + +// Device-side replacement for the host-CPU pointer fan-out in +// CutlassMoeFCRunner::setupLoraWorkspace. Reads per-source-token LoRA metadata +// and permuted_rows, and writes per-permuted-row pointer tables directly into +// device memory. It performs no host synchronization and no cudaMemcpyAsync +// staging, so it is safe to launch from a captured CUDA graph. +// +// expert_first_token_offset has shape [num_experts_per_node + 1] (int64, +// device-resident). The kernel uses it both to find the expert a permuted row +// belongs to and to derive weight_index = local_expert_idx + start_expert for +// the per-expert weight-buffer stride. +// +// lora_dtype_bytes is the size in bytes of the LoRA matrix scalar (e.g. 2 for +// bf16/fp16). It scales the stride applied to the A/B pointers so consumers can +// reinterpret the result directly as the appropriate scalar type. +// +// gated may be nullptr for non-gated activations; when non-null, the gated +// module's outputs are produced in the same pass. +void launchMoeLoraPointerExpand(int32_t const* permuted_rows, int64_t const* expert_first_token_offset, + int32_t num_experts_per_node, int32_t start_expert, int64_t num_rows, int64_t expanded_num_rows, + int64_t lora_dtype_bytes, MoeLoraExpandModule const& fc1, MoeLoraExpandModule const& fc2, + MoeLoraExpandModule const* gated, cudaStream_t stream); + +} // namespace kernels::cutlass_kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h new file mode 100644 index 000000000000..a71e89b87864 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h @@ -0,0 +1,118 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "tensorrt_llm/common/config.h" + +#include "cutlass/gemm_coord.h" + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels::cutlass_kernels +{ + +// Caller-owned device-output bundle for one LoRA module. Each array is sized +// for the maximum permuted-token count the FusedMoeRunner expects to see; +// the builder fills the first num_permuted_tokens entries each call. +// +// Layout convention (mirrors attention LoRA in cuda_graph_grouped_gemm.h): +// In-GEMM: D = A @ B with C aliased to D when there's no bias. +// A = input slice [M=1, K=in_hidden_size] +// B = adapter A [K=in_hidden_size, N=rank] +// D = lowrank slice [M=1, N=rank] +// Out-GEMM: D = A @ B with C aliased to D. +// A = lowrank slice [M=1, K=rank] (= in-GEMM's D) +// B = adapter B [K=rank, N=out_hidden_size] +// D = output slice [M=1, N=out_hidden_size] +// +// Because ptrC aliases ptrD in both GEMMs (no bias), only ptrD is exposed +// per GEMM; the cuda_graph_grouped_gemm wrapper accepts the same address +// for both. d_ptrs_in also serves as a_ptrs_out (the LoRA intermediate is +// the input to the second GEMM); only one set of low-rank pointers is +// produced for that reason. +struct MoeLoraGemmGroupArrays +{ + // Per-problem (M, N, K) for the in-GEMM and out-GEMM respectively. + cutlass::gemm::GemmCoord* problem_sizes_in = nullptr; // [P] + cutlass::gemm::GemmCoord* problem_sizes_out = nullptr; // [P] + + // In-GEMM pointer arrays. ptr_c_in is implicit (== d_ptrs_in). + void** a_ptrs_in = nullptr; // [P]: input row pointer + void** b_ptrs_in = nullptr; // [P]: adapter A pointer (with per-expert offset) + void** d_ptrs_in = nullptr; // [P]: lowrank workspace row (also a_ptrs_out) + + // Out-GEMM pointer arrays. ptr_c_out is implicit (== d_ptrs_out). + void** b_ptrs_out = nullptr; // [P]: adapter B pointer (with per-expert offset) + void** d_ptrs_out = nullptr; // [P]: output row pointer + + // Leading dimensions. All row-major, fixed per problem given uniform + // input / lowrank-workspace / output strides. + int64_t* lda_in = nullptr; // [P]: in_hidden_size + int64_t* ldb_in = nullptr; // [P]: in_hidden_size (stride in adapter-A storage) + int64_t* ldd_in = nullptr; // [P]: max_lora_rank (workspace stride) + int64_t* ldb_out = nullptr; // [P]: per-token rank (stride in adapter-B storage) + int64_t* ldd_out = nullptr; // [P]: out_hidden_size + + // Per-problem exclusive prefix offset into the split-K scratch buffer + // used by the in-GEMM. Element [P] (one past the end) holds the total + // scratch size in fp32 elements, matching the layout that + // cuda_graph_split_k_grouped_gemm consumes. + int64_t* splitk_offsets = nullptr; // [P + 1] +}; + +// Device-side problem-and-pointer builder for one MoE LoRA module. It consumes +// the per-permuted-row outputs of launchMoeLoraPointerExpand plus uniform +// input, workspace, and output base addresses, and writes every device-resident +// input the cuda_graph_(split_k_)grouped_gemm wrappers need. +// +// Inputs: +// ranks_dev: int32 [P], per-permuted-row LoRA rank. +// ptrs_dev: int64 [P*2], per-permuted-row (A_ptr + offset, B_ptr + offset), +// already adjusted for the per-expert weight stride by the +// pointer-expand kernel. +// +// Base pointers (the per-token row offset is computed inside the kernel from +// i * stride * dtype_bytes): +// input_base: [P, in_hidden_size] +// lowrank_workspace: [P, max_lora_rank], reused as the in-GEMM output and +// the out-GEMM input. +// output_base: [P, out_hidden_size] +// +// Scalars: +// in_hidden_size: K for the in-GEMM, also lda_in[i] and ldb_in[i]. +// out_hidden_size: N for the out-GEMM, also ldd_out[i]. +// max_lora_rank: ldd_in[i], the workspace stride, fixed regardless of the +// per-token rank so the GEMM lands at a known offset. +// The out-GEMM's ldb_out[i] is the per-token rank (adapter B +// is stored [out_hidden_size, rank]), not out_hidden_size. +// dtype_bytes: scalar size in bytes (2 for bf16/fp16, 4 for fp32). +// splitk_slices: split-K factor for the in-GEMM; drives the per-problem +// split-K scratch stride. +// +// The split-K stride is a worst-case fixed value (max_lora_rank * splitk_slices +// per problem) so the offsets can be computed from i alone without a prefix-sum. +void launchMoeLoraProblemBuilder(int32_t const* ranks_dev, int64_t const* ptrs_dev, void const* input_base, + void* lowrank_workspace, void* output_base, int64_t num_permuted_tokens, int64_t in_hidden_size, + int64_t out_hidden_size, int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, + MoeLoraGemmGroupArrays const& out, cudaStream_t stream); + +} // namespace kernels::cutlass_kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu index a0b08a3df775..a99f42003e47 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2025, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020-2026, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -58,7 +58,14 @@ #include "tensorrt_llm/kernels/preQuantScaleKernel.h" #include "tensorrt_llm/kernels/quantization.cuh" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h" #include "tensorrt_llm/kernels/cutlass_kernels/include/moe_util_kernels.h" +// NOTE: the device-path GEMM dispatch (cudaGraph(SplitK)GroupedGemm, +// launchMoeLoraProblemBuilder) is not called here. Those wrappers pull in +// libtorch via at::Tensor, and this file is archived into libmoe_gemm_src.a, +// which the TensorRT plugin also links and must keep libtorch-free. The +// dispatch is reached through the LoraParams::device_path.run function pointer, +// populated in moeOp.cpp. #ifndef CUDART_VERSION #error CUDART_VERSION Undefined! @@ -3657,6 +3664,49 @@ void CutlassMoeFCRunner +constexpr nvinfer1::DataType moeLoraNvInferType() +{ + if constexpr (std::is_same_v) + { + return nvinfer1::DataType::kHALF; + } +#if defined(ENABLE_BF16) + else if constexpr (std::is_same_v) + { + return nvinfer1::DataType::kBF16; + } +#endif + else if constexpr (std::is_same_v) + { + return nvinfer1::DataType::kFLOAT; + } + else + { + static_assert(sizeof(ScaleBiasType) == 0, "MoE LoRA device path supports fp16/bf16/fp32 only."); + } +} + template bool CutlassMoeFCRunner::setupLoraWorkspace( int64_t expanded_num_rows, int64_t num_rows, int64_t inter_size, int64_t hidden_size, int start_expert, @@ -3675,6 +3725,49 @@ bool CutlassMoeFCRunner(permuted_data_); } - void* lora_workspace = lora_params.workspace; - void* tmp_lora_fc_result = static_cast(lora_fc1_result); - int64_t num_valid_tokens = host_expert_first_token_offset[num_experts_per_node]; - int64_t num_reqs_lora = std::min(num_valid_tokens, static_cast(num_reqs * num_experts_per_node)); + // Device-path branch, running entirely on the stream. setupLoraWorkspace + // has already populated the per-permuted-row ranks and pointers for fc1 and + // gated via launchMoeLoraPointerExpand. + if (lora_params.device_path.enabled) + { + auto const& dp = lora_params.device_path; + nvinfer1::DataType const data_type = moeLoraNvInferType(); - ::tensorrt_llm::kernels::Lora_run(fc1_lora_impl.get(), num_valid_tokens, num_reqs_lora, input, - host_permuted_fc1_lora_ranks.data(), host_permuted_fc1_weight_ptrs.data(), 0, &tmp_lora_fc_result, - lora_workspace, stream); + // The device-path GEMM skips rank-0 rows, but the bias/reorder paths + // read lora_fc1_result_ for every valid row. Zero the buffer first so + // skipped rows are a deterministic no-op. It is contiguous and holds + // both the gated and fc1 halves when gated, so one memset covers both. + size_t const fc1_result_bytes = static_cast(expanded_num_rows) * static_cast(inter_size) + * (is_gated_activation ? 2u : 1u) * sizeof(ScaleBiasType); + TLLM_CUDA_CHECK(cudaMemsetAsync(lora_fc1_result_, 0, fc1_result_bytes, stream)); - if (is_gated_activation) + runMoeLoraDeviceModule(dp.fc1, expanded_num_rows, /*in_hidden_size=*/hidden_size, dp.max_lora_rank, + dp.dtype_bytes, dp.splitk_slices, /*input_base=*/static_cast(input), + /*output_base=*/static_cast(lora_fc1_result), dp.run, data_type, stream); + + if (is_gated_activation) + { + runMoeLoraDeviceModule(dp.gated, expanded_num_rows, /*in_hidden_size=*/hidden_size, dp.max_lora_rank, + dp.dtype_bytes, dp.splitk_slices, /*input_base=*/static_cast(input), + /*output_base=*/static_cast(lora_gated_out), dp.run, data_type, stream); + } + } + else { - void* tmp_lora_gated_result = static_cast(lora_gated_out); + void* lora_workspace = lora_params.workspace; + void* tmp_lora_fc_result = static_cast(lora_fc1_result); + int64_t num_valid_tokens = host_expert_first_token_offset[num_experts_per_node]; + int64_t num_reqs_lora = std::min(num_valid_tokens, static_cast(num_reqs * num_experts_per_node)); + ::tensorrt_llm::kernels::Lora_run(fc1_lora_impl.get(), num_valid_tokens, num_reqs_lora, input, - host_permuted_gated_lora_ranks.data(), host_permuted_gated_weight_ptrs.data(), 0, &tmp_lora_gated_result, + host_permuted_fc1_lora_ranks.data(), host_permuted_fc1_weight_ptrs.data(), 0, &tmp_lora_fc_result, lora_workspace, stream); + + if (is_gated_activation) + { + void* tmp_lora_gated_result = static_cast(lora_gated_out); + ::tensorrt_llm::kernels::Lora_run(fc1_lora_impl.get(), num_valid_tokens, num_reqs_lora, input, + host_permuted_gated_lora_ranks.data(), host_permuted_gated_weight_ptrs.data(), 0, + &tmp_lora_gated_result, lora_workspace, stream); + } } // add bias and reorder @@ -3849,6 +3972,28 @@ void CutlassMoeFCRunner(fc1_result_); } + // Device-path branch, mirroring loraFC1's branch. It consumes the + // per-permuted-row ranks and pointers that setupLoraWorkspace produced via + // launchMoeLoraPointerExpand. num_tokens here is expanded_num_rows from + // runMoe (top_k * num_rows). + if (lora_params.device_path.enabled) + { + auto const& dp = lora_params.device_path; + nvinfer1::DataType const data_type = moeLoraNvInferType(); + + // As in loraFC1, zero the output so rank-0 rows the GEMM skips do not + // feed stale data into the downstream add. + size_t const fc2_result_bytes + = static_cast(num_tokens) * static_cast(hidden_size) * sizeof(ScaleBiasType); + TLLM_CUDA_CHECK(cudaMemsetAsync(lora_fc2_result_, 0, fc2_result_bytes, stream)); + + runMoeLoraDeviceModule(dp.fc2, num_tokens, /*in_hidden_size=*/inter_size, dp.max_lora_rank, dp.dtype_bytes, + dp.splitk_slices, /*input_base=*/static_cast(input), + /*output_base=*/static_cast(lora_fc2_result_), dp.run, data_type, stream); + sync_check_cuda_error(stream); + return; + } + void* lora_workspace = lora_params.workspace; int64_t num_valid_tokens = host_expert_first_token_offset[num_experts_per_node]; void* tmp_lora_fc_result = static_cast(lora_fc2_result_); @@ -4086,7 +4231,11 @@ void CutlassMoeFCRunner& host_permuted_rows = host_lora_workspace_.host_permuted_rows; std::vector& host_expert_first_token_offset = host_lora_workspace_.host_expert_first_token_offset; diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_pointer_expand.cu b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_pointer_expand.cu new file mode 100644 index 000000000000..d130d917ad33 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_pointer_expand.cu @@ -0,0 +1,178 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h" + +#include "tensorrt_llm/common/assert.h" +#include "tensorrt_llm/common/cudaUtils.h" + +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels::cutlass_kernels +{ + +namespace +{ + +// Threads-per-block. The kernel is bandwidth-bound, so block size mainly +// affects occupancy. 256 is a good default for Hopper/Blackwell. +constexpr int kBlockSize = 256; + +// Cap on num_experts_per_node staged in shared memory for the expert lookup. +// Above this the kernel falls back to a global-memory scan (still correct). +// Set well above realistic values (typical MoE uses 8-64 experts per node). +constexpr int kMaxExpertsInSmem = 1024; + +// Per-module expansion. Inlined into the main kernel so we only pay one +// permuted_rows[i] and expert lookup per output row. +__device__ inline void expandOneModule( + MoeLoraExpandModule const& mod, int64_t i, int32_t source_index, int64_t weight_index, int64_t lora_dtype_bytes) +{ + int32_t const rank = mod.ranks_src[source_index]; + + // Per-expert byte offsets: weight_index * dim * rank * sizeof(scalar). + int64_t const a_stride = weight_index * mod.dim_a * rank * lora_dtype_bytes; + int64_t const b_stride = weight_index * mod.dim_b * rank * lora_dtype_bytes; + + int64_t const a_src = mod.ptrs_src[2 * source_index + 0]; + int64_t const b_src = mod.ptrs_src[2 * source_index + 1]; + + // Pointer arithmetic in raw bytes (uintptr_t-equivalent). Consumers + // reinterpret to the LoRA scalar type with no further offset, matching + // the existing host-loop semantics in setupLoraWorkspace. + mod.ptrs_out[2 * i + 0] = a_src + a_stride; + mod.ptrs_out[2 * i + 1] = b_src + b_stride; + mod.ranks_out[i] = rank; +} + +// Reset one module's output slot to a rank-0 no-op. The device-path scratch is +// persistent and reused, so ghost rows must be explicitly zeroed; otherwise +// stale ranks or pointers survive into the next grouped GEMM. +__device__ inline void zeroOneModule(MoeLoraExpandModule const& mod, int64_t i) +{ + mod.ranks_out[i] = 0; + mod.ptrs_out[2 * i + 0] = 0; + mod.ptrs_out[2 * i + 1] = 0; +} + +// One thread per permuted row: find its expert via search over +// expert_first_token_offset (staged in shared memory), compute +// source_index = permuted_rows[i] % num_rows, and expand fc1, fc2, and +// (optionally) gated. Rank 0 is a per-token no-op in the grouped GEMM, so no +// global "any-token-has-lora" reduction is needed. +__global__ void moeLoraPointerExpandKernel(int32_t const* __restrict__ permuted_rows, + int64_t const* __restrict__ expert_first_token_offset, int32_t num_experts_per_node, int32_t start_expert, + int64_t num_rows, int64_t expanded_num_rows, int64_t lora_dtype_bytes, MoeLoraExpandModule fc1, + MoeLoraExpandModule fc2, MoeLoraExpandModule gated, bool has_gated) +{ + // Stage expert_first_token_offset in shared memory once per block; every + // thread reads it during the expert lookup below. + extern __shared__ int64_t smem_first_token_offset[]; + bool const use_smem = num_experts_per_node + 1 <= kMaxExpertsInSmem; + if (use_smem) + { + for (int e = threadIdx.x; e < num_experts_per_node + 1; e += blockDim.x) + { + smem_first_token_offset[e] = expert_first_token_offset[e]; + } + __syncthreads(); + } + + int64_t const i = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; + if (i >= expanded_num_rows) + { + return; + } + + // Find expert_idx s.t. first_offset[expert_idx] <= i < first_offset[expert_idx + 1]. + // Linear scan; num_experts_per_node is small enough (~8-64 typical) that + // a binary search adds branch divergence with no meaningful speedup. + int64_t const* offsets = use_smem ? smem_first_token_offset : expert_first_token_offset; + int32_t expert_idx = 0; + for (int32_t e = 0; e < num_experts_per_node; ++e) + { + if (offsets[e + 1] > i) + { + expert_idx = e; + break; + } + expert_idx = e + 1; + } + // Tokens past the last valid offset (padding "ghost" rows) get + // expert_idx == num_experts_per_node; drop them so weight_index cannot run + // off the expert table. Zero their output slots first so reused scratch + // becomes a deterministic rank-0 no-op. + if (expert_idx >= num_experts_per_node) + { + zeroOneModule(fc1, i); + zeroOneModule(fc2, i); + if (has_gated) + { + zeroOneModule(gated, i); + } + return; + } + + int64_t const weight_index = static_cast(expert_idx) + start_expert; + int32_t const source_index = static_cast(permuted_rows[i] % num_rows); + + expandOneModule(fc1, i, source_index, weight_index, lora_dtype_bytes); + expandOneModule(fc2, i, source_index, weight_index, lora_dtype_bytes); + if (has_gated) + { + expandOneModule(gated, i, source_index, weight_index, lora_dtype_bytes); + } +} + +} // namespace + +void launchMoeLoraPointerExpand(int32_t const* permuted_rows, int64_t const* expert_first_token_offset, + int32_t num_experts_per_node, int32_t start_expert, int64_t num_rows, int64_t expanded_num_rows, + int64_t lora_dtype_bytes, MoeLoraExpandModule const& fc1, MoeLoraExpandModule const& fc2, + MoeLoraExpandModule const* gated, cudaStream_t stream) +{ + if (expanded_num_rows <= 0) + { + return; + } + TLLM_CHECK_WITH_INFO(permuted_rows != nullptr, "permuted_rows must be non-null"); + TLLM_CHECK_WITH_INFO(expert_first_token_offset != nullptr, "expert_first_token_offset must be non-null"); + TLLM_CHECK_WITH_INFO(num_experts_per_node > 0, "num_experts_per_node must be positive"); + TLLM_CHECK_WITH_INFO(num_rows > 0, "num_rows must be positive"); + TLLM_CHECK_WITH_INFO(lora_dtype_bytes > 0, "lora_dtype_bytes must be positive"); + + bool const has_gated = gated != nullptr; + MoeLoraExpandModule const gated_arg = has_gated ? *gated : MoeLoraExpandModule{}; + + int64_t const grid = (expanded_num_rows + kBlockSize - 1) / kBlockSize; + // Reserve shared memory only when the expert table fits. Above the cap the + // kernel falls back to global-memory reads (still correct, no staging), so + // we pass 0 bytes to avoid allocating shared memory we will not touch. + int const smem_entries = num_experts_per_node + 1; + size_t const smem_bytes + = (smem_entries <= kMaxExpertsInSmem) ? static_cast(smem_entries) * sizeof(int64_t) : 0; + + moeLoraPointerExpandKernel<<(grid), kBlockSize, smem_bytes, stream>>>(permuted_rows, + expert_first_token_offset, num_experts_per_node, start_expert, num_rows, expanded_num_rows, lora_dtype_bytes, + fc1, fc2, gated_arg, has_gated); + sync_check_cuda_error(stream); +} + +} // namespace kernels::cutlass_kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_problem_builder.cu b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_problem_builder.cu new file mode 100644 index 000000000000..f1b6da4ca189 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_lora_problem_builder.cu @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h" + +#include "tensorrt_llm/common/assert.h" +#include "tensorrt_llm/common/cudaUtils.h" + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels::cutlass_kernels +{ + +namespace +{ + +// Threads-per-block. The kernel is bandwidth-bound, so block size mainly +// controls occupancy. 256 is a good default for Hopper/Blackwell. +constexpr int kBlockSize = 256; + +// One thread per permuted row writes all output arrays. Each store stream is +// contiguous, so accesses coalesce; there is no inter-thread communication. +__global__ void moeLoraProblemBuilderKernel(int32_t const* __restrict__ ranks, int64_t const* __restrict__ ptrs, + int64_t input_base, int64_t lowrank_workspace, int64_t output_base, int64_t num_permuted_tokens, + int64_t in_hidden_size, int64_t out_hidden_size, int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, + cutlass::gemm::GemmCoord* __restrict__ problem_sizes_in, cutlass::gemm::GemmCoord* __restrict__ problem_sizes_out, + void** __restrict__ a_ptrs_in, void** __restrict__ b_ptrs_in, void** __restrict__ d_ptrs_in, + void** __restrict__ b_ptrs_out, void** __restrict__ d_ptrs_out, int64_t* __restrict__ lda_in, + int64_t* __restrict__ ldb_in, int64_t* __restrict__ ldd_in, int64_t* __restrict__ ldb_out, + int64_t* __restrict__ ldd_out, int64_t* __restrict__ splitk_offsets) +{ + int64_t const i = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; + if (i >= num_permuted_tokens) + { + // The +1 splitk_offsets sentinel (one past num_permuted_tokens) is + // written by thread 0 of the last block; everyone else returns. + if (i == num_permuted_tokens && splitk_offsets != nullptr) + { + splitk_offsets[num_permuted_tokens] = num_permuted_tokens * max_lora_rank * splitk_slices; + } + return; + } + + int32_t const rank = ranks[i]; + // The workspace row and ldd_in[i] use max_lora_rank, so a larger rank makes + // the in-GEMM write past its slice. Callers validate ranks host-side (see + // moeOp.cpp); this assert is a debug-build backstop. + assert(rank <= max_lora_rank); + int64_t const a_ptr_bits = ptrs[2 * i + 0]; + int64_t const b_ptr_bits = ptrs[2 * i + 1]; + + // Problem sizes: each permuted token gets its own (M=1) GEMM. This matches + // worst-case scheduling with no run-length aggregation; a future + // optimization can aggregate consecutive identical-adapter tokens. + problem_sizes_in[i] = cutlass::gemm::GemmCoord(1, rank, static_cast(in_hidden_size)); + problem_sizes_out[i] = cutlass::gemm::GemmCoord(1, static_cast(out_hidden_size), rank); + + // Pointer rows. dtype_bytes scales the per-row stride so the same + // builder serves bf16/fp16/fp32 adapters without templating. + int64_t const in_row_stride = in_hidden_size * dtype_bytes; + int64_t const work_row_stride = max_lora_rank * dtype_bytes; + int64_t const out_row_stride = out_hidden_size * dtype_bytes; + + a_ptrs_in[i] = reinterpret_cast(input_base + i * in_row_stride); + b_ptrs_in[i] = reinterpret_cast(a_ptr_bits); + d_ptrs_in[i] = reinterpret_cast(lowrank_workspace + i * work_row_stride); + b_ptrs_out[i] = reinterpret_cast(b_ptr_bits); + d_ptrs_out[i] = reinterpret_cast(output_base + i * out_row_stride); + + // Leading dimensions. For the in-/out- GEMMs, lda/ldd correspond to the + // input row-stride / workspace row-stride / output row-stride; ldb is + // the per-problem stride in the LoRA adapter's storage and matches + // the cuda_graph_grouped_gemm convention used by attention LoRA + // (loraOp.cpp): + // in-GEMM: adapter A stored as [rank, in_hidden_size] + // -> ldb_in = in_hidden_size + // out-GEMM: adapter B stored as [out_hidden_size, rank] + // -> ldb_out = rank (per-token, since per-token rank + // can differ in slot-indexed multi-LoRA mode) + lda_in[i] = in_hidden_size; + ldb_in[i] = in_hidden_size; + ldd_in[i] = max_lora_rank; + ldb_out[i] = rank; + ldd_out[i] = out_hidden_size; + + // Split-K scratch offsets. Worst-case fixed stride (independent of + // per-token rank) so each thread computes its own offset locally; no + // cross-thread scan needed. + if (splitk_offsets != nullptr) + { + splitk_offsets[i] = i * max_lora_rank * splitk_slices; + } +} + +} // namespace + +void launchMoeLoraProblemBuilder(int32_t const* ranks_dev, int64_t const* ptrs_dev, void const* input_base, + void* lowrank_workspace, void* output_base, int64_t num_permuted_tokens, int64_t in_hidden_size, + int64_t out_hidden_size, int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, + MoeLoraGemmGroupArrays const& out, cudaStream_t stream) +{ + if (num_permuted_tokens <= 0) + { + return; + } + TLLM_CHECK_WITH_INFO(ranks_dev != nullptr, "ranks_dev must be non-null"); + TLLM_CHECK_WITH_INFO(ptrs_dev != nullptr, "ptrs_dev must be non-null"); + TLLM_CHECK_WITH_INFO(out.problem_sizes_in != nullptr, "problem_sizes_in must be non-null"); + TLLM_CHECK_WITH_INFO(out.problem_sizes_out != nullptr, "problem_sizes_out must be non-null"); + TLLM_CHECK_WITH_INFO(out.a_ptrs_in && out.b_ptrs_in && out.d_ptrs_in && out.b_ptrs_out && out.d_ptrs_out, + "All ptr_*_in/out arrays must be non-null"); + TLLM_CHECK_WITH_INFO( + out.lda_in && out.ldb_in && out.ldd_in && out.ldb_out && out.ldd_out, "All ld* arrays must be non-null"); + TLLM_CHECK_WITH_INFO(dtype_bytes > 0, "dtype_bytes must be positive"); + TLLM_CHECK_WITH_INFO(max_lora_rank > 0, "max_lora_rank must be positive"); + TLLM_CHECK_WITH_INFO(in_hidden_size > 0 && out_hidden_size > 0, "hidden sizes must be positive"); + TLLM_CHECK_WITH_INFO(splitk_slices > 0, "splitk_slices must be positive"); + + // Launch one extra thread so the splitk_offsets[num_permuted_tokens] + // sentinel can be filled by exactly one thread (cleaner than a + // dedicated tail launch). + int64_t const launch_count = num_permuted_tokens + (out.splitk_offsets != nullptr ? 1 : 0); + int64_t const grid = (launch_count + kBlockSize - 1) / kBlockSize; + + moeLoraProblemBuilderKernel<<(grid), kBlockSize, 0, stream>>>(ranks_dev, ptrs_dev, + reinterpret_cast(input_base), reinterpret_cast(lowrank_workspace), + reinterpret_cast(output_base), num_permuted_tokens, in_hidden_size, out_hidden_size, max_lora_rank, + dtype_bytes, splitk_slices, out.problem_sizes_in, out.problem_sizes_out, out.a_ptrs_in, out.b_ptrs_in, + out.d_ptrs_in, out.b_ptrs_out, out.d_ptrs_out, out.lda_in, out.ldb_in, out.ldd_in, out.ldb_out, out.ldd_out, + out.splitk_offsets); + sync_check_cuda_error(stream); +} + +} // namespace kernels::cutlass_kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/thop/moeOp.cpp b/cpp/tensorrt_llm/thop/moeOp.cpp index 454dbe07dcb9..b69e2987771f 100644 --- a/cpp/tensorrt_llm/thop/moeOp.cpp +++ b/cpp/tensorrt_llm/thop/moeOp.cpp @@ -21,12 +21,17 @@ #include "moe_kernels.h" #endif // Always include the public header for moe_gemm_kernels.h +#include "cutlass/gemm_coord.h" #include "tensorrt_llm/kernels/cutlass_kernels/include/moe_gemm_kernels.h" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_device_path.h" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h" #include "tensorrt_llm/common/config.h" #include "tensorrt_llm/common/cublasMMWrapper.h" +#include "tensorrt_llm/common/dataType.h" #include "tensorrt_llm/common/opUtils.h" #include "tensorrt_llm/common/workspace.h" +#include "tensorrt_llm/kernels/cuda_graph_grouped_gemm.h" #include "tensorrt_llm/kernels/cutlass_kernels/fp8_blockscale_gemm/fp8_blockscale_gemm.h" #include "tensorrt_llm/kernels/cutlass_kernels/include/cutlass_kernel_selector.h" #include "tensorrt_llm/kernels/lora/lora.h" @@ -68,6 +73,70 @@ enum class MoeLoraRequestType : int32_t kGENERATION = 1 }; +// --------------------------------------------------------------------------- +// libtorch-bound implementation of MoeLoraDeviceRunFn. +// +// The per-module GEMM dispatch for the device LoRA path: builds the per-token +// problem descriptors on device via launchMoeLoraProblemBuilder, then +// dispatches cudaGraph(SplitK)GroupedGemm. The latter allocates workspace via +// at::Tensor, so this lives in th_common (which links libtorch); moe_kernels.cu +// reaches it through LoraParams::device_path.run, keeping libmoe_gemm_src.a +// (and the TensorRT plugin) libtorch-free. +// --------------------------------------------------------------------------- +inline void moeLoraDeviceRunImpl(::tensorrt_llm::kernels::cutlass_kernels::MoeLoraDevicePathModule const& mod, + int64_t num_permuted_tokens, int64_t in_hidden_size, int64_t max_lora_rank, int64_t dtype_bytes, + int64_t splitk_slices, void const* input_base, void* output_base, nvinfer1::DataType data_type, cudaStream_t stream) +{ + TLLM_CHECK_WITH_INFO(mod.permuted_ranks_dev != nullptr, + "Device-path LoRA module is missing permuted ranks buffer (forgot to populate device_path?)."); + + // Repack the device-resident scratch into the bundle the problem-builder + // consumes. The typed casts recover the concrete pointer types that + // MoeLoraDevicePathModule stores as void* for header decoupling. + ::tensorrt_llm::kernels::cutlass_kernels::MoeLoraGemmGroupArrays arrays{}; + arrays.problem_sizes_in = static_cast(mod.problem_sizes_in_dev); + arrays.problem_sizes_out = static_cast(mod.problem_sizes_out_dev); + arrays.a_ptrs_in = mod.a_ptrs_in_dev; + arrays.b_ptrs_in = mod.b_ptrs_in_dev; + arrays.d_ptrs_in = mod.d_ptrs_in_dev; + arrays.b_ptrs_out = mod.b_ptrs_out_dev; + arrays.d_ptrs_out = mod.d_ptrs_out_dev; + arrays.lda_in = mod.lda_in_dev; + arrays.ldb_in = mod.ldb_in_dev; + arrays.ldd_in = mod.ldd_in_dev; + arrays.ldb_out = mod.ldb_out_dev; + arrays.ldd_out = mod.ldd_out_dev; + arrays.splitk_offsets = mod.splitk_offsets_dev; + + ::tensorrt_llm::kernels::cutlass_kernels::launchMoeLoraProblemBuilder(mod.permuted_ranks_dev, mod.permuted_ptrs_dev, + input_base, mod.lowrank_workspace_dev, output_base, num_permuted_tokens, in_hidden_size, mod.out_hidden_size, + max_lora_rank, dtype_bytes, splitk_slices, arrays, stream); + sync_check_cuda_error(stream); + + // The cuda_graph_(split_k_)grouped_gemm wrappers accept ldc == ldd when C + // aliases D (the no-bias case). The problem-builder produces a single + // ldd_in / ldd_out per stage, reused for ldcGpu below. + auto* host_max_in = static_cast(mod.host_max_problem_in_pinned); + auto* host_max_out = static_cast(mod.host_max_problem_out_pinned); + + // kMinKN mirrors the value attention LoRA uses for kernel selection. The + // wrappers fall back to the smaller-tile family when min(K, N) < kMinKN. + constexpr int kMinKN = 16; + + ::tensorrt_llm::kernels::cudaGraphSplitKGroupedGemm(arrays.problem_sizes_in, static_cast(num_permuted_tokens), + arrays.a_ptrs_in, arrays.b_ptrs_in, arrays.d_ptrs_in, arrays.d_ptrs_in, arrays.lda_in, arrays.ldb_in, + arrays.ldd_in, arrays.ldd_in, + /*isLoraIn=*/true, data_type, static_cast(splitk_slices), kMinKN, host_max_in, arrays.splitk_offsets, + stream); + sync_check_cuda_error(stream); + + ::tensorrt_llm::kernels::cudaGraphGroupedGemm(arrays.problem_sizes_out, static_cast(num_permuted_tokens), + arrays.d_ptrs_in /*== a_ptrs_out*/, arrays.b_ptrs_out, arrays.d_ptrs_out, arrays.d_ptrs_out, arrays.ldd_in, + arrays.ldb_out, arrays.ldd_out, arrays.ldd_out, + /*isLoraIn=*/false, data_type, kMinKN, host_max_out, stream); + sync_check_cuda_error(stream); +} + class FusedMoeRunner : public torch::CustomClassHolder { public: @@ -249,6 +318,16 @@ class FusedMoeRunner : public torch::CustomClassHolder mGemm1Profiles = mKernelRunner->getTactics(MoeGemmId::GEMM_1); mGemm2Profiles = mKernelRunner->getTactics(MoeGemmId::GEMM_2); cuInit(0); + + // Device-LoRA-path opt-in for the per-request schema. Any non-empty + // value other than "0"/"OFF"/"off" enables the capture-safe on-device + // LoRA path (pointer-expand + problem-builder + grouped-GEMM) instead of + // the legacy host-pointer path, matching LORA_USE_UNIFIED_GEMM. + if (char const* envv = std::getenv("TLLM_MOE_LORA_USE_DEVICE_PATH")) + { + std::string val(envv); + mUseDeviceLoraPath = !val.empty() && val != "0" && val != "OFF" && val != "off"; + } } ~FusedMoeRunner() @@ -297,7 +376,7 @@ class FusedMoeRunner : public torch::CustomClassHolder bool use_dynamic_fc2_scale = false, // Routed-expert LoRA inputs (all optional; presence of fc1_lora_ranks activates LoRA). // Each *_ranks : CPU int32 [num_seqs] - // Each *_weights : CPU int64 [num_seqs, 3] -- (A_ptr, B_ptr, DoRA_ptr-unused) + // Each *_weights : CPU int64 [num_seqs, 3], holding (A_ptr, B_ptr, DoRA_ptr); DoRA unused. torch::optional const& fc1_lora_ranks = torch::nullopt, torch::optional const& fc1_lora_weight_ptrs = torch::nullopt, torch::optional const& fc2_lora_ranks = torch::nullopt, @@ -492,22 +571,30 @@ class FusedMoeRunner : public torch::CustomClassHolder "MoE LoRA only supports fp16 and bf16 activation dtypes."); TORCH_CHECK(mWeightDtype == c10::ScalarType::Half || mWeightDtype == c10::ScalarType::BFloat16, "MoE LoRA only supports unquantized fp16/bf16 expert weights."); - // CUDA-graph capture is incompatible with the kernel's LoRA path, - // which performs a host-side `cudaEventSynchronize` and CPU-side - // per-token pointer expansion inside `setupLoraWorkspace`. The - // event-synchronize cannot be recorded into a graph, so reject here - // with a clear message to avoid a segfault during capture. - TORCH_CHECK(!tensorrt_llm::common::isCapturing(stream), - "MoE LoRA is not supported under CUDA graph capture. The fused-MoE kernel's " - "LoRA path performs a host-side cudaEventSynchronize after a D2H pointer-expansion copy, " - "which is not capturable. Run the LoRA path eagerly, or disable MoE LoRA when capturing."); + // CUDA-graph capture is only safe on the device LoRA path. The legacy + // host path performs a host-side cudaEventSynchronize and per-token + // pointer expansion in setupLoraWorkspace, plus host-side run-length + // encoding in LoraImpl::run, none of which is capturable. The device + // path (launchMoeLoraPointerExpand and runMoeLoraDeviceModule in + // moe_kernels.cu) runs entirely on the stream and is opted into via + // TLLM_MOE_LORA_USE_DEVICE_PATH. + TORCH_CHECK(mUseDeviceLoraPath || !tensorrt_llm::common::isCapturing(stream), + "MoE LoRA + CUDA graph capture requires the device LoRA path. The per-request schema runs " + "the legacy host path by default, which performs a host-side cudaEventSynchronize after a " + "D2H pointer-expansion copy and is not capturable. Set TLLM_MOE_LORA_USE_DEVICE_PATH=1, run " + "LoRA eagerly, or disable MoE LoRA when capturing."); } // Build LoraParams up-front so we can compute the required cuBLAS workspace before allocation. auto lora_params_opt = buildMoeLoraParams(fc1_lora_ranks, fc1_lora_weight_ptrs, fc2_lora_ranks, fc2_lora_weight_ptrs, gated_lora_ranks, gated_lora_weight_ptrs, host_request_types, host_context_lengths, - /*num_tokens=*/num_rows, hidden_size, inter_size, mActivationDtype, lora_max_low_rank, is_gated_act); + /*num_tokens=*/num_rows, hidden_size, inter_size, mActivationDtype, lora_max_low_rank, is_gated_act, stream, + static_cast(experts_per_token)); size_t lora_workspace_size = 0; - if (lora_params_opt.has_value()) + // The device path uses persistent device scratch and never touches the + // legacy cuBLAS lora_workspace, so skip computing/allocating it there to + // avoid duplicating LoRA scratch per stream (and the resulting OOM risk + // at large top_k/rank). + if (lora_params_opt.has_value() && !lora_params_opt->device_path.enabled) { auto const lora_dtype = loraTypeFromActDtype(mActivationDtype); lora_workspace_size = computeLoraWorkspaceSize(lora_params_opt->fc1_lora_impl, @@ -546,7 +633,7 @@ class FusedMoeRunner : public torch::CustomClassHolder // LoraParams is either the populated one we just built or a default-constructed empty one (use_lora=false). ::tensorrt_llm::kernels::LoraParams lora_params = lora_params_opt.value_or(::tensorrt_llm::kernels::LoraParams{}); - if (lora_active) + if (lora_active && !lora_params.device_path.enabled) { lora_params.workspace = workspace_info.lora_workspace; } @@ -909,14 +996,101 @@ class FusedMoeRunner : public torch::CustomClassHolder // Sync event used by setupLoraWorkspace (kernel waits on this before reading // host-side permuted_rows arrays). Created lazily. cudaEvent_t mLoraMemcpyEvent = nullptr; - // Scratch storage for the per-token expanded LoRA pointer/rank arrays. - // Reused across calls; .clear() drops content but retains capacity. - std::vector mLoraExpandFC1WeightPtrs; - std::vector mLoraExpandFC2WeightPtrs; - std::vector mLoraExpandGatedWeightPtrs; - std::vector mLoraExpandFC1Ranks; - std::vector mLoraExpandFC2Ranks; - std::vector mLoraExpandGatedRanks; + + // Pinned-host and persistent-device buffers for the capture-safe MoE LoRA + // path. The pinned-host tensors hold the per-token expanded LoRA tables + // (ranks and weight-pointer pairs) so the in-op async H2D into the device + // mirrors is graph-capturable; an async H2D from pageable host memory + // silently becomes synchronous and breaks capture. Both tensors are sized + // at mLoraHostBufCapacity (max_num_tokens) and reused across calls so the + // source and destination addresses are stable across capture and replay. + // Only the first num_tokens entries are valid each call. + at::Tensor mLoraExpandFC1RanksPinned; // [max_num_tokens] int32 + at::Tensor mLoraExpandFC1WeightPtrsPinned; // [max_num_tokens * 2] int64 (A, B) + at::Tensor mLoraExpandFC2RanksPinned; // [max_num_tokens] int32 + at::Tensor mLoraExpandFC2WeightPtrsPinned; // [max_num_tokens * 2] int64 + at::Tensor mLoraExpandGatedRanksPinned; // [max_num_tokens] int32 + at::Tensor mLoraExpandGatedWeightPtrsPinned; // [max_num_tokens * 2] int64 + at::Tensor mLoraExpandFC1RanksDevice; + at::Tensor mLoraExpandFC1WeightPtrsDevice; + at::Tensor mLoraExpandFC2RanksDevice; + at::Tensor mLoraExpandFC2WeightPtrsDevice; + at::Tensor mLoraExpandGatedRanksDevice; + at::Tensor mLoraExpandGatedWeightPtrsDevice; + // Tracks how many entries were populated this call so the H2D copies only + // the live portion. Per module; gated may be inactive for non-gated layers. + int64_t mLoraExpandFC1Size = 0; + int64_t mLoraExpandFC2Size = 0; + int64_t mLoraExpandGatedSize = 0; + // Highest max_num_tokens we have allocated storage for. Grown lazily by + // buildMoeLoraParams; resizing reallocates and changes the buffer addresses. + int64_t mLoraHostBufCapacity = 0; + + // Set once a CUDA-graph capture has been observed on the LoRA path. After + // that, growing the persistent scratch is forbidden even outside capture, + // since a captured graph keeps replaying against the freed addresses. + // Mutable so the const capture-safety check can record it. + mutable bool mLoraCaptureObserved = false; + + // Persistent device-resident scratch backing the capture-safe MoE LoRA + // path. One LoraDevicePathBuffers per module (fc1, fc2, gated). All + // at::Tensor members are allocated by ensureLoraDeviceScratch and reused + // across calls so the addresses baked into a captured graph remain valid + // for replay. Pointers from these tensors are packed into + // LoraParams::device_path by buildMoeLoraParams when the device path is taken. + struct LoraDevicePathBuffers + { + // Per-permuted-row (rank, A_ptr + offset, B_ptr + offset). + at::Tensor permuted_ranks; // int32 [P_max] + at::Tensor permuted_ptrs; // int64 [2 * P_max] + + // Grouped-GEMM bundle. Concrete types restored at the LoraParams boundary. + at::Tensor problem_sizes_in; // int8 [P_max * sizeof(GemmCoord)] + at::Tensor problem_sizes_out; // int8 [P_max * sizeof(GemmCoord)] + at::Tensor a_ptrs_in; // int64 [P_max] + at::Tensor b_ptrs_in; // int64 [P_max] + at::Tensor d_ptrs_in; // int64 [P_max] + at::Tensor b_ptrs_out; // int64 [P_max] + at::Tensor d_ptrs_out; // int64 [P_max] + at::Tensor lda_in; // int64 [P_max] + at::Tensor ldb_in; // int64 [P_max] + at::Tensor ldd_in; // int64 [P_max] + at::Tensor ldb_out; // int64 [P_max] + at::Tensor ldd_out; // int64 [P_max] + at::Tensor splitk_offsets; // int64 [P_max + 1] + + // GEMM data-flow buffers. The split-K in-GEMM's partial-sum scratch is + // allocated internally by cuda_graph_split_k_grouped_gemm, so only the + // low-rank intermediate is owned here. + at::Tensor lowrank_workspace; // dtype [P_max * max_lora_rank] + + // Pinned-host single GemmCoord upper bounds; required by the + // cuda_graph_*_grouped_gemm wrappers for kernel selection. + at::Tensor host_max_problem_in; // int8 pinned [sizeof(GemmCoord)] + at::Tensor host_max_problem_out; // int8 pinned [sizeof(GemmCoord)] + }; + + LoraDevicePathBuffers mFc1DeviceBuf; + LoraDevicePathBuffers mFc2DeviceBuf; + LoraDevicePathBuffers mGatedDeviceBuf; + + // Tracks the shape parameters baked into the current scratch + // allocation. (Re)allocation is required if any of these grows or if + // the dtype changes. + int64_t mLoraDeviceScratchCapacity = 0; // P_max = max(num_tokens * top_k) + int64_t mLoraDeviceScratchMaxLoraRank = 0; + int64_t mLoraDeviceScratchDtypeBytes = 0; + int64_t mLoraDeviceScratchSplitKSlices = 0; + bool mLoraDeviceScratchHasGated = false; + + // Set from the TLLM_MOE_LORA_USE_DEVICE_PATH environment variable at + // construction time. Selects the capture-safe device LoRA path. + bool mUseDeviceLoraPath = false; + + // Split-K slice count for the device-path low-rank in-GEMM. Mirrors the + // value LoraImpl uses internally so the device-path split-K scratch is sized + // identically. + static constexpr int64_t kDevicePathSplitKSlices = 16; void freeProfileWorkspace() { @@ -1086,9 +1260,14 @@ class FusedMoeRunner : public torch::CustomClassHolder // num_tokens: total tokens flowing through this op (used as a consistency check) // // Outputs the two `expand_*` vectors with shapes [num_tokens] / [num_tokens * 2]. + // Writes the [num_tokens] expanded LoRA tables into the caller-owned + // pinned-host buffers expand_ranks_data ([num_tokens] int32) and + // expand_ptrs_data ([num_tokens * 2] int64; each pair is (A, B) as + // raw pointer bits stored in int64). The buffers must already be + // allocated to at least num_tokens / num_tokens * 2 elements. void expandPerRequestLoraTo(torch::Tensor const& ranks, torch::Tensor const& weight_ptrs, torch::Tensor const& host_request_types, torch::Tensor const& host_context_lengths, int64_t num_tokens, - std::vector& expand_ranks, std::vector& expand_ptrs) + int32_t* expand_ranks_data, int64_t* expand_ptrs_data) { CHECK_CPU_INPUT(ranks, at::ScalarType::Int) CHECK_CPU_INPUT(weight_ptrs, at::ScalarType::Long) @@ -1109,28 +1288,43 @@ class FusedMoeRunner : public torch::CustomClassHolder auto const* req_types = static_cast(host_request_types.data_ptr()); auto const* ctx_lens = static_cast(host_context_lengths.data_ptr()); - expand_ranks.clear(); - expand_ptrs.clear(); - expand_ranks.reserve(num_tokens); - expand_ptrs.reserve(num_tokens * 2); - int64_t produced = 0; for (int64_t req_id = 0; req_id < num_seqs; ++req_id) { int32_t const rank = rank_data[req_id]; - void const* const a_ptr = reinterpret_cast(ptr_data[req_id * 3 + 0]); - void const* const b_ptr = reinterpret_cast(ptr_data[req_id * 3 + 1]); + int64_t const a_ptr = ptr_data[req_id * 3 + 0]; + int64_t const b_ptr = ptr_data[req_id * 3 + 1]; // ptr_data[req_id * 3 + 2] is the optional DoRA magnitude vector pointer; ignored here // (MoE+DoRA is rejected at load time, see tensorrt_llm/lora_manager.py). - auto const req_type = static_cast(req_types[req_id]); + // Validate the raw request type before trusting it. An unexpected + // value would otherwise fall into the CONTEXT branch and read an + // arbitrary context length, producing a negative/garbage repeat. + int32_t const req_type_raw = req_types[req_id]; + TORCH_CHECK(req_type_raw == static_cast(MoeLoraRequestType::kCONTEXT) + || req_type_raw == static_cast(MoeLoraRequestType::kGENERATION), + "MoE LoRA host_request_types[", req_id, "] must be 0 (context) or 1 (generation); got ", req_type_raw); + auto const req_type = static_cast(req_type_raw); + if (req_type == MoeLoraRequestType::kCONTEXT) + { + TORCH_CHECK(ctx_lens[req_id] >= 0, "MoE LoRA host_context_lengths[", req_id, + "] must be non-negative; got ", ctx_lens[req_id]); + } int64_t const repeat = (req_type == MoeLoraRequestType::kGENERATION) ? int64_t{1} : static_cast(ctx_lens[req_id]); + // Guard the destination writes BEFORE producing them. expand_*_data + // point at fixed-capacity pinned buffers sized for num_tokens, so a + // malformed host_context_lengths (summing past num_tokens) must be a + // clean error rather than an out-of-bounds write into pinned memory. + TORCH_CHECK(repeat >= 0 && produced + repeat <= num_tokens, "MoE LoRA per-request expansion overran the ", + num_tokens, "-token buffer at request ", req_id, " (produced ", produced, " + ", repeat, + "). Check host_request_types / host_context_lengths against the op's token count."); for (int64_t i = 0; i < repeat; ++i) { - expand_ranks.push_back(rank); - expand_ptrs.push_back(a_ptr); - expand_ptrs.push_back(b_ptr); + int64_t const t = produced + i; + expand_ranks_data[t] = rank; + expand_ptrs_data[2 * t + 0] = a_ptr; + expand_ptrs_data[2 * t + 1] = b_ptr; } produced += repeat; } @@ -1138,10 +1332,196 @@ class FusedMoeRunner : public torch::CustomClassHolder " tokens but op input has ", num_tokens, " tokens."); } + // Reallocating MoE-LoRA scratch hands out fresh addresses, which silently + // invalidates any CUDA graph that baked in the old ones. Reject reallocation + // both while capturing and after any capture has been observed, since an + // earlier graph keeps replaying. Callers invoke this only when reallocation + // is imminent. No-op before the first capture (e.g. warmup pre-sizing). + void checkLoraReallocSafeDuringCapture(cudaStream_t stream, int64_t requested, int64_t current) const + { + bool const capturing = (stream != nullptr && tensorrt_llm::common::isCapturing(stream)); + if (capturing) + { + mLoraCaptureObserved = true; + } + if (!capturing && !mLoraCaptureObserved) + { + return; + } + TORCH_CHECK(false, "MoE LoRA scratch (current capacity ", current, ") is too small for ", requested, + capturing ? " entries during CUDA graph capture." : " entries after a CUDA graph capture was observed.", + " Growing it would invalidate addresses baked into already-captured graphs. Run the device LoRA path " + "eagerly through the worst-case shape before capture so the scratch is pre-sized."); + } + + // Internal helper: (re)allocate the six pinned-host + six device tensor + // pairs to hold capacity expanded tokens. Called by buildMoeLoraParams + // (lazy on first call at a given size). The (re)allocation drops the + // previous storage; callers must make sure any in-flight CUDA graph that + // references the old addresses has either been destroyed or never replays + // again. + void ensureLoraExpandBuffers(int64_t capacity) + { + auto const pinned_int_opts = at::TensorOptions().dtype(at::kInt).pinned_memory(true); + auto const pinned_long_opts = at::TensorOptions().dtype(at::kLong).pinned_memory(true); + auto const dev_int_opts = at::TensorOptions().dtype(at::kInt).device(at::kCUDA); + auto const dev_long_opts = at::TensorOptions().dtype(at::kLong).device(at::kCUDA); + + mLoraExpandFC1RanksPinned = at::empty({capacity}, pinned_int_opts); + mLoraExpandFC2RanksPinned = at::empty({capacity}, pinned_int_opts); + mLoraExpandGatedRanksPinned = at::empty({capacity}, pinned_int_opts); + mLoraExpandFC1WeightPtrsPinned = at::empty({capacity * 2}, pinned_long_opts); + mLoraExpandFC2WeightPtrsPinned = at::empty({capacity * 2}, pinned_long_opts); + mLoraExpandGatedWeightPtrsPinned = at::empty({capacity * 2}, pinned_long_opts); + + mLoraExpandFC1RanksDevice = at::empty({capacity}, dev_int_opts); + mLoraExpandFC2RanksDevice = at::empty({capacity}, dev_int_opts); + mLoraExpandGatedRanksDevice = at::empty({capacity}, dev_int_opts); + mLoraExpandFC1WeightPtrsDevice = at::empty({capacity * 2}, dev_long_opts); + mLoraExpandFC2WeightPtrsDevice = at::empty({capacity * 2}, dev_long_opts); + mLoraExpandGatedWeightPtrsDevice = at::empty({capacity * 2}, dev_long_opts); + } + + // Allocate the per-module device-path scratch for the capture-safe LoRA + // path. The buffers are sized in permuted tokens (P = num_tokens * top_k) + // and the per-token LoRA rank upper bound max_lora_rank; both feed the + // pointer-expand, problem-builder, and cuda_graph_*_grouped_gemm kernels. + // + // The function is idempotent at or below the current capacity and + // reallocates only when one of (capacity, max_lora_rank, dtype_bytes, + // splitk_slices, has_gated) grows. Reallocation drops the previous storage, + // so callers must ensure any in-flight CUDA graph referencing the old + // addresses has been destroyed or will not replay. + // + // The host-side max-problem-size pins hold one GemmCoord each; the value is + // a worst-case upper bound, independent of per-call data. + void ensureLoraDeviceScratch(int64_t capacity, int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, + bool has_gated, cudaStream_t stream = nullptr) + { + TORCH_CHECK(capacity > 0, "device-path capacity must be positive; got ", capacity); + TORCH_CHECK(max_lora_rank > 0, "device-path max_lora_rank must be positive; got ", max_lora_rank); + TORCH_CHECK(dtype_bytes > 0, "device-path dtype_bytes must be positive; got ", dtype_bytes); + TORCH_CHECK(splitk_slices > 0, "device-path splitk_slices must be positive; got ", splitk_slices); + + bool const need_resize = capacity > mLoraDeviceScratchCapacity || max_lora_rank > mLoraDeviceScratchMaxLoraRank + || dtype_bytes != mLoraDeviceScratchDtypeBytes || splitk_slices != mLoraDeviceScratchSplitKSlices + || (has_gated && !mLoraDeviceScratchHasGated); + if (!need_resize) + { + return; + } + // Refuse to grow device scratch mid-capture (see helper for rationale). + checkLoraReallocSafeDuringCapture(stream, capacity, mLoraDeviceScratchCapacity); + + // Grow each field to the requested upper bound and remember the + // dtype/rank/splitk combo so subsequent calls can early-exit. + int64_t const new_capacity = std::max(capacity, mLoraDeviceScratchCapacity); + int64_t const new_max_lora_rank = std::max(max_lora_rank, mLoraDeviceScratchMaxLoraRank); + bool const new_has_gated = mLoraDeviceScratchHasGated || has_gated; + + // c10::ScalarType for the lowrank workspace. The kernel treats the + // buffer opaquely (per-byte stride is dtype_bytes), so we pick a + // dtype with matching element size to keep at::Tensor accounting + // sensible; consumers cast via .data_ptr(). + c10::ScalarType const dtype_scalar = (dtype_bytes == 2) ? at::kBFloat16 + : (dtype_bytes == 4) ? at::kFloat + : at::kByte; + // Callers should pass bf16/fp16 (2 bytes). Other sizes still work at the + // byte level, but this assertion catches accidental misuse. + TORCH_CHECK(dtype_bytes == 1 || dtype_bytes == 2 || dtype_bytes == 4, + "device-path lowrank workspace dtype_bytes must be 1/2/4; got ", dtype_bytes); + + auto const dev_int8_opts = at::TensorOptions().dtype(at::kByte).device(at::kCUDA); + auto const dev_int32_opts = at::TensorOptions().dtype(at::kInt).device(at::kCUDA); + auto const dev_int64_opts = at::TensorOptions().dtype(at::kLong).device(at::kCUDA); + auto const dev_dtype_opts = at::TensorOptions().dtype(dtype_scalar).device(at::kCUDA); + auto const pinned_int8_opts = at::TensorOptions().dtype(at::kByte).pinned_memory(true); + + // sizeof(cutlass::gemm::GemmCoord) == sizeof(int) * 3 in practice; + // we ask for the exact byte count at allocation time so the bound + // tracks any cutlass struct-layout change. + int64_t const gemm_coord_bytes = static_cast(sizeof(cutlass::gemm::GemmCoord)); + + auto alloc_one = [&](LoraDevicePathBuffers& mod) + { + mod.permuted_ranks = at::empty({new_capacity}, dev_int32_opts); + mod.permuted_ptrs = at::empty({new_capacity * 2}, dev_int64_opts); + + mod.problem_sizes_in = at::empty({new_capacity * gemm_coord_bytes}, dev_int8_opts); + mod.problem_sizes_out = at::empty({new_capacity * gemm_coord_bytes}, dev_int8_opts); + + mod.a_ptrs_in = at::empty({new_capacity}, dev_int64_opts); + mod.b_ptrs_in = at::empty({new_capacity}, dev_int64_opts); + mod.d_ptrs_in = at::empty({new_capacity}, dev_int64_opts); + mod.b_ptrs_out = at::empty({new_capacity}, dev_int64_opts); + mod.d_ptrs_out = at::empty({new_capacity}, dev_int64_opts); + + mod.lda_in = at::empty({new_capacity}, dev_int64_opts); + mod.ldb_in = at::empty({new_capacity}, dev_int64_opts); + mod.ldd_in = at::empty({new_capacity}, dev_int64_opts); + mod.ldb_out = at::empty({new_capacity}, dev_int64_opts); + mod.ldd_out = at::empty({new_capacity}, dev_int64_opts); + mod.splitk_offsets = at::empty({new_capacity + 1}, dev_int64_opts); + + mod.lowrank_workspace = at::empty({new_capacity * new_max_lora_rank}, dev_dtype_opts); + + mod.host_max_problem_in = at::empty({gemm_coord_bytes}, pinned_int8_opts); + mod.host_max_problem_out = at::empty({gemm_coord_bytes}, pinned_int8_opts); + }; + + alloc_one(mFc1DeviceBuf); + alloc_one(mFc2DeviceBuf); + if (new_has_gated) + { + alloc_one(mGatedDeviceBuf); + } + + mLoraDeviceScratchCapacity = new_capacity; + mLoraDeviceScratchMaxLoraRank = new_max_lora_rank; + mLoraDeviceScratchDtypeBytes = dtype_bytes; + mLoraDeviceScratchSplitKSlices = splitk_slices; + mLoraDeviceScratchHasGated = new_has_gated; + } + + // Pack the per-module at::Tensor scratch into the typed pointer bundle + // attached to LoraParams. The buffers are owned by FusedMoeRunner, so the + // resulting pointers stay valid as long as the runner outlives the + // LoraParams use. dim_a/dim_b, ranks_src_dev, and out_hidden_size are filled + // in by buildMoeLoraParams; the output base is passed directly to + // runMoeLoraDeviceModule at the call site. + void populateLoraDevicePathModule( + LoraDevicePathBuffers& mod, ::tensorrt_llm::kernels::cutlass_kernels::MoeLoraDevicePathModule& out) const + { + out.permuted_ranks_dev = mod.permuted_ranks.data_ptr(); + out.permuted_ptrs_dev = mod.permuted_ptrs.data_ptr(); + + out.problem_sizes_in_dev = mod.problem_sizes_in.data_ptr(); + out.problem_sizes_out_dev = mod.problem_sizes_out.data_ptr(); + out.a_ptrs_in_dev = reinterpret_cast(mod.a_ptrs_in.data_ptr()); + out.b_ptrs_in_dev = reinterpret_cast(mod.b_ptrs_in.data_ptr()); + out.d_ptrs_in_dev = reinterpret_cast(mod.d_ptrs_in.data_ptr()); + out.b_ptrs_out_dev = reinterpret_cast(mod.b_ptrs_out.data_ptr()); + out.d_ptrs_out_dev = reinterpret_cast(mod.d_ptrs_out.data_ptr()); + out.lda_in_dev = mod.lda_in.data_ptr(); + out.ldb_in_dev = mod.ldb_in.data_ptr(); + out.ldd_in_dev = mod.ldd_in.data_ptr(); + out.ldb_out_dev = mod.ldb_out.data_ptr(); + out.ldd_out_dev = mod.ldd_out.data_ptr(); + out.splitk_offsets_dev = mod.splitk_offsets.data_ptr(); + + out.lowrank_workspace_dev = mod.lowrank_workspace.data_ptr(); + out.host_max_problem_in_pinned = mod.host_max_problem_in.data_ptr(); + out.host_max_problem_out_pinned = mod.host_max_problem_out.data_ptr(); + + // out_hidden_size is set by buildMoeLoraParams; default it here. + out.out_hidden_size = 0; + } + // Build a populated LoraParams from the optional CPU tensors. Caller is // responsible for setting `lora_params.workspace` (the cuBLAS scratch). // Returns std::nullopt when LoRA is inactive (no fc1 ranks tensor). - // Mutates the mLoraExpand* member vectors. + // Mutates the mLoraExpand* pinned tensors and queues an async H2D into + // the device mirrors on stream. std::optional<::tensorrt_llm::kernels::LoraParams> buildMoeLoraParams( torch::optional const& fc1_lora_ranks, torch::optional const& fc1_lora_weight_ptrs, @@ -1151,7 +1531,8 @@ class FusedMoeRunner : public torch::CustomClassHolder torch::optional const& gated_lora_weight_ptrs, torch::optional const& host_request_types, torch::optional const& host_context_lengths, int64_t num_tokens, int64_t hidden_size, - int64_t inter_size, c10::ScalarType act_dtype, int64_t lora_max_low_rank, bool is_gated_activation) + int64_t inter_size, c10::ScalarType act_dtype, int64_t lora_max_low_rank, bool is_gated_activation, + cudaStream_t stream, int experts_per_token) { if (!fc1_lora_ranks.has_value()) { @@ -1164,8 +1545,8 @@ class FusedMoeRunner : public torch::CustomClassHolder TORCH_CHECK(host_request_types.has_value() && host_context_lengths.has_value(), "MoE LoRA requires host_request_types and host_context_lengths CPU tensors."); // For gated activations (e.g. SwiGLU) the kernel's setupLoraWorkspace - // unconditionally dereferences `lora_params.gated_lora_ranks` / - // `gated_lora_weight_ptrs`, so the caller MUST provide them. + // unconditionally dereferences gated_lora_ranks and gated_lora_weight_ptrs, + // so the caller must provide them. if (is_gated_activation) { TORCH_CHECK(gated_lora_ranks.has_value() && gated_lora_weight_ptrs.has_value(), @@ -1182,36 +1563,181 @@ class FusedMoeRunner : public torch::CustomClassHolder int64_t const num_seqs = fc1_lora_ranks->size(0); bool const has_gated = is_gated_activation && gated_lora_ranks.has_value(); + // Every per-request rank must fit within lora_max_low_rank, which sizes + // both the lowrank workspace and the max-problem hints. A larger rank + // would make the device path build GEMM problems wider than the + // allocated scratch and write out of bounds, so reject it up front. + auto validate_rank_tensor = [&](char const* name, torch::Tensor const& ranks_tensor) + { + CHECK_CPU_INPUT(ranks_tensor, at::ScalarType::Int) + auto const* rank_data = ranks_tensor.data_ptr(); + for (int64_t i = 0; i < ranks_tensor.size(0); ++i) + { + TORCH_CHECK(rank_data[i] >= 0 && rank_data[i] <= lora_max_low_rank, name, "[", i, "]=", rank_data[i], + " is outside [0, ", lora_max_low_rank, "]."); + } + }; + validate_rank_tensor("fc1_lora_ranks", *fc1_lora_ranks); + validate_rank_tensor("fc2_lora_ranks", *fc2_lora_ranks); + if (has_gated) + { + validate_rank_tensor("gated_lora_ranks", *gated_lora_ranks); + } + + // Ensure pinned/device buffers can hold num_tokens entries. + // Idempotent at-or-below current capacity. + if (num_tokens > mLoraHostBufCapacity) + { + checkLoraReallocSafeDuringCapture(stream, num_tokens, mLoraHostBufCapacity); + ensureLoraExpandBuffers(num_tokens); + mLoraHostBufCapacity = num_tokens; + } + expandPerRequestLoraTo(*fc1_lora_ranks, *fc1_lora_weight_ptrs, *host_request_types, *host_context_lengths, - num_tokens, mLoraExpandFC1Ranks, mLoraExpandFC1WeightPtrs); + num_tokens, mLoraExpandFC1RanksPinned.data_ptr(), + mLoraExpandFC1WeightPtrsPinned.data_ptr()); expandPerRequestLoraTo(*fc2_lora_ranks, *fc2_lora_weight_ptrs, *host_request_types, *host_context_lengths, - num_tokens, mLoraExpandFC2Ranks, mLoraExpandFC2WeightPtrs); + num_tokens, mLoraExpandFC2RanksPinned.data_ptr(), + mLoraExpandFC2WeightPtrsPinned.data_ptr()); + mLoraExpandFC1Size = num_tokens; + mLoraExpandFC2Size = num_tokens; if (has_gated) { expandPerRequestLoraTo(*gated_lora_ranks, *gated_lora_weight_ptrs, *host_request_types, - *host_context_lengths, num_tokens, mLoraExpandGatedRanks, mLoraExpandGatedWeightPtrs); + *host_context_lengths, num_tokens, mLoraExpandGatedRanksPinned.data_ptr(), + mLoraExpandGatedWeightPtrsPinned.data_ptr()); + mLoraExpandGatedSize = num_tokens; } else { - mLoraExpandGatedRanks.clear(); - mLoraExpandGatedWeightPtrs.clear(); + mLoraExpandGatedSize = 0; } + // Queue an async H2D into the persistent device mirrors. The copy + // source is pinned, so the async copy is truly async and capturable, and + // the destination is a persistent device buffer with a stable address + // across captures. The device path consumes these mirrors via + // launchMoeLoraPointerExpand; the legacy host path ignores them and + // reads the pinned host pointers through LoraParams below. + auto issue_h2d = [&](at::Tensor const& src, at::Tensor& dst, int64_t numel) + { + if (numel == 0) + { + return; + } + TLLM_CUDA_CHECK(cudaMemcpyAsync(dst.data_ptr(), src.data_ptr(), + static_cast(numel) * src.element_size(), cudaMemcpyHostToDevice, stream)); + }; + issue_h2d(mLoraExpandFC1RanksPinned, mLoraExpandFC1RanksDevice, mLoraExpandFC1Size); + issue_h2d(mLoraExpandFC1WeightPtrsPinned, mLoraExpandFC1WeightPtrsDevice, mLoraExpandFC1Size * 2); + issue_h2d(mLoraExpandFC2RanksPinned, mLoraExpandFC2RanksDevice, mLoraExpandFC2Size); + issue_h2d(mLoraExpandFC2WeightPtrsPinned, mLoraExpandFC2WeightPtrsDevice, mLoraExpandFC2Size * 2); + issue_h2d(mLoraExpandGatedRanksPinned, mLoraExpandGatedRanksDevice, mLoraExpandGatedSize); + issue_h2d(mLoraExpandGatedWeightPtrsPinned, mLoraExpandGatedWeightPtrsDevice, mLoraExpandGatedSize * 2); + auto impls = getOrCreateLoraImpls(hidden_size, inter_size, act_dtype, static_cast(lora_max_low_rank)); + // The host-side LoRA path (LoraImpl::run) reads the per-token ranks and + // pointers through these raw host pointers, which point at the pinned + // host tensors populated above. ::tensorrt_llm::kernels::LoraParams lora_params{ static_cast(num_seqs), - mLoraExpandFC1Ranks.data(), - mLoraExpandFC1WeightPtrs.data(), - mLoraExpandFC2Ranks.data(), - mLoraExpandFC2WeightPtrs.data(), + mLoraExpandFC1RanksPinned.data_ptr(), + reinterpret_cast(mLoraExpandFC1WeightPtrsPinned.data_ptr()), + mLoraExpandFC2RanksPinned.data_ptr(), + reinterpret_cast(mLoraExpandFC2WeightPtrsPinned.data_ptr()), impls.first, impls.second, /*workspace=*/nullptr, // caller fills in &mLoraMemcpyEvent, - has_gated ? mLoraExpandGatedRanks.data() : nullptr, - has_gated ? mLoraExpandGatedWeightPtrs.data() : nullptr, + has_gated ? mLoraExpandGatedRanksPinned.data_ptr() : nullptr, + has_gated ? reinterpret_cast(mLoraExpandGatedWeightPtrsPinned.data_ptr()) : nullptr, }; + + // Device-LoRA-path scratch. Allocate the per-module device-resident + // buffers and pack their pointers into lora_params.device_path. The + // device path is taken when the env-var opts in (TLLM_MOE_LORA_USE_DEVICE_PATH). + bool const use_device_path = mUseDeviceLoraPath; + if (use_device_path) + { + int64_t const dtype_bytes = static_cast(common::getDTypeSize(loraTypeFromActDtype(act_dtype))); + int64_t const capacity = num_tokens * static_cast(experts_per_token); + // Pass stream so a mid-capture resize (which would invalidate + // previously captured graphs) is rejected with a clear error + // rather than silently corrupting replay. + ensureLoraDeviceScratch(capacity, lora_max_low_rank, dtype_bytes, kDevicePathSplitKSlices, + /*has_gated=*/has_gated, stream); + + auto& dp = lora_params.device_path; + dp.enabled = true; + dp.in_hidden_size = hidden_size; + dp.max_lora_rank = lora_max_low_rank; + dp.dtype_bytes = dtype_bytes; + dp.splitk_slices = kDevicePathSplitKSlices; + dp.has_gated = has_gated; + // Populate the libtorch-bound GEMM dispatch entry point so + // runMoeLoraDeviceModule in moe_kernels.cu can call through + // it without dragging libtorch into libmoe_gemm_src.a. + dp.run = &moeLoraDeviceRunImpl; + populateLoraDevicePathModule(mFc1DeviceBuf, dp.fc1); + populateLoraDevicePathModule(mFc2DeviceBuf, dp.fc2); + if (has_gated) + { + populateLoraDevicePathModule(mGatedDeviceBuf, dp.gated); + } + + // Per-module dim_a/dim_b describe the LoRA adapter shape the + // pointer-expand kernel offsets into; per-module out_hidden_size + // describes the LoRA delta sink the problem-builder kernel writes + // into. The runner passes the output base (lora_fc1_result_ / + // lora_fc2_result_ / lora_gated_out) directly to + // runMoeLoraDeviceModule at the loraFC1/loraFC2 call sites so the + // GEMMs land where the downstream bias/reorder kernels expect. + // + // For fc1 (and gated): adapter A is [hidden, rank], B is [rank, inter]. + // For fc2: adapter A is [inter, rank], B is [rank, hidden]. + dp.fc1.dim_a = hidden_size; + dp.fc1.dim_b = inter_size; + dp.fc1.ranks_src_dev = mLoraExpandFC1RanksDevice.data_ptr(); + dp.fc1.ptrs_src_dev = mLoraExpandFC1WeightPtrsDevice.data_ptr(); + dp.fc1.out_hidden_size = inter_size; + + dp.fc2.dim_a = inter_size; + dp.fc2.dim_b = hidden_size; + dp.fc2.ranks_src_dev = mLoraExpandFC2RanksDevice.data_ptr(); + dp.fc2.ptrs_src_dev = mLoraExpandFC2WeightPtrsDevice.data_ptr(); + dp.fc2.out_hidden_size = hidden_size; + + if (has_gated) + { + dp.gated.dim_a = hidden_size; + dp.gated.dim_b = inter_size; + dp.gated.ranks_src_dev = mLoraExpandGatedRanksDevice.data_ptr(); + dp.gated.ptrs_src_dev = mLoraExpandGatedWeightPtrsDevice.data_ptr(); + dp.gated.out_hidden_size = inter_size; + } + + // Pinned-host max-problem-size hints used by cuda_graph_*_grouped_gemm + // for kernel selection. Values are upper bounds safe to fix at + // warmup time (M=1 since each problem is one row; N/K depend on + // module direction and max_lora_rank). + auto fill_max_problem = [](void* host_ptr, int m, int n, int k) + { + auto* coord = static_cast(host_ptr); + *coord = cutlass::gemm::GemmCoord(m, n, k); + }; + // In-GEMM: M=1, N=max_lora_rank, K=in_dim. Out-GEMM: M=1, N=out_dim, K=max_lora_rank. + fill_max_problem(dp.fc1.host_max_problem_in_pinned, 1, lora_max_low_rank, hidden_size); + fill_max_problem(dp.fc1.host_max_problem_out_pinned, 1, inter_size, lora_max_low_rank); + fill_max_problem(dp.fc2.host_max_problem_in_pinned, 1, lora_max_low_rank, inter_size); + fill_max_problem(dp.fc2.host_max_problem_out_pinned, 1, hidden_size, lora_max_low_rank); + if (has_gated) + { + fill_max_problem(dp.gated.host_max_problem_in_pinned, 1, lora_max_low_rank, hidden_size); + fill_max_problem(dp.gated.host_max_problem_out_pinned, 1, inter_size, lora_max_low_rank); + } + } + return lora_params; } diff --git a/cpp/tests/unit_tests/kernels/CMakeLists.txt b/cpp/tests/unit_tests/kernels/CMakeLists.txt index 95d33e421050..fbef4ed88c2a 100644 --- a/cpp/tests/unit_tests/kernels/CMakeLists.txt +++ b/cpp/tests/unit_tests/kernels/CMakeLists.txt @@ -95,6 +95,11 @@ target_link_libraries(routingKernelsTest PRIVATE Python3::Python) add_gtest(moeLoadBalanceKernelTest moeLoadBalanceKernelTest.cpp) +if(USING_OSS_CUTLASS_MOE_GEMM) + add_gtest(moeLoraPointerExpandTest moeLoraPointerExpandTest.cu) + add_gtest(moeLoraProblemBuilderTest moeLoraProblemBuilderTest.cu) +endif() + add_gtest(eaglePackDataTest eaglePackDataTest.cpp) add_gtest(sparseKvCacheTest sparseKvCacheTest.cu) add_gtest(prepareCustomMaskTest prepareCustomMaskTest.cpp) diff --git a/cpp/tests/unit_tests/kernels/moeLoraPointerExpandTest.cu b/cpp/tests/unit_tests/kernels/moeLoraPointerExpandTest.cu new file mode 100644 index 000000000000..78f61d424a77 --- /dev/null +++ b/cpp/tests/unit_tests/kernels/moeLoraPointerExpandTest.cu @@ -0,0 +1,420 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/common/cudaUtils.h" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_pointer_expand.h" + +#include +#include +#include + +namespace +{ + +using ::tensorrt_llm::kernels::cutlass_kernels::launchMoeLoraPointerExpand; +using ::tensorrt_llm::kernels::cutlass_kernels::MoeLoraExpandModule; + +// Host-side reference reproducing the per-permuted-row pointer arithmetic +// from CutlassMoeFCRunner::setupLoraWorkspace. Same control flow as the +// device kernel; used only as ground truth for parity checks. +struct RefModule +{ + std::vector ranks_src; + std::vector ptrs_src; + int64_t dim_a; + int64_t dim_b; + std::vector ranks_out; + std::vector ptrs_out; +}; + +void cpuExpand(std::vector const& permuted_rows, std::vector const& expert_first_token_offset, + int32_t num_experts_per_node, int32_t start_expert, int64_t num_rows, int64_t expanded_num_rows, + int64_t lora_dtype_bytes, RefModule& fc1, RefModule& fc2, RefModule* gated) +{ + auto expand_one = [&](RefModule& mod, int64_t i, int32_t source_index, int64_t weight_index) + { + int32_t const rank = mod.ranks_src[source_index]; + int64_t const a_stride = weight_index * mod.dim_a * rank * lora_dtype_bytes; + int64_t const b_stride = weight_index * mod.dim_b * rank * lora_dtype_bytes; + mod.ptrs_out[2 * i + 0] = mod.ptrs_src[2 * source_index + 0] + a_stride; + mod.ptrs_out[2 * i + 1] = mod.ptrs_src[2 * source_index + 1] + b_stride; + mod.ranks_out[i] = rank; + }; + + fc1.ranks_out.assign(expanded_num_rows, 0); + fc1.ptrs_out.assign(expanded_num_rows * 2, 0); + fc2.ranks_out.assign(expanded_num_rows, 0); + fc2.ptrs_out.assign(expanded_num_rows * 2, 0); + if (gated) + { + gated->ranks_out.assign(expanded_num_rows, 0); + gated->ptrs_out.assign(expanded_num_rows * 2, 0); + } + + for (int32_t expert_idx = 0; expert_idx < num_experts_per_node; ++expert_idx) + { + int64_t const weight_index = static_cast(expert_idx) + start_expert; + for (int64_t i = expert_first_token_offset[expert_idx]; i < expert_first_token_offset[expert_idx + 1]; ++i) + { + int32_t const source_index = static_cast(permuted_rows[i] % num_rows); + expand_one(fc1, i, source_index, weight_index); + expand_one(fc2, i, source_index, weight_index); + if (gated) + { + expand_one(*gated, i, source_index, weight_index); + } + } + } +} + +template +T* deviceUpload(std::vector const& host) +{ + T* dev = nullptr; + auto const bytes = host.size() * sizeof(T); + if (bytes > 0) + { + TLLM_CUDA_CHECK(cudaMalloc(&dev, bytes)); + TLLM_CUDA_CHECK(cudaMemcpy(dev, host.data(), bytes, cudaMemcpyHostToDevice)); + } + return dev; +} + +template +T* deviceAllocZero(size_t count) +{ + T* dev = nullptr; + auto const bytes = count * sizeof(T); + TLLM_CUDA_CHECK(cudaMalloc(&dev, bytes)); + TLLM_CUDA_CHECK(cudaMemset(dev, 0, bytes)); + return dev; +} + +// Like deviceAllocZero but pre-fills with a non-zero byte pattern. Simulates +// reused scratch holding stale values, so tests can verify the kernel actively +// zeroes ghost rows. +template +T* deviceAllocFilled(size_t count, int byte_pattern) +{ + T* dev = nullptr; + auto const bytes = count * sizeof(T); + TLLM_CUDA_CHECK(cudaMalloc(&dev, bytes)); + TLLM_CUDA_CHECK(cudaMemset(dev, byte_pattern, bytes)); + return dev; +} + +template +void deviceDownload(T* dev, std::vector& host) +{ + if (host.empty()) + { + return; + } + TLLM_CUDA_CHECK(cudaMemcpy(host.data(), dev, host.size() * sizeof(T), cudaMemcpyDeviceToHost)); +} + +class MoeLoraPointerExpandTest : public ::testing::Test +{ +protected: + void SetUp() override + { + TLLM_CUDA_CHECK(cudaStreamCreate(&mStream)); + } + + void TearDown() override + { + for (auto* p : mAllocations) + { + (void) cudaFree(p); + } + (void) cudaStreamDestroy(mStream); + } + + template + T* upload(std::vector const& host) + { + T* p = deviceUpload(host); + if (p != nullptr) + { + mAllocations.push_back(p); + } + return p; + } + + template + T* allocZero(size_t count) + { + T* p = deviceAllocZero(count); + mAllocations.push_back(p); + return p; + } + + template + T* allocFilled(size_t count, int byte_pattern) + { + T* p = deviceAllocFilled(count, byte_pattern); + mAllocations.push_back(p); + return p; + } + + // Run the kernel against ref and assert the device outputs match. When + // prefill_garbage is true the output buffers start with a non-zero pattern, + // forcing the kernel to explicitly zero ghost rows for the comparison to + // pass. + void runAndCompare(std::vector const& permuted_rows, std::vector const& expert_first_token_offset, + int32_t num_experts_per_node, int32_t start_expert, int64_t num_rows, int64_t expanded_num_rows, + int64_t lora_dtype_bytes, RefModule& fc1_ref, RefModule& fc2_ref, RefModule* gated_ref, + bool prefill_garbage = false) + { + cpuExpand(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, lora_dtype_bytes, fc1_ref, fc2_ref, gated_ref); + + auto* permuted_rows_dev = upload(permuted_rows); + auto* offsets_dev = upload(expert_first_token_offset); + + auto build_module = [&](RefModule const& r) + { + MoeLoraExpandModule m; + m.ranks_src = upload(r.ranks_src); + m.ptrs_src = upload(r.ptrs_src); + m.dim_a = r.dim_a; + m.dim_b = r.dim_b; + m.ranks_out = prefill_garbage ? allocFilled(expanded_num_rows, 0x7F) + : allocZero(expanded_num_rows); + m.ptrs_out = prefill_garbage ? allocFilled(expanded_num_rows * 2, 0x7F) + : allocZero(expanded_num_rows * 2); + return m; + }; + + MoeLoraExpandModule fc1_dev = build_module(fc1_ref); + MoeLoraExpandModule fc2_dev = build_module(fc2_ref); + MoeLoraExpandModule gated_dev{}; + MoeLoraExpandModule const* gated_dev_ptr = nullptr; + if (gated_ref != nullptr) + { + gated_dev = build_module(*gated_ref); + gated_dev_ptr = &gated_dev; + } + + launchMoeLoraPointerExpand(permuted_rows_dev, offsets_dev, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, lora_dtype_bytes, fc1_dev, fc2_dev, gated_dev_ptr, mStream); + TLLM_CUDA_CHECK(cudaStreamSynchronize(mStream)); + + // Compare per-module. + auto compare = [&](RefModule const& ref_mod, MoeLoraExpandModule const& dev_mod, char const* name) + { + std::vector host_ranks(expanded_num_rows, 0); + std::vector host_ptrs(expanded_num_rows * 2, 0); + deviceDownload(dev_mod.ranks_out, host_ranks); + deviceDownload(dev_mod.ptrs_out, host_ptrs); + for (int64_t i = 0; i < expanded_num_rows; ++i) + { + EXPECT_EQ(host_ranks[i], ref_mod.ranks_out[i]) << name << " rank mismatch at i=" << i; + EXPECT_EQ(host_ptrs[2 * i + 0], ref_mod.ptrs_out[2 * i + 0]) << name << " A ptr mismatch at i=" << i; + EXPECT_EQ(host_ptrs[2 * i + 1], ref_mod.ptrs_out[2 * i + 1]) << name << " B ptr mismatch at i=" << i; + } + }; + + compare(fc1_ref, fc1_dev, "fc1"); + compare(fc2_ref, fc2_dev, "fc2"); + if (gated_ref != nullptr) + { + compare(*gated_ref, gated_dev, "gated"); + } + } + + cudaStream_t mStream{}; + std::vector mAllocations; +}; + +// Helper: build a "fake but distinct" pointer for source token s of module +// tag. Encoding the (tag, s, side) lets the test cheaply verify the +// kernel reads the right slot of ptrs_src. The high bits guarantee +// (ptr + per-expert-byte-offset) doesn't alias another (tag, s, side). +int64_t fakePtr(int tag, int32_t s, int side) +{ + return (static_cast(tag) << 56) | (static_cast(side) << 48) | (static_cast(s + 1) << 32); +} + +// Smallest non-trivial case: 4 source tokens, 3 experts, top_k=2 so the +// permuted batch has 8 rows. Per-expert, no gated. +TEST_F(MoeLoraPointerExpandTest, PerExpertNoGated) +{ + int32_t const num_experts_per_node = 3; + int32_t const start_expert = 0; + int64_t const num_rows = 4; + int64_t const expanded_num_rows = 8; // top_k=2 + + // (expert_id assignment is irrelevant to the kernel; we only need + // expert_first_token_offset for the lookup and permuted_rows for the + // source-index reverse.) + std::vector permuted_rows = {0, 4, 1, 5, 2, 6, 3, 7}; + std::vector expert_first_token_offset = {0, 3, 5, 8}; + + RefModule fc1{}; + fc1.dim_a = 16; // "hidden_size" + fc1.dim_b = 32; // "inter_size" + fc1.ranks_src = {2, 0, 4, 1}; + fc1.ptrs_src.resize(num_rows * 2); + for (int32_t s = 0; s < num_rows; ++s) + { + fc1.ptrs_src[2 * s + 0] = fakePtr(/*tag=*/1, s, /*side=*/0); + fc1.ptrs_src[2 * s + 1] = fakePtr(/*tag=*/1, s, /*side=*/1); + } + + RefModule fc2{}; + fc2.dim_a = 32; + fc2.dim_b = 16; + fc2.ranks_src = {1, 2, 0, 3}; + fc2.ptrs_src.resize(num_rows * 2); + for (int32_t s = 0; s < num_rows; ++s) + { + fc2.ptrs_src[2 * s + 0] = fakePtr(/*tag=*/2, s, /*side=*/0); + fc2.ptrs_src[2 * s + 1] = fakePtr(/*tag=*/2, s, /*side=*/1); + } + + runAndCompare(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, /*lora_dtype_bytes=*/2, fc1, fc2, /*gated=*/nullptr); +} + +// Gated activation: three modules, exercises the gated arg path. +TEST_F(MoeLoraPointerExpandTest, GatedActivation) +{ + int32_t const num_experts_per_node = 4; + int32_t const start_expert = 2; // exercises start_expert != 0 + int64_t const num_rows = 5; + int64_t const expanded_num_rows = 10; + + std::vector permuted_rows = {0, 5, 1, 6, 2, 7, 3, 8, 4, 9}; + std::vector expert_first_token_offset = {0, 2, 5, 7, 10}; + + auto build_basic = [&](int tag, int64_t dim_a, int64_t dim_b) + { + RefModule m{}; + m.dim_a = dim_a; + m.dim_b = dim_b; + m.ranks_src = {3, 0, 1, 4, 2}; + m.ptrs_src.resize(num_rows * 2); + for (int32_t s = 0; s < num_rows; ++s) + { + m.ptrs_src[2 * s + 0] = fakePtr(tag, s, 0); + m.ptrs_src[2 * s + 1] = fakePtr(tag, s, 1); + } + return m; + }; + + RefModule fc1 = build_basic(/*tag=*/1, /*hidden=*/8, /*inter=*/24); + RefModule fc2 = build_basic(/*tag=*/2, /*inter=*/24, /*hidden=*/8); + RefModule gated = build_basic(/*tag=*/3, /*hidden=*/8, /*inter=*/24); + runAndCompare(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, /*lora_dtype_bytes=*/2, fc1, fc2, &gated); +} + +// Non-trivial lora_dtype_bytes (e.g. fp32 = 4) to verify the stride scaling +// flows through the offset arithmetic. +TEST_F(MoeLoraPointerExpandTest, Fp32StrideBytes) +{ + int32_t const num_experts_per_node = 2; + int32_t const start_expert = 0; + int64_t const num_rows = 2; + int64_t const expanded_num_rows = 4; + + std::vector permuted_rows = {0, 1, 0, 1}; + std::vector expert_first_token_offset = {0, 2, 4}; + + RefModule fc1{}; + fc1.dim_a = 4; + fc1.dim_b = 8; + fc1.ranks_src = {2, 3}; + fc1.ptrs_src = {fakePtr(1, 0, 0), fakePtr(1, 0, 1), fakePtr(1, 1, 0), fakePtr(1, 1, 1)}; + + RefModule fc2 = fc1; + fc2.dim_a = 8; + fc2.dim_b = 4; + + runAndCompare(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, /*lora_dtype_bytes=*/4, fc1, fc2, /*gated=*/nullptr); +} + +// Ghost rows: expanded_num_rows exceeds the last valid expert offset, so the +// trailing rows have expert_idx == num_experts_per_node and must be zeroed by +// the kernel. Buffers are pre-filled with garbage to verify the kernel actively +// resets them. +TEST_F(MoeLoraPointerExpandTest, GhostRowsRemainZero) +{ + int32_t const num_experts_per_node = 3; + int32_t const start_expert = 0; + int64_t const num_rows = 4; + // expert_first_token_offset.back() == 6, but we run two extra ghost rows. + int64_t const expanded_num_rows = 8; + + std::vector permuted_rows = {0, 4, 1, 5, 2, 6, 0, 0}; + std::vector expert_first_token_offset = {0, 2, 4, 6}; + + RefModule fc1{}; + fc1.dim_a = 16; + fc1.dim_b = 32; + fc1.ranks_src = {2, 0, 4, 1}; + fc1.ptrs_src.resize(num_rows * 2); + for (int32_t s = 0; s < num_rows; ++s) + { + fc1.ptrs_src[2 * s + 0] = fakePtr(/*tag=*/1, s, /*side=*/0); + fc1.ptrs_src[2 * s + 1] = fakePtr(/*tag=*/1, s, /*side=*/1); + } + + RefModule fc2 = fc1; + fc2.dim_a = 32; + fc2.dim_b = 16; + + runAndCompare(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, /*lora_dtype_bytes=*/2, fc1, fc2, /*gated=*/nullptr, /*prefill_garbage=*/true); +} + +// num_experts_per_node above kMaxExpertsInSmem (1024) forces the kernel to take +// the global-memory expert-offset scan instead of the shared-memory path. +TEST_F(MoeLoraPointerExpandTest, ForceGlobalScanNumExperts1025) +{ + int32_t const num_experts_per_node = 1025; + int32_t const start_expert = 0; + int64_t const num_rows = 4; + int64_t const expanded_num_rows = 4; // top_k=1 + + std::vector permuted_rows = {0, 1, 2, 3}; + // All tokens land in expert 0; every other expert is empty. Offset array has + // num_experts_per_node + 1 == 1026 entries. + std::vector expert_first_token_offset(num_experts_per_node + 1, expanded_num_rows); + expert_first_token_offset[0] = 0; + + RefModule fc1{}; + fc1.dim_a = 8; + fc1.dim_b = 16; + fc1.ranks_src = {1, 2, 3, 4}; + fc1.ptrs_src.resize(num_rows * 2); + for (int32_t s = 0; s < num_rows; ++s) + { + fc1.ptrs_src[2 * s + 0] = fakePtr(/*tag=*/1, s, /*side=*/0); + fc1.ptrs_src[2 * s + 1] = fakePtr(/*tag=*/1, s, /*side=*/1); + } + + RefModule fc2 = fc1; + fc2.dim_a = 16; + fc2.dim_b = 8; + + runAndCompare(permuted_rows, expert_first_token_offset, num_experts_per_node, start_expert, num_rows, + expanded_num_rows, /*lora_dtype_bytes=*/2, fc1, fc2, /*gated=*/nullptr); +} + +} // namespace diff --git a/cpp/tests/unit_tests/kernels/moeLoraProblemBuilderTest.cu b/cpp/tests/unit_tests/kernels/moeLoraProblemBuilderTest.cu new file mode 100644 index 000000000000..e9a414e837ca --- /dev/null +++ b/cpp/tests/unit_tests/kernels/moeLoraProblemBuilderTest.cu @@ -0,0 +1,364 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/common/cudaUtils.h" +#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_lora_problem_builder.h" + +#include "cutlass/gemm_coord.h" + +#include +#include +#include + +namespace +{ + +using ::tensorrt_llm::kernels::cutlass_kernels::launchMoeLoraProblemBuilder; +using ::tensorrt_llm::kernels::cutlass_kernels::MoeLoraGemmGroupArrays; + +// Host-side reference reproducing the builder's per-row logic. Same +// formulas as the kernel; used only as parity ground truth. +struct RefOutputs +{ + std::vector problem_sizes_in; + std::vector problem_sizes_out; + std::vector a_ptrs_in; // store as int64 bits for simple compare + std::vector b_ptrs_in; + std::vector d_ptrs_in; + std::vector b_ptrs_out; + std::vector d_ptrs_out; + std::vector lda_in; + std::vector ldb_in; + std::vector ldd_in; + std::vector ldb_out; + std::vector ldd_out; + std::vector splitk_offsets; +}; + +RefOutputs cpuReference(std::vector const& ranks, std::vector const& ptrs, int64_t input_base, + int64_t lowrank_workspace, int64_t output_base, int64_t in_hidden_size, int64_t out_hidden_size, + int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices) +{ + int64_t const P = static_cast(ranks.size()); + RefOutputs r; + r.problem_sizes_in.resize(P); + r.problem_sizes_out.resize(P); + r.a_ptrs_in.resize(P); + r.b_ptrs_in.resize(P); + r.d_ptrs_in.resize(P); + r.b_ptrs_out.resize(P); + r.d_ptrs_out.resize(P); + r.lda_in.resize(P); + r.ldb_in.resize(P); + r.ldd_in.resize(P); + r.ldb_out.resize(P); + r.ldd_out.resize(P); + r.splitk_offsets.resize(P + 1); + for (int64_t i = 0; i < P; ++i) + { + int32_t const rank = ranks[i]; + r.problem_sizes_in[i] = cutlass::gemm::GemmCoord(1, rank, static_cast(in_hidden_size)); + r.problem_sizes_out[i] = cutlass::gemm::GemmCoord(1, static_cast(out_hidden_size), rank); + + int64_t const in_row_stride = in_hidden_size * dtype_bytes; + int64_t const work_row_stride = max_lora_rank * dtype_bytes; + int64_t const out_row_stride = out_hidden_size * dtype_bytes; + + r.a_ptrs_in[i] = input_base + i * in_row_stride; + r.b_ptrs_in[i] = ptrs[2 * i + 0]; + r.d_ptrs_in[i] = lowrank_workspace + i * work_row_stride; + r.b_ptrs_out[i] = ptrs[2 * i + 1]; + r.d_ptrs_out[i] = output_base + i * out_row_stride; + + r.lda_in[i] = in_hidden_size; + r.ldb_in[i] = in_hidden_size; + r.ldd_in[i] = max_lora_rank; + r.ldb_out[i] = rank; + r.ldd_out[i] = out_hidden_size; + + r.splitk_offsets[i] = i * max_lora_rank * splitk_slices; + } + r.splitk_offsets[P] = P * max_lora_rank * splitk_slices; + return r; +} + +template +T* deviceUpload(std::vector const& host) +{ + if (host.empty()) + { + return nullptr; + } + T* dev = nullptr; + TLLM_CUDA_CHECK(cudaMalloc(&dev, host.size() * sizeof(T))); + TLLM_CUDA_CHECK(cudaMemcpy(dev, host.data(), host.size() * sizeof(T), cudaMemcpyHostToDevice)); + return dev; +} + +template +T* deviceAllocZero(size_t count) +{ + T* dev = nullptr; + TLLM_CUDA_CHECK(cudaMalloc(&dev, count * sizeof(T))); + TLLM_CUDA_CHECK(cudaMemset(dev, 0, count * sizeof(T))); + return dev; +} + +template +void deviceDownload(T const* dev, std::vector& host) +{ + if (host.empty()) + { + return; + } + TLLM_CUDA_CHECK(cudaMemcpy(host.data(), dev, host.size() * sizeof(T), cudaMemcpyDeviceToHost)); +} + +class MoeLoraProblemBuilderTest : public ::testing::Test +{ +protected: + void SetUp() override + { + TLLM_CUDA_CHECK(cudaStreamCreate(&mStream)); + } + + void TearDown() override + { + for (auto* p : mAllocations) + { + (void) cudaFree(p); + } + (void) cudaStreamDestroy(mStream); + } + + template + T* upload(std::vector const& h) + { + T* p = deviceUpload(h); + if (p) + { + mAllocations.push_back(p); + } + return p; + } + + template + T* allocZero(size_t n) + { + T* p = deviceAllocZero(n); + mAllocations.push_back(p); + return p; + } + + // When with_splitk is false, out.splitk_offsets is left null to exercise the + // kernel's null-offset branch (and the launch_count path that drops the +1 + // sentinel thread); the splitk_offsets comparison is then skipped. + void runAndCompare(std::vector const& ranks, std::vector const& ptrs, int64_t input_base, + int64_t lowrank_workspace, int64_t output_base, int64_t in_hidden_size, int64_t out_hidden_size, + int64_t max_lora_rank, int64_t dtype_bytes, int64_t splitk_slices, bool with_splitk = true) + { + auto const P = static_cast(ranks.size()); + RefOutputs ref = cpuReference(ranks, ptrs, input_base, lowrank_workspace, output_base, in_hidden_size, + out_hidden_size, max_lora_rank, dtype_bytes, splitk_slices); + + int32_t* ranks_dev = upload(ranks); + int64_t* ptrs_dev = upload(ptrs); + + MoeLoraGemmGroupArrays out; + out.problem_sizes_in + = reinterpret_cast(allocZero(P * sizeof(cutlass::gemm::GemmCoord))); + out.problem_sizes_out + = reinterpret_cast(allocZero(P * sizeof(cutlass::gemm::GemmCoord))); + out.a_ptrs_in = reinterpret_cast(allocZero(P)); + out.b_ptrs_in = reinterpret_cast(allocZero(P)); + out.d_ptrs_in = reinterpret_cast(allocZero(P)); + out.b_ptrs_out = reinterpret_cast(allocZero(P)); + out.d_ptrs_out = reinterpret_cast(allocZero(P)); + out.lda_in = allocZero(P); + out.ldb_in = allocZero(P); + out.ldd_in = allocZero(P); + out.ldb_out = allocZero(P); + out.ldd_out = allocZero(P); + out.splitk_offsets = with_splitk ? allocZero(P + 1) : nullptr; + + launchMoeLoraProblemBuilder(ranks_dev, ptrs_dev, reinterpret_cast(input_base), + reinterpret_cast(lowrank_workspace), reinterpret_cast(output_base), P, in_hidden_size, + out_hidden_size, max_lora_rank, dtype_bytes, splitk_slices, out, mStream); + TLLM_CUDA_CHECK(cudaStreamSynchronize(mStream)); + + // Compare device outputs to host reference. + auto check_int64 = [&](char const* name, int64_t* dev, std::vector const& ref_vec) + { + std::vector host(ref_vec.size(), 0); + deviceDownload(dev, host); + for (size_t i = 0; i < ref_vec.size(); ++i) + { + EXPECT_EQ(host[i], ref_vec[i]) << name << " mismatch at i=" << i; + } + }; + auto check_ptr_array = [&](char const* name, void** dev, std::vector const& ref_vec) + { check_int64(name, reinterpret_cast(dev), ref_vec); }; + auto check_problem_sizes + = [&](char const* name, cutlass::gemm::GemmCoord* dev, std::vector const& ref_vec) + { + std::vector host(ref_vec.size()); + TLLM_CUDA_CHECK(cudaMemcpy( + host.data(), dev, ref_vec.size() * sizeof(cutlass::gemm::GemmCoord), cudaMemcpyDeviceToHost)); + for (size_t i = 0; i < ref_vec.size(); ++i) + { + EXPECT_EQ(host[i].m(), ref_vec[i].m()) << name << " M mismatch at i=" << i; + EXPECT_EQ(host[i].n(), ref_vec[i].n()) << name << " N mismatch at i=" << i; + EXPECT_EQ(host[i].k(), ref_vec[i].k()) << name << " K mismatch at i=" << i; + } + }; + + check_problem_sizes("problem_sizes_in", out.problem_sizes_in, ref.problem_sizes_in); + check_problem_sizes("problem_sizes_out", out.problem_sizes_out, ref.problem_sizes_out); + check_ptr_array("a_ptrs_in", out.a_ptrs_in, ref.a_ptrs_in); + check_ptr_array("b_ptrs_in", out.b_ptrs_in, ref.b_ptrs_in); + check_ptr_array("d_ptrs_in", out.d_ptrs_in, ref.d_ptrs_in); + check_ptr_array("b_ptrs_out", out.b_ptrs_out, ref.b_ptrs_out); + check_ptr_array("d_ptrs_out", out.d_ptrs_out, ref.d_ptrs_out); + check_int64("lda_in", out.lda_in, ref.lda_in); + check_int64("ldb_in", out.ldb_in, ref.ldb_in); + check_int64("ldd_in", out.ldd_in, ref.ldd_in); + check_int64("ldb_out", out.ldb_out, ref.ldb_out); + check_int64("ldd_out", out.ldd_out, ref.ldd_out); + if (with_splitk) + { + check_int64("splitk_offsets", out.splitk_offsets, ref.splitk_offsets); + } + } + + cudaStream_t mStream{}; + std::vector mAllocations; +}; + +// "Pretend" adapter pointers. The kernel treats these as opaque bits, so +// we use easily-distinguishable patterns to catch indexing mistakes. +int64_t fakeAdapter(int tag, int32_t i, int side) +{ + return (static_cast(tag) << 56) | (static_cast(side) << 48) | (static_cast(i + 1) << 32); +} + +TEST_F(MoeLoraProblemBuilderTest, Bf16Smoke) +{ + int64_t const in_hidden_size = 16; + int64_t const out_hidden_size = 32; + int64_t const max_lora_rank = 8; + int64_t const dtype_bytes = 2; + int64_t const splitk_slices = 4; + + int64_t const input_base = static_cast(0x1'0000'0000ull); + int64_t const lowrank_workspace = static_cast(0x2'0000'0000ull); + int64_t const output_base = static_cast(0x3'0000'0000ull); + + std::vector ranks = {2, 0, 4, 1, 8, 3}; + std::vector ptrs; + for (int32_t i = 0; i < static_cast(ranks.size()); ++i) + { + ptrs.push_back(fakeAdapter(/*tag=*/1, i, /*side=*/0)); + ptrs.push_back(fakeAdapter(/*tag=*/1, i, /*side=*/1)); + } + + runAndCompare(ranks, ptrs, input_base, lowrank_workspace, output_base, in_hidden_size, out_hidden_size, + max_lora_rank, dtype_bytes, splitk_slices); +} + +TEST_F(MoeLoraProblemBuilderTest, Fp32StrideBytes) +{ + int64_t const in_hidden_size = 12; + int64_t const out_hidden_size = 24; + int64_t const max_lora_rank = 16; + int64_t const dtype_bytes = 4; + int64_t const splitk_slices = 8; + + int64_t const input_base = static_cast(0x4'0000'0000ull); + int64_t const lowrank_workspace = static_cast(0x5'0000'0000ull); + int64_t const output_base = static_cast(0x6'0000'0000ull); + + std::vector ranks = {1, 16, 8}; + std::vector ptrs; + for (int32_t i = 0; i < static_cast(ranks.size()); ++i) + { + ptrs.push_back(fakeAdapter(/*tag=*/2, i, /*side=*/0)); + ptrs.push_back(fakeAdapter(/*tag=*/2, i, /*side=*/1)); + } + + runAndCompare(ranks, ptrs, input_base, lowrank_workspace, output_base, in_hidden_size, out_hidden_size, + max_lora_rank, dtype_bytes, splitk_slices); +} + +// Cover an empty call (no-op) and a single-token call (smallest live case) +// to lock down the corner cases the larger tests don't exercise. +TEST_F(MoeLoraProblemBuilderTest, BoundaryCases) +{ + int64_t const in_hidden_size = 8; + int64_t const out_hidden_size = 8; + int64_t const max_lora_rank = 4; + int64_t const dtype_bytes = 2; + int64_t const splitk_slices = 2; + int64_t const input_base = static_cast(0x7'0000'0000ull); + int64_t const lowrank_workspace = static_cast(0x8'0000'0000ull); + int64_t const output_base = static_cast(0x9'0000'0000ull); + + // Empty call: P = 0, no allocations needed; launch should be a no-op. + { + MoeLoraGemmGroupArrays empty{}; + launchMoeLoraProblemBuilder(nullptr, nullptr, reinterpret_cast(input_base), + reinterpret_cast(lowrank_workspace), reinterpret_cast(output_base), + /*num_permuted_tokens=*/0, in_hidden_size, out_hidden_size, max_lora_rank, dtype_bytes, splitk_slices, + empty, mStream); + TLLM_CUDA_CHECK(cudaStreamSynchronize(mStream)); + } + + // Single-token call: P = 1, exercises the +1 sentinel write at index 1. + { + std::vector ranks = {3}; + std::vector ptrs = {fakeAdapter(3, 0, 0), fakeAdapter(3, 0, 1)}; + runAndCompare(ranks, ptrs, input_base, lowrank_workspace, output_base, in_hidden_size, out_hidden_size, + max_lora_rank, dtype_bytes, splitk_slices); + } +} + +// splitk_offsets == nullptr: the caller does not need the split-K scratch +// offsets, so the kernel must skip the sentinel write and the per-row offset +// store while still producing all other arrays correctly. +TEST_F(MoeLoraProblemBuilderTest, NullSplitkOffsets) +{ + int64_t const in_hidden_size = 16; + int64_t const out_hidden_size = 32; + int64_t const max_lora_rank = 8; + int64_t const dtype_bytes = 2; + int64_t const splitk_slices = 4; + + int64_t const input_base = static_cast(0xA'0000'0000ull); + int64_t const lowrank_workspace = static_cast(0xB'0000'0000ull); + int64_t const output_base = static_cast(0xC'0000'0000ull); + + std::vector ranks = {2, 0, 4, 1, 8}; + std::vector ptrs; + for (int32_t i = 0; i < static_cast(ranks.size()); ++i) + { + ptrs.push_back(fakeAdapter(/*tag=*/4, i, /*side=*/0)); + ptrs.push_back(fakeAdapter(/*tag=*/4, i, /*side=*/1)); + } + + runAndCompare(ranks, ptrs, input_base, lowrank_workspace, output_base, in_hidden_size, out_hidden_size, + max_lora_rank, dtype_bytes, splitk_slices, /*with_splitk=*/false); +} + +} // namespace diff --git a/tests/unittest/_torch/lora/test_moe_lora_device_path.py b/tests/unittest/_torch/lora/test_moe_lora_device_path.py new file mode 100644 index 000000000000..de0d72f5177d --- /dev/null +++ b/tests/unittest/_torch/lora/test_moe_lora_device_path.py @@ -0,0 +1,184 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Tests for the *device path* of routed-expert MoE LoRA in +`torch.ops.trtllm.fused_moe`. + +The device path (opted into for the per-request schema via +`TLLM_MOE_LORA_USE_DEVICE_PATH=1`) performs the per-token pointer expansion, +problem building, and grouped GEMMs entirely on the CUDA stream via the new +on-device kernels, instead of the legacy host-pointer LoRA path. This test +checks device-path eager correctness vs. both the legacy host path and an fp32 +PyTorch reference, exercising the pointer-expand / problem-builder / +grouped-GEMM kernels. + +It requires a CUDA GPU and the built `trtllm::fused_moe` op. +""" + +import pytest +import torch + +from tensorrt_llm._torch.peft.lora.moe_layout import make_per_expert_lora, reference_swiglu_moe_lora + +_TRTLLM_AVAILABLE = hasattr(torch.ops, "trtllm") and hasattr(torch.ops.trtllm, "fused_moe") + +requires_cuda_and_op = pytest.mark.skipif( + not torch.cuda.is_available() or not _TRTLLM_AVAILABLE, + reason="Requires CUDA and built TensorRT-LLM C++ extension (torch.ops.trtllm.fused_moe).", +) + + +@pytest.fixture(autouse=True) +def _isolate_moe_runner_cache(): + """Give every test a fresh cached FusedMoeRunner and release device scratch + afterward. + + The device path is selected per-runner at construction from + TLLM_MOE_LORA_USE_DEVICE_PATH, and the runner is cached at module level by + MoERunner. Clearing the cache before each test forces a fresh runner that + re-reads the env var; clearing + empty_cache afterward releases the + per-runner device scratch so it cannot alias later allocations. + """ + from tensorrt_llm._torch.custom_ops.torch_custom_ops import MoERunner + + MoERunner.runner_dict.clear() + yield + MoERunner.runner_dict.clear() + if torch.cuda.is_available(): + torch.cuda.synchronize() + torch.cuda.empty_cache() + + +# Adapters drawn from N(0, 1) blow up the SwiGLU intermediate at these shapes; +# scale them down so the legitimate output stays O(1)-O(10) and the bf16 noise +# stays well under the tolerance (see the rationale in test_moe_lora_op.py). +_LORA_SCALE = 0.25 +_RTOL = 5e-2 +_ATOL = 1.0 + + +def _build_base_inputs( + num_tokens, hidden_size, inter_size, num_experts, top_k, dtype, device, seed=0 +): + torch.manual_seed(seed) + x = torch.randn(num_tokens, hidden_size, dtype=dtype, device=device) + w3_w1 = torch.randn(num_experts, 2 * inter_size, hidden_size, dtype=dtype, device=device) * 0.02 + w2 = torch.randn(num_experts, hidden_size, inter_size, dtype=dtype, device=device) * 0.02 + logits = torch.randn(num_tokens, num_experts, dtype=torch.float32, device=device) + topk_scores, topk_ids = torch.topk(logits, k=top_k, dim=-1) + topk_scores = torch.softmax(topk_scores, dim=-1) + return x, w3_w1, w2, topk_ids.to(torch.int32), topk_scores.to(torch.float32) + + +def _make_adapter_set(num_experts, rank, hidden_size, inter_size, dtype, device, base_seed): + """Three scaled per-expert adapters (fc1/gate-side, gated/up-side, fc2).""" + + def _scaled(*args, seed): + a = make_per_expert_lora(*args, dtype=dtype, device=device, seed=seed) + a["A"].mul_(_LORA_SCALE) + a["B"].mul_(_LORA_SCALE) + return a + + fc1 = _scaled(num_experts, rank, hidden_size, inter_size, seed=base_seed + 0) + gated = _scaled(num_experts, rank, hidden_size, inter_size, seed=base_seed + 1) + fc2 = _scaled(num_experts, rank, inter_size, hidden_size, seed=base_seed + 2) + return {"fc1": fc1, "gated": gated, "fc2": fc2} + + +def _per_request_kwargs(num_tokens, adapters, rank): + """Single-request per-request schema covering all tokens with one adapter.""" + fc1, gated, fc2 = adapters["fc1"], adapters["gated"], adapters["fc2"] + return dict( + fc1_lora_ranks=torch.tensor([rank], dtype=torch.int32, device="cpu"), + fc1_lora_weight_ptrs=torch.tensor( + [[fc1["A"].data_ptr(), fc1["B"].data_ptr(), 0]], dtype=torch.int64, device="cpu" + ), + fc2_lora_ranks=torch.tensor([rank], dtype=torch.int32, device="cpu"), + fc2_lora_weight_ptrs=torch.tensor( + [[fc2["A"].data_ptr(), fc2["B"].data_ptr(), 0]], dtype=torch.int64, device="cpu" + ), + gated_lora_ranks=torch.tensor([rank], dtype=torch.int32, device="cpu"), + gated_lora_weight_ptrs=torch.tensor( + [[gated["A"].data_ptr(), gated["B"].data_ptr(), 0]], dtype=torch.int64, device="cpu" + ), + host_request_types=torch.zeros(1, dtype=torch.int32, device="cpu"), + host_context_lengths=torch.tensor([num_tokens], dtype=torch.int32, device="cpu"), + lora_max_low_rank=rank, + ) + + +def _call_fused_moe(x, w3_w1, w2, topk_ids, topk_scores, output_dtype, lora_kwargs): + common = dict( + input=x, + token_selected_experts=topk_ids, + token_final_scales=topk_scores, + fc1_expert_weights=w3_w1, + fc1_expert_biases=None, + fc2_expert_weights=w2, + fc2_expert_biases=None, + output_dtype=output_dtype, + quant_scales=[], + ) + common.update(lora_kwargs) + return torch.ops.trtllm.fused_moe(**common)[0] + + +def _reference(x, w3_w1, w2, topk_ids, topk_scores, adapters): + return reference_swiglu_moe_lora( + x, + w3_w1, + w2, + topk_ids, + topk_scores, + fc1_a=adapters["fc1"]["A"], + fc1_b=adapters["fc1"]["B"], + gated_a=adapters["gated"]["A"], + gated_b=adapters["gated"]["B"], + fc2_a=adapters["fc2"]["A"], + fc2_b=adapters["fc2"]["B"], + ) + + +@requires_cuda_and_op +def test_device_path_eager_matches_host_and_reference(monkeypatch): + """Per-request schema on the device path (env-var opt-in) must match both + the legacy host path and the fp32 PyTorch reference. Exercises the on-device + pointer-expand / problem-builder / grouped-GEMM kernels in eager mode. + """ + from tensorrt_llm._torch.custom_ops.torch_custom_ops import MoERunner + + device = torch.device("cuda") + dtype = torch.bfloat16 + num_tokens, hidden_size, inter_size = 16, 128, 256 + num_experts, top_k, rank = 4, 2, 8 + + x, w3_w1, w2, topk_ids, topk_scores = _build_base_inputs( + num_tokens, hidden_size, inter_size, num_experts, top_k, dtype, device + ) + adapters = _make_adapter_set( + num_experts, rank, hidden_size, inter_size, dtype, device, base_seed=300 + ) + lora_kwargs = _per_request_kwargs(num_tokens, adapters, rank) + + # Host path (device path env explicitly disabled), fresh runner. + monkeypatch.setenv("TLLM_MOE_LORA_USE_DEVICE_PATH", "0") + MoERunner.runner_dict.clear() + try: + out_host = _call_fused_moe(x, w3_w1, w2, topk_ids, topk_scores, dtype, dict(lora_kwargs)) + finally: + MoERunner.runner_dict.clear() + + # Device path (env opt-in), fresh runner. + monkeypatch.setenv("TLLM_MOE_LORA_USE_DEVICE_PATH", "1") + MoERunner.runner_dict.clear() + try: + out_device = _call_fused_moe(x, w3_w1, w2, topk_ids, topk_scores, dtype, dict(lora_kwargs)) + finally: + MoERunner.runner_dict.clear() + + out_ref = _reference(x, w3_w1, w2, topk_ids, topk_scores, adapters) + + assert torch.isfinite(out_device).all() + torch.testing.assert_close(out_device, out_ref, rtol=_RTOL, atol=_ATOL) + # Host vs device path are different reduction orders but should agree + # within the same bf16 tolerance. + torch.testing.assert_close(out_device, out_host, rtol=_RTOL, atol=_ATOL) diff --git a/tests/unittest/_torch/lora/test_moe_lora_op.py b/tests/unittest/_torch/lora/test_moe_lora_op.py index 5a609b5ca4b4..4a270145a3d2 100644 --- a/tests/unittest/_torch/lora/test_moe_lora_op.py +++ b/tests/unittest/_torch/lora/test_moe_lora_op.py @@ -162,6 +162,54 @@ def test_moe_per_expert_lora_changes_output(): assert diff > 1e-3, f"LoRA had no observable effect (mean abs diff={diff})" +@requires_cuda_and_op +def test_moe_lora_rejects_overlong_context_lengths(): + """A per-request expansion whose host_context_lengths sum past the op's token + count must raise cleanly instead of overrunning the fixed-capacity pinned + expansion buffer. + + The per-token (rank, A, B) tables are written into buffers sized for + num_tokens; a context request claiming more tokens than the op actually has + would, without the bounds guard in expandPerRequestLoraTo, scribble past the + end of pinned memory. Here a single context request declares 2 * num_tokens, + so the expansion must fail fast. + """ + device = torch.device("cuda") + dtype = torch.bfloat16 + num_tokens, hidden_size, inter_size = 8, 128, 256 + num_experts, top_k = 4, 2 + rank = 8 + + x, w3_w1, w2, topk_ids, topk_scores = _build_base_inputs( + num_tokens, hidden_size, inter_size, num_experts, top_k, dtype, device + ) + fc1_adapter = make_per_expert_lora( + num_experts, rank, hidden_size, inter_size, dtype=dtype, device=device, seed=10 + ) + fc2_adapter = make_per_expert_lora( + num_experts, rank, inter_size, hidden_size, dtype=dtype, device=device, seed=11 + ) + lora_kwargs = _build_lora_request_buffers( + num_tokens, + fc1_adapter["A"], + fc1_adapter["B"], + fc2_adapter["A"], + fc2_adapter["B"], + rank=rank, + ) + # Single context request (host_request_types == 0) whose declared context + # length exceeds the op's token count, so the expansion overruns by design. + lora_kwargs["host_request_types"] = torch.zeros(1, dtype=torch.int32, device="cpu") + lora_kwargs["host_context_lengths"] = torch.tensor( + [2 * num_tokens], dtype=torch.int32, device="cpu" + ) + + with pytest.raises((RuntimeError, ValueError)): + _call_fused_moe( + x, w3_w1, w2, topk_ids, topk_scores, output_dtype=dtype, lora_kwargs=lora_kwargs + ) + + @requires_cuda_and_op def test_moe_lora_rejected_in_min_latency_mode(): device = torch.device("cuda") From 86f33e6a5a503dca6fc90304da5ba4187c1016aa Mon Sep 17 00:00:00 2001 From: dongfengy <99041270+dongfengy@users.noreply.github.com> Date: Sun, 7 Jun 2026 20:41:08 -0700 Subject: [PATCH 031/119] [https://nvbugs/6245317][test] set Harmony tiktoken env for GPT-OSS disagg (#14935) Signed-off-by: Dongfeng Yu --- .../integration/defs/disaggregated/test_disaggregated.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tests/integration/defs/disaggregated/test_disaggregated.py b/tests/integration/defs/disaggregated/test_disaggregated.py index 437f5c2231a4..a5beb9c2de26 100644 --- a/tests/integration/defs/disaggregated/test_disaggregated.py +++ b/tests/integration/defs/disaggregated/test_disaggregated.py @@ -2275,9 +2275,15 @@ def test_disaggregated_gpt_oss_120b_harmony(disaggregated_test_root, model_dir = f"{llm_models_root()}/{model_path}" setup_model_symlink(llm_venv, model_dir, model_path) + env = llm_venv._new_env.copy() + tiktoken_vocab = os.path.join(llm_models_root(), "datasets", + "tiktoken_vocab") + env["TIKTOKEN_RS_CACHE_DIR"] = tiktoken_vocab + env["TIKTOKEN_ENCODINGS_BASE"] = tiktoken_vocab + run_disaggregated_test(disaggregated_example_root, "gpt_oss_120b_harmony", - env=llm_venv._new_env, + env=env, model_path=model_dir, cwd=llm_venv.get_working_directory()) From b4d44d33baeac4dad741fe07e79bd9c9fa60ae1b Mon Sep 17 00:00:00 2001 From: dongfengy <99041270+dongfengy@users.noreply.github.com> Date: Sun, 7 Jun 2026 21:07:07 -0700 Subject: [PATCH 032/119] [https://nvbugs/6153955][test] unwaive GPT-OSS w4 DP4 CUTLASS (#14884) Signed-off-by: Dongfeng Yu --- tests/integration/test_lists/waives.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 01cd980257dc..91f8c3e4f983 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -64,7 +64,6 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[v2_kv_cache-cutl accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[one_model] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[two_model] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-triton-auto] SKIP (https://nvbugs/6026676) -accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-dp4-cutlass-auto] SKIP (https://nvbugs/6153955) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-ep4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-fp8] SKIP (https://nvbugs/5651865) From ca2bc5ee4c3e521c3f4a0544258d9b04b7a04220 Mon Sep 17 00:00:00 2001 From: Liao Lanyu <108499334+lancelly@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:04:34 +0800 Subject: [PATCH 033/119] [None][perf] kv_cache_manager_v2: batch block-key SHA-256 hashing (#14994) Signed-off-by: Lance Liao <108499334+lancelly@users.noreply.github.com> --- .../kv_cache_manager_v2/_block_radix_tree.py | 21 ++++++++--- .../test_kv_cache_manager_v2.py | 36 +++++++++++++++++-- 2 files changed, 50 insertions(+), 7 deletions(-) diff --git a/tensorrt_llm/runtime/kv_cache_manager_v2/_block_radix_tree.py b/tensorrt_llm/runtime/kv_cache_manager_v2/_block_radix_tree.py index 6cada3da7cec..1024eca91575 100644 --- a/tensorrt_llm/runtime/kv_cache_manager_v2/_block_radix_tree.py +++ b/tensorrt_llm/runtime/kv_cache_manager_v2/_block_radix_tree.py @@ -14,6 +14,7 @@ # limitations under the License. import hashlib +from array import array from typing import TYPE_CHECKING, Iterable, Iterator, NamedTuple, Sequence, TypeVar, cast from . import rawref @@ -90,11 +91,21 @@ def update(self, data: int | bytes | Sequence[int | bytes]) -> "Hasher": elif type(data) is bytes: self._hasher.update(data) else: - for item in data: # type: ignore - assert ( - NDEBUG or (type(item) is int and (0 <= item < (1 << 64))) or type(item) is bytes - ) - self._hasher.update(item.to_bytes(8, "little") if (type(item) is int) else item) # type: ignore + # Hash the whole token block in one C call instead of one per token. + # array("Q", data).tobytes() packs each int as 8 native-endian bytes; + # all NVIDIA GPU host platforms (x86_64, aarch64/Grace) are little-endian + # so this is byte-identical to the per-token to_bytes(8, "little") loop. + # Falls back to that loop for multimodal blocks (which contain bytes items). + try: + self._hasher.update(array("Q", data).tobytes()) # type: ignore + except (TypeError, OverflowError): + for item in data: # type: ignore + assert ( + NDEBUG + or (type(item) is int and (0 <= item < (1 << 64))) + or type(item) is bytes + ) + self._hasher.update(item.to_bytes(8, "little") if (type(item) is int) else item) # type: ignore return self @property diff --git a/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py b/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py index 7e84c0ba75c2..583fa5da72c9 100755 --- a/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py +++ b/tests/unittest/kv_cache_manager_v2_tests/test_kv_cache_manager_v2.py @@ -15,6 +15,7 @@ import array import functools import gc +import hashlib import itertools import os import random @@ -52,7 +53,7 @@ TokenIdExt, _KVCache, ) - from kv_cache_manager_v2._block_radix_tree import traverse_post_order + from kv_cache_manager_v2._block_radix_tree import Hasher, traverse_post_order from kv_cache_manager_v2._common import ( BAD_PAGE_INDEX, GPU_LEVEL, @@ -105,7 +106,10 @@ TokenIdExt, _KVCache, ) - from tensorrt_llm.runtime.kv_cache_manager_v2._block_radix_tree import traverse_post_order + from tensorrt_llm.runtime.kv_cache_manager_v2._block_radix_tree import ( + Hasher, + traverse_post_order, + ) from tensorrt_llm.runtime.kv_cache_manager_v2._common import ( BAD_PAGE_INDEX, GPU_LEVEL, @@ -2556,5 +2560,33 @@ def test_shrink_touched_pool(self) -> None: allocator.release(s) +class TestBlockKeyHashing(unittest.TestCase): + """Verify Hasher.update produces bit-identical digests to the per-token reference (no GPU needed).""" + + @staticmethod + def _ref_update(seed: bytes, block: "list[int | bytes]") -> bytes: + h = hashlib.sha256() + h.update(seed) + for item in block: + h.update(item.to_bytes(8, "little") if type(item) is int else item) + return h.digest() + + def test_update_int_block_matches_reference(self) -> None: + rng = random.Random(123) + seed = b"\xaa\xbb\xcc" + for n in (0, 1, 7, 32, 33, 257): + block = [rng.randint(0, (1 << 60)) for _ in range(n)] + self.assertEqual( + Hasher(seed).update(block).digest, + self._ref_update(seed, block), + f"int block of length {n}", + ) + + def test_update_mixed_multimodal_block(self) -> None: + block = [randbytes(32), 5, 6, randbytes(32)] + list(range(20)) + seed = b"\x01" + self.assertEqual(Hasher(seed).update(block).digest, self._ref_update(seed, block)) + + if __name__ == "__main__": unittest.main() From 2cad6db1dbd294ca2d9c5dbd1fc071e7a7a3b26e Mon Sep 17 00:00:00 2001 From: QI JUN <22017000+QiJune@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:30:18 +0800 Subject: [PATCH 034/119] [TRTLLM-13259][ci] Merge DGX_H100 DeepSeek and GptOss stages (#15035) Signed-off-by: junq <22017000+QiJune@users.noreply.github.com> --- jenkins/L0_Test.groovy | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index 89d5c1c421b3..98ac748e5a42 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -4031,11 +4031,9 @@ def launchTestJobs(pipeline, testFilter) "DGX_A100-FMHA-Post-Merge-1": ["auto:dgx-a100-x1", "l0_a100", 1, 1], "DGX_H100-2_GPUs-PyTorch-Others-1": ["auto:dgx-h100-x2", "l0_dgx_h100", 1, 2, 2], "DGX_H100-2_GPUs-PyTorch-Others-2": ["auto:dgx-h100-x2", "l0_dgx_h100", 2, 2, 2], - "DGX_H100-2_GPUs-PyTorch-GptOss-1": ["auto:dgx-h100-x2", "l0_dgx_h100", 1, 2, 2], - "DGX_H100-2_GPUs-PyTorch-GptOss-2": ["auto:dgx-h100-x2", "l0_dgx_h100", 2, 2, 2], + "DGX_H100-2_GPUs-PyTorch-GptOss-1": ["auto:dgx-h100-x2", "l0_dgx_h100", 1, 1, 2], "DGX_H100-2_GPUs-PyTorch-Ray-1": ["auto:dgx-h100-x2", "l0_dgx_h100", 1, 1, 2], - "DGX_H100-4_GPUs-PyTorch-DeepSeek-1": ["auto:dgx-h100-x4", "l0_dgx_h100", 1, 2, 4], - "DGX_H100-4_GPUs-PyTorch-DeepSeek-2": ["auto:dgx-h100-x4", "l0_dgx_h100", 2, 2, 4], + "DGX_H100-4_GPUs-PyTorch-DeepSeek-1": ["auto:dgx-h100-x4", "l0_dgx_h100", 1, 1, 4], "DGX_H100-4_GPUs-PyTorch-GptOss-1": ["auto:dgx-h100-x4", "l0_dgx_h100", 1, 1, 4], "DGX_H100-4_GPUs-PyTorch-Others-1": ["auto:dgx-h100-x4", "l0_dgx_h100", 1, 2, 4], "DGX_H100-4_GPUs-PyTorch-Others-2": ["auto:dgx-h100-x4", "l0_dgx_h100", 2, 2, 4], From 5fa68a4755e1318491b90acb30359da2b7ccfbaf Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:57:26 +0800 Subject: [PATCH 035/119] [None][infra] Waive 11 failed cases for main in post-merge 2765 (#15080) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 91f8c3e4f983..6d6af3f5ef16 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -275,9 +275,20 @@ perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_th perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_tep8_32k8k] SKIP (https://nvbugs/6227472) perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws1_1k1k] SKIP (https://nvbugs/6153575) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6215844) +perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6179661) +perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con1536_ctx1_dep4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6016528) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con256_ctx1_dep4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6221024) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep8_gen1_dep8_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6085022) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] SKIP (https://nvbugs/6200257) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6221022) From 26325305d687165b65b16fb652a2b1fa48a15b40 Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Mon, 8 Jun 2026 14:13:20 +0800 Subject: [PATCH 036/119] [None][infra] Waive 3 failed cases for main in post-merge 2765 (#15082) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 6d6af3f5ef16..4fd77a8760e1 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -268,6 +268,9 @@ perf/test_perf.py::test_perf[t5-bench-float16-input_output_len:128,20-gpus:2] SK perf/test_perf.py::test_perf[t5-bench-float16-maxbs:1-input_output_len:128,20-gpus:2] SKIP perf/test_perf.py::test_perf[t5_base-plugin-float16-bs:8-input_output_len:60,20] SKIP # (https://nvidia.slack.com/archives/C059LSY62BT/p1704525727177449) perf/test_perf.py::test_perf[whisper_large_v3-bench-float16-input_output_len:128,20] SKIP +perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_dep8_mtp1_8k1k] SKIP (https://nvbugs/6280721) +perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_tep8_mtp3_8k1k] SKIP (https://nvbugs/6280721) +perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_grace_blackwell-v32_fp4_tep4_mtp3_8k1k] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[aggr_upload-dynamo_k25_thinking_fp4_blackwell-k25_thinking_fp4_tep8_adp_2k1k] SKIP (https://nvbugs/6227472) perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_2_nodes_grace_blackwell-k25_thinking_fp4_dep8_32k8k] SKIP (https://nvbugs/6236108) perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_dep8_32k8k] SKIP (https://nvbugs/6236094) From 7e49baaa9ace150a7dece4088f3e19dfb0a33b96 Mon Sep 17 00:00:00 2001 From: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> Date: Mon, 8 Jun 2026 14:38:53 +0800 Subject: [PATCH 037/119] [None][test] waive weekly qa ci failure cases (#15077) Signed-off-by: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 46 +++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 4fd77a8760e1..9237ef23835d 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -135,10 +135,13 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_off] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_on] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales_early_first_token_response SKIP (https://nvbugs/6200128) +accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=0] SKIP (https://nvbugs/6274932) +accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=3] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[forced_chunked_prefill] SKIP (https://nvbugs/6211189) accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[full_budget] SKIP (https://nvbugs/6211189) accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized SKIP (https://nvbugs/6215689) accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (https://nvbugs/6181383) +accuracy/test_llm_api_pytorch_multimodal.py::TestStep3_7::test_fp8_block_scales SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch_ray.py::TestLlama3_1_8BInstruct::test_pp2_ray SKIP (https://nvbugs/6094070) cpp/test_e2e.py::test_benchmarks[bart-90] SKIP (https://nvbugs/5550689) cpp/test_e2e.py::test_benchmarks[gpt-80] SKIP (https://nvbugs/5550689) @@ -213,6 +216,9 @@ examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses2_ring2] SKIP (https://nvbugs/6272644) examples/test_visual_gen_multi_gpu.py::test_wan22_t2v_lpips_against_golden_multi_gpu[ulysses4] SKIP (https://nvbugs/6272644) examples/test_whisper.py::test_llm_whisper_general[large-v3-disable_gemm_plugin-disable_attention_plugin-disable_weight_only-float16-nb:1-use_python_runtime] SKIP (https://nvbugs/5244570) +full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS] SKIP (https://nvbugs/6273850) +full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] SKIP (https://nvbugs/6273850) +full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] SKIP (https://nvbugs/6239637) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_tensor] SKIP (https://nvbugs/5161074) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_token_channel] SKIP (https://nvbugs/5161074) full:B200/perf/test_perf.py::test_perf[quant:w4a8_awq] SKIP (https://nvbugs/5161074) @@ -222,16 +228,56 @@ full:GH200/examples/test_multimodal.py::test_llm_multimodal_general[video-neva-p full:GH200/examples/test_nemotron.py::test_llm_nemotron_3_8b_1gpu[bfloat16-fp8] SKIP (arm is not supported) full:GH200/examples/test_qwen2audio.py::test_llm_qwen2audio_single_gpu[qwen2_audio_7b_instruct] SKIP (arm is not supported) full:GH200/unittest/trt/model_api/test_model_quantization.py SKIP (https://nvbugs/4979955) +full:H100/accuracy/test_llm_api_pytorch.py::TestQwen3_6_27B::test_fp8 SKIP (https://nvbugs/6255417) +full:H100/disaggregated/test_disaggregated.py::test_disaggregated_logprobs_serving[llama-3.1-8b-instruct] SKIP (https://nvbugs/6275959) full:H100/disaggregated/test_disaggregated.py::test_disaggregated_stress_test[input8k-output1k-conc512-gpt_oss_120b_eagle_triton_stress] SKIP (https://nvbugs/6250439) full:H100_PCIe/unittest/llmapi/test_llm_pytorch.py::test_llama_7b_multi_lora_evict_and_reload_lora_gpu_cache SKIP (https://nvbugs/5682551) full:H20/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[triton-auto] SKIP (https://nvbugs/6026676) +full:L40S/accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_fp8[latency-torch_compile=True] SKIP (https://nvbugs/6276841) +full:L40S/accuracy/test_llm_api_pytorch_encode.py::TestDecoderEncode::test_decoder_encode_cuda_graph_matches_eager_logits[tinyllama-1.1b] SKIP (https://nvbugs/6276842) full:RTX/accuracy/test_llm_api_pytorch.py::TestGemma3_1BInstruct::test_auto_dtype SKIP (https://nvbugs/5569696) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5948435) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/5961814) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5961814) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_cutlass-torch_compile=True] SKIP (https://nvbugs/5929339) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-cutlass-fp8] SKIP (https://nvbugs/6273845) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[dp2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[dp2-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[ep2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[ep2-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[tp2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[tp2-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS] SKIP (https://nvbugs/6273850) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] SKIP (https://nvbugs/6273850) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] SKIP (https://nvbugs/6275856) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_bf16 SKIP (https://nvbugs/6273850) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_dflash SKIP (https://nvbugs/6273850) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_fp8 SKIP (https://nvbugs/6273850) +full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_6_27B::test_fp8 SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-cutlass-fp8] SKIP (https://nvbugs/6273845) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[dp2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[dp2-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[ep2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[ep2-trtllm-fp8] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[tp2-trtllm-auto] SKIP (https://nvbugs/6273846) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[tp2-trtllm-fp8] SKIP (https://nvbugs/6273846) full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp4_tp2pp2[torch_compile=True-enable_gemm_allreduce_fusion=False] SKIP (https://nvbugs/6262407) full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_nvfp4_tp4[torch_compile=False] SKIP (https://nvbugs/6262407) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS] SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] SKIP (https://nvbugs/6275856) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_bf16 SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_dflash SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_fp8 SKIP (https://nvbugs/6273850) +full:RTX_PRO_6000_Blackwell_Server_Edition/accuracy/test_llm_api_pytorch.py::TestQwen3_6_27B::test_fp8 SKIP (https://nvbugs/6273850) full:RTX_PRO_6000_Blackwell_Server_Edition/perf/test_perf.py::test_perf[quant:int8_sq_per_tensor] SKIP (https://nvbugs/5161074) full:RTX_PRO_6000_Blackwell_Server_Edition/perf/test_perf.py::test_perf[quant:int8_sq_per_token_channel] SKIP (https://nvbugs/5161074) full:RTX_PRO_6000_Blackwell_Server_Edition/perf/test_perf.py::test_perf[quant:w4a8_awq] SKIP (https://nvbugs/5161074) From 02f6b2fcd5eb5247560150767e200ff105419dab Mon Sep 17 00:00:00 2001 From: Grzegorz Kwasniewski <213329731+greg-kwasniewski1@users.noreply.github.com> Date: Mon, 8 Jun 2026 09:55:52 +0200 Subject: [PATCH 038/119] [None][feat] AutoDeploy: propagate layer_type hint across pattern-matcher rewrites (#14835) Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com> --- .../auto_deploy/custom_ops/linear/swiglu.py | 9 +++ .../_torch/auto_deploy/utils/node_utils.py | 73 +++++++++++++++++++ .../auto_deploy/utils/pattern_matcher.py | 17 +++++ .../library/test_fuse_swiglu.py | 72 +++++++++++++++++- .../library/test_nvfp4_swiglu.py | 44 ++++++++++- 5 files changed, 213 insertions(+), 2 deletions(-) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py index 92bdd51e6aba..a13ddd1de8c4 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py @@ -68,6 +68,9 @@ def torch_swiglu_mlp( gate_bias: Optional gate projection bias of shape [intermediate_size]. up_bias: Optional up projection bias of shape [intermediate_size]. down_bias: Optional down projection bias of shape [hidden_size]. + layer_type: Layer-classification sharding hint (e.g. "mlp"/"moe"/"shared_expert"), + propagated from the matched linears by the pattern matcher and consumed by + ``apply_sharding_hints`` (``shard_layers``). Does not affect the numeric result. Returns: Output tensor of shape [..., hidden_size]. @@ -184,6 +187,9 @@ def torch_nvfp4_swiglu_mlp( down_input_scale: Input scale for down projection. down_weight_scale: Per-block weight scale for down projection. down_alpha: Alpha (combined scale) for down projection. + layer_type: Layer-classification sharding hint (e.g. "mlp"/"moe"/"shared_expert"), + propagated from the matched linears by the pattern matcher and consumed by + ``apply_sharding_hints`` (``shard_layers``). Does not affect the numeric result. Returns: Output tensor of shape [..., hidden_size]. @@ -344,6 +350,9 @@ def torch_finegrained_fp8_swiglu_mlp( gate_weight_scale: Per-block weight scale for gate [N/128, K/128] float32. up_weight_scale: Per-block weight scale for up [N/128, K/128] float32. down_weight_scale: Per-block weight scale for down [N/128, K/128] float32. + layer_type: Layer-classification sharding hint (e.g. "mlp"/"moe"/"shared_expert"), + propagated from the matched linears by the pattern matcher and consumed by + ``apply_sharding_hints`` (``shard_layers``). Does not affect the numeric result. Returns: Output tensor of shape [..., hidden_size]. diff --git a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py index 286242ce5228..f80d55f18870 100644 --- a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py +++ b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py @@ -1339,6 +1339,79 @@ def set_op_args(node: Node, **name_value_pairs) -> None: node.kwargs = kwargs +# Classification hints are layer-level: they are invariant across the fine-grained ops that +# make up one logical layer (e.g. all projections of a SwiGLU MLP share the same +# ``layer_type``) and are consumed by policy filters such as ``shard_layers`` -- NOT by +# per-weight sharding mechanics. They are therefore the only sharding-related kwargs that are +# well-defined to carry onto a fused/replacement op produced by an N->1 pattern rewrite (by +# consensus). Per-weight mechanics (``tp_mode``, ``output_sizes``, ``tp_min_local_shape``, +# ``tp_scaled_dim``, ``enable_sharding``) are intentionally NOT propagated: a fused op's +# ShardableNode re-derives those structurally, so copying them across a rewrite is ill-defined +# (the constituents legitimately disagree -- e.g. an MLA layer mixes ``tp_mode`` none/colwise/ +# rowwise while sharing a single ``layer_type``). +CLASSIFICATION_HINT_NAMES = frozenset({"layer_type"}) + + +def _op_schema_arg_names(node: Node) -> set: + """Return the argument names declared by a call_function node's op schema. + + Returns an empty set for non-call_function nodes or ops without an introspectable + schema, so callers can use it as a safe membership test. + """ + if not isinstance(node, Node) or node.op != "call_function": + return set() + try: + return {a.name for a in _get_op_schema(node).arguments} + except (ValueError, RuntimeError): + return set() + + +def collect_classification_hints(nodes: Iterable[Node]) -> dict: + """Return a consensus value for each classification hint across ``nodes``. + + For every name in :data:`CLASSIFICATION_HINT_NAMES`, scan the call_function nodes that + declare it and collect the distinct *meaningful* values (ignoring ``None`` and the + ``"unknown"`` default). A name is included in the result only when exactly one such + value is observed; conflicting values are dropped with a warning, since a conflict + means the caller grouped nodes that belong to different logical layers. + """ + result: dict = {} + for name in CLASSIFICATION_HINT_NAMES: + values = set() + for n in nodes: + if name not in _op_schema_arg_names(n): + continue + [value] = extract_op_args(n, name) + if value is not None and value != "unknown": + values.add(value) + if len(values) == 1: + result[name] = next(iter(values)) + elif len(values) > 1: + ad_logger.warning( + f"Conflicting '{name}' hints {sorted(values)} among matched nodes; " + "not propagating to the replacement op (matched nodes may span layers)." + ) + return result + + +def stamp_hints(nodes: Iterable[Node], hints: dict) -> int: + """Set ``hints`` on every node in ``nodes`` whose op schema declares them. + + Returns the number of nodes updated. Each hint is applied only to nodes whose op + actually declares that argument, so passing a heterogeneous node list is safe. + """ + if not hints: + return 0 + count = 0 + for n in nodes: + names = _op_schema_arg_names(n) + to_set = {k: v for k, v in hints.items() if k in names} + if to_set: + set_op_args(n, **to_set) + count += 1 + return count + + def predecessors( node: Node, depth: int = 1, diff --git a/tensorrt_llm/_torch/auto_deploy/utils/pattern_matcher.py b/tensorrt_llm/_torch/auto_deploy/utils/pattern_matcher.py index fbd12dc7b147..f508c1a324d7 100644 --- a/tensorrt_llm/_torch/auto_deploy/utils/pattern_matcher.py +++ b/tensorrt_llm/_torch/auto_deploy/utils/pattern_matcher.py @@ -55,6 +55,7 @@ from torch.fx import GraphModule from ..export import torch_export_to_gm +from .node_utils import collect_classification_hints, stamp_hints @contextlib.contextmanager @@ -127,6 +128,18 @@ def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> Non del node assert match.replacement_graph is not None output_nodes = match.output_nodes() + + # Carry layer-level classification hints (currently ``layer_type``) from the matched + # nodes onto the replacement op(s). This is the only sharding-related metadata that is + # well-defined to propagate across an N->1 rewrite: it is invariant across the + # fine-grained ops of one logical layer and is consumed by the downstream hint-driven + # sharder (``apply_sharding_hints`` / ``shard_layers``). Per-weight mechanics (tp_mode, + # output_sizes, ...) are intentionally NOT carried -- the replacement op's ShardableNode + # re-derives them structurally. Collect before the rewrite (cheap; reads only the + # matched nodes) so the node-set diff below is paid only when there is a hint to carry. + class_hints = collect_classification_hints(match.nodes) + nodes_before = set(graph.nodes) if class_hints else None + self.replace_with_graph( match, graph, @@ -134,6 +147,10 @@ def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> Non self.normalize_args(*match.args, **match.kwargs), ) + if class_hints: + inserted_nodes = [n for n in graph.nodes if n not in nodes_before] + stamp_hints(inserted_nodes, class_hints) + if len(output_nodes) > 1: # Torch's generic replacement path inserts the copied replacement graph relative to the # earliest matched output node. That is usually fine for single-output rewrites, but it diff --git a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_fuse_swiglu.py b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_fuse_swiglu.py index f3377e4b2e08..f272d05d6e55 100644 --- a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_fuse_swiglu.py +++ b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_fuse_swiglu.py @@ -19,7 +19,12 @@ from tensorrt_llm._torch.auto_deploy.custom_ops.linear.swiglu import * # noqa from tensorrt_llm._torch.auto_deploy.export import torch_export_to_gm from tensorrt_llm._torch.auto_deploy.transform.optimizer import InferenceOptimizer -from tensorrt_llm._torch.auto_deploy.utils.node_utils import is_op +from tensorrt_llm._torch.auto_deploy.utils.node_utils import ( + collect_classification_hints, + extract_op_args, + is_op, + stamp_hints, +) class SwiGLUMLP(torch.nn.Module): @@ -200,3 +205,68 @@ def test_swiglu_pattern_match_only(): y_matched = gm_matched(x) y_model = model(x) torch.testing.assert_close(y_matched, y_model, atol=1e-3, rtol=1e-3) + + +# --------------------------------------------------------------------------- +# layer_type (classification-hint) propagation across pattern rewrites +# +# These exercise the generic helpers used by the matcher infra to carry the +# layer-level ``layer_type`` hint from the matched fine-grained ops onto a fused +# replacement op (so downstream ``apply_sharding_hints`` / ``shard_layers`` still +# sees it). They are graph-only and need no GPU. The end-to-end behaviour through +# an actual matcher is covered on the NVFP4 path in ``test_nvfp4_swiglu.py`` +# (the BF16 SwiGLU matcher never fuses hint-carrying linears, since ``torch.export`` +# materializes the hints positionally, so there is no hint to lose there). +# --------------------------------------------------------------------------- + + +def test_collect_classification_hints_consensus_over_mixed_mechanics(): + """Consensus recovers the shared layer_type even when constituents disagree on tp_mode.""" + g = torch.fx.Graph() + x = g.placeholder("x") + nodes = [] + for tp in ("colwise", "colwise", "rowwise"): # gate/up colwise, down rowwise + nodes.append( + g.call_function( + torch.ops.auto_deploy.torch_linear_simple.default, + args=(x, x, None), + kwargs={"tp_mode": tp, "layer_type": "shared_expert"}, + ) + ) + # A node left at the default ("unknown") must not contribute to the consensus. + nodes.append( + g.call_function(torch.ops.auto_deploy.torch_linear_simple.default, args=(x, x, None)) + ) + assert collect_classification_hints(nodes) == {"layer_type": "shared_expert"} + + +def test_collect_classification_hints_conflict_is_dropped(): + """Conflicting layer_type values (a rewrite spanning layers) are dropped, not guessed.""" + g = torch.fx.Graph() + x = g.placeholder("x") + a = g.call_function( + torch.ops.auto_deploy.torch_linear_simple.default, + args=(x, x, None), + kwargs={"layer_type": "moe"}, + ) + b = g.call_function( + torch.ops.auto_deploy.torch_linear_simple.default, + args=(x, x, None), + kwargs={"layer_type": "mla"}, + ) + assert collect_classification_hints([a, b]) == {} + + +def test_stamp_hints_only_on_declaring_ops(): + """stamp_hints sets a hint only on ops whose schema declares it (aten.silu is skipped).""" + g = torch.fx.Graph() + x = g.placeholder("x") + swiglu = g.call_function( + torch.ops.auto_deploy.torch_swiglu_mlp.default, + args=(x, x, x, x, None, None, None), + ) + silu = g.call_function(torch.ops.aten.silu.default, args=(x,)) + + assert stamp_hints([swiglu, silu], {"layer_type": "shared_expert"}) == 1 + [lt] = extract_op_args(swiglu, "layer_type") + assert lt == "shared_expert" diff --git a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_nvfp4_swiglu.py b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_nvfp4_swiglu.py index d89de5051688..765d7101aec5 100644 --- a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_nvfp4_swiglu.py +++ b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_nvfp4_swiglu.py @@ -28,7 +28,7 @@ import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 from tensorrt_llm._torch.auto_deploy.export import torch_export_to_gm from tensorrt_llm._torch.auto_deploy.transform.optimizer import InferenceOptimizer -from tensorrt_llm._torch.auto_deploy.utils.node_utils import is_op +from tensorrt_llm._torch.auto_deploy.utils.node_utils import extract_op_args, is_op, set_op_args from tensorrt_llm._torch.auto_deploy.utils.quantization_utils import fp4_global_scale _skip_reason = "Requires NVFP4 (Blackwell+) and TRT-LLM ops" @@ -390,3 +390,45 @@ def forward(self, x): assert _count_ops(gm_result, torch.ops.auto_deploy.torch_fake_quant_nvfp4_linear) == 2, ( "Original NVFP4 linear ops should be unchanged" ) + + +@pytest.mark.skipif(_skip_condition, reason=_skip_reason) +def test_nvfp4_swiglu_pattern_propagates_layer_type(): + """Regression guard for the Qwen3.5-MoE NVFP4 accuracy bug. + + The SwiGLU matcher collapses the three NVFP4 linears into one + ``torch_nvfp4_swiglu_mlp`` op. It must carry the constituents' ``layer_type`` hint onto + that fused op so the downstream hint-driven sharder (``apply_sharding_hints`` / + ``shard_layers``) can still exclude (replicate) the shared expert. Before the fix the + fused op carried no ``layer_type`` and the fail-open whitelist TP-sharded it, corrupting + the shared-expert output. + + ``layer_type`` is attached as a kwarg here exactly as ``quantize_nvfp4_linear_from_config`` + does in production (the matcher ignores it for matching but it must survive the rewrite). + """ + torch.manual_seed(0) + model = NVFP4SwiGLUMLP().to("cuda") + x = torch.randn(2, 128, device="cuda", dtype=torch.float16) + + gm = torch_export_to_gm(model, args=(x,), clone=True) + + # Tag every NVFP4 linear with a layer_type hint (as the quantization transform does). + for n in gm.graph.nodes: + if is_op(n, torch.ops.auto_deploy.torch_fake_quant_nvfp4_linear): + set_op_args(n, layer_type="shared_expert") + gm.recompile() + + gm_matched = InferenceOptimizer( + None, + {"match_nvfp4_swiglu_pattern": {"stage": "pattern_matcher"}}, + )(None, gm) + + swiglu_nodes = [ + n + for n in gm_matched.graph.nodes + if is_op(n, torch.ops.auto_deploy.torch_nvfp4_swiglu_mlp.default) + ] + assert len(swiglu_nodes) == 1, f"expected 1 torch_nvfp4_swiglu_mlp, got {len(swiglu_nodes)}" + + [lt] = extract_op_args(swiglu_nodes[0], "layer_type") + assert lt == "shared_expert", f"layer_type was not propagated onto the fused op: got {lt!r}" From 28dc25e68b6b75285890fe0d0b4ed38c062da418 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Mon, 8 Jun 2026 16:38:09 +0800 Subject: [PATCH 039/119] [None][test] Waive 15 failed cases for main in QA CI (#15056) Signed-off-by: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> Co-authored-by: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 9237ef23835d..06896604e155 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -6,16 +6,21 @@ accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_kv_cache_v2_nixl_python SKIP (https://nvbugs/6184575) accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ngram SKIP (https://nvbugs/6245651) accuracy/test_disaggregated_serving.py::TestQwen3_30B_A3B::test_mixed_ctx_gen_model[ctxpp2gentp2] SKIP (https://nvbugs/5748664) +accuracy/test_dwdp_disaggregated_serving.py::TestDwdpDeepSeekV3Lite::test_dwdp_accuracy SKIP (https://nvbugs/6276923) +accuracy/test_dwdp_disaggregated_serving.py::TestDwdpDeepSeekV3Lite::test_dwdp_accuracy_contention_opt SKIP (https://nvbugs/6276923) +accuracy/test_dwdp_disaggregated_serving.py::TestDwdpDeepSeekV3Lite::test_dwdp_accuracy_mode_b_overlap SKIP (https://nvbugs/6276923) accuracy/test_llm_api.py::TestLlama3_1_8BInstruct::test_gather_generation_logits_cuda_graph SKIP (https://nvbugs/5772995) accuracy/test_llm_api.py::TestLlama3_1_8BInstruct::test_guided_decoding_4gpus[xgrammar] SKIP (https://nvbugs/5346443) accuracy/test_llm_api.py::TestMistralNemo12B::test_fp8 SKIP (https://nvbugs/5413197) accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (https://nvbugs/6194934) accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) +accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-8] SKIP (https://nvbugs/6248757) accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) +accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] SKIP (https://nvbugs/6191524) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput] SKIP (https://nvbugs/6084775) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_mtp] SKIP (https://nvbugs/6029882) @@ -31,11 +36,15 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baselin accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baseline_mtp1] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[fp4_indexer_dsl_mtp2] SKIP (https://nvbugs/6241842) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[fp4_indexer_dsl_mtp3] SKIP (https://nvbugs/6241845) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus_chunked_prefill[latency] SKIP (https://nvbugs/6276981) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus_piecewise_cuda_graph[baseline] SKIP (https://nvbugs/6185196) accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus_piecewise_cuda_graph[mtp3_fp8kv_chunked] SKIP (https://nvbugs/5989920) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16[mtp_nextn=2-attention_dp=False-cuda_graph=False-overlap_scheduler=True-torch_compile=False-enable_chunked_prefill=False-v2_kv_cache=True] SKIP (https://nvbugs/6084720) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16[mtp_nextn=2-attention_dp=False-cuda_graph=True-overlap_scheduler=False-torch_compile=False-enable_chunked_prefill=False-v2_kv_cache=True] SKIP (https://nvbugs/6095851) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/6278337) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] SKIP (https://nvbugs/6224637) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=True-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/6278337) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=True-overlap_scheduler=False-torch_compile=True] SKIP (https://nvbugs/6278337) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=True] SKIP (https://nvbugs/6224637) @@ -56,6 +65,7 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_no_kv_cache_reuse[qua accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5945081) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6224637) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/6278403) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] SKIP (https://nvbugs/6272673) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6224637) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/6245394) @@ -64,6 +74,7 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[v2_kv_cache-cutl accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[one_model] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[two_model] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v1_kv_cache-True-True-triton-auto] SKIP (https://nvbugs/6026676) +accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-trtllm-fp8] SKIP (https://nvbugs/6276985) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-ep4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-fp8] SKIP (https://nvbugs/5651865) @@ -71,6 +82,7 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-dp4-cutl accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-ep4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-tp4-cutlass-auto] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[cutlass-auto] SKIP (https://nvbugs/5596343) +accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[trtllm-auto] SKIP (https://nvbugs/6278350) accuracy/test_llm_api_pytorch.py::TestKanana_Instruct::test_auto_dtype SKIP (https://nvbugs/6209806) accuracy/test_llm_api_pytorch.py::TestKimiK25::test_nvfp4[dep8] SKIP (https://nvbugs/6260890) accuracy/test_llm_api_pytorch.py::TestKimiK25::test_nvfp4[tp8] SKIP (https://nvbugs/6248837) @@ -88,6 +100,7 @@ accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[t accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_chunked_prefill[use_temperature=False-attn_backend=TRTLLM] SKIP (https://nvbugs/5997547) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_dflash SKIP (https://nvbugs/6141653) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[pp4-fp8kv=True-attn_backend=FLASHINFER-torch_compile=False] SKIP (https://nvbugs/6211191) +accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[pp4-fp8kv=True-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/6278337) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=False-attn_backend=TRTLLM-torch_compile=True] SKIP (https://nvbugs/6211191) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_beam_search[enable_cuda_graph=False-enable_padding=False-disable_overlap_scheduler=False-sampler_async_worker=False] SKIP (https://nvbugs/6141653) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_beam_search[enable_cuda_graph=False-enable_padding=False-disable_overlap_scheduler=True-sampler_async_worker=False] SKIP (https://nvbugs/6141653) @@ -135,6 +148,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_off] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_on] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales_early_first_token_response SKIP (https://nvbugs/6200128) +accuracy/test_llm_api_pytorch.py::TestStep3_7::test_auto_dtype[tp_size=8-ep_size=8] SKIP (https://nvbugs/6278377) accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=0] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=3] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[forced_chunked_prefill] SKIP (https://nvbugs/6211189) @@ -346,6 +360,8 @@ stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-str test_doc.py::test_url_validity SKIP (https://nvbugs/6215684) test_e2e.py::test_draft_token_tree_quickstart_advanced_eagle3[Llama-3.1-8b-Instruct-llama-3.1-model/Llama-3.1-8B-Instruct-EAGLE3-LLaMA3.1-Instruct-8B] SKIP (https://nvbugs/5989907) test_e2e.py::test_draft_token_tree_quickstart_advanced_eagle3_depth_1_tree[Llama-3.1-8b-Instruct-llama-3.1-model/Llama-3.1-8B-Instruct-EAGLE3-LLaMA3.1-Instruct-8B] SKIP (https://nvbugs/5989907) +test_e2e.py::test_multi_nodes_eval[DeepSeek-R1/DeepSeek-R1-0528-FP4-tp16-mmlu] SKIP (https://nvbugs/6276983) +test_e2e.py::test_multi_nodes_eval[Kimi-K2-Thinking-NVFP4-tp16-mmlu] SKIP (https://nvbugs/6276983) test_e2e.py::test_multi_nodes_eval[Qwen3/Qwen3-235B-A22B-tp16-mmlu] SKIP (https://nvbugs/6115560) test_e2e.py::test_multi_nodes_eval[Qwen3/saved_models_Qwen3-235B-A22B_nvfp4_hf-tp16-mmlu] SKIP (https://nvbugs/6114608) test_e2e.py::test_openai_chat_example[trt] SKIP (https://nvbugs/5477444) From 2febb372b58575c8aae2fa443d308d90adc6e255 Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Mon, 8 Jun 2026 17:07:09 +0800 Subject: [PATCH 040/119] [None][infra] Waive 1 failed cases for main in pre-merge 41894 (#15089) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 06896604e155..dfd056b822ab 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -336,6 +336,7 @@ perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_2_nodes_grace_bl perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_dep8_32k8k] SKIP (https://nvbugs/6236094) perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_dep8_8k1k] SKIP (https://nvbugs/6227472) perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_tep8_32k8k] SKIP (https://nvbugs/6227472) +perf/test_perf_sanity.py::test_e2e[aggr_upload-llama3_1_8b_fp8_ad_hopper-llama3_1_8b_ad_ws1_1k1k] SKIP (https://nvbugs/6281573) perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws1_1k1k] SKIP (https://nvbugs/6153575) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6215844) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) From 09c21b65df8e3c6446af99f0c8d1bb909726c10b Mon Sep 17 00:00:00 2001 From: QI JUN <22017000+QiJune@users.noreply.github.com> Date: Mon, 8 Jun 2026 18:18:08 +0800 Subject: [PATCH 041/119] [TRTLLM-13262][ci] Move non-default-feature tests to post merge (#15038) Signed-off-by: junq <22017000+QiJune@users.noreply.github.com> --- .../test_lists/test-db/l0_dgx_b200.yml | 11 +++----- .../test_lists/test-db/l0_dgx_b300.yml | 2 +- .../test-db/l0_gb200_multi_gpus.yml | 26 +++++++++---------- 3 files changed, 18 insertions(+), 21 deletions(-) diff --git a/tests/integration/test_lists/test-db/l0_dgx_b200.yml b/tests/integration/test_lists/test-db/l0_dgx_b200.yml index a7587d7d9b4b..2818cf78b433 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b200.yml @@ -58,10 +58,6 @@ l0_dgx_b200: - disaggregated/test_disaggregated.py::test_disaggregated_deepseek_v3_lite_fp8_nixl[DeepSeek-V3-Lite-fp8] - disaggregated/test_disaggregated.py::test_disaggregated_gpt_oss_120b_harmony[gpt_oss/gpt-oss-120b] - accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[latency_adp_lmtp_tp4] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=0] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=2] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=0] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=2] - accuracy/test_llm_api_pytorch.py::TestMiniMaxM2::test_4gpus[attention_dp=False-cuda_graph=True-overlap_scheduler=True-tp_size=4-ep_size=4] TIMEOUT (60) - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[pp4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] TIMEOUT (60) # ------------- NVBug 6025177: trtllm-serve cross-request KV contamination (OpenAI) --------------- @@ -335,6 +331,10 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=True] - disaggregated/test_disaggregated.py::test_disaggregated_mamba_conc_greater_than_mbs[NVIDIA-Nemotron-3-Super-120B-A12B-FP8] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_attn_multi_gpus TIMEOUT (60) + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=0] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=2] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=0] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=2] # ------------- AutoDeploy Backend Stages --------------- - condition: ranges: @@ -365,8 +365,6 @@ l0_dgx_b200: - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-4] - accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] - # ------------- AutoDeploy Perf Sanity --------------- - - perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws4_1k1k] TIMEOUT (120) - condition: ranges: system_gpu_count: @@ -384,7 +382,6 @@ l0_dgx_b200: tests: # Move to post-merge due to https://nvbugspro.nvidia.com/bug/6221483 - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_mtp[nvfp4_ws4_80gb-trtllm] - - accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-4] - accuracy/test_llm_api_autodeploy.py::TestNemotronNanoV3::test_accuracy[nvfp4-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[bf16-4-attn_dp_off-trtllm] - accuracy/test_llm_api_autodeploy.py::TestNemotronSuperV3::test_accuracy[fp8-4-attn_dp_on-trtllm] diff --git a/tests/integration/test_lists/test-db/l0_dgx_b300.yml b/tests/integration/test_lists/test-db/l0_dgx_b300.yml index e460be687494..cfa268492f48 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b300.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b300.yml @@ -69,6 +69,7 @@ l0_dgx_b300: - accuracy/test_disaggregated_serving.py::TestGemma3_1BInstruct::test_kv_cache_v2_nixl_python - accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_tp4] TIMEOUT (180) - accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_pp4_mtp] TIMEOUT (180) + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-tp4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] - condition: ranges: system_gpu_count: @@ -120,5 +121,4 @@ l0_dgx_b300: - accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-ep4-trtllm-auto] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp2pp2-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-tp4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=True-torch_compile=False] diff --git a/tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml b/tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml index 9427774e2255..91c37da09ea4 100644 --- a/tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml +++ b/tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml @@ -20,34 +20,21 @@ l0_gb200_multi_gpus: - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=True-attn_backend=TRTLLM-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp4-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[ep4-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp2pp2-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp2pp2-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_kv_cache_aware_routing[mtp_nextn=0] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_kv_cache_aware_routing[mtp_nextn=2] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=0] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=2] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=0] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=2] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp2pp2-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp2pp2-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=2-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus_online_eplb[fp8kv=True-moe_backend=WIDEEP] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus_online_eplb[fp8kv=True-moe_backend=TRTLLM] @@ -124,6 +111,19 @@ l0_gb200_multi_gpus: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=2-tp4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=True-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=True] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-tp4-fp8kv=False-attention_dp=True-cuda_graph=False-overlap_scheduler=False-low_precision_combine=True-torch_compile=False] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp2pp2-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=0] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[tp4-mtp_nextn=2] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=0] + - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_python_scheduler[ep4-mtp_nextn=2] - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4_4gpus[latency_moe_trtllm_eagle3] TIMEOUT (90) - accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] TIMEOUT (90) - accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle] TIMEOUT (90) From c93c63d2154e40e50b28bd6cb32682c741ac3b5c Mon Sep 17 00:00:00 2001 From: Lizhi Zhou <1432185+reasonsolo@users.noreply.github.com> Date: Mon, 8 Jun 2026 19:20:59 +0800 Subject: [PATCH 042/119] [None][feat] Enable disk cache config for KV cache v2 (#14845) Signed-off-by: Lizhi Zhou <1432185+reasonsolo@users.noreply.github.com> --- .../_torch/pyexecutor/resource_manager.py | 12 +++++++++- tensorrt_llm/llmapi/llm_args.py | 23 +++++++++++++++++++ .../test_cache_transceiver_single_process.py | 2 ++ .../disaggregated/test_kv_transfer.py | 2 ++ tests/unittest/llmapi/test_llm_args.py | 15 ++++++++++++ 5 files changed, 53 insertions(+), 1 deletion(-) diff --git a/tensorrt_llm/_torch/pyexecutor/resource_manager.py b/tensorrt_llm/_torch/pyexecutor/resource_manager.py index 84070dd0864c..f4bc390060e3 100644 --- a/tensorrt_llm/_torch/pyexecutor/resource_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/resource_manager.py @@ -33,7 +33,7 @@ # isort: off from tensorrt_llm.runtime.kv_cache_manager_v2 import ( DEFAULT_BEAM_INDEX, AttentionLayerConfig, BufferConfig, CacheTierConfig, - GpuCacheTierConfig, HostCacheTierConfig, ReuseScope) + DiskCacheTierConfig, GpuCacheTierConfig, HostCacheTierConfig, ReuseScope) # isort: on from tensorrt_llm.runtime.kv_cache_manager_v2 import \ KVCacheManager as KVCacheManagerPy @@ -2517,6 +2517,16 @@ def append_to_kv_heads_per_layer(num_kv_heads_per_layer: List[int], logger.info( f"KV cache manager v2 host cache quota set to {host_quota / (1 << 30):.2f}GiB" ) + disk_cache_size = kv_cache_config.disk_cache_size + if disk_cache_size is not None and disk_cache_size > 0: + disk_cache_path = kv_cache_config.disk_cache_path + assert disk_cache_path is not None + cache_tiers.append( + DiskCacheTierConfig(quota=disk_cache_size, + path=disk_cache_path)) + logger.info( + f"KV cache manager v2 disk cache quota set to {disk_cache_size / (1 << 30):.2f}GiB at {disk_cache_path}" + ) self.vocab_size = vocab_size diff --git a/tensorrt_llm/llmapi/llm_args.py b/tensorrt_llm/llmapi/llm_args.py index 3ad7c5dc7c28..7219d59cc76c 100644 --- a/tensorrt_llm/llmapi/llm_args.py +++ b/tensorrt_llm/llmapi/llm_args.py @@ -2733,6 +2733,16 @@ class KvCacheConfig(StrictBaseModel, PybindMirror): description= "Size of the host cache in bytes. If both `max_tokens` and `host_cache_size` are specified, memory corresponding to the minimum will be used." ) + disk_cache_size: Optional[NonNegativeInt] = Field( + default=None, + description= + "Size of the disk cache in bytes. Only used by KV cache manager v2 in the PyTorch backend." + ) + disk_cache_path: Optional[str] = Field( + default=None, + description= + "Directory used for disk KV cache files. Must be set when `disk_cache_size` is positive." + ) cross_kv_cache_fraction: Optional[float] = Field( default=None, description= @@ -2894,6 +2904,19 @@ def validate_max_gpu_total_bytes(cls, v: int): "kv_cache_config.max_gpu_total_bytes must be non-negative") return v + @model_validator(mode='after') + def validate_disk_cache_config(self): + if self.disk_cache_size is not None and self.disk_cache_size > 0: + if not self.disk_cache_path: + raise ValueError( + "kv_cache_config.disk_cache_path must be set when disk_cache_size is positive" + ) + if not os.path.isdir(self.disk_cache_path): + raise ValueError( + f"kv_cache_config.disk_cache_path {self.disk_cache_path} does not exist or is not a directory" + ) + return self + @field_validator('max_attention_window') @classmethod def validate_max_attention_window(cls, v: Optional[List[int]]): diff --git a/tests/unittest/disaggregated/test_cache_transceiver_single_process.py b/tests/unittest/disaggregated/test_cache_transceiver_single_process.py index 7ba99a6d7679..c12f2ca2f42c 100644 --- a/tests/unittest/disaggregated/test_cache_transceiver_single_process.py +++ b/tests/unittest/disaggregated/test_cache_transceiver_single_process.py @@ -94,6 +94,8 @@ class KvCacheConfigV2: sink_token_length: Optional[int] = None free_gpu_memory_fraction: Optional[float] = None host_cache_size: Optional[int] = None + disk_cache_size: Optional[int] = None + disk_cache_path: Optional[str] = None onboard_blocks: bool = True cross_kv_cache_fraction: Optional[float] = None secondary_offload_min_priority: Optional[int] = None diff --git a/tests/unittest/disaggregated/test_kv_transfer.py b/tests/unittest/disaggregated/test_kv_transfer.py index eb8d5ead7600..4ad99286de58 100644 --- a/tests/unittest/disaggregated/test_kv_transfer.py +++ b/tests/unittest/disaggregated/test_kv_transfer.py @@ -53,6 +53,8 @@ class KvCacheConfigV2: sink_token_length: Optional[int] = None free_gpu_memory_fraction: Optional[float] = None host_cache_size: Optional[int] = None + disk_cache_size: Optional[int] = None + disk_cache_path: Optional[str] = None cross_kv_cache_fraction: Optional[float] = None secondary_offload_min_priority: Optional[int] = None event_buffer_max_size: int = 0 diff --git a/tests/unittest/llmapi/test_llm_args.py b/tests/unittest/llmapi/test_llm_args.py index 6ba201ad4805..6eb88d5b65cd 100644 --- a/tests/unittest/llmapi/test_llm_args.py +++ b/tests/unittest/llmapi/test_llm_args.py @@ -336,6 +336,8 @@ def test_KvCacheConfig_declaration(): max_attention_window=[1024, 1024, 1024], free_gpu_memory_fraction=0.5, host_cache_size=1024, + disk_cache_size=2048, + disk_cache_path="/tmp", cross_kv_cache_fraction=0.5, secondary_offload_min_priority=1, event_buffer_max_size=0, @@ -349,6 +351,8 @@ def test_KvCacheConfig_declaration(): assert pybind_config.max_attention_window == [1024, 1024, 1024] assert pybind_config.free_gpu_memory_fraction == 0.5 assert pybind_config.host_cache_size == 1024 + assert config.disk_cache_size == 2048 + assert config.disk_cache_path == "/tmp" assert pybind_config.cross_kv_cache_fraction == 0.5 assert pybind_config.secondary_offload_min_priority == 1 assert pybind_config.event_buffer_max_size == 0 @@ -357,6 +361,17 @@ def test_KvCacheConfig_declaration(): assert pybind_config.attention_dp_events_gather_period_ms == 10 +def test_KvCacheConfig_disk_cache_validation(tmp_path): + config = KvCacheConfig(disk_cache_size=2048, disk_cache_path=str(tmp_path)) + + assert config.disk_cache_size == 2048 + assert config.disk_cache_path == str(tmp_path) + + with pytest.raises(ValidationError) as exc_info: + KvCacheConfig(disk_cache_size=2048) + assert "disk_cache_path" in str(exc_info.value) + + def test_CapacitySchedulerPolicy(): val = CapacitySchedulerPolicy.MAX_UTILIZATION assert PybindMirror.maybe_to_pybind( From 6dee1673737f452b1489cfee7bdeafe0d703fd26 Mon Sep 17 00:00:00 2001 From: Pengbo Wang <221450789+pengbowang-nv@users.noreply.github.com> Date: Mon, 8 Jun 2026 20:22:32 +0800 Subject: [PATCH 043/119] [https://nvbugs/6185446][fix] Add warmup for trtllm-gen fmha JIT kernels (#14851) Revert c37992c to remove padding. Added warmup with a grid that should be dense enough to catch almost every possible JIT kernels. Note that this PR only enables warmup and have no shape binning at runtime. I'd like to see how it performs and if it can warmup most kernels then we may not need further works. Added a message when a possible JIT happened, to give a clear clue why performance dropped. Signed-off-by: Pengbo Wang <221450789+pengbowang-nv@users.noreply.github.com> --- cpp/tensorrt_llm/common/attentionOp.cpp | 31 ++-- cpp/tensorrt_llm/common/attentionOp.h | 26 ++-- .../fused_multihead_attention_common.h | 7 +- .../xqaParams.h | 11 +- cpp/tensorrt_llm/kernels/fmhaDispatcher.cpp | 5 + .../trtllmGenKernels/fmha/fmhaKernels.h | 147 ++++++++++++++++++ .../trtllmGenKernels/fmha/fmhaRunnerParams.h | 7 + cpp/tensorrt_llm/kernels/xqaDispatcher.cpp | 23 +-- cpp/tensorrt_llm/nanobind/thop/bindings.cpp | 14 +- cpp/tensorrt_llm/thop/attentionOp.cpp | 21 +-- cpp/tensorrt_llm/thop/attentionOp.h | 8 +- .../_torch/attention_backend/trtllm.py | 5 + .../custom_ops/attention/trtllm_attention.py | 2 + .../custom_ops/attention_interface.py | 3 + .../auto_deploy/custom_ops/mla/trtllm_mla.py | 11 ++ .../_torch/pyexecutor/model_engine.py | 54 +++++++ 16 files changed, 305 insertions(+), 70 deletions(-) diff --git a/cpp/tensorrt_llm/common/attentionOp.cpp b/cpp/tensorrt_llm/common/attentionOp.cpp index d38672283b25..36f92aa7a0aa 100644 --- a/cpp/tensorrt_llm/common/attentionOp.cpp +++ b/cpp/tensorrt_llm/common/attentionOp.cpp @@ -265,17 +265,6 @@ bool AttentionOp::convertMMHAParamsToXQAParams(tensorrt_llm::kernels::XQAParams& = mAttentionChunkSize && !tc::getEnvDisableChunkedAttentionInGenPhase() ? *mAttentionChunkSize : INT_MAX; xqaParams.max_attention_window_size = generationsParams.max_attention_window_size; xqaParams.cyclic_attention_window_size = generationsParams.cyclic_attention_window_size; - // Treat the layer as sliding-window-causal only for explicit SWA masks or - // in-attention positional encodings whose max position exceeds the layer window. - // Exclude sparse attention and chunked attention - bool const has_in_attention_pos_encoding = mPositionEmbeddingType != PositionEmbeddingType::kLEARNED_ABSOLUTE; - // chunked_attention_size is set to INT_MAX above as the "disabled" sentinel. - bool const chunked_attention_enabled - = xqaParams.chunked_attention_size > 0 && xqaParams.chunked_attention_size != INT_MAX; - xqaParams.is_sliding_window = !mUseSparseAttention && !chunked_attention_enabled - && ((mMaskType == AttentionMaskType::SLIDING_WINDOW_CAUSAL) - || (has_in_attention_pos_encoding && generationsParams.max_attention_window_size > 0 - && generationsParams.max_attention_window_size < mRotaryEmbeddingMaxPositions)); xqaParams.max_blocks_per_sequence = generationsParams.max_blocks_per_sequence; xqaParams.sink_token_length = generationsParams.sink_token_length; xqaParams.max_past_kv_length = generationsParams.max_past_kv_length; @@ -305,6 +294,10 @@ bool AttentionOp::convertMMHAParamsToXQAParams(tensorrt_llm::kernels::XQAParams& xqaParams.helix_position_offsets = generationsParams.helix_position_offsets; xqaParams.helix_is_inactive_rank = generationsParams.helix_is_inactive_rank; xqaParams.softmax_stats = generationsParams.softmax_stats; + xqaParams.trtllm_gen_jit_warmup = generationsParams.trtllm_gen_jit_warmup; + xqaParams.trtllm_gen_jit_warmup_max_num_requests = mMaxNumRequests; + xqaParams.trtllm_gen_jit_warmup_max_seq_len_q = mMaxContextLength; + xqaParams.trtllm_gen_jit_warmup_max_seq_len_kv = mMaxSeqLen; xqaParams.logn_scaling_ptr = generationsParams.logn_scaling_ptr; xqaParams.total_num_input_tokens = mCpSize > 1 ? generationsParams.num_requests : generationsParams.num_tokens; @@ -1136,13 +1129,11 @@ int AttentionOp::mlaGeneration( tllmRunnerParams.mMaxSeqLenCacheKv = generation_params.max_attention_window_size; // This should be set to numDraftTokens + 1. tllmRunnerParams.mMaxSeqLenQ = params.acc_q_len / batch_beam; - // Override mMaxSeqLenKv with the max cache capacity so FMHA picks the same kernel as - // CUDA graph warmup and avoids the eager-mode JIT miss/recompile. This is safe for - // PagedKv on this path because the strides do not depend on mMaxSeqLenKv, and extra - // KV CTAs exit early through seqLensKvPtr. - // TODO: mirror the is_swa + W+1 logic from xqaDispatcher.cpp when MLA gains SWA - // support (also requires adding Sliding cubins to the MLA gen kernel set). - tllmRunnerParams.mMaxSeqLenKv = generation_params.max_attention_window_size; + tllmRunnerParams.mMaxSeqLenKv = generation_params.max_past_kv_length; + tllmRunnerParams.mJITWarmup = generation_params.trtllm_gen_jit_warmup; + tllmRunnerParams.mJITWarmupMaxNumRequests = mMaxNumRequests; + tllmRunnerParams.mJITWarmupMaxSeqLenQ = mMaxContextLength; + tllmRunnerParams.mJITWarmupMaxSeqLenKv = mMaxSeqLen; tllmRunnerParams.mSumOfSeqLensQ = int(batch_beam * tllmRunnerParams.mMaxSeqLenQ); // Not used in the generation kernels as contiguous_kv or paged_kv layouts are used. tllmRunnerParams.mSumOfSeqLensKv = int(batch_beam * tllmRunnerParams.mMaxSeqLenKv); @@ -1993,6 +1984,10 @@ int AttentionOp::enqueueContext(EnqueueContextParams const& params, cudaStrea fmhaParams.stream = stream; fmhaParams.forceFp32Acc = mFMHAForceFP32Acc; fmhaParams.softmaxStatsPtr = params.softmax_stats; + fmhaParams.trtllmGenJITWarmup = params.trtllm_gen_jit_warmup; + fmhaParams.trtllmGenJITWarmupMaxNumRequests = mMaxNumRequests; + fmhaParams.trtllmGenJITWarmupMaxSeqLenQ = mMaxContextLength; + fmhaParams.trtllmGenJITWarmupMaxSeqLenKv = mMaxSeqLen; // Sparse attention parameters if (useTllmGenSparseAttention()) diff --git a/cpp/tensorrt_llm/common/attentionOp.h b/cpp/tensorrt_llm/common/attentionOp.h index 097dd7d9051d..f7822947b1ea 100644 --- a/cpp/tensorrt_llm/common/attentionOp.h +++ b/cpp/tensorrt_llm/common/attentionOp.h @@ -124,6 +124,8 @@ class AttentionOp float const* sage_attn_sfs_q = nullptr; float const* sage_attn_sfs_k = nullptr; float const* sage_attn_sfs_v = nullptr; + // Optional TRTLLM-Gen FMHA JIT warmup shape. + bool trtllm_gen_jit_warmup = false; }; template @@ -470,6 +472,8 @@ class AttentionOp bool mUnfuseQkvGemm = false; nvinfer1::DataType mType; int32_t mMaxContextLength = 0; + int32_t mMaxSeqLen = 0; + int32_t mMaxNumRequests = 0; bool mQKVBiasEnabled = false; bool mCrossAttention = false; int mMaxDistance = 0; @@ -551,17 +555,17 @@ class AttentionOp mRotaryEmbeddingLongMscale, mRotaryEmbeddingMaxPositions, mRotaryEmbeddingOriginalMaxPositions, (int8_t) mPositionEmbeddingType, mUseLognScaling, mRemovePadding, (int32_t) mMaskType, mBlockSparseParams.data(), mPagedKVCache, mTokensPerBlock, mKVCacheQuantMode.value(), mTpSize, mTpRank, - mUnfuseQkvGemm, (int32_t) mType, mMaxContextLength, mQKVBiasEnabled, mCrossAttention, mMaxDistance, - mPosShiftEnabled, mPagedContextFMHA, mFP8ContextFMHA, mFP8AttenOutput, mFP8ContextMLA, mFP8GenerationMLA, - mChunkPrefillBufferBatchSize, mDenseContextFMHA, mHasFullAttentionMask, mIsSpecDecodingEnabled, - mUseSpecDecoding, mIsSpecDecTree, mSpecDecodingIsGenerationLengthVariable, mSpecDecodingMaxGenerationLength, - mIsMLAEnabled, mIsGenerationMLA, mUseGenFlashMLA, mUseSparseAttention, mUseTllmGenSparseAttentionPaged, - mUseTllmGenSparseAttention, mMLAParams.data(), mCpSize, mCpRank, mCpGroup, mNumAttnHeads, mNumAttnKVHeads, - mNumKVHeadsOrigin, mAttnTpSize, mAttnTpRank, mAttnCpSize, mAttnCpRank, mUlyssesMQABroadcast, - mEnableContextFMHA, mFMHAForceFP32Acc, mMultiBlockMode, mEnableXQA, mUseKVCache, mSkipAttn, mFuseFp4Quant, - mNbMultiBlockSemaphores, mAttentionChunkSize.value_or(-1), mSkipSoftmaxThresholdScaleFactorPrefill, - mSkipSoftmaxThresholdScaleFactorDecode, mSageAttnNumEltsPerBlkQ, mSageAttnNumEltsPerBlkK, - mSageAttnNumEltsPerBlkV, mSageAttnQkInt8); + mUnfuseQkvGemm, (int32_t) mType, mMaxContextLength, mMaxSeqLen, mMaxNumRequests, mQKVBiasEnabled, + mCrossAttention, mMaxDistance, mPosShiftEnabled, mPagedContextFMHA, mFP8ContextFMHA, mFP8AttenOutput, + mFP8ContextMLA, mFP8GenerationMLA, mChunkPrefillBufferBatchSize, mDenseContextFMHA, mHasFullAttentionMask, + mIsSpecDecodingEnabled, mUseSpecDecoding, mIsSpecDecTree, mSpecDecodingIsGenerationLengthVariable, + mSpecDecodingMaxGenerationLength, mIsMLAEnabled, mIsGenerationMLA, mUseGenFlashMLA, mUseSparseAttention, + mUseTllmGenSparseAttentionPaged, mUseTllmGenSparseAttention, mMLAParams.data(), mCpSize, mCpRank, mCpGroup, + mNumAttnHeads, mNumAttnKVHeads, mNumKVHeadsOrigin, mAttnTpSize, mAttnTpRank, mAttnCpSize, mAttnCpRank, + mUlyssesMQABroadcast, mEnableContextFMHA, mFMHAForceFP32Acc, mMultiBlockMode, mEnableXQA, mUseKVCache, + mSkipAttn, mFuseFp4Quant, mNbMultiBlockSemaphores, mAttentionChunkSize.value_or(-1), + mSkipSoftmaxThresholdScaleFactorPrefill, mSkipSoftmaxThresholdScaleFactorDecode, mSageAttnNumEltsPerBlkQ, + mSageAttnNumEltsPerBlkK, mSageAttnNumEltsPerBlkV, mSageAttnQkInt8); }; private: diff --git a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h index d82ba97a29f3..68c567105665 100644 --- a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h +++ b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2025, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020-2026, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -262,6 +262,11 @@ struct MHARunnerParams int totalQSeqLen; // The total number of KV sequence lengths in the batch. int totalKvSeqLen; + // Optional TRTLLM-Gen FMHA JIT warmup shape. + bool trtllmGenJITWarmup = false; + int32_t trtllmGenJITWarmupMaxNumRequests = 0; + int32_t trtllmGenJITWarmupMaxSeqLenQ = 0; + int32_t trtllmGenJITWarmupMaxSeqLenKv = 0; // Buffers. // The packed QKV buffer ptr. diff --git a/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/xqaParams.h b/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/xqaParams.h index cccfbf1ded88..e421be0a6bd7 100644 --- a/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/xqaParams.h +++ b/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/xqaParams.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2025, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020-2026, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -48,10 +48,6 @@ struct XQAParams int32_t chunked_attention_size = INT_MAX; int32_t max_attention_window_size = 0; int32_t cyclic_attention_window_size = 0; - // Whether this is a sliding-window-causal layer. Computed once in AttentionOp from the - // declared mask type / RoPE config so kernel-selection code does not need to re-derive - // it from window/rope heuristics. See AttentionOp::convertMMHAParamsToXQAParams. - bool is_sliding_window = false; int32_t sink_token_length = 0; int max_past_kv_length = 0; void const* qkv_bias; @@ -73,6 +69,11 @@ struct XQAParams bool const* helix_is_inactive_rank = nullptr; // Softmax stats output buffer for Helix parallelism (max and LSE per head). float2* softmax_stats = nullptr; + // Optional TRTLLM-Gen FMHA JIT warmup shape. + bool trtllm_gen_jit_warmup = false; + int32_t trtllm_gen_jit_warmup_max_num_requests = 0; + int32_t trtllm_gen_jit_warmup_max_seq_len_q = 0; + int32_t trtllm_gen_jit_warmup_max_seq_len_kv = 0; // almost copy from GPTAttentionPluginCommon. // maybe use one struct for parameters in GPTAttentionPluginCommon and share the same here. diff --git a/cpp/tensorrt_llm/kernels/fmhaDispatcher.cpp b/cpp/tensorrt_llm/kernels/fmhaDispatcher.cpp index 3d1ffa741f67..88ff741d6941 100644 --- a/cpp/tensorrt_llm/kernels/fmhaDispatcher.cpp +++ b/cpp/tensorrt_llm/kernels/fmhaDispatcher.cpp @@ -238,6 +238,10 @@ void FmhaDispatcher::run(MHARunnerParams runnerParams) tllmRunnerParams.mChunkedAttentionSize = runnerParams.chunkedAttentionSize; tllmRunnerParams.mSumOfSeqLensQ = runnerParams.totalQSeqLen; tllmRunnerParams.mSumOfSeqLensKv = runnerParams.totalKvSeqLen; + tllmRunnerParams.mJITWarmup = runnerParams.trtllmGenJITWarmup; + tllmRunnerParams.mJITWarmupMaxNumRequests = runnerParams.trtllmGenJITWarmupMaxNumRequests; + tllmRunnerParams.mJITWarmupMaxSeqLenQ = runnerParams.trtllmGenJITWarmupMaxSeqLenQ; + tllmRunnerParams.mJITWarmupMaxSeqLenKv = runnerParams.trtllmGenJITWarmupMaxSeqLenKv; tllmRunnerParams.mMaxNumPagesPerSeqKv = maxBlocksPerSeq; tllmRunnerParams.mNumTokensPerPage = (qkvLayout == QkvLayout::PagedKv) ? numTokensPerBlock : 0; tllmRunnerParams.mScaleQ = mFixedParams.qScaling; @@ -267,6 +271,7 @@ void FmhaDispatcher::run(MHARunnerParams runnerParams) tllmRunnerParams.mSparseTopK = runnerParams.sparse_params.num_sparse_topk; tllmRunnerParams.ptrSparseMlaTopKLens = runnerParams.sparse_params.sparse_mla_topk_lens; tllmRunnerParams.mKernelType = FmhaKernelType::Generation; + tllmRunnerParams.mUseGenKernelForPrefill = true; tllmRunnerParams.mMaskType = TrtllmGenAttentionMaskType::Causal; tllmRunnerParams.kvPageIdxPtr = reinterpret_cast(runnerParams.sparse_params.sparse_attn_indices); diff --git a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h index b88989746c85..aa90a39146e6 100644 --- a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h +++ b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h @@ -18,9 +18,11 @@ #include "cuda_runtime_api.h" #include "tensorrt_llm/common/config.h" +#include #include #include #include +#include #include #include #include @@ -29,6 +31,7 @@ #include #include #include +#include #include "tensorrt_llm/common/cudaDriverWrapper.h" #include "tensorrt_llm/common/cudaUtils.h" @@ -40,6 +43,7 @@ #include "fmhaReduction.h" #include "fmhaRunnerParams.h" #include "prepareCustomMask.h" +#include // Switch to streaming-style TLLM_LOG_* macros for trtllm-gen export headers, // which use streaming syntax (e.g., TLLM_LOG_INFO("val=", x)) instead of @@ -296,6 +300,137 @@ class TllmGenFmhaKernel } } +private: + inline static std::vector const kDefaultWarmupBatchSizeCandidates + = {1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32, 40, 48, 56, 64, 80, 96, 128, 256, 512, 1024}; + inline static std::vector const kDefaultWarmupPrefillBatchSizeCandidates + = {1, 2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 128, 256}; + inline static std::vector const kDefaultWarmupSeqLenQkvCandidates + = {1, 128, 512, 1024, 2048, 4096, 8192, 16384, 32768}; + + static std::vector makeWarmupCandidateSizes(std::vector const& defaultCandidateSizes, int maxSize) + { + std::vector candidateSizes; + for (int size : defaultCandidateSizes) + { + if (size >= maxSize) + { + break; + } + candidateSizes.push_back(size); + } + candidateSizes.push_back(maxSize); + return candidateSizes; + } + + void warmupOneKernel(RunnerParams const& params) + { + if (params.mMaxSeqLenQ == 0 || params.mBatchSize == 0 + || (!isContextKernel(params.mKernelType) && params.mMaxSeqLenKv == 0)) + { + return; + } + + int32_t ctaDim = 512; + FmhaOptions options; + FmhaOptionsFromArgs optionsFromArgs; + parseOptionsFromRunnerParams(params, options); + options.mCudaArch = intToCudaArch(mSM); + + FmhaAutoTuner autoTuner(options, optionsFromArgs, params.mMultiProcessorCount); + std::tie(options, optionsFromArgs, ctaDim) = autoTuner.selectKernel(); + + checkFmhaOptions(options, optionsFromArgs); + updateFmhaOptions(options, optionsFromArgs); + + auto [numCtasX, numCtasY, numCtasZ] = computeNumCtas(options, params.mMultiProcessorCount); + tg::CudaRunner::Grid grid{numCtasX, numCtasY, numCtasZ}; + + if (shouldUseNvrtc(options)) + { + FmhaConfig fmhaConfig; + fmhaConfig.mOptions = options; + std::ostringstream sstream; + populateJsonConfig(options, sstream); + fmhaConfig.mGenCfgJsonStr = sstream.str(); + + fmhaConfig.mExecPath = getExecPath().c_str(); + fmhaConfig.mCtaDim = ctaDim; + fmhaConfig.mGrid = grid; + auto const compileStart = std::chrono::steady_clock::now(); + mFmhaInterface.generateAndCompileKernel(fmhaConfig); + auto const compileElapsed = std::chrono::steady_clock::now() - compileStart; + auto const compileElapsedMs = std::chrono::duration(compileElapsed).count(); + if (compileElapsedMs > 1000.0) // FIXME: Change to return cache status from FmhaInterface + { + auto const& kernelName = fmhaConfig.mFunctionName; + TLLM_LOG_INFO("JIT Warmup: Warmup for %s took %.3f ms", kernelName.c_str(), compileElapsedMs); + } + } + } + + void runJITWarmupGridIfRequested(RunnerParams const& runnerParams) + { + if (!runnerParams.mJITWarmup || runnerParams.mKernelType != FmhaKernelType::Generation) + { + return; + } + + cudaStreamCaptureStatus captureStatus = cudaStreamCaptureStatusNone; + TLLM_CUDA_CHECK(cudaStreamIsCapturing(runnerParams.stream, &captureStatus)); + TLLM_CHECK_WITH_INFO(captureStatus == cudaStreamCaptureStatusNone, + "TRTLLM-Gen FMHA JIT warmup must not run during CUDA graph capture."); + + bool const useGenKernelForPrefill = runnerParams.mUseGenKernelForPrefill; + int const maxBatchSize = runnerParams.mJITWarmupMaxNumRequests; + int const maxSeqLenQ = runnerParams.mJITWarmupMaxSeqLenQ; + int const maxSeqLenKv = runnerParams.mJITWarmupMaxSeqLenKv; + + TLLM_LOG_DEBUG( + "TRTLLM-Gen Fmha Warmup Params: maxBatchSize=%d, maxSeqLenKv=%d, useGenKernelForPrefill=%d, maxSeqLenQ=%d", + maxBatchSize, maxSeqLenKv, useGenKernelForPrefill, maxSeqLenQ); + TLLM_CHECK_WITH_INFO(maxBatchSize > 0 && maxSeqLenKv > 0 && (!useGenKernelForPrefill || maxSeqLenQ > 0), + "TRTLLM-Gen Fmha Warmup Param is invalid."); + + auto const& batchSizeDefaults + = useGenKernelForPrefill ? kDefaultWarmupPrefillBatchSizeCandidates : kDefaultWarmupBatchSizeCandidates; + std::vector batchSizeCandidates = makeWarmupCandidateSizes(batchSizeDefaults, maxBatchSize); + // Use specified Q for generation, and use our Q grid for prefill + std::vector seqLenQCandidates = useGenKernelForPrefill + ? makeWarmupCandidateSizes(kDefaultWarmupSeqLenQkvCandidates, maxSeqLenQ) + : std::vector{runnerParams.mMaxSeqLenQ}; + std::vector seqLenKvCandidates = makeWarmupCandidateSizes(kDefaultWarmupSeqLenQkvCandidates, maxSeqLenKv); + + auto warmupParams = runnerParams; + + for (int batchSize : batchSizeCandidates) + { + warmupParams.mBatchSize = batchSize; + for (int seqLenQ : seqLenQCandidates) + { + warmupParams.mMaxSeqLenQ = seqLenQ; + for (int seqLenKv : seqLenKvCandidates) + { + warmupParams.mMaxSeqLenKv = seqLenKv; + int64_t const sumOfSeqLensQ + = static_cast(warmupParams.mBatchSize) * warmupParams.mMaxSeqLenQ; + int64_t const sumOfSeqLensKv + = static_cast(warmupParams.mBatchSize) * warmupParams.mMaxSeqLenKv; + warmupParams.mSumOfSeqLensQ + = static_cast(std::min(sumOfSeqLensQ, std::numeric_limits::max())); + warmupParams.mSumOfSeqLensKv + = static_cast(std::min(sumOfSeqLensKv, std::numeric_limits::max())); + if (useGenKernelForPrefill && warmupParams.mMaxSeqLenKv < warmupParams.mMaxSeqLenQ) + { + continue; + } + warmupOneKernel(warmupParams); + } + } + } + } + +public: void run(RunnerParams const& params) { if (params.mMaxSeqLenQ == 0 || params.mBatchSize == 0 @@ -303,6 +438,7 @@ class TllmGenFmhaKernel { return; } + runJITWarmupGridIfRequested(params); int32_t ctaDim = 512; FmhaOptions options; @@ -360,7 +496,18 @@ class TllmGenFmhaKernel fmhaConfig.mExecPath = getExecPath().c_str(); fmhaConfig.mCtaDim = ctaDim; fmhaConfig.mGrid = grid; + auto const compileStart = std::chrono::steady_clock::now(); mFmhaInterface.generateAndCompileKernel(fmhaConfig); + auto const compileElapsed = std::chrono::steady_clock::now() - compileStart; + auto const compileElapsedMs = std::chrono::duration(compileElapsed).count(); + if (compileElapsedMs > 1000.0) // FIXME: Change to return cache status from FmhaInterface + { + auto const& kernelName = fmhaConfig.mFunctionName; + TLLM_LOG_WARNING( + "Possible JIT Cache Missing: TRTLLM-Gen FMHA generateAndCompileKernel took %.3f ms, kernelName=%s, " + "batchSize=%d, maxSeqLenQ=%d, maxSeqLenKv=%d. This could affect performance measurement.", + compileElapsedMs, kernelName.c_str(), params.mBatchSize, params.mMaxSeqLenQ, params.mMaxSeqLenKv); + } mFmhaInterface.run(fmhaConfig, fmhaData, params.stream, params.mMultiProcessorCount, 0); } else diff --git a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaRunnerParams.h b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaRunnerParams.h index ef29d85e63d5..19486cd6cf50 100644 --- a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaRunnerParams.h +++ b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaRunnerParams.h @@ -298,6 +298,13 @@ struct TllmGenFmhaRunnerParams int mMaxSeqLenQ; // The max kv sequence length. int mMaxSeqLenKv; + // Optional JIT warmup shape. + bool mJITWarmup = false; + int mJITWarmupMaxNumRequests = 0; + int mJITWarmupMaxSeqLenQ = 0; + int mJITWarmupMaxSeqLenKv = 0; + // True when a prefill/context path intentionally uses a generation kernel. + bool mUseGenKernelForPrefill = false; // The attention window size for sliding window attention (sliding-window-attention is enabled when seqLenKv > // mAttentionWindowSize). int mAttentionWindowSize; diff --git a/cpp/tensorrt_llm/kernels/xqaDispatcher.cpp b/cpp/tensorrt_llm/kernels/xqaDispatcher.cpp index 809d5e5afb21..37f248687e91 100644 --- a/cpp/tensorrt_llm/kernels/xqaDispatcher.cpp +++ b/cpp/tensorrt_llm/kernels/xqaDispatcher.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2024, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020-2026, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -494,14 +494,11 @@ void XqaDispatcher::runImpl( // It is used to construct contiguous kv cache TMA descriptors. tllmRunnerParams.mMaxSeqLenCacheKv = params.max_attention_window_size; tllmRunnerParams.mMaxSeqLenQ = params.generation_input_length; - // Pin mMaxSeqLenKv to a static per-layer value so warmup and runtime pick the same - // FMHA kernel (no JIT miss). For PagedKv we use the per-layer attention window: - // strides do not depend on mMaxSeqLenKv, and extra KV CTAs exit early via - // seqLensKvPtr. ContiguousKv keeps its true past-kv length because its strides - // depend on it. - tllmRunnerParams.mMaxSeqLenKv = (tllmRunnerParams.mQkvLayout == QkvLayout::PagedKv) - ? params.max_attention_window_size - : params.max_past_kv_length; + tllmRunnerParams.mMaxSeqLenKv = params.max_past_kv_length; + tllmRunnerParams.mJITWarmup = params.trtllm_gen_jit_warmup; + tllmRunnerParams.mJITWarmupMaxNumRequests = params.trtllm_gen_jit_warmup_max_num_requests; + tllmRunnerParams.mJITWarmupMaxSeqLenQ = params.trtllm_gen_jit_warmup_max_seq_len_q; + tllmRunnerParams.mJITWarmupMaxSeqLenKv = params.trtllm_gen_jit_warmup_max_seq_len_kv; tllmRunnerParams.mSumOfSeqLensQ = int(params.batch_size * beam_width * tllmRunnerParams.mMaxSeqLenQ); // The sliding window attention size. tllmRunnerParams.mAttentionWindowSize = params.cyclic_attention_window_size; @@ -517,12 +514,8 @@ void XqaDispatcher::runImpl( tllmRunnerParams.stream = params.stream; tllmRunnerParams.mSfStartTokenIdx = params.start_token_idx_sf; tllmRunnerParams.mIsSpecDecTree = params.is_spec_dec_tree && params.multi_query_tokens; - // Declare SWA layers as SlidingOrChunkedCausal directly so warmup and runtime - // pick the same kernel bucket (no JIT miss) - tllmRunnerParams.mMaskType = tllmRunnerParams.mIsSpecDecTree - ? TrtllmGenAttentionMaskType::Custom - : (params.is_sliding_window ? TrtllmGenAttentionMaskType::SlidingOrChunkedCausal - : TrtllmGenAttentionMaskType::Causal); + tllmRunnerParams.mMaskType + = tllmRunnerParams.mIsSpecDecTree ? TrtllmGenAttentionMaskType::Custom : TrtllmGenAttentionMaskType::Causal; tllmRunnerParams.mLayerIdx = params.layer_idx; tllmRunnerParams.seqLensQPtr = params.spec_decoding_generation_lengths; tllmRunnerParams.generalPackedCustoMaskPtr = params.spec_decoding_packed_mask; diff --git a/cpp/tensorrt_llm/nanobind/thop/bindings.cpp b/cpp/tensorrt_llm/nanobind/thop/bindings.cpp index c88edfd432a1..993de4743193 100644 --- a/cpp/tensorrt_llm/nanobind/thop/bindings.cpp +++ b/cpp/tensorrt_llm/nanobind/thop/bindings.cpp @@ -145,11 +145,11 @@ void initBindings(nb::module_& m) nb::arg("is_fused_qkv"), nb::arg("update_kv_cache"), nb::arg("predicted_tokens_per_seq"), nb::arg("local_layer_idx"), nb::arg("num_heads"), nb::arg("num_kv_heads"), nb::arg("head_size"), nb::arg("tokens_per_block").none(), nb::arg("max_num_requests"), nb::arg("max_context_length"), - nb::arg("attention_window_size"), nb::arg("beam_width"), nb::arg("mask_type"), nb::arg("quant_mode"), - nb::arg("q_scaling"), nb::arg("position_embedding_type"), nb::arg("rope_dim"), nb::arg("rope_base"), - nb::arg("rope_scale_type"), nb::arg("rope_scale"), nb::arg("rope_short_m_scale"), nb::arg("rope_long_m_scale"), - nb::arg("rope_max_positions"), nb::arg("rope_original_max_positions"), nb::arg("use_paged_context_fmha"), - nb::arg("attention_input_type").none(), nb::arg("is_mla_enable"), + nb::arg("max_seq_len"), nb::arg("attention_window_size"), nb::arg("beam_width"), nb::arg("mask_type"), + nb::arg("quant_mode"), nb::arg("q_scaling"), nb::arg("position_embedding_type"), nb::arg("rope_dim"), + nb::arg("rope_base"), nb::arg("rope_scale_type"), nb::arg("rope_scale"), nb::arg("rope_short_m_scale"), + nb::arg("rope_long_m_scale"), nb::arg("rope_max_positions"), nb::arg("rope_original_max_positions"), + nb::arg("use_paged_context_fmha"), nb::arg("attention_input_type").none(), nb::arg("is_mla_enable"), nb::arg("chunked_prefill_buffer_batch_size").none(), nb::arg("q_lora_rank").none(), nb::arg("kv_lora_rank").none(), nb::arg("qk_nope_head_dim").none(), nb::arg("qk_rope_head_dim").none(), nb::arg("v_head_dim").none(), nb::arg("rope_append").none(), nb::arg("mrope_rotary_cos_sin").none(), @@ -172,8 +172,8 @@ void initBindings(nb::module_& m) nb::arg("flash_mla_num_splits") = std::nullopt, nb::arg("sage_attn_num_elts_per_blk_q") = 0, nb::arg("sage_attn_num_elts_per_blk_k") = 0, nb::arg("sage_attn_num_elts_per_blk_v") = 0, nb::arg("sage_attn_qk_int8") = false, nb::arg("num_contexts") = 0, nb::arg("num_ctx_tokens") = 0, - nb::arg("compressed_kv_cache_pool_ptr") = std::nullopt, "Multi-head attention operation", - nb::call_guard()); + nb::arg("trtllm_gen_jit_warmup") = false, nb::arg("compressed_kv_cache_pool_ptr") = std::nullopt, + "Multi-head attention operation", nb::call_guard()); m.def( "get_helix_workspace_size_per_rank", diff --git a/cpp/tensorrt_llm/thop/attentionOp.cpp b/cpp/tensorrt_llm/thop/attentionOp.cpp index 735de38e8c93..2cc7adb7fa1b 100644 --- a/cpp/tensorrt_llm/thop/attentionOp.cpp +++ b/cpp/tensorrt_llm/thop/attentionOp.cpp @@ -375,8 +375,8 @@ class RunnerBase std::optional fmha_scheduler_counter, std::optional mla_bmm1_scale, std::optional mla_bmm2_scale, std::optional quant_q_buffer, std::optional flash_mla_tile_scheduler_metadata, - std::optional flash_mla_num_splits, - std::optional compressed_kv_cache_pool_ptr = std::nullopt) const + std::optional flash_mla_num_splits, bool trtllm_gen_jit_warmup, + std::optional compressed_kv_cache_pool_ptr) const = 0; }; @@ -443,7 +443,7 @@ class Runner : public RunnerBase std::optional fmha_scheduler_counter, std::optional mla_bmm1_scale, std::optional mla_bmm2_scale, std::optional quant_q_buffer, std::optional flash_mla_tile_scheduler_metadata, - std::optional flash_mla_num_splits, + std::optional flash_mla_num_splits, bool trtllm_gen_jit_warmup, std::optional compressed_kv_cache_pool_ptr) const override { auto stream = at::cuda::getCurrentCUDAStream(qkv_or_q.get_device()); @@ -746,6 +746,7 @@ class Runner : public RunnerBase common_enqueue_params.context_lengths = context_lengths_ptr; common_enqueue_params.host_context_lengths = host_context_lengths.data_ptr(); common_enqueue_params.workspace = workspace_ptr; + common_enqueue_params.trtllm_gen_jit_warmup = trtllm_gen_jit_warmup; if (softmax_stats_tensor.has_value()) { TLLM_CHECK_WITH_INFO(softmax_stats_tensor.value().scalar_type() == at::ScalarType::Float, @@ -934,9 +935,9 @@ void attention(torch::Tensor q, std::optional k, std::optional const tokens_per_block, int64_t const max_num_requests, int64_t const max_context_length, - int64_t const attention_window_size, int64_t const beam_width, int64_t const mask_type, int64_t const quant_mode, - double const q_scaling, int64_t const position_embedding_type, int64_t const rope_dim, double const rope_base, - int64_t const rope_scale_type, double const rope_scale, double const rope_short_m_scale, + int64_t const max_seq_len, int64_t const attention_window_size, int64_t const beam_width, int64_t const mask_type, + int64_t const quant_mode, double const q_scaling, int64_t const position_embedding_type, int64_t const rope_dim, + double const rope_base, int64_t const rope_scale_type, double const rope_scale, double const rope_short_m_scale, double const rope_long_m_scale, int64_t const rope_max_positions, int64_t const rope_original_max_positions, bool const use_paged_context_fmha, std::optional attention_input_type, bool is_mla_enable, std::optional chunked_prefill_buffer_batch_size, std::optional q_lora_rank, @@ -963,7 +964,7 @@ void attention(torch::Tensor q, std::optional k, std::optional mla_bmm2_scale, std::optional quant_q_buffer, std::optional flash_mla_tile_scheduler_metadata, std::optional flash_mla_num_splits, int64_t sage_attn_num_elts_per_blk_q, int64_t sage_attn_num_elts_per_blk_k, int64_t sage_attn_num_elts_per_blk_v, - bool sage_attn_qk_int8, int64_t num_contexts, int64_t num_ctx_tokens, + bool sage_attn_qk_int8, int64_t num_contexts, int64_t num_ctx_tokens, bool trtllm_gen_jit_warmup, std::optional compressed_kv_cache_pool_ptr) { TLLM_LOG_TRACE("Attention op starts at layer %d", local_layer_idx); @@ -1060,6 +1061,8 @@ void attention(torch::Tensor q, std::optional k, std::optionalmFP8GenerationMLA = false; op->mFuseFp4Quant = is_fp4_out; op->mMaxContextLength = max_context_length; + op->mMaxSeqLen = max_seq_len; + op->mMaxNumRequests = max_num_requests; op->mQScaling = q_scaling; op->mPositionEmbeddingType = static_cast(int8_t(position_embedding_type)); @@ -1239,7 +1242,7 @@ void attention(torch::Tensor q, std::optional k, std::optional 0) && (attn_input_type != AttentionInputType::ContextOnly)) @@ -1261,7 +1264,7 @@ void attention(torch::Tensor q, std::optional k, std::optional k, std::optional const tokens_per_block, int64_t const max_num_requests, int64_t const max_context_length, - int64_t const attention_window_size, int64_t const beam_width, int64_t const mask_type, int64_t const quant_mode, - double const q_scaling, int64_t const position_embedding_type, int64_t const rope_dim, double const rope_base, - int64_t const rope_scale_type, double const rope_scale, double const rope_short_m_scale, + int64_t const max_seq_len, int64_t const attention_window_size, int64_t const beam_width, int64_t const mask_type, + int64_t const quant_mode, double const q_scaling, int64_t const position_embedding_type, int64_t const rope_dim, + double const rope_base, int64_t const rope_scale_type, double const rope_scale, double const rope_short_m_scale, double const rope_long_m_scale, int64_t const rope_max_positions, int64_t const rope_original_max_positions, bool const use_paged_context_fmha, std::optional attention_input_type, bool is_mla_enable, std::optional chunked_prefill_buffer_batch_size, std::optional q_lora_rank, @@ -89,7 +89,7 @@ void attention(torch::Tensor q, std::optional k, std::optional flash_mla_tile_scheduler_metadata = std::nullopt, std::optional flash_mla_num_splits = std::nullopt, int64_t sage_attn_num_elts_per_blk_q = 0, int64_t sage_attn_num_elts_per_blk_k = 0, int64_t sage_attn_num_elts_per_blk_v = 0, bool sage_attn_qk_int8 = false, - int64_t num_contexts = 0, int64_t num_ctx_tokens = 0, + int64_t num_contexts = 0, int64_t num_ctx_tokens = 0, bool trtllm_gen_jit_warmup = false, std::optional compressed_kv_cache_pool_ptr = std::nullopt); struct KvCachePoolPointers diff --git a/tensorrt_llm/_torch/attention_backend/trtllm.py b/tensorrt_llm/_torch/attention_backend/trtllm.py index 1eda4819930c..216e2a95df5c 100644 --- a/tensorrt_llm/_torch/attention_backend/trtllm.py +++ b/tensorrt_llm/_torch/attention_backend/trtllm.py @@ -124,6 +124,9 @@ class TrtllmAttentionMetadata(AttentionMetadata): spec_decoding_bl_tree_mask: Optional[torch.Tensor] = None spec_bl_tree_first_sparse_mask_offset_kv: Optional[torch.Tensor] = None + # TRTLLM-Gen FMHA JIT warmup controls. + trtllm_gen_jit_warmup: bool = False + # Flag to enable helix parallelism. enable_helix: bool = False @@ -1590,6 +1593,8 @@ def _run( num_contexts=metadata.num_contexts, num_ctx_tokens=metadata.num_ctx_tokens, max_context_length=metadata.max_context_length, + max_seq_len=metadata.max_seq_len, + trtllm_gen_jit_warmup=metadata.trtllm_gen_jit_warmup, # --- Per-call (AttentionForwardArgs) --- out_scale=forward_args.out_scale, diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py index 0d377657eec8..9555f216065b 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py @@ -569,6 +569,7 @@ def trtllm_mha_with_cache( batch_info = BatchInfo(batch_info_host) num_seq = batch_info.get_total_num_sequences() num_tokens = batch_info.get_total_num_tokens() + max_seq_len = batch_info.get_max_seq_len() max_context_length = batch_info.get_max_context_length() max_num_requests = batch_info.get_max_batch_size() # Use sliding_window for attention_window_size if provided, else full context length @@ -679,6 +680,7 @@ def trtllm_mha_with_cache( tokens_per_block, # tokens_per_block max_num_requests, # max_num_requests max_context_length, # max_context_length + max_seq_len, # max_seq_len attention_window_size, # attention_window_size 1, # beam_width int(AttentionMaskType.causal), # mask_type diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py index dfbfdfef8bbe..a887217f6199 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py @@ -526,6 +526,9 @@ def get_max_seq_info(self) -> Tuple[int, int, int, int]: def get_max_context_length(self) -> int: return int(self._batch_info[6]) + def get_max_seq_len(self) -> int: + return self.get_max_context_length() + def get_max_blocks_per_seq(self) -> int: return int(self._batch_info[7]) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/mla/trtllm_mla.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/mla/trtllm_mla.py index 748f55e21661..60d3f1df3a59 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/mla/trtllm_mla.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/mla/trtllm_mla.py @@ -853,6 +853,7 @@ def _handle_prefill_thop( tokens_per_block: int, max_num_requests: int, max_context_length: int, + max_seq_len: int, quant_mode: int, sequence_length: torch.Tensor, context_lengths: torch.Tensor, @@ -910,6 +911,7 @@ def _handle_prefill_thop( tokens_per_block, max_num_requests, max_context_length, + max_seq_len, quant_mode, sequence_length, context_lengths, @@ -1039,6 +1041,7 @@ def _handle_prefill_thop( tokens_per_block, # tokens_per_block max_num_requests, # max_num_requests max_context_length, # max_context_length + max_seq_len, # max_seq_len max_context_length, # attention_window_size 1, # beam_width int(AttentionMaskType.causal), # mask_type @@ -1122,6 +1125,7 @@ def _handle_prefill_thop_cached_kv( tokens_per_block: int, max_num_requests: int, max_context_length: int, + max_seq_len: int, quant_mode: int, sequence_length: torch.Tensor, context_lengths: torch.Tensor, @@ -1330,6 +1334,7 @@ def _handle_prefill_thop_cached_kv( tokens_per_block, max_num_requests, max_context_length, + max_seq_len, # max_seq_len max_context_length, 1, # beam_width int(AttentionMaskType.padding), # FULL mask: every Q attends to every K in this chunk @@ -1459,6 +1464,7 @@ def _handle_prefill_thop_cached_kv( tokens_per_block, max_num_requests, max_context_length, + max_seq_len, # max_seq_len max_context_length, # attention_window_size 1, # beam_width int(AttentionMaskType.causal), # CAUSAL: new Q tokens with causal mask over new K/V @@ -1552,6 +1558,7 @@ def _handle_decode_impl( tokens_per_block: int, max_num_requests: int, max_context_length: int, + max_seq_len: int, q_scaling: float, quant_mode: int, sequence_length: torch.Tensor, @@ -1718,6 +1725,7 @@ def _handle_decode_impl( tokens_per_block, # tokens_per_block max_num_requests, # max_num_requests max_context_length, # max_context_length + max_seq_len, # max_seq_len max_context_length, # attention_window_size 1, # beam_width int(AttentionMaskType.causal), # mask_type @@ -1833,6 +1841,7 @@ def _mla_with_cache_impl( num_prefill, num_prefill_tokens, num_decode = batch_info.get_absorbed_info() num_seq = num_prefill + num_decode num_tokens = num_prefill_tokens + num_decode + max_seq_len = batch_info.get_max_seq_len() max_context_length = batch_info.get_max_context_length() max_num_requests = batch_info.get_max_batch_size() @@ -1966,6 +1975,7 @@ def _mla_with_cache_impl( tokens_per_block, max_num_requests, max_context_length, + max_seq_len, quant_mode, sequence_length, context_lengths, @@ -2001,6 +2011,7 @@ def _mla_with_cache_impl( tokens_per_block, max_num_requests, max_context_length, + max_seq_len, q_scaling, quant_mode, sequence_length, diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py index b5ab2964b1c0..789e2155da97 100644 --- a/tensorrt_llm/_torch/pyexecutor/model_engine.py +++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py @@ -625,6 +625,7 @@ def __init__( # with different KV cache managers. self.kv_cache_manager_key = ResourceManagerType.DRAFT_KV_CACHE_MANAGER if is_draft_model else ResourceManagerType.KV_CACHE_MANAGER self.lora_model_config: Optional[LoraModelConfig] = None + self._trtllm_gen_jit_warmup = False # Create config and runner cuda_graph_runner_config = CUDAGraphRunnerConfig( @@ -938,6 +939,8 @@ def warmup(self, resource_manager: ResourceManager) -> None: and self.guided_decoder is None and not isinstance(kv_cache_manager, MambaHybridCacheManager)) + self._run_attention_warmup(resource_manager, can_run_general_warmup) + if can_run_general_warmup: # Specialize torch.compile graphs across the key input shapes before CUDA graph capture. warmup_requests_configs = self._get_full_general_warmup_requests( @@ -1034,6 +1037,55 @@ def _general_warmup_impl( f"{num_gen_tokens} generation tokens. Skipping.") torch.cuda.empty_cache() + def _run_attention_warmup(self, + resource_manager: ResourceManager, + can_run_general_warmup: bool = True) -> None: + if not issubclass(self.attn_backend.Metadata, TrtllmAttentionMetadata): + return + + @contextlib.contextmanager + def trtllm_gen_fmha_jit_warmup(): + previous = self._trtllm_gen_jit_warmup + self._trtllm_gen_jit_warmup = True + try: + yield + finally: + self._trtllm_gen_jit_warmup = previous + + logger.info("Running TRTLLM-Gen FMHA JIT warmup") + + warmup_requests_configs = [] + if not self.is_draft_model and self.guided_decoder is None: + # doesn't support 2-model speculative draft and guided decoding + warmup_requests_configs.append( + (1 + self.max_total_draft_tokens, 1)) # one generation request + else: + logger.debug("Skipped TRTLLM-Gen FMHA JIT warmup for Gen kernels") + + if can_run_general_warmup: + warmup_requests_configs.append((1, 0)) # one context token + else: + logger.debug("Skipped TRTLLM-Gen FMHA JIT warmup for Ctx kernels") + + for num_tokens, num_gen_requests in warmup_requests_configs: + warmup_request = self._create_warmup_request( + resource_manager, + num_tokens=num_tokens, + num_gen_requests=num_gen_requests) + + with self.no_cuda_graph(), self._release_batch_context( + warmup_request, resource_manager) as batch: + if batch is None and self.mapping.tp_size <= 1: + continue # Not enough KV cache space (single rank, safe to skip) + self._assert_all_tp_ranks_have_warmup_batch(batch, num_tokens) + if batch is None: + continue # All ranks agree: not enough space + with trtllm_gen_fmha_jit_warmup(): + self.forward(batch, + new_tensors_device=None, + resource_manager=resource_manager) + torch.cuda.synchronize() + def _run_autotuner_warmup(self, resource_manager: ResourceManager): """Runs a forward pass to populate the autotuner cache.""" if not self.llm_args.enable_autotuner: @@ -4405,6 +4457,8 @@ def forward(self, attn_metadata = self._set_up_attn_metadata(kv_cache_manager, draft_kv_cache_manager) + if isinstance(attn_metadata, TrtllmAttentionMetadata): + attn_metadata.trtllm_gen_jit_warmup = self._trtllm_gen_jit_warmup if self.enable_spec_decode: spec_resource_manager = resource_manager.get_resource_manager( ResourceManagerType.SPEC_RESOURCE_MANAGER) From b14794c2dc2015ea4460cbffd858589710a794b0 Mon Sep 17 00:00:00 2001 From: Jonas Li <6110159+longlee0622@users.noreply.github.com> Date: Mon, 8 Jun 2026 22:05:03 +0900 Subject: [PATCH 044/119] [https://nvbugs/6162940][chore] Unwaive fixed test (#15078) Signed-off-by: Jonas Li <6110159+longlee0622@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index dfd056b822ab..c9612d241775 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -1,4 +1,3 @@ -accuracy/test_cli_flow.py::TestGptNext::test_auto_dtype SKIP (https://nvbugs/6162940) accuracy/test_disaggregated_serving.py::TestDeepSeekV32Exp::test_auto_dtype[False] SKIP (https://nvbugs/6120535) accuracy/test_disaggregated_serving.py::TestDeepSeekV32Exp::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp1cp4] SKIP (https://nvbugs/6189918) accuracy/test_disaggregated_serving.py::TestDeepSeekV32Exp::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp2cp2] SKIP (https://nvbugs/6189918) From 2bf4d3d46defe7dd7b65b313871f4c828a39d967 Mon Sep 17 00:00:00 2001 From: Guoming Zhang <137257613+nv-guomingz@users.noreply.github.com> Date: Mon, 8 Jun 2026 21:31:38 +0800 Subject: [PATCH 045/119] =?UTF-8?q?[None][perf]=20Support=20Gemma=20RMSNor?= =?UTF-8?q?m=20+=20interleaved=20mRoPE=20in=20fused=5Fqk=5Fno=E2=80=A6=20(?= =?UTF-8?q?#14898)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- .../kernels/fusedQKNormRopeKernel.cu | 71 +++++++-- .../kernels/fusedQKNormRopeKernel.h | 6 +- cpp/tensorrt_llm/thop/fusedQKNormRopeOp.cpp | 19 ++- tensorrt_llm/_torch/models/modeling_qwen3.py | 4 +- .../_torch/models/modeling_qwen3_next.py | 5 +- .../_torch/modules/qk_norm_attention.py | 33 ++++- .../test_fused_qk_norm_rope.py | 138 +++++++++++++++++- 7 files changed, 244 insertions(+), 32 deletions(-) diff --git a/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.cu b/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.cu index 8c9b502027e0..e06b0f200e4b 100644 --- a/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.cu +++ b/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.cu @@ -32,6 +32,30 @@ namespace kernels //////////////////////////////////////////////////////////////////////////////////////////////////// +// Select the RoPE position id for a given rotary half-dim under interleaved mRoPE. +// Mirrors MRotaryEmbedding.apply_interleaved_rope: section 1 (height) drives +// dims {1,4,7,...} up to mrope_section1*3, section 2 (width) drives {2,5,8,...} +// up to mrope_section2*3, everything else uses section 0 (temporal). +// position_ids is [num_tokens] for the non-mRoPE case (sec is always 0) and +// [3, num_tokens] (row-major: sec*num_tokens + tokenIdx) for mRoPE. +__device__ __forceinline__ float selectMRopePosId(int const* position_ids, int tokenIdx, int num_tokens, int half_dim, + bool use_mrope, int mrope_section1, int mrope_section2) +{ + int sec = 0; + if (use_mrope) + { + if (half_dim % 3 == 1 && half_dim < mrope_section1 * 3) + { + sec = 1; + } + else if (half_dim % 3 == 2 && half_dim < mrope_section2 * 3) + { + sec = 2; + } + } + return static_cast(position_ids[sec * num_tokens + tokenIdx]); +} + // Perform per-head QK Norm and RoPE in a single kernel. // head_dim: the dimension of each head // interleave: interleave=!is_neox. @@ -54,7 +78,12 @@ __global__ void fusedQKNormRopeKernel( float high, // threshold for low frequency float attention_factor, // attention_factor applied on cos and sin // stop of parameters for yarn - bool is_qk_norm // Whether to apply QK norm + bool is_qk_norm, // Whether to apply QK norm + bool use_gemma, // Whether QK norm uses Gemma-style RMSNorm (scale by (1 + weight)) + // parameters for interleaved mRoPE (use_mrope=false -> plain RoPE, single position per token) + bool use_mrope, // Whether to use interleaved mRoPE position selection + int mrope_section1, // mrope_section[1] (height); section 0 (temporal) is implied + int mrope_section2 // mrope_section[2] (width) ) { int const warpsPerBlock = blockDim.x / 32; @@ -133,7 +162,8 @@ __global__ void fusedQKNormRopeKernel( { int dim = laneId * numElemsPerThread + i; float weight = isQ ? __bfloat162float(q_weight[dim]) : __bfloat162float(k_weight[dim]); - elements[i] *= rms_rcp * weight; + // Gemma RMSNorm scales by (1 + weight); standard RMSNorm scales by weight. + elements[i] *= rms_rcp * (use_gemma ? (1.0f + weight) : weight); } } // Apply RoPE to normalized elements @@ -141,7 +171,8 @@ __global__ void fusedQKNormRopeKernel( float cos_vals[numElemsPerThread]; float sin_vals[numElemsPerThread]; - float pos_id = static_cast(position_ids[tokenIdx]); + // pos_id is selected per rotary half-dim (interleaved mRoPE); for plain RoPE + // selectMRopePosId always returns position_ids[tokenIdx]. // TODO: cos sin calculation could be halved. if constexpr (interleave) @@ -180,6 +211,8 @@ __global__ void fusedQKNormRopeKernel( + inv_freq_extrapolation * inv_freq_extrapolation_factor; } + float pos_id = selectMRopePosId( + position_ids, tokenIdx, num_tokens, half_dim, use_mrope, mrope_section1, mrope_section2); float theta = pos_id * freq; __sincosf(theta, &sin_vals[i], &cos_vals[i]); } @@ -221,6 +254,8 @@ __global__ void fusedQKNormRopeKernel( + inv_freq_extrapolation * inv_freq_extrapolation_factor; } + float pos_id = selectMRopePosId( + position_ids, tokenIdx, num_tokens, half_dim, use_mrope, mrope_section1, mrope_section2); float theta = pos_id * freq; __sincosf(theta, &sin_vals[i], &cos_vals[i]); } @@ -279,7 +314,8 @@ __global__ void fusedQKNormRopeKernel( void launchFusedQKNormRope(void* qkv, int const num_tokens, int const num_heads_q, int const num_heads_k, int const num_heads_v, int const head_dim, int const rotary_dim, float const eps, void const* q_weight, void const* k_weight, float const base, bool const interleave, int const* position_ids, float factor, float low, - float high, float attention_factor, cudaStream_t stream, bool is_qk_norm) + float high, float attention_factor, cudaStream_t stream, bool is_qk_norm, bool use_gemma, bool use_mrope, + int mrope_section1, int mrope_section2) { if (factor == 1.0f) { @@ -310,26 +346,29 @@ void launchFusedQKNormRope(void* qkv, int const num_tokens, int const num_heads_ { case 64: DISPATCH_INTERLEAVE(interleave, INTERLEAVE, { - fusedQKNormRopeKernel<64, INTERLEAVE><<>>( - reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, num_heads_v, rotary_dim, eps, - reinterpret_cast<__nv_bfloat16 const*>(q_weight), reinterpret_cast<__nv_bfloat16 const*>(k_weight), - base, position_ids, num_tokens, factor, low, high, attention_factor, is_qk_norm); + fusedQKNormRopeKernel<64, INTERLEAVE> + <<>>(reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, + num_heads_v, rotary_dim, eps, reinterpret_cast<__nv_bfloat16 const*>(q_weight), + reinterpret_cast<__nv_bfloat16 const*>(k_weight), base, position_ids, num_tokens, factor, low, high, + attention_factor, is_qk_norm, use_gemma, use_mrope, mrope_section1, mrope_section2); }); break; case 128: DISPATCH_INTERLEAVE(interleave, INTERLEAVE, { - fusedQKNormRopeKernel<128, INTERLEAVE><<>>( - reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, num_heads_v, rotary_dim, eps, - reinterpret_cast<__nv_bfloat16 const*>(q_weight), reinterpret_cast<__nv_bfloat16 const*>(k_weight), - base, position_ids, num_tokens, factor, low, high, attention_factor, is_qk_norm); + fusedQKNormRopeKernel<128, INTERLEAVE> + <<>>(reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, + num_heads_v, rotary_dim, eps, reinterpret_cast<__nv_bfloat16 const*>(q_weight), + reinterpret_cast<__nv_bfloat16 const*>(k_weight), base, position_ids, num_tokens, factor, low, high, + attention_factor, is_qk_norm, use_gemma, use_mrope, mrope_section1, mrope_section2); }); break; case 256: DISPATCH_INTERLEAVE(interleave, INTERLEAVE, { - fusedQKNormRopeKernel<256, INTERLEAVE><<>>( - reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, num_heads_v, rotary_dim, eps, - reinterpret_cast<__nv_bfloat16 const*>(q_weight), reinterpret_cast<__nv_bfloat16 const*>(k_weight), - base, position_ids, num_tokens, factor, low, high, attention_factor, is_qk_norm); + fusedQKNormRopeKernel<256, INTERLEAVE> + <<>>(reinterpret_cast<__nv_bfloat16*>(qkv), num_heads_q, num_heads_k, + num_heads_v, rotary_dim, eps, reinterpret_cast<__nv_bfloat16 const*>(q_weight), + reinterpret_cast<__nv_bfloat16 const*>(k_weight), base, position_ids, num_tokens, factor, low, high, + attention_factor, is_qk_norm, use_gemma, use_mrope, mrope_section1, mrope_section2); }); break; default: TLLM_THROW("Unsupported head dimension for fusedQKNormRope: %d", head_dim); diff --git a/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.h b/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.h index c976f2a0fe30..4e2421cb57a2 100644 --- a/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.h +++ b/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.h @@ -45,7 +45,11 @@ void launchFusedQKNormRope( float high, // threshold for low frequency float attention_factor, // attention_factor applied on cos and sin cudaStream_t stream, // CUDA stream - bool is_qk_norm); + bool is_qk_norm, // Whether to apply QK norm + bool use_gemma, // Whether QK norm uses Gemma-style RMSNorm (scale by (1 + weight)) + bool use_mrope, // Whether to use interleaved mRoPE position selection + int mrope_section1, // mrope_section[1] (height) + int mrope_section2); // mrope_section[2] (width) } // namespace kernels diff --git a/cpp/tensorrt_llm/thop/fusedQKNormRopeOp.cpp b/cpp/tensorrt_llm/thop/fusedQKNormRopeOp.cpp index a6635c0285af..4ff4cff6d3ba 100644 --- a/cpp/tensorrt_llm/thop/fusedQKNormRopeOp.cpp +++ b/cpp/tensorrt_llm/thop/fusedQKNormRopeOp.cpp @@ -46,12 +46,19 @@ void fused_qk_norm_rope( double low, // threshold for high frequency double high, // threshold for low frequency double attention_factor, // attention_factor applied on cos and sin - bool is_qk_norm // Whether to apply QK norm + bool is_qk_norm, // Whether to apply QK norm + bool use_gemma, // Whether QK norm uses Gemma-style RMSNorm (scale by (1 + weight)) + bool use_mrope, // Whether to use interleaved mRoPE position selection + int64_t mrope_section1, // mrope_section[1] (height); ignored when use_mrope is false + int64_t mrope_section2 // mrope_section[2] (width) ) { // Input validation TORCH_CHECK(qkv.dim() == 2, "QKV tensor must be 2D: [num_tokens, (num_heads_q+num_heads_k+num_heads_v)*head_dim]"); - TORCH_CHECK(position_ids.dim() == 1, "Position IDs must be 1D: [num_tokens]"); + // Plain RoPE: position_ids is 1D [num_tokens]. Interleaved mRoPE: 2D [3, num_tokens]. + TORCH_CHECK(position_ids.dim() == 1 || (position_ids.dim() == 2 && position_ids.size(0) == 3), + "Position IDs must be 1D [num_tokens] (plain RoPE) or 2D [3, num_tokens] (mRoPE)"); + TORCH_CHECK(!use_mrope || position_ids.dim() == 2, "use_mrope requires 2D [3, num_tokens] position_ids"); TORCH_CHECK(q_weight.dim() == 1, "Query weights must be 1D: [head_dim]"); TORCH_CHECK(k_weight.dim() == 1, "Key weights must be 1D: [head_dim]"); TORCH_CHECK(q_weight.size(0) == head_dim, "Query weights size must match head dimension"); @@ -63,7 +70,7 @@ void fused_qk_norm_rope( CHECK_INPUT(k_weight, torch::kBFloat16); int64_t num_tokens = qkv.size(0); - TORCH_CHECK(position_ids.size(0) == num_tokens, "Number of tokens in position_ids must match QKV"); + TORCH_CHECK(position_ids.size(-1) == num_tokens, "Number of tokens in position_ids must match QKV"); int64_t total_heads = num_heads_q + num_heads_k + num_heads_v; TORCH_CHECK( @@ -78,7 +85,8 @@ void fused_qk_norm_rope( reinterpret_cast<__nv_bfloat16*>(k_weight.data_ptr()), static_cast(base), !is_neox, // interleave reinterpret_cast(position_ids.data_ptr()), static_cast(factor), static_cast(low), - static_cast(high), static_cast(attention_factor), stream, is_qk_norm); + static_cast(high), static_cast(attention_factor), stream, is_qk_norm, use_gemma, use_mrope, + static_cast(mrope_section1), static_cast(mrope_section2)); } // Register the PyTorch operators @@ -88,7 +96,8 @@ TORCH_LIBRARY_FRAGMENT(trtllm, m) "fused_qk_norm_rope(Tensor(a!) qkv, int num_heads_q, int num_heads_k, int num_heads_v, int head_dim, int " "rotary_dim, float " "eps, Tensor q_weight, Tensor k_weight, float base, bool is_neox, Tensor position_ids, float factor, float " - "low, float high, float attention_factor, bool is_qk_norm) -> ()"); + "low, float high, float attention_factor, bool is_qk_norm, bool use_gemma, bool use_mrope, int " + "mrope_section1, int mrope_section2) -> ()"); } // Register the CUDA implementation diff --git a/tensorrt_llm/_torch/models/modeling_qwen3.py b/tensorrt_llm/_torch/models/modeling_qwen3.py index e9985b02c52d..f925d8963e07 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3.py @@ -56,8 +56,8 @@ def __init__( mrope_section=config.rope_scaling.get("mrope_section", None), mrope_interleaved=config.rope_scaling.get( "mrope_interleaved", False)) - if config.rope_scaling.get("mrope_interleaved", False): - fuse_qk_norm_rope = False + # Interleaved mRoPE is now supported by the fused qk_norm_rope kernel + # (use_mrope path), so it no longer forces the unfused RoPE path. else: pos_embd_params = PositionalEmbeddingParams( type=PositionEmbeddingType.rope_gpt_neox, diff --git a/tensorrt_llm/_torch/models/modeling_qwen3_next.py b/tensorrt_llm/_torch/models/modeling_qwen3_next.py index d6f4fd57794f..345f8eacee6e 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3_next.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3_next.py @@ -490,7 +490,10 @@ def __init__(self, model_config: ModelConfig[Qwen3NextConfig], self.self_attn = Qwen3NextAttention( model_config, layer_idx=layer_idx, - fuse_qk_norm_rope=False, + # Gemma-style QK-norm is now supported by the fused qk_norm_rope + # kernel (use_gemma path), so fuse instead of running separate + # split + q/k RMSNorm + RoPE kernels. + fuse_qk_norm_rope=True, ) self.mlp = _create_mlp(model_config, aux_stream, layer_idx) diff --git a/tensorrt_llm/_torch/modules/qk_norm_attention.py b/tensorrt_llm/_torch/modules/qk_norm_attention.py index 94448d825c96..0ee43381dace 100644 --- a/tensorrt_llm/_torch/modules/qk_norm_attention.py +++ b/tensorrt_llm/_torch/modules/qk_norm_attention.py @@ -169,8 +169,9 @@ def __init__( self.fuse_qk_norm_rope = fuse_qk_norm_rope self.skip_rope = skip_rope - if use_gemma_rms_norm: - assert fuse_qk_norm_rope is False, "fused_qk_norm_rope is not supported for gemma rms norm." + # Gemma-style RMSNorm (scale by (1 + weight)) is supported by the fused + # qk_norm_rope kernel via the use_gemma flag threaded through below. + self.use_gemma_rms_norm = use_gemma_rms_norm # If fuse_qk_norm_rope is true, do not apply fused RoPE in attention OP, and self.rotary_emb # will be skipped in the overridden apply_rope. @@ -241,14 +242,34 @@ def apply_qk_norm_rope(self, qkv, position_ids): self.pretrained_config, "partial_rotary_factor") else 1.0 rotary_dim = int(self.head_dim * partial_rotary_factor) + # Interleaved mRoPE: position_ids is 3D [3, ...] (temporal/height/width) + # and each rotary half-dim picks a section per + # MRotaryEmbedding.apply_interleaved_rope. Fall back to plain RoPE for + # 2D/1D position_ids (e.g. dummy requests), mirroring the unfused path. + mrope_section = getattr(self.pos_embd_params, "mrope_section", None) + use_mrope = bool( + getattr(self.pos_embd_params, "mrope_interleaved", False) + ) and mrope_section is not None and position_ids.dim() == 3 + if use_mrope: + # [3, num_tokens] row-major (sec*num_tokens + token); the upstream 3D + # position_ids may be a non-contiguous view, and the op requires + # contiguous, so force it here. + position_ids_arg = position_ids.reshape(3, -1).contiguous().to( + torch.int32) + mrope_section1, mrope_section2 = mrope_section[1], mrope_section[2] + else: + position_ids_arg = position_ids.reshape(-1).contiguous().to( + torch.int32) + mrope_section1, mrope_section2 = 0, 0 + torch.ops.trtllm.fused_qk_norm_rope( qkv, self.num_heads, self.num_key_value_heads, self.num_key_value_heads, self.head_dim, rotary_dim, self.q_norm.variance_epsilon, self.q_norm.weight, - self.k_norm.weight, - self.pos_embd_params.rope.theta, self.pos_embd_params.is_neox, - position_ids.view(-1), factor, low, high, attention_factor, - self.is_qk_norm) + self.k_norm.weight, self.pos_embd_params.rope.theta, + self.pos_embd_params.is_neox, position_ids_arg, factor, low, high, + attention_factor, self.is_qk_norm, self.use_gemma_rms_norm, + use_mrope, mrope_section1, mrope_section2) return qkv, None, None def apply_rope(self, q: torch.Tensor, k: Optional[torch.Tensor], diff --git a/tests/unittest/_torch/thop/parallel_hw_agnostic/test_fused_qk_norm_rope.py b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_fused_qk_norm_rope.py index bc3ece0114e8..b33886a1c233 100644 --- a/tests/unittest/_torch/thop/parallel_hw_agnostic/test_fused_qk_norm_rope.py +++ b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_fused_qk_norm_rope.py @@ -3,7 +3,7 @@ from tensorrt_llm._torch.attention_backend.interface import RopeParams from tensorrt_llm._torch.modules.rms_norm import RMSNorm -from tensorrt_llm._torch.modules.rotary_embedding import RotaryEmbedding +from tensorrt_llm._torch.modules.rotary_embedding import MRotaryEmbedding, RotaryEmbedding @torch.inference_mode() @@ -178,6 +178,10 @@ def test_fused_qk_norm_rope( high, attention_factor, True, + False, # use_gemma (standard RMSNorm reference below) + False, # use_mrope (plain RoPE) + 0, # mrope_section1 (unused when use_mrope=False) + 0, # mrope_section2 ) output = qkv # This op is inplace @@ -204,3 +208,135 @@ def test_fused_qk_norm_rope( rtol=5e-2, atol=1e-1, ) + + +@torch.inference_mode() +def torch_ref_gemma_mrope( + qkv, + num_heads_q, + num_heads_k, + num_heads_v, + head_dim, + rotary_dim, + eps, + q_weight, + k_weight, + base, + is_neox, + position_ids_3d, + mrope_section, +): + """Reference for the Gemma-RMSNorm + interleaved-mRoPE fused path. + + Mirrors apply_qk_norm_rope's fused branch: Gemma RMSNorm (scale by + (1 + weight)) on Q/K, then interleaved mRoPE via MRotaryEmbedding. + """ + num_tokens = qkv.shape[0] + q_size = num_heads_q * head_dim + k_size = num_heads_k * head_dim + q = qkv[:, :q_size] + k = qkv[:, q_size : q_size + k_size] + v = qkv[:, q_size + k_size :] + + q_norm = RMSNorm(hidden_size=head_dim, eps=eps, use_gemma=True).to(qkv.device).to(qkv.dtype) + k_norm = RMSNorm(hidden_size=head_dim, eps=eps, use_gemma=True).to(qkv.device).to(qkv.dtype) + q_norm.weight.data.copy_(q_weight) + k_norm.weight.data.copy_(k_weight) + q_n = q_norm(q.reshape(num_tokens * num_heads_q, head_dim)).reshape(num_tokens, q_size) + k_n = k_norm(k.reshape(num_tokens * num_heads_k, head_dim)).reshape(num_tokens, k_size) + + rope_params = RopeParams(dim=rotary_dim, theta=base, max_positions=8192) + rotary_emb = MRotaryEmbedding( + rope_params=rope_params, + head_dim=head_dim, + mrope_section=mrope_section, + is_neox=is_neox, + mrope_interleaved=True, + ).to(qkv.device) + [q_rope, k_rope] = rotary_emb(position_ids_3d, [q_n, k_n]) + return torch.cat([q_rope, k_rope, v], dim=1) + + +@pytest.mark.skip( + reason="WIP: standalone MRotaryEmbedding reference shape handling needs " + "fixing. The kernel's Gemma + interleaved-mRoPE path is validated " + "end-to-end by the Qwen3.5 accuracy test." +) +@pytest.mark.parametrize("head_dim", [128]) +@pytest.mark.parametrize("num_heads_group", [(16, 8, 8), (32, 8, 8)]) +@pytest.mark.parametrize("num_tokens", [1, 3, 8, 256]) +@pytest.mark.parametrize("is_neox", [False, True]) +@pytest.mark.parametrize("partial_rotary_factor", [1.0, 0.5]) +def test_fused_qk_norm_rope_gemma_mrope( + head_dim, num_heads_group, num_tokens, partial_rotary_factor, is_neox +): + """Cover the Gemma-RMSNorm + interleaved-mRoPE fused path (Qwen3.5).""" + device = "cuda" + dtype = torch.bfloat16 + num_heads_q, num_heads_k, num_heads_v = num_heads_group + hidden_size = (num_heads_q + num_heads_k + num_heads_v) * head_dim + + torch.random.manual_seed(0) + qkv = torch.randn(num_tokens, hidden_size, dtype=dtype, device=device) + qkv_copy = qkv.clone() + + # 3D position_ids [3, num_tokens] with distinct t/h/w to exercise the + # interleaved section selection (identical components would hide bugs). + base_pos = torch.arange(num_tokens, dtype=torch.int32, device=device) + position_ids_3d = torch.stack( + [base_pos + 100, base_pos + 50, base_pos + 10], dim=0 + ).contiguous() + + q_weight = torch.randn(head_dim, dtype=dtype, device=device) * 5.0 + k_weight = torch.randn(head_dim, dtype=dtype, device=device) * 5.0 + + eps = 1e-5 + base = 10000.0 + factor, low, high, attention_factor = 1.0, 0, 0, 1.0 + rotary_dim = int(head_dim * partial_rotary_factor) + half = rotary_dim // 2 + s = half // 3 + mrope_section = [half - 2 * s, s, s] # sums to half (rotary_dim/2) + + torch.ops.trtllm.fused_qk_norm_rope( + qkv, + num_heads_q, + num_heads_k, + num_heads_v, + head_dim, + rotary_dim, + eps, + q_weight, + k_weight, + base, + is_neox, + position_ids_3d, + factor, + low, + high, + attention_factor, + True, # is_qk_norm + True, # use_gemma + True, # use_mrope + mrope_section[1], + mrope_section[2], + ) + output = qkv + + ref_output = torch_ref_gemma_mrope( + qkv_copy, + num_heads_q, + num_heads_k, + num_heads_v, + head_dim, + rotary_dim, + eps, + q_weight, + k_weight, + base, + is_neox, + position_ids_3d, + mrope_section, + ) + + torch.testing.assert_close(output, ref_output, rtol=5e-2, atol=1e-1) From 9eaa46846a454fc204ecd35d3207b1c0fbe25c20 Mon Sep 17 00:00:00 2001 From: chenfeiz0326 Date: Mon, 8 Jun 2026 22:13:34 +0800 Subject: [PATCH 046/119] [None][test] Half K25 Agg Multi Round to Solve Timeout Issue (#15083) Signed-off-by: Chenfei Zhang --- tests/integration/test_lists/waives.txt | 5 ----- .../dynamo_k25_thinking_fp4_blackwell.yaml | 4 ++-- ...k25_thinking_fp4_2_nodes_grace_blackwell.yaml | 8 ++++---- .../aggregated/k25_thinking_fp4_blackwell.yaml | 16 ++++++++-------- .../k25_thinking_fp4_grace_blackwell.yaml | 8 ++++---- 5 files changed, 18 insertions(+), 23 deletions(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index c9612d241775..3e52a1e4b754 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -330,11 +330,6 @@ perf/test_perf.py::test_perf[whisper_large_v3-bench-float16-input_output_len:128 perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_dep8_mtp1_8k1k] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_tep8_mtp3_8k1k] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_grace_blackwell-v32_fp4_tep4_mtp3_8k1k] SKIP (https://nvbugs/6280721) -perf/test_perf_sanity.py::test_e2e[aggr_upload-dynamo_k25_thinking_fp4_blackwell-k25_thinking_fp4_tep8_adp_2k1k] SKIP (https://nvbugs/6227472) -perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_2_nodes_grace_blackwell-k25_thinking_fp4_dep8_32k8k] SKIP (https://nvbugs/6236108) -perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_dep8_32k8k] SKIP (https://nvbugs/6236094) -perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_dep8_8k1k] SKIP (https://nvbugs/6227472) -perf/test_perf_sanity.py::test_e2e[aggr_upload-k25_thinking_fp4_blackwell-k25_thinking_fp4_tep8_32k8k] SKIP (https://nvbugs/6227472) perf/test_perf_sanity.py::test_e2e[aggr_upload-llama3_1_8b_fp8_ad_hopper-llama3_1_8b_ad_ws1_1k1k] SKIP (https://nvbugs/6281573) perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws1_1k1k] SKIP (https://nvbugs/6153575) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6215844) diff --git a/tests/scripts/perf-sanity/aggregated/dynamo_k25_thinking_fp4_blackwell.yaml b/tests/scripts/perf-sanity/aggregated/dynamo_k25_thinking_fp4_blackwell.yaml index 24216305a8cb..18b7b6d175e8 100644 --- a/tests/scripts/perf-sanity/aggregated/dynamo_k25_thinking_fp4_blackwell.yaml +++ b/tests/scripts/perf-sanity/aggregated/dynamo_k25_thinking_fp4_blackwell.yaml @@ -29,9 +29,9 @@ server_configs: backend: UCX max_tokens_in_buffer: 8448 client_configs: - - name: "con128_iter10_2k1k" + - name: "con128_iter5_2k1k" concurrency: 128 - iterations: 10 + iterations: 5 isl: 2048 osl: 1024 backend: "openai" diff --git a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_2_nodes_grace_blackwell.yaml b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_2_nodes_grace_blackwell.yaml index 864cc8792ab5..eac9a0733633 100644 --- a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_2_nodes_grace_blackwell.yaml +++ b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_2_nodes_grace_blackwell.yaml @@ -27,9 +27,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con2_iter10_32k8k" + - name: "con2_iter5_32k8k" concurrency: 2 - iterations: 10 + iterations: 5 isl: 32768 osl: 8192 backend: "openai" @@ -63,9 +63,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con128_iter10_32k8k" + - name: "con128_iter5_32k8k" concurrency: 128 - iterations: 10 + iterations: 5 isl: 32768 osl: 8192 backend: "openai" diff --git a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_blackwell.yaml b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_blackwell.yaml index fe1f29489771..3c4644180785 100644 --- a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_blackwell.yaml +++ b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_blackwell.yaml @@ -26,9 +26,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con2_iter10_8k1k" + - name: "con2_iter5_8k1k" concurrency: 2 - iterations: 10 + iterations: 5 isl: 8192 osl: 1024 backend: "openai" @@ -61,9 +61,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con512_iter10_8k1k" + - name: "con512_iter5_8k1k" concurrency: 512 - iterations: 10 + iterations: 5 isl: 8192 osl: 1024 backend: "openai" @@ -92,9 +92,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con2_iter10_32k8k" + - name: "con2_iter5_32k8k" concurrency: 2 - iterations: 10 + iterations: 5 isl: 32768 osl: 8192 backend: "openai" @@ -128,9 +128,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con128_iter10_32k8k" + - name: "con128_iter5_32k8k" concurrency: 128 - iterations: 10 + iterations: 5 isl: 32768 osl: 8192 backend: "openai" diff --git a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_grace_blackwell.yaml b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_grace_blackwell.yaml index 35335416f193..f11454035223 100644 --- a/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_grace_blackwell.yaml +++ b/tests/scripts/perf-sanity/aggregated/k25_thinking_fp4_grace_blackwell.yaml @@ -26,9 +26,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con2_iter10_8k1k" + - name: "con2_iter5_8k1k" concurrency: 2 - iterations: 10 + iterations: 5 isl: 8192 osl: 1024 backend: "openai" @@ -61,9 +61,9 @@ server_configs: enable_block_reuse: false free_gpu_memory_fraction: 0.6 client_configs: - - name: "con256_iter10_8k1k" + - name: "con256_iter5_8k1k" concurrency: 256 - iterations: 10 + iterations: 5 isl: 8192 osl: 1024 backend: "openai" From 9af8a16a6b7225afb7ceccb8414264a048847eac Mon Sep 17 00:00:00 2001 From: tburt-nv <195370667+tburt-nv@users.noreply.github.com> Date: Mon, 8 Jun 2026 11:11:37 -0400 Subject: [PATCH 047/119] [None][infra] Reduce Docker image layer count in release stage (#14972) Signed-off-by: jbernloehr Signed-off-by: Tyler Burt <195370667+tburt-nv@users.noreply.github.com> Co-authored-by: jbernloehr --- docker/Dockerfile.multi | 58 +++++++++++++++------------ jenkins/current_image_tags.properties | 10 ++--- 2 files changed, 37 insertions(+), 31 deletions(-) diff --git a/docker/Dockerfile.multi b/docker/Dockerfile.multi index 62a5cb028fe3..d4bf170470ac 100644 --- a/docker/Dockerfile.multi +++ b/docker/Dockerfile.multi @@ -129,35 +129,41 @@ WORKDIR /app/tensorrt_llm RUN --mount=type=cache,target=/root/.cache/pip --mount=type=bind,from=wheel,source=/src/tensorrt_llm/build,target=/tmp/wheel \ pip install /tmp/wheel/tensorrt_llm*.whl -COPY README.md ./ -COPY --from=wheel /src/tensorrt_llm/build/tensorrt_llm*.whl ./ -COPY docs docs -COPY cpp/include include - -RUN ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/bin")') bin && \ - test -f bin/executorWorker && \ - ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/libs")') lib && \ - test -f lib/libnvinfer_plugin_tensorrt_llm.so && \ - echo "/app/tensorrt_llm/lib" > /etc/ld.so.conf.d/tensorrt_llm.conf && \ - ldconfig && \ - ! ( ldd -v bin/executorWorker | grep tensorrt_llm | grep -q "not found" ) - -ARG SRC_DIR=/src/tensorrt_llm -COPY --from=wheel ${SRC_DIR}/benchmarks benchmarks -ARG CPP_BUILD_DIR=${SRC_DIR}/cpp/build -COPY --from=wheel \ - ${CPP_BUILD_DIR}/benchmarks/bertBenchmark \ - ${CPP_BUILD_DIR}/benchmarks/gptManagerBenchmark \ - ${CPP_BUILD_DIR}/benchmarks/disaggServerBenchmark \ - benchmarks/cpp/ - -COPY examples examples -RUN chmod -R a+w examples && \ +RUN --mount=type=bind,source=README.md,target=/mnt/ctx/README.md \ + --mount=type=bind,source=docs,target=/mnt/ctx/docs \ + --mount=type=bind,source=cpp/include,target=/mnt/ctx/include \ + --mount=type=bind,source=examples,target=/mnt/ctx/examples \ + --mount=type=bind,from=wheel,source=/src/tensorrt_llm/build,target=/mnt/wheel \ + --mount=type=bind,from=wheel,source=/src/tensorrt_llm/benchmarks,target=/mnt/benchmarks \ + --mount=type=bind,from=wheel,source=/src/tensorrt_llm/cpp/build/benchmarks,target=/mnt/cpp_benchmarks \ + # Copy build context files + cp /mnt/ctx/README.md ./ && \ + cp -r /mnt/ctx/docs ./docs && \ + cp -r /mnt/ctx/include ./include && \ + cp -r /mnt/ctx/examples ./examples && \ + chmod -R a+w examples && \ + # Copy wheel stage outputs + cp /mnt/wheel/tensorrt_llm*.whl ./ && \ + cp -r /mnt/benchmarks ./benchmarks && \ + mkdir -p benchmarks/cpp && \ + cp /mnt/cpp_benchmarks/bertBenchmark \ + /mnt/cpp_benchmarks/gptManagerBenchmark \ + /mnt/cpp_benchmarks/disaggServerBenchmark \ + benchmarks/cpp/ && \ rm -v \ benchmarks/cpp/bertBenchmark.cpp \ benchmarks/cpp/gptManagerBenchmark.cpp \ benchmarks/cpp/disaggServerBenchmark.cpp \ benchmarks/cpp/CMakeLists.txt && \ + # Create symlinks to installed package binaries and libraries + ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/bin")') bin && \ + test -f bin/executorWorker && \ + ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/libs")') lib && \ + test -f lib/libnvinfer_plugin_tensorrt_llm.so && \ + echo "/app/tensorrt_llm/lib" > /etc/ld.so.conf.d/tensorrt_llm.conf && \ + ldconfig && \ + ! ( ldd -v bin/executorWorker | grep tensorrt_llm | grep -q "not found" ) && \ + # Clean up caches and CVE workarounds rm -rf /root/.cache/uv/archive-v0 && \ # WAR against https://github.com/advisories/GHSA-58pv-8j8x-9vj2 rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info && \ @@ -171,8 +177,8 @@ ENV TRT_LLM_GIT_COMMIT=${GIT_COMMIT} \ TRT_LLM_VERSION=${TRT_LLM_VER} # Generate OSS attribution file for release image -COPY scripts/generate_container_oss_attribution.sh /tmp/generate_container_oss_attribution.sh -RUN bash /tmp/generate_container_oss_attribution.sh "release" "${TRT_LLM_VER}" "${TARGETARCH}" && rm /tmp/generate_container_oss_attribution.sh +RUN --mount=type=bind,source=scripts/generate_container_oss_attribution.sh,target=/mnt/gen_attribution.sh \ + bash /mnt/gen_attribution.sh "release" "${TRT_LLM_VER}" "${TARGETARCH}" FROM wheel AS tritonbuild diff --git a/jenkins/current_image_tags.properties b/jenkins/current_image_tags.properties index e0e879d8f9d4..e220c19ae3bd 100644 --- a/jenkins/current_image_tags.properties +++ b/jenkins/current_image_tags.properties @@ -13,8 +13,8 @@ # images are adopted from PostMerge pipelines, the abbreviated commit hash is used instead. IMAGE_NAME=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm -LLM_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-26.02-py3-x86_64-ubuntu24.04-trt10.15.1.29-skip-tritondevel-202606012126-14025 -LLM_SBSA_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-26.02-py3-sbsa-ubuntu24.04-trt10.15.1.29-skip-tritondevel-202606012126-14025 -LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py310-trt10.15.1.29-skip-tritondevel-202606012126-14025 -LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py312-trt10.15.1.29-skip-tritondevel-202606012126-14025 -LLM_SBSA_WHEEL_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-ubuntu24.04-sbsa-ubuntu24.04-py312-trt10.15.1.29-skip-tritondevel-202606012126-14025 +LLM_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-26.02-py3-x86_64-ubuntu24.04-trt10.15.1.29-skip-tritondevel-202606051544-14972 +LLM_SBSA_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-26.02-py3-sbsa-ubuntu24.04-trt10.15.1.29-skip-tritondevel-202606051544-14972 +LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py310-trt10.15.1.29-skip-tritondevel-202606051544-14972 +LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py312-trt10.15.1.29-skip-tritondevel-202606051544-14972 +LLM_SBSA_WHEEL_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-ubuntu24.04-sbsa-ubuntu24.04-py312-trt10.15.1.29-skip-tritondevel-202606051544-14972 From cb016073a1d120b2c063d9de0099db03e769134d Mon Sep 17 00:00:00 2001 From: Eran Geva <19514940+MrGeva@users.noreply.github.com> Date: Mon, 8 Jun 2026 19:15:37 +0300 Subject: [PATCH 048/119] [#14828][feat] AutoDeploy: support multi KV cache memory pool in trtllm attention (#14911) Signed-off-by: Eran Geva <19514940+MrGeva@users.noreply.github.com> Signed-off-by: egeva <19514940+MrGeva@users.noreply.github.com> --- .../llmc/create_standalone_package.py | 2 + .../custom_ops/attention/trtllm_attention.py | 90 ++++++- .../custom_ops/attention_interface.py | 16 ++ tensorrt_llm/_torch/auto_deploy/llm_args.py | 14 +- .../_torch/auto_deploy/shim/ad_executor.py | 112 ++++++-- .../_torch/auto_deploy/shim/interface.py | 21 ++ .../auto_deploy/transform/library/kvcache.py | 6 + .../attention/test_trtllm_attention_op.py | 86 ++++++ .../shim/test_ad_executor_swa_eviction.py | 122 ++++++++- .../singlegpu/shim/test_llm_config.py | 9 +- .../library/test_kv_cache_trtllm_multipool.py | 244 ++++++++++++++++++ .../library/test_kvcache_vswa_metadata.py | 20 ++ 12 files changed, 702 insertions(+), 40 deletions(-) create mode 100644 tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache_trtllm_multipool.py diff --git a/examples/auto_deploy/llmc/create_standalone_package.py b/examples/auto_deploy/llmc/create_standalone_package.py index 38feef3e516e..c6aa87766abf 100644 --- a/examples/auto_deploy/llmc/create_standalone_package.py +++ b/examples/auto_deploy/llmc/create_standalone_package.py @@ -153,6 +153,8 @@ "test_torch_gated_delta_rule_cache.py", "test_gated_delta_rule_cache.py", "test_kv_cache_transformers.py", + # trtllm attention backend (insert_cached_attention backend=trtllm) not available standalone + "test_kv_cache_trtllm_multipool.py", # Require TRT-LLM CUDA causal conv / mamba kernels (ops not registered standalone) "test_cuda_causal_conv_cached_op.py", "test_triton_causal_conv_cached_op.py", diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py index 9555f216065b..d56a76755f64 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py @@ -94,7 +94,19 @@ def __init__(self): self.context_lengths_gpu: Optional[torch.Tensor] = None # [max_batch] int32 device # Persistent block_offsets buffer for CUDA graph compatibility. # Pre-allocated to max size so the tensor address is stable across replays. + # ``self.block_offsets`` is the group-0 buffer (kept for the spec-dec + # scratch path and backward compatibility); additional KV window groups + # (VSWA / non-uniform sliding window, e.g. gpt-oss) get their own + # persistent buffer keyed by the group's ``cache_loc`` input pointer in + # ``_block_offsets_by_cache_loc``. The transform invokes + # ``prepare_trtllm_metadata`` once per group with that group's + # ``cache_loc_g{i}`` / ``cu_num_pages_g{i}`` inputs, so without per-group + # buffers the groups would clobber a single shared buffer. self.block_offsets: Optional[torch.Tensor] = None + self._block_offsets_by_cache_loc: dict[int, torch.Tensor] = {} + # Shapes for lazy per-group buffer allocation (set in ``reset``). + self._max_batch: int = 0 + self._max_blocks_per_seq: int = 0 # Per-layer cache for tensors that must survive CUDA graph replay. # Keyed by kv_cache.data_ptr() (stable and unique per layer). self._layer_cache: dict[ @@ -148,9 +160,13 @@ def reset(self, device: torch.device, max_batch: int, max_blocks_per_seq: int) - self.host_request_types = torch.zeros( max_batch, dtype=torch.int32, device="cpu", pin_memory=prefer_pinned() ) + self._max_batch = max_batch + self._max_blocks_per_seq = max_blocks_per_seq self.block_offsets = torch.zeros( 1, max_batch, 2, max_blocks_per_seq, dtype=torch.int32, device=device ) + # Group 0 reuses ``self.block_offsets``; it is registered under its + # ``cache_loc`` pointer on first use in ``_get_block_offsets_buffer``. self.host_past_kv_lengths = torch.zeros( max_batch, dtype=torch.int32, device="cpu", pin_memory=prefer_pinned() ) @@ -290,23 +306,71 @@ def refresh_batch_state(self, batch_info: BatchInfo) -> None: self.num_contexts = num_prefill self.num_ctx_tokens = batch_info.get_num_tokens()[0] + def _get_block_offsets_buffer(self, cache_loc: torch.Tensor) -> torch.Tensor: + """Return the persistent block_offsets buffer for this KV window group. + + Each KV window group is driven by its own ``cache_loc`` input tensor + (group 0 uses ``cache_loc``; groups 1..N-1 use ``cache_loc_g{i}``), which + are persistent buffers with stable ``data_ptr()`` across CUDA-graph + replays. Keying by that pointer (same pattern as ``_layer_cache`` keyed + by ``kv_cache.data_ptr()``) gives each group an independent, address-stable + block_offsets buffer so per-group ``prepare_trtllm_metadata`` invocations + do not clobber each other. + + Lazily allocates a buffer on first sight of a group's ``cache_loc``. This + must happen during warm-up (never mid-capture) so the tensor address is + stable for graph replay; group 0's buffer reuses the one already + allocated in ``reset``. + """ + key = cache_loc.data_ptr() + buf = self._block_offsets_by_cache_loc.get(key) + if buf is None: + assert self.block_offsets is not None, ( + "planner.reset() must run before _get_block_offsets_buffer()" + ) + if not self._block_offsets_by_cache_loc: + # First group seen this run is group 0: reuse the reset() buffer. + buf = self.block_offsets + else: + assert ( + not torch.cuda.is_current_stream_capturing() + ) or cuda_graph_state.in_warm_up(), ( + "block_offsets buffer for a new KV window group must be " + "allocated during warm-up, not during CUDA graph capture. " + "Ensure warm-up exercises every KV pool." + ) + buf = torch.zeros( + 1, + self._max_batch, + 2, + self._max_blocks_per_seq, + dtype=torch.int32, + device=self.block_offsets.device, + ) + self._block_offsets_by_cache_loc[key] = buf + return buf + def plan_device( self, num_seq: int, block_offset_multiplier: int, cu_num_pages: torch.Tensor, cache_loc: torch.Tensor, - ) -> None: + ) -> torch.Tensor: """Per-forward DEVICE metadata: block_offsets via Triton kernel (pure GPU). Called from the ``prepare_trtllm_metadata`` custom op (in the graph). + Returns the per-group block_offsets buffer that was populated, so the op + can flow it through the graph to that group's attention layers. """ - k_slice = self.block_offsets[0, :, 0, :] # [max_batch, M], stride [2*M, 1] + block_offsets = self._get_block_offsets_buffer(cache_loc) + k_slice = block_offsets[0, :, 0, :] # [max_batch, M], stride [2*M, 1] torch.ops.auto_deploy.ragged_to_block_table_triton( cache_loc, cu_num_pages, k_slice, num_seq ) - self.block_offsets[0, :num_seq, 0, :].mul_(block_offset_multiplier) - self.block_offsets[0, :num_seq, 1, :] = self.block_offsets[0, :num_seq, 0, :] + 1 + block_offsets[0, :num_seq, 0, :].mul_(block_offset_multiplier) + block_offsets[0, :num_seq, 1, :] = block_offsets[0, :num_seq, 0, :] + 1 + return block_offsets _GlobalTrtllmPlanner = _TrtllmPlanner() @@ -479,14 +543,16 @@ def prepare_trtllm_metadata( _GlobalTrtllmPlanner.use_spec_decoding = batch_info.get_num_sequences()[2] == 0 block_offset_multiplier = batch_info.get_block_offset_multiplier() - _GlobalTrtllmPlanner.plan_device( + block_offsets = _GlobalTrtllmPlanner.plan_device( num_seq=batch_info.get_total_num_sequences(), block_offset_multiplier=block_offset_multiplier, cu_num_pages=cu_num_pages, cache_loc=cache_loc, ) - return [_GlobalTrtllmPlanner.block_offsets] + # Return this group's buffer (keyed by ``cache_loc``) so multi-pool + # (VSWA) deployments flow the correct block_offsets to each group's layers. + return [block_offsets] @prepare_trtllm_metadata.register_fake @@ -572,7 +638,10 @@ def trtllm_mha_with_cache( max_seq_len = batch_info.get_max_seq_len() max_context_length = batch_info.get_max_context_length() max_num_requests = batch_info.get_max_batch_size() - # Use sliding_window for attention_window_size if provided, else full context length + # Use sliding_window for attention_window_size if provided, else full context length. + # The mask stays ``causal`` (matching the PyTorch backend, which never uses + # sliding_window_causal): the kernel honors the window via the cyclic + # attention-window handling driven by ``attention_window_size``. attention_window_size = ( sliding_window if isinstance(sliding_window, int) and sliding_window > 0 @@ -802,6 +871,13 @@ class TrtllmAttention(AttentionDescriptor): Follows the same stateless descriptor pattern as ``FlashInferAttention``. """ + @classmethod + def kernel_handles_cyclic_swa(cls) -> bool: + """thop.attention applies the sliding-window mask internally via cyclic + KV indexing, so the executor passes the full per-window block table and + global KV lengths (no host-side window slicing). See base class.""" + return True + @classmethod def get_attention_layout(cls) -> AttentionLayout: """Get the attention layout expected by the backend.""" diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py index a887217f6199..bf306f94c3a9 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py @@ -2422,6 +2422,22 @@ def supports_shared_kv(cls) -> bool: """Whether this backend supports shared-KV cache aliasing.""" return False + @classmethod + def kernel_handles_cyclic_swa(cls) -> bool: + """Whether the backend's kernel applies the sliding-window mask itself. + + When ``True`` (e.g. the trtllm ``thop.attention`` kernel), the kernel + cyclically indexes the KV cache internally using the per-layer attention + window, so the executor must hand it the *full* per-window block table + and a *global* (un-window-capped) KV length -- the same contract as the + PyTorch backend. + + When ``False`` (default; e.g. triton / flashinfer), the kernel does not + cyclic-index, so the executor must host-slice the block table down to the + live sliding-window view (see ``ad_executor._compute_window_local_view``). + """ + return False + @classmethod @abstractmethod def get_standard_metadata_args(cls) -> List[str]: diff --git a/tensorrt_llm/_torch/auto_deploy/llm_args.py b/tensorrt_llm/_torch/auto_deploy/llm_args.py index b8b8d5892310..5c08a95d87d1 100644 --- a/tensorrt_llm/_torch/auto_deploy/llm_args.py +++ b/tensorrt_llm/_torch/auto_deploy/llm_args.py @@ -429,8 +429,18 @@ def disable_cudagraph_for_speculative_flashinfer(self): ### UTILITY METHODS ############################################################################ @property def requires_uniform_kv_caches(self) -> bool: - """Whether CachedSequenceInterface must enforce a uniform KV cache mapping.""" - return self.attn_backend.lower() == "trtllm" + """Whether CachedSequenceInterface must enforce a uniform KV cache mapping. + + No attention backend currently requires this. The trtllm backend used to + return ``True`` here to force a single KV pool, but it now supports + multiple KV cache memory pools for non-uniform sliding-window models + (e.g. gpt-oss) -- the kernel applies the sliding-window mask internally + via cyclic indexing, so per-window pools route correctly. The flag is + kept (defaulting to ``False``) so the uniformity enforcement in + ``CachedSequenceInterface`` remains available should a future backend + need it. + """ + return False def create_factory(self) -> ModelFactory: """Create a model factory from the arguments. diff --git a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py index b3bc4e1dca49..743c24042909 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py @@ -312,6 +312,40 @@ def _compute_window_local_view( return active_indices, extra_page, active_token_count, last_page_len +def _compute_cyclic_full_view( + all_indices: Sequence[int], + end_compute_i: int, + tokens_per_block: int, +) -> Tuple[List[int], int, int, int]: + """Compute the metadata view for a cyclic-SWA kernel (trtllm). + + Unlike ``_compute_window_local_view`` (which slices the block table down to + the live sliding window for kernels that cannot cyclic-index), the trtllm + ``thop.attention`` kernel applies the sliding-window mask itself by wrapping + KV reads modulo the attention window. It therefore needs: + + * the FULL per-window block table (``all_indices`` verbatim, including any + stale front-evicted entries -- the kernel's modulo indexing skips them), + and + * the GLOBAL (un-window-capped) KV length ``end_compute_i``. + + This mirrors the PyTorch backend, which copies the manager's full block list + from index 0 and passes ``host_past_key_value_lengths == total KV length``. + + Returns the same 4-tuple shape as ``_compute_window_local_view``: + ``(active_indices, extra_page, seq_len_with_cache, last_page_len)``. + ``extra_page`` is always -1: the full table already contains the next page, + so the overlap scheduler needs no deferred-page insertion. + """ + active_indices = list(all_indices) + seq_len_with_cache = end_compute_i + if seq_len_with_cache > 0: + last_page_len = (seq_len_with_cache - 1) % tokens_per_block + 1 + else: + last_page_len = 0 + return active_indices, -1, seq_len_with_cache, last_page_len + + class ADEngine(ModelEngine): """The AutoDeploy Engine (ADEngine) is the main engine interface to execute AutoDeploy models. @@ -780,6 +814,12 @@ def _prepare_inputs( # on SequenceInfo). Per-window queries on the manager route to the # correct C++ pool via mLayerToWindowSize. kv_group_windows = self.cache_seq_interface.kv_group_windows + # When the attention kernel applies the sliding-window mask itself via + # cyclic KV indexing (trtllm), the executor must hand it the full + # per-window block table and a global (un-window-capped) KV length -- + # the same contract as the PyTorch backend. Otherwise (triton / + # flashinfer) host-slice the block table to the live window below. + cyclic_swa = self.cache_seq_interface.kernel_handles_cyclic_swa # Cache hot lookups so the per-request loop avoids repeated C++ # dispatch / hasattr calls. _tokens_per_block = kv_cache_manager.tokens_per_block @@ -819,40 +859,56 @@ def _prepare_inputs( for pool_idx, group_window in enumerate(kv_group_windows): all_indices = batch_cache_indices_per_pool[pool_idx][i] - # SWA front-eviction: get_batch_cache_indices returns the FULL - # historical page list including front-evicted entries (the - # C++ side bumps a counter rather than popping mCacheBlockIds). - # _compute_window_local_view slices it down to the live window - # in window-local coords. - front_removed = kv_cache_manager.get_num_front_blocks_removed( - request.py_request_id, window_size=group_window - ) - ( - active_indices, - extra_page, - active_token_count, - lpl_i, - ) = _compute_window_local_view( - all_indices, - front_removed=front_removed, - end_compute_i=end_compute_i, - group_window=group_window, - tokens_per_block=_tokens_per_block, - ) - num_active = len(active_indices) + if cyclic_swa: + # Cyclic-SWA kernels (trtllm) want the FULL per-window block + # table and the GLOBAL KV length; the kernel masks the window + # internally. No front-eviction slicing, so the + # get_num_front_blocks_removed C++ dispatch is skipped here. + ( + active_indices, + extra_page, + active_token_count, + lpl_i, + ) = _compute_cyclic_full_view( + all_indices, + end_compute_i=end_compute_i, + tokens_per_block=_tokens_per_block, + ) + num_active = len(active_indices) + else: + # SWA front-eviction: get_batch_cache_indices returns the FULL + # historical page list including front-evicted entries (the + # C++ side bumps a counter rather than popping mCacheBlockIds). + # _compute_window_local_view slices it down to the live window + # in window-local coords. + front_removed = kv_cache_manager.get_num_front_blocks_removed( + request.py_request_id, window_size=group_window + ) + ( + active_indices, + extra_page, + active_token_count, + lpl_i, + ) = _compute_window_local_view( + all_indices, + front_removed=front_removed, + end_compute_i=end_compute_i, + group_window=group_window, + tokens_per_block=_tokens_per_block, + ) + num_active = len(active_indices) cache_loc_per_pool[pool_idx].extend(active_indices) cu_num_pages_per_pool[pool_idx].append( cu_num_pages_per_pool[pool_idx][i] + num_active ) extra_page_per_seq_per_pool[pool_idx].append(extra_page) - # Window-local seq_len_with_cache / last_page_len for every - # pool (including 0). For full-attention pools the helper - # returns the unclamped global value (group_window equals - # max_seq_len, no clamping kicks in), so this is identical to - # the legacy single-pool path for non-SWA models. For SWA - # pools (whether pool 0 or pool 1+), it carries the - # window-local coords the kernel needs under front-eviction. + # seq_len_with_cache / last_page_len per pool (including 0). + # Cyclic-SWA (trtllm): the global KV length for every pool. + # Host-sliced (triton/flashinfer): the unclamped global value for + # full-attention pools (window == max_seq_len, no clamping), and + # the window-local coords for SWA pools under front-eviction -- + # identical to the legacy single-pool path for non-SWA models. seq_len_with_cache_per_pool[pool_idx].append(active_token_count) last_page_len_per_pool[pool_idx].append(lpl_i) diff --git a/tensorrt_llm/_torch/auto_deploy/shim/interface.py b/tensorrt_llm/_torch/auto_deploy/shim/interface.py index 5aff117f139c..c204b4b548c6 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/interface.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/interface.py @@ -147,6 +147,12 @@ def __init__( # same order as the C++ manager's internal pool ordering (i.e. the # insertion order of the per-window shape map keys). self._kv_group_windows: List[int] = [] + # Whether the attention backend's kernel applies the sliding-window mask + # itself via cyclic KV indexing (trtllm). When True the executor passes + # the full per-window block table and global KV lengths instead of + # host-slicing to the live window. Set by the kvcache transform from the + # attention descriptor's ``kernel_handles_cyclic_swa()``. + self._kernel_handles_cyclic_swa: bool = False # lookup of unmanaged resources self._unmanaged_resources: List[str] = [] self._spec_config = spec_config @@ -1307,6 +1313,21 @@ def set_kv_groups(self, group_windows: List[int]) -> None: """ self._kv_group_windows = list(group_windows) + @property + def kernel_handles_cyclic_swa(self) -> bool: + """Whether the attention kernel applies the sliding-window mask itself. + + When True (trtllm), the executor passes the full per-window block table + and global KV lengths; when False (triton/flashinfer), it host-slices to + the live sliding window. + """ + return self._kernel_handles_cyclic_swa + + def set_kernel_handles_cyclic_swa(self, value: bool) -> None: + """Record the attention backend's cyclic-SWA capability (called by the + kvcache transform from ``AttentionDescriptor.kernel_handles_cyclic_swa``).""" + self._kernel_handles_cyclic_swa = bool(value) + @property def kv_cache_manager(self) -> Optional[KVCacheManager]: """Return the unified KVCacheManager, or None if not initialized.""" diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py b/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py index e4aadeab3d6e..58a8862cd504 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py @@ -349,6 +349,12 @@ def _apply( skipped=True, num_matches=0, is_clean=True, has_valid_shapes=True ) + # Record whether this backend's kernel applies the sliding-window mask + # itself (cyclic KV indexing, e.g. trtllm). The executor uses this to + # decide between passing the full per-window block table + global KV + # lengths (cyclic) and host-slicing to the live window (triton/flashinfer). + cm.set_kernel_handles_cyclic_swa(attn_descriptor.kernel_handles_cyclic_swa()) + # get standard metadata nodes for all source attention nodes meta_nodes_std = self._process_metadata_std(gm, cm) diff --git a/tests/unittest/auto_deploy/singlegpu/custom_ops/attention/test_trtllm_attention_op.py b/tests/unittest/auto_deploy/singlegpu/custom_ops/attention/test_trtllm_attention_op.py index 353938830fa1..2b1c9934dfef 100644 --- a/tests/unittest/auto_deploy/singlegpu/custom_ops/attention/test_trtllm_attention_op.py +++ b/tests/unittest/auto_deploy/singlegpu/custom_ops/attention/test_trtllm_attention_op.py @@ -881,3 +881,89 @@ def test_metadata_handles_two_sequences_with_different_lengths(self): assert metadata["host_total_kv_lens"][0] == 300 if "context_lengths_gpu" in metadata: assert metadata["context_lengths_gpu"] == [100, 200] + + +# --------------------------------------------------------------------------- +# Multi-pool (VSWA / non-uniform sliding window) block_offsets +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("device", ["cuda"]) +class TestTrtllmMultiPoolBlockOffsets: + """Per-KV-window-group block_offsets buffers must not clobber one another. + + The trtllm planner keeps a separate, address-stable block_offsets buffer + per KV window group (keyed by the group's ``cache_loc`` input ptr), so that + per-group ``prepare_trtllm_metadata`` invocations -- as emitted by the + kvcache transform for non-uniform sliding-window models like gpt-oss -- do + not clobber one another. + """ + + @staticmethod + def _host_prepare(num_seq, max_seq_len, tokens_per_block, max_batch_size, device): + max_blocks_per_seq = math.ceil(max_seq_len / tokens_per_block) + _bi = BatchInfo() + _bi.update([num_seq, num_seq, 0, 0, 0, 0]) # all-prefill, 1 token/seq + _bi.update_max_seq_info(max_seq_len, max_blocks_per_seq, 2, max_batch_size) + batch_info_host = _bi.serialize() + ones = torch.ones(num_seq, dtype=torch.int32) + zeros = torch.zeros(num_seq, dtype=torch.int32) + prepare_trtllm_metadata_host( + batch_info_host, + ones.clone().pin_memory(), # seq_len_with_cache_host + zeros.clone().pin_memory(), # input_pos_host + ones.clone().pin_memory(), # seq_len_host + ones.clone().pin_memory(), # prompt_lens_host + ones.clone().to(device), # prompt_lens + ) + return batch_info_host + + def test_per_group_buffers_are_distinct_and_not_clobbered(self, device): + _reset_trtllm_planner() + batch_info_host = self._host_prepare( + num_seq=2, max_seq_len=2048, tokens_per_block=32, max_batch_size=4, device=device + ) + + # Two groups, each with its own (distinct-ptr) cache_loc / cu_num_pages. + cache_loc_a = torch.tensor([10, 11, 12, 13], dtype=torch.int32, device=device) + cache_loc_b = torch.tensor([20, 21, 22, 23], dtype=torch.int32, device=device) + cu_num_pages = torch.tensor([0, 2, 4], dtype=torch.int32, device=device) + assert cache_loc_a.data_ptr() != cache_loc_b.data_ptr() + + (buf_a,) = torch.ops.auto_deploy.trtllm_attention_prepare_metadata( + batch_info_host, cu_num_pages, cache_loc_a + ) + # group 0 reuses the pre-allocated reset() buffer + assert buf_a.data_ptr() == _GlobalTrtllmPlanner.block_offsets.data_ptr() + a_after_a = buf_a[0, :2, 0, :].clone() + + (buf_b,) = torch.ops.auto_deploy.trtllm_attention_prepare_metadata( + batch_info_host, cu_num_pages, cache_loc_b + ) + # second group gets its own, distinct buffer + assert buf_b.data_ptr() != buf_a.data_ptr() + assert len(_GlobalTrtllmPlanner._block_offsets_by_cache_loc) == 2 + + # group 0's buffer must be untouched by group 1's prepare + torch.testing.assert_close(buf_a[0, :2, 0, :], a_after_a) + # block_offsets reflect each group's own cache_loc (× multiplier 2) + assert buf_a[0, 0, 0, 0].item() == 10 * 2 + assert buf_b[0, 0, 0, 0].item() == 20 * 2 + + def test_same_cache_loc_returns_stable_buffer(self, device): + _reset_trtllm_planner() + batch_info_host = self._host_prepare( + num_seq=1, max_seq_len=1024, tokens_per_block=32, max_batch_size=2, device=device + ) + cache_loc = torch.tensor([5, 6], dtype=torch.int32, device=device) + cu_num_pages = torch.tensor([0, 2], dtype=torch.int32, device=device) + + (buf1,) = torch.ops.auto_deploy.trtllm_attention_prepare_metadata( + batch_info_host, cu_num_pages, cache_loc + ) + (buf2,) = torch.ops.auto_deploy.trtllm_attention_prepare_metadata( + batch_info_host, cu_num_pages, cache_loc + ) + # Same cache_loc ptr -> identical (address-stable) buffer across replays. + assert buf1.data_ptr() == buf2.data_ptr() + assert len(_GlobalTrtllmPlanner._block_offsets_by_cache_loc) == 1 diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_ad_executor_swa_eviction.py b/tests/unittest/auto_deploy/singlegpu/shim/test_ad_executor_swa_eviction.py index 95c873f8a160..210b12d4fa36 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_ad_executor_swa_eviction.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_ad_executor_swa_eviction.py @@ -26,7 +26,10 @@ from tensorrt_llm._torch.auto_deploy._compat import KvCacheConfig from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import KVPagedResourceHandler -from tensorrt_llm._torch.auto_deploy.shim.ad_executor import _compute_window_local_view +from tensorrt_llm._torch.auto_deploy.shim.ad_executor import ( + _compute_cyclic_full_view, + _compute_window_local_view, +) from tensorrt_llm._torch.auto_deploy.shim.interface import CachedSequenceInterface pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @@ -312,5 +315,122 @@ def test_helper_does_not_consume_evicted_extra_slot(two_window_interface, monkey assert extra_page == -1 +# --------------------------------------------------------------------------- +# Multi-pool gate: trtllm (and any backend) may host >1 KV pool +# --------------------------------------------------------------------------- + + +def _build_two_pool_interface(requires_uniform_kv_caches: bool): + interface = CachedSequenceInterface( + max_seq_len=FULL_WINDOW, + max_batch_size=2, + max_num_tokens=default_max_num_tokens(FULL_WINDOW, 2), + device="cuda", + kv_cache_config=KvCacheConfig( + tokens_per_block=TOKENS_PER_BLOCK, + max_tokens=1024, + free_gpu_memory_fraction=0.0, + ), + requires_uniform_kv_caches=requires_uniform_kv_caches, + ) + interface.add_resource( + "kv_swa", KVPagedResourceHandler(4, 32, dtype=torch.float16, sliding_window=SWA_WINDOW) + ) + interface.add_resource("kv_full", KVPagedResourceHandler(4, 32, dtype=torch.float16)) + return interface + + +def test_two_distinct_windows_allowed_by_default(): + """Default (requires_uniform_kv_caches=False, the trtllm setting) hosts two pools.""" + interface = _build_two_pool_interface(requires_uniform_kv_caches=False) + interface.initialize_resources() # must not raise + # SWA pool + full-attention pool == two distinct windows. + windows = sorted( + {SWA_WINDOW, FULL_WINDOW} + & {pc.window_size for pc in interface._identify_managed_kv_resources()[1]} + ) + assert windows == [SWA_WINDOW, FULL_WINDOW] + + +def test_uniform_kv_caches_still_enforced_when_requested(): + """The uniformity mechanism is intact: opting in still rejects >1 pool. + + (No backend opts in today; trtllm now defaults to False -- this guards the + mechanism so a future single-pool backend can still rely on it.) + """ + interface = _build_two_pool_interface(requires_uniform_kv_caches=True) + with pytest.raises(RuntimeError, match="not uniform"): + interface.initialize_resources() + + +# --------------------------------------------------------------------------- +# Cyclic-SWA view (trtllm): full block table + global KV length, no slicing +# --------------------------------------------------------------------------- + + +def test_cyclic_view_passes_full_table_and_global_length(two_window_interface): + """Trtllm path: hand the kernel the FULL block table and the GLOBAL length. + + The trtllm kernel masks the sliding window internally via cyclic indexing, + so -- unlike the host-sliced triton/flashinfer path -- the executor must NOT + front-slice and must report the un-window-capped KV length. + """ + manager = two_window_interface.kv_cache_manager + # A prefill that exceeds the SWA window so window-local slicing WOULD differ. + prefill_len = SWA_WINDOW * 3 # 192 tokens + req = _add_request(manager, request_id=50, token_num=prefill_len) + all_indices = manager.get_cache_indices(req, window_size=SWA_WINDOW) + + active_indices, extra_page, swc, lpl = _compute_cyclic_full_view( + all_indices, + end_compute_i=prefill_len, + tokens_per_block=TOKENS_PER_BLOCK, + ) + + # Full table verbatim (no front-slice, no window cap). + assert active_indices == list(all_indices) + # Global (un-capped) KV length -- matches host_past_key_value_lengths. + assert swc == prefill_len + assert lpl == (prefill_len - 1) % TOKENS_PER_BLOCK + 1 + # No deferred-page insertion in cyclic mode. + assert extra_page == -1 + + +def test_cyclic_view_differs_from_window_local_when_evicted(two_window_interface, monkeypatch): + """Cyclic view ignores front-eviction; window-local view slices it off. + + Guards that the two staging paths genuinely diverge once the window has + been exceeded (so a backend mix-up would be caught). + """ + manager = two_window_interface.kv_cache_manager + front_removed = 2 + total_tokens = front_removed * TOKENS_PER_BLOCK + SWA_WINDOW + 1 + req = _add_request(manager, request_id=51, token_num=total_tokens) + monkeypatch.setattr( + manager, "get_num_front_blocks_removed", lambda req_id, window_size=None: front_removed + ) + all_indices = manager.get_cache_indices(req, window_size=SWA_WINDOW) + + cyc_indices, _, cyc_swc, _ = _compute_cyclic_full_view( + all_indices, end_compute_i=total_tokens, tokens_per_block=TOKENS_PER_BLOCK + ) + win_indices, _, win_swc, _ = _compute_window_local_view( + all_indices, + front_removed=front_removed, + end_compute_i=total_tokens, + group_window=SWA_WINDOW, + tokens_per_block=TOKENS_PER_BLOCK, + ) + + # Cyclic keeps the full list + global length; window-local slices + caps. + assert cyc_indices == list(all_indices) + assert cyc_swc == total_tokens + # Window-local view drops the stale front pages and starts at front_removed. + assert win_indices == list(all_indices[front_removed : front_removed + len(win_indices)]) + assert len(win_indices) < len(cyc_indices) + assert win_swc == total_tokens - front_removed * TOKENS_PER_BLOCK + assert cyc_swc != win_swc + + if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py index 73f73b48621e..a6f37875223f 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py @@ -54,8 +54,13 @@ def test_custom_values(): def test_requires_uniform_kv_caches_follows_attention_backend(): - """TRTLLM requires stricter KV cache compatibility than FlashInfer.""" - assert LlmArgs(model="test-model", attn_backend="TRTLLM").requires_uniform_kv_caches is True + """No attention backend currently requires uniform KV caches. + + The trtllm backend used to force a single KV pool, but it now supports + multiple KV cache memory pools for non-uniform sliding-window models, so the + flag defaults to False for all backends. + """ + assert LlmArgs(model="test-model", attn_backend="TRTLLM").requires_uniform_kv_caches is False assert ( LlmArgs(model="test-model", attn_backend="flashinfer").requires_uniform_kv_caches is False ) diff --git a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache_trtllm_multipool.py b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache_trtllm_multipool.py new file mode 100644 index 000000000000..9dad926f2510 --- /dev/null +++ b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache_trtllm_multipool.py @@ -0,0 +1,244 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Forward-level test for the trtllm attention backend with MULTIPLE KV pools. + +Builds a tiny two-layer model whose layers use different attention windows +(layer 0 = sliding window, layer 1 = full attention), so the AutoDeploy +kvcache transform creates two KV cache memory pools. Runs a prefill that +exceeds the sliding window through the cached ``trtllm`` attention op and +checks it matches the eager (uncached) reference. + +This is the on-GPU forward validation for issue #14828: it exercises the +unblocked multi-pool gate, the per-group block_offsets buffers, the +cyclic-SWA metadata staging (full block table + global KV length), and that +each pool's kernel receives its own attention window. +""" + +import pytest +import torch +import torch.nn as nn +from _torch_test_utils import all_close, trtllm_ops_available + +import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 +from tensorrt_llm._torch.auto_deploy._compat import KvCacheConfig +from tensorrt_llm._torch.auto_deploy.models.factory import FullModelExportInfo, ModelFactory +from tensorrt_llm._torch.auto_deploy.shim.interface import CachedSequenceInterface +from tensorrt_llm._torch.auto_deploy.transform.optimizer import InferenceOptimizer + +pytestmark = pytest.mark.skipif( + not torch.cuda.is_available() or not trtllm_ops_available(), + reason="Requires CUDA and TRT-LLM attention backend", +) + + +class _DummyFactory(ModelFactory): + def __init__(self, model): + self._model = model + + def build_model(self, device: str): + return self._model.to(device=device) + + def _build_model(self, device: str): + return + + def _load_checkpoint(self, model, device): + return + + def get_cache_config_updates(self): + return {} + + def get_export_infos(self, model): + return [FullModelExportInfo()] + + @property + def max_seq_len(self) -> int: + return 512 + + +class _WindowedAttnLayer(nn.Module): + def __init__(self, hidden: int, n_heads: int, sliding_window, layer_idx: int): + super().__init__() + self.n_heads = n_heads + self.head_dim = hidden // n_heads + self.sliding_window = sliding_window + self.layer_idx = layer_idx + self.q_proj = nn.Linear(hidden, hidden, bias=False) + self.k_proj = nn.Linear(hidden, hidden, bias=False) + self.v_proj = nn.Linear(hidden, hidden, bias=False) + self.o_proj = nn.Linear(hidden, hidden, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + b, s, _ = x.shape + q = self.q_proj(x).view(b, s, self.n_heads, self.head_dim) + k = self.k_proj(x).view(b, s, self.n_heads, self.head_dim) + v = self.v_proj(x).view(b, s, self.n_heads, self.head_dim) + o = torch.ops.auto_deploy.torch_attention( + q, + k, + v, + None, # attn_mask + 0.0, # dropout_p + True, # is_causal + None, # scale + None, # sinks + self.sliding_window, # sliding_window (int for SWA layer, None for full) + None, # logit_cap + "bsnd", # layout + self.layer_idx, # layer_idx + ) + return x + self.o_proj(o.reshape(b, s, -1)) + + +class _TwoWindowModel(nn.Module): + """Layer 0 = sliding window, layer 1 = full attention -> two KV pools.""" + + def __init__(self, vocab: int, hidden: int, n_heads: int, sliding_window: int): + super().__init__() + self.embed_tokens = nn.Embedding(vocab, hidden) + self.layer0 = _WindowedAttnLayer(hidden, n_heads, sliding_window, layer_idx=0) + self.layer1 = _WindowedAttnLayer(hidden, n_heads, None, layer_idx=1) + + @torch.no_grad() + def forward(self, input_ids: torch.Tensor, position_ids=None) -> torch.Tensor: + x = self.embed_tokens(input_ids) + x = self.layer0(x) + x = self.layer1(x) + return x + + +def _build_and_stage(sliding_window, seq_len, dtype=torch.float16): + """Build a 2-window model and run a single cyclic-staged prefill. + + Inserts trtllm cached attention (2 pools) and stages the prefill the way + ad_executor does for the cyclic (trtllm) path. Returns (eager_ref, cached_out). + """ + vocab, hidden, n_heads = 1000, 128, 2 + batch_size = 2 + tokens_per_block = 128 # >= max_seq_len -> 1 page per sequence per pool + max_seq_len = 128 + + kv_cache_config = KvCacheConfig( + tokens_per_block=tokens_per_block, + max_tokens=batch_size * tokens_per_block, + free_gpu_memory_fraction=0.0, + ) + cm = CachedSequenceInterface( + max_seq_len=max_seq_len, + max_batch_size=batch_size, + max_num_tokens=batch_size * max_seq_len, + device="cuda", + kv_cache_config=kv_cache_config, + ) + + model = _TwoWindowModel(vocab, hidden, n_heads, sliding_window).to(dtype=dtype, device="cuda") + input_ids = torch.randint(0, vocab, (batch_size, seq_len), device="cuda") + position_ids = torch.arange(seq_len, device="cuda").unsqueeze(0).repeat(batch_size, 1) + + y_ref = model(input_ids, position_ids) # eager reference (per-layer SWA masking) + + optimizer = InferenceOptimizer( + _DummyFactory(model), + { + "build_model": { + "stage": "factory", + "run_per_gm": False, + "device": "cuda", + "run_graph_cleanup": False, + "requires_clean_graph": False, + }, + "export_to_gm": { + "stage": "export", + "strict": False, + "run_per_gm": False, + "clone_state_dict": True, + "run_graph_cleanup": False, + "requires_clean_graph": False, + }, + "cleanup_input_constraints": {"stage": "post_export"}, + "insert_cached_attention": {"stage": "cache_init", "backend": "trtllm"}, + }, + ) + gm = optimizer(cm) + gm.to("cuda") + cm.initialize_resources() + + # Two distinct windows -> two pools, and trtllm uses the cyclic-SWA path. + assert len(cm.kv_group_windows) == 2, cm.kv_group_windows + assert cm.kernel_handles_cyclic_swa is True + + # Stage prefill metadata the way ad_executor does for the cyclic (trtllm) + # path: full per-window block table (1 page/seq here) + GLOBAL kv length. + n_pools = len(cm.kv_group_windows) + cache_loc_per_pool = [list(range(batch_size)) for _ in range(n_pools)] + cu_num_pages_per_pool = [list(range(batch_size + 1)) for _ in range(n_pools)] + seq_len_with_cache_per_pool = [[seq_len] * batch_size for _ in range(n_pools)] + last_page_len_per_pool = [ + [seq_len % tokens_per_block or tokens_per_block] * batch_size for _ in range(n_pools) + ] + extra_page_per_seq_per_pool = [[-1] * batch_size for _ in range(n_pools)] + + cm.info.reset() + cm.info.nest_sequences( + input_ids.flatten().tolist(), + cu_seqlen=list(range(0, batch_size * seq_len + 1, seq_len)), + input_pos=[0] * batch_size, + batch_info=[batch_size, batch_size * seq_len, 0, 0, 0, 0], + cache_loc_per_pool=cache_loc_per_pool, + cu_num_pages_per_pool=cu_num_pages_per_pool, + extra_page_per_seq_per_pool=extra_page_per_seq_per_pool, + seq_len_with_cache_per_pool=seq_len_with_cache_per_pool, + last_page_len_per_pool=last_page_len_per_pool, + slot_idx=list(range(batch_size)), + prompt_lens=[seq_len] * batch_size, + gather_context_logits=True, + ) + y_cached = torch.stack(cm.info.unnest_sequences(gm(**cm.named_args))) + return y_ref, y_cached + + +@torch.inference_mode() +def test_trtllm_two_pools_no_mask_matches_eager(): + """Two DISTINCT KV pools with no masking (both windows >= seq_len). + + Both layers do full causal attention. + Strict match against the eager reference validates the multi-pool feature: + two pools are created, each layer reads its OWN pool's block_offsets buffer + (no clobbering), and the cyclic full-table staging is correct. + """ + y_ref, y_cached = _build_and_stage(sliding_window=64, seq_len=48) + assert all_close(y_ref, y_cached, atol=2e-2, rtol=2e-2) + + +@torch.inference_mode() +def test_trtllm_two_pools_swa_engaged_runs(): + """Two pools with the SWA window strictly below the sequence length. + + Exercises the cyclic-SWA staging with a real sub-sequence window through + both pools. We assert it runs and produces finite, correctly-shaped output + rather than exact-matching the eager reference: the prefill sliding-window + mask is applied by the trtllm kernel, and on SMs where the trtllm-gen FMHA + is unavailable for the layer's shape the op falls back to an unfused MHA + that does not apply the context-phase window. Exact SWA-prefill correctness + on the supported kernel is covered by the PyTorch-backend contract (causal + mask + attention_window_size) this op mirrors. + """ + y_ref, y_cached = _build_and_stage(sliding_window=32, seq_len=96) + assert y_cached.shape == y_ref.shape + assert torch.isfinite(y_cached).all() + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kvcache_vswa_metadata.py b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kvcache_vswa_metadata.py index 2c98ee893551..4c39289745e1 100644 --- a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kvcache_vswa_metadata.py +++ b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kvcache_vswa_metadata.py @@ -17,6 +17,7 @@ import pytest import torch +from _torch_test_utils import trtllm_ops_available import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 from tensorrt_llm._torch.auto_deploy.export import torch_export_to_gm @@ -209,5 +210,24 @@ def find_swc_dep(node, visited=None): ) +@pytest.mark.parametrize( + "backend, expected_cyclic", + [("triton", False), ("trtllm", True)], +) +def test_vswa_sets_kernel_handles_cyclic_swa(backend, expected_cyclic): + """The transform records the backend's cyclic-SWA capability on the interface. + + trtllm's kernel masks the sliding window internally (cyclic), so the + executor must pass full block tables + global lengths; triton must not. + """ + if backend == "trtllm" and not trtllm_ops_available(): + pytest.skip("trtllm attention backend requires TRT-LLM ops (unavailable in standalone)") + gm, info, cm = _run_transform(backend=backend) + assert info.num_matches == 2 + assert cm.kernel_handles_cyclic_swa is expected_cyclic + # Both backends still register two window groups regardless of cyclic-ness. + assert len(cm.kv_group_windows) == 2 + + if __name__ == "__main__": pytest.main([__file__, "-v"]) From 15d06c0923b63ac1781784d5f59e1747bb47d5f1 Mon Sep 17 00:00:00 2001 From: Guoming Zhang <137257613+nv-guomingz@users.noreply.github.com> Date: Tue, 9 Jun 2026 00:16:39 +0800 Subject: [PATCH 049/119] [None][doc] Refine Nemotron Ultra doc (#15113) Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- docs/source/models/supported-models.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index fbcd3b96cc92..73e075d949ac 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -69,8 +69,7 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl | `GptOssForCausalLM` | Yes | Yes | Yes | Yes | Yes | No | Yes | No | Yes | Yes | Yes | Yes | N/A | Yes | Yes | | `Qwen3_5MoeForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Yes | N/A | Untested | Untested | | `Glm4MoeLiteForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Untested | N/A | Untested | Untested | -| `NemotronHForCausalLM` (Super) | Yes | Yes | Untested | Untested | Yes | Yes | No | No | No | Yes | Yes | Untested | N/A | Untested | Untested | -| `NemotronHForCausalLM` (Ultra) | Yes | Yes | Untested | Yes | Yes | Yes | No | No | No | Yes | Yes | Yes | N/A | Untested | Untested | +| `NemotronHForCausalLM` | Yes | Yes | Yes | Yes | Yes | Yes | No | No | No | Yes | Yes | Yes | N/A | Untested | Untested | | `Gemma4ForConditionalGeneration` | Untested | Yes | Untested | No | Yes | No | No | No | No | Yes | Untested | No | Yes | Untested | Untested | | `Step3p7ForConditionalGeneration`| Yes | Yes | Yes | Untested | Untested | Yes | No | No | No | Yes | Untested | Untested | Yes | Untested | Untested | From 8036cde5f3b4bf3975e920dfe8d10e69bd8bb334 Mon Sep 17 00:00:00 2001 From: Abby Wei <18545893+mzweilz@users.noreply.github.com> Date: Tue, 9 Jun 2026 01:02:31 +0800 Subject: [PATCH 050/119] [None][infra] Waive TestQwen3NextInstruct nvfp4 cases (#15086) Signed-off-by: Abby Wei <18545893+mzweilz@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 3e52a1e4b754..bb3fc542a0ed 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -135,7 +135,9 @@ accuracy/test_llm_api_pytorch.py::TestNemotronV3Super::test_nvfp4_8gpus_mtp SKIP accuracy/test_llm_api_pytorch.py::TestPhi4MiniInstruct::test_auto_dtype SKIP (https://nvbugs/6076767) accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_bf16_4gpu[tp4ep4_cudagraph_overlap_adp_off] SKIP (https://nvbugs/6255417) accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_bf16_4gpu[tp4ep4_cudagraph_overlap_adp_on] SKIP (https://nvbugs/6094068) +accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[no_cuda_graph_overlap-cutlass] SKIP (https://nvbugs/6281014) accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp1-cutlass] SKIP (https://nvbugs/6116088) +accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp4ep1-cutlass] SKIP (https://nvbugs/6281014) accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp4ep4_adp_on-trtllm] SKIP (https://nvbugs/6094068) accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_fp8[latency] SKIP (https://nvbugs/6177390) accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_fp8[throughput_latency] SKIP (https://nvbugs/6177390) From 19983240828b019852b260fbe3b37542bf239fb4 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 01:36:29 +0800 Subject: [PATCH 051/119] [https://nvbugs/6248757][fix] Avoid running all reduce in aux stream (#14917) Signed-off-by: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> --- .../transform/library/multi_stream_moe.py | 102 +++- .../_torch/auto_deploy/utils/node_utils.py | 31 +- tests/integration/test_lists/waives.txt | 1 - ...est_multi_stream_moe_trailing_allreduce.py | 541 ++++++++++++++++++ .../custom_ops/test_multi_stream_moe.py | 403 +++++++++++++ 5 files changed, 1049 insertions(+), 29 deletions(-) create mode 100644 tests/unittest/auto_deploy/multigpu/custom_ops/test_multi_stream_moe_trailing_allreduce.py diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_moe.py b/tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_moe.py index dc71bf1ff7a6..33ba897f916e 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_moe.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_moe.py @@ -29,7 +29,7 @@ end_aux_stream_passthrough, wait_aux_stream_passthrough, ) -from ...utils.node_utils import has_shape, is_op +from ...utils.node_utils import all_reduce_ops, has_shape, is_op from ..interface import BaseTransform, SharedConfig, TransformInfo, TransformRegistry @@ -194,7 +194,32 @@ def _execute_shared_expert_in_aux_stream( # Order shared nodes by their position in the graph. shared_nodes.sort(key=lambda n: node_order.get(n, 0)) - first_shared = shared_nodes[0] + + # Collectives (all-reduce) in the shared-expert branch must stay on the + # MAIN stream. A collective synchronizes across ranks, and that + # rendezvous does not compose with per-rank aux-stream overlap: when the + # shared-expert all-reduce is captured on the aux stream while the + # routed-expert all-reduce runs on the main stream, the two symm-mem + # MULTIMEM collectives (world_size >= 6 on SM100) interleave across ranks + # under monolithic CUDA-graph replay and silently corrupt the output. + # We therefore overlap only the shared-expert GEMMs on the aux stream and + # run the trailing all-reduce on the main stream. + ar_ops = all_reduce_ops() + collective_node = shared_output if is_op(shared_output, ar_ops) else None + aux_region = [n for n in shared_nodes if n is not collective_node] + + # The aux-stream region must contain compute and must not itself contain + # a collective (only a trailing shared-output collective can be split + # off safely). + if not aux_region or any(is_op(n, ar_ops) for n in aux_region): + ad_logger.warning( + f"Shared-expert branch of MoE node {moe_node.name} has no aux-stream " + "compute outside of a collective; skipping multi-stream transform for " + "this node." + ) + continue + + first_shared = aux_region[0] # Sanity check: the first shared op must directly consume the fork # point so we can wire begin_aux_stream_passthrough into it. @@ -222,28 +247,67 @@ def _execute_shared_expert_in_aux_stream( begin_aux_node if arg is fork_point else arg for arg in first_shared.args ) - # ---- Step 5: Insert end_aux after the last shared-expert op. ---- - with graph.inserting_after(shared_output): - end_aux_node = graph.call_function( - end_aux_stream_passthrough, - args=(shared_output,), + if collective_node is None: + # ---- Step 5: Insert end_aux after the last shared-expert op. ---- + with graph.inserting_after(shared_output): + end_aux_node = graph.call_function( + end_aux_stream_passthrough, + args=(shared_output,), + ) + + # Replace shared-expert input to the merge node with end_aux output. + merge_node.args = tuple( + end_aux_node if arg is shared_output else arg for arg in merge_node.args ) - # Replace shared-expert input to the merge node with end_aux output. - merge_node.args = tuple( - end_aux_node if arg is shared_output else arg for arg in merge_node.args - ) + # ---- Step 6: Insert wait_aux before the merge node. ---- + with graph.inserting_before(merge_node): + wait_aux_node = graph.call_function( + wait_aux_stream_passthrough, + args=(routed_output,), + ) - # ---- Step 6: Insert wait_aux before the merge node. ---- - with graph.inserting_before(merge_node): - wait_aux_node = graph.call_function( - wait_aux_stream_passthrough, - args=(routed_output,), + merge_node.args = tuple( + wait_aux_node if arg is routed_output else arg for arg in merge_node.args + ) + else: + # The trailing all-reduce stays on the main stream. End the aux + # region after the last aux-stream compute op (e.g. the rowwise + # down-projection) and make the main stream wait for it before the + # collective consumes the result. + aux_boundary = max( + (a for a in collective_node.all_input_nodes if a in aux_region), + key=lambda n: node_order.get(n, 0), + default=None, ) + if aux_boundary is None: + ad_logger.warning( + f"Could not find aux-stream input to the shared-expert collective " + f"for MoE node {moe_node.name}; skipping multi-stream transform." + ) + continue - merge_node.args = tuple( - wait_aux_node if arg is routed_output else arg for arg in merge_node.args - ) + # ---- Step 5: end_aux after the last aux-stream op, switching the + # current stream back to main before the collective. ---- + with graph.inserting_after(aux_boundary): + end_aux_node = graph.call_function( + end_aux_stream_passthrough, + args=(aux_boundary,), + ) + + # ---- Step 6: wait_aux so the main stream waits for the aux compute + # before running the collective on the main stream. ---- + with graph.inserting_after(end_aux_node): + wait_aux_node = graph.call_function( + wait_aux_stream_passthrough, + args=(end_aux_node,), + ) + + # The collective now consumes the synced aux output and runs on the + # main stream; the merge node continues to consume the collective. + collective_node.args = tuple( + wait_aux_node if arg is aux_boundary else arg for arg in collective_node.args + ) num_replaced += 1 diff --git a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py index f80d55f18870..17b4012a83e4 100644 --- a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py +++ b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py @@ -808,23 +808,36 @@ def all_gather_ops() -> frozenset: Strategy (AUTO/SYMM_MEM) and workspace_id (for symm-mem ProcessGroup selection) flow through as op arguments, not as separate op identities. + + The TRT-LLM-backed ops are silently skipped if their custom_ops module + failed to register (e.g. in the standalone ``llmc`` package, where + ``trtllm_dist`` is not importable). """ return frozenset( - { - torch.ops.auto_deploy.trtllm_dist_all_gather, - torch.ops.auto_deploy.torch_dist_all_gather, - } + op + for op in ( + _auto_deploy_op("trtllm_dist_all_gather"), + _auto_deploy_op("torch_dist_all_gather"), + ) + if op is not None ) @functools.cache def all_reduce_ops() -> frozenset: - """All AllReduce custom op packets recognized by AutoDeploy.""" + """All AllReduce custom op packets recognized by AutoDeploy. + + The TRT-LLM-backed op is silently skipped if its custom_ops module + failed to register (e.g. in the standalone ``llmc`` package, where + ``trtllm_dist`` is not importable). + """ return frozenset( - { - torch.ops.auto_deploy.trtllm_dist_all_reduce, - torch.ops.auto_deploy.torch_dist_all_reduce, - } + op + for op in ( + _auto_deploy_op("trtllm_dist_all_reduce"), + _auto_deploy_op("torch_dist_all_reduce"), + ) + if op is not None ) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index bb3fc542a0ed..fca32c8a951e 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -16,7 +16,6 @@ accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (http accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) -accuracy/test_llm_api_autodeploy.py::TestNemotronUltraV3::test_accuracy[nvfp4-8] SKIP (https://nvbugs/6248757) accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6278380) diff --git a/tests/unittest/auto_deploy/multigpu/custom_ops/test_multi_stream_moe_trailing_allreduce.py b/tests/unittest/auto_deploy/multigpu/custom_ops/test_multi_stream_moe_trailing_allreduce.py new file mode 100644 index 000000000000..139d76cd5a97 --- /dev/null +++ b/tests/unittest/auto_deploy/multigpu/custom_ops/test_multi_stream_moe_trailing_allreduce.py @@ -0,0 +1,541 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Multi-GPU regression tests for nvbugs/6248757. + +Background +---------- +``fuse_rmsnorm_quant_nvfp4`` restructures the shared-expert branch of a +TP-sharded MoE layer so that a standalone ``trtllm_dist_all_reduce`` directly +feeds the merge node:: + + hidden ─┬─ gate ─ topk ─── moe_fused ─── moe_out ───────────────────┐ + └─ up_proj ─ relu² ─ down_proj ─ all_reduce ─ shared_out ─┴─ add + +Before PR #14917, ``_execute_shared_expert_in_aux_stream`` placed ``end_aux`` +*after* the all_reduce, putting the collective on the aux stream. With +AllReduceStrategy.SYMM_MEM (used in Nemotron Ultra V3 production config) two +concurrent SYMM_MEM ops on different streams interleave across ranks under +monolithic CUDA-graph replay and silently corrupt the output. + +Three test scenarios +-------------------- +1. ``test_structural_multigpu`` — graph-level check: after the transform the + all_reduce must appear *after* ``end_aux``. FAILS pre-PR, PASSES with fix. + +2. ``test_correctness_nccl_cuda_graph`` — NCCL correctness under CUDA graph. + NCCL serialises submissions CPU-side so the race does not manifest; serves + as a regression guard. + +3. ``test_corruption_symm_mem_cuda_graph`` — explicitly builds the *buggy* + graph, forces asymmetric submission ordering across ranks, and verifies the + buggy graph produces wrong output while the fixed graph is correct. + Skipped when SYMM_MEM is unavailable. +""" + +import traceback + +import pytest +import torch +from torch.distributed import DistNetworkError + +# MPI pool leaks a thread on shutdown — suppress the threadleak warning. +pytestmark = pytest.mark.threadleak(enabled=False) + + +# --------------------------------------------------------------------------- +# Worker helpers (everything torch.ops-related is inside workers to avoid +# cloudpickle issues when serialising across MPI) +# --------------------------------------------------------------------------- + + +def _init_dist(port): + import torch.distributed as dist + + import tensorrt_llm + import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 — registers custom ops + from tensorrt_llm._torch.auto_deploy.distributed.common import initialize_or_skip + from tensorrt_llm._utils import get_free_port, mpi_broadcast + + rank = tensorrt_llm.mpi_rank() + torch.cuda.set_device(rank) + # Rank 0 picks a free port and broadcasts it so all workers use the same one. + if port is None: + port = mpi_broadcast(get_free_port() if rank == 0 else None) + initialize_or_skip(port=port) + return rank, dist.get_world_size() + + +def _cleanup(): + import torch.distributed as dist + + from tensorrt_llm._torch.auto_deploy.distributed.common import cleanup + + if dist.is_initialized() and dist.get_world_size() > 1: + dist.barrier() + cleanup() + + +def _make_model_and_example(hidden_dim, inter_dim, strategy, device="cuda"): + """Build the mock MoE layer with trailing all_reduce and an example input. + + Defined inside worker functions to avoid cloudpickle capturing torch.ops + at module level. + """ + import torch.nn as nn + + # Register mock MoE op if not already registered. + op_name = "auto_deploy::mock_moe_trailing_ar" + if not hasattr(torch.ops.auto_deploy, "mock_moe_trailing_ar"): + + @torch.library.custom_op(op_name, mutates_args=()) + def _mock_moe( + x: torch.Tensor, sel: torch.Tensor, w: torch.Tensor, ew: torch.Tensor + ) -> torch.Tensor: + return torch.ops.aten.linear(x, ew) + + @_mock_moe.register_fake + def _mock_moe_fake(x, sel, w, ew): + return torch.ops.aten.linear(x, ew) + + moe_op = torch.ops.auto_deploy.mock_moe_trailing_ar + ar_op = torch.ops.auto_deploy.trtllm_dist_all_reduce + + class _Layer(nn.Module): + def __init__(self): + super().__init__() + self.strategy = strategy + self.gate = nn.Linear(hidden_dim, 8, bias=False) + self.up = nn.Linear(hidden_dim, inter_dim, bias=False) + self.down = nn.Linear(inter_dim, hidden_dim, bias=False) + self.expert_w = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) + self.ln = nn.LayerNorm(hidden_dim) + + def forward(self, x): + logits = self.gate(x) + rw, sel = torch.topk(logits, k=2, dim=-1) + shared = self.down(torch.relu(self.up(x)) ** 2) + shared_out = ar_op(shared, self.strategy) + moe_out = moe_op(x, sel, rw, self.expert_w) + return self.ln(shared_out + moe_out) + + model = _Layer().eval().to(device) + example = torch.randn(4, hidden_dim, device=device) + moe_ops = [moe_op] + return model, example, moe_ops + + +def _build_gm(model, example): + return torch.export.export(model, (example,)).module() + + +# --------------------------------------------------------------------------- +# Worker 1 — structural check +# --------------------------------------------------------------------------- + + +def _worker_structural(world_size, port): + import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 + from tensorrt_llm._torch.auto_deploy.transform.library.multi_stream_moe import ( + _execute_shared_expert_in_aux_stream, + ) + from tensorrt_llm._torch.auto_deploy.utils.multi_stream_utils import ( + cuda_stream_manager, + end_aux_stream_passthrough, + wait_aux_stream_passthrough, + ) + from tensorrt_llm._torch.auto_deploy.utils.node_utils import all_reduce_ops, is_op + + rank, _ = _init_dist(port) + try: + cuda_stream_manager.add_device(rank) + model, example, moe_ops = _make_model_and_example(128, 256, "NCCL") + gm = _build_gm(model, example) + gm, num = _execute_shared_expert_in_aux_stream(gm, moe_ops) + + assert num == 1, f"[rank {rank}] Expected 1 replacement, got {num}" + + node_order = {n: i for i, n in enumerate(gm.graph.nodes)} + ar_ops = all_reduce_ops() + ar_node = next((n for n in gm.graph.nodes if is_op(n, ar_ops)), None) + end_aux_nodes = [ + n + for n in gm.graph.nodes + if n.op == "call_function" and n.target is end_aux_stream_passthrough + ] + wait_aux_nodes = [ + n + for n in gm.graph.nodes + if n.op == "call_function" and n.target is wait_aux_stream_passthrough + ] + + assert ar_node is not None, f"[rank {rank}] No all_reduce node" + assert end_aux_nodes, f"[rank {rank}] No end_aux node" + assert wait_aux_nodes, f"[rank {rank}] No wait_aux node" + + end_aux = end_aux_nodes[0] + wait_aux = wait_aux_nodes[0] + + # Core invariant: collective must come AFTER the stream switch back to main. + assert node_order[ar_node] > node_order[end_aux], ( + f"[rank {rank}] BUG: all_reduce before end_aux — on aux stream" + ) + assert node_order[ar_node] > node_order[wait_aux], ( + f"[rank {rank}] BUG: all_reduce before wait_aux" + ) + assert end_aux.args[0] is not ar_node, ( + f"[rank {rank}] BUG: end_aux wraps the all_reduce directly" + ) + assert wait_aux in ar_node.all_input_nodes, ( + f"[rank {rank}] wait_aux must feed the all_reduce" + ) + return True + except Exception: + traceback.print_exc() + raise + finally: + _cleanup() + + +# --------------------------------------------------------------------------- +# Worker 2 — NCCL correctness under CUDA graph +# --------------------------------------------------------------------------- + + +def _worker_nccl_cuda_graph(world_size, port): + import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 + from tensorrt_llm._torch.auto_deploy.transform.library.multi_stream_moe import ( + _execute_shared_expert_in_aux_stream, + ) + from tensorrt_llm._torch.auto_deploy.utils.multi_stream_utils import cuda_stream_manager + + rank, _ = _init_dist(port) + try: + cuda_stream_manager.add_device(rank) + torch.manual_seed(42 + rank) + model, example, moe_ops = _make_model_and_example(128, 256, "NCCL") + gm = _build_gm(model, example) + gm, num = _execute_shared_expert_in_aux_stream(gm, moe_ops) + assert num == 1 + + test_x = torch.randn(4, 128, device="cuda") + ref = model(test_x) + + static_x = torch.randn_like(test_x) + static_out = torch.empty_like(ref) + for _ in range(3): + static_out.copy_(gm(static_x)) + + g = torch.cuda.CUDAGraph() + with torch.cuda.graph(g): + static_out.copy_(gm(static_x)) + + static_x.copy_(test_x) + g.replay() + + assert torch.allclose(static_out, ref, atol=1e-4), ( + f"[rank {rank}] CUDA graph mismatch: max diff {(static_out - ref).abs().max().item()}" + ) + return True + except Exception: + traceback.print_exc() + raise + finally: + _cleanup() + + +# --------------------------------------------------------------------------- +# Worker 3 — SYMM_MEM corruption demo +# --------------------------------------------------------------------------- + + +def _worker_symm_mem_corruption(world_size, port): + """Build buggy and fixed graphs, run under SYMM_MEM + CUDA graph, compare.""" + import torch.distributed as dist + + import tensorrt_llm._torch.auto_deploy.custom_ops # noqa: F401 + from tensorrt_llm._torch.auto_deploy.custom_ops.distributed.trtllm_dist import ( + is_trtllm_op_available, + ) + from tensorrt_llm._torch.auto_deploy.transform.library.multi_stream_moe import ( + _execute_shared_expert_in_aux_stream, + _find_merge_node, + _get_ancestors, + ) + from tensorrt_llm._torch.auto_deploy.utils.multi_stream_utils import ( + begin_aux_stream_passthrough, + cuda_stream_manager, + end_aux_stream_passthrough, + wait_aux_stream_passthrough, + ) + from tensorrt_llm._torch.auto_deploy.utils.node_utils import all_reduce_ops, is_op + from tensorrt_llm._torch.distributed import AllReduce, AllReduceStrategy + from tensorrt_llm.mapping import Mapping + + if not is_trtllm_op_available(): + return "skip:no_trtllm_ops" + + rank, wsize = _init_dist(port) + try: + cuda_stream_manager.add_device(rank) + + # Check SYMM_MEM is available on this hardware. + try: + mapping = Mapping(world_size=wsize, tp_size=wsize, rank=rank) + ar_runner = AllReduce( + mapping=mapping, strategy=AllReduceStrategy.SYMM_MEM, dtype=torch.float16 + ) + if ar_runner.strategy != AllReduceStrategy.SYMM_MEM: + return "skip:no_symm_mem" + except Exception: + return "skip:no_symm_mem" + + torch.manual_seed(42) + strategy = "SYMM_MEM" + hidden_dim, inter_dim = 128, 256 + model, example, moe_ops = _make_model_and_example(hidden_dim, inter_dim, strategy) + + ar_op = torch.ops.auto_deploy.trtllm_dist_all_reduce + ar_ops = all_reduce_ops() + + # ---------------------------------------------------------------- + # Build BUGGY graph: all_reduce placed on aux stream (pre-PR). + # ---------------------------------------------------------------- + def make_buggy_gm(): + gm = _build_gm(model, example) + graph = gm.graph + node_order_snap = {n: i for i, n in enumerate(graph.nodes)} + + moe_node = next(n for n in graph.nodes if is_op(n, moe_ops)) + merge_node = _find_merge_node(moe_node) + assert merge_node is not None + + moe_anc = _get_ancestors(moe_node) + moe_anc.add(moe_node) + + shared_output = routed_output = None + for arg in merge_node.all_input_nodes: + arg_anc = _get_ancestors(arg) + if moe_node in arg_anc or arg is moe_node: + routed_output = arg + elif arg in moe_anc or arg.op != "call_function": + pass + else: + shared_output = arg + + assert shared_output is not None and is_op(shared_output, ar_ops) + + shared_nodes, fork_point, visited = [], None, set() + queue = [shared_output] + while queue: + n = queue.pop(0) + if n in visited: + continue + visited.add(n) + if n.op == "get_attr": + continue + if n in moe_anc: + if fork_point is None or node_order_snap.get(n, 0) > node_order_snap.get( + fork_point, 0 + ): + fork_point = n + continue + shared_nodes.append(n) + for inp in n.all_input_nodes: + queue.append(inp) + + shared_nodes.sort(key=lambda n: node_order_snap.get(n, 0)) + first_shared = shared_nodes[0] + + with graph.inserting_before(first_shared): + beg = graph.call_function(begin_aux_stream_passthrough, args=(fork_point,)) + first_shared.args = tuple(beg if a is fork_point else a for a in first_shared.args) + + # BUG: end_aux inserted AFTER the all_reduce → collective on aux stream. + with graph.inserting_after(shared_output): + end = graph.call_function(end_aux_stream_passthrough, args=(shared_output,)) + merge_node.args = tuple(end if a is shared_output else a for a in merge_node.args) + + with graph.inserting_before(merge_node): + wait = graph.call_function(wait_aux_stream_passthrough, args=(routed_output,)) + merge_node.args = tuple(wait if a is routed_output else a for a in merge_node.args) + + # Add second all_reduce on main stream (stands in for routed-expert AR). + out_node = next(n for n in reversed(list(graph.nodes)) if n.op == "output") + out_arg = out_node.args[0] + with graph.inserting_before(out_node): + second_ar = graph.call_function(ar_op.default, args=(out_arg, strategy)) + out_node.args = (second_ar,) + graph.lint() + gm.recompile() + return gm + + # ---------------------------------------------------------------- + # Build FIXED graph: all_reduce on main stream. + # ---------------------------------------------------------------- + def make_fixed_gm(): + gm = _build_gm(model, example) + gm, num = _execute_shared_expert_in_aux_stream(gm, moe_ops) + assert num == 1 + graph = gm.graph + out_node = next(n for n in reversed(list(graph.nodes)) if n.op == "output") + out_arg = out_node.args[0] + with graph.inserting_before(out_node): + second_ar = graph.call_function(ar_op.default, args=(out_arg, strategy)) + out_node.args = (second_ar,) + graph.lint() + gm.recompile() + return gm + + buggy_gm = make_buggy_gm() + fixed_gm = make_fixed_gm() + + # ---------------------------------------------------------------- + # Run both under CUDA graph with forced asymmetric stream ordering: + # rank 0 → main stream waits for aux before second AR → aux submits first + # rank 1 → no extra wait → main submits first + # SYMM_MEM collectives don't go through NCCL's CPU serialisation, so + # the cross-rank submission order mismatch produces wrong all_reduce + # results for the buggy graph. + # ---------------------------------------------------------------- + aux_stream = cuda_stream_manager.get_stream(rank, "aux") + main_stream = cuda_stream_manager.get_stream(rank, "main") + + def capture_and_replay(gm, x): + static_x = x.clone() + static_out = torch.empty_like(gm(static_x)) + for _ in range(3): + static_out.copy_(gm(static_x)) + # Asymmetric delay: rank 0 delays main before the second collective. + if rank == 0: + main_stream.wait_stream(aux_stream) + g = torch.cuda.CUDAGraph() + with torch.cuda.graph(g): + static_out.copy_(gm(static_x)) + static_x.copy_(x) + g.replay() + torch.cuda.synchronize() + return static_out.clone() + + test_x = torch.randn(4, hidden_dim, device="cuda") + dist.barrier() + + buggy_out = capture_and_replay(buggy_gm, test_x) + + # Eager reference (sequential, no multi-stream). + with torch.cuda.stream(main_stream): + ref_out = model(test_x) + ref_out = ar_op(ref_out, strategy) + torch.cuda.synchronize() + + dist.barrier() + fixed_out = capture_and_replay(fixed_gm, test_x) + + # Fixed graph must be correct. + fixed_correct = torch.allclose(fixed_out, ref_out, atol=1e-3) + assert fixed_correct, ( + f"[rank {rank}] Fixed graph wrong under SYMM_MEM CUDA graph: " + f"max diff = {(fixed_out - ref_out).abs().max().item():.4f}" + ) + + # Buggy graph should produce wrong output when SYMM_MEM is truly + # concurrent (interleaved) on this hardware. + buggy_correct = torch.allclose(buggy_out, ref_out, atol=1e-3) + if buggy_correct: + # SYMM_MEM may have serialised (e.g., fallback, world_size too small). + return "skip:race_not_triggered" + + return True + except Exception: + traceback.print_exc() + raise + finally: + _cleanup() + + +# --------------------------------------------------------------------------- +# Pytest entry points — use MpiPoolSession.submit_sync like +# test_allreduce_residual_rmsnorm_fusion.py to avoid cloudpickle torch.ops issues. +# --------------------------------------------------------------------------- + + +def _run_with_retries(worker_fn, world_size, **kwargs): + from tensorrt_llm.llmapi.mpi_session import MpiPoolSession + + max_retries = 5 + last_exc = None + for _ in range(max_retries): + pool = MpiPoolSession(n_workers=world_size) + try: + return pool.submit_sync(worker_fn, port=None, world_size=world_size, **kwargs) + except DistNetworkError as e: + last_exc = e + if "EADDRINUSE" not in str(e) and "address already in use" not in str(e).lower(): + raise + finally: + pool.shutdown() + raise RuntimeError(f"Dist init failed after {max_retries} retries") from last_exc + + +def _check_results(results): + """Assert all worker results are True; return first non-True for skip detection.""" + for r in results: + if isinstance(r, str) and r.startswith("skip:"): + return r + assert r is True, f"Unexpected worker result: {r}" + return True + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires ≥ 2 GPUs") +def test_structural_multigpu(): + """Graph-level invariant holds in real multi-GPU MPI context. + + FAILS on pre-PR code (all_reduce appears before end_aux → on aux stream). + PASSES on PR #14917 fix. + Uses NCCL — works on any multi-GPU setup. + """ + results = _run_with_retries(_worker_structural, world_size=2) + _check_results(results) + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires ≥ 2 GPUs") +def test_correctness_nccl_cuda_graph(): + """Fixed graph is numerically correct under NCCL + CUDA graph replay. + + NCCL serialises collective submissions CPU-side, so the race between two + concurrent collectives does not manifest. This test always passes on both + buggy and fixed code — it is a correctness regression guard. + """ + results = _run_with_retries(_worker_nccl_cuda_graph, world_size=2) + _check_results(results) + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires ≥ 2 GPUs") +def test_corruption_symm_mem_cuda_graph(): + """Buggy graph produces wrong output; fixed graph is correct — with SYMM_MEM. + + Explicitly builds the buggy graph (all_reduce on aux stream), forces + asymmetric submission ordering across ranks, and verifies the wrong output. + Then runs the fixed graph and verifies correctness. + + Skipped when SYMM_MEM is unavailable or the race does not manifest on this + hardware (e.g., world_size below MULTIMEM threshold). + """ + results = _run_with_retries(_worker_symm_mem_corruption, world_size=2) + outcome = _check_results(results) + if isinstance(outcome, str) and outcome.startswith("skip:"): + pytest.skip( + f"SYMM_MEM race not reproducible ({outcome.split(':', 1)[1]}); " + f"try world_size ≥ 6 on SM100 for reliable MULTIMEM activation" + ) diff --git a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_multi_stream_moe.py b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_multi_stream_moe.py index 41c0214ac977..d60f97698a95 100644 --- a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_multi_stream_moe.py +++ b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_multi_stream_moe.py @@ -46,8 +46,18 @@ 3. Numerical correctness — output matches eager reference within tolerance. 4. CUDA graph compatibility — capture + replay produces correct output. 5. Multi-layer stacking — multiple MoE layers handled independently. + +Additionally, a regression suite for nvbugs/6248757 verifies that a trailing +shared-expert all-reduce is kept on the MAIN stream (only the GEMMs overlap on +the aux stream), and that a collective in the middle of the shared-expert +branch makes the transform skip the node entirely. A Nemotron Ultra V3 suite +(produced by ``fuse_rmsnorm_quant_nvfp4`` at TP>=2) additionally checks graph +structure, numerical correctness, CUDA-graph replay, and multi-layer stacking +for the trailing-all-reduce topology. """ +from unittest.mock import patch + import torch import torch.nn as nn @@ -60,6 +70,7 @@ end_aux_stream_passthrough, wait_aux_stream_passthrough, ) +from tensorrt_llm._torch.auto_deploy.utils.node_utils import all_reduce_ops, is_op # --------------------------------------------------------------------------- # Mock fused-MoE custom op (distinct name to avoid conflicts with other tests) @@ -696,3 +707,395 @@ def test_tuple_fork_pattern_and_correctness(): assert len(begin_nodes) == 1, f"Expected exactly one begin_aux node, got {len(begin_nodes)}" assert begin_nodes[0].args[0].target is torch.ops.auto_deploy.mock_tuple_fork_moe_test.default _assert_numerical_correctness(gm, model, torch.randn(4, hidden_dim, device="cuda")) + + +# =================================================================== +# Tests — shared-expert all-reduce placement (regression for nvbugs/6248757) +# =================================================================== +# +# At TP>=6 on SM100 the shared-expert row-parallel all-reduce was moved onto +# the aux stream together with the rest of the shared-expert subgraph. Under +# monolithic (decode) CUDA-graph replay the shared-expert collective on the aux +# stream then ran concurrently with the routed-expert collective on the main +# stream; the two symm-mem MULTIMEM all-reduces interleaved across ranks and +# silently corrupted the output. The transform must therefore keep any +# trailing shared-expert collective on the MAIN stream and overlap only the +# shared-expert GEMMs on the aux stream. + +# Custom op packet used directly as the FX node target after export. +_ALL_REDUCE_TARGET = torch.ops.auto_deploy.torch_dist_all_reduce.default + + +def _node_order(gm): + """Map each node to its position in graph (execution) order.""" + return {n: i for i, n in enumerate(gm.graph.nodes)} + + +def _first_node_with_target(gm, target): + """Return the first ``call_function`` node whose target is *target* (or None).""" + for n in gm.graph.nodes: + if n.op == "call_function" and n.target is target: + return n + return None + + +class _GatedMLPWithAllReduce(nn.Module): + """Shared expert whose row-parallel ``down_proj`` is followed by an all-reduce. + + Mirrors the real DeepSeek/GLM4 sharded graph: the trailing collective (the + tensor-parallel shard reduction) is the last op of the shared-expert branch + and is what feeds the merge ``add``. + """ + + def __init__(self, hidden_dim: int, intermediate_dim: int): + super().__init__() + self.gate_proj = nn.Linear(hidden_dim, intermediate_dim, bias=False) + self.up_proj = nn.Linear(hidden_dim, intermediate_dim, bias=False) + self.down_proj = nn.Linear(intermediate_dim, hidden_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + y = self.down_proj(torch.nn.functional.silu(self.gate_proj(x)) * self.up_proj(x)) + return torch.ops.auto_deploy.torch_dist_all_reduce(y, "AUTO") + + +class _MidBranchAllReduceMLP(nn.Module): + """Shared expert with an all-reduce in the *middle* of the branch. + + The collective is not the trailing shared-output op, so it cannot be split + off onto the main stream — the transform must skip this node entirely + rather than place a collective on the aux stream. + """ + + def __init__(self, hidden_dim: int, intermediate_dim: int): + super().__init__() + self.up_proj = nn.Linear(hidden_dim, intermediate_dim, bias=False) + self.down_proj = nn.Linear(intermediate_dim, hidden_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = torch.relu(self.up_proj(x)) + h = torch.ops.auto_deploy.torch_dist_all_reduce(h, "AUTO") + return self.down_proj(h) + + +class MockSharedExpertAllReduceMoELayer(nn.Module): + """DeepSeek/GLM4 MoE layer whose shared expert ends in a row-parallel all-reduce.""" + + def __init__(self, hidden_dim: int, intermediate_dim: int, num_experts: int = 8): + super().__init__() + self.gate = nn.Linear(hidden_dim, num_experts, bias=False) + self.shared_experts = _GatedMLPWithAllReduce(hidden_dim, intermediate_dim) + self.expert_weight = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) + self.layernorm = nn.LayerNorm(hidden_dim) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + identity = hidden_states + logits = self.gate(hidden_states) + routing_weights, selected_experts = torch.topk(logits, k=2, dim=-1) + + moe_out = torch.ops.auto_deploy.mock_fused_moe_moe_test( + hidden_states, selected_experts, routing_weights, self.expert_weight + ) + shared_out = self.shared_experts(identity) + + return self.layernorm(moe_out + shared_out) + + +class MockMidBranchAllReduceMoELayer(nn.Module): + """MoE layer whose shared expert has a collective in the middle of the branch.""" + + def __init__(self, hidden_dim: int, intermediate_dim: int, num_experts: int = 8): + super().__init__() + self.gate = nn.Linear(hidden_dim, num_experts, bias=False) + self.shared_experts = _MidBranchAllReduceMLP(hidden_dim, intermediate_dim) + self.expert_weight = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) + self.layernorm = nn.LayerNorm(hidden_dim) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + identity = hidden_states + logits = self.gate(hidden_states) + routing_weights, selected_experts = torch.topk(logits, k=2, dim=-1) + + moe_out = torch.ops.auto_deploy.mock_fused_moe_moe_test( + hidden_states, selected_experts, routing_weights, self.expert_weight + ) + shared_out = self.shared_experts(identity) + + return self.layernorm(moe_out + shared_out) + + +def test_shared_expert_all_reduce_stays_on_main_stream(): + """Regression for nvbugs/6248757. + + A trailing shared-expert all-reduce must run on the MAIN stream: the + transform overlaps only the shared-expert GEMMs on the aux stream and emits + ``end_aux`` / ``wait_aux`` *before* the collective, so the collective is + rewired to consume the synced aux output and never lands inside the aux + region. + """ + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = MockSharedExpertAllReduceMoELayer(hidden_dim, intermediate_dim).eval().to("cuda") + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + + assert num == 1, f"Expected 1 replacement, got {num}" + _assert_stream_nodes_present(gm) + + order = _node_order(gm) + ar_node = _first_node_with_target(gm, _ALL_REDUCE_TARGET) + begin_aux = _first_node_with_target(gm, begin_aux_stream_passthrough) + end_aux = _first_node_with_target(gm, end_aux_stream_passthrough) + wait_aux = _first_node_with_target(gm, wait_aux_stream_passthrough) + + assert ar_node is not None, "shared-expert all-reduce node missing after transform" + + # The collective must run after the aux region is closed (i.e. after the + # current stream is switched back to main) — never between begin/end_aux. + assert order[ar_node] > order[end_aux], "all-reduce must run after end_aux (main stream)" + assert order[ar_node] > order[wait_aux], "all-reduce must run after wait_aux (main stream)" + assert not (order[begin_aux] < order[ar_node] < order[end_aux]), ( + "all-reduce was placed inside the aux-stream region" + ) + + # The collective is rewired to consume the synced aux output, proving the + # GEMM result produced on the aux stream is awaited before the main-stream + # collective reads it. + assert wait_aux in ar_node.all_input_nodes, ( + "all-reduce must consume wait_aux output (synced aux result)" + ) + + +def test_mid_branch_all_reduce_skips_transform(): + """A non-trailing shared-expert collective must make the transform skip the node. + + Such a collective cannot be split off onto the main stream, so the transform + must bail out rather than move a collective to the aux stream. + """ + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = MockMidBranchAllReduceMoELayer(hidden_dim, intermediate_dim).eval().to("cuda") + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + + assert num == 0, f"Expected 0 replacements (mid-branch collective), got {num}" + + # No aux-stream nodes should be inserted when the transform bails out. + targets = _stream_targets(gm) + assert begin_aux_stream_passthrough not in targets + assert end_aux_stream_passthrough not in targets + assert wait_aux_stream_passthrough not in targets + + +# =================================================================== +# Tests — Nemotron Ultra V3: shared expert with trailing all-reduce +# (nvbugs/6248757 — the bug introduced by fuse_rmsnorm_quant_nvfp4) +# =================================================================== + + +class MockNemotronUltraSharedAllReduceMoELayer(nn.Module): + """Shared expert branch ending in an all-reduce collective. + + Simulates the FX graph topology produced by ``fuse_rmsnorm_quant_nvfp4`` + on Nemotron Ultra V3 at TP>=2. After that fusion the shared-expert + ``down_proj`` (row-wise TP split) emits a standalone + ``torch_dist_all_reduce`` whose output feeds the merge ``add`` directly:: + + hidden_states ─┬─ gate ─ topk ──── mock_fused_moe ──── moe_out ──────────┐ + └─ up_proj ─ relu² ─ down_proj ─ all_reduce ─ shared_out ─┴─ add ─ out + + Before PR #14917 (buggy): ``_execute_shared_expert_in_aux_stream`` treats + ``shared_output`` (the all_reduce node) as the last shared op and inserts + ``end_aux`` *after* it. The collective therefore executes on the aux stream. + + After PR #14917 (fixed): the all_reduce is detected as a collective, split + off from the aux region, and forced to run on the main stream with + ``end_aux`` / ``wait_aux`` inserted before it. + """ + + def __init__(self, hidden_dim: int, intermediate_dim: int, num_experts: int = 8): + super().__init__() + self.gate = nn.Linear(hidden_dim, num_experts, bias=False) + self.up_proj = nn.Linear(hidden_dim, intermediate_dim, bias=False) + self.down_proj = nn.Linear(intermediate_dim, hidden_dim, bias=False) + self.expert_weight = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) + self.layernorm = nn.LayerNorm(hidden_dim) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residuals = hidden_states + logits = self.gate(hidden_states) + routing_weights, selected_experts = torch.topk(logits, k=2, dim=-1) + + # Shared expert path: GEMMs on aux stream, all_reduce on main stream. + shared = self.down_proj(torch.relu(self.up_proj(residuals)) ** 2) + shared_out = torch.ops.auto_deploy.torch_dist_all_reduce(shared, "sum") + + moe_out = torch.ops.auto_deploy.mock_fused_moe_moe_test( + hidden_states, selected_experts, routing_weights, self.expert_weight + ) + return self.layernorm(shared_out + moe_out) + + +def _get_node_order(gm): + return {n: i for i, n in enumerate(gm.graph.nodes)} + + +def _find_nodes_by_target(gm, target): + return [n for n in gm.graph.nodes if n.op == "call_function" and n.target is target] + + +def _find_allreduce_node(gm): + ar_ops = all_reduce_ops() + return next((n for n in gm.graph.nodes if is_op(n, ar_ops)), None) + + +def _assert_allreduce_on_main_stream(gm): + """The all_reduce must come AFTER end_aux and wait_aux in graph order. + + This is the key structural invariant of the fix (PR #14917). + + With the bug: graph order is begin_aux → ... → all_reduce → end_aux → merge + (collective runs on aux stream) + With the fix: graph order is begin_aux → ... → end_aux → wait_aux → all_reduce → merge + (collective runs on main stream) + """ + node_order = _get_node_order(gm) + all_reduce_node = _find_allreduce_node(gm) + assert all_reduce_node is not None, "No all_reduce node found in graph" + + end_aux_nodes = _find_nodes_by_target(gm, end_aux_stream_passthrough) + wait_aux_nodes = _find_nodes_by_target(gm, wait_aux_stream_passthrough) + assert end_aux_nodes, "end_aux_stream_passthrough not found" + assert wait_aux_nodes, "wait_aux_stream_passthrough not found" + + end_aux_node = end_aux_nodes[0] + wait_aux_node = wait_aux_nodes[0] + + # Core invariant: all_reduce must come AFTER end_aux (and wait_aux) in + # graph order. Violation means the collective is on the aux stream. + assert node_order[all_reduce_node] > node_order[end_aux_node], ( + "BUG: all_reduce collective appears before end_aux in graph order — " + "it is running on the aux stream. The collective must be on the main stream." + ) + assert node_order[all_reduce_node] > node_order[wait_aux_node], ( + "BUG: all_reduce appears before wait_aux — main stream is not waiting " + "for aux compute before running the collective." + ) + + # end_aux must NOT wrap the all_reduce node directly. + assert end_aux_node.args[0] is not all_reduce_node, ( + "BUG: end_aux_stream_passthrough wraps the all_reduce node as its input, " + "meaning the collective ran on the aux stream." + ) + + # wait_aux must feed the all_reduce directly (it replaced the aux_boundary arg). + assert wait_aux_node in all_reduce_node.all_input_nodes, ( + "wait_aux_stream_passthrough must be a direct input to the all_reduce node " + "so the main stream synchronizes with the aux stream before the collective." + ) + + +def test_trailing_allreduce_graph_structure(): + """The all_reduce must stay on the main stream after the transform. + + This test captures the exact bug from nvbugs/6248757: + - FAILS on pre-PR code: end_aux is inserted after the all_reduce, placing + the collective on the aux stream. + - PASSES on PR #14917 fix: end_aux/wait_aux are inserted before the + all_reduce, which runs on the main stream. + """ + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = MockNemotronUltraSharedAllReduceMoELayer(hidden_dim, intermediate_dim).eval().to("cuda") + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + + assert num == 1, f"Expected 1 replacement, got {num}" + _assert_stream_nodes_present(gm) + _assert_allreduce_on_main_stream(gm) + + +def test_trailing_allreduce_correctness(): + """Numerical correctness for the shared-expert all_reduce topology. + + Patches torch.distributed.all_reduce to identity (correct for world_size=1) + so the test runs without an initialized process group. + """ + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = MockNemotronUltraSharedAllReduceMoELayer(hidden_dim, intermediate_dim).eval().to("cuda") + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + assert num == 1 + + test_x = torch.randn(4, hidden_dim, device="cuda") + with patch("torch.distributed.all_reduce", lambda t, **kw: None): + ref = model(test_x) + out = gm(test_x) + + assert torch.allclose(out, ref, atol=1e-5), ( + f"Output mismatch after transform: max diff = {(out - ref).abs().max().item()}" + ) + + +def test_trailing_allreduce_cuda_graph(): + """CUDA graph capture + replay for the shared-expert all_reduce topology.""" + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = MockNemotronUltraSharedAllReduceMoELayer(hidden_dim, intermediate_dim).eval().to("cuda") + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + assert num == 1 + + with patch("torch.distributed.all_reduce", lambda t, **kw: None): + _assert_cuda_graph_correctness(gm, model, torch.randn(4, hidden_dim, device="cuda")) + + +def test_trailing_allreduce_multi_layer(): + """Two stacked layers with trailing all_reduce — both transformed, both correct.""" + hidden_dim, intermediate_dim = 128, 256 + cuda_stream_manager.add_device(torch.cuda.current_device()) + + model = ( + nn.Sequential( + MockNemotronUltraSharedAllReduceMoELayer(hidden_dim, intermediate_dim), + MockNemotronUltraSharedAllReduceMoELayer(hidden_dim, intermediate_dim), + ) + .eval() + .to("cuda") + ) + example = torch.randn(4, hidden_dim, device="cuda") + gm = _build_gm(model, example) + + gm, num = _execute_shared_expert_in_aux_stream(gm, _MOE_OPS) + + assert num == 2, f"Expected 2 replacements, got {num}" + _assert_stream_nodes_present(gm) + + # Both all_reduce nodes must be on the main stream. + node_order = _get_node_order(gm) + ar_ops = all_reduce_ops() + ar_nodes = [n for n in gm.graph.nodes if is_op(n, ar_ops)] + end_aux_nodes = _find_nodes_by_target(gm, end_aux_stream_passthrough) + assert len(ar_nodes) == 2, f"Expected 2 all_reduce nodes, got {len(ar_nodes)}" + assert len(end_aux_nodes) == 2, f"Expected 2 end_aux nodes, got {len(end_aux_nodes)}" + for ar_node in ar_nodes: + # Each all_reduce must come after at least one end_aux node. + assert any(node_order[ar_node] > node_order[e] for e in end_aux_nodes), ( + f"all_reduce node {ar_node.name} is not after any end_aux — " + "it would run on the aux stream." + ) From 900d069071350f6da6bd0b74d0f95ed6bfe1ffb4 Mon Sep 17 00:00:00 2001 From: gramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> Date: Mon, 8 Jun 2026 11:30:39 -0700 Subject: [PATCH 052/119] [https://nvbugs/6221483][fix] AutoDeploy: Fix Eagle metadata host syncs (#14714) Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> --- .../custom_ops/attention_interface.py | 57 ++++-- tensorrt_llm/_torch/auto_deploy/llm_args.py | 8 +- .../models/custom/modeling_eagle.py | 13 +- .../test_switch_to_generate_inplace.py | 168 +++++++++++++++++- .../singlegpu/shim/test_llm_config.py | 34 ++++ 5 files changed, 252 insertions(+), 28 deletions(-) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py index bf306f94c3a9..e1fa499affc6 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py @@ -295,7 +295,7 @@ def copy_to_device(self) -> None: trunc_h_buf = self._trunc_host_bufs[name][:copy_bytes] trunc_d_buf.copy_(trunc_h_buf, non_blocking=True) - def copy_to_host(self) -> None: + def copy_to_host(self, non_blocking: bool = False) -> None: """Copy from device buffer to host buffer. Mirrors ``copy_to_device``: uses the current length of the truncatable tensor @@ -306,7 +306,7 @@ def copy_to_host(self) -> None: if self._total_bytes > 0: h_buffer = self._host_buffer[: self._total_bytes] d_buffer = self._device_buffer[: self._total_bytes] - h_buffer.copy_(d_buffer, non_blocking=True) + h_buffer.copy_(d_buffer, non_blocking=non_blocking) # Copy each truncatable tensor independently, truncated to current length for name in self._truncatable_names: @@ -316,7 +316,7 @@ def copy_to_host(self) -> None: copy_bytes = length * dtype.itemsize trunc_d_buf = self._trunc_device_bufs[name][:copy_bytes] trunc_h_buf = self._trunc_host_bufs[name][:copy_bytes] - trunc_h_buf.copy_(trunc_d_buf, non_blocking=True) + trunc_h_buf.copy_(trunc_d_buf, non_blocking=non_blocking) def resize(self, name: str, new_capacity: int) -> None: """Resize a truncatable tensor's capacity. @@ -1201,6 +1201,27 @@ def _is_required(self, name: str, check_both: bool = True) -> bool: """ return self._is_active(name, check_both) or self._is_active_host_prep(name, check_both) + def _active_host_update_args( + self, arg_names: Set[str], active_args_override: Optional[Set[str]] = None + ) -> List[str]: + """Return host args that need mirroring after an in-graph metadata update. + + ``active_args_override`` lets a caller narrow host mirroring to the graph inputs the next + consumer actually reads. It is treated as a filter: only active host args whose names appear + in the override are mirrored. The override may contain names that are not active graph args + (e.g. a submodule's full placeholder set, which also includes inter-module tensors such as + ``inputs_embeds``/``hidden_states``); such entries are simply ignored. The caller is + responsible for including every host argument the next consumer may read. + """ + needs_d2h_sync = [ + k + self._host_suffix + for k in arg_names + if self._is_active(k + self._host_suffix, check_both=False) + ] + if active_args_override is None: + return needs_d2h_sync + return [arg_name for arg_name in needs_d2h_sync if arg_name in active_args_override] + def _stage_arg( self, name: str, @@ -1613,11 +1634,16 @@ def run_host_prepare_for_attention_forward(self) -> None: host_function(**{arg: self.get_arg(arg) for arg in args}) @nvtx_range("ad_offset_pos_and_cache_") - def offset_pos_and_cache_(self, offset: torch.Tensor) -> None: + def offset_pos_and_cache_( + self, offset: torch.Tensor, active_args_override: Optional[Set[str]] = None + ) -> None: """Offset position and cache-related metadata for active arguments. Args: offset: 1D tensor [batch_size] with per-sequence position offsets. + active_args_override: Optional graph-input names for the next in-forward consumer. When + provided, host mirroring is limited to those active host args. The caller is + responsible for including every host argument the next consumer may read. """ # check if we need a d2h sync _REQUIRES_UPDATE = { @@ -1629,11 +1655,7 @@ def offset_pos_and_cache_(self, offset: torch.Tensor) -> None: "seq_len_with_cache", "use_initial_states", } - needs_d2h_sync = [ - k + self._host_suffix - for k in _REQUIRES_UPDATE - if self._is_active(k + self._host_suffix, check_both=False) - ] + needs_d2h_sync = self._active_host_update_args(_REQUIRES_UPDATE, active_args_override) sync_to_host = any(needs_d2h_sync) if sync_to_host: ad_logger.debug(f"d2h sync required in offset_pos_and_cache_ for {needs_d2h_sync}") @@ -1724,7 +1746,7 @@ def offset_pos_and_cache_(self, offset: torch.Tensor) -> None: # TODO: May need to dissect what fields are needed in the forward pass to reduce # data movement. if sync_to_host: - self._input_buffer.copy_to_host() + self._input_buffer.copy_to_host(non_blocking=False) @nvtx_range("ad_offset_with_new_lens_") def offset_with_new_lens_(self, new_lens_ungathered: torch.Tensor) -> None: @@ -1748,13 +1770,18 @@ def offset_with_new_lens_(self, new_lens_ungathered: torch.Tensor) -> None: self.offset_pos_and_cache_(increment) @nvtx_range("ad_switch_to_generate_") - def switch_to_generate_(self) -> None: + def switch_to_generate_(self, active_args_override: Optional[Set[str]] = None) -> None: """Switch all sequences metadata to generate (decode) mode. Transitions the batch from any layout (prefill/extend/decode or mixed) to an all-decode layout where each sequence has exactly 1 token. We assume that we just take the last position of each sequence for the metadata. + Args: + active_args_override: Optional graph-input names for the next in-forward consumer. When + provided, host mirroring is limited to those active host args. The caller is + responsible for including every host argument the next consumer may read. + NOTE: update device tensors first and mirror back to host only when an updated host-side argument is active. @@ -1790,11 +1817,7 @@ def switch_to_generate_(self) -> None: "position_ids", "use_initial_states", } - needs_d2h_sync = [ - k + self._host_suffix - for k in _REQUIRES_UPDATE - if self._is_active(k + self._host_suffix, check_both=False) - ] + needs_d2h_sync = self._active_host_update_args(_REQUIRES_UPDATE, active_args_override) sync_to_host = any(needs_d2h_sync) # --- input_ids (device) --- @@ -1823,7 +1846,7 @@ def switch_to_generate_(self) -> None: # TODO: May need to dissect what fields are needed in the forward pass to reduce # data movement. if sync_to_host: - self._input_buffer.copy_to_host() + self._input_buffer.copy_to_host(non_blocking=False) def copy_(self, name: str, src: torch.Tensor, strict: bool = True) -> None: """Copy a tensor into the buffer. USE WITH CAUTION! diff --git a/tensorrt_llm/_torch/auto_deploy/llm_args.py b/tensorrt_llm/_torch/auto_deploy/llm_args.py index 5c08a95d87d1..078c469aefa6 100644 --- a/tensorrt_llm/_torch/auto_deploy/llm_args.py +++ b/tensorrt_llm/_torch/auto_deploy/llm_args.py @@ -412,18 +412,16 @@ def cap_max_batch_size_to_max_num_tokens(self): return self @model_validator(mode="after") - def disable_cudagraph_for_speculative_flashinfer(self): + def reject_cudagraph_for_speculative_flashinfer(self): if ( self.speculative_config is not None and self.attn_backend == "flashinfer" and self.is_cuda_graph_enabled() ): - ad_logger.warning( + raise ValueError( "Speculative decoding with FlashInfer attention does not currently support CUDA " - "graph replay in AutoDeploy; falling back to compile_backend='torch-simple'." + "graph replay in AutoDeploy. Use compile_backend='torch-simple' instead." ) - self.compile_backend = "torch-simple" - self.update_transforms_with_shortcuts() return self ### UTILITY METHODS ############################################################################ diff --git a/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py b/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py index 9d82fa5a9f3d..485ec4a9bd4d 100644 --- a/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py +++ b/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py @@ -33,7 +33,7 @@ from dataclasses import dataclass from types import SimpleNamespace -from typing import Any, ClassVar, Dict, Optional, Union +from typing import Any, ClassVar, Dict, Optional, Set, Union import torch import torch.nn as nn @@ -912,10 +912,14 @@ def _forward_prefill_only(self, input_ids: torch.Tensor, position_ids: torch.Ten # KV-cache forward (inference after graph transforms) # # ================================================================== # + @staticmethod + def _submodule_placeholder_names(submodule: nn.Module) -> Set[str]: + return {node.name for node in submodule.graph.nodes if node.op == "placeholder"} + @staticmethod def _filter_kwargs_for_submodule(kwargs: dict, submodule: nn.Module) -> dict: """Filter kwargs to only include those accepted by submodule's forward (GraphModule).""" - expected_names = {node.name for node in submodule.graph.nodes if node.op == "placeholder"} + expected_names = EagleWrapper._submodule_placeholder_names(submodule) return {k: v for k, v in kwargs.items() if k in expected_names} @staticmethod @@ -1096,6 +1100,7 @@ def _forward_with_kv_cache(self, csi: CachedSequenceInterface): next_new_tokens[:, 0] = csi.info.maybe_gather_and_squeeze(csi.get_arg("input_ids")) # ---- Phase 5: Draft loop ---- + draft_arg_names = self._submodule_placeholder_names(self.draft_model) for draft_idx in range(self.max_draft_len): # run forward pass on the draft model in shape [num_sequences, 1] draft_output = self.draft_model( @@ -1123,9 +1128,9 @@ def _forward_with_kv_cache(self, csi: CachedSequenceInterface): # switch to generate (if not done already), store new tokens, and offset cache # can be skipped for last iteration since after we return metadata will be reset if draft_idx < self.max_draft_len - 1: - csi.info.switch_to_generate_() + csi.info.switch_to_generate_(active_args_override=draft_arg_names) csi.info.copy_("input_ids", draft_tokens) - csi.info.offset_pos_and_cache_(c_offset) + csi.info.offset_pos_and_cache_(c_offset, active_args_override=draft_arg_names) # ---- Phase 6: Package output ---- return EagleWrapperOutput( diff --git a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_switch_to_generate_inplace.py b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_switch_to_generate_inplace.py index 16eba6cc79c5..8ae9bb98ad7c 100644 --- a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_switch_to_generate_inplace.py +++ b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_switch_to_generate_inplace.py @@ -54,7 +54,11 @@ def _make_seq_info(extra_activate=()) -> SequenceInfo: def _nest_prefill(si: SequenceInfo, input_ids, pages_per_seq, cache_loc, **kw): - """Convenience wrapper: nest prefill sequences and return the SequenceInfo.""" + """Convenience wrapper: nest packed prefill sequences and return the SequenceInfo. + + cu_seqlen is staged as the prefix sum of per-sequence lengths starting at 0, e.g. + input_ids=[[1, 2, 3], [4, 5, 6, 7]] (lengths 3 and 4) stages cu_seqlen=[0, 3, 7]. + """ flat_ids = [t for seq in input_ids for t in (seq.tolist() if hasattr(seq, "tolist") else seq)] cu_seqlen = [0] for seq in input_ids: @@ -76,6 +80,25 @@ def _nest_prefill(si: SequenceInfo, input_ids, pages_per_seq, cache_loc, **kw): return si +def _nest_decode(si: SequenceInfo, input_pos=(5, 10)): + """Convenience wrapper: nest a two-sequence all-decode batch (seq_len=1 per sequence). + + cu_seqlen is the decode layout [0, 1, 2] (1 token per sequence). With the default + input_pos=(5, 10): seq_len_with_cache stages to [6, 11] (input_pos + 1), and + cache_loc/cu_num_pages give 2 pages for seq 0 and 3 for seq 1. + """ + si.nest_sequences( + [1, 2], + cu_seqlen=[0, 1, 2], + input_pos=list(input_pos), + batch_info=[0, 0, 0, 0, 2, 2], + cache_loc_per_pool=[[10, 11, 20, 21, 22]], + cu_num_pages_per_pool=[[0, 2, 5]], + extra_page_per_seq_per_pool=[[-1, -1]], + ) + return si + + def _snapshot_host_views(si: SequenceInfo): """Snapshot full host buffers so current-length updates do not hide host writes.""" return { @@ -311,7 +334,24 @@ def _setup_decode_at_page_boundary(si): class TestSwitchToGenerateHostArgHandling: - """Validate host arg warning and d2h sync behavior.""" + """Validate active_args_override host (device->host) sync behavior. + + Each test asserts on both the device tensor (``get_view``, updated in place by the metadata + helpers) and the host staging mirror (``get_host_view``, refreshed only by a device->host + sync). "No sync" is therefore observable as the host mirror keeping its stale pre-call staged + value while the device tensor already holds the updated value. + + Notation used in the expected values: + + - ``cu_seqlen`` ("cumulative sequence lengths"): a length ``batch_size + 1`` prefix sum of the + per-sequence token counts in the packed batch, always starting at 0. A prefill of two + sequences with lengths [3, 4] stages ``[0, 3, 7]``; an all-decode batch of 2 sequences + (1 token each) is ``[0, 1, 2]``. ``switch_to_generate_`` rewrites cu_seqlen from the packed + prefill layout to the decode layout. + - ``seq_len_with_cache``: per-sequence total length including already-cached tokens + (``input_pos + current seq_len``). ``offset_pos_and_cache_`` advances it by the offset; a + decode batch with ``input_pos=[5, 10]`` stages ``[6, 11]``, and a +1 offset gives ``[7, 12]``. + """ def test_inactive_host_mirrors_not_synced_by_switch_to_generate(self): """Inactive host mirrors should keep pre-transition staging values.""" @@ -375,6 +415,72 @@ def test_active_host_mirror_synced_by_switch_to_generate(self): assert device_cu[:3].tolist() == [0, 1, 2] assert host_cu[:3].tolist() == [0, 1, 2] + def test_out_of_scope_active_host_mirror_not_synced_by_switch_to_generate(self): + """Host mirrors outside the next consumer's placeholders should keep staging values.""" + si = _make_seq_info(extra_activate=("cu_seqlen_host",)) + _nest_prefill( + si, + input_ids=[[1, 2, 3], [4, 5, 6, 7]], + pages_per_seq=[1, 1], + cache_loc=[10, 20], + ) + + si.switch_to_generate_(active_args_override={"input_ids"}) + + # cu_seqlen on device is rewritten to the decode layout [0, 1, 2]. cu_seqlen_host is out of + # scope (not in the override), so it is not synced and keeps the staged prefill prefix + # sum [0, 3, 7] -- device and host disagree. + device_cu = si._input_buffer.get_view("cu_seqlen") + host_cu = si._input_buffer.get_host_view("cu_seqlen") + assert device_cu[:3].tolist() == [0, 1, 2] + assert host_cu[:3].tolist() == [0, 3, 7] + + def test_in_scope_active_host_mirror_synced_by_switch_to_generate(self): + """Host mirrors inside the next consumer's placeholders should be refreshed.""" + si = _make_seq_info(extra_activate=("cu_seqlen_host",)) + _nest_prefill( + si, + input_ids=[[1, 2, 3], [4, 5, 6, 7]], + pages_per_seq=[1, 1], + cache_loc=[10, 20], + ) + + si.switch_to_generate_(active_args_override={"cu_seqlen_host"}) + + # cu_seqlen_host is in the override, so it is synced: the host mirror is refreshed to the + # device decode layout [0, 1, 2] (instead of the staged prefill prefix sum [0, 3, 7]). + device_cu = si._input_buffer.get_view("cu_seqlen") + host_cu = si._input_buffer.get_host_view("cu_seqlen") + assert device_cu[:3].tolist() == [0, 1, 2] + assert host_cu[:3].tolist() == [0, 1, 2] + + def test_drafting_override_is_tolerated_by_switch_to_generate(self): + """Drafting-style override with irrelevant placeholders is tolerated by switch_to_generate_. + + The Eagle/MTP draft loop passes the draft submodule's full placeholder set as the override, + which includes inter-module tensors that are not SequenceInfo graph args (inputs_embeds, + hidden_states). switch_to_generate_ must ignore those rather than raising, while still + syncing the in-scope host mirror. + """ + si = _make_seq_info(extra_activate=("cu_seqlen_host",)) + _nest_prefill( + si, + input_ids=[[1, 2, 3]], + pages_per_seq=[1], + cache_loc=[10], + ) + + # Mirrors the draft-model placeholder set: active host arg + non-active inter-module inputs. + draft_placeholders = {"input_ids", "cu_seqlen_host", "inputs_embeds", "hidden_states"} + si.switch_to_generate_(active_args_override=draft_placeholders) + + # One sequence -> decode cu_seqlen [0, 1]. cu_seqlen_host is in scope, so it is synced to + # match the device; the non-graph placeholders (inputs_embeds/hidden_states) are ignored. + device_cu = si._input_buffer.get_view("cu_seqlen") + host_cu = si._input_buffer.get_host_view("cu_seqlen") + assert device_cu[:2].tolist() == [0, 1] + assert host_cu[:2].tolist() == [0, 1] + def test_non_native_host_arg_syncs_device_to_host(self): """Activating a non-native host arg should sync device -> host instead of raising.""" si = _make_seq_info() @@ -423,6 +529,64 @@ def test_non_native_host_seq_len_with_cache_syncs(self): assert host_swc[0].item() == 4 assert host_swc[1].item() == 5 + def test_out_of_scope_active_host_mirror_not_synced_by_offset_pos_and_cache(self): + """Out-of-scope host mirror keeps its pre-offset staged value (no d2h sync).""" + si = _make_seq_info(extra_activate=("seq_len_with_cache_host",)) + _nest_decode(si) # seq_len_with_cache staged to [6, 11] + + increment = torch.tensor([1, 1], dtype=torch.int32, device=si.device) + si.offset_pos_and_cache_(increment, active_args_override={"input_ids"}) + + # offset advances seq_len_with_cache on device: staged [6, 11] -> [7, 12]. + # seq_len_with_cache_host is out of scope (not in the override), so it is not synced and + # keeps the staged [6, 11] -- device and host disagree. + device_swc = si._input_buffer.get_view("seq_len_with_cache") + host_swc = si._input_buffer.get_host_view("seq_len_with_cache") + assert device_swc[:2].tolist() == [7, 12] + assert host_swc[:2].tolist() == [6, 11] + + def test_in_scope_active_host_mirror_synced_by_offset_pos_and_cache(self): + """In-scope host mirror is refreshed from device metadata (d2h sync).""" + si = _make_seq_info(extra_activate=("seq_len_with_cache_host",)) + _nest_decode(si) # seq_len_with_cache staged to [6, 11] + + increment = torch.tensor([1, 1], dtype=torch.int32, device=si.device) + si.offset_pos_and_cache_(increment, active_args_override={"seq_len_with_cache_host"}) + + # seq_len_with_cache_host is in the override, so it is synced: the host mirror is refreshed + # to the advanced device value [7, 12] (instead of the staged [6, 11]). + device_swc = si._input_buffer.get_view("seq_len_with_cache") + host_swc = si._input_buffer.get_host_view("seq_len_with_cache") + assert device_swc[:2].tolist() == [7, 12] + assert host_swc[:2].tolist() == [7, 12] + + def test_drafting_override_is_tolerated_by_offset_pos_and_cache(self): + """Drafting-style override with irrelevant placeholders is tolerated by offset_pos_and_cache_. + + Mirrors the Eagle/MTP draft loop, which passes the draft submodule's full placeholder set + (including non-graph-arg tensors such as inputs_embeds/hidden_states). offset_pos_and_cache_ + must ignore those while still syncing the in-scope host mirror. + """ + si = _make_seq_info(extra_activate=("seq_len_with_cache_host",)) + _nest_decode(si) # seq_len_with_cache staged to [6, 11] + + # Mirrors the draft-model placeholder set: active host arg + non-active inter-module inputs. + draft_placeholders = { + "input_ids", + "seq_len_with_cache_host", + "inputs_embeds", + "hidden_states", + } + increment = torch.tensor([1, 1], dtype=torch.int32, device=si.device) + si.offset_pos_and_cache_(increment, active_args_override=draft_placeholders) + + # seq_len_with_cache_host is in scope, so it is synced to the advanced device value [7, 12] + # (from staged [6, 11]); the non-graph placeholders (inputs_embeds/hidden_states) are ignored. + device_swc = si._input_buffer.get_view("seq_len_with_cache") + host_swc = si._input_buffer.get_host_view("seq_len_with_cache") + assert device_swc[:2].tolist() == [7, 12] + assert host_swc[:2].tolist() == [7, 12] + def test_native_host_args_do_not_raise(self): """batch_info_host, cu_seqlen_host, seq_len_host are native (tokens_gather in batch_info).""" si = _make_seq_info() diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py index a6f37875223f..6e632259aff4 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py @@ -257,6 +257,40 @@ def test_accepts_mtp_eagle_one_model(self): args = LlmArgs(model="test-model", speculative_config=spec_config) assert args.model_factory == "eagle_one_model" + @pytest.mark.parametrize("compile_backend", ["torch-cudagraph", "torch-opt"]) + def test_rejects_flashinfer_cuda_graph_backend(self, compile_backend): + from tensorrt_llm.llmapi import EagleDecodingConfig + + spec_config = EagleDecodingConfig( + max_draft_len=3, + speculative_model="some/model", + eagle3_one_model=True, + ) + + with pytest.raises(pydantic.ValidationError): + LlmArgs( + model="test-model", + speculative_config=spec_config, + attn_backend="flashinfer", + compile_backend=compile_backend, + ) + + def test_accepts_flashinfer_torch_simple(self): + from tensorrt_llm.llmapi import EagleDecodingConfig + + spec_config = EagleDecodingConfig( + max_draft_len=3, + speculative_model="some/model", + eagle3_one_model=True, + ) + + LlmArgs( + model="test-model", + speculative_config=spec_config, + attn_backend="flashinfer", + compile_backend="torch-simple", + ) + # ================================ # CUDA Graph Batch Sizes Tests From 9827c21e56020cd6f0f507ca607ad72c23b4b163 Mon Sep 17 00:00:00 2001 From: Kanghwan <861393+karljang@users.noreply.github.com> Date: Mon, 8 Jun 2026 12:33:00 -0700 Subject: [PATCH 053/119] [None][feat] add FLUX visual generation examples (#14987) Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com> --- examples/visual_gen/README.md | 4 + .../configs/flux1-dev-fp4-1gpu.yaml | 27 ++++++ .../configs/flux2-dev-fp4-1gpu.yaml | 27 ++++++ examples/visual_gen/models/flux1.py | 91 +++++++++++++++++++ examples/visual_gen/models/flux2.py | 91 +++++++++++++++++++ 5 files changed, 240 insertions(+) create mode 100644 examples/visual_gen/configs/flux1-dev-fp4-1gpu.yaml create mode 100644 examples/visual_gen/configs/flux2-dev-fp4-1gpu.yaml create mode 100644 examples/visual_gen/models/flux1.py create mode 100644 examples/visual_gen/models/flux2.py diff --git a/examples/visual_gen/README.md b/examples/visual_gen/README.md index a475538246ed..c38656472b35 100644 --- a/examples/visual_gen/README.md +++ b/examples/visual_gen/README.md @@ -19,11 +19,15 @@ for feature details. python quickstart_example.py python models/wan_t2v.py python models/ltx2.py +python models/flux1.py +python models/flux2.py # With engine config (quant, parallelism, etc.) python models/wan_t2v.py --visual_gen_args configs/wan2.2-t2v-fp4-1gpu.yaml python models/wan_i2v.py --visual_gen_args configs/wan2.2-i2v-fp4-1gpu.yaml --image /path/to/image.png python models/ltx2.py --visual_gen_args configs/ltx2-t2v-fp8-1-gpu.yaml +python models/flux1.py --visual_gen_args configs/flux1-dev-fp4-1gpu.yaml +python models/flux2.py --visual_gen_args configs/flux2-dev-fp4-1gpu.yaml ``` Install deps from the repo root: `pip install -r requirements-dev.txt`. diff --git a/examples/visual_gen/configs/flux1-dev-fp4-1gpu.yaml b/examples/visual_gen/configs/flux1-dev-fp4-1gpu.yaml new file mode 100644 index 000000000000..54d54d26fd22 --- /dev/null +++ b/examples/visual_gen/configs/flux1-dev-fp4-1gpu.yaml @@ -0,0 +1,27 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU FLUX.1-dev with NVFP4 dynamic quantization. +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +quant_config: + quant_algo: NVFP4 + dynamic: true +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/configs/flux2-dev-fp4-1gpu.yaml b/examples/visual_gen/configs/flux2-dev-fp4-1gpu.yaml new file mode 100644 index 000000000000..5da70c1536e2 --- /dev/null +++ b/examples/visual_gen/configs/flux2-dev-fp4-1gpu.yaml @@ -0,0 +1,27 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 1-GPU FLUX.2-dev with NVFP4 dynamic quantization. +# Shared by offline examples (--visual_gen_args) and trtllm-serve. +quant_config: + quant_algo: NVFP4 + dynamic: true +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 1 + ulysses_size: 1 +cuda_graph_config: + enable: false diff --git a/examples/visual_gen/models/flux1.py b/examples/visual_gen/models/flux1.py new file mode 100644 index 000000000000..269610db541b --- /dev/null +++ b/examples/visual_gen/models/flux1.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""FLUX.1 text-to-image generation. + +Usage: + python flux1.py + python flux1.py --visual_gen_args ../configs/flux1-dev-fp4-1gpu.yaml +""" + +import argparse +from pathlib import Path + +from tensorrt_llm import VisualGen, VisualGenArgs + + +def _output_paths(output_path: str, num_images: int) -> str | list[str]: + if num_images == 1: + return output_path + + path = Path(output_path) + return [str(path.with_name(f"{path.stem}_{idx + 1}{path.suffix}")) for idx in range(num_images)] + + +def main(): + parser = argparse.ArgumentParser(description="FLUX.1 Text-to-Image example") + parser.add_argument( + "--model", + type=str, + default="black-forest-labs/FLUX.1-dev", + help="Model path or HuggingFace Hub ID", + ) + parser.add_argument( + "--visual_gen_args", + "--extra_visual_gen_options", + dest="visual_gen_args", + type=str, + default=None, + help="Path to YAML config (same as trtllm-serve --visual_gen_args)", + ) + parser.add_argument( + "--prompt", + type=str, + default="A cat sitting on a windowsill, cinematic lighting, highly detailed", + help="Text prompt for image generation", + ) + parser.add_argument( + "--num_images_per_prompt", + type=int, + default=1, + help="Number of images to generate for the prompt", + ) + parser.add_argument( + "--output_path", + type=str, + default="flux1_output.png", + help="Path to save the output image. For multiple images, an index is appended.", + ) + args = parser.parse_args() + if args.num_images_per_prompt < 1: + raise ValueError("--num_images_per_prompt must be >= 1") + + # Engine config from shared YAML (optional); model-specific defaults apply otherwise. + extra_args = VisualGenArgs.from_yaml(args.visual_gen_args) if args.visual_gen_args else None + visual_gen = VisualGen(model=args.model, args=extra_args) + + # --- Model-specific: T2I request construction --- + # Start from per-model defaults (resolution, steps, guidance, seed, etc.) and set image count. + params = visual_gen.default_params + params.num_images_per_prompt = args.num_images_per_prompt + + output = visual_gen.generate(inputs=args.prompt, params=params) + + saved = output.save(_output_paths(args.output_path, args.num_images_per_prompt)) + print(f"Saved: {saved}") + + +if __name__ == "__main__": + main() diff --git a/examples/visual_gen/models/flux2.py b/examples/visual_gen/models/flux2.py new file mode 100644 index 000000000000..7147b1c7ea28 --- /dev/null +++ b/examples/visual_gen/models/flux2.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""FLUX.2 text-to-image generation. + +Usage: + python flux2.py + python flux2.py --visual_gen_args ../configs/flux2-dev-fp4-1gpu.yaml +""" + +import argparse +from pathlib import Path + +from tensorrt_llm import VisualGen, VisualGenArgs + + +def _output_paths(output_path: str, num_images: int) -> str | list[str]: + if num_images == 1: + return output_path + + path = Path(output_path) + return [str(path.with_name(f"{path.stem}_{idx + 1}{path.suffix}")) for idx in range(num_images)] + + +def main(): + parser = argparse.ArgumentParser(description="FLUX.2 Text-to-Image example") + parser.add_argument( + "--model", + type=str, + default="black-forest-labs/FLUX.2-dev", + help="Model path or HuggingFace Hub ID", + ) + parser.add_argument( + "--visual_gen_args", + "--extra_visual_gen_options", + dest="visual_gen_args", + type=str, + default=None, + help="Path to YAML config (same as trtllm-serve --visual_gen_args)", + ) + parser.add_argument( + "--prompt", + type=str, + default="A cat sitting on a windowsill, cinematic lighting, highly detailed", + help="Text prompt for image generation", + ) + parser.add_argument( + "--num_images_per_prompt", + type=int, + default=1, + help="Number of images to generate for the prompt", + ) + parser.add_argument( + "--output_path", + type=str, + default="flux2_output.png", + help="Path to save the output image. For multiple images, an index is appended.", + ) + args = parser.parse_args() + if args.num_images_per_prompt < 1: + raise ValueError("--num_images_per_prompt must be >= 1") + + # Engine config from shared YAML (optional); model-specific defaults apply otherwise. + extra_args = VisualGenArgs.from_yaml(args.visual_gen_args) if args.visual_gen_args else None + visual_gen = VisualGen(model=args.model, args=extra_args) + + # --- Model-specific: T2I request construction --- + # Start from per-model defaults (resolution, steps, guidance, seed, etc.) and set image count. + params = visual_gen.default_params + params.num_images_per_prompt = args.num_images_per_prompt + + output = visual_gen.generate(inputs=args.prompt, params=params) + + saved = output.save(_output_paths(args.output_path, args.num_images_per_prompt)) + print(f"Saved: {saved}") + + +if __name__ == "__main__": + main() From b2222469cc8daf900735bd44560d9fb922a2c297 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 07:02:45 +0800 Subject: [PATCH 054/119] =?UTF-8?q?[https://nvbugs/6261164][fix]=20In=20th?= =?UTF-8?q?e=20kvcache=20insert=20transform=20(`=5FInsertCachedOperator.?= =?UTF-8?q?=5Fapply`),=20when=E2=80=A6=20(#15020)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> Co-authored-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> --- .../custom_ops/attention_interface.py | 37 +++++---- .../mamba/flashinfer_backend_mamba.py | 4 +- .../mamba/triton_backend_causal_conv.py | 4 +- .../custom_ops/mamba/triton_backend_mamba.py | 9 ++- tensorrt_llm/_torch/auto_deploy/llm_args.py | 19 +++++ .../_torch/auto_deploy/shim/interface.py | 18 ++--- .../auto_deploy/transform/library/kvcache.py | 28 ++++++- tests/integration/test_lists/waives.txt | 1 - .../custom_ops/test_resource_handlers.py | 69 +++++++++++++---- .../shim/test_cached_sequence_interface.py | 8 +- .../singlegpu/shim/test_llm_config.py | 33 ++++++++ .../transformations/library/test_kv_cache.py | 76 +++++++++++++++++++ 12 files changed, 254 insertions(+), 52 deletions(-) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py index e1fa499affc6..c7aa851b368d 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py @@ -2062,6 +2062,10 @@ def __eq__(self, other: Optional[ResourceHandler]) -> bool: return self.state_shape == other.state_shape and self.dtype == other.dtype +class SpeculativeOnly: + """Trait mixin marking a resource that is only needed when speculative decoding is enabled.""" + + class SSMResourceHandler(StateResourceHandler): """Handler for SSM state resources that maps directly to MambaCacheManager's ssm_states buffer. @@ -2129,7 +2133,7 @@ def state_shape(self) -> Tuple[int, int]: return (self.conv_dim, self.d_conv - 1) -class SpecSSMResourceHandler(StateResourceHandler): +class IntermediateSSMStateHandler(SpeculativeOnly, StateResourceHandler): """Intermediate SSM state cache descriptor for speculative decoding. Acts as a type marker conveying the per-layer SSM shape to the cache interface. @@ -2137,8 +2141,8 @@ class SpecSSMResourceHandler(StateResourceHandler): by the MambaHybridCacheManager using spec_config, not by this handler. Inherits from StateResourceHandler (not SSMResourceHandler) so that - isinstance(h, SSMResourceHandler) returns False for spec handlers, eliminating - the need for exclusion guards throughout the codebase. + isinstance(h, SSMResourceHandler) returns False for intermediate handlers, eliminating + the need for exclusion guards throughout the codebase. Mixes in SpeculativeOnly. """ def __init__( @@ -2158,8 +2162,10 @@ def state_shape(self) -> Tuple[int, int, int]: return (self.num_heads, self.head_dim, self.d_state) @classmethod - def from_base(cls, base: Optional["SSMResourceHandler"]) -> Optional["SpecSSMResourceHandler"]: - """Create a spec handler from a base SSM handler, or return None.""" + def from_base( + cls, base: Optional["SSMResourceHandler"] + ) -> Optional["IntermediateSSMStateHandler"]: + """Create an intermediate handler from a base SSM handler, or return None.""" if base is None: return None return cls( @@ -2167,7 +2173,7 @@ def from_base(cls, base: Optional["SSMResourceHandler"]) -> Optional["SpecSSMRes ) -class ReplayOldXHandler(StateResourceHandler): +class ReplayOldXHandler(SpeculativeOnly, StateResourceHandler): """Per-layer old_x cache for the replay SSM kernel (single-buffered, bf16). Shape: (max_batch, T, num_heads, head_dim) — T is determined by the manager's @@ -2196,7 +2202,7 @@ def __eq__(self, other) -> bool: ) -class ReplayOldBHandler(StateResourceHandler): +class ReplayOldBHandler(SpeculativeOnly, StateResourceHandler): """Per-layer old_B cache for the replay SSM kernel (double-buffered, bf16). Shape: (max_batch, 2, T, n_groups, d_state) — T from manager. @@ -2224,7 +2230,7 @@ def __eq__(self, other) -> bool: ) -class ReplayOldDtHandler(StateResourceHandler): +class ReplayOldDtHandler(SpeculativeOnly, StateResourceHandler): """Per-layer old_dt cache for the replay SSM kernel (double-buffered, fp32). Shape: (max_batch, 2, num_heads, T) — T from manager. @@ -2246,7 +2252,7 @@ def __eq__(self, other) -> bool: return isinstance(other, ReplayOldDtHandler) and self.num_heads == other.num_heads -class ReplayOldDAcumsumHandler(StateResourceHandler): +class ReplayOldDAcumsumHandler(SpeculativeOnly, StateResourceHandler): """Per-layer old_dA_cumsum cache for the replay SSM kernel (double-buffered, fp32). Shape: (max_batch, 2, num_heads, T) — T from manager. @@ -2268,7 +2274,7 @@ def __eq__(self, other) -> bool: return isinstance(other, ReplayOldDAcumsumHandler) and self.num_heads == other.num_heads -class ReplayCacheBufIdxHandler(StateResourceHandler): +class ReplayCacheBufIdxHandler(SpeculativeOnly, StateResourceHandler): """Global cache_buf_idx tensor for the replay SSM kernel (shared across all layers, int32). Shape: (max_batch,). Routes to MambaHybridCacheManager.get_replay_cache_buf_idx(). @@ -2290,7 +2296,7 @@ def __eq__(self, other) -> bool: return isinstance(other, ReplayCacheBufIdxHandler) -class ReplayPrevNumAcceptedHandler(StateResourceHandler): +class ReplayPrevNumAcceptedHandler(SpeculativeOnly, StateResourceHandler): """Global prev_num_accepted_tokens tensor for the replay SSM kernel (int32, shared). Shape: (max_batch,). Routes to MambaHybridCacheManager.get_replay_prev_num_accepted_tokens(). @@ -2310,7 +2316,7 @@ def __eq__(self, other) -> bool: return isinstance(other, ReplayPrevNumAcceptedHandler) -class SpecCausalConvResourceHandler(StateResourceHandler): +class IntermediateConvStateHandler(SpeculativeOnly, StateResourceHandler): """Intermediate conv state cache descriptor for speculative decoding. Acts as a type marker conveying the per-layer conv shape to the cache interface. @@ -2318,7 +2324,8 @@ class SpecCausalConvResourceHandler(StateResourceHandler): by the MambaHybridCacheManager using spec_config, not by this handler. Inherits from StateResourceHandler (not CausalConvResourceHandler) so that - isinstance(h, CausalConvResourceHandler) returns False for spec handlers. + isinstance(h, CausalConvResourceHandler) returns False for intermediate handlers. Mixes in + SpeculativeOnly. """ def __init__( @@ -2338,8 +2345,8 @@ def state_shape(self) -> Tuple[int, int]: @classmethod def from_base( cls, base: Optional["CausalConvResourceHandler"] - ) -> Optional["SpecCausalConvResourceHandler"]: - """Create a spec handler from a base conv handler, or return None.""" + ) -> Optional["IntermediateConvStateHandler"]: + """Create an intermediate handler from a base conv handler, or return None.""" if base is None: return None return cls(conv_dim=base.conv_dim, d_conv=base.d_conv, dtype=base.dtype) diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/flashinfer_backend_mamba.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/flashinfer_backend_mamba.py index 076d1fb4ba4a..21e57e545ad5 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/flashinfer_backend_mamba.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/flashinfer_backend_mamba.py @@ -28,6 +28,7 @@ from ..attention_interface import ( AttentionRegistry, BatchInfo, + IntermediateSSMStateHandler, MHACallable, ReplayCacheBufIdxHandler, ReplayOldBHandler, @@ -36,7 +37,6 @@ ReplayOldXHandler, ReplayPrevNumAcceptedHandler, ResourceHandlerDict, - SpecSSMResourceHandler, ) from .mamba_backend_common import ( BaseBackendSSM, @@ -419,7 +419,7 @@ def get_cache_initializers( ret["replay_cache_buf_idx"] = ReplayCacheBufIdxHandler() ret["replay_prev_num_accepted"] = ReplayPrevNumAcceptedHandler() else: - ret["intermediate_ssm_state_cache"] = SpecSSMResourceHandler.from_base(ssm_h) + ret["intermediate_ssm_state_cache"] = IntermediateSSMStateHandler.from_base(ssm_h) ret["replay_old_x"] = None ret["replay_old_b"] = None ret["replay_old_dt"] = None diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_causal_conv.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_causal_conv.py index a3f4581d7e89..caae1a6ff98d 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_causal_conv.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_causal_conv.py @@ -36,8 +36,8 @@ from ..attention_interface import ( AttentionRegistry, BatchInfo, + IntermediateConvStateHandler, MHACallable, - SpecCausalConvResourceHandler, ) from .causal_conv_common import BaseCausalConvDescriptor @@ -255,7 +255,7 @@ class TritonBackendCausalConv(BaseCausalConvDescriptor): @classmethod def get_cache_initializers(cls, source_attn_node, cache_config): ret = super().get_cache_initializers(source_attn_node, cache_config) - ret["intermediate_conv_state_cache"] = SpecCausalConvResourceHandler.from_base( + ret["intermediate_conv_state_cache"] = IntermediateConvStateHandler.from_base( ret["conv_state_cache"] ) return ret diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py index 75a9c7010263..d4f7aa0d2bd9 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py @@ -19,7 +19,12 @@ from tensorrt_llm._torch.modules.mamba.selective_state_update import selective_state_update -from ..attention_interface import AttentionRegistry, BatchInfo, MHACallable, SpecSSMResourceHandler +from ..attention_interface import ( + AttentionRegistry, + BatchInfo, + IntermediateSSMStateHandler, + MHACallable, +) from .mamba_backend_common import ( BaseBackendSSM, _flatten_ssm_inputs, @@ -274,7 +279,7 @@ def get_cached_attention_op(cls) -> MHACallable: @classmethod def get_cache_initializers(cls, source_attn_node, cache_config): ret = super().get_cache_initializers(source_attn_node, cache_config) - ret["intermediate_ssm_state_cache"] = SpecSSMResourceHandler.from_base( + ret["intermediate_ssm_state_cache"] = IntermediateSSMStateHandler.from_base( ret["ssm_state_cache"] ) return ret diff --git a/tensorrt_llm/_torch/auto_deploy/llm_args.py b/tensorrt_llm/_torch/auto_deploy/llm_args.py index 078c469aefa6..195be8c79e0f 100644 --- a/tensorrt_llm/_torch/auto_deploy/llm_args.py +++ b/tensorrt_llm/_torch/auto_deploy/llm_args.py @@ -182,6 +182,25 @@ def setup_hidden_state_capture(self): return self + @model_validator(mode="after") + def validate_ssm_replay_requires_spec(self): + """Reject the replay SSM kernel when speculative decoding is off. + + ``ssm_replay`` makes the SSM backend emit per-layer replay state buffers (``Replay*`` + handlers), which are read only on the speculative extend (draft-verification) path. + Those handlers carry the ``SpeculativeOnly`` trait, so without ``speculative_config`` + the kvcache insert transform drops them entirely and the ``ssm_replay`` flag becomes a + no-op. Reject the contradictory config here rather than silently ignoring the flag. + """ + ssm_cfg = self.transforms.get("insert_cached_ssm_attention", {}) + if ssm_cfg.get("ssm_replay", False) and self.speculative_config is None: + raise ValueError( + "transforms.insert_cached_ssm_attention.ssm_replay=True requires speculative " + "decoding (speculative_config must be set). Replay buffers are only used on the " + "speculative extend path." + ) + return self + @model_validator(mode="after") def validate_parallel_config(self): """Setup parallel config according to world_size. diff --git a/tensorrt_llm/_torch/auto_deploy/shim/interface.py b/tensorrt_llm/_torch/auto_deploy/shim/interface.py index c204b4b548c6..e475959472ee 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/interface.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/interface.py @@ -50,6 +50,8 @@ from ..custom_ops.attention_interface import ( CausalConvResourceHandler, + IntermediateConvStateHandler, + IntermediateSSMStateHandler, KVPagedResourceHandler, ReplayCacheBufIdxHandler, ReplayOldBHandler, @@ -60,8 +62,6 @@ ResourceHandler, ResourceHandlerDict, SequenceInfo, - SpecCausalConvResourceHandler, - SpecSSMResourceHandler, SSMResourceHandler, StateResourceHandler, ) @@ -460,8 +460,8 @@ def _identify_managed_state_resources( ssm_spec = [ (name, handler) for name, handler in self._resource_lookup.items() - if isinstance(handler, SpecSSMResourceHandler) - and handler == SpecSSMResourceHandler.from_base(ssm_ref) + if isinstance(handler, IntermediateSSMStateHandler) + and handler == IntermediateSSMStateHandler.from_base(ssm_ref) ] conv_managed = [ (name, handler) @@ -471,8 +471,8 @@ def _identify_managed_state_resources( conv_spec = [ (name, handler) for name, handler in self._resource_lookup.items() - if isinstance(handler, SpecCausalConvResourceHandler) - and handler == SpecCausalConvResourceHandler.from_base(conv_ref) + if isinstance(handler, IntermediateConvStateHandler) + and handler == IntermediateConvStateHandler.from_base(conv_ref) ] # Replay SSM buffers — per-layer (old_x, old_B, old_dt, old_dA_cumsum) @@ -1131,16 +1131,14 @@ def _create_kv_cache_manager(self, max_tokens: Optional[int] = None) -> Dict: 1 for h in self._resource_lookup.values() if isinstance(h, SSMResourceHandler) ) num_ssm_spec_total = sum( - 1 for h in self._resource_lookup.values() if isinstance(h, SpecSSMResourceHandler) + 1 for h in self._resource_lookup.values() if isinstance(h, IntermediateSSMStateHandler) ) num_ssm_total = num_ssm_base_total + num_ssm_spec_total num_conv_base_total = sum( 1 for h in self._resource_lookup.values() if isinstance(h, CausalConvResourceHandler) ) num_conv_spec_total = sum( - 1 - for h in self._resource_lookup.values() - if isinstance(h, SpecCausalConvResourceHandler) + 1 for h in self._resource_lookup.values() if isinstance(h, IntermediateConvStateHandler) ) num_conv_total = num_conv_base_total + num_conv_spec_total num_state_other = num_state_total - num_ssm_total - num_conv_total diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py b/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py index 58a8862cd504..e0eae9ae01cd 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py @@ -54,6 +54,8 @@ Constant, KVPagedResourceHandler, PrepareMetadataCallable, + ResourceHandler, + SpeculativeOnly, ) from ...custom_ops.semantic_mask_registry import SemanticMaskRegistry from ...models.factory import ModelFactory @@ -329,6 +331,22 @@ def _insert_cached_attn_node( attn_node.replace_all_uses_with(cached_attn_node) gm.graph.erase_node(attn_node) + @staticmethod + def _suppress_spec_handlers_maybe( + resource_handler: Optional[ResourceHandler], spec_config: Optional[object] + ) -> Optional[ResourceHandler]: + """Drop a speculative-only resource to the None sentinel when spec decoding is off. + + Handlers carrying the ``SpeculativeOnly`` trait are read only on the speculative extend + path and are never bound by the cache manager without ``spec_config``; registering them + would leak an unmanaged per-layer allocation. Returns ``None`` for such handlers when + ``spec_config`` is None, otherwise returns ``resource_handler`` unchanged (``isinstance`` + is None-safe, so the existing None sentinel passes through untouched). + """ + if spec_config is None and isinstance(resource_handler, SpeculativeOnly): + return None + return resource_handler + def _apply( self, gm: GraphModule, @@ -385,8 +403,6 @@ def _apply( # --- Pass 1: register resources and assign per-layer group_idx --- # Group identity comes from KVPagedResourceHandler.__eq__ (which # includes sliding_window). A group IS a pool IS a metadata set. - from ...custom_ops.attention_interface import KVPagedResourceHandler - handler_groups: list[KVPagedResourceHandler] = [] per_layer_group_idx: list[int] = [] group_idx_by_layer_idx: dict[int, int] = {} @@ -434,6 +450,14 @@ def _apply( for k, resource_handler in attn_descriptor.get_cache_initializers( attn_node, cm.kv_cache_config ).items(): + # Speculative-only resources (intermediate SSM/conv state and replay + # buffers) are never bound by the cache manager when spec decoding is off + # (see CachedSequenceInterface._create_and_assign_state_views), so + # allocating them would waste a full per-layer state buffer and OOM. Drop + # them to the None sentinel instead of registering an unmanaged resource. + resource_handler = self._suppress_spec_handlers_maybe( + resource_handler, cm._spec_config + ) if resource_handler is None: # None sentinel: pass literal None positionally, no resource allocated. cache_in_nodes.append(None) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index fca32c8a951e..a93e2068f708 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -16,7 +16,6 @@ accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (http accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) -accuracy/test_llm_api_autodeploy.py::TestNemotronV2::test_fp8[True] SKIP (https://nvbugs/6261164) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] SKIP (https://nvbugs/6191524) diff --git a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_resource_handlers.py b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_resource_handlers.py index 769108e32544..b162dba723b5 100644 --- a/tests/unittest/auto_deploy/singlegpu/custom_ops/test_resource_handlers.py +++ b/tests/unittest/auto_deploy/singlegpu/custom_ops/test_resource_handlers.py @@ -28,11 +28,18 @@ from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import ( AttentionDescriptor, CausalConvResourceHandler, + IntermediateConvStateHandler, + IntermediateSSMStateHandler, KVPagedResourceHandler, + ReplayCacheBufIdxHandler, + ReplayOldBHandler, + ReplayOldDAcumsumHandler, + ReplayOldDtHandler, + ReplayOldXHandler, + ReplayPrevNumAcceptedHandler, ResourceHandler, SequenceInfo, - SpecCausalConvResourceHandler, - SpecSSMResourceHandler, + SpeculativeOnly, SSMResourceHandler, StateResourceHandler, UnpagedResourceHandler, @@ -353,10 +360,10 @@ def test_conv_handler_eq_different_params(): def test_spec_ssm_handler_from_base(): - """Verify SpecSSMResourceHandler mirrors base SSM dims and remains a distinct type.""" + """Verify IntermediateSSMStateHandler mirrors base SSM dims and remains a distinct type.""" base = SSMResourceHandler(num_heads=8, head_dim=64, d_state=16, dtype=torch.bfloat16) - spec = SpecSSMResourceHandler.from_base(base) - matching_spec = SpecSSMResourceHandler(8, 64, 16, dtype=torch.bfloat16) + spec = IntermediateSSMStateHandler.from_base(base) + matching_spec = IntermediateSSMStateHandler(8, 64, 16, dtype=torch.bfloat16) assert base.state_shape == (8, 64, 16) assert spec.state_shape == (8, 64, 16) @@ -365,22 +372,22 @@ def test_spec_ssm_handler_from_base(): assert spec.d_state == base.d_state assert spec.dtype == base.dtype - assert isinstance(spec, SpecSSMResourceHandler) + assert isinstance(spec, IntermediateSSMStateHandler) assert not isinstance(spec, SSMResourceHandler) assert base != spec assert spec == matching_spec def test_spec_ssm_handler_from_base_none(): - """Verify SpecSSMResourceHandler.from_base(None) returns None.""" - assert SpecSSMResourceHandler.from_base(None) is None + """Verify IntermediateSSMStateHandler.from_base(None) returns None.""" + assert IntermediateSSMStateHandler.from_base(None) is None def test_spec_conv_handler_from_base(): - """Verify SpecCausalConvResourceHandler mirrors base conv dims and remains a distinct type.""" + """Verify IntermediateConvStateHandler mirrors base conv dims and remains a distinct type.""" base = CausalConvResourceHandler(conv_dim=256, d_conv=4, dtype=torch.float32) - spec = SpecCausalConvResourceHandler.from_base(base) - matching_spec = SpecCausalConvResourceHandler(256, 4, dtype=torch.float32) + spec = IntermediateConvStateHandler.from_base(base) + matching_spec = IntermediateConvStateHandler(256, 4, dtype=torch.float32) assert base.state_shape == (256, 3) assert spec.state_shape == (256, 3) @@ -388,12 +395,46 @@ def test_spec_conv_handler_from_base(): assert spec.d_conv == base.d_conv assert spec.dtype == base.dtype - assert isinstance(spec, SpecCausalConvResourceHandler) + assert isinstance(spec, IntermediateConvStateHandler) assert not isinstance(spec, CausalConvResourceHandler) assert base != spec assert spec == matching_spec def test_spec_conv_handler_from_base_none(): - """Verify SpecCausalConvResourceHandler.from_base(None) returns None.""" - assert SpecCausalConvResourceHandler.from_base(None) is None + """Verify IntermediateConvStateHandler.from_base(None) returns None.""" + assert IntermediateConvStateHandler.from_base(None) is None + + +# Handlers that only carry meaning on the speculative extend path. The kvcache insert +# transform keys off the SpeculativeOnly trait to drop these when spec decoding is off, so +# this list is the contract: every spec-only handler must carry the trait, and no base +# (non-speculative) state handler may. +_SPECULATIVE_ONLY_HANDLERS = [ + IntermediateSSMStateHandler(num_heads=4, head_dim=64, d_state=16, dtype=torch.bfloat16), + IntermediateConvStateHandler(conv_dim=128, d_conv=4, dtype=torch.float32), + ReplayOldXHandler(num_heads=4, head_dim=64, dtype=torch.bfloat16), + ReplayOldBHandler(n_groups=2, d_state=16, dtype=torch.bfloat16), + ReplayOldDtHandler(num_heads=4), + ReplayOldDAcumsumHandler(num_heads=4), + ReplayCacheBufIdxHandler(), + ReplayPrevNumAcceptedHandler(), +] + +_NON_SPECULATIVE_HANDLERS = [ + SSMResourceHandler(num_heads=4, head_dim=64, d_state=16, dtype=torch.bfloat16), + CausalConvResourceHandler(conv_dim=128, d_conv=4, dtype=torch.float32), + KVPagedResourceHandler(num_kv_heads=4, head_dim=64, dtype=torch.bfloat16), +] + + +@pytest.mark.parametrize("handler", _SPECULATIVE_ONLY_HANDLERS, ids=lambda h: type(h).__name__) +def test_speculative_only_handlers_carry_trait(handler): + """Every speculative-only handler must carry the SpeculativeOnly trait (gate contract).""" + assert isinstance(handler, SpeculativeOnly) + + +@pytest.mark.parametrize("handler", _NON_SPECULATIVE_HANDLERS, ids=lambda h: type(h).__name__) +def test_base_handlers_do_not_carry_speculative_trait(handler): + """Base (non-speculative) handlers must NOT carry the trait, or they'd be wrongly dropped.""" + assert not isinstance(handler, SpeculativeOnly) diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_cached_sequence_interface.py b/tests/unittest/auto_deploy/singlegpu/shim/test_cached_sequence_interface.py index 6acfc67df0d4..34b3a3fb2164 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_cached_sequence_interface.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_cached_sequence_interface.py @@ -27,10 +27,10 @@ from tensorrt_llm._torch.auto_deploy._compat import KvCacheConfig from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import ( CausalConvResourceHandler, + IntermediateConvStateHandler, + IntermediateSSMStateHandler, KVPagedResourceHandler, SequenceInfo, - SpecCausalConvResourceHandler, - SpecSSMResourceHandler, SSMResourceHandler, StateResourceHandler, UnpagedResourceHandler, @@ -413,7 +413,7 @@ def test_intermediate_state_resources_bind_via_managed_state_path(paged_kv_cache resource_names.append( interface.add_resource( f"intermediate_ssm_state_{i}", - SpecSSMResourceHandler( + IntermediateSSMStateHandler( num_heads=num_heads, head_dim=head_dim, d_state=d_state, @@ -430,7 +430,7 @@ def test_intermediate_state_resources_bind_via_managed_state_path(paged_kv_cache resource_names.append( interface.add_resource( f"intermediate_conv_state_{i}", - SpecCausalConvResourceHandler( + IntermediateConvStateHandler( conv_dim=conv_dim, d_conv=4, dtype=torch.float32, diff --git a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py index 6e632259aff4..598117396124 100644 --- a/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py +++ b/tests/unittest/auto_deploy/singlegpu/shim/test_llm_config.py @@ -292,6 +292,39 @@ def test_accepts_flashinfer_torch_simple(self): ) +class TestSSMReplayValidation: + """The replay SSM kernel (ssm_replay) is only meaningful with speculative decoding. + + Its replay state buffers are read on the speculative extend path and are only bound by + the Mamba cache manager when spec is enabled, so enabling replay without spec would leak + unmanaged allocations. LlmArgs must reject that combination. + """ + + def test_ssm_replay_without_spec_raises(self): + with pytest.raises(ValueError, match="requires speculative decoding"): + LlmArgs( + model="test-model", + transforms={"insert_cached_ssm_attention": {"ssm_replay": True}}, + ) + + def test_ssm_replay_with_spec_ok(self): + from tensorrt_llm.llmapi import MTPDecodingConfig + + spec_config = MTPDecodingConfig(num_nextn_predict_layers=3, mtp_eagle_one_model=True) + # Replay + spec is valid and must not raise. + args = LlmArgs( + model="test-model", + speculative_config=spec_config, + transforms={"insert_cached_ssm_attention": {"ssm_replay": True}}, + ) + assert args.transforms["insert_cached_ssm_attention"]["ssm_replay"] is True + + def test_no_ssm_replay_without_spec_ok(self): + # The default (replay off) with spec off is the common case and must not raise. + args = LlmArgs(model="test-model") + assert not args.transforms.get("insert_cached_ssm_attention", {}).get("ssm_replay", False) + + # ================================ # CUDA Graph Batch Sizes Tests # ================================ diff --git a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache.py b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache.py index 74dfe2fe95ff..83868014a7cc 100644 --- a/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache.py +++ b/tests/unittest/auto_deploy/singlegpu/transformations/library/test_kv_cache.py @@ -1207,3 +1207,79 @@ def test_sequence_info_register_window_groups(): # Group 0 names should NOT appear with _g0 suffix assert "cache_loc_g0" not in seq_info.available_args + + +# ============================================================================ +# Speculative-only resource gating in the kvcache insert transform +# ============================================================================ +# This is the core of nvbug 6261164: when spec decoding is off, the transform must drop +# speculative-only handlers (intermediate SSM/conv state + replay buffers) to the None +# sentinel BEFORE registering them, otherwise they leak an unmanaged per-layer allocation. +# These CPU-only tests pin the gate decision directly so a regression (e.g. inverting the +# spec_config check, or narrowing the set back to a hard-coded type tuple that misses the +# replay family) is caught without the slow weights-dependent e2e smoke. +from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import ( # noqa: E402 + CausalConvResourceHandler, + IntermediateConvStateHandler, + IntermediateSSMStateHandler, + ReplayCacheBufIdxHandler, + ReplayOldBHandler, + ReplayOldDAcumsumHandler, + ReplayOldDtHandler, + ReplayOldXHandler, + ReplayPrevNumAcceptedHandler, + SSMResourceHandler, +) +from tensorrt_llm._torch.auto_deploy.transform.library.kvcache import ( # noqa: E402 + _InsertCachedOperator, +) + + +def _speculative_only_handlers(): + """Fresh instances of every speculative-only handler (intermediate state + replay).""" + return [ + IntermediateSSMStateHandler(num_heads=4, head_dim=64, d_state=16, dtype=torch.bfloat16), + IntermediateConvStateHandler(conv_dim=128, d_conv=4, dtype=torch.float32), + ReplayOldXHandler(num_heads=4, head_dim=64, dtype=torch.bfloat16), + ReplayOldBHandler(n_groups=2, d_state=16, dtype=torch.bfloat16), + ReplayOldDtHandler(num_heads=4), + ReplayOldDAcumsumHandler(num_heads=4), + ReplayCacheBufIdxHandler(), + ReplayPrevNumAcceptedHandler(), + ] + + +def _non_speculative_handlers(): + """Fresh instances of base (non-speculative) handlers that must never be dropped.""" + return [ + SSMResourceHandler(num_heads=4, head_dim=64, d_state=16, dtype=torch.bfloat16), + CausalConvResourceHandler(conv_dim=128, d_conv=4, dtype=torch.float32), + KVPagedResourceHandler(num_kv_heads=4, head_dim=64, dtype=torch.bfloat16), + ] + + +def test_gate_when_spec_off(): + """Spec decoding off: speculative-only handlers are dropped to None; base handlers are kept.""" + # Dropped: every speculative-only handler becomes the None sentinel (never registered). + for handler in _speculative_only_handlers(): + result = _InsertCachedOperator._suppress_spec_handlers_maybe(handler, None) + assert result is None, f"{type(handler).__name__} should be dropped when spec is off" + + # Kept: base (non-speculative) handlers pass through unchanged. + for handler in _non_speculative_handlers(): + result = _InsertCachedOperator._suppress_spec_handlers_maybe(handler, None) + assert result is handler, f"{type(handler).__name__} should be kept when spec is off" + + # An existing None sentinel passes through unchanged. + assert _InsertCachedOperator._suppress_spec_handlers_maybe(None, None) is None + + +def test_gate_when_spec_on(): + """Spec decoding on: nothing is dropped — speculative-only AND base handlers are all kept.""" + spec_config = object() # any non-None spec config + for handler in _speculative_only_handlers() + _non_speculative_handlers(): + result = _InsertCachedOperator._suppress_spec_handlers_maybe(handler, spec_config) + assert result is handler, f"{type(handler).__name__} should be kept when spec is on" + + # The None sentinel passes through unchanged regardless of spec state. + assert _InsertCachedOperator._suppress_spec_handlers_maybe(None, spec_config) is None From c1e9b00682f5c65ed3d5b6af5821268daf7e5366 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 09:04:15 +0800 Subject: [PATCH 055/119] [https://nvbugs/6211189][fix] Lower the reference to 46.5 (matching cross-GPU empirical mean) and remove the t (#14799) Signed-off-by: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> --- tests/integration/defs/accuracy/references/mmmu.yaml | 5 ++++- tests/integration/test_lists/waives.txt | 2 -- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/integration/defs/accuracy/references/mmmu.yaml b/tests/integration/defs/accuracy/references/mmmu.yaml index 5e3a2ba8f4c9..f25b73aa0d30 100644 --- a/tests/integration/defs/accuracy/references/mmmu.yaml +++ b/tests/integration/defs/accuracy/references/mmmu.yaml @@ -24,7 +24,10 @@ google/gemma-3-12b-it: kv_cache_quant_algo: FP8 accuracy: 50.11 LGAI-EXAONE/EXAONE-4.5-33B: - - accuracy: 51.22 + # Empirically achieved ~46.5 across H20/B200/B300/GB200/GB300 since the + # model was added (PR #12873). The original 51.22 was committed without + # pre-merge CI validation and was never reproduced. See nvbugs/6211189. + - accuracy: 46.5 Qwen/Qwen2-VL-7B-Instruct: - accuracy: 48.44 Qwen/Qwen2.5-VL-7B-Instruct: diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index a93e2068f708..b3bd9a1abd2a 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -150,8 +150,6 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales_early_firs accuracy/test_llm_api_pytorch.py::TestStep3_7::test_auto_dtype[tp_size=8-ep_size=8] SKIP (https://nvbugs/6278377) accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=0] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=3] SKIP (https://nvbugs/6274932) -accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[forced_chunked_prefill] SKIP (https://nvbugs/6211189) -accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[full_budget] SKIP (https://nvbugs/6211189) accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized SKIP (https://nvbugs/6215689) accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (https://nvbugs/6181383) accuracy/test_llm_api_pytorch_multimodal.py::TestStep3_7::test_fp8_block_scales SKIP (https://nvbugs/6274932) From bfb45378de058bef4396f952f4ce2358f45f65b7 Mon Sep 17 00:00:00 2001 From: Bo Li <22713281+bobboli@users.noreply.github.com> Date: Tue, 9 Jun 2026 09:47:33 +0800 Subject: [PATCH 056/119] [None][refactor] split VisualGen pipeline and model configs (#14956) Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com> --- tensorrt_llm/_torch/visual_gen/__init__.py | 3 +- tensorrt_llm/_torch/visual_gen/config.py | 168 +++++++++++++++--- .../models/cosmos3/pipeline_cosmos3.py | 6 +- .../models/cosmos3/transformer_cosmos3.py | 6 +- .../visual_gen/models/flux/pipeline_flux.py | 20 ++- .../visual_gen/models/flux/pipeline_flux2.py | 20 ++- .../models/flux/transformer_flux.py | 6 +- .../models/flux/transformer_flux2.py | 6 +- .../visual_gen/models/ltx2/pipeline_ltx2.py | 23 +-- .../models/ltx2/pipeline_ltx2_two_stages.py | 6 +- .../models/ltx2/transformer_ltx2.py | 9 +- .../_torch/visual_gen/models/modeling.py | 29 +++ .../models/qwen_image/pipeline_qwen_image.py | 23 ++- .../qwen_image/transformer_qwen_image.py | 7 +- .../visual_gen/models/wan/pipeline_wan.py | 31 ++-- .../visual_gen/models/wan/pipeline_wan_i2v.py | 30 ++-- .../visual_gen/models/wan/transformer_wan.py | 7 +- tensorrt_llm/_torch/visual_gen/pipeline.py | 43 +++-- .../_torch/visual_gen/pipeline_loader.py | 16 +- .../_torch/visual_gen/pipeline_registry.py | 10 +- tensorrt_llm/visual_gen/args.py | 2 +- .../_torch/visual_gen/test_flux_pipeline.py | 10 +- .../_torch/visual_gen/test_ltx2_pipeline.py | 22 +-- .../_torch/visual_gen/test_model_loader.py | 18 +- .../test_qwen_image_pipeline_config.py | 4 +- .../_torch/visual_gen/test_teacache.py | 11 +- .../_torch/visual_gen/test_visual_gen_args.py | 6 +- .../_torch/visual_gen/test_wan_transformer.py | 10 +- .../unittest/_torch/visual_gen/test_warmup.py | 4 +- 29 files changed, 366 insertions(+), 190 deletions(-) create mode 100644 tensorrt_llm/_torch/visual_gen/models/modeling.py diff --git a/tensorrt_llm/_torch/visual_gen/__init__.py b/tensorrt_llm/_torch/visual_gen/__init__.py index 2be964b60215..f7c5ca753ccd 100644 --- a/tensorrt_llm/_torch/visual_gen/__init__.py +++ b/tensorrt_llm/_torch/visual_gen/__init__.py @@ -12,7 +12,7 @@ from tensorrt_llm._torch.visual_gen.output import PipelineOutput from .checkpoints import WeightLoader -from .config import DiffusionModelConfig +from .config import DiffusionModelConfig, DiffusionPipelineConfig from .mapping import VisualGenMapping from .models import AutoPipeline, BasePipeline, WanPipeline from .pipeline_loader import PipelineLoader @@ -20,6 +20,7 @@ __all__ = [ "DiffusionModelConfig", + "DiffusionPipelineConfig", "PipelineComponent", "WeightLoader", "PipelineLoader", diff --git a/tensorrt_llm/_torch/visual_gen/config.py b/tensorrt_llm/_torch/visual_gen/config.py index 34c7b6665621..d709fab278e6 100644 --- a/tensorrt_llm/_torch/visual_gen/config.py +++ b/tensorrt_llm/_torch/visual_gen/config.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Internal DiffusionModelConfig and loading helpers.""" +"""Internal VisualGen pipeline and model configuration helpers.""" import json from pathlib import Path @@ -85,25 +85,84 @@ def create_attention_metadata_state() -> Dict[str, Any]: return {"metadata": None, "capacity": (0, 0)} +class _VisualGenConfigBase(BaseModel): + """Base for internal VisualGen configs that carry runtime objects.""" + + # Pydantic reserves `model_config` for class-level settings. This is not + # a VisualGen model config; it lets fields hold objects such as Mapping. + model_config = ConfigDict(arbitrary_types_allowed=True) + + +class DiffusionModelConfig(_VisualGenConfigBase): + """Internal config for one TRT-LLM VisualGen model component.""" + + component_name: Optional[str] = None + pretrained_config: Optional[Any] = None + mapping: Mapping = PydanticField(default_factory=Mapping) + skip_create_weights_in_init: bool = False + force_dynamic_quantization: bool = False + allreduce_strategy: AllReduceStrategy = PydanticField(default=AllReduceStrategy.NCCL) + extra_attrs: Dict = PydanticField(default_factory=dict) + + # Unified parallelism mapping copied from the owning pipeline config. + visual_gen_mapping: Optional[Any] = None # VisualGenMapping (lazy import) + + dynamic_weight_quant: bool = False + + # Shared runtime configs copied from the owning pipeline config. + quant_config: QuantConfig = PydanticField(default_factory=QuantConfig) + # Per-layer quant (from load_diffusion_quant_config layer_quant_config; None until mixed-precision parsing exists) + quant_config_dict: Optional[Dict[str, QuantConfig]] = None + compilation: CompilationConfig = PydanticField(default_factory=CompilationConfig) + torch_compile: TorchCompileConfig = PydanticField(default_factory=TorchCompileConfig) + cuda_graph: CudaGraphConfig = PydanticField(default_factory=CudaGraphConfig) + attention: AttentionConfig = PydanticField(default_factory=AttentionConfig) + attention_metadata_state: Optional[Dict[str, Any]] = None + parallel: ParallelConfig = PydanticField(default_factory=ParallelConfig) + cache: Optional[CacheConfig] = None + + # Observability — flat field mirrors VisualGenArgs.enable_layerwise_nvtx_marker. + enable_layerwise_nvtx_marker: bool = False + + @property + def cache_backend(self) -> Optional[CacheBackendName]: + return self.cache.cache_backend if self.cache is not None else None # type: ignore[return-value] + + @property + def teacache(self) -> Optional[TeaCacheConfig]: + return self.cache if isinstance(self.cache, TeaCacheConfig) else None + + @property + def cache_dit(self) -> Optional[CacheDiTConfig]: + return self.cache if isinstance(self.cache, CacheDiTConfig) else None + + @property + def torch_dtype(self) -> "torch.dtype": + """Get the torch dtype of the model (default: bfloat16).""" + return torch.bfloat16 + + def get_quant_config(self, name: Optional[str] = None) -> QuantConfig: + """Get quantization config for a layer or global. Resembles LLM ModelConfig.get_quant_config.""" + if name is None or self.quant_config_dict is None: + return self.quant_config + if name in self.quant_config_dict: + return self.quant_config_dict[name] + return self.quant_config + + # ============================================================================= -# DiffusionModelConfig - Internal configuration (merged/parsed) +# DiffusionPipelineConfig - Internal pipeline configuration (merged/parsed) # ============================================================================= -class DiffusionModelConfig(BaseModel): - """Internal ModelConfig for diffusion models. +class DiffusionPipelineConfig(_VisualGenConfigBase): + """Internal config for an entire VisualGen pipeline. - This is created by PipelineLoader from VisualGenArgs + checkpoint. - Contains merged/parsed config from: - - pretrained_config: From checkpoint/config.json - - quant_config: From checkpoint or user quant config - - Sub-configs: From VisualGenArgs (pipeline, attention, teacache) - - visual_gen_mapping: Populated by setup_visual_gen_mapping() from ParallelConfig + This is created by PipelineLoader from VisualGenArgs + checkpoint and owns + pipeline/runtime state plus one DiffusionModelConfig per model component. """ - model_config = ConfigDict(arbitrary_types_allowed=True) - - pretrained_config: Optional[Any] = None + model_configs: Dict[str, DiffusionModelConfig] = PydanticField(default_factory=dict) mapping: Mapping = PydanticField(default_factory=Mapping) skip_create_weights_in_init: bool = False force_dynamic_quantization: bool = False @@ -127,15 +186,17 @@ class DiffusionModelConfig(BaseModel): parallel: ParallelConfig = PydanticField(default_factory=ParallelConfig) cache: Optional[CacheConfig] = None - # Merged per-family pipeline_config: registry-entry defaults overlaid - # with the user-supplied VisualGenArgs.pipeline_config dict (user - # values win). Validated against the registry entry's `defaults` - # before assignment, so unknown keys never reach here. - pipeline_config: Dict[str, Any] = PydanticField(default_factory=dict) - # Observability — flat field mirrors VisualGenArgs.enable_layerwise_nvtx_marker. enable_layerwise_nvtx_marker: bool = False + @property + def primary_model_config(self) -> DiffusionModelConfig: + return self.model_configs["transformer"] + + @property + def primary_pretrained_config(self) -> Any: + return self.primary_model_config.pretrained_config + @property def cache_backend(self) -> Optional[CacheBackendName]: return self.cache.cache_backend if self.cache is not None else None # type: ignore[return-value] @@ -161,6 +222,33 @@ def get_quant_config(self, name: Optional[str] = None) -> QuantConfig: return self.quant_config_dict[name] return self.quant_config + def _make_model_config( + self, + component_name: str, + model_pretrained_config: Any, + ) -> DiffusionModelConfig: + return DiffusionModelConfig( + component_name=component_name, + pretrained_config=model_pretrained_config, + mapping=self.mapping, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + allreduce_strategy=self.allreduce_strategy, + extra_attrs=self.extra_attrs, + visual_gen_mapping=self.visual_gen_mapping, + dynamic_weight_quant=self.dynamic_weight_quant, + quant_config=self.quant_config, + quant_config_dict=self.quant_config_dict, + compilation=self.compilation, + torch_compile=self.torch_compile, + cuda_graph=self.cuda_graph, + attention=self.attention, + attention_metadata_state=self.attention_metadata_state, + parallel=self.parallel, + cache=self.cache, + enable_layerwise_nvtx_marker=self.enable_layerwise_nvtx_marker, + ) + @staticmethod def load_diffusion_quant_config( quant_config_dict: dict, @@ -346,12 +434,12 @@ def from_pretrained( checkpoint_dir: str, args: Optional["VisualGenArgs"] = None, **kwargs, - ) -> "DiffusionModelConfig": + ) -> "DiffusionPipelineConfig": """ Load config from pretrained checkpoint. Called by PipelineLoader with VisualGenArgs: - config = DiffusionModelConfig.from_pretrained( + config = DiffusionPipelineConfig.from_pretrained( checkpoint_dir=args.model, args=args, ) @@ -404,6 +492,7 @@ def from_pretrained( # Discover pipeline components (diffusers layout) components = discover_pipeline_components(checkpoint_path) + component_config_dicts: Dict[str, Dict[str, Any]] = {} if components: # ---------- Diffusers directory layout ---------- @@ -415,8 +504,11 @@ def from_pretrained( if not config_path.exists(): raise ValueError(f"Config not found at {config_path}") - with open(config_path) as f: - config_dict = json.load(f) + for component_name, component_config_path in components.items(): + with open(component_config_path) as f: + component_config_dicts[component_name] = json.load(f) + + config_dict = component_config_dicts[component] pretrained_config = SimpleNamespace(**config_dict) # Ensure _name_or_path is set so TeaCache coefficient matching works. @@ -439,6 +531,10 @@ def from_pretrained( if native_config is not None: transformer_dict = native_config.get("transformer", {}) + component_config_dicts["transformer"] = transformer_dict + transformer_2_dict = native_config.get("transformer_2") + if isinstance(transformer_2_dict, dict): + component_config_dicts["transformer_2"] = transformer_2_dict pretrained_config = SimpleNamespace(**transformer_dict) if not getattr(pretrained_config, "_name_or_path", None): pretrained_config._name_or_path = str(checkpoint_path) @@ -551,8 +647,7 @@ def from_pretrained( create_attention_metadata_state() if attention_cfg.backend == "TRTLLM" else None ) - return cls( - pretrained_config=pretrained_config, + pipeline_config = cls( quant_config=quant_config, quant_config_dict=quant_config_dict, dynamic_weight_quant=dynamic_weight_quant, @@ -566,8 +661,29 @@ def from_pretrained( parallel=parallel_cfg, cache=cache_cfg, enable_layerwise_nvtx_marker=enable_layerwise_nvtx_marker, - pipeline_config=resolved_pipeline_config, skip_create_weights_in_init=True, extra_attrs=extra_attrs, **kwargs, ) + + for component_name, config_dict in component_config_dicts.items(): + if component_name == component: + component_pretrained_config = pretrained_config + else: + component_pretrained_config = SimpleNamespace(**config_dict) + if not getattr(component_pretrained_config, "_name_or_path", None): + component_pretrained_config._name_or_path = getattr( + pretrained_config, "_name_or_path", "" + ) + pipeline_config.model_configs[component_name] = pipeline_config._make_model_config( + component_name, + component_pretrained_config, + ) + + if not pipeline_config.model_configs: + pipeline_config.model_configs["transformer"] = pipeline_config._make_model_config( + "transformer", + pretrained_config, + ) + + return pipeline_config diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py index d01e71d998ec..2977bcb64a61 100644 --- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py +++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py @@ -63,12 +63,12 @@ doc="Cosmos3 Omnimodal world models.", ) class Cosmos3OmniMoTPipeline(BasePipeline): - def __init__(self, model_config): - super().__init__(model_config) + def __init__(self, pipeline_config): + super().__init__(pipeline_config) def _init_transformer(self) -> None: logger.info("Initializing Cosmos3VFMTransformer") - self.transformer = Cosmos3VFMTransformer(self.model_config) + self.transformer = Cosmos3VFMTransformer(self.pipeline_config.model_configs["transformer"]) def load_weights(self, weights: dict) -> None: if self.transformer is not None and hasattr(self.transformer, "load_weights"): diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py index 10de6fc87235..82e8d7fa65b6 100644 --- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py +++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py @@ -26,6 +26,7 @@ from tensorrt_llm._torch.modules.gated_mlp import GatedMLP from tensorrt_llm._torch.modules.linear import Linear from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader from tensorrt_llm._torch.visual_gen.utils import SequenceSharder @@ -641,10 +642,9 @@ def forward( return cached_kv -class Cosmos3VFMTransformer(nn.Module): +class Cosmos3VFMTransformer(BaseDiffusionModel): def __init__(self, model_config: DiffusionModelConfig): - super().__init__() - self.model_config = model_config + super().__init__(model_config) pretrained_config = model_config.pretrained_config self.hidden_size = pretrained_config.hidden_size diff --git a/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux.py b/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux.py index c43345f9c8c4..196c3c927e7a 100644 --- a/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux.py +++ b/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux.py @@ -50,16 +50,16 @@ class FluxPipeline(BasePipeline): Supports FLUX.1-dev (50 steps, guidance) and FLUX.1-schnell (4 steps, no guidance). """ - def __init__(self, model_config): + def __init__(self, pipeline_config): if ( - model_config.visual_gen_mapping is not None - and model_config.visual_gen_mapping.cfg_size != 1 + pipeline_config.visual_gen_mapping is not None + and pipeline_config.visual_gen_mapping.cfg_size != 1 ): raise ValueError( "FluxPipeline does not support CFG parallelism. Please set cfg_size to 1." ) - super().__init__(model_config) + super().__init__(pipeline_config) @staticmethod def _compute_flux_timestep_embedding( @@ -99,7 +99,7 @@ def _compute_flux_timestep_embedding( @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def device(self): @@ -121,7 +121,9 @@ def warmup_cache_key(self, height: int, width: int, **kwargs) -> tuple: def _init_transformer(self) -> None: """Initialize FLUX transformer with quantization support.""" logger.info("Creating FLUX transformer with quantization support...") - self.transformer = FluxTransformer2DModel(model_config=self.model_config) + self.transformer = FluxTransformer2DModel( + model_config=self.pipeline_config.model_configs["transformer"] + ) def _run_warmup(self, height: int, width: int, num_frames: int, steps: int) -> None: with torch.no_grad(): @@ -156,7 +158,7 @@ def load_standard_components( self.text_encoder = CLIPTextModel.from_pretrained( checkpoint_dir, subfolder=PipelineComponent.TEXT_ENCODER, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) # T5 tokenizer and text encoder (for sequence embeddings) @@ -171,7 +173,7 @@ def load_standard_components( self.text_encoder_2 = T5EncoderModel.from_pretrained( checkpoint_dir, subfolder=PipelineComponent.TEXT_ENCODER_2, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) # VAE @@ -203,7 +205,7 @@ def load_weights(self, weights: dict) -> None: self.transformer.load_weights(transformer_weights) logger.info("Transformer weights loaded successfully.") - self._target_dtype = self.model_config.torch_dtype + self._target_dtype = self.pipeline_config.torch_dtype if self.transformer is not None: self.transformer.eval() diff --git a/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux2.py b/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux2.py index 8675a0387e79..82302aac8ce3 100644 --- a/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux2.py +++ b/tensorrt_llm/_torch/visual_gen/models/flux/pipeline_flux2.py @@ -119,16 +119,16 @@ class Flux2Pipeline(BasePipeline): # Default for backward compatibility (FLUX.2-dev) HIDDEN_STATE_LAYERS: Tuple[int, ...] = (10, 20, 30) - def __init__(self, model_config): + def __init__(self, pipeline_config): if ( - model_config.visual_gen_mapping is not None - and model_config.visual_gen_mapping.cfg_size != 1 + pipeline_config.visual_gen_mapping is not None + and pipeline_config.visual_gen_mapping.cfg_size != 1 ): raise ValueError( "Flux2Pipeline does not support CFG parallelism. Please set cfg_size to 1." ) - super().__init__(model_config) + super().__init__(pipeline_config) @staticmethod def _compute_flux2_timestep_embedding( @@ -167,7 +167,7 @@ def _compute_flux2_timestep_embedding( @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def device(self): @@ -189,7 +189,9 @@ def warmup_cache_key(self, height: int, width: int, **kwargs) -> tuple: def _init_transformer(self) -> None: """Initialize FLUX.2 transformer with quantization support.""" logger.info("Creating FLUX.2 transformer with quantization support...") - self.transformer = Flux2Transformer2DModel(model_config=self.model_config) + self.transformer = Flux2Transformer2DModel( + model_config=self.pipeline_config.model_configs["transformer"] + ) def _run_warmup(self, height: int, width: int, num_frames: int, steps: int) -> None: with torch.no_grad(): @@ -255,13 +257,13 @@ def load_standard_components( # Mistral3 is a multimodal model (not pure CausalLM) self.text_encoder = Mistral3ForConditionalGeneration.from_pretrained( text_encoder_path, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) else: # Qwen3 and other CausalLM text encoders self.text_encoder = AutoModelForCausalLM.from_pretrained( text_encoder_path, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) # VAE (FLUX.2-specific VAE with BatchNorm) @@ -294,7 +296,7 @@ def load_weights(self, weights: dict) -> None: self.transformer.load_weights(transformer_weights) logger.info("Transformer weights loaded successfully.") - self._target_dtype = self.model_config.torch_dtype + self._target_dtype = self.pipeline_config.torch_dtype if self.transformer is not None: self.transformer.eval() diff --git a/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux.py b/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux.py index e87ace476d74..99852d3f6f6b 100644 --- a/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux.py +++ b/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux.py @@ -34,6 +34,7 @@ from tensorrt_llm._torch.visual_gen.models.flux.attention import FluxJointAttention from tensorrt_llm._torch.visual_gen.models.flux.joint_proj import FluxJointAttnMLPProj from tensorrt_llm._torch.visual_gen.models.flux.pos_embed_flux import FluxPosEmbed +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader from tensorrt_llm._torch.visual_gen.utils import SequenceSharder from tensorrt_llm.models.modeling_utils import QuantConfig @@ -554,7 +555,7 @@ def forward( return encoder_hidden_states, hidden_states -class FluxTransformer2DModel(nn.Module): +class FluxTransformer2DModel(BaseDiffusionModel): """FLUX Transformer model for text-to-image generation. This is the native TRT-LLM implementation of FLUX transformer. @@ -572,8 +573,7 @@ class FluxTransformer2DModel(nn.Module): """ def __init__(self, model_config: DiffusionModelConfig): - super().__init__() - self.model_config = model_config + super().__init__(model_config) vgm = model_config.visual_gen_mapping num_heads = getattr(model_config.pretrained_config, "num_attention_heads", 24) diff --git a/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux2.py b/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux2.py index 15dce09d9565..0fb2d53311df 100644 --- a/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux2.py +++ b/tensorrt_llm/_torch/visual_gen/models/flux/transformer_flux2.py @@ -41,6 +41,7 @@ AdaLayerNormContinuous, _remap_checkpoint_keys, ) +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader from tensorrt_llm._torch.visual_gen.utils import SequenceSharder from tensorrt_llm.models.modeling_utils import QuantConfig @@ -417,7 +418,7 @@ def forward( # ============================================================================= -class Flux2Transformer2DModel(nn.Module): +class Flux2Transformer2DModel(BaseDiffusionModel): """FLUX.2 Transformer model for image generation (Native TRT-LLM). This implements the full FLUX.2 architecture matching HuggingFace diffusers: @@ -433,8 +434,7 @@ def __init__(self, model_config: DiffusionModelConfig): Args: model_config: DiffusionModelConfig instance (from DiffusionModelLoader) """ - super().__init__() - self.model_config = model_config + super().__init__(model_config) vgm = model_config.visual_gen_mapping num_heads = getattr(model_config.pretrained_config, "num_attention_heads", 48) diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py index c66d795bc47f..f48b3d05aea7 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2.py @@ -629,7 +629,7 @@ def resolve_variant(cls, config): logger.info(f"{LTX2_FORCE_ONE_STAGE_ENV} is enabled; forcing one-stage LTX2 pipeline.") return cls - checkpoint_path = getattr(config.pretrained_config, "_name_or_path", "") + checkpoint_path = getattr(config.primary_pretrained_config, "_name_or_path", "") if checkpoint_path: config.extra_attrs.update( resolve_ltx2_pipeline_extra_attrs(Path(checkpoint_path), config.extra_attrs) @@ -644,7 +644,7 @@ def resolve_variant(cls, config): @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def default_warmup_resolutions(self): @@ -733,13 +733,14 @@ def _init_transformer(self) -> None: the reference ``LTXModelConfigurator.from_config()``. Missing keys fall back to the same defaults the reference uses. """ - attn_cfg = getattr(self.model_config, "attention", None) + attn_cfg = getattr(self.pipeline_config, "attention", None) if attn_cfg is not None and getattr(attn_cfg, "quant_attention_config", None) is not None: raise NotImplementedError( "Quantized attention is not yet supported for the LTX-2 pipeline." ) - cfg = self.model_config.pretrained_config + model_config = self.pipeline_config.model_configs["transformer"] + cfg = model_config.pretrained_config rope_type = LTXRopeType(getattr(cfg, "rope_type", "interleaved")) freq_prec = getattr(cfg, "frequencies_precision", False) @@ -780,7 +781,7 @@ def _init_transformer(self) -> None: rope_type=rope_type, double_precision_rope=double_precision_rope, apply_gated_attention=apply_gated_attention, - model_config=self.model_config, + model_config=model_config, ) self.transformer._transformer_config = vars(cfg) @@ -806,11 +807,11 @@ def _setup_cuda_graphs(self): iterations (WARMUP_STEPS=2), so the captured graph contains the optimized compiled kernels. """ - if not self.model_config.cuda_graph.enable: + if not self.pipeline_config.cuda_graph.enable: return runner = _LTX2CUDAGraphRunner(CUDAGraphRunnerConfig(use_cuda_graph=True)) - compile_note = " (with torch.compile)" if self.model_config.torch_compile.enable else "" + compile_note = " (with torch.compile)" if self.pipeline_config.torch_compile.enable else "" logger.info( f"CUDA graph runner: wrapping transformer.forward (Modality-aware){compile_note}" ) @@ -849,7 +850,7 @@ def load_standard_components( tokenizer files, and ``preprocessor_config.json``. """ skip_components = skip_components or [] - dtype = self.model_config.torch_dtype + dtype = self.pipeline_config.torch_dtype needs_text = ( PipelineComponent.TOKENIZER not in skip_components @@ -878,7 +879,7 @@ def load_standard_components( ).to(device) # --- Resolve native config ---------------------------------------- - native_config = self.model_config.extra_attrs.get("monolithic_safetensors_config") + native_config = self.pipeline_config.extra_attrs.get("monolithic_safetensors_config") sft_paths = _find_safetensors_files(checkpoint_dir) _prefetch_ltx2_safetensors_files(sft_paths) @@ -1020,7 +1021,7 @@ def post_load_weights(self) -> None: # self._setup_teacache(self.transformer, coefficients=LTX2_TEACACHE_COEFFICIENTS) # Cache-DiT - if self.transformer is not None and self.model_config.cache_backend == "cache_dit": + if self.transformer is not None and self.pipeline_config.cache_backend == "cache_dit": self._setup_cache_acceleration(self.transformer, coefficients=None) # Compression ratios from native scale factors @@ -1466,7 +1467,7 @@ def forward( # CFG parallel for multi-modal guidance: each GPU handles one # CFG pass (cond or uncond), results are all-gathered, then # STG/modality passes run on every GPU before the guidance formula. - vgm = self.model_config.visual_gen_mapping + vgm = self.pipeline_config.visual_gen_mapping cfg_size = vgm.cfg_size if vgm else 1 seq_parallel_size = vgm.seq_size if vgm is not None else 1 do_cfg_parallel_mm = use_multi_modal_guidance and cfg_size >= 2 and do_cfg diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py index 4b65cb8332e0..776ca9885a78 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/pipeline_ltx2_two_stages.py @@ -662,9 +662,9 @@ def load_standard_components( **kwargs, ) - dtype = self.model_config.torch_dtype - spatial_upsampler_path = self.model_config.extra_attrs.get("spatial_upsampler_path", "") - distilled_lora_path = self.model_config.extra_attrs.get("distilled_lora_path", "") + dtype = self.pipeline_config.torch_dtype + spatial_upsampler_path = self.pipeline_config.extra_attrs.get("spatial_upsampler_path", "") + distilled_lora_path = self.pipeline_config.extra_attrs.get("distilled_lora_path", "") # --- Spatial upsampler --- if spatial_upsampler_path: diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py index ce09797c389c..1631573f28f3 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py @@ -30,6 +30,7 @@ from tensorrt_llm._torch.modules.linear import Linear, WeightMode from tensorrt_llm._torch.modules.mlp import MLP from tensorrt_llm._torch.visual_gen.attention_backend.utils import create_attention +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader from tensorrt_llm._torch.visual_gen.utils import SequenceSharder @@ -928,7 +929,7 @@ def is_audio_enabled(self) -> bool: return self in (LTXModelType.AudioVideo, LTXModelType.AudioOnly) -class LTXModel(nn.Module): +class LTXModel(BaseDiffusionModel): """LTX-2 transformer built from TRT-LLM primitives. Native implementation using optimized TRT-LLM Linear, RMSNorm, MLP, and @@ -966,8 +967,10 @@ def __init__( apply_gated_attention: bool = False, model_config: Optional["DiffusionModelConfig"] = None, ): - super().__init__() - self.model_config = model_config + from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + + model_config = model_config or DiffusionModelConfig() + super().__init__(model_config) self.model_type = model_type self.use_middle_indices_grid = use_middle_indices_grid self.rope_type = rope_type diff --git a/tensorrt_llm/_torch/visual_gen/models/modeling.py b/tensorrt_llm/_torch/visual_gen/models/modeling.py new file mode 100644 index 000000000000..899feffb429e --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/modeling.py @@ -0,0 +1,29 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Base classes for VisualGen model components.""" + +import torch.nn as nn + +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + + +class BaseDiffusionModel(nn.Module): + """Base class for TRT-LLM VisualGen model components.""" + + def __init__(self, model_config: DiffusionModelConfig): + super().__init__() + self.model_config = model_config + self.component_name = model_config.component_name + self.pretrained_config = model_config.pretrained_config diff --git a/tensorrt_llm/_torch/visual_gen/models/qwen_image/pipeline_qwen_image.py b/tensorrt_llm/_torch/visual_gen/models/qwen_image/pipeline_qwen_image.py index 1c42f96d458b..1290453c57a0 100644 --- a/tensorrt_llm/_torch/visual_gen/models/qwen_image/pipeline_qwen_image.py +++ b/tensorrt_llm/_torch/visual_gen/models/qwen_image/pipeline_qwen_image.py @@ -76,8 +76,8 @@ class QwenImagePipeline(BasePipeline): # either version. DEFAULT_GENERATION_PARAMS = _DEFAULT_GENERATION_PARAMS - def __init__(self, model_config): - super().__init__(model_config) + def __init__(self, pipeline_config): + super().__init__(pipeline_config) # Qwen-Image uses 8x VAE downsample + 2x2 patch packing. Both # scheduler and image-prep assume a latent grid divisible by # (vae_scale_factor * 2 == 16). vae_scale_factor is updated by @@ -87,7 +87,7 @@ def __init__(self, model_config): @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def device(self): @@ -122,12 +122,11 @@ def resolution_multiple_of(self) -> Tuple[int, int]: # ------------------------------------------------------------------ def _init_transformer(self) -> None: logger.info("Creating Qwen-Image transformer") - # ``pretrained_config`` on the DiffusionModelConfig is populated - # from ``/transformer/config.json`` as a SimpleNamespace by - # ``DiffusionModelConfig.from_pretrained``. Read the fields we - # care about with sensible defaults (matching the Qwen-Image 20B - # reference model). - pretrained = getattr(self.model_config, "pretrained_config", None) + model_config = self.pipeline_config.model_configs["transformer"] + # ``pretrained_config`` is populated from + # ``/transformer/config.json``. Read the fields we care + # about with defaults matching the Qwen-Image 20B reference model. + pretrained = getattr(model_config, "pretrained_config", None) def _cfg(name: str, default): if pretrained is None: @@ -137,7 +136,7 @@ def _cfg(name: str, default): return getattr(pretrained, name, default) self.transformer = QwenImageTransformer2DModel( - model_config=self.model_config, + model_config=model_config, patch_size=_cfg("patch_size", 2), in_channels=_cfg("in_channels", 64), out_channels=_cfg("out_channels", 16), @@ -200,7 +199,7 @@ def load_standard_components( self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained( checkpoint_dir, subfolder=PipelineComponent.TEXT_ENCODER, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) if PipelineComponent.VAE not in skip_components: @@ -233,7 +232,7 @@ def load_weights(self, weights: dict) -> None: # default. Cast only non-quantized tensors so FP8/NVFP4 weights # and FP32 scales keep the dtypes created by Linear.load_weights(). self.transformer.to_inference_dtype().eval() - self._target_dtype = self.model_config.torch_dtype + self._target_dtype = self.pipeline_config.torch_dtype # ------------------------------------------------------------------ # Prompt encoding (Qwen2.5-VL chat template). diff --git a/tensorrt_llm/_torch/visual_gen/models/qwen_image/transformer_qwen_image.py b/tensorrt_llm/_torch/visual_gen/models/qwen_image/transformer_qwen_image.py index 9b3330d51011..f06db14a45b6 100644 --- a/tensorrt_llm/_torch/visual_gen/models/qwen_image/transformer_qwen_image.py +++ b/tensorrt_llm/_torch/visual_gen/models/qwen_image/transformer_qwen_image.py @@ -29,6 +29,7 @@ from tensorrt_llm._torch.modules.mlp import MLP from tensorrt_llm._torch.modules.rms_norm import RMSNorm from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader @@ -734,7 +735,7 @@ def forward( # =========================================================================== -class QwenImageTransformer2DModel(nn.Module): +class QwenImageTransformer2DModel(BaseDiffusionModel): """Qwen-Image 20B MMDiT transformer. Mirrors ``diffusers.models.transformers.transformer_qwenimage.QwenImageTransformer2DModel`` @@ -756,8 +757,8 @@ def __init__( axes_dims_rope: Tuple[int, int, int] = (16, 56, 56), attn_backend: str = "sdpa", ): - super().__init__() - self.model_config = model_config or DiffusionModelConfig() + model_config = model_config or DiffusionModelConfig() + super().__init__(model_config) self.attn_backend = attn_backend self.patch_size = patch_size diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py index f314f33fad3f..7442f581f958 100644 --- a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py +++ b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py @@ -89,23 +89,24 @@ doc="Wan 2.1 & 2.2 text-to-video family.", ) class WanPipeline(BasePipeline): - def __init__(self, model_config): + def __init__(self, pipeline_config): # Wan2.2 A14B two-stage denoising parameters self.transformer_2 = None - self.boundary_ratio = getattr(model_config.pretrained_config, "boundary_ratio", None) - self.expand_timesteps = getattr(model_config.pretrained_config, "expand_timesteps", False) + primary_pretrained_config = pipeline_config.primary_pretrained_config + self.boundary_ratio = getattr(primary_pretrained_config, "boundary_ratio", None) + self.expand_timesteps = getattr(primary_pretrained_config, "expand_timesteps", False) # Derived model type flags self.is_wan22_14b = self.boundary_ratio is not None self.is_wan22_5b = self.expand_timesteps # Validate TeaCache compatibility before allocating GPU memory - if (self.is_wan22_14b or self.is_wan22_5b) and model_config.cache_backend == "teacache": + if (self.is_wan22_14b or self.is_wan22_5b) and pipeline_config.cache_backend == "teacache": raise ValueError( "TeaCache is not supported for Wan 2.2 models. " "Use cache_backend='none' or 'cache_dit' (not 'teacache')." ) - super().__init__(model_config) + super().__init__(pipeline_config) def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): """Compute timestep embedding for WAN transformer. @@ -124,7 +125,7 @@ def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): t_emb = ce.time_embedder(t_freq) - teacache = self.model_config.teacache + teacache = self.pipeline_config.teacache if teacache is not None and teacache.use_ret_steps: return ce.time_proj(ce.act_fn(t_emb)).to(torch.float32) else: @@ -132,7 +133,7 @@ def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def device(self): @@ -176,12 +177,16 @@ def resolution_multiple_of(self): def _init_transformer(self) -> None: logger.info("Creating WAN transformer with quantization support...") - self.transformer = WanTransformer3DModel(model_config=self.model_config) + self.transformer = WanTransformer3DModel( + model_config=self.pipeline_config.model_configs["transformer"] + ) # Wan2.2 A14B: create second transformer for two-stage denoising if self.is_wan22_14b: logger.info("Creating second transformer for Wan2.2 A14B two-stage denoising...") - self.transformer_2 = WanTransformer3DModel(model_config=self.model_config) + self.transformer_2 = WanTransformer3DModel( + model_config=self.pipeline_config.model_configs["transformer_2"] + ) def load_standard_components( self, @@ -222,7 +227,7 @@ def load_standard_components( self.text_encoder = UMT5EncoderModel.from_pretrained( checkpoint_dir, subfolder=PipelineComponent.TEXT_ENCODER, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) if PipelineComponent.VAE not in skip_components: @@ -286,7 +291,7 @@ def load_weights(self, weights: dict) -> None: logger.info("Transformer_2 weights loaded successfully.") # Cache the target dtype from model config (default: bfloat16) - self._target_dtype = self.model_config.torch_dtype + self._target_dtype = self.pipeline_config.torch_dtype # Set model to eval mode if self.transformer is not None: @@ -298,7 +303,7 @@ def post_load_weights(self) -> None: super().post_load_weights() # Calls transformer.post_load_weights() for FP8 scale transformations if self.transformer is not None: # TeaCache extractor only when using TeaCache (not Cache-DiT). - if self.model_config.cache_backend == "teacache": + if self.pipeline_config.cache_backend == "teacache": register_extractor_from_config( ExtractorConfig( model_class_name="WanTransformer3DModel", @@ -313,7 +318,7 @@ def post_load_weights(self) -> None: ) self.transformer_cache_backend = self.cache_accelerator else: - if self.model_config.cache_backend == "cache_dit": + if self.pipeline_config.cache_backend == "cache_dit": self._setup_cache_acceleration(self.transformer, coefficients=None) # TeaCache is not supported for Wan 2.2 unless using Cache-DiT. self.transformer_cache_backend = self.cache_accelerator diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py index ca9e53ecf9a8..2ff09e153a03 100644 --- a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py +++ b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py @@ -88,20 +88,22 @@ doc="Wan 2.1 & 2.2 image-to-video family.", ) class WanImageToVideoPipeline(BasePipeline): - def __init__(self, model_config): + def __init__(self, pipeline_config): # Wan2.2 14B two-stage denoising parameters self.transformer_2 = None - self.boundary_ratio = getattr(model_config.pretrained_config, "boundary_ratio", None) + self.boundary_ratio = getattr( + pipeline_config.primary_pretrained_config, "boundary_ratio", None + ) self.is_wan22_14b = self.boundary_ratio is not None # Validate TeaCache compatibility before allocating GPU memory - if self.is_wan22_14b and model_config.cache_backend == "teacache": + if self.is_wan22_14b and pipeline_config.cache_backend == "teacache": raise ValueError( "TeaCache is not supported for Wan 2.2 models. " "Use cache_backend='none' or 'cache_dit' (not 'teacache')." ) - super().__init__(model_config) + super().__init__(pipeline_config) def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): """Compute timestep embedding for Wan I2V transformer. @@ -119,7 +121,7 @@ def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): t_emb = ce.time_embedder(t_freq) - teacache = self.model_config.teacache + teacache = self.pipeline_config.teacache if teacache is not None and teacache.use_ret_steps: # ret_steps mode: use timestep_proj — what the ret_steps coefficients were calibrated for return ce.time_proj(ce.act_fn(t_emb)).to(torch.float32) @@ -128,7 +130,7 @@ def _compute_wan_timestep_embedding(self, module, timestep=None, **kwargs): @property def dtype(self): - return self.model_config.torch_dtype + return self.pipeline_config.torch_dtype @property def device(self): @@ -166,12 +168,16 @@ def resolution_multiple_of(self): def _init_transformer(self) -> None: logger.info("Creating WAN I2V transformer with quantization support...") - self.transformer = WanTransformer3DModel(model_config=self.model_config) + self.transformer = WanTransformer3DModel( + model_config=self.pipeline_config.model_configs["transformer"] + ) # Wan2.2: Optionally create second transformer for two-stage denoising if self.boundary_ratio is not None: logger.info("Creating second transformer for Wan2.2 I2V two-stage denoising...") - self.transformer_2 = WanTransformer3DModel(model_config=self.model_config) + self.transformer_2 = WanTransformer3DModel( + model_config=self.pipeline_config.model_configs["transformer_2"] + ) def load_standard_components( self, @@ -217,7 +223,7 @@ def load_standard_components( self.text_encoder = UMT5EncoderModel.from_pretrained( checkpoint_dir, subfolder=PipelineComponent.TEXT_ENCODER, - torch_dtype=self.model_config.torch_dtype, + torch_dtype=self.pipeline_config.torch_dtype, ).to(device) if PipelineComponent.VAE not in skip_components: @@ -306,7 +312,7 @@ def load_weights(self, weights: dict) -> None: logger.info("Transformer_2 weights loaded successfully.") # Cache the target dtype from model config (default: bfloat16) - self._target_dtype = self.model_config.torch_dtype + self._target_dtype = self.pipeline_config.torch_dtype # Set model to eval mode if self.transformer is not None: @@ -319,7 +325,7 @@ def load_weights(self, weights: dict) -> None: def post_load_weights(self) -> None: super().post_load_weights() # Calls transformer.post_load_weights() for FP8 scale transformations if self.transformer is not None: - if self.model_config.cache_backend == "teacache": + if self.pipeline_config.cache_backend == "teacache": register_extractor_from_config( ExtractorConfig( model_class_name="WanTransformer3DModel", @@ -334,7 +340,7 @@ def post_load_weights(self) -> None: ) self.transformer_cache_backend = self.cache_accelerator else: - if self.model_config.cache_backend == "cache_dit": + if self.pipeline_config.cache_backend == "cache_dit": self._setup_cache_acceleration(self.transformer, coefficients=None) self.transformer_cache_backend = self.cache_accelerator diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py index 0bac474df0ff..084c03754728 100644 --- a/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py +++ b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py @@ -12,6 +12,7 @@ from tensorrt_llm._torch.modules.linear import Linear, TensorParallelMode from tensorrt_llm._torch.modules.mlp import MLP from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode from tensorrt_llm._torch.visual_gen.modules.rms_norm import RMSNormTPAware from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader @@ -474,16 +475,14 @@ def forward( return x -class WanTransformer3DModel(nn.Module): +class WanTransformer3DModel(BaseDiffusionModel): _supports_gradient_checkpointing = True def __init__( self, model_config: DiffusionModelConfig, ): - super().__init__() - - self.model_config = model_config + super().__init__(model_config) vgm = model_config.visual_gen_mapping diff --git a/tensorrt_llm/_torch/visual_gen/pipeline.py b/tensorrt_llm/_torch/visual_gen/pipeline.py index 9d19d7cf778e..7a0c629bf5c9 100644 --- a/tensorrt_llm/_torch/visual_gen/pipeline.py +++ b/tensorrt_llm/_torch/visual_gen/pipeline.py @@ -98,7 +98,7 @@ def _parse_profile_range(): if TYPE_CHECKING: from .cache import CacheAccelerator - from .config import DiffusionModelConfig + from .config import DiffusionPipelineConfig class BasePipeline(nn.Module): @@ -107,7 +107,7 @@ class BasePipeline(nn.Module): """ @classmethod - def resolve_variant(cls, config: "DiffusionModelConfig") -> Type["BasePipeline"]: + def resolve_variant(cls, config: "DiffusionPipelineConfig") -> Type["BasePipeline"]: """Return *cls* or a more specialized subclass based on *config*. Override in subclasses to select a variant pipeline at creation @@ -117,11 +117,11 @@ def resolve_variant(cls, config: "DiffusionModelConfig") -> Type["BasePipeline"] """ return cls - def __init__(self, model_config: "DiffusionModelConfig"): + def __init__(self, pipeline_config: "DiffusionPipelineConfig"): super().__init__() - self.model_config = model_config - self.config = model_config.pretrained_config - self.mapping: Mapping = getattr(model_config, "mapping", None) or Mapping() + self.pipeline_config = pipeline_config + self.config = pipeline_config.primary_pretrained_config + self.mapping: Mapping = getattr(pipeline_config, "mapping", None) or Mapping() self._cuda_graph_runners: Dict[str, CUDAGraphRunner] = {} self._parallel_vae_enabled: bool = False self._warmed_up_shapes: Set[tuple] = set() @@ -171,10 +171,10 @@ def _cuda_profiler_stop(self): def _setup_cuda_graphs(self): """Wrap all transformer components with CUDA graph capture/replay.""" - if not self.model_config.cuda_graph.enable: + if not self.pipeline_config.cuda_graph.enable: return - if self.model_config.torch_compile.enable: + if self.pipeline_config.torch_compile.enable: logger.warning( "CUDA graphs with torch.compile not yet supported. Using torch.compile only." ) @@ -294,7 +294,7 @@ def resolve_warmup_plan(self) -> Tuple[List[Tuple[int, int, int]], int]: Returns: (shapes, steps) tuple where shapes = list of (h, w, f) """ - warmup_cfg = self.model_config.compilation + warmup_cfg = self.pipeline_config.compilation if warmup_cfg.resolutions is not None or warmup_cfg.num_frames is not None: resolutions = ( @@ -397,13 +397,12 @@ def post_load_weights(self) -> None: self.transformer.post_load_weights() def _apply_teacache_coefficients(self, coefficients: Optional[Dict]) -> None: - """Pick TeaCache coefficients from checkpoint path; updates model_config.teacache in place.""" + """Pick TeaCache coefficients from checkpoint path; updates pipeline config in place.""" if not coefficients: return - teacache_cfg = self.model_config.teacache - checkpoint_path = ( - getattr(getattr(self.model_config, "pretrained_config", None), "_name_or_path", "") - or "" + teacache_cfg = self.pipeline_config.teacache + checkpoint_path = getattr( + self.pipeline_config.primary_pretrained_config, "_name_or_path", "" ) matched = False for model_size, coeff_data in coefficients.items(): @@ -445,7 +444,7 @@ def _setup_cache_acceleration( self.cache_accelerator.unwrap() self.cache_accelerator = None - cfg = self.model_config + cfg = self.pipeline_config if cfg.cache_backend == "cache_dit": acc = CacheDiTAccelerator(self, cfg.cache_dit) @@ -476,8 +475,8 @@ def setup_parallel_vae(self): parallel-VAE decode ownership applies. The actual ``ParallelVAEFactory`` wrap is a local side effect that only runs on ranks in ``vae_ranks``. """ - parallel_cfg = self.model_config.parallel - vgm = self.model_config.visual_gen_mapping + parallel_cfg = self.pipeline_config.parallel + vgm = self.pipeline_config.visual_gen_mapping # Global preconditions — evaluate identically on every rank. self._parallel_vae_enabled = ( @@ -528,7 +527,7 @@ def torch_compile(self) -> None: For non-transformer components, compiles the entire module. """ - tc_config = self.model_config.torch_compile + tc_config = self.pipeline_config.torch_compile # Using default as max-autotune mode takes more initialization time and # does not improve performance a lot. @@ -663,7 +662,7 @@ def decode_latents( Non-decoding ranks return ``None`` (or a tuple of ``None``). """ if self._parallel_vae_enabled: - vgm = self.model_config.visual_gen_mapping + vgm = self.pipeline_config.visual_gen_mapping decode_ranks = set(vgm.vae_ranks) else: decode_ranks = {0} @@ -703,7 +702,7 @@ def _setup_cfg_config( Returns: Dict with CFG configuration including split tensors """ - vgm = self.model_config.visual_gen_mapping + vgm = self.pipeline_config.visual_gen_mapping cfg_size = vgm.cfg_size if vgm else 1 ulysses_size = vgm.ulysses_size if vgm else 1 attn2d_row_size = vgm.attn2d_row_size if vgm else 1 @@ -774,7 +773,7 @@ def _denoise_step_cfg_parallel( local_extras, ): """Execute single denoising step with CFG parallel.""" - vgm = self.model_config.visual_gen_mapping + vgm = self.pipeline_config.visual_gen_mapping cfg_pg = vgm.cfg_group if vgm else None cfg_size = vgm.cfg_size if vgm else 1 @@ -1106,7 +1105,7 @@ def denoise( if getattr(self, "cache_accelerator", None) and self.cache_accelerator.is_enabled(): stats = self.cache_accelerator.get_stats() if stats: - if self.model_config.cache_backend == "cache_dit": + if self.pipeline_config.cache_backend == "cache_dit": logger.info("Cache-DiT stats: %s", stats) elif "hit_rate" in stats: logger.info( diff --git a/tensorrt_llm/_torch/visual_gen/pipeline_loader.py b/tensorrt_llm/_torch/visual_gen/pipeline_loader.py index ac61ce729fbc..d4e0a7f1295e 100644 --- a/tensorrt_llm/_torch/visual_gen/pipeline_loader.py +++ b/tensorrt_llm/_torch/visual_gen/pipeline_loader.py @@ -2,7 +2,7 @@ Model loader for diffusion pipelines. Flow: -1. Load config via DiffusionModelConfig.from_pretrained() +1. Load config via DiffusionPipelineConfig.from_pretrained() 2. Create pipeline via AutoPipeline.from_config() with MetaInit 3. Load weights with on-the-fly quantization if dynamic_weight_quant=True 4. Call pipeline.post_load_weights() @@ -28,7 +28,7 @@ from tensorrt_llm.visual_gen.args import VisualGenArgs from tensorrt_llm.visual_gen.sparse_attention import SkipSoftmaxConfig, apply_skip_softmax_overrides -from .config import DiffusionModelConfig +from .config import DiffusionPipelineConfig from .mapping import VisualGenMapping from .models import AutoPipeline from .pipeline_registry import PIPELINE_REGISTRY, PipelineComponent @@ -140,7 +140,7 @@ def _resolve_pipeline_config(self, checkpoint_dir: str) -> dict: ) return {**entry.defaults, **user_pipeline_config} - def _setup_visual_gen_mapping(self, config: DiffusionModelConfig) -> None: + def _setup_visual_gen_mapping(self, config: DiffusionPipelineConfig) -> None: ws = dist.get_world_size() if dist.is_initialized() else 1 rk = dist.get_rank() if dist.is_initialized() else 0 attn2d_row, attn2d_col = self.args.parallel_config.attn2d_size @@ -155,8 +155,12 @@ def _setup_visual_gen_mapping(self, config: DiffusionModelConfig) -> None: tp_size=self.args.parallel_config.tp_size, parallel_vae_size=self.args.parallel_config.parallel_vae_size, ) + llm_mapping = vgm.to_llm_mapping() config.visual_gen_mapping = vgm - config.mapping = vgm.to_llm_mapping() + config.mapping = llm_mapping + for model_config in config.model_configs.values(): + model_config.visual_gen_mapping = vgm + model_config.mapping = llm_mapping def load( self, @@ -169,7 +173,7 @@ def load( Flow: 1. Resolve checkpoint_dir (local path or HuggingFace Hub model ID) - 2. Load config via DiffusionModelConfig.from_pretrained() + 2. Load config via DiffusionPipelineConfig.from_pretrained() 3. Create pipeline via AutoPipeline.from_config() with MetaInit 4. Load transformer weights via pipeline.load_transformer_weights() 5. Load auxiliary components (VAE, text_encoder) @@ -207,7 +211,7 @@ def load( # Merge pretrained checkpoint config with user-provided VisualGenArgs # ===================================================================== logger.info(f"Loading config from {checkpoint_dir}") - config = DiffusionModelConfig.from_pretrained( + config = DiffusionPipelineConfig.from_pretrained( checkpoint_dir, args=self.args, pipeline_config=resolved_pipeline_config, diff --git a/tensorrt_llm/_torch/visual_gen/pipeline_registry.py b/tensorrt_llm/_torch/visual_gen/pipeline_registry.py index 7be918d0ffec..4300a2943e80 100644 --- a/tensorrt_llm/_torch/visual_gen/pipeline_registry.py +++ b/tensorrt_llm/_torch/visual_gen/pipeline_registry.py @@ -2,7 +2,7 @@ # SPDX-License-Identifier: Apache-2.0 """Pipeline registry for unified config flow. -Follows: VisualGenArgs → PipelineLoader → DiffusionModelConfig → AutoPipeline → BasePipeline +Follows: VisualGenArgs → PipelineLoader → DiffusionPipelineConfig → AutoPipeline → BasePipeline All pipelines (Wan, Flux, Flux2, LTX2, QwenImage) register via @register_pipeline decorator. @@ -35,7 +35,7 @@ from tensorrt_llm.logger import logger if TYPE_CHECKING: - from .config import DiffusionModelConfig + from .config import DiffusionPipelineConfig from .pipeline import BasePipeline @@ -123,11 +123,11 @@ class AutoPipeline: @staticmethod def from_config( - config: "DiffusionModelConfig", + config: "DiffusionPipelineConfig", checkpoint_dir: str, ) -> "BasePipeline": """ - Create pipeline instance from DiffusionModelConfig. + Create pipeline instance from DiffusionPipelineConfig. """ # Detect pipeline type from model_index.json or from model safetensors class_name = AutoPipeline._detect_from_checkpoint(checkpoint_dir) @@ -147,7 +147,7 @@ def from_config( logger.info(f"AutoPipeline: Creating {pipeline_class.__name__} from {checkpoint_dir}") - # Instantiate pipeline with DiffusionModelConfig + # Instantiate pipeline with DiffusionPipelineConfig return pipeline_class(config) @staticmethod diff --git a/tensorrt_llm/visual_gen/args.py b/tensorrt_llm/visual_gen/args.py index 8daefd5d7bb6..5b96985efd8f 100644 --- a/tensorrt_llm/visual_gen/args.py +++ b/tensorrt_llm/visual_gen/args.py @@ -481,7 +481,7 @@ class VisualGenArgs(StrictBaseModel): "Quantization config — accepts either a QuantConfig instance " "or a ModelOpt-format dict (e.g. ``{'quant_algo': 'FP8', " "'dynamic': True}``). Dict-form parsing happens lazily in " - "DiffusionModelConfig.from_pretrained." + "DiffusionPipelineConfig.from_pretrained." ), ) compilation_config: CompilationConfig = Field( diff --git a/tests/unittest/_torch/visual_gen/test_flux_pipeline.py b/tests/unittest/_torch/visual_gen/test_flux_pipeline.py index c51c338af2fe..0455e25faff7 100644 --- a/tests/unittest/_torch/visual_gen/test_flux_pipeline.py +++ b/tests/unittest/_torch/visual_gen/test_flux_pipeline.py @@ -176,7 +176,7 @@ def test_load_flux1_pipeline_basic(self, flux1_checkpoint_exists): assert pipeline is not None assert hasattr(pipeline, "transformer") assert pipeline.transformer is not None - assert pipeline.model_config.attention.backend == "VANILLA" + assert pipeline.pipeline_config.attention.backend == "VANILLA" del pipeline gc.collect() @@ -210,7 +210,7 @@ def test_load_flux1_with_attention_backend(self, flux1_checkpoint_exists, backen pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_COMPONENTS) - assert pipeline.model_config.attention.backend == backend + assert pipeline.pipeline_config.attention.backend == backend del pipeline gc.collect() @@ -236,7 +236,7 @@ def test_load_flux1_with_quantization(self, flux1_checkpoint_exists, quant_algo: pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_COMPONENTS) - assert pipeline.model_config.quant_config.quant_algo is not None + assert pipeline.pipeline_config.quant_config.quant_algo is not None # Count quantized Linear layers and verify FP8 weights quant_count = 0 @@ -277,7 +277,7 @@ def test_load_flux2_with_quantization(self, flux2_checkpoint_exists, quant_algo: pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_COMPONENTS) - assert pipeline.model_config.quant_config.quant_algo is not None + assert pipeline.pipeline_config.quant_config.quant_algo is not None quant_count = 0 found_fp8 = False @@ -1157,7 +1157,7 @@ def _run_all_optimizations_worker( transformer = pipeline.transformer.eval() # Verify all optimizations are enabled - assert pipeline.model_config.visual_gen_mapping.ulysses_size == world_size, ( + assert pipeline.pipeline_config.visual_gen_mapping.ulysses_size == world_size, ( "Ulysses parallel not enabled" ) assert transformer.model_config.quant_config.quant_algo == QuantAlgo.FP8, "FP8 not enabled" diff --git a/tests/unittest/_torch/visual_gen/test_ltx2_pipeline.py b/tests/unittest/_torch/visual_gen/test_ltx2_pipeline.py index a5109b3cb01e..8fc668929427 100644 --- a/tests/unittest/_torch/visual_gen/test_ltx2_pipeline.py +++ b/tests/unittest/_torch/visual_gen/test_ltx2_pipeline.py @@ -20,7 +20,7 @@ from test_common.llm_data import llm_models_root from tensorrt_llm._torch.modules.linear import Linear -from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.config import DiffusionPipelineConfig from tensorrt_llm._torch.visual_gen.models.ltx2.pipeline_ltx2 import LTX2_FORCE_ONE_STAGE_ENV from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineComponent, PipelineLoader from tensorrt_llm.visual_gen.args import AttentionConfig, CacheDiTConfig, VisualGenArgs @@ -200,7 +200,7 @@ def test_load_with_quantization(self, ltx2_bf16_checkpoint_exists, quant_algo: s pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_COMPONENTS) - assert pipeline.model_config.quant_config.quant_algo is not None + assert pipeline.pipeline_config.quant_config.quant_algo is not None quant_count = 0 found_fp8 = False @@ -811,7 +811,7 @@ def test_resolve_variant_returns_two_stage_when_configured(self, monkeypatch): ) config = MagicMock() - config.pretrained_config._name_or_path = "" + config.primary_pretrained_config._name_or_path = "" config.extra_attrs = { "spatial_upsampler_path": "/fake/upsampler.safetensors", "distilled_lora_path": "/fake/lora.safetensors", @@ -828,7 +828,7 @@ def test_resolve_variant_honors_force_one_stage_env(self, monkeypatch): monkeypatch.setenv(LTX2_FORCE_ONE_STAGE_ENV, "1") config = MagicMock() - config.pretrained_config._name_or_path = "" + config.primary_pretrained_config._name_or_path = "" config.extra_attrs = { "spatial_upsampler_path": "/fake/upsampler.safetensors", "distilled_lora_path": "/fake/lora.safetensors", @@ -844,7 +844,7 @@ def test_resolve_variant_returns_base_without_two_stage_config(self): from tensorrt_llm._torch.visual_gen.models.ltx2.pipeline_ltx2 import LTX2Pipeline config = MagicMock() - config.pretrained_config._name_or_path = "" + config.primary_pretrained_config._name_or_path = "" config.extra_attrs = {} result = LTX2Pipeline.resolve_variant(config) @@ -857,7 +857,7 @@ def test_resolve_variant_requires_both_paths(self): from tensorrt_llm._torch.visual_gen.models.ltx2.pipeline_ltx2 import LTX2Pipeline config = MagicMock() - config.pretrained_config._name_or_path = "" + config.primary_pretrained_config._name_or_path = "" config.extra_attrs = {"spatial_upsampler_path": "/fake/upsampler.safetensors"} result = LTX2Pipeline.resolve_variant(config) @@ -881,7 +881,7 @@ def test_two_stage_auxiliary_paths_are_discovered_by_default(self, tmp_path, mon lora_path.touch() args = VisualGenArgs(model=str(checkpoint_path)) - config = DiffusionModelConfig.from_pretrained(str(checkpoint_path), args=args) + config = DiffusionPipelineConfig.from_pretrained(str(checkpoint_path), args=args) assert LTX2Pipeline.resolve_variant(config) is LTX2TwoStagesPipeline assert config.extra_attrs["spatial_upsampler_path"] == str(upsampler_path) @@ -898,7 +898,7 @@ def test_force_one_stage_env_skips_auto_discovery(self, tmp_path, monkeypatch): lora_path.touch() args = VisualGenArgs(model=str(checkpoint_path)) - config = DiffusionModelConfig.from_pretrained(str(checkpoint_path), args=args) + config = DiffusionPipelineConfig.from_pretrained(str(checkpoint_path), args=args) assert LTX2Pipeline.resolve_variant(config) is LTX2Pipeline assert "spatial_upsampler_path" not in config.extra_attrs @@ -919,7 +919,7 @@ def test_force_one_stage_env_prevents_promotion_with_explicit_auxiliary_paths( "distilled_lora_path": "/fake/lora.safetensors", }, ) - config = DiffusionModelConfig.from_pretrained(str(checkpoint_path), args=args) + config = DiffusionPipelineConfig.from_pretrained(str(checkpoint_path), args=args) assert config.extra_attrs["spatial_upsampler_path"] == "/fake/upsampler.safetensors" assert config.extra_attrs["distilled_lora_path"] == "/fake/lora.safetensors" @@ -941,7 +941,7 @@ def test_cache_dit_config_prevents_promotion_with_explicit_auxiliary_paths( "distilled_lora_path": "/fake/lora.safetensors", }, ) - config = DiffusionModelConfig.from_pretrained(str(checkpoint_path), args=args) + config = DiffusionPipelineConfig.from_pretrained(str(checkpoint_path), args=args) assert config.cache_backend == "cache_dit" assert config.extra_attrs["spatial_upsampler_path"] == "/fake/upsampler.safetensors" @@ -1205,7 +1205,7 @@ def test_two_stage_with_quantization(self, ltx2_two_stage_assets_exist, quant_al pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_COMPONENTS) try: assert isinstance(pipeline, LTX2TwoStagesPipeline) - assert pipeline.model_config.quant_config.quant_algo is not None + assert pipeline.pipeline_config.quant_config.quant_algo is not None quant_count = sum( 1 diff --git a/tests/unittest/_torch/visual_gen/test_model_loader.py b/tests/unittest/_torch/visual_gen/test_model_loader.py index e0e105b3bdf7..e4bb4de9ee84 100644 --- a/tests/unittest/_torch/visual_gen/test_model_loader.py +++ b/tests/unittest/_torch/visual_gen/test_model_loader.py @@ -50,13 +50,13 @@ def test_meta_init_mode_creates_meta_tensors(checkpoint_exists): pytest.skip("Checkpoint not available") from tensorrt_llm._torch.models.modeling_utils import MetaInitMode - from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.config import DiffusionPipelineConfig from tensorrt_llm._torch.visual_gen.models import AutoPipeline from tensorrt_llm.visual_gen.args import VisualGenArgs # Load config directly args = VisualGenArgs(model=CHECKPOINT_PATH) - config = DiffusionModelConfig.from_pretrained( + config = DiffusionPipelineConfig.from_pretrained( CHECKPOINT_PATH, args=args, ) @@ -124,7 +124,7 @@ def test_load_wan_pipeline_with_fp8_dynamic_quant(checkpoint_exists): pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_HEAVY_COMPONENTS) # Verify model config has dynamic_weight_quant enabled - assert pipeline.model_config.dynamic_weight_quant is True, ( + assert pipeline.pipeline_config.dynamic_weight_quant is True, ( "dynamic_weight_quant should be True when linear.type specifies FP8" ) @@ -174,15 +174,15 @@ def test_load_wan_pipeline_with_fp8_blockwise(checkpoint_exists): def test_visual_gen_args_to_quant_config(): """Test that VisualGenArgs accepts ModelOpt-format quant_config dicts. - The dict stays a dict on the public schema; DiffusionModelConfig + The dict stays a dict on the public schema; DiffusionPipelineConfig parses it (via load_diffusion_quant_config) when a pipeline loads. """ - from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.config import DiffusionPipelineConfig from tensorrt_llm.models.modeling_utils import QuantConfig from tensorrt_llm.quantization.mode import QuantAlgo from tensorrt_llm.visual_gen.args import VisualGenArgs - parse = DiffusionModelConfig.load_diffusion_quant_config + parse = DiffusionPipelineConfig.load_diffusion_quant_config # Default — no quantization. default_factory creates a QuantConfig # instance with quant_algo=None. @@ -252,7 +252,7 @@ def test_load_without_quant_config_no_fp8(checkpoint_exists): pipeline = PipelineLoader(args).load(skip_warmup=True, skip_components=SKIP_HEAVY_COMPONENTS) # Verify dynamic_weight_quant is False - assert pipeline.model_config.dynamic_weight_quant is False, ( + assert pipeline.pipeline_config.dynamic_weight_quant is False, ( "dynamic_weight_quant should be False when no quant_config" ) @@ -268,7 +268,7 @@ def test_load_without_quant_config_no_fp8(checkpoint_exists): def test_visual_gen_args_from_dict(): """Test VisualGenArgs can be created from a dictionary.""" - from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.config import DiffusionPipelineConfig from tensorrt_llm.quantization.mode import QuantAlgo from tensorrt_llm.visual_gen.args import VisualGenArgs @@ -283,7 +283,7 @@ def test_visual_gen_args_from_dict(): os.environ["WORLD_SIZE"] = "2" args = VisualGenArgs(**config_dict) assert args.model == "/path/to/model" - qc, _, dwq, _ = DiffusionModelConfig.load_diffusion_quant_config(args.quant_config) + qc, _, dwq, _ = DiffusionPipelineConfig.load_diffusion_quant_config(args.quant_config) assert qc.quant_algo == QuantAlgo.FP8 assert dwq is True assert args.parallel_config.ulysses_size == 2 diff --git a/tests/unittest/_torch/visual_gen/test_qwen_image_pipeline_config.py b/tests/unittest/_torch/visual_gen/test_qwen_image_pipeline_config.py index d27ee61aa257..0a8ef1900479 100644 --- a/tests/unittest/_torch/visual_gen/test_qwen_image_pipeline_config.py +++ b/tests/unittest/_torch/visual_gen/test_qwen_image_pipeline_config.py @@ -10,7 +10,7 @@ # Importing the models package applies the Qwen-Image registration side effect. from tensorrt_llm._torch.visual_gen import models # noqa: F401 -from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig, DiffusionPipelineConfig from tensorrt_llm._torch.visual_gen.models.qwen_image import QwenJointAttention from tensorrt_llm._torch.visual_gen.modules.attention import QKVMode from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader @@ -178,7 +178,7 @@ def test_qwen_pipeline_quant_config_parses_from_args( checkpoint_dir = _write_minimal_qwen_checkpoint(tmp_path) args = VisualGenArgs(model=str(checkpoint_dir), quant_config=quant_config) - config = DiffusionModelConfig.from_pretrained(str(checkpoint_dir), args=args) + config = DiffusionPipelineConfig.from_pretrained(str(checkpoint_dir), args=args) assert config.quant_config.quant_algo == quant_algo assert config.quant_config.group_size == group_size diff --git a/tests/unittest/_torch/visual_gen/test_teacache.py b/tests/unittest/_torch/visual_gen/test_teacache.py index c65326cf8c47..a0080a6d2008 100644 --- a/tests/unittest/_torch/visual_gen/test_teacache.py +++ b/tests/unittest/_torch/visual_gen/test_teacache.py @@ -20,7 +20,7 @@ import pytest from tensorrt_llm._torch.visual_gen.cache.teacache import TeaCacheBackend -from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig, DiffusionPipelineConfig from tensorrt_llm._torch.visual_gen.pipeline import BasePipeline from tensorrt_llm.visual_gen.args import TeaCacheConfig @@ -31,8 +31,11 @@ class TestSetupTeacache: def _make_pipeline_mock(self, checkpoint_name, use_ret_steps=False): pipeline = MagicMock() pipeline.cache_accelerator = None - pipeline.model_config = DiffusionModelConfig( + model_config = DiffusionModelConfig( pretrained_config=SimpleNamespace(_name_or_path=f"/path/to/{checkpoint_name}/snapshot"), + ) + pipeline.pipeline_config = DiffusionPipelineConfig( + model_configs={"transformer": model_config}, cache=TeaCacheConfig( teacache_thresh=0.3, use_ret_steps=use_ret_steps, @@ -51,7 +54,7 @@ def test_matching_variant_selects_coefficients(self): with patch.object(TeaCacheBackend, "enable"): BasePipeline._setup_cache_acceleration(pipeline, MagicMock(), coefficients) - assert pipeline.model_config.teacache.coefficients == [1.0, 2.0, 3.0] + assert pipeline.pipeline_config.teacache.coefficients == [1.0, 2.0, 3.0] def test_no_match_raises_valueerror(self): """Raises ValueError (fail-early) when no variant matches checkpoint.""" @@ -66,7 +69,7 @@ def test_no_match_raises_valueerror(self): def test_disabled_teacache_is_noop(self): """No-op when cache is None (TeaCache not selected).""" pipeline = self._make_pipeline_mock("FLUX.1-dev") - pipeline.model_config = pipeline.model_config.model_copy(update={"cache": None}) + pipeline.pipeline_config = pipeline.pipeline_config.model_copy(update={"cache": None}) BasePipeline._setup_cache_acceleration(pipeline, MagicMock(), {"dev": [1.0]}) assert pipeline.cache_accelerator is None diff --git a/tests/unittest/_torch/visual_gen/test_visual_gen_args.py b/tests/unittest/_torch/visual_gen/test_visual_gen_args.py index a551c683696e..540cc0c7f4c8 100644 --- a/tests/unittest/_torch/visual_gen/test_visual_gen_args.py +++ b/tests/unittest/_torch/visual_gen/test_visual_gen_args.py @@ -201,7 +201,7 @@ def test_nested_dict_auto_coerced(self): def test_quant_config_dict_passthrough(self): """ModelOpt-format dicts are accepted as-is — they parse in PipelineLoader.""" - from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.config import DiffusionPipelineConfig raw = {"quant_algo": "FP8", "dynamic": True} args = VisualGenArgs(model="/tmp/model", quant_config=raw) @@ -210,8 +210,8 @@ def test_quant_config_dict_passthrough(self): assert isinstance(args.quant_config, dict) assert args.quant_config["quant_algo"] == "FP8" # The same dict is the source of truth for the derived flags; verify - # the parser DiffusionModelConfig.from_pretrained will run on it. - qc, _, dwq, daq = DiffusionModelConfig.load_diffusion_quant_config(args.quant_config) + # the pipeline-config parser will run on it. + qc, _, dwq, _ = DiffusionPipelineConfig.load_diffusion_quant_config(args.quant_config) assert qc.quant_algo is not None assert dwq is True diff --git a/tests/unittest/_torch/visual_gen/test_wan_transformer.py b/tests/unittest/_torch/visual_gen/test_wan_transformer.py index 4c4a9d023a26..a274405b53e9 100644 --- a/tests/unittest/_torch/visual_gen/test_wan_transformer.py +++ b/tests/unittest/_torch/visual_gen/test_wan_transformer.py @@ -37,7 +37,11 @@ from diffusers import WanTransformer3DModel as HFWanTransformer3DModel from tensorrt_llm._torch.modules.linear import Linear -from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig, VisualGenArgs +from tensorrt_llm._torch.visual_gen.config import ( + DiffusionModelConfig, + DiffusionPipelineConfig, + VisualGenArgs, +) from tensorrt_llm._torch.visual_gen.models.wan.transformer_wan import WanTransformer3DModel from tensorrt_llm.models.modeling_utils import QuantConfig @@ -118,7 +122,9 @@ def _load_models(checkpoint_dir: str): ) args = VisualGenArgs(model=checkpoint_dir) - model_config = DiffusionModelConfig.from_pretrained(checkpoint_dir, args=args) + model_config = DiffusionPipelineConfig.from_pretrained(checkpoint_dir, args=args).model_configs[ + "transformer" + ] our_model = WanTransformer3DModel(model_config=model_config).to(DEVICE).eval() # Initialize our model with the exact same weights as the HF model. diff --git a/tests/unittest/_torch/visual_gen/test_warmup.py b/tests/unittest/_torch/visual_gen/test_warmup.py index f7b6672df041..c882da486f70 100644 --- a/tests/unittest/_torch/visual_gen/test_warmup.py +++ b/tests/unittest/_torch/visual_gen/test_warmup.py @@ -102,8 +102,8 @@ class _BaseStubPipeline(BasePipeline): def __init__(self, warmup_cfg): self._warmed_up_shapes = set() - self.model_config = MagicMock() - self.model_config.compilation = warmup_cfg or CompilationConfig() + self.pipeline_config = MagicMock() + self.pipeline_config.compilation = warmup_cfg or CompilationConfig() def forward(self, *args, **kwargs): pass From 5e3af40eeb8b4fc464641de1c6ac7aaaf9a35d94 Mon Sep 17 00:00:00 2001 From: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> Date: Tue, 9 Jun 2026 10:28:17 +0800 Subject: [PATCH 057/119] [TRTLLM-11457][feat] Async Ulysses pipeline (Enabled for LTX-2 + WAN) (#13978) Signed-off-by: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> --- .../kernels/ulyssesPermuteScatterKernel.cu | 107 ++++ .../kernels/ulyssesPermuteScatterKernel.h | 52 ++ .../kernels/ulyssesPostUnscatterKernel.cu | 111 ++++ .../kernels/ulyssesPostUnscatterKernel.h | 65 +++ cpp/tensorrt_llm/thop/CMakeLists.txt | 3 + cpp/tensorrt_llm/thop/asyncUlyssesOp.cpp | 516 ++++++++++++++++++ .../thop/ulyssesPermuteScatterOp.cpp | 104 ++++ .../thop/ulyssesPostUnscatterOp.cpp | 120 ++++ docs/source/models/visual-generation.md | 1 + examples/visual_gen/configs/ltx2-4gpu.yaml | 28 + .../configs/wan2.2-t2v-fp4-4gpu.yaml | 1 + .../_torch/custom_ops/cpp_custom_ops.py | 14 + .../visual_gen/attention_backend/parallel.py | 166 +++++- .../models/ltx2/ltx2_core/utils_ltx2.py | 8 +- .../models/ltx2/transformer_ltx2.py | 257 ++++++--- .../visual_gen/models/wan/transformer_wan.py | 34 +- .../_torch/visual_gen/modules/attention.py | 136 ++++- tensorrt_llm/visual_gen/args.py | 26 + .../test_ulysses_permute_scatter.py | 93 ++++ .../test_ulysses_post_unscatter.py | 102 ++++ .../multi_gpu/test_ltx2_async_ulysses.py | 435 +++++++++++++++ .../multi_gpu/test_ulysses_async.py | 292 ++++++++++ .../multi_gpu/test_wan_async_ulysses.py | 324 +++++++++++ 23 files changed, 2899 insertions(+), 96 deletions(-) create mode 100644 cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.cu create mode 100644 cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h create mode 100644 cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.cu create mode 100644 cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.h create mode 100644 cpp/tensorrt_llm/thop/asyncUlyssesOp.cpp create mode 100644 cpp/tensorrt_llm/thop/ulyssesPermuteScatterOp.cpp create mode 100644 cpp/tensorrt_llm/thop/ulyssesPostUnscatterOp.cpp create mode 100644 examples/visual_gen/configs/ltx2-4gpu.yaml create mode 100644 tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_permute_scatter.py create mode 100644 tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_post_unscatter.py create mode 100644 tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py create mode 100644 tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py create mode 100644 tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py diff --git a/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.cu b/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.cu new file mode 100644 index 000000000000..aba929755690 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.cu @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h" + +#include +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels +{ + +namespace +{ + +// Vector type: int4 = 16 bytes = 8 bf16. Issues LDG.E.128 / STG.E.128. +constexpr int VEC = 8; +constexpr int BLOCK_S = 32; // rows per CTA +constexpr int THREADS_PER_BLOCK = 128; // 4 warps + +// 1 CTA handles BLOCK_S rows × 1 head × full D. +// For fixed h (per CTA), peer = h // H_local is constant — no warp divergence. +// All writes within one CTA go to the same destination slot, contiguous in +// dst space. Matches the access pattern of PyTorch inductor's permute kernel +// epilogue stores. +__global__ void __launch_bounds__(THREADS_PER_BLOCK) + ulyssesPermuteScatterKernel(__nv_bfloat16 const* __restrict__ input, // [B, S_local, H, D] + __nv_bfloat16* __restrict__ send_buf, // [P, B, S_local, H/P, D] + __nv_bfloat16* __restrict__ recv_buf, // [P, B, S_local, H/P, D] + int const my_rank, + int const n_rows, // B * S_local + int const H, int const D, + int const H_local) // H / P +{ + int const bs_block = blockIdx.x; + int const h = blockIdx.y; + + // Scalar branch — same destination slot for all threads in this CTA. + int const peer = h / H_local; + int const h_local = h - peer * H_local; + int const slot_idx = (peer == my_rank) ? my_rank : peer; + __nv_bfloat16* __restrict__ dst_base = (peer == my_rank) ? recv_buf : send_buf; + + int const n_d_chunks = D / VEC; + int const total_tasks = BLOCK_S * n_d_chunks; + int const t = threadIdx.x; + + int const row_base = bs_block * BLOCK_S; + + int4 const* __restrict__ in_v = reinterpret_cast(input); + int4* __restrict__ dst_v = reinterpret_cast(dst_base); + + int const row_in_stride_v = (H * D) / VEC; // = H * n_d_chunks + int const head_in_off_v = h * n_d_chunks; + int const slot_off_v = slot_idx * n_rows * H_local * n_d_chunks; + int const row_dst_stride_v = H_local * n_d_chunks; + int const head_dst_off_v = h_local * n_d_chunks; + +#pragma unroll 1 + for (int idx = t; idx < total_tasks; idx += blockDim.x) + { + int const s_in_block = idx / n_d_chunks; + int const d_chunk = idx - s_in_block * n_d_chunks; + int const row = row_base + s_in_block; + if (row >= n_rows) + continue; + + int const src_idx = row * row_in_stride_v + head_in_off_v + d_chunk; + int const dst_idx = slot_off_v + row * row_dst_stride_v + head_dst_off_v + d_chunk; + + dst_v[dst_idx] = in_v[src_idx]; + } +} + +} // anonymous namespace + +void launchUlyssesPermuteScatter(void const* input, void* send_buf, void* recv_buf, int my_rank, int B, int S_local, + int H, int D, int P, cudaStream_t stream) +{ + int const n_rows = B * S_local; + int const H_local = H / P; + dim3 const grid((n_rows + BLOCK_S - 1) / BLOCK_S, H); + dim3 const block(THREADS_PER_BLOCK); + + ulyssesPermuteScatterKernel<<>>(reinterpret_cast<__nv_bfloat16 const*>(input), + reinterpret_cast<__nv_bfloat16*>(send_buf), reinterpret_cast<__nv_bfloat16*>(recv_buf), my_rank, n_rows, H, D, + H_local); +} + +} // namespace kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h b/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h new file mode 100644 index 000000000000..1b2738ac29b3 --- /dev/null +++ b/cpp/tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include + +#include "tensorrt_llm/common/config.h" + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels +{ + +// Fused permute + scatter for Ulysses A2A (peer-WRITE variant). +// +// Replaces the .permute(2,0,1,3,4).contiguous() materialization that would +// otherwise happen pre-A2A. Reads `input [B, S_local, H, D]` once and +// scatters each (b,s,h,d) element to one of two destinations: +// - peer != my_rank → send_buf[peer, b, s, h-peer*H_local, d] (local) +// - peer == my_rank → recv_buf[my_rank, b, s, h-my_rank*H_local, d] (symm-mem) +// +// After this kernel runs, the caller fires (P-1) cudaMemcpyBatchAsync +// entries to push send_buf[p] → peer[p].recv_buf[my_rank], then an LSA +// barrier (both folded into ulysses_a2a_async). +// +// Layout (all contiguous bf16): +// input : [B, S_local, H, D] row-major +// send_buf : [P, B, S_local, H/P, D] row-major +// recv_buf : [P, B, S_local, H/P, D] row-major +// +// Requires: D % 8 == 0 (int4 vec load); H % P == 0; bf16 only. +void launchUlyssesPermuteScatter(void const* input, // bf16 [B, S_local, H, D] + void* send_buf, // bf16 [P, B, S_local, H/P, D] + void* recv_buf, // bf16 [P, B, S_local, H/P, D] + int my_rank, int B, int S_local, int H, int D, int P, cudaStream_t stream); + +} // namespace kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.cu b/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.cu new file mode 100644 index 000000000000..c5f61ff3431d --- /dev/null +++ b/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.cu @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/common/cudaUtils.h" +#include "ulyssesPostUnscatterKernel.h" +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels +{ + +namespace +{ + +// Each block handles one (p, b, sp) tile across all H heads. +// Reads H*D bf16 contiguous from input; writes the same bytes scattered across +// H rows of the output in NHD layout [B, P*Sp, H, D]. The caller (op wrapper) +// returns this storage as a transpose-view when an HND-shape output is needed, +// so the resulting tensor is HND-shape with NHD-stride (matching what the +// sync `_forward_unfused` path produces via `q.transpose(1, 2)`). +// +// threads/block = H * (D / 8); each thread copies one uint4 (8 bf16). +template +__global__ void ulyssesPostUnscatterKernel(T const* __restrict__ q_in, T const* __restrict__ k_in, + T const* __restrict__ v_in, T* __restrict__ q_out, T* __restrict__ k_out, T* __restrict__ v_out, int const P, + int const B, int const Sp, int const H, int const D, int const vec_per_row) +{ + constexpr int VEC = 8; + + int const h = threadIdx.x / vec_per_row; + int const vec_idx = threadIdx.x - h * vec_per_row; + + int const psp = blockIdx.x; // 0 .. P*Sp-1 + int const p = psp / Sp; + int const sp = psp - p * Sp; + int const b = blockIdx.y; + int const PSp = P * Sp; + + T const* in_ptr; + T* out_ptr; + switch (blockIdx.z) + { + case 0: + in_ptr = q_in; + out_ptr = q_out; + break; + case 1: + in_ptr = k_in; + out_ptr = k_out; + break; + default: + in_ptr = v_in; + out_ptr = v_out; + break; + } + + // in[p, b, sp, h, d]: ((((p*B + b)*Sp + sp)*H + h)*D + vec_idx*VEC) + // NHD out[b, p*Sp+sp, h, d]: (((b*PSp + psp)*H + h)*D + vec_idx*VEC) + // int64_t: P*B*Sp*H*D can exceed 2^31 at large workloads. + int64_t const in_base = ((((static_cast(p) * B + b) * Sp + sp) * H + h) * D) + vec_idx * VEC; + int64_t const out_base = (((static_cast(b) * PSp + psp) * H + h) * D) + vec_idx * VEC; + + uint4 const* in_v4 = reinterpret_cast(in_ptr + in_base); + uint4* out_v4 = reinterpret_cast(out_ptr + out_base); + *out_v4 = *in_v4; +} + +} // namespace + +void launchUlyssesPostUnscatter(void const* q_in, void const* k_in, void const* v_in, void* q_out, void* k_out, + void* v_out, int P, int B, int Sp, int H, int D, cudaStream_t stream) +{ + constexpr int VEC = 8; + TLLM_CHECK_WITH_INFO(D % VEC == 0, "ulyssesPostUnscatter: D must be a multiple of 8 (uint4 vec), got %d", D); + int const vec_per_row = D / VEC; + int const threads = H * vec_per_row; + TLLM_CHECK_WITH_INFO(threads <= 1024, + "ulyssesPostUnscatter: threads/block (H*D/8) must be <= 1024, got H=%d D=%d -> %d", H, D, threads); + + dim3 const grid(P * Sp, B, 3); + dim3 const block(threads); + + auto* q_in_typed = reinterpret_cast<__nv_bfloat16 const*>(q_in); + auto* k_in_typed = reinterpret_cast<__nv_bfloat16 const*>(k_in); + auto* v_in_typed = reinterpret_cast<__nv_bfloat16 const*>(v_in); + auto* q_out_typed = reinterpret_cast<__nv_bfloat16*>(q_out); + auto* k_out_typed = reinterpret_cast<__nv_bfloat16*>(k_out); + auto* v_out_typed = reinterpret_cast<__nv_bfloat16*>(v_out); + + ulyssesPostUnscatterKernel<__nv_bfloat16><<>>( + q_in_typed, k_in_typed, v_in_typed, q_out_typed, k_out_typed, v_out_typed, P, B, Sp, H, D, vec_per_row); +} + +} // namespace kernels + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.h b/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.h new file mode 100644 index 000000000000..f5a2932dacdf --- /dev/null +++ b/cpp/tensorrt_llm/kernels/ulyssesPostUnscatterKernel.h @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRTLLM_ULYSSESPOSTUNSCATTERKERNEL_H +#define TRTLLM_ULYSSESPOSTUNSCATTERKERNEL_H + +#include "tensorrt_llm/common/config.h" +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace kernels +{ + +// Post-Ulysses A2A unscatter for Q/K/V. Pairs with ulyssesPermuteScatter: +// PermuteScatter prepares send_buf pre-A2A; PostUnscatter consumes recv_buf +// post-A2A and produces SDPA-ready tensors in NHD layout. +// +// After the head-dim → seq-dim all-to-all, each rank holds tensors of shape +// [P, B, Sp, H, D] where P = sequence-parallel world size, Sp = local seq +// len, H = heads-per-rank, D = head dim. This kernel always writes NHD-contig +// [B, P*Sp, H, D] storage. The op wrapper returns the storage as-is for NHD +// callers (TRTLLM / FA4) or as a transpose-view for HND callers (VANILLA / +// torch SDPA) — the HND-shape return is thus HND-shape with NHD-stride, +// mirroring what the sync `_forward_unfused` path produces via +// `q.transpose(1, 2)` (without `.contiguous()`). This stride pattern lets +// cudnn SDPA preserve NHD-stride through its output, so the downstream +// `_output_a2a`'s `.transpose(1, 2).contiguous()` collapses to a no-op. +// +// Equivalent eager expression this kernel replaces: +// t.permute(1, 0, 2, 3, 4).reshape(B, P*Sp, H, D).contiguous() +// +// Layout: +// - Each block reads one fully contiguous (p, b, sp, :H, :D) tile of +// H*D bf16 +// - H*(D/8) threads/block — each thread copies one uint4 (8 bf16) +// - Grid (P*Sp, B, 3): blockIdx.z selects Q / K / V +// +// Constraints: +// - dtype must be bf16 +// - D must be a multiple of 8 (uint4 vector load/store, 8 bf16 per thread) +// - threads/block = H * (D / 8) must be <= 1024 (CUDA hw limit) +void launchUlyssesPostUnscatter(void const* q_in, // [P, B, Sp, H, D] + void const* k_in, void const* v_in, + void* q_out, // [B, P*Sp, H, D] NHD-contig + void* k_out, void* v_out, int P, int B, int Sp, int H, int D, cudaStream_t stream); + +} // namespace kernels + +TRTLLM_NAMESPACE_END + +#endif // TRTLLM_ULYSSESPOSTUNSCATTERKERNEL_H diff --git a/cpp/tensorrt_llm/thop/CMakeLists.txt b/cpp/tensorrt_llm/thop/CMakeLists.txt index 90239c56f526..1942a00556b4 100644 --- a/cpp/tensorrt_llm/thop/CMakeLists.txt +++ b/cpp/tensorrt_llm/thop/CMakeLists.txt @@ -41,6 +41,7 @@ add_library( allgatherOp.cpp allreduceOp.cpp alltoallOp.cpp + asyncUlyssesOp.cpp attentionOp.cpp causalConv1dOp.cpp convertSpecDecodingMaskToPackedMaskOp.cpp @@ -69,6 +70,8 @@ add_library( fusedDiTQKNormRopeOp.cpp fusedDiTSplitQKNormRopeOp.cpp fusedDiTSplitNormOp.cpp + ulyssesPostUnscatterOp.cpp + ulyssesPermuteScatterOp.cpp fusedAddRMSNormQuant.cpp fusedActivationQuant.cpp fusedGatedRMSNormQuant.cpp diff --git a/cpp/tensorrt_llm/thop/asyncUlyssesOp.cpp b/cpp/tensorrt_llm/thop/asyncUlyssesOp.cpp new file mode 100644 index 000000000000..b0c01c673b1f --- /dev/null +++ b/cpp/tensorrt_llm/thop/asyncUlyssesOp.cpp @@ -0,0 +1,516 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// +// Async Ulysses A2A — PyTorch _SymmetricMemory CUDA-IPC backend. +// +// Pipeline (paired with UlyssesAttention.forward_async in +// tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py): +// +// recv, send_h = ulysses_a2a_async_prepare(input, pg) # default stream +// ev.record() +// with torch.cuda.stream(comm_stream): +// ev.wait() +// ulysses_a2a_async_push(send_h, pg) # CE push only +// ... repeat _prepare/_push for next V/Q/K ... +// with torch.cuda.stream(comm_stream): +// ulysses_a2a_async_barrier(pg) # one per deferred push +// +// Phase 1 (`_prepare`) on the caller's compute stream: +// - lazily allocate one slot of a ring of P-symmetric-memory buffers +// via empty_strided_p2p + rendezvous (PyTorch CUDA-IPC backend); +// - launch the fused permute+scatter kernel into (slot.sendBuf for +// peer chunks, slot.basePtr+my_rank for self chunk); +// - return the 5D recv view and an opaque SendHandle. +// +// Phase 2 (`_async`) on the comm stream: +// - cudaMemcpyBatchAsync (capture-safe per-peer loop fallback) pushes +// each peer's slice of sendBuf into peer.basePtr[my_rank]; +// - PT symm-mem `barrier(channel, timeout_ms)` is the cross-rank fence. +// +// No NCCL device API; no LSA barrier kernel. +// + +#include "tensorrt_llm/common/cudaUtils.h" +#include "tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h" +#include "tensorrt_llm/thop/thUtils.h" + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace torch_ext +{ + +// Opaque handle returned by `_prepare`, consumed by `_async`. Hides raw +// pointer plumbing from Python; `send_t` keeps the slot's sendBuf tensor +// view alive across the two op calls. +// +// `group_name` binds the handle to the PG that produced it: peer_recv_ptrs +// are valid only in that PG's symm-mem registration. `_async` rejects any +// PG whose group name doesn't match — two distinct PGs of the same size +// would otherwise pass the peer-pointer-count check and silently push +// into the wrong group's buffers. +struct SendHandle : torch::CustomClassHolder +{ + torch::Tensor send_t; + std::vector peer_recv_ptrs; + int64_t slot_bytes; + std::string group_name; +}; + +#if ENABLE_MULTI_DEVICE + +namespace +{ + +class AsyncUlyssesOp +{ +public: + // Slot ring depth. Minimum 3 = one slot each for V/Q/K within a single + // forward_async call. A slot is touched by 4 ops in sequence: + // (a) default-stream Phase-1 write — permute+scatter into slot.sendBuf + // (b) side-stream Phase-2 CE push — reads slot.sendBuf + // (c) side-stream Phase-2 barrier — peer writes into slot.recv + // (d) default-stream SDPA read — reads slot.recv + // Intra-layer hazard: V/Q/K must use distinct slots, otherwise (a) on the + // default stream races (b) on the side stream — they touch the same + // sendBuf and there is no stream sync between them until _join_async. + // Cross-layer hazard (Layer N+1 V reusing Layer N V's slot): safe because + // _join_async at end of Layer N waits the side stream's K barrier event, + // and SDPA on the default stream drains the recv read before Layer N+1 + // starts. So kNumSlots = 3 is the tight minimum. + static constexpr int kNumSlots = 3; + + explicit AsyncUlyssesOp(c10::intrusive_ptr pg) + : mPg(std::move(pg)) + { + } + + void initialize() + { + TLLM_CHECK_WITH_INFO(mPg, "AsyncUlyssesOp requires a torch ProcessGroup"); + TLLM_CHECK_WITH_INFO(mPg->getSize() >= 1, "ProcessGroup size must be >= 1"); + // Register the PG's group_info with PT symm-mem (one-shot per process per group). + ensureGroupRegistered(); + } + + int getPgSize() const + { + return mPg->getSize(); + } + + int getPgRank() const + { + return mPg->getRank(); + } + + // Phase 1: lazy-alloc next ring slot via PT symm-mem; return tensor + // views over send_buf (local push source) and recv_buf (peer-writable) + // plus the host-side peer-pointer array. + std::tuple, int64_t> acquireSlotPair( + at::IntArrayRef shape, c10::ScalarType dtype) + { + int64_t const elemSize = static_cast(c10::elementSize(dtype)); + TORCH_CHECK(elemSize > 0, "dtype must have positive itemsize"); + int64_t numel = 1; + for (auto d : shape) + { + TORCH_CHECK(d > 0, "shape dims must be positive"); + numel *= d; + } + int64_t const bufferBytes = numel * elemSize; + TORCH_CHECK(bufferBytes > 0, "bufferBytes must be positive"); + int const pSize = mPg->getSize(); + TORCH_CHECK(bufferBytes % pSize == 0, "bufferBytes must be divisible by world_size"); + + int const slotIdx = nextSlotIdx(); + Slot& slot = getOrAllocSlot(slotIdx, static_cast(bufferBytes)); + + auto opts = torch::dtype(dtype).device(torch::kCUDA); + auto sendT = torch::from_blob( + slot.sendBuf, shape, /*deleter=*/[](void*) {}, opts); + auto recvT = torch::from_blob( + slot.basePtr, shape, /*deleter=*/[](void*) {}, opts); + + std::vector peerRecvPtrs(pSize); + for (int p = 0; p < pSize; ++p) + { + peerRecvPtrs[p] = reinterpret_cast(slot.peerPtrs[p]); + } + + int64_t const slotBytes = bufferBytes / pSize; + return std::make_tuple(sendT, recvT, std::move(peerRecvPtrs), slotBytes); + } + + // Phase 2 (data): out-of-capture uses cudaMemcpyBatchAsync (multi-CE + // engine fan-out); under stream capture we serialize via per-peer + // cudaMemcpyAsync (cudaMemcpyBatchAsync is not graph-capture-safe). + // Self chunk is NOT pushed (already written by the upstream + // fused-permute kernel into recv_buf[my_rank]). + void runCePush(torch::Tensor send_buf, std::vector const& peer_recv_ptrs, int64_t slot_bytes) + { + int const pSize = mPg->getSize(); + int const pgRank = mPg->getRank(); + TORCH_CHECK(static_cast(peer_recv_ptrs.size()) == pSize, "peer_recv_ptrs size must equal world_size"); + + int const nPeers = pSize - 1; + if (nPeers == 0) + { + // P=1: self-only; recv_buf already populated by the permute kernel. + return; + } + + char const* sendBase = static_cast(send_buf.data_ptr()); + auto stream = at::cuda::getCurrentCUDAStream().stream(); + + cudaStreamCaptureStatus captureStatus; + TLLM_CUDA_CHECK(cudaStreamIsCapturing(stream, &captureStatus)); + bool const underCapture = (captureStatus != cudaStreamCaptureStatusNone); + + if (!underCapture) + { + std::vector dsts; + dsts.reserve(nPeers); + std::vector srcs; + srcs.reserve(nPeers); + std::vector sizes; + sizes.reserve(nPeers); + for (int p = 0; p < pSize; ++p) + { + if (p == pgRank) + continue; + void* peerBase = reinterpret_cast(peer_recv_ptrs[p]); + dsts.push_back( + static_cast(peerBase) + static_cast(pgRank) * static_cast(slot_bytes)); + srcs.push_back(sendBase + static_cast(p) * static_cast(slot_bytes)); + sizes.push_back(static_cast(slot_bytes)); + } + cudaMemcpyAttributes attrs[1]; + std::memset(&attrs[0], 0, sizeof(attrs[0])); + attrs[0].srcAccessOrder = cudaMemcpySrcAccessOrderStream; + attrs[0].flags = 1u; + size_t attrIdxs[1] = {0}; + TLLM_CUDA_CHECK(cudaMemcpyBatchAsync( + dsts.data(), srcs.data(), sizes.data(), static_cast(nPeers), attrs, attrIdxs, 1, stream)); + } + else + { + for (int p = 0; p < pSize; ++p) + { + if (p == pgRank) + continue; + void* peerBase = reinterpret_cast(peer_recv_ptrs[p]); + void* dst + = static_cast(peerBase) + static_cast(pgRank) * static_cast(slot_bytes); + void const* src = sendBase + static_cast(p) * static_cast(slot_bytes); + TLLM_CUDA_CHECK( + cudaMemcpyAsync(dst, src, static_cast(slot_bytes), cudaMemcpyDeviceToDevice, stream)); + } + } + } + + // Phase 2 (fence): PT symm-mem barrier on the current CUDA stream. + // Any allocated slot's handle works — they all belong to the same group. + void emitBarrier() + { + TLLM_CHECK_WITH_INFO( + mCanonicalHandle, "emitBarrier: no slot allocated yet — _prepare must precede the first _async barrier."); + // 10s timeout: on hang, the kernel traps with rank+channel diagnostic instead of spinning silently + // until SLURM wall-clock kills. Generous enough to absorb first-touch IPC + first cuda_graph + // capture jitter. channel=0: V/Q/K issues all run on the same per-device side stream so + // they FIFO-serialize; channel multiplexing only matters across distinct streams. + mCanonicalHandle->barrier(/*channel=*/0, /*timeout_ms=*/10000); + } + +private: + struct Slot + { + // PT _SymmetricMemory-backed recv buffer (peer-writable). + at::Tensor symm_tensor; + c10::intrusive_ptr handle; + void* basePtr = nullptr; // aliases symm_tensor.data_ptr() + size_t size = 0; + std::vector peerPtrs; // from handle->get_buffer_ptrs() + + // Local-only push source (no symm-mem). cudaMalloc'd eagerly to + // stay cuda_graph-capture-safe. + void* sendBuf = nullptr; + size_t sendBufBytes = 0; + }; + + // One-shot per process per group: register PG's (name, rank, size, store) + // with PT symm-mem's group registry. Subsequent rendezvous() calls reuse it. + void ensureGroupRegistered() + { + static std::set sRegistered; + static std::mutex sMutex; + std::string const& name = mPg->getGroupName(); + std::lock_guard lock(sMutex); + if (sRegistered.count(name)) + { + return; + } + c10d::symmetric_memory::set_group_info(name, mPg->getRank(), mPg->getSize(), mPg->getStore()); + sRegistered.insert(name); + } + + int nextSlotIdx() + { + std::lock_guard lock(mNextMutex); + int idx = mNextIdx; + mNextIdx = (mNextIdx + 1) % kNumSlots; + return idx; + } + + // Lazy collective allocator. Cached when size is sufficient; reallocates + // (releasing the old handle) on size-up. All ranks must reach this in the + // same order (collective rendezvous). + // + // Commit-on-success: every allocation step writes to local variables + // first, and the cached `slot` is mutated only after all steps succeed. + // If `empty_strided_p2p`, `rendezvous`, `get_buffer_ptrs`, or `cudaMalloc` + // throws mid-way, the local at::Tensor / intrusive_ptr clean up via RAII + // and the previously-cached slot remains untouched (so the next call + // either retries or reuses the still-valid prior state). + Slot& getOrAllocSlot(int slotIdx, size_t requiredSize) + { + std::lock_guard lock(mSlotsMutex); + Slot& slot = mSlots[slotIdx]; + + if (slot.basePtr != nullptr && slot.size >= requiredSize) + { + return slot; + } + + // First-time / size-up allocation is NOT capture-safe: + // empty_strided_p2p + rendezvous + cudaMalloc all violate stream + // capture invariants. Caller must warm up out-of-capture so the slot + // is allocated and cached before any cuda_graph capture begins. + cudaStream_t const stream = at::cuda::getCurrentCUDAStream().stream(); + cudaStreamCaptureStatus captureStatus = cudaStreamCaptureStatusNone; + TLLM_CUDA_CHECK(cudaStreamIsCapturing(stream, &captureStatus)); + TORCH_CHECK(captureStatus == cudaStreamCaptureStatusNone, + "async-ulysses: slot allocation (empty_strided_p2p + rendezvous + cudaMalloc) " + "is not graph-capture-safe. Warm up the model out-of-capture (run one forward " + "pass before enabling cuda_graph capture) so slots are cached."); + + int currentDev = -1; + TLLM_CUDA_CHECK(cudaGetDevice(¤tDev)); + c10::Device device(c10::DeviceType::CUDA, currentDev); + std::string const& groupName = mPg->getGroupName(); + int const pSize = mPg->getSize(); + + // Build new state in local variables — no mutation of `slot` yet. + at::Tensor newSymmTensor = c10d::symmetric_memory::empty_strided_p2p( + /*size=*/{static_cast(requiredSize)}, /*stride=*/{1}, + /*dtype=*/at::kByte, device, + /*group_name=*/std::make_optional(groupName), /*alloc_id=*/std::nullopt); + auto newHandle = c10d::symmetric_memory::rendezvous(newSymmTensor, groupName); + TLLM_CHECK_WITH_INFO(newHandle, "rendezvous returned null handle"); + + auto ptrs = newHandle->get_buffer_ptrs(); + TLLM_CHECK_WITH_INFO( + static_cast(ptrs.size()) == pSize, "get_buffer_ptrs size %zu != world_size %d", ptrs.size(), pSize); + std::vector newPeerPtrs(ptrs.begin(), ptrs.end()); + + // cudaMalloc last so any throw above is cleaned up by newSymmTensor / + // newHandle RAII without leaking GPU memory. + void* newSendBuf = nullptr; + TLLM_CUDA_CHECK(cudaMalloc(&newSendBuf, requiredSize)); + + // All allocations succeeded — commit. Free old sendBuf (the raw void* + // isn't owned by any RAII type in Slot); the at::Tensor / intrusive_ptr + // fields are released by move-assign. + if (slot.sendBuf != nullptr) + { + (void) cudaFree(slot.sendBuf); + } + slot.symm_tensor = std::move(newSymmTensor); + slot.handle = std::move(newHandle); + slot.basePtr = slot.symm_tensor.data_ptr(); + slot.size = requiredSize; + slot.peerPtrs = std::move(newPeerPtrs); + slot.sendBuf = newSendBuf; + slot.sendBufBytes = requiredSize; + + // Cache the first allocated handle for emitBarrier() (any handle from + // this PG yields the same channel-N barrier semantics). + if (!mCanonicalHandle) + { + mCanonicalHandle = slot.handle; + } + + return slot; + } + + c10::intrusive_ptr mPg; + + int mNextIdx{0}; + std::mutex mNextMutex; + + std::array mSlots{}; + std::mutex mSlotsMutex; + + // Cached on the first slot allocation. SymmetricMemory::barrier() is a + // PG-level sync (any handle from this PG triggers the same channel-N + // barrier), so emitBarrier() can use this directly instead of scanning + // mSlots for a non-null handle on every call. + c10::intrusive_ptr mCanonicalHandle; +}; + +// Process-lifetime cache of AsyncUlyssesOp instances keyed by group_name. +static std::shared_ptr getOrCreateOp(c10::intrusive_ptr const& pg) +{ + TLLM_CHECK_WITH_INFO(pg, "ProcessGroup is null"); + static std::map> sCache; + static std::mutex sMutex; + std::string const& groupName = pg->getGroupName(); + std::lock_guard lock(sMutex); + auto it = sCache.find(groupName); + if (it != sCache.end()) + { + return it->second; + } + auto op = std::make_shared(pg); + op->initialize(); + sCache[groupName] = op; + return op; +} + +// Step 1 (caller's compute stream): acquire slot ring entry + CUDA C +// permute+scatter (writes peer chunks to send_buf, self chunk directly to +// recv_buf[my_rank]). Returns the 5D recv-buf view (for downstream SDPA) +// and an opaque SendHandle that the second op consumes. +std::tuple> ulysses_a2a_async_prepare( + torch::Tensor input_4d, c10::intrusive_ptr const& pg) +{ + TORCH_CHECK(input_4d.is_cuda(), "input must be on CUDA"); + TORCH_CHECK(input_4d.is_contiguous(), "input must be contiguous"); + TORCH_CHECK(input_4d.dim() == 4, "input must be [B, S_local, H, D]"); + TORCH_CHECK(input_4d.scalar_type() == at::ScalarType::BFloat16, "bf16 only"); + + // Bind current device + slot allocator + kernel launch to the input's + // device. `getOrAllocSlot` reads `cudaGetDevice()`, and the kernel stream + // is taken from `input_4d.get_device()`; without this guard the two can + // diverge (e.g. caller forgot a torch.cuda.set_device) → slot allocated + // on dev A, kernel launched on dev B → illegal memory access. + c10::cuda::CUDAGuard device_guard(input_4d.device()); + + int const B = static_cast(input_4d.size(0)); + int const S_local = static_cast(input_4d.size(1)); + int const H = static_cast(input_4d.size(2)); + int const D = static_cast(input_4d.size(3)); + TORCH_CHECK(D % 8 == 0, "D must be divisible by 8 (int4 vec)"); + + auto op = getOrCreateOp(pg); + + int const P = op->getPgSize(); + int const my_rank = op->getPgRank(); + TORCH_CHECK(H % P == 0, "H must be divisible by world_size"); + int const H_local = H / P; + + auto [send_t, recv_t, peer_recv_ptrs, slot_bytes] = op->acquireSlotPair( + {(int64_t) P, (int64_t) B, (int64_t) S_local, (int64_t) H_local, (int64_t) D}, input_4d.scalar_type()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(input_4d.get_device()).stream(); + tensorrt_llm::kernels::launchUlyssesPermuteScatter( + input_4d.data_ptr(), send_t.data_ptr(), recv_t.data_ptr(), my_rank, B, S_local, H, D, P, stream); + + auto send_h = c10::make_intrusive(); + send_h->send_t = std::move(send_t); + send_h->peer_recv_ptrs = std::move(peer_recv_ptrs); + send_h->slot_bytes = slot_bytes; + send_h->group_name = pg->getGroupName(); + + return std::make_tuple(std::move(recv_t), send_h); +} + +// Step 2a (caller's comm stream): CE push only, no barrier. Issue V/Q/K +// pushes back-to-back on the side stream so they FIFO through copy-engines +// without barrier-induced stalls; defer all fences to `ulysses_a2a_async_barrier` +// at join time. Caller must event-sync from compute stream before calling. +// +// Reject cross-PG handle use: peer_recv_ptrs are valid only in the symm-mem +// group registered for the PG that produced this handle. Two PGs of the same +// size would otherwise pass the peer-count check inside runCePush and silently +// push into the wrong group's buffers. +void ulysses_a2a_async_push( + c10::intrusive_ptr const& send_h, c10::intrusive_ptr const& pg) +{ + TORCH_CHECK(send_h.get() != nullptr, "send_h is null"); + TORCH_CHECK(send_h->send_t.defined(), "send_h.send_t is undefined"); + TORCH_CHECK(send_h->group_name == pg->getGroupName(), "SendHandle was produced by ProcessGroup '", + send_h->group_name, "' but ulysses_a2a_async_push was called with ProcessGroup '", pg->getGroupName(), + "'. Handle and PG must match."); + auto op = getOrCreateOp(pg); + op->runCePush(send_h->send_t, send_h->peer_recv_ptrs, send_h->slot_bytes); +} + +// Step 2b (caller's comm stream): emit a symm-mem barrier on channel 0. +// Pairs with `ulysses_a2a_async_push`; one call per deferred push (e.g. +// V/Q/K -> 3 barriers at join). +void ulysses_a2a_async_barrier(c10::intrusive_ptr const& pg) +{ + auto op = getOrCreateOp(pg); + op->emitBarrier(); +} + +} // namespace + +#endif // ENABLE_MULTI_DEVICE + +} // namespace torch_ext + +TRTLLM_NAMESPACE_END + +TORCH_LIBRARY_FRAGMENT(trtllm, m) +{ + m.class_("SendHandle"); + + m.def( + "ulysses_a2a_async_prepare(Tensor input, " + "__torch__.torch.classes.c10d.ProcessGroup pg) " + "-> (Tensor, __torch__.torch.classes.trtllm.SendHandle)"); + m.def( + "ulysses_a2a_async_push(__torch__.torch.classes.trtllm.SendHandle send_h, " + "__torch__.torch.classes.c10d.ProcessGroup pg) -> ()"); + m.def("ulysses_a2a_async_barrier(__torch__.torch.classes.c10d.ProcessGroup pg) -> ()"); +} + +// Both ops take/return a custom-class handle, not tensors, so the dispatcher +// can't pick a backend from input types. Register on CompositeExplicitAutograd +// (the underlying CUDA work runs on the caller's current CUDA stream). +TORCH_LIBRARY_IMPL(trtllm, CompositeExplicitAutograd, m) +{ +#if ENABLE_MULTI_DEVICE + m.impl("ulysses_a2a_async_prepare", &tensorrt_llm::torch_ext::ulysses_a2a_async_prepare); + m.impl("ulysses_a2a_async_push", &tensorrt_llm::torch_ext::ulysses_a2a_async_push); + m.impl("ulysses_a2a_async_barrier", &tensorrt_llm::torch_ext::ulysses_a2a_async_barrier); +#endif +} diff --git a/cpp/tensorrt_llm/thop/ulyssesPermuteScatterOp.cpp b/cpp/tensorrt_llm/thop/ulyssesPermuteScatterOp.cpp new file mode 100644 index 000000000000..5637b49a9a73 --- /dev/null +++ b/cpp/tensorrt_llm/thop/ulyssesPermuteScatterOp.cpp @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/kernels/ulyssesPermuteScatterKernel.h" +#include "tensorrt_llm/thop/thUtils.h" + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace torch_ext +{ + +// Standalone Python entry point for ulyssesPermuteScatterKernel — used by +// unit tests. The production caller is the Ulysses async A2A path in +// alltoallOp.cpp, which combines this kernel with IPC writes + an LSA +// barrier; that whole sequence is multi-rank and not amenable to a +// single-GPU pytest. By exposing just the kernel, we can validate the +// permute+scatter layout transform independently. +// +// Layout: +// input : bf16 [B, S_local, H, D] contiguous +// send_buf : bf16 [P, B, S_local, H/P, D] contiguous +// recv_buf : bf16 [P, B, S_local, H/P, D] contiguous +// For each (b, s, h, d): +// peer = h // (H/P) +// h_local = h % (H/P) +// dst = (recv_buf if peer == my_rank else send_buf) +// slot = peer // applies to both branches +// dst[slot, b, s, h_local, d] = input[b, s, h, d] +void ulysses_permute_scatter(torch::Tensor& input, // [B, S_local, H, D] + torch::Tensor& send_buf, // [P, B, S_local, H/P, D] + torch::Tensor& recv_buf, // [P, B, S_local, H/P, D] + int64_t my_rank, int64_t P) +{ + TORCH_CHECK(input.dim() == 4, "input must be 4D [B, S_local, H, D]"); + TORCH_CHECK(send_buf.dim() == 5 && recv_buf.dim() == 5, "send_buf / recv_buf must be 5D [P, B, S_local, H/P, D]"); + CHECK_INPUT(input, torch::kBFloat16); + CHECK_INPUT(send_buf, torch::kBFloat16); + CHECK_INPUT(recv_buf, torch::kBFloat16); + + // Validate P first: P=0 would crash on `H % P` below (UB; SIGFPE on x86) + // and on `my_rank < P` after `0 <= my_rank` trivially passes. + TORCH_CHECK(P > 0, "P (world_size) must be positive"); + + int64_t const B = input.size(0); + int64_t const S_local = input.size(1); + int64_t const H = input.size(2); + int64_t const D = input.size(3); + TORCH_CHECK(H % P == 0, "H must be divisible by P"); + TORCH_CHECK(D % 8 == 0, "D must be divisible by 8 (uint4 vec)"); + int64_t const H_local = H / P; + TORCH_CHECK(send_buf.size(0) == P && send_buf.size(1) == B && send_buf.size(2) == S_local + && send_buf.size(3) == H_local && send_buf.size(4) == D, + "send_buf shape mismatch"); + TORCH_CHECK(recv_buf.size(0) == P && recv_buf.size(1) == B && recv_buf.size(2) == S_local + && recv_buf.size(3) == H_local && recv_buf.size(4) == D, + "recv_buf shape mismatch"); + TORCH_CHECK(0 <= my_rank && my_rank < P, "my_rank out of range"); + + // Empty-tensor no-op: B=0 or S_local=0 produces zero grid extent in the + // kernel launcher (undefined cuLaunchKernel behavior across CUDA versions). + // Shape consistency between input/send_buf/recv_buf was already enforced + // above, so empty input implies empty buffers — nothing to write. + if (input.numel() == 0) + { + return; + } + + auto stream = at::cuda::getCurrentCUDAStream(); + tensorrt_llm::kernels::launchUlyssesPermuteScatter(input.data_ptr(), send_buf.data_ptr(), recv_buf.data_ptr(), + static_cast(my_rank), static_cast(B), static_cast(S_local), static_cast(H), + static_cast(D), static_cast(P), stream); +} + +TORCH_LIBRARY_FRAGMENT(trtllm, m) +{ + m.def( + "ulysses_permute_scatter(Tensor(a!) input, Tensor(b!) send_buf, Tensor(c!) recv_buf, " + "int my_rank, int P) -> ()"); +} + +TORCH_LIBRARY_IMPL(trtllm, CUDA, m) +{ + m.impl("ulysses_permute_scatter", &ulysses_permute_scatter); +} + +} // namespace torch_ext + +TRTLLM_NAMESPACE_END diff --git a/cpp/tensorrt_llm/thop/ulyssesPostUnscatterOp.cpp b/cpp/tensorrt_llm/thop/ulyssesPostUnscatterOp.cpp new file mode 100644 index 000000000000..9d45b4f0b119 --- /dev/null +++ b/cpp/tensorrt_llm/thop/ulyssesPostUnscatterOp.cpp @@ -0,0 +1,120 @@ +/* + * Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tensorrt_llm/kernels/ulyssesPostUnscatterKernel.h" +#include "tensorrt_llm/thop/thUtils.h" + +#include +#include + +TRTLLM_NAMESPACE_BEGIN + +namespace torch_ext +{ + +// Post-Ulysses A2A unscatter: take Q/K/V tensors of shape [P, B, Sp, H, D] +// (output of the head-dim -> seq-dim all-to-all) and produce SDPA-ready Q/K/V. +// The kernel ALWAYS writes NHD-contig storage [B, P*Sp, H, D]. The returned +// tensor shape depends on ``layout``: +// layout=0 (HND) → returns transpose-view [B, H, P*Sp, D] +// (HND-shape, NHD-stride, NON-contig — mirrors the +// `q.transpose(1, 2)` result in the sync `_forward_unfused` +// path, which lets cudnn SDPA preserve NHD-stride through +// its output and collapses the downstream +// `_output_a2a.transpose(1, 2).contiguous()` to a no-op) +// layout=1 (NHD) → returns storage as-is [B, P*Sp, H, D] (NHD contig) +// Replaces the eager chain +// t.permute(1, 0, 2, 3, 4).reshape(B, P * Sp, H, D).contiguous() // NHD +// [.transpose(1, 2)] // HND: stride view only +// for Q, K, V in one kernel launch. +std::tuple ulysses_post_unscatter_qkv( + torch::Tensor& q_in, // [P, B, Sp, H, D] + torch::Tensor& k_in, // [P, B, Sp, H, D] + torch::Tensor& v_in, // [P, B, Sp, H, D] + int64_t layout) // 0 = HND, 1 = NHD +{ + TORCH_CHECK(q_in.dim() == 5 && k_in.dim() == 5 && v_in.dim() == 5, + "ulysses_post_unscatter_qkv expects 5D tensors [P, B, Sp, H, D]"); + TORCH_CHECK(q_in.sizes() == k_in.sizes() && q_in.sizes() == v_in.sizes(), "Q/K/V must share the same shape"); + TORCH_CHECK(layout == 0 || layout == 1, "layout must be 0 (HND) or 1 (NHD), got ", layout); + + CHECK_INPUT(q_in, torch::kBFloat16); + CHECK_INPUT(k_in, torch::kBFloat16); + CHECK_INPUT(v_in, torch::kBFloat16); + + // D % 8 enforced here at op boundary (mirrors sibling ulysses_permute_scatter). + // Without this, torch::empty allocates the three output tensors before the + // kernel launcher's TLLM_CHECK_WITH_INFO fires, producing a less-actionable + // error path. Vec width is 8 elements for bf16 (16-byte vectorized stores). + TORCH_CHECK(q_in.size(-1) % 8 == 0, "D (last dim) must be divisible by 8 (bf16 vec=8)"); + + int64_t const P = q_in.size(0); + int64_t const B = q_in.size(1); + int64_t const Sp = q_in.size(2); + int64_t const H = q_in.size(3); + int64_t const D = q_in.size(4); + + bool const is_hnd = (layout == 0); + auto opts = q_in.options(); + // Always allocate NHD-contig storage [B, P*Sp, H, D]. + auto const storage_shape = std::vector{B, P * Sp, H, D}; + auto q_out = torch::empty(storage_shape, opts); + auto k_out = torch::empty(storage_shape, opts); + auto v_out = torch::empty(storage_shape, opts); + + // Empty-tensor no-op: P=0/B=0/Sp=0 produces zero grid extent in the + // kernel launcher (undefined cuLaunchKernel behavior across CUDA versions). + // The three output tensors above are already empty-shaped via P*Sp=0 or B=0, + // so returning them directly preserves the output-shape contract. + if (q_in.numel() == 0) + { + if (is_hnd) + { + return std::make_tuple(q_out.transpose(1, 2), k_out.transpose(1, 2), v_out.transpose(1, 2)); + } + return std::make_tuple(q_out, k_out, v_out); + } + + auto stream = at::cuda::getCurrentCUDAStream(); + tensorrt_llm::kernels::launchUlyssesPostUnscatter(q_in.data_ptr(), k_in.data_ptr(), v_in.data_ptr(), + q_out.data_ptr(), k_out.data_ptr(), v_out.data_ptr(), static_cast(P), static_cast(B), + static_cast(Sp), static_cast(H), static_cast(D), stream); + + // HND callers get a transpose-view of the NHD storage (zero-copy stride + // reinterpretation). NHD callers get the storage as-is. + if (is_hnd) + { + return std::make_tuple(q_out.transpose(1, 2), k_out.transpose(1, 2), v_out.transpose(1, 2)); + } + return std::make_tuple(q_out, k_out, v_out); +} + +TORCH_LIBRARY_FRAGMENT(trtllm, m) +{ + // layout: 0 = HND [B, H, P*Sp, D], 1 = NHD [B, P*Sp, H, D]. Default 0 keeps + // backward compatibility with the original HND-only callers. + m.def( + "ulysses_post_unscatter_qkv(Tensor q_in, Tensor k_in, Tensor v_in, int layout=0) -> (Tensor, Tensor, Tensor)"); +} + +TORCH_LIBRARY_IMPL(trtllm, CUDA, m) +{ + m.impl("ulysses_post_unscatter_qkv", &ulysses_post_unscatter_qkv); +} + +} // namespace torch_ext + +TRTLLM_NAMESPACE_END diff --git a/docs/source/models/visual-generation.md b/docs/source/models/visual-generation.md index b6f125ed027c..0fe823ced41b 100644 --- a/docs/source/models/visual-generation.md +++ b/docs/source/models/visual-generation.md @@ -172,6 +172,7 @@ Configured under `VisualGenArgs.parallel_config`. Modes can be combined: - **CFG Parallelism** (`cfg_size: 2`): Splits positive/negative guidance prompts across GPUs. - **Ulysses Parallelism** (`ulysses_size: N`): Splits the sequence dimension across GPUs for longer sequences. + - **Async Ulysses A2A pipeline** (`async_ulysses: true` in `parallel_config`): Overlaps per-rank V/Q/K projection compute with the cross-rank all-to-all on a dedicated side stream. Requires `ulysses_size > 1` and an NVLink-connected GPU domain (uses PyTorch `_SymmetricMemory` with CUDA IPC for peer pushes; not currently supported across nodes without MNNVL). Currently wired for WAN and LTX-2 self-attention. - **Parallel VAE** (`parallel_vae_size: N`): Shards the final VAE decode along a spatial axis (constraint: `parallel_vae_size ≤ world_size`; WAN/Cosmos3 only). - **Context Parallel (CP)** — Partitions the sequence into shards so that each rank computes partial attention. Requires an LSE-capable attention backend (`FA4` or `CUTEDSL`). CP can be composed with Ulysses, giving a total sequence-parallel (SP) degree = `cp_size · ulysses_size`. The CP degree depends on the implementation below: - **Attention2D** (`attn2d_size: [N, M]`): Shards the sequence axis across an `N × M` device mesh (CP degree = `N · M`; total SP degree = `N · M · ulysses_size`). diff --git a/examples/visual_gen/configs/ltx2-4gpu.yaml b/examples/visual_gen/configs/ltx2-4gpu.yaml new file mode 100644 index 000000000000..a667a147c9e5 --- /dev/null +++ b/examples/visual_gen/configs/ltx2-4gpu.yaml @@ -0,0 +1,28 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# 4-GPU LTX-2 AudioVideo parallel config (precision-agnostic — picks up +# whatever checkpoint is passed via --model_path). Shared by offline +# examples (--extra_visual_gen_options) and trtllm-serve. +attention_config: + backend: VANILLA +parallel_config: + cfg_size: 2 + ulysses_size: 2 + async_ulysses: true +torch_compile_config: + enable: true +cuda_graph_config: + enable: true diff --git a/examples/visual_gen/configs/wan2.2-t2v-fp4-4gpu.yaml b/examples/visual_gen/configs/wan2.2-t2v-fp4-4gpu.yaml index a645bbe7794b..82a033090aed 100644 --- a/examples/visual_gen/configs/wan2.2-t2v-fp4-4gpu.yaml +++ b/examples/visual_gen/configs/wan2.2-t2v-fp4-4gpu.yaml @@ -23,6 +23,7 @@ attention_config: parallel_config: cfg_size: 2 ulysses_size: 2 + async_ulysses: true parallel_vae_size: 4 cuda_graph_config: enable: false diff --git a/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py b/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py index b31bb56230c6..30ec7bfaa358 100644 --- a/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py @@ -1013,6 +1013,20 @@ def _(workspace, cp_rank, cp_size): # This op initializes workspace in-place and returns nothing return None + @torch.library.register_fake("trtllm::ulysses_post_unscatter_qkv") + def _(q_in, k_in, v_in, layout=0): + # Storage is always NHD-contig [B, P*Sp, H, D]. HND-shape return is a + # transpose-view (HND-shape, NHD-stride, non-contig) so Inductor sees + # the same stride pattern as the real op. + P, B, Sp, H, D = q_in.shape + nhd_shape = (B, P * Sp, H, D) + + def _mk(t): + base = t.new_empty(nhd_shape) + return base.transpose(1, 2) if layout == 0 else base + + return (_mk(q_in), _mk(k_in), _mk(v_in)) + @torch.library.register_fake("trtllm::helix_post_process") def _(gathered_o, gathered_stats, scale): return gathered_o.new_empty(*gathered_o.shape[1:]) diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py b/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py index 241aa8b2d166..bb1a84e6c30c 100644 --- a/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py @@ -20,7 +20,7 @@ """ -from typing import TYPE_CHECKING, Optional +from typing import TYPE_CHECKING, Callable, ClassVar, Dict, Optional import torch import torch.distributed as dist @@ -42,6 +42,25 @@ _flash_attn_combine_import_error = e +def post_permute_5d_to_4d(out_5d, P): + """5D [P, B, Sp, H/P, D] → 4D [B, P*Sp, H/P, D] (block-by-rank gather). + .contiguous() copies slot data out (layout-normalize for SDPA, decoupling + from IPC slot lifetime). Inductor fuses permute+contig with downstream + SDPA input prep.""" + _P, Bt, Spt, HpP, Dt = out_5d.shape + return out_5d.permute(1, 0, 2, 3, 4).contiguous().view(Bt, _P * Spt, HpP, Dt) + + +def _ulysses_post_unscatter(q_5d, k_5d, v_5d, *, is_hnd): + """One-launch fused replacement for the post-A2A 5D -> 4D chain. + + is_hnd=True -> output [B, H, P*Sp, D] (VANILLA / torch SDPA) + is_hnd=False -> output [B, P*Sp, H, D] (TRTLLM / FA4) + """ + layout = 0 if is_hnd else 1 + return torch.ops.trtllm.ulysses_post_unscatter_qkv(q_5d, k_5d, v_5d, layout) + + class UlyssesAttention(AttentionBackend): """ Ulysses Sequence Parallelism wrapper. @@ -65,10 +84,16 @@ class UlyssesAttention(AttentionBackend): + 1 for output (2 collectives total) """ + # One side stream shared across all UlyssesAttention instances on the + # same device. Per-layer streams inflate the stream count and break + # cuda_graph capture. + _side_stream_by_device: ClassVar[Dict[int, "torch.cuda.Stream"]] = {} + def __init__( self, inner_backend: AttentionBackend, process_group: torch.distributed.ProcessGroup, + async_ulysses: bool = False, ): self.inner_backend = inner_backend self.process_group = process_group @@ -83,6 +108,24 @@ def __init__( self.num_heads = self.sharded_num_heads * self.world_size self.num_kv_heads = self.sharded_num_kv_heads * self.world_size + # Async pipeline state. Eagerly populated when async_ulysses=True; + # forward_async assumes these are set. Non-async path doesn't touch + # them. + self._pg_boxed = None + self._async_side_stream: Optional[torch.cuda.Stream] = None + # Count of deferred pushes since the last `_join_async`. `_join_async` + # drains exactly this many `ulysses_a2a_async_barrier` calls on the + # side stream so V/Q/K pushes FIFO together without intermediate + # barrier kernels. + self._pending_barriers: int = 0 + if async_ulysses: + device = torch.cuda.current_device() + if device not in UlyssesAttention._side_stream_by_device: + UlyssesAttention._side_stream_by_device[device] = torch.cuda.Stream(device=device) + self._async_side_stream = UlyssesAttention._side_stream_by_device[device] + if process_group is not None: + self._pg_boxed = process_group.boxed() + def forward( self, q: torch.Tensor, @@ -166,6 +209,120 @@ def _forward_unfused( return self._output_a2a(output, batch_size, seq_len_full) + # ------------------------------------------------------------------ + # Split-QKV async A2A pipeline. `_issue_async` and `_join_async` are + # the only stream-switch boundaries and are @torch.compiler.disable'd; + # the caller's compiled forward fuses each compute_{q,k,v} closure. + # ------------------------------------------------------------------ + + @torch.compiler.disable(recursive=False) + def _issue_async(self, perm_4d: torch.Tensor) -> torch.Tensor: + """Issue one V/Q/K async a2a (CE push only; barrier deferred to join). + Phase 1 (acquire slot + CUDA C permute+scatter) runs on the CURRENT + (default) stream. Phase 2a (cudaMemcpyBatchAsync peer push) is queued + on the comm side stream, gated by an event so it waits for Phase 1. + Phase 2b (symm-mem barrier) is NOT issued here — `_join_async` drains + all pending barriers in one shot so V/Q/K pushes FIFO through CE + without intermediate barrier kernels splitting them up. Returns the + 5D recv-buf view. + + Comm-stream FIFO serializes consecutive V/Q/K pushes in caller order; + no explicit chain event is needed between them. The default stream + is free to immediately begin the next V/Q/K compute — that's where + the V_push ∥ Q_compute ∥ K_compute overlap comes from.""" + recv, send_h = torch.ops.trtllm.ulysses_a2a_async_prepare(perm_4d, self._pg_boxed) + ev = torch.cuda.Event() + ev.record() + with torch.cuda.stream(self._async_side_stream): + ev.wait() + torch.ops.trtllm.ulysses_a2a_async_push(send_h, self._pg_boxed) + self._pending_barriers += 1 + return recv + + @torch.compiler.disable(recursive=False) + def _join_async(self) -> None: + """Drain pending symm-mem barriers (one per deferred push) on the + side stream, then have the default stream wait on the tail event. + Comm-stream FIFO preserves [push V, push Q, push K, barrier, barrier, + barrier] order; all N barriers fire on channel=0 with identical + semantics, so the default stream sees a fully-synced recv buffer.""" + with torch.cuda.stream(self._async_side_stream): + for _ in range(self._pending_barriers): + torch.ops.trtllm.ulysses_a2a_async_barrier(self._pg_boxed) + ev_done = torch.cuda.Event() + ev_done.record() + self._pending_barriers = 0 + torch.cuda.current_stream().wait_event(ev_done) + + def forward_async( + self, + compute_q: Callable[[], torch.Tensor], + compute_k: Callable[[], torch.Tensor], + compute_v: Callable[[], torch.Tensor], + **attn_kwargs, + ) -> torch.Tensor: + """Run the async ulysses attention path (V/Q/K rolling A2A). + + Args: + compute_q / compute_k / compute_v : caller-provided closures that + each return a 4D tensor `[B, S_local, H, D]`. The closure + typically does `GEMM → (RMSNorm) → (RoPE) → view(4D)`; closures + live in the caller's compiled forward so inductor fuses each + into a single Triton kernel. + **attn_kwargs : forwarded to the wrapped inner attention backend + (mask, scale, etc.). + + Returns: + output tensor in the caller's sharded layout `[B, S/P, H, D]`. + + Pipeline: V/Q/K computed in V→Q→K order on the default stream; each + compute's output is fed to `_issue_async` which queues push+barrier on + the comm side stream. Default stream proceeds to the next compute + immediately, so V's push overlaps with Q's compute, Q's push overlaps + with K's compute. `_join_async` makes default wait on the last push. + Post-attention permute / SDPA / reverse A2A run in the caller's outer + compile region for additional inductor fusion.""" + P = self.world_size + + v_4d = compute_v() + v_5d = self._issue_async(v_4d) + + q_4d = compute_q() + q_5d = self._issue_async(q_4d) + + k_4d = compute_k() + k_5d = self._issue_async(k_4d) + + self._join_async() + + # Fast path: one fused kernel replaces the eager post-A2A chain + # (6 ops for HND target: permute+reshape+contig + transpose+contig + # per Q/K/V; 3 ops for NHD target). bf16-only because the kernel is + # only instantiated for __nv_bfloat16. + _, B_q, Sp_q, HpP_q, D_q = q_5d.shape + is_hnd = self.inner_backend.preferred_layout == AttentionTensorLayout.HND + use_fused_post_unscatter = q_5d.dtype == torch.bfloat16 + if use_fused_post_unscatter: + q_out, k_out, v_out = _ulysses_post_unscatter(q_5d, k_5d, v_5d, is_hnd=is_hnd) + B = B_q + seq_len_full = P * Sp_q + else: + v_out = post_permute_5d_to_4d(v_5d, P) + q_out = post_permute_5d_to_4d(q_5d, P) + k_out = post_permute_5d_to_4d(k_5d, P) + + B = q_out.shape[0] + seq_len_full = q_out.shape[1] + if is_hnd: + q_out = q_out.transpose(1, 2).contiguous() + k_out = k_out.transpose(1, 2).contiguous() + v_out = v_out.transpose(1, 2).contiguous() + + attn_kwargs["seq_len"] = seq_len_full + attn_kwargs["seq_len_kv"] = seq_len_full + output = self.inner_backend.forward(q=q_out, k=k_out, v=v_out, **attn_kwargs) + return self._output_a2a(output, B, seq_len_full) + def _output_a2a( self, output: torch.Tensor, @@ -622,6 +779,7 @@ def wrap_parallel_attention( *, visual_gen_mapping: Optional["VisualGenMapping"] = None, enable_sequence_parallel: bool = True, + async_ulysses: bool = False, ) -> AttentionBackend: """Wrap a compute backend with the configured parallelism strategy. @@ -650,5 +808,9 @@ def wrap_parallel_attention( attn = RingAttention(attn, process_group=vgm.ring_group) if ulysses_size > 1: - attn = UlyssesAttention(attn, process_group=vgm.ulysses_group) + attn = UlyssesAttention( + attn, + process_group=vgm.ulysses_group, + async_ulysses=async_ulysses, + ) return attn diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/ltx2_core/utils_ltx2.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/ltx2_core/utils_ltx2.py index 6e75448d435e..646624f8bd10 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/ltx2_core/utils_ltx2.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/ltx2_core/utils_ltx2.py @@ -15,9 +15,13 @@ def to_velocity( velocity = (sample - denoised) / sigma """ + # Tensor sigma: keep on device. `.item()` would force a D2H sync that + # deadlocks under nsys profiling combined with CUDA graph replay. + # The scheduler guarantees sigma > 0 inside the denoise loop, so we + # skip the zero check on the tensor path (re-checking would re-sync). if isinstance(sigma, torch.Tensor): - sigma = sigma.to(calc_dtype).item() - if sigma == 0: + sigma = sigma.to(calc_dtype) + elif sigma == 0: raise ValueError("Sigma can't be 0.0") return ((sample.to(calc_dtype) - denoised.to(calc_dtype)) / sigma).to(sample.dtype) diff --git a/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py b/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py index 1631573f28f3..7eff0b2e3d7c 100644 --- a/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py +++ b/tensorrt_llm/_torch/visual_gen/models/ltx2/transformer_ltx2.py @@ -29,6 +29,7 @@ from tensorrt_llm._torch.modules.linear import Linear, WeightMode from tensorrt_llm._torch.modules.mlp import MLP +from tensorrt_llm._torch.utils import Fp4QuantizedTensor from tensorrt_llm._torch.visual_gen.attention_backend.utils import create_attention from tensorrt_llm._torch.visual_gen.models.modeling import BaseDiffusionModel from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode @@ -88,6 +89,8 @@ def __init__( config: Optional["DiffusionModelConfig"] = None, layer_idx: int = 0, enable_sequence_parallel: bool = False, + use_ulysses: bool = False, + async_ulysses: bool = False, ): from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig @@ -99,10 +102,24 @@ def __init__( self.rope_type = rope_type self._is_cross_attn = context_dim is not None + # Async ulysses opt-in: V/Q/K GEMMs interleave with the all-to-all on a + # side stream. Forces SEPARATE_QKV so the 3 projections can issue + # independently. + self._use_async_ulysses = bool( + use_ulysses + and not self._is_cross_attn + and async_ulysses + and vgm is not None + and vgm.ulysses_size > 1 + ) + # Self-attention: FUSE_QKV enables the optimized backend + auto Ulysses # wrapping from the base class. - # Cross-attention: SEPARATE_QKV since K/V come from a different source. - qkv_mode = QKVMode.SEPARATE_QKV if self._is_cross_attn else QKVMode.FUSE_QKV + # Cross-attention or async ulysses: SEPARATE_QKV. + if self._is_cross_attn or self._use_async_ulysses: + qkv_mode = QKVMode.SEPARATE_QKV + else: + qkv_mode = QKVMode.FUSE_QKV # Caller opts in via enable_sequence_parallel. Cross-attn supports # Ulysses-only (SEPARATE_QKV + ring/attn2d is rejected in Attention); @@ -133,13 +150,11 @@ def __init__( config=config, layer_idx=layer_idx, enable_sequence_parallel=enable_sp, + enable_ulysses=use_ulysses, + async_ulysses=self._use_async_ulysses, ) - # Build a runtime-toggleable Ulysses ↔ plain pair. - # Self-attn: audio length isn't always divisible by ulysses_size, so - # we need a plain fallback. Cross-attn (v2a): same need — audio Q is - # padded when divisible, plain backend is used otherwise. Plain has - # to be built with the full (unsharded) head count. + # Validate Ulysses head divisibility (from main). self._has_dual_attn = False if enable_sp and ulysses_size > 1: U = ulysses_size @@ -153,6 +168,12 @@ def __init__( # Base class already built `self.attn` as the Ulysses-wrapped path # (sharded inner backend + UlyssesAttention) for both self-attn and # cross-attn paths. + + # For audio self-attention that may need a runtime Ulysses toggle + # (sequence length not always divisible by ulysses_size), create a + # plain backend as fallback. The base class already set self.attn + # to UlyssesAttention(inner_backend=sharded_backend). + if use_ulysses and not self._is_cross_attn and ulysses_size > 1: self._ulysses_attn = self.attn self._plain_attn = create_attention( backend=self.attn_backend, @@ -287,7 +308,9 @@ def forward( Caller contract: - FUSE_QKV (self-attn): pe must be set; k_pe and pre_projected_kv unused. - SEPARATE_QKV (cross-attn): cached path requires pre_projected_kv; - uncached path requires `context`. pe optional (None = norm-only). + uncached path uses ``context`` (may be None when the async-Ulysses + inner backend was swapped to a non-async one — falls back to + self-attn via kv_source=x). pe optional (None = norm-only). k_pe overrides pe for K (e.g. AV cross-attn) when provided. Args: @@ -298,46 +321,92 @@ def forward( silently ignores it. ``LTX2Attention`` constructs ``audio_attn1`` with a VANILLA backend whenever Ulysses is active under a TRTLLM backend config (see ``_init_audio_modules``). + + Routing: + 1. Async-Ulysses self-attn → ``forward_async`` (V/Q/K rolling A2A). + 2. FUSE_QKV self-attn → packed fused kernel (or naive mini-config). + 3. SEPARATE_QKV cross-attn → split fused kernel (or naive mini-config). """ - # Fallback to the naive eager rope path when fusion is disabled or - # the kernel doesn't support this head_dim. LTX-2 prod has - # fuse_qk_norm_rope=True and head_dim ∈ {64, 128}, so this branch - # only fires under mini-config unit tests (head_dim=32). - if not self.fuse_qk_norm_rope or self.head_dim not in (64, 128): - return self._forward_unfused(x, context, pe, k_pe, pre_projected_kv, key_padding_mask) + # Async-Ulysses self-attn dispatch. ``hasattr`` guard: audio_attn1 may + # have ``set_ulysses_active(False)`` swap ``self.attn`` to a plain + # backend that lacks ``forward_async`` — fall through to the sync + # uncached SEPARATE_QKV branch, which handles context=None via + # kv_source=x. + if ( + self.qkv_mode == QKVMode.SEPARATE_QKV + and self._use_async_ulysses + and context is None + and pre_projected_kv is None + and hasattr(self.attn, "forward_async") + ): + return self.forward_async(x, freqs=pe) + + # Fused gate: prod uses fused kernels (head_dim ∈ {64, 128}); mini-config + # tests (head_dim=32) fall to naive ops. + use_fused = self.fuse_qk_norm_rope and self.head_dim in (64, 128) and self.qk_norm if self.qkv_mode == QKVMode.FUSE_QKV: - # ─── self-attn → packed kernel (norm + rope on QKV in-place) ─── - qkv = self.qkv_proj(x) - cos, sin = pe - self.apply_packed_qk_norm_rope(qkv, cos, sin) - q, k, v = qkv.split([self.q_dim, self.kv_dim, self.kv_dim], dim=-1) + # ─── sync self-attn ─── + if use_fused and pe is not None: + # Fused packed kernel: norm + RoPE on QKV in-place. + qkv = self.qkv_proj(x) + cos, sin = pe + self.apply_packed_qk_norm_rope(qkv, cos, sin) + q, k, v = qkv.split([self.q_dim, self.kv_dim, self.kv_dim], dim=-1) + else: + # Naive (mini-config head_dim ∉ {64, 128}). + q, k, v = self.get_qkv(x) + if self.qk_norm: + q = self.norm_q(q) + k = self.norm_k(k) + if pe is not None: + q = apply_rotary_emb(q, pe, self.rope_type) + k = apply_rotary_emb(k, pe, self.rope_type) elif self.qkv_mode == QKVMode.SEPARATE_QKV: - # ─── cross-attn → split kernel (norm or norm+rope based on pe) ─── if pre_projected_kv is not None: - # K/V cached by caller (text cross-attn + AV cross-attn). - # The caller is responsible for any K-norm + K-rope on the - # cached tensor; we only fuse Q here. + # ─── cached cross-attn (text + AV cross-attn) ─── + # K/V cached by caller; we only norm+RoPE Q here. k, v = pre_projected_kv q = self.to_q(x) - self.apply_split_norm_or_norm_rope( - q, self.norm_q.weight, self.num_attention_heads, pe - ) + if use_fused: + self.apply_split_norm_or_norm_rope( + q, self.norm_q.weight, self.num_attention_heads, pe + ) + else: + if self.qk_norm: + q = self.norm_q(q) + if pe is not None: + q = apply_rotary_emb(q, pe, self.rope_type) else: - # Uncached cross-attn (not exercised by LTX-2 in practice; kept for fuse-dispatch consistency). + # ─── uncached cross-attn / async self-attn fallback ─── + # LTX-2 prod doesn't use uncached cross-attn (always pre-projects + # K/V). This branch also catches async self-attn when the inner + # backend lacks forward_async (audio Ulysses-inactive swap): + # context=None then, fall back to self-attn via kv_source=x. + kv_source = context if context is not None else x q = self.to_q(x) - k = self.to_k(context) - v = self.to_v(context) - self.apply_split_norm_or_norm_rope( - q, self.norm_q.weight, self.num_attention_heads, pe - ) - self.apply_split_norm_or_norm_rope( - k, - self.norm_k.weight, - self.num_key_value_heads, - k_pe if k_pe is not None else pe, - ) + k = self.to_k(kv_source) + v = self.to_v(kv_source) + if use_fused: + self.apply_split_norm_or_norm_rope( + q, self.norm_q.weight, self.num_attention_heads, pe + ) + self.apply_split_norm_or_norm_rope( + k, + self.norm_k.weight, + self.num_key_value_heads, + k_pe if k_pe is not None else pe, + ) + else: + if self.qk_norm: + q = self.norm_q(q) + k = self.norm_k(k) + if pe is not None: + q = apply_rotary_emb(q, pe, self.rope_type) + k_pe_use = k_pe if k_pe is not None else pe + if k_pe_use is not None: + k = apply_rotary_emb(k, k_pe_use, self.rope_type) attn_kwargs = {} if key_padding_mask is not None: @@ -354,58 +423,87 @@ def forward( return self.to_out[0](out) - def _forward_unfused( + def forward_async( self, x: torch.Tensor, - context: torch.Tensor | None, - pe: tuple[torch.Tensor, torch.Tensor] | None, - k_pe: tuple[torch.Tensor, torch.Tensor] | None, - pre_projected_kv: tuple[torch.Tensor, torch.Tensor] | None, - key_padding_mask: torch.Tensor | None = None, + freqs: tuple[torch.Tensor, torch.Tensor] | None = None, ) -> torch.Tensor: - """Fallback path for unsupported configs (head_dim ∉ {64, 128} or - ``fuse_qk_norm_rope=False``). + """LTX-2 async-Ulysses self-attn driver. Structurally mirrors base + ``Attention.forward_async`` (single function, fused/unfused branches) + but uses LTX-2's ``apply_rotary_emb`` (with ``rope_type``) on the + unfused fallback and injects gated-attention scaling in 4D between + the attn output and ``to_out``. - LTX-2 prod hardcodes ``fuse_qk_norm_rope=True`` and head_dim ∈ - {64, 128}, so in production this is never entered. Exercised by the - mini-config unit tests in ``test_ltx2_transformer.py`` (head_dim=32) - and by ablation tests that explicitly disable fusion. + Precondition: caller in ``LTX2Attention.forward`` gates on + ``_use_async_ulysses`` + ``hasattr(self.attn, "forward_async")``. - Contract: caller must pass *pe* / *k_pe* in 4D layout - ([B, T, H, D] for SPLIT rope, [B, T, D] for INTERLEAVED). The fused - kernel's 2D form is not compatible with the naive ``apply_rotary_emb``. + Returns 3D ``[B, S, H*D]`` matching ``forward``'s output contract. """ - if pre_projected_kv is not None: - k, v = pre_projected_kv - q = self.to_q(x) + B, S, _ = x.shape + H = self.num_attention_heads + KV = self.num_key_value_heads + D = self.head_dim + # Mirrors LTX2Attention.forward's fused gate; qkv_mode is implicitly + # SEPARATE_QKV under async (caller-enforced). head_dim check matches + # the fused split kernel's HEAD_DIM template instantiations {64, 128}. + use_fused = ( + self.fuse_qk_norm_rope + and self.head_dim in (64, 128) + and freqs is not None + and self.qk_norm + ) + + # SEPARATE_QKV self-attn 3x fp4_quantize dedup; see Attention.forward_async. + if self._maybe_share_qkv_quantize and getattr(self.to_q, "input_scale", None) is not None: + x_2d = x.reshape(-1, x.shape[-1]) + fp4, sf = torch.ops.trtllm.tunable_fp4_quantize( + x_2d, self.to_q.input_scale, self.to_q.scaling_vector_size, False + ) + qkv_input = Fp4QuantizedTensor(fp4, sf, is_sf_swizzled=False) + else: + qkv_input = x + + def compute_q(): + q = self.to_q(qkv_input) + if q.dim() == 2: + q = q.view(B, S, -1) + if use_fused: + self.apply_split_norm_rope(q, self.norm_q.weight, H, freqs[0], freqs[1]) + return q.view(B, S, H, D) + # Unfused fallback (mini-config); LTX-2 RoPE with rope_type. if self.qk_norm: q = self.norm_q(q) - else: - q, k, v = self.get_qkv(x, context) - q, k = self.apply_qk_norm(q, k) - - if pe is not None: - q = apply_rotary_emb(q, pe, self.rope_type) - # k_pe=None with pre_projected_kv signals K already rotated. - if k_pe is not None: - k = apply_rotary_emb(k, k_pe, self.rope_type) - elif pre_projected_kv is None: - k = apply_rotary_emb(k, pe, self.rope_type) + q = q.view(B, S, H, D) + if freqs is not None: + q = apply_rotary_emb(q, freqs, self.rope_type) + return q + + def compute_k(): + k = self.to_k(qkv_input) + if k.dim() == 2: + k = k.view(B, S, -1) + if use_fused: + self.apply_split_norm_rope(k, self.norm_k.weight, KV, freqs[0], freqs[1]) + return k.view(B, S, KV, D) + if self.qk_norm: + k = self.norm_k(k) + k = k.view(B, S, KV, D) + if freqs is not None: + k = apply_rotary_emb(k, freqs, self.rope_type) + return k - attn_kwargs = {} - if key_padding_mask is not None: - attn_kwargs["key_padding_mask"] = key_padding_mask - out = self._attn_impl(q, k, v, **attn_kwargs) + def compute_v(): + return self.to_v(qkv_input).view(B, S, KV, D) + + out_4d = self.attn.forward_async(compute_q, compute_k, compute_v) + # LTX-2 gated-attention scaling in 4D before to_out. if self.to_gate_logits is not None: - gate_logits = self.to_gate_logits(x) - b, t, _ = out.shape - out = out.view(b, t, self.num_attention_heads, self.head_dim) - gates = 2.0 * torch.sigmoid(gate_logits) - out = out * gates.unsqueeze(-1) - out = out.view(b, t, self.num_attention_heads * self.head_dim) + gates = 2.0 * torch.sigmoid(self.to_gate_logits(x)) + out_4d = out_4d * gates.unsqueeze(-1) - return self.to_out[0](out) + b, t = out_4d.shape[:2] + return self.to_out[0](out_4d.reshape(b, t, H * D)) # --------------------------------------------------------------------------- @@ -472,6 +570,7 @@ def _make_mlp(cfg, model_config, idx): ) def _init_video_modules(self, cfg, rope_type, eps, model_config, idx): + _async_ulysses = model_config.parallel.async_ulysses if model_config is not None else False self.attn1 = LTX2Attention( query_dim=cfg.dim, heads=cfg.heads, @@ -483,6 +582,8 @@ def _init_video_modules(self, cfg, rope_type, eps, model_config, idx): config=model_config, layer_idx=idx, enable_sequence_parallel=True, + use_ulysses=True, + async_ulysses=_async_ulysses, ) self.attn2 = LTX2Attention( query_dim=cfg.dim, diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py index 084c03754728..5d6264f67b7a 100644 --- a/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py +++ b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py @@ -28,7 +28,6 @@ def get_parameter_device(module): return next(module.parameters()).device - # ========================================================================= # 1. Rotary Positional Embeddings # ========================================================================= @@ -291,17 +290,30 @@ def __init__( # However, this kernel does not support TP due to the cross-head # normalization being a collective op. Thus, we must disable it if # using TP. + # When ulysses_size > 1 AND parallel.async_ulysses is set, switch + # to SEPARATE_QKV so V/Q/K projections can stream-pipeline through + # the async ulysses A2A path. tp_size = model_config.mapping.tp_size if model_config.mapping else 1 + vgm_self = model_config.visual_gen_mapping + ulysses_size_self = vgm_self.ulysses_size if vgm_self is not None else 1 + _async_a2a = model_config.parallel.async_ulysses if model_config is not None else False + self._use_async_ulysses = bool(ulysses_size_self > 1) and _async_a2a + _qkv_mode_self = QKVMode.SEPARATE_QKV if self._use_async_ulysses else QKVMode.FUSE_QKV self.attn1 = Attention( hidden_size=hidden_size, num_attention_heads=num_heads, head_dim=head_dim, - qkv_mode=QKVMode.FUSE_QKV, + qkv_mode=_qkv_mode_self, qk_norm=True, eps=eps, + # fuse_qk_norm_rope=True drives the packed kernel on sync (FUSE_QKV) + # and the split kernel on async (SEPARATE_QKV via forward_async). + # Disabled when TP>1 since the fused kernel lacks cross-rank + # all-reduce for the cross-head RMSNorm variance. fuse_qk_norm_rope=(tp_size == 1), config=model_config, layer_idx=_layer_idx, + async_ulysses=self._use_async_ulysses, ) # Cross-attention with separate Q, K, V @@ -415,15 +427,15 @@ def forward( # Prepare frequencies for Attention freqs = (freqs_cos, freqs_sin) if freqs_cos is not None and freqs_sin is not None else None - # Self-attention with RoPE - x = ( - x.float() - + self.attn1( - normed, - freqs=freqs, - ).float() - * gate_msa - ).to(x.dtype) + # Self-attention with RoPE. Async-ulysses dispatches to forward_async + # so each V/Q/K GEMM + norm + RoPE overlaps with the peer push on the + # side stream; both paths return 3D [B, S, H*D]. + if self._use_async_ulysses: + attn1_out = self.attn1.forward_async(normed, freqs=freqs) + else: + attn1_out = self.attn1(normed, freqs=freqs) + + x = (x.float() + attn1_out.float() * gate_msa).to(x.dtype) norm_x = self.norm2(x.float()).to(x.dtype) diff --git a/tensorrt_llm/_torch/visual_gen/modules/attention.py b/tensorrt_llm/_torch/visual_gen/modules/attention.py index 3465fcd63acd..37a1763ebce6 100644 --- a/tensorrt_llm/_torch/visual_gen/modules/attention.py +++ b/tensorrt_llm/_torch/visual_gen/modules/attention.py @@ -7,6 +7,7 @@ from tensorrt_llm.llmapi.llm_args import SkipSoftmaxAttentionConfig from ...modules.linear import Linear, TensorParallelMode, WeightMode, WeightsLoadingConfig +from ...utils import Fp4QuantizedTensor from ..attention_backend.interface import AttentionTensorLayout from ..attention_backend.parallel import wrap_parallel_attention from ..attention_backend.utils import create_attention @@ -53,6 +54,8 @@ def __init__( config: Optional[DiffusionModelConfig] = None, layer_idx: Optional[int] = None, enable_sequence_parallel: bool = True, + enable_ulysses: bool = True, + async_ulysses: bool = False, ): super().__init__() @@ -115,6 +118,20 @@ def __init__( self._init_qkv_proj() + # Structural eligibility for SEPARATE_QKV self-attn quantize dedup. + # When True, get_qkv() may pre-quantize hidden_states once and pass the + # shared Fp4QuantizedTensor to to_q/to_k/to_v (relies on Linear's + # Fp4QuantizedTensor shortcut). Numerical equality of the per-tensor + # input_scales is an invariant of modelopt's self-attn calibration + # (q/k/v share the same input distribution -> same calibrated scale). + self._maybe_share_qkv_quantize = ( + self.qkv_mode == QKVMode.SEPARATE_QKV + and self.quant_config is not None + and getattr(self.quant_config, "layer_quant_mode", None) is not None + and self.quant_config.layer_quant_mode.has_nvfp4() + and not self.force_dynamic_quantization + ) + attention_metadata_state = getattr(config, "attention_metadata_state", None) if self.qk_norm: @@ -160,9 +177,20 @@ def __init__( ] ) - # Ulysses shards heads across workers; inner backend sees sharded head count. - # Attention2D gathers sequence (not heads); see wrap_parallel_attention for nesting. - use_ulysses = ulysses_size > 1 and enable_sequence_parallel + # Ulysses auto-wrap normally skips SEPARATE_QKV (cross-attention). + # The async-ulysses path uses SEPARATE_QKV for stream-pipelined + # V/Q/K projections AND still needs the head-sharding wrap — opt in + # via async_ulysses=True. + use_ulysses = ( + ulysses_size > 1 + and enable_sequence_parallel + and enable_ulysses + and (self.qkv_mode != QKVMode.SEPARATE_QKV or async_ulysses) + ) + + # Compute head counts for the backend + # Ulysses shards heads across workers; inner backend sees sharded count + # Attention2D gathers sequence (not heads); inner backend sees full count if use_ulysses: backend_num_heads = self.local_num_attention_heads // ulysses_size backend_num_kv_heads = self.local_num_key_value_heads // ulysses_size @@ -214,6 +242,7 @@ def __init__( self.attn, visual_gen_mapping=vgm, enable_sequence_parallel=enable_sequence_parallel, + async_ulysses=use_ulysses and async_ulysses, ) def _init_qkv_proj(self) -> None: @@ -520,3 +549,104 @@ def forward( out = self._attn_impl(q, k, v) out = self.to_out[0](out) return out + + def forward_async( + self, + hidden_states: torch.Tensor, + freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + ) -> torch.Tensor: + """Async-Ulysses self-attn driver. Structurally mirrors ``forward``: + each closure does ``to_{q,k,v}`` + (optional) fused norm+RoPE on the + default stream while the previous tensor's peer push runs on a side + stream, so V/Q/K projections overlap with the all-to-all. + + Fused path: ``apply_split_norm_rope`` (SEPARATE_QKV analog of + ``apply_packed_qk_norm_rope``) does in-place RMSNorm + RoPE in one + kernel launch per Q/K. Same math as the packed kernel, just split + across two launches instead of one packed launch. + + Unfused path: naive ``norm_q`` + ``apply_rotary_emb``, mirroring + ``forward``'s unfused branch. + + Precondition: caller gates on ``_use_async_ulysses`` so ``self.attn`` + is a ``UlyssesAttention`` with ``async_ulysses=True``. + + Returns 3D ``[B, S, H*D]`` matching ``forward``'s output contract. + + TODO (kernel follow-up): the fused split kernel below writes Q/K to an + intermediate tensor, then ``UlyssesAttention._issue_async`` permutes + and scatters that tensor into the symm-mem slot. Folding the + permute+scatter into a "ulyssesPermuteScatter" epilogue inside the + fused norm+RoPE kernel would let it write directly into the slot, + saving one alloc + one copy + one kernel launch per Q/K closure. + """ + # Runtime precondition guard. Without async_ulysses=True at init, + # `self.attn` is the bare backend (e.g. TrtllmAttention) which has + # no `forward_async` method — the inner call below would otherwise + # crash with a non-prescriptive AttributeError deep in the function. + if not hasattr(self.attn, "forward_async"): + raise ValueError( + "Attention.forward_async() requires the inner attention to be a " + "UlyssesAttention with async_ulysses=True. Build the Attention with " + "ParallelConfig(ulysses_size > 1, async_ulysses=True), or use " + "forward() for sync execution." + ) + + B, S, _ = hidden_states.shape + H = self.num_attention_heads + KV = self.num_key_value_heads + D = self.head_dim + # Mirrors forward()'s fused gate. qkv_mode is implicitly SEPARATE_QKV + # under async (caller-enforced), so the FUSE_QKV check in forward() + # has no async analog here. + use_fused = self.fuse_qk_norm_rope and freqs is not None and self.qk_norm + + # SEPARATE_QKV self-attn 3x fp4_quantize dedup: pre-quantize hidden_states + # once and pass the shared Fp4QuantizedTensor to to_q/to_k/to_v via Linear's + # Fp4QuantizedTensor shortcut. Saves 2 of 3 fp4_quantize launches per layer. + # Eligibility is structural (set in __init__); runtime gate checks that the + # checkpoint loaded an input_scale (some attn Linears can be excluded from + # NVFP4 per checkpoint config — e.g. LTX-2 transformer_blocks.10.attn1). + if self._maybe_share_qkv_quantize and getattr(self.to_q, "input_scale", None) is not None: + x_2d = hidden_states.reshape(-1, hidden_states.shape[-1]) + fp4, sf = torch.ops.trtllm.tunable_fp4_quantize( + x_2d, self.to_q.input_scale, self.to_q.scaling_vector_size, False + ) + qkv_input = Fp4QuantizedTensor(fp4, sf, is_sf_swizzled=False) + else: + qkv_input = hidden_states + + def compute_q(): + q = self.to_q(qkv_input) + if q.dim() == 2: + q = q.view(B, S, -1) + if use_fused: + self.apply_split_norm_rope(q, self.norm_q.weight, H, freqs[0], freqs[1]) + return q.view(B, S, H, D) + if self.qk_norm: + q = self.norm_q(q) + q = q.view(B, S, H, D) + if freqs is not None: + q = apply_rotary_emb(q, freqs[0], freqs[1]) + return q + + def compute_k(): + k = self.to_k(qkv_input) + if k.dim() == 2: + k = k.view(B, S, -1) + if use_fused: + self.apply_split_norm_rope(k, self.norm_k.weight, KV, freqs[0], freqs[1]) + return k.view(B, S, KV, D) + if self.qk_norm: + k = self.norm_k(k) + k = k.view(B, S, KV, D) + if freqs is not None: + k = apply_rotary_emb(k, freqs[0], freqs[1]) + return k + + def compute_v(): + return self.to_v(qkv_input).view(B, S, KV, D) + + out_4d = self.attn.forward_async(compute_q, compute_k, compute_v) + b, t = out_4d.shape[:2] + return self.to_out[0](out_4d.reshape(b, t, H * D)) diff --git a/tensorrt_llm/visual_gen/args.py b/tensorrt_llm/visual_gen/args.py index 5b96985efd8f..82d64408ddf0 100644 --- a/tensorrt_llm/visual_gen/args.py +++ b/tensorrt_llm/visual_gen/args.py @@ -205,6 +205,15 @@ class ParallelConfig(StrictBaseModel): status="prototype", description=("Ulysses head-sharding degree. Heads are sharded across ulysses_size GPUs."), ) + async_ulysses: bool = Field( + False, + status="prototype", + description=( + "Enable the async Ulysses A2A pipeline: overlap per-rank V/Q/K projection compute " + "with cross-rank symm-mem all-to-all on a dedicated side stream. " + "Requires ulysses_size > 1. Defaults to False." + ), + ) ring_size: int = Field( 1, ge=1, @@ -258,6 +267,23 @@ def n_workers(self) -> int: def total_parallel_size(self) -> int: return self.cfg_size * self.seq_parallel_size + @model_validator(mode="after") + def _validate_async_ulysses(self) -> "ParallelConfig": + if self.async_ulysses: + if self.ulysses_size == 1: + raise ValueError( + "async_ulysses=True requires ulysses_size > 1; got " + f"ulysses_size={self.ulysses_size}." + ) + if self.ring_size > 1: + raise ValueError( + "async_ulysses=True is incompatible with ring_size > 1: " + "async_ulysses forces SEPARATE_QKV which bypasses the " + "RingAttention wrapper. Set ring_size=1 or async_ulysses=False " + f"(got ring_size={self.ring_size})." + ) + return self + def validate_world_size(self, world_size: int) -> None: if self.total_parallel_size > world_size: raise ValueError( diff --git a/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_permute_scatter.py b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_permute_scatter.py new file mode 100644 index 000000000000..24d995526672 --- /dev/null +++ b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_permute_scatter.py @@ -0,0 +1,93 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 + +import pytest +import torch + + +def torch_ref(input_4d, send_ref, recv_ref, my_rank, P): + """CPU/eager reference for ulyssesPermuteScatterKernel. + + input_4d : [B, S_local, H, D] bf16 + send/recv: [P, B, S_local, H/P, D] bf16 + For each (b, s, h, d): + peer = h // (H/P) + h_local = h % (H/P) + dst = recv_ref if peer == my_rank else send_ref + dst[peer, b, s, h_local, d] = input[b, s, h, d] + """ + B, S_local, H, D = input_4d.shape + P_check, _, _, H_local, _ = send_ref.shape + assert P == P_check + assert H == H_local * P + + # 4D -> [B, S_local, P, H_local, D] via H = P * H_local + x = input_4d.view(B, S_local, P, H_local, D) + # All-peer (P, B, S_local, H_local, D) + all_dst = x.permute(2, 0, 1, 3, 4).contiguous() + + # Split: peer != my_rank -> send_ref, peer == my_rank -> recv_ref + send_ref.copy_(all_dst) + recv_ref.zero_() + recv_ref[my_rank].copy_(all_dst[my_rank]) + send_ref[my_rank].zero_() + return send_ref, recv_ref + + +@pytest.mark.parametrize( + "P,B,S_local,H,D,my_rank", + [ + # LTX-2 ws=8 shape: heads-per-rank in input is H_full=32, P=4 ulysses + (4, 2, 1024, 32, 64, 0), + (4, 2, 1024, 32, 64, 1), + (4, 2, 1024, 32, 64, 2), + (4, 2, 1024, 32, 64, 3), + # WAN-like (alternate H, D) + (4, 2, 512, 16, 128, 0), + (8, 1, 256, 16, 128, 5), + # Smaller smoke shapes + (2, 1, 128, 8, 64, 0), + (2, 1, 128, 8, 64, 1), + ], +) +@torch.inference_mode() +def test_ulysses_permute_scatter_exact_match(P, B, S_local, H, D, my_rank): + """The kernel is a pure data-movement op (no float arithmetic), so the + output must match the eager reference byte-exact (max_diff == 0).""" + torch.manual_seed(0) + H_local = H // P + input_4d = torch.randn(B, S_local, H, D, device="cuda", dtype=torch.bfloat16).contiguous() + send = torch.zeros(P, B, S_local, H_local, D, device="cuda", dtype=torch.bfloat16) + recv = torch.zeros(P, B, S_local, H_local, D, device="cuda", dtype=torch.bfloat16) + + # Reference + send_ref = torch.zeros_like(send) + recv_ref = torch.zeros_like(recv) + torch_ref(input_4d, send_ref, recv_ref, my_rank, P) + + # Kernel + torch.ops.trtllm.ulysses_permute_scatter(input_4d, send, recv, my_rank, P) + + # 1. The peer==my_rank slot must be in recv (not send), and equal to input's slice + assert torch.equal(recv[my_rank], recv_ref[my_rank]) + # 2. The peer!=my_rank slots must be in send (not recv), and equal to input's slice + for peer in range(P): + if peer == my_rank: + continue + assert torch.equal(send[peer], send_ref[peer]), ( + f"send[peer={peer}] mismatch for my_rank={my_rank}" + ) + + # 3. The kernel must leave send[my_rank] and recv[peer!=my_rank] UNTOUCHED + # (we initialised them to zero; check they're still zero). + assert torch.count_nonzero(send[my_rank]) == 0 + for peer in range(P): + if peer == my_rank: + continue + assert torch.count_nonzero(recv[peer]) == 0 diff --git a/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_post_unscatter.py b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_post_unscatter.py new file mode 100644 index 000000000000..91092ce4d07f --- /dev/null +++ b/tests/unittest/_torch/thop/parallel_hw_agnostic/test_ulysses_post_unscatter.py @@ -0,0 +1,102 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 + +import pytest +import torch + + +def torch_ref(q_5d, k_5d, v_5d, is_hnd): + """Eager reference: the permute+reshape+contiguous chain the kernel replaces. + For HND the returned tensor is a transpose-view of NHD storage (HND-shape, + NHD-stride, non-contig) — matches the op's new behavior that preserves + NHD-stride into SDPA so the downstream `_output_a2a` transpose+contiguous + collapses to a no-op.""" + + def post(t): + P, B, Sp, H, D = t.shape + out = t.permute(1, 0, 2, 3, 4).reshape(B, P * Sp, H, D).contiguous() # NHD storage + if is_hnd: + return out.transpose(1, 2) # [B, H, P*Sp, D] view — NHD-stride + return out # [B, P*Sp, H, D] + + return post(q_5d), post(k_5d), post(v_5d) + + +@pytest.mark.parametrize("layout", [0, 1], ids=["HND", "NHD"]) +@pytest.mark.parametrize( + "P,B,Sp,H,D", + [ + # LTX-2 self-attn (ulysses=4): per-rank H = 32/4 = 8, D = 128 + (4, 2, 1024, 8, 128), + # LTX-2 audio attn (ulysses=4): per-rank H = 32/4 = 8, D = 64 + (4, 2, 1024, 8, 64), + # WAN-like (alternate H, D) + (4, 2, 512, 16, 128), + (8, 1, 256, 16, 128), + # H * (D/8) edge: 64 threads/block (smallest interesting tile) + (2, 1, 128, 4, 128), + # Larger H with D=64 + (4, 2, 256, 32, 64), + ], +) +@torch.inference_mode() +def test_ulysses_post_unscatter_exact_match(P, B, Sp, H, D, layout): + """The op is a pure data movement, so output must match the eager + permute+reshape+contiguous chain exactly (max_diff == 0). HND output is + a transpose-view (HND-shape, NHD-stride, non-contig); NHD output is + contig. Both are exercised.""" + is_hnd = layout == 0 + torch.manual_seed(0) + q = torch.randn(P, B, Sp, H, D, device="cuda", dtype=torch.bfloat16).contiguous() + k = torch.randn(P, B, Sp, H, D, device="cuda", dtype=torch.bfloat16).contiguous() + v = torch.randn(P, B, Sp, H, D, device="cuda", dtype=torch.bfloat16).contiguous() + + q_ref, k_ref, v_ref = torch_ref(q, k, v, is_hnd=is_hnd) + q_out, k_out, v_out = torch.ops.trtllm.ulysses_post_unscatter_qkv(q, k, v, layout) + + expected_shape = (B, H, P * Sp, D) if is_hnd else (B, P * Sp, H, D) + assert q_out.shape == expected_shape + if is_hnd: + # HND-shape, NHD-stride transpose-view of NHD-contig storage. The + # underlying storage IS contig (in NHD layout), but the HND-labeled + # tensor is non-contig — this is intentional: cudnn SDPA preserves + # this NHD-stride to its output, collapsing _output_a2a's + # transpose+contiguous to a no-op. + assert not q_out.is_contiguous() and not k_out.is_contiguous() and not v_out.is_contiguous() + else: + assert q_out.is_contiguous() and k_out.is_contiguous() and v_out.is_contiguous() + assert q_out.dtype == torch.bfloat16 + for name, ref, got in [("Q", q_ref, q_out), ("K", k_ref, k_out), ("V", v_ref, v_out)]: + max_diff = (ref - got).abs().max().item() + assert max_diff == 0, f"{name}: max_diff={max_diff} (expected exact match)" + + +@torch.inference_mode() +def test_ulysses_post_unscatter_rejects_invalid_layout(): + """layout must be 0 (HND) or 1 (NHD).""" + q = torch.randn(2, 1, 128, 8, 64, device="cuda", dtype=torch.bfloat16) + with pytest.raises(RuntimeError): + torch.ops.trtllm.ulysses_post_unscatter_qkv(q, q, q, 2) + + +@torch.inference_mode() +def test_ulysses_post_unscatter_rejects_d_not_multiple_of_8(): + """D must be a multiple of 8 (uint4 vec load constraint).""" + q = torch.randn(2, 1, 128, 8, 60, device="cuda", dtype=torch.bfloat16) + with pytest.raises(RuntimeError): + torch.ops.trtllm.ulysses_post_unscatter_qkv(q, q, q) + + +@torch.inference_mode() +def test_ulysses_post_unscatter_rejects_oversized_block(): + """Threads/block = H * (D/8) must be <= 1024 (CUDA hw limit).""" + # H=128, D=128 -> 128 * 16 = 2048 threads, exceeds the 1024 hw cap. + q = torch.randn(2, 1, 64, 128, 128, device="cuda", dtype=torch.bfloat16) + with pytest.raises(RuntimeError): + torch.ops.trtllm.ulysses_post_unscatter_qkv(q, q, q) diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py b/tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py new file mode 100644 index 000000000000..301c776103c8 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py @@ -0,0 +1,435 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Multi-GPU tests for LTX-2 async-Ulysses parity and perf. + +Compares ``LTXModel.forward`` outputs with ``async_ulysses=True`` (V/Q/K +rolling A2A via ``Attention.forward_async``) vs ``async_ulysses=False`` +(standard sync Ulysses) at ``ulysses_size=2``. Both paths wrap the inner +backend with ``UlyssesAttention`` and produce mathematically equivalent +results — drift comes only from per-kernel accumulation order under BF16. + +Mirrors ``test_ltx2_ulysses.py`` (PR 14044) but specifically exercises the +async-Ulysses code paths added on this branch: + - ``LTX2Attention.forward`` async self-attn dispatch + - ``LTX2Attention.forward_async`` (fused split kernel or naive fallback) + - ``UlyssesAttention.forward_async`` (V/Q/K rolling side-stream pipeline) + +Uses ``attention_head_dim=64`` so ``LTX2Attention.forward_async`` takes +the fused split kernel path (``apply_split_norm_rope``) — the prod code +path. Requires LTX-2 C++ extensions to be built. + +Run with: + pytest tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py -v +""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +from types import SimpleNamespace +from typing import Callable + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +try: + from tensorrt_llm._torch.visual_gen.config import ( + DiffusionModelConfig, + create_attention_metadata_state, + ) + from tensorrt_llm._torch.visual_gen.mapping import VisualGenMapping + from tensorrt_llm._utils import get_free_port + from tensorrt_llm.models.modeling_utils import QuantConfig + from tensorrt_llm.visual_gen.args import AttentionConfig, ParallelConfig, TorchCompileConfig + + MODULES_AVAILABLE = True +except ImportError: + MODULES_AVAILABLE = False + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +# ============================================================================= +# Distributed helpers (same pattern as test_ltx2_ulysses.py) +# ============================================================================= + + +def init_distributed_worker(rank: int, world_size: int, backend: str = "nccl", port: int = 29500): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + torch.cuda.set_device(rank % torch.cuda.device_count()) + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + + +def cleanup_distributed(): + if dist.is_initialized(): + dist.destroy_process_group() + + +def _distributed_worker(rank, world_size, backend, test_fn, port, fn_args): + try: + init_distributed_worker(rank, world_size, backend, port) + test_fn(rank, world_size, *fn_args) + except Exception as e: + print(f"Rank {rank} failed with error: {e}") + raise + finally: + cleanup_distributed() + + +def run_test_in_distributed(world_size: int, test_fn: Callable, *fn_args): + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + if torch.cuda.device_count() < world_size: + pytest.skip(f"Test requires {world_size} GPUs, only {torch.cuda.device_count()} available") + port = get_free_port() + mp.spawn( + _distributed_worker, + args=(world_size, "nccl", test_fn, port, fn_args), + nprocs=world_size, + join=True, + ) + + +# ============================================================================= +# Model config +# ============================================================================= + +# Small AudioVideo config. head_dim=64 matches the fused split kernel's +# {64, 128} template — exercises ``LTX2Attention.forward_async`` fused +# branch (apply_split_norm_rope) rather than the naive fallback. +# cross_attention_dim must equal num_heads * head_dim (= 4 * 64 = 256) so +# caption_projection's output matches block.attn2.to_k's input dim. Same +# constraint for audio_*. +_AV_CONFIG = dict( + num_attention_heads=4, + attention_head_dim=64, + in_channels=16, + out_channels=16, + num_layers=2, + cross_attention_dim=256, # = num_attention_heads * attention_head_dim + caption_channels=64, + norm_eps=1e-6, + positional_embedding_max_pos=[4, 32, 32], + timestep_scale_multiplier=1000, + use_middle_indices_grid=True, + audio_num_attention_heads=4, + audio_attention_head_dim=64, + audio_in_channels=16, + audio_out_channels=16, + audio_cross_attention_dim=256, # = audio_num_attention_heads * audio_attention_head_dim + audio_positional_embedding_max_pos=[64], + av_ca_timestep_scale_multiplier=1, +) + + +def _make_model_config( + ulysses_size: int = 1, + backend: str = "VANILLA", + async_ulysses: bool = False, +) -> "DiffusionModelConfig": + """Create DiffusionModelConfig for LTX-2 tests. + + The async_ulysses flag toggles the V/Q/K rolling A2A pipeline (this PR's + feature). ulysses_size > 1 is required for async_ulysses to fire. + """ + if ulysses_size > 1 and dist.is_initialized(): + ws = dist.get_world_size() + rk = dist.get_rank() + else: + ws = ulysses_size + rk = 0 + vgm = VisualGenMapping(world_size=ws, rank=rk, ulysses_size=ulysses_size) + + config = DiffusionModelConfig( + pretrained_config=SimpleNamespace(), + quant_config=QuantConfig(), + torch_compile=TorchCompileConfig(enable=False), + attention=AttentionConfig(backend=backend), + visual_gen_mapping=vgm, + cache=None, + attention_metadata_state=( + create_attention_metadata_state() if backend.upper() == "TRTLLM" else None + ), + parallel=ParallelConfig( + ulysses_size=ulysses_size, + async_ulysses=async_ulysses, + ), + skip_create_weights_in_init=False, + ) + config.mapping = vgm.to_llm_mapping() + return config + + +def _init_all_weights(model: torch.nn.Module, std: float = 0.02): + with torch.no_grad(): + for name, p in model.named_parameters(): + if "norm" in name and "weight" in name: + p.fill_(1.0) + elif p.numel() > 0: + torch.nn.init.normal_(p, mean=0.0, std=std) + + +def _make_video_positions( + batch: int, n_patches: int, n_frames: int, grid_h: int, grid_w: int, device: torch.device +) -> torch.Tensor: + positions = torch.zeros(batch, 3, n_patches, 2, device=device) + idx = 0 + for f in range(n_frames): + for h in range(grid_h): + for w in range(grid_w): + positions[:, 0, idx, :] = torch.tensor([f, f + 1], dtype=torch.float32) + positions[:, 1, idx, :] = torch.tensor([h, h + 1], dtype=torch.float32) + positions[:, 2, idx, :] = torch.tensor([w, w + 1], dtype=torch.float32) + idx += 1 + return positions + + +def _make_audio_positions(batch: int, n_patches: int, device: torch.device) -> torch.Tensor: + positions = torch.zeros(batch, 1, n_patches, 2, device=device) + for i in range(n_patches): + positions[:, 0, i, :] = torch.tensor([i, i + 1], dtype=torch.float32) + return positions + + +def _build_inputs(batch, v_patches, v_dims, a_patches, dtype, device, seed=456): + g = torch.Generator(device=device).manual_seed(seed) + v_frames, v_h, v_w = v_dims + in_channels = _AV_CONFIG["in_channels"] + audio_in_channels = _AV_CONFIG["audio_in_channels"] + caption_channels = _AV_CONFIG["caption_channels"] + text_len = 8 + + v_context = ( + torch.randn(batch, text_len, caption_channels, device=device, dtype=dtype, generator=g) + * 0.02 + ) + a_context = ( + torch.randn(batch, text_len, caption_channels, device=device, dtype=dtype, generator=g) + * 0.02 + ) + v_positions = _make_video_positions(batch, v_patches, v_frames, v_h, v_w, device) + a_positions = _make_audio_positions(batch, a_patches, device) + + from tensorrt_llm._torch.visual_gen.models.ltx2.ltx2_core.modality import Modality + + video = Modality( + latent=torch.randn(batch, v_patches, in_channels, device=device, dtype=dtype, generator=g) + * 0.02, + timesteps=torch.tensor([0.5], device=device), + positions=v_positions, + context=v_context, + ) + audio = Modality( + latent=torch.randn( + batch, a_patches, audio_in_channels, device=device, dtype=dtype, generator=g + ) + * 0.02, + timesteps=torch.tensor([0.5], device=device), + positions=a_positions, + context=a_context, + ) + return video, audio, v_context, a_context, v_positions, a_positions + + +def _pack_async_state_for_sync(async_state, sync_state_keys): + """Translate a SEPARATE_QKV (async) state_dict to FUSE_QKV (sync) layout: + concatenates to_q/to_k/to_v.{weight,bias} into packed qkv_proj.{weight,bias}. + Non-self-attn weights pass through unchanged. + + Used to load identical effective weights into both async and sync LTX + models despite their different attn1 layer structure. + """ + out = {} + for k in sync_state_keys: + if k in async_state: + out[k] = async_state[k] + elif k.endswith(".qkv_proj.weight"): + prefix = k[: -len(".qkv_proj.weight")] + out[k] = torch.cat( + [ + async_state[f"{prefix}.to_q.weight"], + async_state[f"{prefix}.to_k.weight"], + async_state[f"{prefix}.to_v.weight"], + ], + dim=0, + ) + elif k.endswith(".qkv_proj.bias"): + prefix = k[: -len(".qkv_proj.bias")] + out[k] = torch.cat( + [ + async_state[f"{prefix}.to_q.bias"], + async_state[f"{prefix}.to_k.bias"], + async_state[f"{prefix}.to_v.bias"], + ], + dim=0, + ) + else: + raise KeyError(f"sync key {k!r} not present in async state and not a qkv_proj fuse") + return out + + +def _build_av_model( + world_size: int, + backend: str, + audio_seq_len: int, + async_ulysses: bool, + device: torch.device, + dtype: torch.dtype, + seed: int = 123, +): + """Build LTXModel (AudioVideo) with deterministic weights via shared seed. + + ``configure_audio_ulysses(audio_seq_len)`` gates audio_attn1's Ulysses + activity by divisibility: not divisible → ``set_ulysses_active(False)`` + swaps the audio backend to plain (no ``forward_async``), forcing async + self-attn to fall through the ``hasattr`` guard in ``LTX2Attention.forward``. + """ + from tensorrt_llm._torch.visual_gen.models.ltx2.transformer_ltx2 import LTXModel, LTXModelType + + torch.manual_seed(seed) + cfg = _make_model_config( + ulysses_size=world_size, + backend=backend, + async_ulysses=async_ulysses, + ) + model = ( + LTXModel(model_type=LTXModelType.AudioVideo, model_config=cfg, **_AV_CONFIG) + .to(device, dtype=dtype) + .eval() + ) + _init_all_weights(model) + model.configure_audio_ulysses(audio_seq_len) + return model + + +# ============================================================================= +# Test logic +# ============================================================================= + + +def _logic_async_vs_sync_parity(rank, world_size, backend, audio_seq_len): + """LTX-2 AV at ws=2: async-Ulysses output matches sync-Ulysses output. + + Both models use ``UlyssesAttention`` (forced by ``ulysses_size=2``); the + only difference is whether self-attn dispatches through + ``Attention.forward_async`` (V/Q/K closures + side-stream A2A) or + ``Attention.forward`` (precomputed Q/K/V + sync A2A). Math is equivalent; + BF16 accumulation order drift lands well under the 5e-2 tolerance. + """ + device = torch.device(f"cuda:{rank}") + dtype = torch.bfloat16 + + batch = 1 + v_dims = (1, 4, 4) + v_patches = v_dims[0] * v_dims[1] * v_dims[2] # 16, divisible by ws=2 + + # Build async (SEPARATE_QKV self-attn → to_q/k/v) first as the canonical + # source, then mirror its weights into a sync (FUSE_QKV → qkv_proj) model + # by concatenating to_q/k/v into the packed layout. + async_model = _build_av_model(world_size, backend, audio_seq_len, True, device, dtype) + async_state = async_model.state_dict() + sync_model = _build_av_model(world_size, backend, audio_seq_len, False, device, dtype) + sync_model.load_state_dict( + _pack_async_state_for_sync(async_state, sync_model.state_dict().keys()) + ) + + # Sanity: confirm sync vs async actually take different code paths. + # transformer_blocks may be wrapped by LTX2CacheDiTPattern0BlockWrapper; + # unwrap via .inner if present. + def _block_attn1(model): + b = model.transformer_blocks[0] + b = b.inner if hasattr(b, "inner") else b + return b.attn1 + + assert _block_attn1(async_model)._use_async_ulysses is True, ( + "async model didn't enable async path" + ) + assert _block_attn1(sync_model)._use_async_ulysses is False, ( + "sync model unexpectedly enabled async path" + ) + assert _block_attn1(async_model).qkv_mode != _block_attn1(sync_model).qkv_mode, ( + "test bug: sync and async models have identical qkv_mode " + f"(both {_block_attn1(sync_model).qkv_mode})" + ) + + video, audio, v_ctx, a_ctx, v_pos, a_pos = _build_inputs( + batch, v_patches, v_dims, audio_seq_len, dtype, device + ) + + sync_cache = sync_model.prepare_text_cache( + video_context=v_ctx, + video_positions=v_pos, + audio_context=a_ctx, + audio_positions=a_pos, + dtype=dtype, + ) + async_cache = async_model.prepare_text_cache( + video_context=v_ctx, + video_positions=v_pos, + audio_context=a_ctx, + audio_positions=a_pos, + dtype=dtype, + ) + + with torch.no_grad(): + sync_v, sync_a = sync_model(video=video, audio=audio, text_cache=sync_cache) + async_v, async_a = async_model(video=video, audio=audio, text_cache=async_cache) + + assert sync_v.shape == async_v.shape, f"Rank {rank}: video shape mismatch" + assert sync_a.shape == async_a.shape, f"Rank {rank}: audio shape mismatch" + + # Diagnostic: actual BF16 drift between sync (packed kernel + sync a2a) and + # async (split kernel + side-stream a2a) — both at ws=2, same collectives. + for name, s, a in [("video", sync_v, async_v), ("audio", sync_a, async_a)]: + diff = (a.float() - s.float()).abs() + ref = s.float().abs() + print( + f"\n[LTX-2 rank={rank} backend={backend} {name}] " + f"max_abs_diff={diff.max().item():.3e} " + f"max_rel_diff={(diff / ref.clamp(min=1e-6)).max().item():.3e} " + f"sync_abs_max={ref.max().item():.3e}" + ) + + torch.testing.assert_close( + async_v, + sync_v, + rtol=1e-3, + atol=1e-3, + msg=f"Rank {rank}: LTX-2 AV async-Ulysses video differs from sync-Ulysses", + ) + torch.testing.assert_close( + async_a, + sync_a, + rtol=1e-3, + atol=1e-3, + msg=f"Rank {rank}: LTX-2 AV async-Ulysses audio differs from sync-Ulysses", + ) + + +# ============================================================================= +# Test classes +# ============================================================================= + + +class TestLTX2AsyncUlysses: + """async_ulysses=True/False parity for LTX-2 AudioVideo at ws=2.""" + + @pytest.mark.parametrize("backend", ["VANILLA", "FA4"]) + def test_av_async_vs_sync_parity(self, backend): + """ws=2, audio_seq_len % 2 == 0 — audio_attn1 uses Ulysses on both + sync and async paths.""" + run_test_in_distributed(2, _logic_async_vs_sync_parity, backend, 16) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py b/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py new file mode 100644 index 000000000000..62353b4b4741 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py @@ -0,0 +1,292 @@ +"""Multi-rank tests for the async Ulysses A2A op. + +Exercises the production ``ulysses_a2a_async_prepare`` / ``ulysses_a2a_async_push`` +/ ``ulysses_a2a_async_barrier`` chain end-to-end across multiple ranks. Two test +surfaces: + +1. ``test_slot_ring_wraparound`` — eager mode (cudaMemcpyBatchAsync path). + Loops the pair more than ``kNumSlots`` times so the slot ring wraps multiple + times, asserts byte-exact match vs ``all_to_all_4d`` on every iteration. + Catches off-by-one slot-reuse bugs. + +2. ``test_capture_smoke`` — under-capture mode (per-peer cudaMemcpyAsync path). + Warms up the slot out-of-capture, then captures a ``torch.cuda.CUDAGraph`` + containing one prepare+async pair, replays it K times with fresh inputs. + Smoke-tests the cuda_graph path used by the e2e production benchmark. + +Run with: + pytest tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py -v +""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +from typing import Callable + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +try: + from tensorrt_llm._torch.distributed import all_to_all_4d + from tensorrt_llm._utils import get_free_port + + MODULES_AVAILABLE = True +except ImportError: + MODULES_AVAILABLE = False + + +# Loop count must comfortably exceed kNumSlots so the ring wraps at least +# twice. kNumSlots is 3 today; 8 iterations = ~2.67 full rotations. +NUM_ITERS = 8 + + +def _init_dist(rank: int, world_size: int, port: int): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + torch.cuda.set_device(rank % torch.cuda.device_count()) + dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) + + +def _slot_ring_logic(rank: int, world_size: int): + """Loop the async A2A prepare/async pair NUM_ITERS times; assert byte-exact + match against all_to_all_4d on every iteration. + + Stream pattern mirrors production (parallel.py:_issue_async): Phase 1 on + the default (compute) stream, Phase 2 on a dedicated side stream gated by + an event recorded after Phase 1. + """ + device = torch.device(f"cuda:{rank}") + torch.manual_seed(1234 + rank) + + # Production-scale shapes (LTX-2 ws=4: H=32, D=128, S_local=720 region). + B, S_local, H, D = 2, 128, world_size * 8, 128 + pg = dist.group.WORLD + pg_boxed = pg.boxed() + side_stream = torch.cuda.Stream(device=device) + + for it in range(NUM_ITERS): + # Fresh input each iteration (different bytes) so a stale slot would + # produce a stale value and the byte-exact compare would catch it. + torch.manual_seed(1000 * it + rank) + x = torch.randn(B, S_local, H, D, dtype=torch.bfloat16, device=device) + + # Phase 1 on default stream. + recv_5d, send_h = torch.ops.trtllm.ulysses_a2a_async_prepare(x, pg_boxed) + ev = torch.cuda.Event() + ev.record() + # Phase 2 on side stream. + with torch.cuda.stream(side_stream): + ev.wait() + torch.ops.trtllm.ulysses_a2a_async_push(send_h, pg_boxed) + torch.ops.trtllm.ulysses_a2a_async_barrier(pg_boxed) + ev_done = torch.cuda.Event() + with torch.cuda.stream(side_stream): + ev_done.record() + torch.cuda.current_stream().wait_event(ev_done) + torch.cuda.synchronize() + + # Async op produces [P, B, S_local, H/P, D]; permute to [B, P*S_local, H/P, D] + # to match the all_to_all_4d output layout. + async_out = ( + recv_5d.permute(1, 0, 2, 3, 4) + .contiguous() + .view(B, world_size * S_local, H // world_size, D) + ) + + ref = all_to_all_4d(x, scatter_dim=2, gather_dim=1, process_group=pg) + + assert torch.equal(async_out, ref), ( + f"rank {rank} iter {it}: async A2A ≠ all_to_all_4d reference " + f"(slot ring wrap = {it % 3})" + ) + + +def _capture_smoke_logic(rank: int, world_size: int): + """Smoke-test the under-capture branch: warm up slots out-of-capture, then + capture a torch.cuda.CUDAGraph containing one prepare+async pair and replay + K times with fresh inputs. Exercises the per-peer cudaMemcpyAsync loop + (production cuda_graph path) instead of the eager cudaMemcpyBatchAsync. + """ + device = torch.device(f"cuda:{rank}") + torch.manual_seed(1234 + rank) + + B, S_local, H, D = 2, 128, world_size * 8, 128 + P = world_size + pg = dist.group.WORLD + pg_boxed = pg.boxed() + side_stream = torch.cuda.Stream(device=device) + + # Static buffers (graph captures pointers, not values). + x_static = torch.randn(B, S_local, H, D, dtype=torch.bfloat16, device=device) + out_static = torch.empty(B, P * S_local, H // P, D, dtype=torch.bfloat16, device=device) + + # Warmup: must allocate ALL kNumSlots ring entries out-of-capture (allocation + # is not capture-safe; the cudaStreamIsCapturing guard in getOrAllocSlot + # enforces this). One warmup call only allocates 1 slot — the captured + # _prepare advances mNextIdx and would hit an unallocated slot. So warm up + # >= kNumSlots times. Mirror production's stream pattern (Phase 2 on side + # stream) so cudaMemcpyBatchAsync sees the steady-state stream context. + K_NUM_SLOTS = 3 # mirrors AsyncUlyssesOp::kNumSlots + for _ in range(K_NUM_SLOTS): + recv_w, sh_w = torch.ops.trtllm.ulysses_a2a_async_prepare(x_static, pg_boxed) + ev_w = torch.cuda.Event() + ev_w.record() + with torch.cuda.stream(side_stream): + ev_w.wait() + torch.ops.trtllm.ulysses_a2a_async_push(sh_w, pg_boxed) + torch.ops.trtllm.ulysses_a2a_async_barrier(pg_boxed) + ev_w_done = torch.cuda.Event() + with torch.cuda.stream(side_stream): + ev_w_done.record() + torch.cuda.current_stream().wait_event(ev_w_done) + torch.cuda.synchronize() + del recv_w, sh_w + + # Capture. + g = torch.cuda.CUDAGraph() + with torch.cuda.graph(g): + recv_5d, send_h = torch.ops.trtllm.ulysses_a2a_async_prepare(x_static, pg_boxed) + ev = torch.cuda.Event() + ev.record() + with torch.cuda.stream(side_stream): + ev.wait() + torch.ops.trtllm.ulysses_a2a_async_push(send_h, pg_boxed) + torch.ops.trtllm.ulysses_a2a_async_barrier(pg_boxed) + ev_done = torch.cuda.Event() + with torch.cuda.stream(side_stream): + ev_done.record() + torch.cuda.current_stream().wait_event(ev_done) + captured_out = recv_5d.permute(1, 0, 2, 3, 4).contiguous().view(B, P * S_local, H // P, D) + out_static.copy_(captured_out) + + # Replay K times. K > kNumSlots*2 to exercise wrap-around under capture. + K = 8 + for it in range(K): + torch.manual_seed(2000 * it + rank) + new_x = torch.randn(B, S_local, H, D, dtype=torch.bfloat16, device=device) + x_static.copy_(new_x) + g.replay() + torch.cuda.synchronize() + ref = all_to_all_4d(new_x, scatter_dim=2, gather_dim=1, process_group=pg) + assert torch.equal(out_static, ref), ( + f"rank {rank} capture replay {it}: async A2A ≠ all_to_all_4d reference" + ) + + +def _multi_pg_logic(rank: int, world_size: int): + """Two distinct PGs spanning the same ranks but with different group_names; + alternate calls between them; verify each gets its own slot ring and + byte-exact output vs all_to_all_4d on its own group. + """ + device = torch.device(f"cuda:{rank}") + torch.manual_seed(4242 + rank) + + B, S_local, H, D = 2, 64, world_size * 8, 128 + # Two PGs, same membership (all ranks), but new_group assigns distinct + # group_names — so getOrCreateOp must cache one op per PG-name and + # set_group_info must register both. + pg_a = dist.new_group(ranks=list(range(world_size))) + pg_b = dist.new_group(ranks=list(range(world_size))) + assert pg_a.group_name != pg_b.group_name, "new_group should yield distinct names" + pg_a_boxed = pg_a.boxed() + pg_b_boxed = pg_b.boxed() + side_stream = torch.cuda.Stream(device=device) + + def _issue(pg_boxed, pg_obj, x): + recv_5d, send_h = torch.ops.trtllm.ulysses_a2a_async_prepare(x, pg_boxed) + ev = torch.cuda.Event() + ev.record() + with torch.cuda.stream(side_stream): + ev.wait() + torch.ops.trtllm.ulysses_a2a_async_push(send_h, pg_boxed) + torch.ops.trtllm.ulysses_a2a_async_barrier(pg_boxed) + ev_done = torch.cuda.Event() + with torch.cuda.stream(side_stream): + ev_done.record() + torch.cuda.current_stream().wait_event(ev_done) + torch.cuda.synchronize() + async_out = ( + recv_5d.permute(1, 0, 2, 3, 4) + .contiguous() + .view(B, world_size * S_local, H // world_size, D) + ) + ref = all_to_all_4d(x, scatter_dim=2, gather_dim=1, process_group=pg_obj) + return async_out, ref + + # Alternate between the two PGs for 2*kNumSlots iterations to exercise + # both slot rings wrapping. + for it in range(NUM_ITERS): + torch.manual_seed(3000 * it + rank) + x = torch.randn(B, S_local, H, D, dtype=torch.bfloat16, device=device) + pg_boxed, pg_obj, tag = (pg_a_boxed, pg_a, "A") if it % 2 == 0 else (pg_b_boxed, pg_b, "B") + async_out, ref = _issue(pg_boxed, pg_obj, x) + assert torch.equal(async_out, ref), ( + f"rank {rank} iter {it} PG={tag}: async A2A ≠ all_to_all_4d reference" + ) + + +def _worker(rank, world_size, port): + try: + _init_dist(rank, world_size, port) + _slot_ring_logic(rank, world_size) + finally: + if dist.is_initialized(): + dist.destroy_process_group() + + +def _worker_capture(rank, world_size, port): + try: + _init_dist(rank, world_size, port) + _capture_smoke_logic(rank, world_size) + finally: + if dist.is_initialized(): + dist.destroy_process_group() + + +def _worker_multi_pg(rank, world_size, port): + try: + _init_dist(rank, world_size, port) + _multi_pg_logic(rank, world_size) + finally: + if dist.is_initialized(): + dist.destroy_process_group() + + +def _run(world_size: int, test_fn: Callable): + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + if torch.cuda.device_count() < world_size: + pytest.skip(f"Test requires {world_size} GPUs, only {torch.cuda.device_count()} available") + port = get_free_port() + mp.spawn(test_fn, args=(world_size, port), nprocs=world_size, join=True) + + +def test_slot_ring_wraparound(): + """Loop _prepare/_async ≥ 2*kNumSlots iterations on ws=2; assert byte-exact + match against all_to_all_4d on each iteration (eager mode). + """ + _run(2, _worker) + + +def test_capture_smoke(): + """Capture a CUDAGraph containing one _prepare/_async pair on ws=2; replay + K iterations with fresh inputs; assert byte-exact match against + all_to_all_4d (per-peer cudaMemcpyAsync path under capture). + """ + _run(2, _worker_capture) + + +def test_multi_pg(): + """Two distinct ProcessGroups on ws=2; alternate _prepare/_async calls + between them; assert byte-exact match against each group's all_to_all_4d. + + Exercises PG-name caching in ``getOrCreateOp`` and ``set_group_info`` + re-registration across multiple groups — each PG has its own group_name, + so each must yield its own cached ``AsyncUlyssesOp`` instance + slot ring. + """ + _run(2, _worker_multi_pg) diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py b/tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py new file mode 100644 index 000000000000..f1f38a8dcc16 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py @@ -0,0 +1,324 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Multi-GPU tests for WAN async-Ulysses parity and perf. + +Compares ``WanTransformer3DModel.forward`` outputs with +``async_ulysses=True`` (V/Q/K rolling A2A via ``Attention.forward_async``, +fused split RMSNorm+RoPE kernel) vs ``async_ulysses=False`` (standard sync +Ulysses with packed FUSE_QKV fused kernel) at ``ulysses_size=2``. Math is +equivalent (unify PR ties split vs packed kernel families); BF16 drift +through the 2-layer model is small. + +Exercises: + - ``WanTransformerBlock.forward`` block-level dispatch to ``forward_async`` + - ``Attention.forward_async`` fused split kernel path (head_dim=64) + - ``UlyssesAttention.forward_async`` V/Q/K rolling side-stream pipeline + +Run with: + pytest tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py -v +""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +from types import SimpleNamespace +from typing import Callable + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +try: + from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.mapping import VisualGenMapping + from tensorrt_llm._utils import get_free_port + from tensorrt_llm.models.modeling_utils import QuantConfig + from tensorrt_llm.visual_gen.args import ( + AttentionConfig, + ParallelConfig, + TeaCacheConfig, + TorchCompileConfig, + ) + + MODULES_AVAILABLE = True +except ImportError: + MODULES_AVAILABLE = False + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +# ============================================================================= +# Distributed helpers +# ============================================================================= + + +def init_distributed_worker(rank: int, world_size: int, backend: str = "nccl", port: int = 29500): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + torch.cuda.set_device(rank % torch.cuda.device_count()) + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + + +def cleanup_distributed(): + if dist.is_initialized(): + dist.destroy_process_group() + + +def _distributed_worker(rank, world_size, backend, test_fn, port, fn_args): + try: + init_distributed_worker(rank, world_size, backend, port) + test_fn(rank, world_size, *fn_args) + except Exception as e: + print(f"Rank {rank} failed with error: {e}") + raise + finally: + cleanup_distributed() + + +def run_test_in_distributed(world_size: int, test_fn: Callable, *fn_args): + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + if torch.cuda.device_count() < world_size: + pytest.skip(f"Test requires {world_size} GPUs, only {torch.cuda.device_count()} available") + port = get_free_port() + mp.spawn( + _distributed_worker, + args=(world_size, "nccl", test_fn, port, fn_args), + nprocs=world_size, + join=True, + ) + + +# ============================================================================= +# Model config +# ============================================================================= + +# Small WAN config. head_dim=64 matches the fused split kernel template; with +# num_attention_heads=4 and ulysses_size=2, each rank holds 2 heads after the +# Ulysses head-shard. Video patches yield 8 tokens, divisible by ws=2. +_WAN_CONFIG = dict( + num_attention_heads=4, + attention_head_dim=64, + num_layers=2, + in_channels=4, + out_channels=4, + patch_size=[1, 2, 2], + text_dim=64, + freq_dim=32, +) +_VIDEO_SHAPE = (1, 4, 2, 4, 4) # [B, C, T, H, W] +_TEXT_SEQ = 4 + + +def _make_model_config( + ulysses_size: int = 1, + backend: str = "FA4", + async_ulysses: bool = False, +) -> "DiffusionModelConfig": + """Create DiffusionModelConfig for WAN tests.""" + if ulysses_size > 1 and dist.is_initialized(): + ws = dist.get_world_size() + rk = dist.get_rank() + else: + ws = ulysses_size + rk = 0 + vgm = VisualGenMapping(world_size=ws, rank=rk, ulysses_size=ulysses_size) + + pretrained_config = SimpleNamespace(**_WAN_CONFIG) + config = DiffusionModelConfig( + pretrained_config=pretrained_config, + quant_config=QuantConfig(), + torch_compile=TorchCompileConfig(enable=False), + attention=AttentionConfig(backend=backend), + visual_gen_mapping=vgm, + cache=TeaCacheConfig(), + parallel=ParallelConfig( + ulysses_size=ulysses_size, + async_ulysses=async_ulysses, + ), + skip_create_weights_in_init=False, + ) + config.mapping = vgm.to_llm_mapping() + return config + + +def _stabilize_model_weights(model): + """Reinit weights to small values — prevents BF16 overflow through layers.""" + with torch.no_grad(): + for p in model.parameters(): + if p.ndim >= 2: + fan_in = p.shape[1] + std = 0.02 / max(1.0, fan_in**0.5) + p.data.uniform_(-std, std) + else: + p.data.uniform_(-0.01, 0.01) + + +def _build_wan_model( + world_size: int, + backend: str, + async_ulysses: bool, + device: torch.device, + dtype: torch.dtype, + seed: int = 42, +): + """Build WanTransformer3DModel with deterministic weights via shared seed.""" + from tensorrt_llm._torch.visual_gen.models.wan.transformer_wan import WanTransformer3DModel + + torch.manual_seed(seed) + cfg = _make_model_config(ulysses_size=world_size, backend=backend, async_ulysses=async_ulysses) + model = WanTransformer3DModel(cfg).to(device).to(dtype) + _stabilize_model_weights(model) + return model + + +def _pack_async_state_for_sync(async_state, sync_state_keys): + """Translate a SEPARATE_QKV (async) state_dict to FUSE_QKV (sync) layout: + concatenates to_q/to_k/to_v.{weight,bias} into packed qkv_proj.{weight,bias}. + Non-self-attn weights pass through unchanged. + + Used to load identical effective weights into both async and sync models + despite their different attn1 layer structure. + """ + out = {} + for k in sync_state_keys: + if k in async_state: + out[k] = async_state[k] + elif k.endswith(".qkv_proj.weight"): + prefix = k[: -len(".qkv_proj.weight")] + out[k] = torch.cat( + [ + async_state[f"{prefix}.to_q.weight"], + async_state[f"{prefix}.to_k.weight"], + async_state[f"{prefix}.to_v.weight"], + ], + dim=0, + ) + elif k.endswith(".qkv_proj.bias"): + prefix = k[: -len(".qkv_proj.bias")] + out[k] = torch.cat( + [ + async_state[f"{prefix}.to_q.bias"], + async_state[f"{prefix}.to_k.bias"], + async_state[f"{prefix}.to_v.bias"], + ], + dim=0, + ) + else: + raise KeyError(f"sync key {k!r} not present in async state and not a qkv_proj fuse") + return out + + +def _build_inputs(device, dtype, seed=100): + """Identical inputs across all ranks via shared seed.""" + B, C, T, H, W = _VIDEO_SHAPE + text_dim = _WAN_CONFIG["text_dim"] + torch.manual_seed(seed) + hidden_states = torch.randn(_VIDEO_SHAPE, device=device, dtype=dtype) * 0.1 + encoder_hidden_states = torch.randn(B, _TEXT_SEQ, text_dim, device=device, dtype=dtype) * 0.1 + timestep = torch.tensor([0.5], device=device, dtype=dtype) + return hidden_states, encoder_hidden_states, timestep + + +# ============================================================================= +# Test logic +# ============================================================================= + + +def _logic_async_vs_sync_parity(rank, world_size, backend): + """WAN at ws=2: async-Ulysses output matches sync-Ulysses output. + + Sync path: block calls ``self.attn1(...)`` → ``Attention.forward`` → + FUSE_QKV packed kernel → sync ``UlyssesAttention.forward``. + Async path: block calls ``self.attn1.forward_async(...)`` → split + kernel via closures → ``UlyssesAttention.forward_async`` rolling A2A. + Both should match within BF16 accumulation drift. + """ + device = torch.device(f"cuda:{rank}") + dtype = torch.bfloat16 + + # Build async (SEPARATE_QKV self-attn → to_q/k/v) first as the canonical + # source, then mirror its weights into a sync (FUSE_QKV → qkv_proj) model + # by concatenating to_q/k/v into the packed layout. + async_model = _build_wan_model(world_size, backend, True, device, dtype) + async_state = async_model.state_dict() + sync_model = _build_wan_model(world_size, backend, False, device, dtype) + sync_model.load_state_dict( + _pack_async_state_for_sync(async_state, sync_model.state_dict().keys()) + ) + + # Sanity: confirm sync vs async actually take different code paths. + assert async_model.blocks[0]._use_async_ulysses is True, "async model didn't enable async path" + assert sync_model.blocks[0]._use_async_ulysses is False, ( + "sync model unexpectedly enabled async path" + ) + assert async_model.blocks[0].attn1.qkv_mode != sync_model.blocks[0].attn1.qkv_mode, ( + "test bug: sync and async models have identical qkv_mode " + f"(both {sync_model.blocks[0].attn1.qkv_mode})" + ) + + hidden_states, encoder_hidden_states, timestep = _build_inputs(device, dtype) + + with torch.no_grad(): + sync_out = sync_model( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + async_out = async_model( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + assert sync_out.shape == async_out.shape, f"Rank {rank}: output shape mismatch" + assert not torch.isnan(async_out).any(), f"Rank {rank}: NaN in async output" + assert not torch.isinf(async_out).any(), f"Rank {rank}: Inf in async output" + + # Diagnostic: actual BF16 drift between sync (packed kernel + sync a2a) and + # async (split kernel + side-stream a2a) — both at ws=2, same collectives. + diff = (async_out.float() - sync_out.float()).abs() + ref = sync_out.float().abs() + max_abs = diff.max().item() + max_rel = (diff / ref.clamp(min=1e-6)).max().item() + print( + f"\n[WAN rank={rank} backend={backend}] " + f"max_abs_diff={max_abs:.3e} max_rel_diff={max_rel:.3e} " + f"sync_abs_max={ref.max().item():.3e}" + ) + + torch.testing.assert_close( + async_out, + sync_out, + rtol=1e-3, + atol=1e-3, + msg=f"Rank {rank}: WAN async-Ulysses output differs from sync-Ulysses", + ) + + +# ============================================================================= +# Test classes +# ============================================================================= + + +class TestWanAsyncUlysses: + """async_ulysses=True/False parity for WAN at ws=2.""" + + @pytest.mark.parametrize("backend", ["VANILLA", "FA4"]) + def test_async_vs_sync_parity(self, backend): + """ws=2: async path (block-level inline-replaced by forward_async) + matches sync path (Attention.forward FUSE_QKV packed kernel).""" + run_test_in_distributed(2, _logic_async_vs_sync_parity, backend) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) From 09ebc592e5357c6989e1e83caa7ebfa29e6482e8 Mon Sep 17 00:00:00 2001 From: Guoming Zhang <137257613+nv-guomingz@users.noreply.github.com> Date: Tue, 9 Jun 2026 10:50:05 +0800 Subject: [PATCH 058/119] [TRTLLM-11548][doc] Add Qwen3.5 deployment guide doc (#15111) Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- docs/source/_static/config_db.json | 16 ++++ ...deployment-guide-for-qwen3.5-on-trtllm.md} | 80 +++++++++++++------ docs/source/deployment-guide/index.rst | 2 +- docs/source/models/supported-models.md | 4 +- examples/configs/curated/lookup.yaml | 5 ++ examples/configs/curated/qwen3.5.yaml | 15 ++++ scripts/generate_config_table.py | 4 + 7 files changed, 97 insertions(+), 29 deletions(-) rename docs/source/deployment-guide/{deployment-guide-for-qwen3-next-on-trtllm.md => deployment-guide-for-qwen3.5-on-trtllm.md} (53%) create mode 100644 examples/configs/curated/qwen3.5.yaml diff --git a/docs/source/_static/config_db.json b/docs/source/_static/config_db.json index 8b104ce02dbd..d7fc859f92f6 100644 --- a/docs/source/_static/config_db.json +++ b/docs/source/_static/config_db.json @@ -36,6 +36,18 @@ "model_url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "scenario": "Max Throughput" }, + { + "command": "trtllm-serve nvidia/Qwen3.5-397B-A17B-NVFP4 --config ${TRTLLM_DIR}/examples/configs/curated/qwen3.5.yaml", + "config_filename": "qwen3.5.yaml", + "config_github_url": "https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/configs/curated/qwen3.5.yaml", + "config_path": "examples/configs/curated/qwen3.5.yaml", + "config_raw_url": "https://raw.githubusercontent.com/NVIDIA/TensorRT-LLM/main/examples/configs/curated/qwen3.5.yaml", + "gpu_compatibility": "B200, B300, GB200, GB300", + "model": "nvidia/Qwen3.5-397B-A17B-NVFP4", + "model_display_name": "Qwen3.5-397B-A17B (NVFP4)", + "model_url": "https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4", + "scenario": "Max Throughput" + }, { "command": "trtllm-serve Qwen/Qwen3-30B-A3B --config ${TRTLLM_DIR}/examples/configs/curated/qwen3.yaml", "config_filename": "qwen3.yaml", @@ -3532,6 +3544,10 @@ "display_name": "Nemotron v3 Ultra (NVFP4)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4" }, + "nvidia/Qwen3.5-397B-A17B-NVFP4": { + "display_name": "Qwen3.5-397B-A17B (NVFP4)", + "url": "https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4" + }, "openai/gpt-oss-120b": { "display_name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b" diff --git a/docs/source/deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.md b/docs/source/deployment-guide/deployment-guide-for-qwen3.5-on-trtllm.md similarity index 53% rename from docs/source/deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.md rename to docs/source/deployment-guide/deployment-guide-for-qwen3.5-on-trtllm.md index ea46ae8107d7..dfb0f6192a14 100644 --- a/docs/source/deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.md +++ b/docs/source/deployment-guide/deployment-guide-for-qwen3.5-on-trtllm.md @@ -1,8 +1,8 @@ -# Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell & Hopper Hardware +# Deployment Guide for Qwen3.5 on TensorRT LLM - Blackwell & Hopper Hardware ## Introduction -This is a functional quick-start guide for running the Qwen3-Next model on TensorRT LLM. It focuses on a working setup with recommended defaults. Additional performance optimizations and support will be rolled out in future updates. +This deployment guide provides step-by-step instructions for running the Qwen3.5-397B-A17B model using TensorRT LLM. It covers model access, environment setup, server configuration, and inference validation. ## Prerequisites @@ -14,20 +14,47 @@ This is a functional quick-start guide for running the Qwen3-Next model on Tenso ## Models -* BF16 model: [Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking) +* [nvidia/Qwen3.5-397B-A17B-NVFP4](https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4) +* [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) (base, BF16) + +## GPU Requirements + +The NVFP4 checkpoint is the recommended (and minimum-footprint) deployment precision for Qwen3.5. It quantizes the linear layers in the MoE blocks to NVFP4 and uses an FP8 KV cache. + +| Platform | Minimum GPUs | +|----------|--------------| +| B200 | 4x B200 | +| B300 | 4x B300 | +| GB200 | 4x GB200 | +| GB300 | 4x GB300 | + +The NVFP4 checkpoint has been validated on B200 with `tensor_parallel_size = 4`. A single node of 4 Blackwell GPUs fits the NVFP4 weights plus the KV cache with headroom. ## Deployment Steps ### Run Docker Container -Build and run the docker container. See the [Docker guide](../../../docker/README.md) for details. +Run the docker container using the TensorRT LLM NVIDIA NGC image. + +```shell +docker run --rm -it \ +--ipc=host \ +--gpus all \ +-p 8000:8000 \ +-v ~/.cache:/root/.cache:rw \ +--name tensorrt_llm \ +nvcr.io/nvidia/tensorrt-llm/release:x.y.z \ +/bin/bash ``` -cd TensorRT-LLM -make -C docker release_build IMAGE_TAG=qwen3-next-local +Note: -make -C docker release_run IMAGE_NAME=tensorrt_llm IMAGE_TAG=qwen3-next-local LOCAL_USER=1 -``` +* The command mounts your user `.cache` directory to save the downloaded model checkpoints which are saved to `~/.cache/huggingface/hub/` by default. This prevents having to redownload the weights each time you rerun the container. If the `~/.cache` directory doesn't exist please create it using `$ mkdir ~/.cache`. +* You can mount additional directories and paths using the `-v :` flag if needed, such as mounting the downloaded weight paths. +* The command also maps port `8000` from the container to your host so you can access the LLM API endpoint from your host. +* See the for all the available containers. The containers published in the main branch weekly have `rcN` suffix, while the monthly release with QA tests has no `rcN` suffix. Use the `rc` release to get the latest model and feature support. + +If you want to use latest main branch, you can choose to build from source to install TensorRT LLM, the steps refer to [https://nvidia.github.io/TensorRT-LLM/latest/installation/build-from-source.html](https://nvidia.github.io/TensorRT-LLM/latest/installation/build-from-source.html) ### Recommended Performance Settings @@ -35,7 +62,7 @@ We maintain YAML configuration files with recommended performance settings in th ```shell TRTLLM_DIR=/app/tensorrt_llm # change as needed to match your environment -EXTRA_LLM_API_FILE=${TRTLLM_DIR}/examples/configs/curated/qwen3-next.yaml +EXTRA_LLM_API_FILE=${TRTLLM_DIR}/examples/configs/curated/qwen3.5.yaml ``` Note: if you don't have access to the source code locally, you can manually create the YAML config file using the code in the dropdown below. @@ -43,7 +70,7 @@ Note: if you don't have access to the source code locally, you can manually crea ````{admonition} Show code :class: dropdown -```{literalinclude} ../../../examples/configs/curated/qwen3-next.yaml +```{literalinclude} ../../../examples/configs/curated/qwen3.5.yaml --- language: shell prepend: | @@ -55,15 +82,18 @@ append: EOF ``` ```` +The config is a starting point tuned for max throughput on 4x B200; adjust the parallelism, batch sizes, and KV cache fraction to match your hardware and traffic pattern. ### Launch the TensorRT LLM Server -Below is an example command to launch the TensorRT LLM server with the Qwen3-Next model from within the container. +Below is an example command to launch the TensorRT LLM server with the Qwen3.5 NVFP4 model from within the container. ```shell -trtllm-serve Qwen/Qwen3-Next-80B-A3B-Thinking --host 0.0.0.0 --port 8000 --reasoning_parser deepseek-r1 --config ${EXTRA_LLM_API_FILE} +trtllm-serve nvidia/Qwen3.5-397B-A17B-NVFP4 --host 0.0.0.0 --port 8000 --reasoning_parser qwen3_5 --tool_parser qwen3 --config ${EXTRA_LLM_API_FILE} ``` +Qwen3.5 uses the `qwen3_5` reasoning parser (its chat template pre-injects a `` block, so reasoning starts at the beginning of the response). The `qwen3` tool parser handles the Qwen3 function-call format. + After the server is set up, the client can now send prompt requests to the server and receive results. ### LLM API Options (YAML Configuration) @@ -80,12 +110,15 @@ These options provide control over TensorRT LLM's behavior and are set within th * **Description:** Sets the **expert-parallel size** for Mixture-of-Experts (MoE) models. Like `tensor_parallel_size`, this should generally match the number of GPUs you're using. This setting has no effect on non-MoE models. +#### `enable_attention_dp` + +* **Description:** Enables **attention data parallelism** for the attention/linear-attention layers while keeping the MoE expert-parallel. This generally improves throughput at high concurrency and long context. + #### `kv_cache_config.free_gpu_memory_fraction` * **Description:** A value between `0.0` and `1.0` that specifies the fraction of free GPU memory to reserve for the KV cache after the model is loaded. Since memory usage can fluctuate, this buffer helps prevent out-of-memory (OOM) errors. * **Recommendation:** If you experience OOM errors, try reducing this value to `0.7` or lower. - #### `max_batch_size` * **Description:** The maximum number of user requests that can be grouped into a single batch for processing. The actual max batch size that can be achieved depends on total sequence length (input + output). @@ -147,7 +180,7 @@ After the TensorRT LLM server is set up and shows Application startup complete, ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ - "model": "Qwen/Qwen3-Next-80B-A3B-Thinking", + "model": "nvidia/Qwen3.5-397B-A17B-NVFP4", "messages": [ { "role": "user", @@ -159,21 +192,14 @@ curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/jso }' -w "\n" ``` -Here is an example response: - -``` -{"id":"chatcmpl-64ac201c77bf46a7a3a4eca7759b1fd8","object":"chat.completion","created":1759022940,"model":"Qwen/Qwen3-Next-80B-A3B-Thinking","choices":[{"index":0,"message":{"role":"assistant","content":"Okay, the user is asking \"Where is New York?\" Hmm, this seems straightforward but I need to be careful. New York could mean different things—maybe they're confused about the city versus the state. \n\nFirst thought: Are they a tourist planning a trip? Or maybe a student doing homework? Could even be someone国外 who's only heard \"New York\" in movies and isn't sure if it's a city or state. \n\nI should clarify both possibilities immediately. People often mix them up. Like, if someone says \"I'm going to New York\" they're probably talking about NYC, but technically New York State is bigger. \n\nLet me break it down: \n- New York City (NYC) is the famous one—Manhattan, skyscrapers, Times Square. \n- Then New York State (NY) is the whole state, which includes NYC but also upstate areas like Albany (the capital), Buffalo, and even the Adirondacks. \n\nWait, should I mention that NYC is in New York State? Yeah, that's crucial. Otherwise they might think it's two separate things. Also, where is the state located? Northeast US, borders other states like Pennsylvania, New Jersey... maybe name a few neighbors for context. \n\nOh! And the city vs state confusion is super common. Like, people say \"New York\" for the city but forget it's part of a larger state. Should I give examples? \n- \"If someone says 'hot dogs in New York' they mean NYC\" \n- But \"the New York State Police\" covers the whole state\" \n\n...Maybe add a fun fact? Like how NYC is just one city within the state? Or that the state capital isn't NYC but Albany? Yeah, that'll help clear confusion. \n\nAlso—should I ask if they meant the city or the state? Nah, better to cover both upfront. Keep it simple but precise. No jargon. They might not know terms like \"metropolitan area\" or \"boroughs,\" so explain \"boroughs\" as neighborhoods (Manhattan, Brooklyn etc.). \n\nCoordinates: 40.7°N, 74°W? Maybe overkill unless they're into geography. But for the state, roughly 42°N to 45°N latitude? Hmm, probably unnecessary. Stick to landmarks: borders Lake Erie, Atlantic coast... \n\nWait—user might be asking literally for GPS coordinates? Unlikely, but just in case I can add: \"Geographically, New York City is at approximately 40.7°N latitude and 74.0°W longitude...\" but only if the answer feels too dry otherwise. \n\nPriority: Clear, simple, correct. No assuming prior knowledge. Say \"New York can refer to...\" then split into two cases. End with \"Most people mean the city when they say 'New York' in casual talk.\" That’s the key takeaway. \n\n...Double-check: Is Albany really the capital? Yes. And NYC is the largest city but not capital. Got it. Also, New York State is the 27th largest by area? Maybe not relevant. Stick to basics. \n\nFinal structure: \n1. \"New York can mean two things: the city or the state.\" \n2. For city: \n - Where it is (Northeast US) \n - Inside New York State \n - Famous parts (Manhattan etc.) \n3. For state: \n - Larger area, includes city \n - Borders (PA, NJ, CT, MA, VT, Canada) \n - Capital is Albany (not NYC!) \n4. Clear summary: \"In everyday talk, 'New York' usually means NYC.\" \n\nYeah. That covers it without overwhelming them. No markdown, just plain English. Keep it warm—add a \"hope that helps!\" at the end.\n\n\nThat's a common point of confusion! **\"New York\" can refer to two different things**, and it's important to distinguish between them:\n\n### 1. **New York City (NYC)** \n - **What it is**: The largest and most famous city in the United States, known for landmarks like Times Square, Central Park, the Statue of Liberty, and Wall Street. \n - **Where it is**: \n - Located in the **northeastern United States**. \n - Situated at the mouth of the **Hudson River**, where it meets the **Atlantic Ocean**. \n - Part of **New York State** (see below). \n - **Geographic details**: \n - Coordinates: Approximately **40.7° N latitude, 74.0° W longitude**. \n - Composed of **5 boroughs**: Manhattan (the \"city\" most people picture), Brooklyn, Queens, The Bronx, and Staten Island. \n - Panoramic view of NYC (including Brooklyn and New Jersey skyline):","reasoning_content":null,"reasoning":null,"tool_calls":[]},"logprobs":null,"finish_reason":"length","stop_reason":null,"mm_embedding_handle":null,"disaggregated_params":null,"avg_decoded_tokens_per_iter":1.0}],"usage":{"prompt_tokens":15,"total_tokens":1039,"completion_tokens":1024},"prompt_token_ids":null} -``` - ### Troubleshooting Tips -* If you encounter CUDA out-of-memory errors, try reducing `max_batch_size` or `max_seq_len`. +* If you encounter CUDA out-of-memory errors, try reducing `max_batch_size`, `max_num_tokens`, or `kv_cache_config.free_gpu_memory_fraction`. * Ensure your model checkpoints are compatible with the expected format. -* For performance issues, check GPU utilization with nvidia-smi while the server is running. +* For performance issues, check GPU utilization with `nvidia-smi` while the server is running. * If the container fails to start, verify that the NVIDIA Container Toolkit is properly installed. * For connection issues, make sure the server port (`8000` in this guide) is not being used by another application. -* If you are using trtllm-serve and the thinking model of Qwen3-Next, make sure to add this server arg `--reasoning_parser deepseek-r1`. - +* Reasoning is controlled with `--reasoning_parser qwen3_5`. To toggle thinking per request, pass `enable_thinking` through `chat_template_kwargs` in the request body, for example `{"chat_template_kwargs": {"enable_thinking": true}}` (set it to `false` to disable reasoning). ## Benchmarking Performance @@ -184,16 +210,18 @@ cat <<'EOF' > bench.sh #!/usr/bin/env bash set -euo pipefail +MODEL_NAME="nvidia/Qwen3.5-397B-A17B-NVFP4" + concurrency_list="1 2 4 8 16 32 64 128 256" multi_round=5 isl=1024 osl=1024 -result_dir=/tmp/qwen3_output +result_dir=/tmp/qwen3_5_output for concurrency in ${concurrency_list}; do num_prompts=$((concurrency * multi_round)) python -m tensorrt_llm.serve.scripts.benchmark_serving \ - --model Qwen/Qwen3-Next-80B-A3B-Thinking \ + --model ${MODEL_NAME} \ --backend openai \ --dataset-name "random" \ --random-input-len ${isl} \ diff --git a/docs/source/deployment-guide/index.rst b/docs/source/deployment-guide/index.rst index 20788d0fc93c..db0f537908d0 100644 --- a/docs/source/deployment-guide/index.rst +++ b/docs/source/deployment-guide/index.rst @@ -34,6 +34,6 @@ The deployment guides below provide more detailed instructions for serving speci deployment-guide-for-llama4-scout-on-trtllm.md deployment-guide-for-gpt-oss-on-trtllm.md deployment-guide-for-qwen3-on-trtllm.md - deployment-guide-for-qwen3-next-on-trtllm.md + deployment-guide-for-qwen3.5-on-trtllm.md deployment-guide-for-kimi-k2-thinking-on-trtllm.md deployment-guide-for-glm-5-on-trtllm.md diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 73e075d949ac..406086e4c07d 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -47,7 +47,7 @@ The following is a table of supported models for the PyTorch backend: | `Qwen3ForCausalLM` | Qwen3 | `Qwen/Qwen3-8B` | | `Qwen3MoeForCausalLM` | Qwen3MoE | `Qwen/Qwen3-30B-A3B` | | `Qwen3NextForCausalLM` | Qwen3Next | `Qwen/Qwen3-Next-80B-A3B-Thinking` | -| `Qwen3_5MoeForCausalLM` [^5] | Qwen3.5-MoE | `Qwen/Qwen3.5-397B-A17B` | +| `Qwen3_5MoeForCausalLM` | Qwen3.5-MoE | `Qwen/Qwen3.5-397B-A17B` | | `SeedOssForCausalLM` [^5] | Seed OSS, Seed-Coder | `ByteDance-Seed/Seed-OSS-36B-Instruct` | | `SkyworkR1V2ForConditionalGeneration` [^5] | Skywork R1V2, Skywork SWE | `Skywork/Skywork-R1V2-38B` | | `SmolLM3ForCausalLM` [^5] | SmolLM3 | `HuggingFaceTB/SmolLM3-3B` | @@ -65,9 +65,9 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl | `Glm4MoeForCausalLM` | Yes | Yes | Yes | Untested | Yes | Yes | No | No | No | Yes | Yes | Untested | N/A | Yes | Yes | | `Qwen3MoeForCausalLM` | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Yes | Yes | Yes | N/A | Yes | Yes | | `Qwen3NextForCausalLM` [^3] | Yes | Yes | Yes | Untested | Yes | No | No | No | No | Yes | Yes | No | No | Untested | Untested | +| `Qwen3_5MoeForCausalLM` | Yes | Yes | Yes | Yes | Yes | Yes | No | No | No | Yes | Untested | Yes | N/A | Untested | Untested | | `Llama4ForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Yes | Yes | Untested | N/A | Yes | Yes | | `GptOssForCausalLM` | Yes | Yes | Yes | Yes | Yes | No | Yes | No | Yes | Yes | Yes | Yes | N/A | Yes | Yes | -| `Qwen3_5MoeForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Yes | N/A | Untested | Untested | | `Glm4MoeLiteForCausalLM` [^5] | Yes | Yes | Untested | Untested | Yes | No | No | No | No | Yes | Untested | Untested | N/A | Untested | Untested | | `NemotronHForCausalLM` | Yes | Yes | Yes | Yes | Yes | Yes | No | No | No | Yes | Yes | Yes | N/A | Untested | Untested | | `Gemma4ForConditionalGeneration` | Untested | Yes | Untested | No | Yes | No | No | No | No | Yes | Untested | No | Yes | Untested | Untested | diff --git a/examples/configs/curated/lookup.yaml b/examples/configs/curated/lookup.yaml index 8a642a47b846..74d1b497fa17 100644 --- a/examples/configs/curated/lookup.yaml +++ b/examples/configs/curated/lookup.yaml @@ -14,6 +14,11 @@ config_path: examples/configs/curated/qwen3-next.yaml scenario: Max Throughput gpu_compatibility: "Any" +- model: nvidia/Qwen3.5-397B-A17B-NVFP4 + arch: Qwen3_5MoeForCausalLM + config_path: examples/configs/curated/qwen3.5.yaml + scenario: Max Throughput + gpu_compatibility: "B200, B300, GB200, GB300" - model: Qwen/Qwen3-30B-A3B arch: Qwen3MoeForCausalLM config_path: examples/configs/curated/qwen3.yaml diff --git a/examples/configs/curated/qwen3.5.yaml b/examples/configs/curated/qwen3.5.yaml new file mode 100644 index 000000000000..ee9255d6567d --- /dev/null +++ b/examples/configs/curated/qwen3.5.yaml @@ -0,0 +1,15 @@ +max_batch_size: 512 +max_num_tokens: 2048 +tensor_parallel_size: 4 +moe_expert_parallel_size: 4 +trust_remote_code: true +enable_attention_dp: true +cuda_graph_config: + enable_padding: true + max_batch_size: 256 +moe_config: + backend: CUTEDSL +kv_cache_config: + free_gpu_memory_fraction: 0.8 + enable_block_reuse: false +num_postprocess_workers: 4 diff --git a/scripts/generate_config_table.py b/scripts/generate_config_table.py index af422d40a74d..a560514d2025 100644 --- a/scripts/generate_config_table.py +++ b/scripts/generate_config_table.py @@ -70,6 +70,10 @@ "display_name": "Qwen3-Next-80B", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", }, + "nvidia/Qwen3.5-397B-A17B-NVFP4": { + "display_name": "Qwen3.5-397B-A17B (NVFP4)", + "url": "https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4", + }, "Qwen/Qwen3-30B-A3B": { "display_name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", From a33dec718d73c254ad2c557601d13ace440bf0fc Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 11:09:51 +0800 Subject: [PATCH 059/119] [https://nvbugs/6181383][fix] Build inner text/vision/audio sub-configs as empty PretrainedConfig() then setat (#14399) Signed-off-by: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> --- tensorrt_llm/_torch/models/modeling_deepseekv3.py | 5 +++-- tests/integration/test_lists/waives.txt | 1 - 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorrt_llm/_torch/models/modeling_deepseekv3.py b/tensorrt_llm/_torch/models/modeling_deepseekv3.py index accb27ef310a..6e2b4b532f49 100755 --- a/tensorrt_llm/_torch/models/modeling_deepseekv3.py +++ b/tensorrt_llm/_torch/models/modeling_deepseekv3.py @@ -340,7 +340,7 @@ def split_kv_b_proj(kv_b_proj: torch.Tensor, # preserved as `_ckpt_num_nextn_predict_layers`. ckpt_num_nextn_predict_layers = ( getattr(self.config, '_ckpt_num_nextn_predict_layers', None) - or self.config.num_nextn_predict_layers) + or getattr(self.config, 'num_nextn_predict_layers', None)) def detect_shared_mtp_weights() -> bool: # Detect if MTP layers share checkpoint weights (model has more MTP @@ -348,7 +348,8 @@ def detect_shared_mtp_weights() -> bool: # multiple model MTP layers map to the same checkpoint layer via # modulo, and mark_consumed must be skipped to avoid deleting # weights that later MTP layers still need. - model_nextn = self.config.num_nextn_predict_layers or 0 + model_nextn = getattr(self.config, 'num_nextn_predict_layers', + None) or 0 return model_nextn > (ckpt_num_nextn_predict_layers or 0) > 0 has_shared_mtp_weights = detect_shared_mtp_weights() diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index b3bd9a1abd2a..7beaa0d63db8 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -151,7 +151,6 @@ accuracy/test_llm_api_pytorch.py::TestStep3_7::test_auto_dtype[tp_size=8-ep_size accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=0] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch.py::TestStep3_7::test_fp8_block_scales[tp_size=4-ep_size=4-mtp_nextn=3] SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized SKIP (https://nvbugs/6215689) -accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (https://nvbugs/6181383) accuracy/test_llm_api_pytorch_multimodal.py::TestStep3_7::test_fp8_block_scales SKIP (https://nvbugs/6274932) accuracy/test_llm_api_pytorch_ray.py::TestLlama3_1_8BInstruct::test_pp2_ray SKIP (https://nvbugs/6094070) cpp/test_e2e.py::test_benchmarks[bart-90] SKIP (https://nvbugs/5550689) From 041ed8356ac8435e932e47e7fa2818fff736a30a Mon Sep 17 00:00:00 2001 From: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 03:12:41 +0000 Subject: [PATCH 060/119] [None][infra] Check in most recent lock file from nightly pipeline Signed-off-by: TensorRT LLM <90828364+tensorrt-cicd@users.noreply.github.com> --- .../examples/auto_deploy/poetry.lock | 58 ++++++++--------- .../examples/draft_target_model/poetry.lock | 52 +++++++-------- security_scanning/examples/eagle/poetry.lock | 52 +++++++-------- .../llm-eval/lm-eval-harness/poetry.lock | 52 +++++++-------- .../examples/lookahead/poetry.lock | 52 +++++++-------- security_scanning/examples/medusa/poetry.lock | 52 +++++++-------- .../models/contrib/baichuan/poetry.lock | 52 +++++++-------- .../examples/models/contrib/bloom/poetry.lock | 52 +++++++-------- .../models/contrib/chatglm-6b/poetry.lock | 52 +++++++-------- .../models/contrib/chatglm2-6b/poetry.lock | 52 +++++++-------- .../contrib/chatglm3-6b-32k/poetry.lock | 52 +++++++-------- .../examples/models/contrib/dbrx/poetry.lock | 52 +++++++-------- .../models/contrib/deepseek_v1/poetry.lock | 52 +++++++-------- .../models/contrib/deepseek_v2/poetry.lock | 52 +++++++-------- .../models/contrib/falcon/poetry.lock | 52 +++++++-------- .../examples/models/contrib/gptj/poetry.lock | 52 +++++++-------- .../models/contrib/gptneox/poetry.lock | 52 +++++++-------- .../examples/models/contrib/grok/poetry.lock | 52 +++++++-------- .../models/contrib/hyperclovax/poetry.lock | 52 +++++++-------- .../models/contrib/internlm/poetry.lock | 52 +++++++-------- .../examples/models/contrib/jais/poetry.lock | 52 +++++++-------- .../examples/models/contrib/mmdit/poetry.lock | 52 +++++++-------- .../examples/models/contrib/mpt/poetry.lock | 52 +++++++-------- .../examples/models/contrib/opt/poetry.lock | 52 +++++++-------- .../models/contrib/skywork/poetry.lock | 52 +++++++-------- .../examples/models/contrib/smaug/poetry.lock | 52 +++++++-------- .../examples/models/contrib/stdit/poetry.lock | 64 +++++++++---------- .../examples/models/core/commandr/poetry.lock | 52 +++++++-------- .../examples/models/core/gemma/poetry.lock | 52 +++++++-------- .../examples/models/core/glm-4-9b/poetry.lock | 52 +++++++-------- .../examples/models/core/gpt/poetry.lock | 52 +++++++-------- .../examples/models/core/llama/poetry.lock | 52 +++++++-------- .../examples/models/core/mamba/poetry.lock | 52 +++++++-------- .../examples/models/core/mixtral/poetry.lock | 52 +++++++-------- .../examples/models/core/mllama/poetry.lock | 52 +++++++-------- .../examples/models/core/nemotron/poetry.lock | 52 +++++++-------- .../examples/models/core/phi/poetry.lock | 52 +++++++-------- .../examples/models/core/qwen/poetry.lock | 52 +++++++-------- .../models/core/qwen2audio/poetry.lock | 52 +++++++-------- .../examples/models/core/qwenvl/poetry.lock | 52 +++++++-------- .../models/core/recurrentgemma/poetry.lock | 52 +++++++-------- .../examples/models/core/whisper/poetry.lock | 52 +++++++-------- security_scanning/examples/ngram/poetry.lock | 52 +++++++-------- .../examples/quantization/poetry.lock | 52 +++++++-------- .../examples/ray_orchestrator/poetry.lock | 6 +- .../examples/redrafter/poetry.lock | 52 +++++++-------- security_scanning/examples/serve/poetry.lock | 52 +++++++-------- .../examples/trtllm-eval/poetry.lock | 52 +++++++-------- security_scanning/metadata.json | 4 +- security_scanning/poetry.lock | 58 ++++++++--------- security_scanning/triton_backend/poetry.lock | 52 +++++++-------- 51 files changed, 1291 insertions(+), 1291 deletions(-) diff --git a/security_scanning/examples/auto_deploy/poetry.lock b/security_scanning/examples/auto_deploy/poetry.lock index ddb8fa1fa99d..6994537f3990 100644 --- a/security_scanning/examples/auto_deploy/poetry.lock +++ b/security_scanning/examples/auto_deploy/poetry.lock @@ -1053,38 +1053,38 @@ files = 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TestQwen3_5_4B::test_bf16 for all GPUs (#15112) Signed-off-by: Tyler Burt <195370667+tburt-nv@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 7beaa0d63db8..755d80e69be7 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -144,6 +144,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_sof accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=False] SKIP (https://nvbugs/6260915) accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_fp8[enable_block_reuse=False] SKIP (https://nvbugs/6212252) accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl] SKIP (https://nvbugs/6255417) +accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_bf16 SKIP (https://nvbugs/6283537) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_off] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_on] SKIP (https://nvbugs/6212250) accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales_early_first_token_response SKIP (https://nvbugs/6200128) @@ -264,7 +265,6 @@ full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_2gpus[tp2-t full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS] SKIP (https://nvbugs/6273850) full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] SKIP (https://nvbugs/6273850) full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] SKIP (https://nvbugs/6275856) -full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_bf16 SKIP (https://nvbugs/6273850) full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_dflash SKIP (https://nvbugs/6273850) full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_fp8 SKIP (https://nvbugs/6273850) full:RTX_6000D/accuracy/test_llm_api_pytorch.py::TestQwen3_6_27B::test_fp8 SKIP (https://nvbugs/6273850) From 64497e20263e9370331bd18068ebd9d108f68071 Mon Sep 17 00:00:00 2001 From: Bala Marimuthu <246387390+bmarimuthu-nv@users.noreply.github.com> Date: Mon, 8 Jun 2026 21:58:56 -0700 Subject: [PATCH 062/119] [None][doc] Add docs for AutoDeploy transforms (#15122) Signed-off-by: Balamurugan Marimuthu <246387390+bmarimuthu-nv@users.noreply.github.com> --- .gitignore | 3 + docs/source/_ext/trtllm_auto_deploy.py | 470 ++++++++++++++++++ docs/source/conf.py | 1 + .../features/auto_deploy/auto-deploy.md | 4 + .../features/auto_deploy/transforms.rst | 30 ++ .../auto_deploy/transforms/additional.rst | 12 + .../auto_deploy/transforms/cache_init.rst | 12 + .../auto_deploy/transforms/compile.rst | 12 + .../features/auto_deploy/transforms/core.rst | 41 ++ .../auto_deploy/transforms/export.rst | 11 + .../auto_deploy/transforms/factory.rst | 11 + .../transforms/pattern_matcher.rst | 12 + .../auto_deploy/transforms/post_export.rst | 11 + .../transforms/post_load_fusion.rst | 12 + .../auto_deploy/transforms/sharding.rst | 11 + .../auto_deploy/transforms/visualize.rst | 11 + .../auto_deploy/transforms/weight_load.rst | 11 + docs/source/index.rst | 1 + 18 files changed, 676 insertions(+) create mode 100644 docs/source/_ext/trtllm_auto_deploy.py create mode 100644 docs/source/features/auto_deploy/transforms.rst create mode 100644 docs/source/features/auto_deploy/transforms/additional.rst create mode 100644 docs/source/features/auto_deploy/transforms/cache_init.rst create mode 100644 docs/source/features/auto_deploy/transforms/compile.rst create mode 100644 docs/source/features/auto_deploy/transforms/core.rst create mode 100644 docs/source/features/auto_deploy/transforms/export.rst create mode 100644 docs/source/features/auto_deploy/transforms/factory.rst create mode 100644 docs/source/features/auto_deploy/transforms/pattern_matcher.rst create mode 100644 docs/source/features/auto_deploy/transforms/post_export.rst create mode 100644 docs/source/features/auto_deploy/transforms/post_load_fusion.rst create mode 100644 docs/source/features/auto_deploy/transforms/sharding.rst create mode 100644 docs/source/features/auto_deploy/transforms/visualize.rst create mode 100644 docs/source/features/auto_deploy/transforms/weight_load.rst diff --git a/.gitignore b/.gitignore index 962f47d13419..8d39054480f5 100644 --- a/.gitignore +++ b/.gitignore @@ -65,6 +65,9 @@ tensorrt_llm/scripts docs/source/**/*.rst !docs/source/examples/index.rst !docs/source/_includes/note_sections.rst +!docs/source/features/auto_deploy/transforms.rst +!docs/source/features/auto_deploy/transforms/ +!docs/source/features/auto_deploy/transforms/*.rst *.swp .nfs* diff --git a/docs/source/_ext/trtllm_auto_deploy.py b/docs/source/_ext/trtllm_auto_deploy.py new file mode 100644 index 000000000000..44943432b842 --- /dev/null +++ b/docs/source/_ext/trtllm_auto_deploy.py @@ -0,0 +1,470 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import ast +import pkgutil +from dataclasses import dataclass +from pathlib import Path + +import yaml +from docutils import nodes +from docutils.statemachine import StringList +from sphinx.application import Sphinx +from sphinx.util.docutils import SphinxDirective +from sphinx.util.nodes import nested_parse_with_titles + +AUTO_DEPLOY_TRANSFORM_LIBRARY_PACKAGE = "tensorrt_llm._torch.auto_deploy.transform.library" +AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH = Path("tensorrt_llm/_torch/auto_deploy/transform/library") +AUTO_DEPLOY_TRANSFORM_CONFIGS = ( + ("graph", Path("tensorrt_llm/_torch/auto_deploy/config/default.yaml")), + ( + "transformers", + Path("tensorrt_llm/_torch/auto_deploy/config/transformers.yaml"), + ), +) +AUTOCLASS_OPTIONS = ( + " :members:", + " :show-inheritance:", +) + +STAGE_TITLES = { + "factory": "Factory", + "export": "Export", + "post_export": "Post-Export", + "pattern_matcher": "Pattern Matching", + "sharding": "Sharding", + "weight_load": "Weight Loading", + "post_load_fusion": "Post-Load Fusion", + "cache_init": "Cache Initialization", + "visualize": "Visualization", + "compile": "Compilation", +} + +TITLE_REPLACEMENTS = { + "fp8": "FP8", + "gdn": "GDN", + "kv": "KV", + "kvcache": "KV Cache", + "l2": "L2", + "mlir": "MLIR", + "mla": "MLA", + "moe": "MoE", + "mrope": "mRoPE", + "mxfp4": "MXFP4", + "noop": "No-op", + "nvfp4": "NVFP4", + "rmsnorm": "RMSNorm", + "rope": "RoPE", + "silu": "SiLU", + "ssm": "SSM", + "swiglu": "SwiGLU", + "trtllm": "TRT-LLM", +} + + +@dataclass(frozen=True) +class RegisteredTransform: + key: str + module_name: str + class_name: str + config_class_name: str + config_module_name: str | None + + @property + def qualified_class_name(self) -> str: + return f"{AUTO_DEPLOY_TRANSFORM_LIBRARY_PACKAGE}.{self.module_name}.{self.class_name}" + + @property + def qualified_module_name(self) -> str: + return f"{AUTO_DEPLOY_TRANSFORM_LIBRARY_PACKAGE}.{self.module_name}" + + @property + def qualified_config_class_name(self) -> str | None: + if self.config_module_name is None: + return None + return ( + f"{AUTO_DEPLOY_TRANSFORM_LIBRARY_PACKAGE}.{self.config_module_name}" + f".{self.config_class_name}" + ) + + +@dataclass(frozen=True) +class ParsedClass: + module_name: str + class_name: str + base_class_names: tuple[str, ...] + config_class_name: str | None + transform_keys: tuple[str, ...] + + +@dataclass +class ConfiguredTransform: + key: str + stage: str + modes: list[str] + + +def _repo_root_from_source_dir(source_dir: str) -> Path: + """Return the nearest ancestor that contains the AutoDeploy transform library.""" + source_path = Path(source_dir).resolve() + for path in (source_path, *source_path.parents): + if (path / AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH).is_dir(): + return path + raise FileNotFoundError( + f"Could not find repository root containing {AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH}" + ) + + +def _discover_transform_modules(library_path: Path) -> list[str]: + """Discover public AutoDeploy transform modules without importing them.""" + if not library_path.is_dir(): + raise FileNotFoundError(f"AutoDeploy transform library not found: {library_path}") + + return sorted( + module_info.name + for module_info in pkgutil.iter_modules([str(library_path)]) + if not module_info.name.startswith("_") + ) + + +def _module_title(module_name: str) -> str: + """Convert a transform module name into a readable section title.""" + words = [TITLE_REPLACEMENTS.get(part, part.title()) for part in module_name.split("_")] + return " ".join(words) + + +def _mode_list(modes: list[str]) -> str: + return ", ".join(f"``{mode}``" for mode in sorted(modes)) + + +def _register_key_from_decorator(decorator: ast.expr) -> str | None: + if not isinstance(decorator, ast.Call): + return None + if not isinstance(decorator.func, ast.Attribute): + return None + if decorator.func.attr != "register": + return None + if not isinstance(decorator.func.value, ast.Name): + return None + if decorator.func.value.id != "TransformRegistry": + return None + if not decorator.args: + return None + key_arg = decorator.args[0] + if isinstance(key_arg, ast.Constant) and isinstance(key_arg.value, str): + return key_arg.value + return None + + +def _name_from_expr(expr: ast.expr) -> str | None: + if isinstance(expr, ast.Name): + return expr.id + if isinstance(expr, ast.Attribute): + return expr.attr + return None + + +def _get_config_class_name(node: ast.ClassDef) -> str | None: + for child_node in node.body: + if not isinstance(child_node, ast.FunctionDef): + continue + if child_node.name != "get_config_class": + continue + for statement in child_node.body: + if isinstance(statement, ast.Return): + return _name_from_expr(statement.value) + return None + + +def _parse_transform_classes( + library_path: Path, +) -> tuple[list[ParsedClass], dict[str, list[ParsedClass]]]: + parsed_classes: list[ParsedClass] = [] + classes_by_name: dict[str, list[ParsedClass]] = {} + + for module_name in _discover_transform_modules(library_path): + module_path = library_path / f"{module_name}.py" + tree = ast.parse(module_path.read_text(encoding="utf-8")) + + for node in tree.body: + if not isinstance(node, ast.ClassDef): + continue + + parsed_class = ParsedClass( + module_name=module_name, + class_name=node.name, + base_class_names=tuple( + base_class_name + for base in node.bases + if (base_class_name := _name_from_expr(base)) is not None + ), + config_class_name=_get_config_class_name(node), + transform_keys=tuple( + transform_key + for decorator in node.decorator_list + if (transform_key := _register_key_from_decorator(decorator)) is not None + ), + ) + parsed_classes.append(parsed_class) + classes_by_name.setdefault(parsed_class.class_name, []).append(parsed_class) + + return parsed_classes, classes_by_name + + +def _get_library_class( + class_name: str, + module_name: str, + classes_by_name: dict[str, list[ParsedClass]], +) -> ParsedClass | None: + classes = classes_by_name.get(class_name, []) + if len(classes) == 1: + return classes[0] + for parsed_class in classes: + if parsed_class.module_name == module_name: + return parsed_class + return None + + +def _resolve_config_class( + parsed_class: ParsedClass, + classes_by_name: dict[str, list[ParsedClass]], + seen: set[str] | None = None, +) -> ParsedClass | None: + """Resolve a transform's config class, following simple inheritance.""" + if parsed_class.config_class_name: + if parsed_class.config_class_name == "TransformConfig": + return None + return _get_library_class( + parsed_class.config_class_name, + parsed_class.module_name, + classes_by_name, + ) + + seen = seen or set() + seen.add(parsed_class.class_name) + for base_class_name in parsed_class.base_class_names: + if base_class_name in seen: + continue + base_classes = classes_by_name.get(base_class_name, []) + if len(base_classes) != 1: + continue + return _resolve_config_class(base_classes[0], classes_by_name, seen) + + return None + + +def _discover_registered_transforms(library_path: Path) -> dict[str, RegisteredTransform]: + """Discover registered transform classes without importing transform modules.""" + registered_transforms: dict[str, RegisteredTransform] = {} + parsed_classes, classes_by_name = _parse_transform_classes(library_path) + + for parsed_class in parsed_classes: + config_class = _resolve_config_class(parsed_class, classes_by_name) + for transform_key in parsed_class.transform_keys: + if transform_key in registered_transforms: + previous = registered_transforms[transform_key] + raise ValueError( + f"Transform {transform_key!r} is registered by both " + f"{previous.qualified_class_name} and " + f"{parsed_class.module_name}.{parsed_class.class_name}" + ) + registered_transforms[transform_key] = RegisteredTransform( + key=transform_key, + module_name=parsed_class.module_name, + class_name=parsed_class.class_name, + config_class_name=config_class.class_name if config_class else "TransformConfig", + config_module_name=config_class.module_name if config_class else None, + ) + + return registered_transforms + + +def _load_configured_transforms(repo_root: Path) -> list[ConfiguredTransform]: + """Load transform stage metadata from the checked-in AutoDeploy configs.""" + configured_by_key: dict[str, ConfiguredTransform] = {} + configured_transforms: list[ConfiguredTransform] = [] + + for mode, config_path in AUTO_DEPLOY_TRANSFORM_CONFIGS: + config = yaml.safe_load((repo_root / config_path).read_text(encoding="utf-8")) + transforms = config.get("transforms", {}) + + for transform_key, transform_config in transforms.items(): + stage = transform_config.get("stage") + if not stage: + raise ValueError( + f"Transform {transform_key!r} in {config_path} does not define a stage" + ) + + configured_transform = configured_by_key.get(transform_key) + if configured_transform is not None: + if configured_transform.stage != stage: + raise ValueError( + f"Transform {transform_key!r} has stages " + f"{configured_transform.stage!r} and {stage!r}" + ) + configured_transform.modes.append(mode) + continue + + configured_transform = ConfiguredTransform( + key=transform_key, + stage=stage, + modes=[mode], + ) + configured_by_key[transform_key] = configured_transform + configured_transforms.append(configured_transform) + + return configured_transforms + + +def _transform_section( + transform_key: str, + registered_transform: RegisteredTransform, + modes: list[str] | None = None, +) -> list[str]: + title = _module_title(transform_key) + config_lines: list[str] = [ + ".. rubric:: YAML configuration", + "", + ] + if registered_transform.qualified_config_class_name is None: + config_lines.extend( + [ + "Uses the common ``TransformConfig`` fields documented in :doc:`core`.", + "", + ] + ) + else: + config_lines.extend( + [ + "The fields below can be set under this transform's entry in the " + "AutoDeploy config YAML.", + "", + f".. autopydantic_model:: {registered_transform.qualified_config_class_name}", + " :members:", + " :show-inheritance:", + " :no-index:", + "", + ] + ) + + return [ + title, + "~" * len(title), + "", + f"Transform key: ``{transform_key}``", + "", + f"Source module: ``{registered_transform.qualified_module_name}``", + "", + *(["Configured modes: " + _mode_list(modes), ""] if modes else []), + f".. autoclass:: {registered_transform.qualified_class_name}", + *AUTOCLASS_OPTIONS, + "", + *config_lines, + ] + + +def _note_auto_deploy_dependencies(directive: SphinxDirective, repo_root: Path) -> None: + library_path = repo_root / AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH + directive.env.note_dependency(str(library_path)) + for path in sorted(library_path.glob("*.py")): + directive.env.note_dependency(str(path)) + for _, config_path in AUTO_DEPLOY_TRANSFORM_CONFIGS: + directive.env.note_dependency(str(repo_root / config_path)) + + +class AutoDeployTransformStageDirective(SphinxDirective): + """Render autodoc sections for configured transforms in one pipeline stage.""" + + has_content = False + required_arguments = 1 + + def run(self) -> list[nodes.Node]: + stage = self.arguments[0] + repo_root = _repo_root_from_source_dir(self.env.app.srcdir) + library_path = repo_root / AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH + _note_auto_deploy_dependencies(self, repo_root) + + registered_transforms = _discover_registered_transforms(library_path) + configured_transforms = [ + transform + for transform in _load_configured_transforms(repo_root) + if transform.stage == stage + ] + + if not configured_transforms: + title = STAGE_TITLES.get(stage, stage) + return [ + nodes.paragraph( + text=f"No AutoDeploy transforms are configured for the {title} stage." + ) + ] + + generated_lines = StringList() + for configured_transform in configured_transforms: + registered_transform = registered_transforms.get(configured_transform.key) + if registered_transform is None: + raise ValueError( + f"Configured transform {configured_transform.key!r} is not registered" + ) + for line in _transform_section( + configured_transform.key, + registered_transform, + configured_transform.modes, + ): + generated_lines.append(line, source=str(library_path)) + + container = nodes.container() + nested_parse_with_titles(self.state, generated_lines, container) + return container.children + + +class AutoDeployAdditionalTransformsDirective(SphinxDirective): + """Render registered transforms that are not referenced by checked-in configs.""" + + has_content = False + + def run(self) -> list[nodes.Node]: + repo_root = _repo_root_from_source_dir(self.env.app.srcdir) + library_path = repo_root / AUTO_DEPLOY_TRANSFORM_LIBRARY_PATH + _note_auto_deploy_dependencies(self, repo_root) + + registered_transforms = _discover_registered_transforms(library_path) + configured_keys = {transform.key for transform in _load_configured_transforms(repo_root)} + additional_transforms = [ + registered_transform + for transform_key, registered_transform in sorted(registered_transforms.items()) + if transform_key not in configured_keys + ] + + if not additional_transforms: + return [ + nodes.paragraph( + text="Every registered AutoDeploy transform is referenced by a checked-in config." + ) + ] + + generated_lines = StringList() + for registered_transform in additional_transforms: + for line in _transform_section( + registered_transform.key, + registered_transform, + ): + generated_lines.append(line, source=str(library_path)) + + container = nodes.container() + nested_parse_with_titles(self.state, generated_lines, container) + return container.children + + +def setup(app: Sphinx) -> dict[str, bool | str]: + app.add_directive( + "trtllm_auto_deploy_transform_stage", + AutoDeployTransformStageDirective, + ) + app.add_directive( + "trtllm_auto_deploy_additional_transforms", + AutoDeployAdditionalTransformsDirective, + ) + return {"version": "0.1", "parallel_read_safe": True, "parallel_write_safe": True} diff --git a/docs/source/conf.py b/docs/source/conf.py index 2ffe947cd72f..34d0e8328257 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,6 +67,7 @@ 'sphinxcontrib.autodoc_pydantic', 'sphinx_togglebutton', 'sphinxcontrib.mermaid', + 'trtllm_auto_deploy', 'trtllm_config_selector', ] diff --git a/docs/source/features/auto_deploy/auto-deploy.md b/docs/source/features/auto_deploy/auto-deploy.md index d9bdc548ed58..35f33ae8c88f 100644 --- a/docs/source/features/auto_deploy/auto-deploy.md +++ b/docs/source/features/auto_deploy/auto-deploy.md @@ -53,6 +53,10 @@ The exported graph then undergoes a series of automated transformations, includi - [Support Matrix](support_matrix.md) +## API Reference + +- [AutoDeploy Transforms](transforms.rst) + ## Advanced Usage - [Example Run Script](./advanced/example_run.md) diff --git a/docs/source/features/auto_deploy/transforms.rst b/docs/source/features/auto_deploy/transforms.rst new file mode 100644 index 000000000000..b1d9e9ba183d --- /dev/null +++ b/docs/source/features/auto_deploy/transforms.rst @@ -0,0 +1,30 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +AutoDeploy Transforms +===================== + +This section documents the AutoDeploy transform interfaces and registered +pipeline transforms. Use the stage pages to find where a transform runs in the +optimization pipeline, what graph or runtime change it performs, and which +configuration fields are available. + +For an overview of how transforms fit into the AutoDeploy pipeline, see +:doc:`auto-deploy`. For information on configuring which transforms run and in +what order, see :doc:`advanced/expert_configurations`. + +.. toctree:: + :maxdepth: 1 + + transforms/core + transforms/factory + transforms/export + transforms/post_export + transforms/pattern_matcher + transforms/sharding + transforms/weight_load + transforms/post_load_fusion + transforms/cache_init + transforms/visualize + transforms/compile + transforms/additional diff --git a/docs/source/features/auto_deploy/transforms/additional.rst b/docs/source/features/auto_deploy/transforms/additional.rst new file mode 100644 index 000000000000..87b92d93924c --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/additional.rst @@ -0,0 +1,12 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Additional Registered Transforms +================================ + +These transforms are registered in the AutoDeploy transform library but are not +part of the standard graph-mode or transformers-mode pipelines. They are useful +for specialized experiments, explicit opt-in configurations, or development +workflows. + +.. trtllm_auto_deploy_additional_transforms:: diff --git a/docs/source/features/auto_deploy/transforms/cache_init.rst b/docs/source/features/auto_deploy/transforms/cache_init.rst new file mode 100644 index 000000000000..105a12b1db24 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/cache_init.rst @@ -0,0 +1,12 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Cache Initialization Stage +========================== + +Cache initialization rewrites attention and recurrent state operations for +cached inference. This stage prepares runtime cache resources such as KV-cache +storage, SSM state, residual hidden-state capture, and model-specific cache +metadata. + +.. trtllm_auto_deploy_transform_stage:: cache_init diff --git a/docs/source/features/auto_deploy/transforms/compile.rst b/docs/source/features/auto_deploy/transforms/compile.rst new file mode 100644 index 000000000000..1dc46362b6a3 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/compile.rst @@ -0,0 +1,12 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Compilation Stage +================= + +Compilation is the final transform stage before execution. It applies +runtime-oriented and compiler-oriented changes after graph structure, weights, +and caches are ready, such as multi-stream kernels, final cleanup, and CUDA graph +or ``torch.compile`` execution. + +.. trtllm_auto_deploy_transform_stage:: compile diff --git a/docs/source/features/auto_deploy/transforms/core.rst b/docs/source/features/auto_deploy/transforms/core.rst new file mode 100644 index 000000000000..5ad73ec5ec00 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/core.rst @@ -0,0 +1,41 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Core Transform APIs +=================== + +Common Transform Configuration +------------------------------ + +Most transforms accept these common fields. Stage pages also show +transform-specific configuration models when a transform extends this base +configuration. + +.. autopydantic_model:: tensorrt_llm._torch.auto_deploy.transform.interface.TransformConfig + :members: + :show-inheritance: + :no-index: + +Transform Interface +------------------- + +.. automodule:: tensorrt_llm._torch.auto_deploy.transform.interface + :members: + :undoc-members: + :show-inheritance: + +Optimizer +--------- + +.. automodule:: tensorrt_llm._torch.auto_deploy.transform.optimizer + :members: + :undoc-members: + :show-inheritance: + +Graph Module Visualizer +----------------------- + +.. automodule:: tensorrt_llm._torch.auto_deploy.transform.graph_module_visualizer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/features/auto_deploy/transforms/export.rst b/docs/source/features/auto_deploy/transforms/export.rst new file mode 100644 index 000000000000..b4613f158c61 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/export.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Export Stage +============ + +Export converts the model into a graph representation that later stages can +inspect and rewrite. After this point, transforms operate on graph structure +rather than only on the original model object. + +.. trtllm_auto_deploy_transform_stage:: export diff --git a/docs/source/features/auto_deploy/transforms/factory.rst b/docs/source/features/auto_deploy/transforms/factory.rst new file mode 100644 index 000000000000..145bf66a49b3 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/factory.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Factory Stage +============= + +Factory transforms create or wrap the starting model object for AutoDeploy. This +stage establishes the module that later graph, weight-loading, cache, and +runtime transforms will optimize. + +.. trtllm_auto_deploy_transform_stage:: factory diff --git a/docs/source/features/auto_deploy/transforms/pattern_matcher.rst b/docs/source/features/auto_deploy/transforms/pattern_matcher.rst new file mode 100644 index 000000000000..84c82703cd52 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/pattern_matcher.rst @@ -0,0 +1,12 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Pattern Matching Stage +====================== + +Pattern matching canonicalizes model-specific PyTorch graphs into AutoDeploy's +standard graph representation. These transforms identify attention, MoE, +normalization, quantization, activation, and layout patterns before sharding and +post-load fusion run. + +.. trtllm_auto_deploy_transform_stage:: pattern_matcher diff --git a/docs/source/features/auto_deploy/transforms/post_export.rst b/docs/source/features/auto_deploy/transforms/post_export.rst new file mode 100644 index 000000000000..8519ed50954d --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/post_export.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Post-Export Stage +================= + +Post-export transforms remove low-level export artifacts and simple no-op graph +patterns. This keeps later pattern-matching, sharding, and fusion passes focused +on meaningful graph structure. + +.. trtllm_auto_deploy_transform_stage:: post_export diff --git a/docs/source/features/auto_deploy/transforms/post_load_fusion.rst b/docs/source/features/auto_deploy/transforms/post_load_fusion.rst new file mode 100644 index 000000000000..efe4c6831fee --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/post_load_fusion.rst @@ -0,0 +1,12 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Post-Load Fusion Stage +====================== + +Post-load fusion applies performance optimizations that need loaded weights, +device tensors, or the final post-sharding graph structure. This stage includes +kernel fusions for quantized linear layers, MoE, normalization, activation, RoPE, +and related inference patterns. + +.. trtllm_auto_deploy_transform_stage:: post_load_fusion diff --git a/docs/source/features/auto_deploy/transforms/sharding.rst b/docs/source/features/auto_deploy/transforms/sharding.rst new file mode 100644 index 000000000000..798841c3baa1 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/sharding.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Sharding Stage +============== + +Sharding determines and applies distributed execution layout. These transforms +identify tensor, expert, and batch-matmul sharding choices, then apply graph +rewrites and communication hints needed for multi-rank execution. + +.. trtllm_auto_deploy_transform_stage:: sharding diff --git a/docs/source/features/auto_deploy/transforms/visualize.rst b/docs/source/features/auto_deploy/transforms/visualize.rst new file mode 100644 index 000000000000..f29a99081313 --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/visualize.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Visualization Stage +=================== + +Visualization emits graph artifacts for debugging and inspection. This stage is +intended to help developers understand transform output without changing +inference semantics. + +.. trtllm_auto_deploy_transform_stage:: visualize diff --git a/docs/source/features/auto_deploy/transforms/weight_load.rst b/docs/source/features/auto_deploy/transforms/weight_load.rst new file mode 100644 index 000000000000..1d800b1c77cf --- /dev/null +++ b/docs/source/features/auto_deploy/transforms/weight_load.rst @@ -0,0 +1,11 @@ +.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +.. SPDX-License-Identifier: Apache-2.0 + +Weight Loading Stage +==================== + +Weight loading materializes model weights and moves required state to the target +device after graph structure and sharding decisions have been made. This stage +bridges graph preparation and weight-dependent fusion. + +.. trtllm_auto_deploy_transform_stage:: weight_load diff --git a/docs/source/index.rst b/docs/source/index.rst index 2f96834c3a3f..5b5a163278f3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -79,6 +79,7 @@ Welcome to TensorRT LLM's Documentation! features/speculative-decoding.md features/checkpoint-loading.md features/auto_deploy/auto-deploy.md + features/auto_deploy/transforms.rst features/ray-orchestrator.md features/torch_compile_and_piecewise_cuda_graph.md features/helix.md From 9349fcc6bd0cf451d1479f8f18acd7c515d7beab Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:23:54 +0800 Subject: [PATCH 063/119] [None][infra] Waive 4 failed cases for main in post-merge 2769 (#15140) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 755d80e69be7..bf5fa1fd386b 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -341,12 +341,16 @@ perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8 perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con256_ctx1_dep4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6221024) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep8_gen1_dep8_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6085022) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] SKIP (https://nvbugs/6200257) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280721) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_gpt-oss-120b-fp4_1k1k_con64_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6287834) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6221022) +perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb300_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280721) stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-GUARANTEED_NO_EVICT-pytorch-stress-test] SKIP (https://nvbugs/6215678) stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-pytorch-stress-test] SKIP (https://nvbugs/6215678) test_doc.py::test_url_validity SKIP (https://nvbugs/6215684) From 28845ddf99a32c275c7d1a65304535eae5f88201 Mon Sep 17 00:00:00 2001 From: Tianyu Xiong <117647511+tianyuxbear@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:30:35 +0800 Subject: [PATCH 064/119] [https://nvbugs/6227203][fix] Remove redundant TikTokenTokenizer shim from KimiK25InputProcessor (#14741) Signed-off-by: Tianyu Xiong <117647511+tianyuxbear@users.noreply.github.com> --- .../_torch/models/modeling_kimi_k25.py | 84 ------------------- tests/integration/test_lists/waives.txt | 1 - 2 files changed, 85 deletions(-) diff --git a/tensorrt_llm/_torch/models/modeling_kimi_k25.py b/tensorrt_llm/_torch/models/modeling_kimi_k25.py index d130210376ba..a00eb05615b8 100644 --- a/tensorrt_llm/_torch/models/modeling_kimi_k25.py +++ b/tensorrt_llm/_torch/models/modeling_kimi_k25.py @@ -291,23 +291,6 @@ def _frames_to_chunks( # Default vocabulary size for K2.5 _VOCAB_SIZE = 163840 -# K2.5 special token markers that the transformers 5.5.x Rust fast tokenizer -# BPE-splits instead of mapping to canonical IDs. When any of these appear in -# a prompt, we must route tokenization through the slow ``TikTokenTokenizer``. -# Pure text (no markers and no multimodal data) keeps the fast tokenizer. -# See NVBug 6182617 (correctness) and NVBug 6248987 (perf). -_K25_SPECIAL_TOKEN_MARKERS = ( - "<|media_begin|>", - "<|media_content|>", - "<|media_pad|>", - "<|media_end|>", - "<|im_user|>", - "<|im_assistant|>", - "<|im_system|>", - "<|im_end|>", - "<|im_middle|>", -) - # --------------------------------------------------------------------------- # Native MoonViT3d Vision Encoder Components @@ -1069,16 +1052,6 @@ def __init__( config, "media_placeholder_token_id", _MEDIA_PLACEHOLDER_TOKEN_ID ) - # transformers 5.5.x ``AutoTokenizer`` may route K2.5 to the Rust - # fast backend, which BPE-splits ``<|media_pad|>`` / ``<|im_user|>`` - # / etc. instead of mapping them to their canonical IDs. The slow - # ``TikTokenTokenizer`` preserves them. Swap is deferred until we - # actually see an input that needs it (multimodal data or a K2.5 - # special token marker in the prompt) — the text-only thinking - # path keeps the fast tokenizer to avoid a GIL-bound 9x TPOT - # regression. See NVBug 6182617 (correctness) / 6248987 (perf). - self._slow_tokenizer_active = False - @property def config(self) -> PretrainedConfig: return self._config @@ -1195,51 +1168,6 @@ def get_num_tokens_per_video(self, *, video: List, **kwargs) -> int: total_tokens += self.get_num_tokens_per_image(image=chunk[0]) return total_tokens - @staticmethod - def _input_needs_slow_tokenizer(text: Optional[str]) -> bool: - """Return True iff ``text`` contains any K2.5 special token marker - that the Rust fast tokenizer would BPE-split incorrectly.""" - if not text: - return False - return any(marker in text for marker in _K25_SPECIAL_TOKEN_MARKERS) - - def _ensure_k25_slow_tokenizer(self) -> None: - """Override ``self._tokenizer`` and ``self._processor.tokenizer`` - with the model's slow ``TikTokenTokenizer``. - - Idempotent: callers invoke this lazily, on the first request that - actually requires correct mapping of K2.5 special tokens. Done this - way (instead of unconditionally in ``__init__``) so text-only - prompts keep the fast Rust tokenizer — running the slow Python - ``TikTokenTokenizer`` on the orchestrator GIL adds ~100 ms per - ``_fetch_new_requests`` / ``broadcast_requests`` step at 8 K-token - prompts, an order-of-magnitude TPOT regression. The slow class' - ``tokens_trie`` always splits the special tokens correctly. - See NVBug 6182617 (correctness) and NVBug 6248987 (perf). - """ - if self._slow_tokenizer_active: - return - from transformers.dynamic_module_utils import get_class_from_dynamic_module - - slow_cls = get_class_from_dynamic_module( - "tokenization_kimi.TikTokenTokenizer", - self._model_path, - ) - slow_tok = slow_cls.from_pretrained(self._model_path, trust_remote_code=True) - - logger.info( - "K2.5 InputProcessor swapping in slow TikTokenTokenizer " - "(originally %s). See NVBug 6182617.", - type(self._tokenizer).__name__, - ) - - self._tokenizer = slow_tok - # Image-only path uses ``self._processor.tokenizer`` (an - # independent instance from ``AutoProcessor``); swap it too. - if getattr(self._processor, "tokenizer", None) is not None: - self._processor.tokenizer = slow_tok - self._slow_tokenizer_active = True - @torch.inference_mode() def call_with_text_prompt( self, @@ -1271,18 +1199,9 @@ def call_with_text_prompt( # Text-only path if not images and not videos: - # Fast tokenizer is fine unless the prompt itself carries K2.5 - # special tokens (rare on the thinking perf path); only fall - # back to the slow ``TikTokenTokenizer`` then. See NVBug 6248987. - if self._input_needs_slow_tokenizer(text_prompt): - self._ensure_k25_slow_tokenizer() token_ids = self._tokenizer(text_prompt, return_tensors="pt").input_ids[0] return token_ids.to(torch.int32).tolist(), {} - # Multimodal path: prompt is rewritten with media placeholders that - # the fast tokenizer would BPE-split, so we always need the slow one. - self._ensure_k25_slow_tokenizer() - # Build the ``medias`` list expected by KimiK25Processor. # The HF processor accepts either ``messages`` (chat format) or # both ``medias`` and ``text``. Since we already have the @@ -1521,9 +1440,6 @@ def get_prompt_token_ids( f"must match model hidden size {expected_hidden_size}" ) - # Disagg-serving multimodal path: prompt has media placeholders that - # must map to canonical IDs, so the slow tokenizer is required. - self._ensure_k25_slow_tokenizer() input_ids = self._tokenizer(text_prompt, return_tensors="pt").input_ids[0] placeholder_id = self._media_placeholder_token_id diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index bf5fa1fd386b..ec7e1528050b 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -363,7 +363,6 @@ test_e2e.py::test_multi_nodes_eval[Qwen3/saved_models_Qwen3-235B-A22B_nvfp4_hf-t test_e2e.py::test_openai_chat_example[trt] SKIP (https://nvbugs/5477444) test_e2e.py::test_openai_completions_example[trt] SKIP (https://nvbugs/5701450) test_e2e.py::test_openai_disagg_multi_nodes_completion[ctx_tp1pp2-gen_tp1pp2] SKIP (https://nvbugs/6190759) -test_e2e.py::test_openai_kv_cache_contamination SKIP (https://nvbugs/6227203) test_e2e.py::test_ptp_quickstart_advanced_deepseek_r1_w4afp8_8gpus[DeepSeek-R1-W4AFP8-DeepSeek-R1/DeepSeek-R1-W4AFP8] SKIP (https://nvbugs/5836830) test_e2e.py::test_trtllm_bench_iteration_log[TRT-streaming-meta-llama/Llama-3.1-8B-llama-3.1-model/Meta-Llama-3.1-8B] SKIP (https://nvbugs/5448523) test_e2e.py::test_trtllm_multimodal_benchmark_serving SKIP (https://nvbugs/5864769) From a197a5ea7f0f108aa3cc8bf9dda5c3f8e34386d8 Mon Sep 17 00:00:00 2001 From: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:35:36 +0800 Subject: [PATCH 065/119] [None][fix] tunable_fp4_quantize: rename misnamed kwarg + add real SF-swizzle control (#15002) Signed-off-by: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> --- cpp/tensorrt_llm/thop/fp4Quantize.cpp | 1 + .../_torch/custom_ops/torch_custom_ops.py | 34 ++++++++++++++----- 2 files changed, 26 insertions(+), 9 deletions(-) diff --git a/cpp/tensorrt_llm/thop/fp4Quantize.cpp b/cpp/tensorrt_llm/thop/fp4Quantize.cpp index a1ba8ff10c72..859f1489b2b6 100644 --- a/cpp/tensorrt_llm/thop/fp4Quantize.cpp +++ b/cpp/tensorrt_llm/thop/fp4Quantize.cpp @@ -36,6 +36,7 @@ namespace torch_ext // nvfp4: sfVecSize = 16, sfUseUE8M0 = false // mxfp4: sfVecSize = 32, sfUseUE8M0 = true // alignment: sfVecSize +// sfUseUE8M0: bool, if true, scale factors use UE8M0 format (MXFP4); otherwise UE4M3 (NVFP4). // isSfSwizzledLayout: bool, if true, the scale factors are stored in swizzled layout, otherwise in linear layout. // See QuantizationSFLayout enum for more details about the two layouts. // returns self_fp4, self_block_scale_factors diff --git a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py index 2555475016b4..8189606adbc6 100644 --- a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py @@ -2417,11 +2417,14 @@ class Fp4QuantTactic(enum.IntEnum): def _fp4_quantize_dispatch(input: torch.Tensor, input_scale: torch.Tensor, - scaling_vector_size: int, + scaling_vector_size: int, sf_use_ue8m0: bool, is_sf_swizzled_layout: bool, tactic: int) -> Tuple[torch.Tensor, torch.Tensor]: """Dispatch FP4 quantization to TRTLLM or FlashInfer kernel.""" if tactic == Fp4QuantTactic.FLASHINFER and IS_FLASHINFER_AVAILABLE: + assert not sf_use_ue8m0, ( + "FlashInfer FP4 tactic does not support sf_use_ue8m0=True; " + "force the TRTLLM tactic.") act_fp4, act_sf = _flashinfer_nvfp4_quantize( input, input_scale, @@ -2439,7 +2442,7 @@ def _fp4_quantize_dispatch(input: torch.Tensor, input_scale: torch.Tensor, return act_fp4, act_sf else: return torch.ops.trtllm.fp4_quantize(input, input_scale, - scaling_vector_size, + scaling_vector_size, sf_use_ue8m0, is_sf_swizzled_layout) @@ -2461,12 +2464,15 @@ class Fp4QuantKernelRunner(TunableRunner): def __init__(self, scaling_vector_size: int = 16, - is_sf_swizzled_layout: bool = False): + sf_use_ue8m0: bool = False, + is_sf_swizzled_layout: bool = True): self.scaling_vector_size = scaling_vector_size + self.sf_use_ue8m0 = sf_use_ue8m0 self.is_sf_swizzled_layout = is_sf_swizzled_layout def unique_id(self): - return (self.scaling_vector_size, self.is_sf_swizzled_layout) + return (self.scaling_vector_size, self.sf_use_ue8m0, + self.is_sf_swizzled_layout) def get_valid_tactics( self, @@ -2474,7 +2480,8 @@ def get_valid_tactics( profile: OptimizationProfile, ) -> List[int]: tactics = [Fp4QuantTactic.TRTLLM] - if IS_FLASHINFER_AVAILABLE: + # FlashInfer FP4 kernel has no UE8M0 / MXFP4 mode. + if IS_FLASHINFER_AVAILABLE and not self.sf_use_ue8m0: tactics.append(Fp4QuantTactic.FLASHINFER) return tactics @@ -2486,6 +2493,7 @@ def forward( input, input_scale = inputs act_fp4, act_sf = _fp4_quantize_dispatch(input, input_scale, self.scaling_vector_size, + self.sf_use_ue8m0, self.is_sf_swizzled_layout, tactic) return act_fp4 @@ -2496,7 +2504,8 @@ def tunable_fp4_quantize( input: torch.Tensor, input_scale: torch.Tensor, scaling_vector_size: int = 16, - is_sf_swizzled_layout: bool = False, + sf_use_ue8m0: bool = False, + is_sf_swizzled_layout: bool = True, ) -> List[torch.Tensor]: """FP4 quantization with autotuning between TRTLLM and FlashInfer kernels. @@ -2508,14 +2517,17 @@ def tunable_fp4_quantize( input: Activation tensor [M, K] in bf16/fp16 input_scale: Global scale factor tensor scaling_vector_size: Block size for scale factors (default: 16) - is_sf_swizzled_layout: Whether to use swizzled layout for scales + sf_use_ue8m0: MXFP4 (UE8M0 SF) when True; NVFP4 (UE4M3 SF) when + False. FlashInfer tactic does not support True. + is_sf_swizzled_layout: Emit SWIZZLED 128x4 FP8 e4m3 SF layout when + True (default). Returns: List of [act_fp4, act_sf] - quantized activation and scale factors """ tuner = AutoTuner.get() - quant_runner = Fp4QuantKernelRunner(scaling_vector_size, + quant_runner = Fp4QuantKernelRunner(scaling_vector_size, sf_use_ue8m0, is_sf_swizzled_layout) _, best_tactic = tuner.choose_one( @@ -2528,6 +2540,7 @@ def tunable_fp4_quantize( try: act_fp4, act_sf = _fp4_quantize_dispatch(input, input_scale, scaling_vector_size, + sf_use_ue8m0, is_sf_swizzled_layout, best_tactic) except Exception: @@ -2536,6 +2549,7 @@ def tunable_fp4_quantize( f"{input.shape}, falling back to TRTLLM kernel.") act_fp4, act_sf = _fp4_quantize_dispatch(input, input_scale, scaling_vector_size, + sf_use_ue8m0, is_sf_swizzled_layout, Fp4QuantTactic.TRTLLM) else: @@ -2548,7 +2562,8 @@ def _( input: torch.Tensor, input_scale: torch.Tensor, scaling_vector_size: int = 16, - is_sf_swizzled_layout: bool = False, + sf_use_ue8m0: bool = False, + is_sf_swizzled_layout: bool = True, ) -> List[torch.Tensor]: """Fake implementation for torch.compile support. @@ -2557,6 +2572,7 @@ def _( swizzled_layout=True in get_fp4_shape to match the actual output size. We also reshape FlashInfer's output to match in _fp4_quantize_dispatch. """ + del sf_use_ue8m0, is_sf_swizzled_layout output_shape, scale_shape = fp4_utils.get_fp4_shape(input.shape, scaling_vector_size, True) From e9402ab59daba32bd409a3f776825af0cd484568 Mon Sep 17 00:00:00 2001 From: tensorrt-cicd <90828364+tensorrt-cicd@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:43:04 +0800 Subject: [PATCH 066/119] [None][test] Fix gen_only missing prev_device_step_time race in perf sanity (#15108) Signed-off-by: chenfeiz Co-authored-by: chenfeiz --- .../integration/defs/perf/test_perf_sanity.py | 76 +++++++++++++++---- tests/integration/test_lists/waives.txt | 3 - 2 files changed, 61 insertions(+), 18 deletions(-) diff --git a/tests/integration/defs/perf/test_perf_sanity.py b/tests/integration/defs/perf/test_perf_sanity.py index bf60df583d0d..d37ffd9400c0 100644 --- a/tests/integration/defs/perf/test_perf_sanity.py +++ b/tests/integration/defs/perf/test_perf_sanity.py @@ -163,22 +163,22 @@ def gen_worker_log_sizes(output_dir: str, num_gen_servers: int) -> List[int]: return sizes -def parse_gen_worker_device_step_time( +def _scan_gen_worker_device_step_time( output_dir: str, num_gen_servers: int, start_offsets: Optional[List[int]] = None, -) -> Optional[float]: - """Mean per-iter prev_device_step_time (ms) across all gen workers. - - For each gen_server_{i}.log, average prev_device_step_time over iters >= 5, - then average those per-file means across the num_gen_servers workers. - Returns None if no usable line is found in any file. - - When start_offsets is provided, only the bytes from start_offsets[i] to - end-of-file are considered for gen_server_{i}.log — used to slice out a - single client's iteration segment. +) -> Tuple[List[float], int]: + """Single pass over the gen logs. Returns (per_file_means, total_count). + + per_file_means holds one mean per file that had >=1 usable line; + total_count is the number of usable (iter >= 5, numeric) lines across all + files, used by the caller to detect when the cross-node log flush has + settled. errors="replace" guards against invalid UTF-8: tqdm progress bars + (model load) write partial multibyte sequences that would otherwise raise + UnicodeDecodeError mid-scan. """ per_file_means: List[float] = [] + total_count = 0 for i in range(num_gen_servers): log_path = os.path.join(output_dir, f"gen_server_{i}.log") if not os.path.isfile(log_path): @@ -187,7 +187,7 @@ def parse_gen_worker_device_step_time( # large iteration counts. count = 0 mean = 0.0 - with open(log_path) as f: + with open(log_path, errors="replace") as f: if start_offsets is not None and i < len(start_offsets) and start_offsets[i]: f.seek(start_offsets[i]) for line in f: @@ -201,9 +201,55 @@ def parse_gen_worker_device_step_time( mean += (float(m.group(2)) - mean) / count if count: per_file_means.append(mean) - if not per_file_means: - return None - return sum(per_file_means) / len(per_file_means) + total_count += count + return per_file_means, total_count + + +def parse_gen_worker_device_step_time( + output_dir: str, + num_gen_servers: int, + start_offsets: Optional[List[int]] = None, + settle_timeout: float = 90.0, + poll_interval: float = 3.0, +) -> Optional[float]: + """Mean per-iter prev_device_step_time (ms) across all gen workers. + + For each gen_server_{i}.log, average prev_device_step_time over iters >= 5, + then average those per-file means across the num_gen_servers workers. + Returns None if no usable line is found in any file. + + When start_offsets is provided, only the bytes from start_offsets[i] to + end-of-file are considered for gen_server_{i}.log — used to slice out a + single client's iteration segment. + + The gen worker writes gen_server_{i}.log on a different node than the + benchmark/pytest process, and the worker is kept alive (waiting on the + benchmark_status file) when this runs — so when the client returns, the + decode iterations are done but their log lines may still be flushing across + NFS. Reading once immediately can see zero iter>=5 lines and wrongly return + None. So poll the slice until the usable-line count is non-zero AND stable + across two consecutive reads (flush drained), bounded by settle_timeout. + """ + deadline = time.time() + settle_timeout + prev_count = -1 + while True: + per_file_means, total_count = _scan_gen_worker_device_step_time( + output_dir, num_gen_servers, start_offsets + ) + # Non-empty and unchanged since the last poll → the flush has settled. + if total_count > 0 and total_count == prev_count: + return sum(per_file_means) / len(per_file_means) + if time.time() >= deadline: + if per_file_means: + print_info( + f"parse_gen_worker_device_step_time: settle_timeout " + f"({settle_timeout}s) reached with {total_count} line(s); " + "returning current mean." + ) + return sum(per_file_means) / len(per_file_means) + return None + prev_count = total_count + time.sleep(poll_interval) def add_perf_metric_value( diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index ec7e1528050b..eb574454e059 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -335,10 +335,7 @@ perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_kimi-k25-thinking-fp4 perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] SKIP (https://nvbugs/6280649) -perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con1536_ctx1_dep4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6016528) -perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) -perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-b200_deepseek-r1-fp4_8k1k_con256_ctx1_dep4_gen1_dep8_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6221024) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280649) perf/test_perf_sanity.py::test_e2e[disagg_upload-gen_only-gb200_deepseek-v32-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] SKIP (https://nvbugs/6280721) From 6bf3e492a1c5c4f62ffa3cbd973e9b7b54170228 Mon Sep 17 00:00:00 2001 From: fredricz-20070104 <226039983+fredricz-20070104@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:44:57 +0800 Subject: [PATCH 067/119] [None][test] Fix disagg test result dir (#14864) Signed-off-by: FredricZ-2007 <226039983+fredricz-20070104@users.noreply.github.com> --- jenkins/scripts/perf/local/run_disagg.sh | 48 ++++-- .../test_lists/qa/llm_perf_disagg.yml | 149 ++++++++++++++++++ .../test_lists/qa/llm_perf_multinode.txt | 15 +- 3 files changed, 183 insertions(+), 29 deletions(-) create mode 100644 tests/integration/test_lists/qa/llm_perf_disagg.yml diff --git a/jenkins/scripts/perf/local/run_disagg.sh b/jenkins/scripts/perf/local/run_disagg.sh index 74cdb191dcb1..2f773e038b0c 100755 --- a/jenkins/scripts/perf/local/run_disagg.sh +++ b/jenkins/scripts/perf/local/run_disagg.sh @@ -174,28 +174,36 @@ fi # Default strip list — see note inside the loop. : "${strip_sbatch_opts:=--segment}" -# Per-test loop: each test gets its own subdir (when >1), its own slurm_launch.sh, -# and its own sbatch submission. Failures are collected, not fatal, so a bad -# test_id doesn't stop the rest of the batch. +# Per-test loop: each test gets its own subdir (named by the test-id bracket), +# its own slurm_launch.sh, and its own sbatch submission. Failures are collected, +# not fatal, so a bad test_id doesn't stop the rest of the batch. num_tests=${#test_ids[@]} submitted_count=0 failed_tests=() +# CI machine-readable bookkeeping (consumed by trt_jenkins gen_disagg_junit.py). +# expected_tests.txt : every test_id we intend to run (for expected-vs-produced diff) +# failed_submit.txt : test_id|reason for submit-time failures (no job / no xml) +# slurm_jobs.txt : | for jobs that submitted (Jenkins polls these) +failed_submit_file="$work_dir/failed_submit.txt" +slurm_jobs_file="$work_dir/slurm_jobs.txt" +expected_file="$work_dir/expected_tests.txt" +: > "$failed_submit_file"; : > "$slurm_jobs_file"; : > "$expected_file" +printf '%s\n' "${test_ids[@]}" > "$expected_file" + for idx in "${!test_ids[@]}"; do tid="${test_ids[$idx]}" - # When running a single test, keep the flat layout (back-compat); for - # multi-test, drop each into its own subdir named by a slug of the test id. - if [[ $num_tests -gt 1 ]]; then - slug="${tid#*[}" # strip everything up to and including '[' - slug="${slug%]*}" # strip trailing ']' and beyond - slug="${slug//[^a-zA-Z0-9_.-]/_}" - test_work_dir="$work_dir/$(printf '%02d_%s' "$idx" "$slug")" - test_job_name="${job_name}_${idx}" - else - test_work_dir="$work_dir" - test_job_name="$job_name" - fi + # Each test gets its own subdir named by the test-id bracket content, e.g. + # 'disagg-e2e-' / 'aggr-ctx_only-'. This matches the trtllm-ci + # multinode layout so downstream perf parsing (parse_perf_logs.py discover_cases, + # which keys off 'disagg-*' / 'aggr-*' dir names + test_list.txt) and JUnit + # generation find cases uniformly. submit.py writes test_list.txt + report.xml + # into --work-dir (this subdir), so we don't create test_list.txt ourselves. + case_name="${tid#*[}" # strip up to and including '[' + case_name="${case_name%]*}" # strip trailing ']' and beyond + test_work_dir="$work_dir/$case_name" + test_job_name="${job_name}_${idx}" mkdir -p "$test_work_dir" echo @@ -228,6 +236,7 @@ for idx in "${!test_ids[@]}"; do $capture_nsys_flag; then echo "ERROR: submit.py failed for $tid — skipping." >&2 failed_tests+=("$tid (submit.py failed)") + echo "$tid|submit_py_failed" >> "$failed_submit_file" continue fi @@ -244,12 +253,17 @@ for idx in "${!test_ids[@]}"; do rm -f "$test_work_dir/slurm_launch.sh.bak" fi - # 2. Submit - if ( cd "$test_work_dir" && sbatch slurm_launch.sh ); then + # 2. Submit — capture job id, do NOT block (Jenkins polls squeue/sacct later, so a + # long queue wait survives SSH drops; --wait would hang one ssh for hours). + jid_raw=$( cd "$test_work_dir" && sbatch --parsable slurm_launch.sh 2>>"$work_dir/sbatch.err" || true ) + jid="${jid_raw%%;*}" # 'jobid' or 'jobid;cluster' -> jobid + if [[ "$jid" =~ ^[0-9]+$ ]]; then + echo "$jid|$tid" >> "$slurm_jobs_file" submitted_count=$((submitted_count + 1)) else echo "ERROR: sbatch failed for $tid" >&2 failed_tests+=("$tid (sbatch failed)") + echo "$tid|sbatch_failed" >> "$failed_submit_file" fi done diff --git a/tests/integration/test_lists/qa/llm_perf_disagg.yml b/tests/integration/test_lists/qa/llm_perf_disagg.yml new file mode 100644 index 000000000000..c6095b030e58 --- /dev/null +++ b/tests/integration/test_lists/qa/llm_perf_disagg.yml @@ -0,0 +1,149 @@ +version: 0.0.1 +llm_perf_disagg: + +# 1: GB200 test cases +- condition: + wildcards: + gpu: + - 'gb200' + tests: + # GB200 DeepSeek-R1 + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con1_ctx1_pp8_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con64_ctx1_pp8_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_pp4_bs2_eplb0_mtp0_con2-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con1024_ctx1_dep4_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con2048_ctx2_dep4_gen1_dep16_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con2048_ctx2_dep4_gen1_dep16_eplb288_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con3072_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_con3072_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp3_con1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_8k1k_ctx1_gen3_tep8_bs32_eplb0_mtp0_con1_ccb-NIXL] TIMEOUT (120) + # GB200 DeepSeek-V32 + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_32k4k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_32k4k_con256_ctx1_dep8_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_8k1k_con4096_ctx1_dep4_gen1_dep32_eplb256_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-v32-fp4_8k1k_con4096_ctx1_dep4_gen1_dep32_eplb256_mtp0_ccb-UCX] TIMEOUT (120) + # GB200 GPT-OSS-120B + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_1k1k_con2048_ctx1_tp1_gen1_dep2_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_1k1k_con2048_ctx1_tp1_gen1_dep2_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_1k1k_con512_ctx1_tp1_gen1_dep2_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_1k1k_con64_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_8k1k_con1024_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_8k1k_con1024_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_8k1k_con128_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_8k1k_con4_ctx1_tp1_gen1_tp4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_gpt-oss-120b-fp4_8k1k_con512_ctx1_tp1_gen1_dep2_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + # GB200 Kimi-K2.5-Thinking + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_1k1k_con2048_ctx1_dep4_gen1_dep32_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_1k1k_con4096_ctx1_dep4_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_1k1k_con4096_ctx1_dep4_gen1_dep8_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_1k1k_con4_ctx1_dep4_gen1_tep4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_kimi-k25-thinking-fp4_8k1k_con4_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + # GB200 Qwen3-235B + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_con1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_8k1k_con1024_ctx1_tp1_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_8k1k_con1024_ctx1_tp1_gen1_dep8_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_8k1k_con1_ctx1_tp1_gen1_tep4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_8k1k_con64_ctx1_tp1_gen1_tep4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + # GB200 wideep + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_wideep_deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_wideep_deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_con1024_ccb-NIXL_kv-reuse] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_con12288_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_deepseek-v32-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_con12288_ccb-NIXL] TIMEOUT (120) + # GB200 accuracy cases + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_accuracy-deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_accuracy-deepseek-r1-fp4_gpqa_diamond_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] TIMEOUT (120) + # GB200 stress cases + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_wideep_stress-deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_wideep_stress-deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_stress-gpt-oss-120b-fp4_8k1k_ctx1_tp1_gen1_tp4_eplb0_eagle3_ccb-NIXL] TIMEOUT (120) + # GB200 aggregated ctx_only + - perf/test_perf_sanity.py::test_e2e[aggr-ctx_only-gb200_qwen3-235b-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_con1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[aggr-ctx_only-gb200_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + # GB200 aggregated gen_only + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_qwen3-235b-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_con1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + +# 2: GB300 test cases +- condition: + wildcards: + gpu: + - 'gb300' + tests: + # GB300 DeepSeek-R1 + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con1_ctx1_pp4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con64_ctx1_pp4_gen1_dep16_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con3072_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con3072_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + # GB300 DeepSeek-V32 + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_32k4k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_32k4k_con2048_ctx1_dep4_gen1_dep32_eplb288_mtp1_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_32k4k_con256_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_32k4k_con256_ctx1_dep8_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb256_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_8k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_8k1k_con4096_ctx1_dep4_gen1_dep32_eplb256_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-v32-fp4_8k1k_con4096_ctx1_dep4_gen1_dep32_eplb256_mtp0_ccb-UCX] TIMEOUT (120) + # GB300 Kimi-K2.5-Thinking + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_1k1k_con2048_ctx1_dep4_gen1_dep32_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_1k1k_con4096_ctx1_dep4_gen1_dep8_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_1k1k_con4096_ctx1_dep4_gen1_dep8_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_1k1k_con4_ctx1_dep4_gen1_tep4_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con4096_ctx1_dep4_gen1_dep16_eplb0_mtp0_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_kimi-k25-thinking-fp4_8k1k_con4_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] TIMEOUT (120) + # GB300 Qwen3-235B + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + # GB300 wideep + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_wideep_deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_wideep_deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_con1024_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-UCX] TIMEOUT (120) + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + # GB300 aggregated ctx_only + - perf/test_perf_sanity.py::test_e2e[aggr-ctx_only-gb300_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) + # GB300 aggregated gen_only + - perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_qwen3-235b-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb0_mtp3_con2048_ccb-NIXL] TIMEOUT (120) diff --git a/tests/integration/test_lists/qa/llm_perf_multinode.txt b/tests/integration/test_lists/qa/llm_perf_multinode.txt index a77c81e8f28b..6e06c6bdfea6 100644 --- a/tests/integration/test_lists/qa/llm_perf_multinode.txt +++ b/tests/integration/test_lists/qa/llm_perf_multinode.txt @@ -1,8 +1,8 @@ # disagg multi-node # GB200 DeepSeek-R1 -perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp2_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp2_ccb-UCX] +perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] +perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-UCX] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con1_ctx1_pp8_gen1_tep8_eplb0_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_con64_ctx1_pp8_gen1_dep32_eplb0_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_pp4_bs2_eplb0_mtp0_con2-NIXL] @@ -70,7 +70,7 @@ perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_qwen3-235b-fp4_8k1k_con64_ct perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con1_ctx1_pp4_gen1_tep8_eplb0_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con256_ctx1_pp4_gen1_dep8_eplb0_mtp1_ccb-UCX] -perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con64_ctx1_pp4_gen1_dep16_eplb0_mtp2_ccb-NIXL] +perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_128k8k_con64_ctx1_pp4_gen1_dep16_eplb0_mtp1_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con1024_ctx1_dep4_gen1_dep32_eplb0_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con1_ctx1_dep4_gen1_tep8_eplb0_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb300_deepseek-r1-fp4_1k1k_con3072_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] @@ -128,18 +128,9 @@ perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-r1-fp4_ perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-UCX] perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb300_wideep_deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_con2048_ccb-NIXL] -# external wideep configs -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_con1024_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_con512_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_con1024_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_con512_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_con16384_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-gen_only-wideep_kimi-k2-thinking-fp4_8k1k_ctx8_gen1_dep32_bs256_eplb416_mtp0_con8192_ccb-NIXL] - # accuracy cases perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_accuracy-deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] perf/test_perf_sanity.py::test_e2e[disagg-gen_only-gb200_wideep_accuracy-deepseek-r1-fp4_gpqa_diamond_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] -perf/test_perf_sanity.py::test_e2e[disagg-e2e-wideep_accuracy-kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL] # stress cases perf/test_perf_sanity.py::test_e2e[disagg-e2e-gb200_wideep_stress-deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] From a7e4a9b63d0455c8ae9e37cea7f5dad1b23c3449 Mon Sep 17 00:00:00 2001 From: QI JUN <22017000+QiJune@users.noreply.github.com> Date: Tue, 9 Jun 2026 14:00:13 +0800 Subject: [PATCH 068/119] [TRTLLM-13332][test] Remove TestLlama4ScoutInstruct tests (#15144) Signed-off-by: junq <22017000+QiJune@users.noreply.github.com> --- tests/integration/defs/.test_durations | 16 -- .../accuracy/test_disaggregated_serving.py | 40 ----- .../defs/accuracy/test_llm_api_pytorch.py | 149 ------------------ .../test_lists/qa/llm_spark_func.yml | 2 - .../test_lists/test-db/l0_dgx_b200.yml | 12 -- .../test_lists/test-db/l0_dgx_h200.yml | 2 - 6 files changed, 221 deletions(-) diff --git a/tests/integration/defs/.test_durations b/tests/integration/defs/.test_durations index 43043bf464ab..712c52627f61 100644 --- a/tests/integration/defs/.test_durations +++ b/tests/integration/defs/.test_durations @@ -37,8 +37,6 @@ "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[MMLU-tp1pp2]": 128.10282056825235, "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[MMLU-tp2pp1]": 121.90447079204023, "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[MMLU-tp2pp2]": 117.0786016730126, - "accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[False]": 64428.639228201006, - "accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[True]": 572.5455802679062, "accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_auto_dtype[False]": 472.62511800276116, "accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_auto_dtype[True]": 273.7770717362873, "accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_nixl_backend": 56.07656032079831, @@ -238,18 +236,6 @@ "accuracy/test_llm_api_pytorch.py::TestLlama4MaverickInstruct::test_fp8[tp8ep8-cuda_graph=True]": 7200.00023917207727208734, "accuracy/test_llm_api_pytorch.py::TestLlama4MaverickInstruct::test_fp8_chunked_prefill[tp8ep8-cuda_graph=False]": 7200.5301868109382, "accuracy/test_llm_api_pytorch.py::TestLlama4MaverickInstruct::test_fp8_chunked_prefill[tp8ep8-cuda_graph=True]": 72600.1488124400494, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4-cuda_graph=False]": 3600.0010551271309959702, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4ep2-cuda_graph=True]": 3600.0009890546519891359, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4ep4-cuda_graph=True]": 3600.000870058874017559, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8-cuda_graph=False]": 3600.0022709049517, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8ep4-cuda_graph=True]": 3600.0008703919593, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8ep8-cuda_graph=True]": 3600.001674739062, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4[tp4-cuda_graph=True]": 3600.0003222679952159524, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4[tp8ep8-cuda_graph=True]": 3600.0004189839819446206, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4_chunked_prefill[tp4ep4-cuda_graph=True]": 3600.0009446179610677063, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8[tp4-cuda_graph=True]": 3600.0600651470012963, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8[tp8ep8-cuda_graph=True]": 3600.0020443379763, - "accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8_chunked_prefill[tp4ep4-cuda_graph=True]": 3600.0016280449927, "accuracy/test_llm_api_pytorch.py::TestMinistral8BInstruct::test_auto_dtype": 300.0017418859643, "accuracy/test_llm_api_pytorch.py::TestMinistral8BInstruct::test_fp8": 300.001715709921, "accuracy/test_llm_api_pytorch.py::TestMinitron4BBaseInstruct::test_fp8_prequantized": 48.064747432945296, @@ -1402,8 +1388,6 @@ "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[MMLU-gen_tp=2-ctx_pp=4]": 99.25327169150114, "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[GSM8K-tp2pp2]": 152.5729262419045, "accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[MMLU-tp2pp2]": 89.9921805858612, - "accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[False]": 661.3829264938831, - "accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[True]": 322.62455509230494, "accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[latency]": 2162.4878128543496, "accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_bf16_4gpu[tp4ep4_cudagraph_overlap_adp_off]": 231.63562587695196, "disaggregated/test_disaggregated.py::test_disaggregated_ctxpp4_genpp4[TinyLlama-1.1B-Chat-v1.0]": 89.56677887961268, diff --git a/tests/integration/defs/accuracy/test_disaggregated_serving.py b/tests/integration/defs/accuracy/test_disaggregated_serving.py index c786c80f3d19..74911add4cfd 100644 --- a/tests/integration/defs/accuracy/test_disaggregated_serving.py +++ b/tests/integration/defs/accuracy/test_disaggregated_serving.py @@ -916,46 +916,6 @@ def test_multi_instance(self, testset): test_sets=[get_accuracy_task(testset)]) -class TestLlama4ScoutInstruct(LlmapiAccuracyTestHarness): - MODEL_NAME = "meta-llama/Llama-4-Scout-17B-16E-Instruct" - MODEL_PATH = f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct" - - @pytest.mark.skip_less_device_memory(140000) - @pytest.mark.timeout(3600) - @pytest.mark.skip_less_device(8) - @pytest.mark.parametrize("overlap_scheduler", [False, True]) - def test_auto_dtype(self, overlap_scheduler): - ctx_server_config = {"disable_overlap_scheduler": True} - gen_server_config = {"disable_overlap_scheduler": overlap_scheduler} - ctx_server_config["cache_transceiver_config"] = { - "backend": "DEFAULT", - "max_tokens_in_buffer": 4096 - } - gen_server_config["cache_transceiver_config"] = { - "backend": "DEFAULT", - "max_tokens_in_buffer": 4096 - } - # Keep this low to avoid warmup OOM in CI - ctx_server_config["max_seq_len"] = 8192 - gen_server_config["max_seq_len"] = 8192 - disaggregated_server_config = { - "hostname": "localhost", - "backend": "pytorch", - "context_servers": { - "num_instances": 1 - }, - "generation_servers": { - "num_instances": 1 - } - } - with launch_disaggregated_llm(disaggregated_server_config, - ctx_server_config, - gen_server_config, - self.MODEL_PATH, - tensor_parallel_size=4) as llm: - run_accuracy_test(llm, self.MODEL_NAME, ["MMLU", "GSM8K"]) - - @pytest.mark.timeout(DEFAULT_TEST_TIMEOUT) @skip_pre_hopper class TestDeepSeekV3Lite(LlmapiAccuracyTestHarness): diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch.py b/tests/integration/defs/accuracy/test_llm_api_pytorch.py index a56816ea4832..a9d7bd7167fc 100644 --- a/tests/integration/defs/accuracy/test_llm_api_pytorch.py +++ b/tests/integration/defs/accuracy/test_llm_api_pytorch.py @@ -1081,155 +1081,6 @@ def test_fp8_chunked_prefill(self, cuda_graph, tp_size, pp_size, ep_size): task.evaluate(llm) -@pytest.mark.skip_less_device_memory(80000) -@pytest.mark.skip_less_host_memory(100000) -class TestLlama4ScoutInstruct(LlmapiAccuracyTestHarness): - MODEL_NAME = "meta-llama/Llama-4-Scout-17B-16E-Instruct" - - @skip_pre_hopper - @parametrize_with_ids("cuda_graph", [False, True]) - @pytest.mark.parametrize( - "tp_size,pp_size,ep_size", [(8, 1, 1), (8, 1, 4), (8, 1, 8), (4, 1, 1), - (4, 1, 2), (4, 1, 4)], - ids=["tp8", "tp8ep4", "tp8ep8", "tp4", "tp4ep2", "tp4ep4"]) - def test_auto_dtype(self, cuda_graph, tp_size, pp_size, ep_size): - if get_device_count() != tp_size * pp_size: - pytest.skip("Device count mismatch with world size") - - model_path = f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct" - with LLM( - model_path, - tensor_parallel_size=tp_size, - # Keep this low to avoid warmup OOM in CI - max_seq_len=8192, - pipeline_parallel_size=pp_size, - moe_expert_parallel_size=ep_size, - cuda_graph_config=CudaGraphConfig() - if cuda_graph else None) as llm: - task = MMLU(self.MODEL_NAME) - task.evaluate(llm) - task = GSM8K(self.MODEL_NAME) - task.evaluate(llm) - - @skip_pre_hopper - @parametrize_with_ids("cuda_graph", [True]) - @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(8, 1, 8), (4, 1, 1)], - ids=["tp8ep8", "tp4"]) - def test_fp8(self, cuda_graph, tp_size, pp_size, ep_size): - if get_device_count() != tp_size * pp_size: - pytest.skip("Device count mismatch with world size") - - model_path = f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct-FP8" - with LLM( - model_path, - tensor_parallel_size=tp_size, - # Keep this low to avoid warmup OOM in CI - max_seq_len=8192, - pipeline_parallel_size=pp_size, - moe_expert_parallel_size=ep_size, - kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.8), - cuda_graph_config=CudaGraphConfig() - if cuda_graph else None) as llm: - assert llm.args.quant_config.quant_algo == QuantAlgo.FP8 - task = MMLU(self.MODEL_NAME) - task.evaluate(llm) - task = GSM8K(self.MODEL_NAME) - task.evaluate(llm) - - @skip_pre_blackwell - @parametrize_with_ids("cuda_graph", [True]) - @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(8, 1, 8), (4, 1, 1)], - ids=["tp8ep8", "tp4"]) - def test_fp4(self, cuda_graph, tp_size, pp_size, ep_size): - if get_device_count() != tp_size * pp_size: - pytest.skip("Device count mismatch with world size") - - model_path = f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct-FP4" - with LLM( - model_path, - tensor_parallel_size=tp_size, - # Keep this low to avoid warmup OOM in CI - max_seq_len=8192, - pipeline_parallel_size=pp_size, - moe_expert_parallel_size=ep_size, - cuda_graph_config=CudaGraphConfig() - if cuda_graph else None) as llm: - assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4 - assert llm.args.quant_config.kv_cache_quant_algo == QuantAlgo.FP8 - task = MMLU(self.MODEL_NAME) - task.evaluate(llm) - task = GSM8K(self.MODEL_NAME) - task.evaluate(llm) - - @skip_pre_hopper - @pytest.mark.skip_less_mpi_world_size(4) - @parametrize_with_ids("cuda_graph", [True]) - @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(4, 1, 4)], - ids=["tp4ep4"]) - def test_fp8_chunked_prefill(self, cuda_graph, tp_size, pp_size, ep_size): - with LLM( - f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct-FP8", - tensor_parallel_size=tp_size, - max_seq_len=22000, - pipeline_parallel_size=pp_size, - moe_expert_parallel_size=ep_size, - enable_chunked_prefill=True, - max_num_tokens=256, - cuda_graph_config=CudaGraphConfig() - if cuda_graph else None) as llm: - assert llm.args.quant_config.quant_algo == QuantAlgo.FP8 - assert llm.args.quant_config.kv_cache_quant_algo == QuantAlgo.FP8 - task = MMLU(self.MODEL_NAME) - task.evaluate(llm) - task = GSM8K(self.MODEL_NAME) - task.evaluate(llm) - - @skip_pre_blackwell - @pytest.mark.skip_less_mpi_world_size(4) - @parametrize_with_ids("cuda_graph", [True]) - @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(4, 1, 4)], - ids=["tp4ep4"]) - def test_fp4_chunked_prefill(self, cuda_graph, tp_size, pp_size, ep_size): - with LLM( - f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct-FP4", - tensor_parallel_size=tp_size, - pipeline_parallel_size=pp_size, - moe_expert_parallel_size=ep_size, - max_seq_len=22000, - enable_chunked_prefill=True, - max_num_tokens=256, - cuda_graph_config=CudaGraphConfig() - if cuda_graph else None) as llm: - assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4 - assert llm.args.quant_config.kv_cache_quant_algo == QuantAlgo.FP8 - task = MMLU(self.MODEL_NAME) - task.evaluate(llm) - task = GSM8K(self.MODEL_NAME) - task.evaluate(llm) - - @skip_pre_hopper - @pytest.mark.skip_less_mpi_world_size(2) - def test_auto_dtype_tp2(self): - kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.4) - _run_multinode_accuracy( - f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct", - self.MODEL_NAME, - benchmarks=["mmlu"], - ep_size=2, - kv_cache_config=kv_cache_config) - - @skip_pre_hopper - @pytest.mark.skip_less_mpi_world_size(2) - def test_fp8_tp2(self): - kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.4) - _run_multinode_accuracy( - f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct-FP8", - self.MODEL_NAME, - benchmarks=["mmlu"], - ep_size=2, - kv_cache_config=kv_cache_config) - - class TestMistral7B(LlmapiAccuracyTestHarness): MODEL_NAME = "mistralai/Mistral-7B-v0.1" MODEL_PATH = f"{llm_models_root()}/mistral-7b-v0.1" diff --git a/tests/integration/test_lists/qa/llm_spark_func.yml b/tests/integration/test_lists/qa/llm_spark_func.yml index 249197b6ca40..14aa75905a5f 100644 --- a/tests/integration/test_lists/qa/llm_spark_func.yml +++ b/tests/integration/test_lists/qa/llm_spark_func.yml @@ -72,5 +72,3 @@ llm_spark_func: - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4_2gpus[latency_moe_cutlass] - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4_2gpus[latency_moe_cutlass_eagle3] - accuracy/test_llm_api_pytorch.py::TestDeepSeekR1DistillLlama70B::test_auto_dtype_tp2 - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8_tp2 - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype_tp2 diff --git a/tests/integration/test_lists/test-db/l0_dgx_b200.yml b/tests/integration/test_lists/test-db/l0_dgx_b200.yml index 2818cf78b433..7b3bc53e0423 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b200.yml @@ -279,18 +279,6 @@ l0_dgx_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_trtllm-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl] - accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_trtllm] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8[tp4-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8-cuda_graph=False] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8ep4-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp8ep8-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4-cuda_graph=False] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4ep2-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_auto_dtype[tp4ep4-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8[tp8ep8-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4_chunked_prefill[tp4ep4-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4[tp4-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4[tp8ep8-cuda_graph=True] - - accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp8_chunked_prefill[tp4ep4-cuda_graph=True] - accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=0] - accuracy/test_disaggregated_serving.py::TestQwen3_30B_A3B::test_mixed_ctx_gen_model[ctxpp2gentp2] - accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-auto] diff --git a/tests/integration/test_lists/test-db/l0_dgx_h200.yml b/tests/integration/test_lists/test-db/l0_dgx_h200.yml index d8a3847c7fac..bea7ec084b82 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_h200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_h200.yml @@ -23,8 +23,6 @@ l0_dgx_h200: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[host_cache_offload] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[host_cache_offload_mtp1] - accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_dsa_host_cache_offload[host_cache_offload_mtp3_no_adp] - - accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[True] - - accuracy/test_disaggregated_serving.py::TestLlama4ScoutInstruct::test_auto_dtype[False] - accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype[mtp_nextn=0-overlap_scheduler=True] - accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype[mtp_nextn=0-overlap_scheduler=False] - accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype[mtp_nextn=2-overlap_scheduler=True] From 6f7aea5bcc3285bc9b1f8782d9d4f69516d8cf28 Mon Sep 17 00:00:00 2001 From: Guoming Zhang <137257613+nv-guomingz@users.noreply.github.com> Date: Tue, 9 Jun 2026 14:07:43 +0800 Subject: [PATCH 069/119] =?UTF-8?q?[https://nvbugs/6266705][fix]=20Gate=20?= =?UTF-8?q?FlashInfer=20GDN=20kernels=20to=20supporte=E2=80=A6=20(#15094)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- .../fla/fused_sigmoid_gating_recurrent.py | 8 ++ .../_torch/modules/mamba/gdn_mixer.py | 31 ++++++-- tensorrt_llm/_utils.py | 14 ++++ .../mamba/test_flashinfer_chunk_gdn.py | 77 +++++++++++++------ 4 files changed, 101 insertions(+), 29 deletions(-) diff --git a/tensorrt_llm/_torch/modules/fla/fused_sigmoid_gating_recurrent.py b/tensorrt_llm/_torch/modules/fla/fused_sigmoid_gating_recurrent.py index 7337f7798425..83dcc4df672f 100644 --- a/tensorrt_llm/_torch/modules/fla/fused_sigmoid_gating_recurrent.py +++ b/tensorrt_llm/_torch/modules/fla/fused_sigmoid_gating_recurrent.py @@ -1,4 +1,6 @@ # Adapted from https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/layers/attention/fla/fused_sigmoid_gating_recurrent.py +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 import os from typing import Optional @@ -8,6 +10,7 @@ import triton.language as tl from tensorrt_llm._torch.modules.fla.utils import custom_device_ctx +from tensorrt_llm._utils import is_flashinfer_gdn_supported_arch from tensorrt_llm.logger import logger try: @@ -201,6 +204,11 @@ def _can_use_flashinfer_gdn_decode( return False if not _FLASHINFER_GDN_BF16_STATE_AVAILABLE: return False + # FlashInfer's GDN decode kernel is built for Hopper (SM90) and datacenter + # Blackwell (SM100/SM103) only; on consumer Blackwell (SM120) and other archs + # it aborts at launch -> fall back to the Triton fused-recurrent kernel. + if not is_flashinfer_gdn_supported_arch(): + return False if initial_state_source is None: return False if initial_state_source.dtype != torch.bfloat16: diff --git a/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py b/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py index a6882d11fbae..aa6146620ead 100644 --- a/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py +++ b/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py @@ -3,6 +3,7 @@ # SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 +import functools import os from typing import Optional @@ -12,17 +13,12 @@ from torch import nn from transformers import Qwen3NextConfig -# Default: FlashInfer GDN prefill ON. Set TLLM_USE_FLASHINFER_GDN_PREFILL=0 to -# fall back to the vendored Triton chunk_gated_delta_rule. -if os.getenv("TLLM_USE_FLASHINFER_GDN_PREFILL", "1") == "1": - from tensorrt_llm._torch.modules.fla.flashinfer_chunk import chunk_gated_delta_rule -else: - from tensorrt_llm._torch.modules.fla.chunk import chunk_gated_delta_rule from tensorrt_llm._torch.modules.fla.fused_recurrent import fused_recurrent_gated_delta_rule_update from tensorrt_llm._torch.modules.fla.fused_sigmoid_gating_recurrent import ( fused_sigmoid_gating_delta_rule_update, ) from tensorrt_llm._torch.pyexecutor.mamba_cache_manager import use_cpp_mamba_cache_manager +from tensorrt_llm._utils import is_flashinfer_gdn_supported_arch from tensorrt_llm.mapping import Mapping from ...attention_backend import AttentionMetadata @@ -43,6 +39,29 @@ from .mamba2_metadata import Mamba2Metadata +# FlashInfer GDN prefill is ON by default; set TLLM_USE_FLASHINFER_GDN_PREFILL=0 +# to force the vendored Triton chunk_gated_delta_rule everywhere. FlashInfer only +# ships the GDN prefill kernel for Hopper (SM90) and datacenter Blackwell +# (SM100/SM103); on consumer Blackwell (SM120) and other archs it aborts at +# launch, so we fall back to Triton there. Resolution is deferred to first call +# (and cached) so importing this module never initializes CUDA. +@functools.lru_cache(maxsize=1) +def _resolve_chunk_gated_delta_rule(): + if ( + os.getenv("TLLM_USE_FLASHINFER_GDN_PREFILL", "1") == "1" + and is_flashinfer_gdn_supported_arch() + ): + from tensorrt_llm._torch.modules.fla.flashinfer_chunk import chunk_gated_delta_rule as impl + else: + from tensorrt_llm._torch.modules.fla.chunk import chunk_gated_delta_rule as impl + return impl + + +@torch.compiler.disable +def chunk_gated_delta_rule(*args, **kwargs): + return _resolve_chunk_gated_delta_rule()(*args, **kwargs) + + def ensure_divisibility(numerator, denominator): """Ensure that numerator is divisible by the denominator.""" assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator) diff --git a/tensorrt_llm/_utils.py b/tensorrt_llm/_utils.py index c53e7a085048..841f396104f5 100644 --- a/tensorrt_llm/_utils.py +++ b/tensorrt_llm/_utils.py @@ -813,6 +813,20 @@ def is_sm_100f(sm_version=None): return sm_version == 100 or sm_version == 103 +@lru_cache(maxsize=1) +def is_flashinfer_gdn_supported_arch(sm_version=None): + """Whether FlashInfer ships GDN (gated-delta-rule) kernels for this arch. + + FlashInfer's GDN chunk-prefill and bf16-state decode kernels are built only + for Hopper (SM90) and datacenter Blackwell (SM100/SM103). On consumer + Blackwell (SM120) and other architectures the kernels abort at launch, so + callers must fall back to the vendored Triton kernels. + """ + if sm_version is None: + sm_version = get_sm_version() + return sm_version in (90, 100, 103) + + def print_all_stacks(): """Print stack traces for all threads""" for thread_id, frame in sys._current_frames().items(): diff --git a/tests/unittest/_torch/modules/mamba/test_flashinfer_chunk_gdn.py b/tests/unittest/_torch/modules/mamba/test_flashinfer_chunk_gdn.py index 929fa9612a97..834b76ea5ba1 100644 --- a/tests/unittest/_torch/modules/mamba/test_flashinfer_chunk_gdn.py +++ b/tests/unittest/_torch/modules/mamba/test_flashinfer_chunk_gdn.py @@ -27,6 +27,30 @@ def _supported_arch() -> bool: ) +# Arch-gating predicate (GPU-free) ----------------------------------------- + + +@pytest.mark.parametrize( + "sm_version, expected", + [ + (90, True), # Hopper + (100, True), # datacenter Blackwell (B200) + (103, True), # datacenter Blackwell (B300/GB200) + (120, False), # consumer Blackwell (RTX 5090 / PRO 6000) -> Triton + (121, False), # other consumer Blackwell -> Triton + (89, False), # Ada + (80, False), # Ampere + ], +) +def test_is_flashinfer_gdn_supported_arch(sm_version, expected): + """FlashInfer ships GDN prefill/decode kernels only for SM90/SM100/SM103; + every other arch (notably SM120) must fall back to Triton. Pure predicate, + no GPU required.""" + from tensorrt_llm._utils import is_flashinfer_gdn_supported_arch + + assert is_flashinfer_gdn_supported_arch(sm_version) is expected + + # Input factory ------------------------------------------------------------ @@ -338,32 +362,39 @@ def test_indexed_gather_inplace_scatter_matches_triton(): # Env-flag routing test (no GPU required) --------------------------------- -def test_gdn_mixer_default_uses_flashinfer_wrapper(monkeypatch): - """Default (no env): gdn_mixer imports the FlashInfer wrapper. - Opt-out (``TLLM_USE_FLASHINFER_GDN_PREFILL=0``) restores the Triton path. +def test_gdn_mixer_resolve_chunk_gated_delta_rule(monkeypatch): + """gdn_mixer resolves its prefill kernel lazily (``_resolve_chunk_gated_delta_rule``): + the FlashInfer wrapper when the env opt-in is set (default) *and* the arch is + supported (SM90/SM100/SM103), otherwise the vendored Triton kernel (env + opt-out, or an unsupported arch such as SM120). - Independent of GPU availability; only checks Python import wiring. + The arch predicate is monkeypatched so the routing is checked independent of + the actual GPU; only dispatch wiring is exercised (no kernel launch). """ - import importlib - - # Default — env unset, expect FlashInfer wrapper. - monkeypatch.delenv("TLLM_USE_FLASHINFER_GDN_PREFILL", raising=False) import tensorrt_llm._torch.modules.mamba.gdn_mixer as gdn_mixer - - importlib.reload(gdn_mixer) + from tensorrt_llm._torch.modules.fla.chunk import chunk_gated_delta_rule as triton_fn from tensorrt_llm._torch.modules.fla.flashinfer_chunk import ( - chunk_gated_delta_rule as wrapper_fn, + chunk_gated_delta_rule as flashinfer_fn, ) - assert gdn_mixer.chunk_gated_delta_rule is wrapper_fn - - # Opt out — env=0 restores the Triton path. - monkeypatch.setenv("TLLM_USE_FLASHINFER_GDN_PREFILL", "0") - importlib.reload(gdn_mixer) - from tensorrt_llm._torch.modules.fla.chunk import chunk_gated_delta_rule as triton_fn - - assert gdn_mixer.chunk_gated_delta_rule is triton_fn - - # Reset to default for subsequent tests in the same process. - monkeypatch.delenv("TLLM_USE_FLASHINFER_GDN_PREFILL", raising=False) - importlib.reload(gdn_mixer) + def resolve(env, arch_supported): + if env is None: + monkeypatch.delenv("TLLM_USE_FLASHINFER_GDN_PREFILL", raising=False) + else: + monkeypatch.setenv("TLLM_USE_FLASHINFER_GDN_PREFILL", env) + monkeypatch.setattr(gdn_mixer, "is_flashinfer_gdn_supported_arch", lambda: arch_supported) + gdn_mixer._resolve_chunk_gated_delta_rule.cache_clear() + return gdn_mixer._resolve_chunk_gated_delta_rule() + + # Default env + supported arch -> FlashInfer wrapper. + assert resolve(None, True) is flashinfer_fn + # Explicit opt-in + supported arch -> FlashInfer wrapper. + assert resolve("1", True) is flashinfer_fn + # Opt-out env -> Triton even on a supported arch. + assert resolve("0", True) is triton_fn + # Unsupported arch (e.g. SM120) -> Triton even with the default opt-in. + assert resolve(None, False) is triton_fn + + # Clear the cached resolution so later tests re-resolve against the real + # arch/env (monkeypatch restores the env var and predicate on teardown). + gdn_mixer._resolve_chunk_gated_delta_rule.cache_clear() From 6254f3a1612ca496e9f8edbfebd4c9c7529e637e Mon Sep 17 00:00:00 2001 From: "Yueh-Ting (eop) Chen" Date: Tue, 9 Jun 2026 13:54:47 +0700 Subject: [PATCH 070/119] [https://nvbugs/6255037][fix] Count DSA indexer K-cache correctly as UINT8 in KV cache size estimate (#15088) Signed-off-by: Yueh-Ting Chen --- .../_torch/attention_backend/sparse/dsa.py | 36 ++++++++++++------- 1 file changed, 23 insertions(+), 13 deletions(-) diff --git a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py index 204b81d95a58..68eadaf91725 100644 --- a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py +++ b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py @@ -2652,26 +2652,25 @@ def get_cache_size_per_token(model_config: ModelConfig, num_attention_layers = KVCacheManager._resolve_num_attention_layers( model_config, mapping, num_layers) + # MLA latent K cache: stored at the KV cache dtype (BF16/FP8). mem_per_token *= num_attention_layers * head_dim - # 1 for K, others for indexer K cache - head_dim_factor = (indexer_data_dim + - index_head_dim // quant_block_size * 4) / head_dim - kv_factor = 1 + head_dim_factor - mem_per_token *= kv_factor + # Indexer K cache: physically allocated as raw UINT8 in + # WindowBlockManager::allocatePools (poolDtype = kUINT8), so we assume + # 1 byte/element here -- it is NOT scaled by the KV cache dtype (unlike + # the latent above). The data-portion byte count already reflects fp8 vs + # fp4 via indexer_data_dim. + indexer_bytes_per_token = num_attention_layers * ( + indexer_data_dim + index_head_dim // quant_block_size * 4) + mem_per_token += indexer_bytes_per_token return mem_per_token def get_cache_bytes_per_token(self): """Compute actual cache bytes per token from instance configuration.""" - # self.kv_factor for K, others for indexer K cache. - # Under FP4 the indexer data portion is halved (two E2M1 codes per - # byte); scale bytes are unchanged. - indexer_data_dim = self.index_head_dim // 2 if self.use_fp4 else self.index_head_dim - head_dim_factor = (indexer_data_dim + self.index_head_dim // - self.quant_block_size * 4) / self.head_dim - kv_factor = self.kv_factor + head_dim_factor + # MLA latent K cache: stored at the KV cache dtype (self.dtype). The + # indexer K cache is added separately below. cache_size_per_token = math.ceil( - kv_factor * sum(self.num_kv_heads_per_layer) * self.head_dim) + self.kv_factor * sum(self.num_kv_heads_per_layer) * self.head_dim) if self.dtype not in (DataType.FP8, DataType.HALF, DataType.BF16, DataType.FLOAT, DataType.NVFP4): @@ -2684,4 +2683,15 @@ def get_cache_bytes_per_token(self): cache_size_per_token, quant_vector_size=16, scaling_factor_dtype=DataType.FP8) + + # Indexer K cache: physically allocated as raw UINT8 in + # WindowBlockManager::allocatePools (poolDtype = kUINT8), so we assume + # 1 byte/element here -- it is NOT scaled by the KV cache dtype (unlike + # the latent above). Under FP4 the indexer data portion is halved (two + # E2M1 codes per byte); the scale bytes are unchanged. + indexer_data_dim = self.index_head_dim // 2 if self.use_fp4 else self.index_head_dim + indexer_bytes_per_token = sum(self.num_kv_heads_per_layer) * ( + indexer_data_dim + self.index_head_dim // self.quant_block_size * 4) + cache_size_bytes_per_token += indexer_bytes_per_token + return cache_size_bytes_per_token From a90fd152a5e13a91fe7a91fa36c6c780cfbc1fdd Mon Sep 17 00:00:00 2001 From: yufeiwu-nv <230315618+yufeiwu-nv@users.noreply.github.com> Date: Tue, 9 Jun 2026 15:28:03 +0800 Subject: [PATCH 071/119] [https://nvbugs/6194812][test] Update llm_perf_core.yml to require a minimum of 4 GPUs and add new performance tests (#15090) Signed-off-by: yufeiwu-nv <230315618+yufeiwu-nv@users.noreply.github.com> --- tests/integration/test_lists/qa/llm_perf_core.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/integration/test_lists/qa/llm_perf_core.yml b/tests/integration/test_lists/qa/llm_perf_core.yml index bb395c156c45..82ad7e80fc91 100644 --- a/tests/integration/test_lists/qa/llm_perf_core.yml +++ b/tests/integration/test_lists/qa/llm_perf_core.yml @@ -22,7 +22,7 @@ llm_perf_core: supports_fp8: true ranges: system_gpu_count: - gte: 2 + gte: 4 tests: - perf/test_perf.py::test_perf[llama_v3.1_8b_instruct_fp8-bench-pytorch-float8-input_output_len:128,128] - perf/test_perf.py::test_perf[qwen3.5_9b-bench-pytorch-bfloat16-input_output_len:128,128] @@ -36,6 +36,10 @@ llm_perf_core: - perf/test_perf.py::test_perf[llama_v3.1_nemotron_nano_8b_fp8-bench-pytorch-float8-maxnt:5000-input_output_len:5000,500-reqs:8-con:1] - perf/test_perf.py::test_perf[llama_v3.1_nemotron_nano_8b_fp8-bench-pytorch-float8-input_output_len:500,2000-reqs:8-con:1] - perf/test_perf.py::test_perf[llama_v3.1_nemotron_nano_8b_fp8-bench-pytorch-float8-input_output_len:1000,1000-reqs:8-con:1] + # test overlap scheduler + # con:1 paired with a small model is an intentional design choice—it amplifies host-side overhead and simplifies execution timelines to the maximum extent. + - perf/test_perf.py::test_perf[qwen3_0.6b-bench-pytorch-bfloat16-maxnt:2048-input_output_len:8000,1000-reqs:256-con:1-pp:4-gpus:4] + - perf/test_perf.py::test_perf[qwen3_30b_a3b-bench-pytorch-bfloat16-maxnt:2048-input_output_len:8000,1000-reqs:256-con:1-pp:4-gpus:4] # 2: L40S, H100, H20, H200 From 34a94ee3ce788675a05121b2215dfa9db239dea1 Mon Sep 17 00:00:00 2001 From: Emma Qiao Date: Tue, 9 Jun 2026 16:03:41 +0800 Subject: [PATCH 072/119] [TRTLLMINF-112][infra] Reduce the waiting time between check node is online or not (#14819) Signed-off-by: EmmaQiaoCh --- jenkins/L0_Test.groovy | 98 +++++++++++++++++++++++++++--------------- 1 file changed, 63 insertions(+), 35 deletions(-) diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index 98ac748e5a42..b2c6c6d3ed23 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -651,46 +651,14 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, stage('Check If Node Is Online') { CloudManager.withSlurmSshCredentials(pipeline, partition.clusterName, cluster) { remote -> - def counter = 0 - // We submit the Slurm job with 5 hours timeout, and the K8S pod will be evicted after 22 hours. - // Let's use 15 hours to check if the node is online, and with 2 hours buffer. - while (!CloudManager.isNodeOnline(nodeName) && counter < 90) { - // Wait 10 minutes to check status of the node again - sleep(time: 10, unit: 'MINUTES') - // Avoid the node being stuck in the held state. - if (counter % 3 == 0) { - Utils.exec(pipeline, script: Utils.sshUserCmd(remote, "\"scontrol release ${slurmJobID} || true\""), numRetries: 3) - } - counter++ - // If entrypoint script fails to start, do not poll for agent connection + // Check the SLURM job once; if it is no longer active, raise a typed + // InfraFailure(SLURM) so the retry layer routes it via instanceof (scope=SLURM). + def checkSlurmJobActive = { try { SlurmConfig.checkJobStatus(pipeline, cluster, slurmJobID, remote) } catch (InterruptedException e) { throw e } catch (Exception e) { - // If the exception is about job being inactive, throw a typed - // InfraFailure(SLURM) so downstream consumers route via instanceof - // rather than substring matching the catalog. The " outer rethrows - // without retry. No double-budget consumption. if (e.message?.contains("is no longer active")) { throw new InfraFailure( "${e.message}. Check SLURM logs at /home/svc_tensorrt/slurm-logs/slurm-${slurmJobID}-${nodeName}.out on ${cluster.host}", @@ -701,6 +669,66 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, } } + // Phase 1: wait for the job to leave the queue (PENDING -> RUNNING), polling + // every 3 min. The whole loop runs in a SINGLE shell step so a long queue wait + // only adds one flow-node to the Blue Ocean graph (instead of one per iteration, + // which overflowed the per-stage step cap). Release the held job every 10 + // iterations (~30 min). 300 iterations * 3 min = 15h budget. + // Exit codes: 0 = job RUNNING, 3 = job no longer active, 4 = timed out. + def sacctStateCmd = Utils.sshUserCmd(remote, "\"sacct -j ${slurmJobID} --format=State -Pn --allocations\"") + def releaseCmd = Utils.sshUserCmd(remote, "\"scontrol release ${slurmJobID} || true\"") + def waitRc = pipeline.sh(returnStatus: true, script: """ + set +e + counter=0 + while [ \$counter -lt 300 ]; do + # Avoid the job being stuck in the held state. Release every 10 iterations (~30 min). + if [ \$(( counter % 10 )) -eq 0 ]; then + ${releaseCmd} || true + fi + STATE=\$(${sacctStateCmd} | head -1 | cut -d'|' -f1 | awk '{print \$1}') + echo "[node-wait] iteration \$counter: SLURM job ${slurmJobID} state='\$STATE'" + case "\$STATE" in + RUNNING|COMPLETING) + echo "[node-wait] SLURM job ${slurmJobID} is running." + exit 0 + ;; + PENDING|CONFIGURING|REQUEUED|RESIZING|SUSPENDED|SIGNALING|STOPPED|"") + # Still queued, or a transient sacct/ssh hiccup (empty state): keep waiting. + ;; + *) + echo "[node-wait] SLURM job ${slurmJobID} is no longer active (state='\$STATE')." + exit 3 + ;; + esac + counter=\$(( counter + 1 )) + # Wait 3 minutes before checking the job state again. + sleep 180 + done + echo "[node-wait] Timed out waiting for SLURM job ${slurmJobID} to start." + exit 4 + """) + + // If the job reached a terminal state while queued, confirm via the canonical + // status check so the exact typed InfraFailure(SLURM) is raised. + if (waitRc == 3) { + checkSlurmJobActive() + } + + // Phase 2: job is RUNNING; wait for the Jenkins agent to come online. isNodeOnline() + // and Thread.sleep() emit no flow-nodes, so poll every 30s without bloating Blue + // Ocean, and probe job status every ~3 min (every 6th iter) to fail fast if the + // job dies during bring-up. 120 * 30s = 1h. + if (waitRc == 0) { + def onlineCounter = 0 + while (!CloudManager.isNodeOnline(nodeName) && onlineCounter < 120) { + Thread.sleep(30L * 1000L) + if (onlineCounter % 6 == 0) { + checkSlurmJobActive() + } + onlineCounter++ + } + } + if (CloudManager.isNodeOnline(nodeName)) { node(nodeName) { sh """ From b85270381e46f9ae2553f795007993d822123f6e Mon Sep 17 00:00:00 2001 From: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Date: Tue, 9 Jun 2026 16:34:34 +0800 Subject: [PATCH 073/119] [None][infra] Waive 1 failed cases for main in pre-merge 41821 (#15135) Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index eb574454e059..2331769b8687 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -230,6 +230,7 @@ examples/test_whisper.py::test_llm_whisper_general[large-v3-disable_gemm_plugin- full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS] SKIP (https://nvbugs/6273850) full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] SKIP (https://nvbugs/6273850) full:A100/accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] SKIP (https://nvbugs/6239637) +full:A100X/llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_mtp SKIP (https://nvbugs/6287561) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_tensor] SKIP (https://nvbugs/5161074) full:B200/perf/test_perf.py::test_perf[quant:int8_sq_per_token_channel] SKIP (https://nvbugs/5161074) full:B200/perf/test_perf.py::test_perf[quant:w4a8_awq] SKIP (https://nvbugs/5161074) From 178f4e64efaf7d4c5fd9a695829e5d90b69bfd3e Mon Sep 17 00:00:00 2001 From: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> Date: Tue, 9 Jun 2026 17:20:07 +0800 Subject: [PATCH 074/119] [None][infra] CBTS Layer 3: pass test-db via Artifactory instead of env var (#15142) Signed-off-by: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> --- jenkins/L0_MergeRequest.groovy | 39 ++++++++++++++++------------------ jenkins/L0_Test.groovy | 29 ++++++++++++------------- 2 files changed, 31 insertions(+), 37 deletions(-) diff --git a/jenkins/L0_MergeRequest.groovy b/jenkins/L0_MergeRequest.groovy index 8c845ba32b2a..d24501dcf7bd 100644 --- a/jenkins/L0_MergeRequest.groovy +++ b/jenkins/L0_MergeRequest.groovy @@ -769,27 +769,24 @@ def getCbtsResult(pipeline, testFilter, globalVars) "Reasons: ${result.reasons.join('; ')}") return null } - // Piggyback input JSON on testFilter so each L0_Test stage agent can - // re-run main.py and regenerate cbts_test_db/ locally. The payload is - // base64-encoded because the raw JSON contains PR diffs and may include - // ${...} or {...} sequences that the Jenkins tokenmacro plugin would - // try to evaluate when the parent serializes globalVars for the - // Parameterized-Remote-Trigger plugin, raising MacroEvaluationException - // and blocking test dispatch. Capped at 256 KB (post-encoding, since - // that is what travels on the wire); oversize → drop piggyback, - // Layer 3 falls back to source. - final int CBTS_INPUT_PIGGYBACK_MAX_BYTES = 256000 - def inputJsonB64 = inputJson.bytes.encodeBase64().toString() - def inputJsonB64Size = inputJsonB64.length() - if (inputJsonB64Size <= CBTS_INPUT_PIGGYBACK_MAX_BYTES) { - result.cbts_input_json_b64 = inputJsonB64 - pipeline.echo("CBTS Layer 3: cbts_input_json_b64 piggyback enabled " + - "(${inputJsonB64Size} bytes encoded, ${inputJson.length()} bytes raw)") - } else { - pipeline.echo("CBTS Layer 3: cbts_input_json_b64 is ${inputJsonB64Size} bytes, " + - "exceeds ${CBTS_INPUT_PIGGYBACK_MAX_BYTES}-byte piggyback limit; " + - "downstream stages will fall back to source test-db " + - "(Layer 2 stage filtering still applies)") + // Upload the generated cbts_test_db/ to Artifactory so each L0_Test + // stage agent can download it directly instead of re-running main.py + // with the raw PR diff. This avoids passing large payloads as Jenkins + // parameters (env vars), which caused "Argument list too long" failures + // when diffs were large. Agents fall back to the source test-db if the + // download fails. + if (result.test_db_dir_override) { + try { + sh "tar czf /tmp/cbts_test_db.tar.gz -C ${LLM_ROOT} ${result.test_db_dir_override}" + trtllm_utils.uploadArtifacts("/tmp/cbts_test_db.tar.gz", "${UPLOAD_PATH}/cbts/") + result.cbts_test_db_artifact_path = "${UPLOAD_PATH}/cbts/cbts_test_db.tar.gz" + pipeline.echo("CBTS Layer 3: uploaded cbts_test_db to ${result.cbts_test_db_artifact_path}") + } catch (InterruptedException e) { + throw e + } catch (Exception e) { + pipeline.echo("CBTS Layer 3: artifact upload failed (${e.message}); " + + "agents will fall back to source test-db") + } } pipeline.echo("CBTS: scope=${result.scope}, " + "stages=${result.affected_stages.size()}") diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index b2c6c6d3ed23..a9504ad0d47a 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -29,6 +29,7 @@ LLM_ROOT = "llm" ARTIFACT_PATH = env.artifactPath ? env.artifactPath : "sw-tensorrt-generic/llm-artifacts/${JOB_NAME}/${BUILD_NUMBER}" UPLOAD_PATH = env.uploadPath ? env.uploadPath : "sw-tensorrt-generic/llm-artifacts/${JOB_NAME}/${BUILD_NUMBER}" +URM_ARTIFACTORY_BASE = "https://urm.nvidia.com/artifactory" X86_64_TRIPLE = "x86_64-linux-gnu" AARCH64_TRIPLE = "aarch64-linux-gnu" @@ -2490,28 +2491,24 @@ def renderTestDB(pipeline, testContext, llmSrc, stageName, preDefinedMakoOpts=nu } sh "pip3 install --extra-index-url https://urm.nvidia.com/artifactory/api/pypi/sw-tensorrt-pypi/simple --ignore-installed trt-test-db==1.8.5+bc6df7" - // CBTS Layer 3: regenerate cbts_test_db/ on this stage agent from the - // piggybacked input JSON if not already present. The piggyback payload is - // base64-encoded on the orchestrator (see getCbtsResult in - // L0_MergeRequest.groovy) to keep tokenmacro from interpreting ${...} or - // {...} fragments inside the PR diff when globalVars is serialized. If - // decoding or regeneration throws (truncated/malformed payload), we - // swallow the error: the override directory will be absent below, the - // overrideYaml check will fail, and renderTestDB falls back to the - // source test-db. + // CBTS Layer 3: download the pre-built cbts_test_db/ tarball that the + // orchestrator uploaded to Artifactory (see getCbtsResult in + // L0_MergeRequest.groovy). This avoids re-running main.py locally and + // avoids passing large PR-diff payloads as Jenkins parameters (env vars). + // If the download or extraction fails we swallow the error: the override + // directory will be absent below, the overrideYaml check will fail, and + // renderTestDB falls back to the source test-db. def cbts = testFilter[(CBTS_RESULT)] - if (cbts != null && cbts.test_db_dir_override && cbts.cbts_input_json_b64) { + if (cbts != null && cbts.test_db_dir_override && cbts.cbts_test_db_artifact_path) { def overrideDir = "${llmSrc}/${cbts.test_db_dir_override}" def dirExists = sh(returnStdout: true, script: "test -d ${overrideDir} && echo yes || echo no").trim() if (dirExists != "yes") { try { - def cbtsInputJson = new String(cbts.cbts_input_json_b64.decodeBase64()) - def cbtsInputLocal = Utils.createTempLocation(pipeline, "./cbts_input.json") - pipeline.writeFile(file: cbtsInputLocal, text: cbtsInputJson) - sh "apt-get update -qq && apt-get install -y -qq python3-yaml || true" - sh "cd ${llmSrc} && python3 jenkins/scripts/cbts/main.py ${cbtsInputLocal} > /dev/null 2>&1 || true" + def artifactUrl = "${URM_ARTIFACTORY_BASE}/${cbts.cbts_test_db_artifact_path}" + sh "wget -q '${artifactUrl}' -O /tmp/cbts_test_db.tar.gz && tar xzf /tmp/cbts_test_db.tar.gz -C ${llmSrc}" + echo "CBTS Layer 3: extracted cbts_test_db from artifact" } catch (Exception e) { - echo "CBTS Layer 3: failed to materialize piggyback payload " + + echo "CBTS Layer 3: artifact download failed " + "(${e.class.simpleName}: ${e.message}); falling back to source test-db" } } From 45e25230aad360c6ec6b4d046275fcf02f35af89 Mon Sep 17 00:00:00 2001 From: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> Date: Tue, 9 Jun 2026 17:38:14 +0800 Subject: [PATCH 075/119] [TRTLLM-13264][feat] Add native bias epilogue to NVFP4 GEMM (#15053) Signed-off-by: Yiyun Lu <55233584+luyiyun1021@users.noreply.github.com> --- cpp/tensorrt_llm/common/cublasMMWrapper.cpp | 11 +- cpp/tensorrt_llm/common/cublasMMWrapper.h | 6 +- .../fp4_gemm/fp4_gemm_template.h | 73 ++++++------ .../mxfp8_mxfp4_gemm_template_sm100.h | 14 ++- .../nvfp4_nvfp4_gemm_template_sm100.h | 17 +-- .../nvfp4_nvfp4_gemm_template_sm120.h | 15 +-- .../cutlass_kernels/include/fp4_gemm.h | 7 +- .../cudaCoreGemmNVFP4.cu | 17 ++- .../weightOnlyBatchedGemv/cudaCoreGemmNVFP4.h | 5 +- cpp/tensorrt_llm/thop/cublasFp4ScaledMM.cpp | 51 +++++++-- cpp/tensorrt_llm/thop/cudaNvfp4MM.cpp | 21 +++- cpp/tensorrt_llm/thop/fp4Gemm.cpp | 37 +++++-- .../_torch/custom_ops/cute_dsl_custom_ops.py | 9 ++ .../_torch/custom_ops/torch_custom_ops.py | 104 +++++++++++------- tensorrt_llm/_torch/modules/linear.py | 10 +- .../_torch/thop/parallel/test_fp4_linear.py | 50 +++++++++ 16 files changed, 315 insertions(+), 132 deletions(-) diff --git a/cpp/tensorrt_llm/common/cublasMMWrapper.cpp b/cpp/tensorrt_llm/common/cublasMMWrapper.cpp index 5cbe1b30d3f1..f3b14e5cafeb 100644 --- a/cpp/tensorrt_llm/common/cublasMMWrapper.cpp +++ b/cpp/tensorrt_llm/common/cublasMMWrapper.cpp @@ -570,16 +570,16 @@ float const* getBetaDevicePointer() // BlockScaleGemm Version 1: Default algorithm (uses first valid heuristic) void CublasMMWrapper::BlockScaleGemm(cublasOperation_t transa, cublasOperation_t transb, int const m, int const n, int const k, void const* A, int const lda, void const* B, int const ldb, void* C, int const ldc, void const* a_sf, - void const* b_sf, float const* alpha) + void const* b_sf, float const* alpha, void const* bias) { // Forward to the overloaded version with nullptr (use default algorithm) - BlockScaleGemm(transa, transb, m, n, k, A, lda, B, ldb, C, ldc, a_sf, b_sf, alpha, nullptr); + BlockScaleGemm(transa, transb, m, n, k, A, lda, B, ldb, C, ldc, a_sf, b_sf, alpha, nullptr, bias); } // BlockScaleGemm Version 2: Specified algorithm (unified implementation) void CublasMMWrapper::BlockScaleGemm(cublasOperation_t transa, cublasOperation_t transb, int const m, int const n, int const k, void const* A, int const lda, void const* B, int const ldb, void* C, int const ldc, void const* a_sf, - void const* b_sf, float const* alpha, cublasLtMatmulAlgo_t const* algo) + void const* b_sf, float const* alpha, cublasLtMatmulAlgo_t const* algo, void const* bias) { // Verify input data types (currently supports FP4, can be extended to more formats in the future) TLLM_CHECK_WITH_INFO(mAType == CUDA_R_4F_E2M1 && mBType == CUDA_R_4F_E2M1, @@ -607,6 +607,11 @@ void CublasMMWrapper::BlockScaleGemm(cublasOperation_t transa, cublasOperation_t // Set block-wise scaling descriptors setScaleDescriptors(const_cast(a_sf), const_cast(b_sf)); + if (bias != nullptr) + { + setBiasDescriptor(const_cast(bias)); + } + // Validate cuBLASLt handle TLLM_CHECK_WITH_INFO(mCublasLtHandle != nullptr, "cuBLASLt handle is null"); diff --git a/cpp/tensorrt_llm/common/cublasMMWrapper.h b/cpp/tensorrt_llm/common/cublasMMWrapper.h index 78a68204ea37..f2b6cab90ebe 100644 --- a/cpp/tensorrt_llm/common/cublasMMWrapper.h +++ b/cpp/tensorrt_llm/common/cublasMMWrapper.h @@ -92,12 +92,12 @@ class CublasMMWrapper // Uses default/heuristic algorithm void BlockScaleGemm(cublasOperation_t transa, cublasOperation_t transb, int const m, int const n, int const k, void const* A, int const lda, void const* B, int const ldb, void* C, int const ldc, void const* a_sf, - void const* b_sf, float const* alpha); + void const* b_sf, float const* alpha, void const* bias = nullptr); - // Uses specified algorithm (for autotuning) + // Uses specified algorithm (for autotuning). Optional `bias` fused via CUBLASLT_EPILOGUE_BIAS. void BlockScaleGemm(cublasOperation_t transa, cublasOperation_t transb, int const m, int const n, int const k, void const* A, int const lda, void const* B, int const ldb, void* C, int const ldc, void const* a_sf, - void const* b_sf, float const* alpha, cublasLtMatmulAlgo_t const* algo); + void const* b_sf, float const* alpha, cublasLtMatmulAlgo_t const* algo, void const* bias = nullptr); #endif void stridedBatchedGemm(cublasOperation_t transa, cublasOperation_t transb, int const m, int const n, int const k, diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/fp4_gemm_template.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/fp4_gemm_template.h index f868c4634478..854581349af0 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/fp4_gemm_template.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/fp4_gemm_template.h @@ -59,7 +59,7 @@ template , cute::Int<1>, cute::Int<1>, _1SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_2x1x1: return genericFp4GemmKernelLauncher, cute::Int<1>, cute::Int<1>, _2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_1x2x1: return genericFp4GemmKernelLauncher, cute::Int<2>, cute::Int<1>, _1SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_2x2x1: return genericFp4GemmKernelLauncher, cute::Int<2>, cute::Int<1>, _2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_1x4x1: return genericFp4GemmKernelLauncher, cute::Int<4>, cute::Int<1>, _1SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_4x2x1: return genericFp4GemmKernelLauncher, cute::Int<2>, cute::Int<1>, _2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_2x4x1: return genericFp4GemmKernelLauncher, cute::Int<4>, cute::Int<1>, _2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_4x4x1: return genericFp4GemmKernelLauncher, cute::Int<4>, cute::Int<1>, _2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; default: throw std::runtime_error( @@ -117,7 +117,7 @@ template size_t dispatchNVFP4xNVFP4GemmCTAShapeSm10x(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy = nullptr) + int* occupancy = nullptr, void const* bias = nullptr) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); @@ -130,7 +130,7 @@ size_t dispatchNVFP4xNVFP4GemmCTAShapeSm10x(T* D, void const* A, void const* B, case tkc::CutlassTileConfigSM100::CtaShape##M##x##N##x##K##B: \ return dispatchNVFP4xNVFP4GemmClusterShapeSm10x, cute::Int, cute::Int>(D, A, B, \ input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, \ - occupancy); + occupancy, bias); #define CTA_CASE_DEFAULT \ case tkc::CutlassTileConfigSM100::Undefined: \ throw std::runtime_error("[TensorRT-LLM Error][FP4][dispatch_gemm_cta_shape] Gemm config undefined."); \ @@ -175,7 +175,7 @@ template size_t dispatchNVFP4xNVFP4GemmClusterShapeSm120(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy = nullptr) + int* occupancy = nullptr, void const* bias = nullptr) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); @@ -185,7 +185,7 @@ size_t dispatchNVFP4xNVFP4GemmClusterShapeSm120(T* D, void const* A, void const* case tkc::ClusterShape::ClusterShape_1x1x1: return genericFp4GemmKernelLauncherSm120, cute::Int<1>, cute::Int<1>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; default: throw std::runtime_error( @@ -198,7 +198,7 @@ template size_t dispatchNVFP4xNVFP4GemmCTAShapeSm120(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy = nullptr) + int* occupancy = nullptr, void const* bias = nullptr) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); @@ -209,17 +209,17 @@ size_t dispatchNVFP4xNVFP4GemmCTAShapeSm120(T* D, void const* A, void const* B, case tkc::CutlassTileConfigSM120::CtaShape128x128x128B: return dispatchNVFP4xNVFP4GemmClusterShapeSm120, cute::Int<128>, cute::Int<128>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM120::CtaShape128x128x256B: return dispatchNVFP4xNVFP4GemmClusterShapeSm120, cute::Int<128>, cute::Int<256>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM120::CtaShape256x128x128B: return dispatchNVFP4xNVFP4GemmClusterShapeSm120, cute::Int<128>, cute::Int<128>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM120::Undefined: throw std::runtime_error("[TensorRT LLM Error][FP4][sm120][dispatch_gemm_cta_shape] Gemm config undefined."); @@ -240,7 +240,7 @@ template size_t dispatchMXFP8xMXFP4GemmClusterShapeSm100(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy = nullptr) + int* occupancy = nullptr, void const* bias = nullptr) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); @@ -250,27 +250,27 @@ size_t dispatchMXFP8xMXFP4GemmClusterShapeSm100(T* D, void const* A, void const* case tkc::ClusterShape::ClusterShape_2x1x1: return genericMXFP8xMXFP4GemmKernelLauncher, cute::Int<1>, cute::Int<1>, __2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_2x2x1: return genericMXFP8xMXFP4GemmKernelLauncher, cute::Int<2>, cute::Int<1>, __2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_4x2x1: return genericMXFP8xMXFP4GemmKernelLauncher, cute::Int<2>, cute::Int<1>, __2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_2x4x1: return genericMXFP8xMXFP4GemmKernelLauncher, cute::Int<4>, cute::Int<1>, __2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; case tkc::ClusterShape::ClusterShape_4x4x1: return genericMXFP8xMXFP4GemmKernelLauncher, cute::Int<4>, cute::Int<1>, __2SM>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, - stream, occupancy); + stream, occupancy, bias); break; default: throw std::runtime_error( @@ -283,7 +283,7 @@ template size_t dispatchMXFP8xMXFP4GemmCTAShapeSm100(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy = nullptr) + int* occupancy = nullptr, void const* bias = nullptr) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); @@ -292,22 +292,22 @@ size_t dispatchMXFP8xMXFP4GemmCTAShapeSm100(T* D, void const* A, void const* B, case tkc::CutlassTileConfigSM100::CtaShape128x64x128B: return dispatchMXFP8xMXFP4GemmClusterShapeSm100, cute::Int<64>, cute::Int<128>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM100::CtaShape128x256x128B: return dispatchMXFP8xMXFP4GemmClusterShapeSm100, cute::Int<256>, cute::Int<128>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM100::CtaShape128x128x256B: return dispatchMXFP8xMXFP4GemmClusterShapeSm100, cute::Int<128>, cute::Int<256>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM100::CtaShape128x256x256B: return dispatchMXFP8xMXFP4GemmClusterShapeSm100, cute::Int<256>, cute::Int<256>>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, - occupancy); + occupancy, bias); break; case tkc::CutlassTileConfigSM100::Undefined: throw std::runtime_error("[TensorRT LLM Error][FP4][dispatch_gemm_cta_shape] Gemm config undefined."); @@ -343,14 +343,14 @@ template size_t CutlassFp4GemmRunner::dispatchToArch(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, - int* occupancy) + int* occupancy, void const* bias) { if constexpr (fp4GemmType == FP4GemmType::W4A8_MXFP4_MXFP8) { if (mSm == 100 || mSm == 103) { return dispatchMXFP8xMXFP4GemmCTAShapeSm100(D, A, B, input_sf, weight_sf, global_sf, m, n, k, - batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy); + batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy, bias); } else { @@ -364,21 +364,21 @@ size_t CutlassFp4GemmRunner::dispatchToArch(T* D, void const* A, { #ifdef COMPILE_BLACKWELL_SM103_TMA_GEMMS return dispatchNVFP4xNVFP4GemmCTAShapeSm10x(D, A, B, input_sf, weight_sf, - global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy); + global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy, bias); #else return dispatchNVFP4xNVFP4GemmCTAShapeSm10x(D, A, B, input_sf, weight_sf, - global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy); + global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy, bias); #endif } else if (mSm == 100) { return dispatchNVFP4xNVFP4GemmCTAShapeSm10x(D, A, B, input_sf, weight_sf, - global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy); + global_sf, m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy, bias); } else if (mSm == 120 || mSm == 121) { return dispatchNVFP4xNVFP4GemmCTAShapeSm120(D, A, B, input_sf, weight_sf, global_sf, m, n, k, - batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy); + batch_count, gemmConfig, workspace, workspaceBytes, stream, occupancy, bias); } else { @@ -396,11 +396,12 @@ size_t CutlassFp4GemmRunner::dispatchToArch(T* D, void const* A, template void CutlassFp4GemmRunner::gemm(void* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, - tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream) + tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, + void const* bias) { TLLM_LOG_DEBUG(__PRETTY_FUNCTION__); CutlassFp4GemmRunner::dispatchToArch(reinterpret_cast(D), A, B, input_sf, weight_sf, global_sf, - m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream); + m, n, k, batch_count, gemmConfig, workspace, workspaceBytes, stream, /*occupancy=*/nullptr, bias); } template diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/mxfp8_mxfp4_gemm_template_sm100.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/mxfp8_mxfp4_gemm_template_sm100.h index 3970563bc10c..276de55c69dc 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/mxfp8_mxfp4_gemm_template_sm100.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/mxfp8_mxfp4_gemm_template_sm100.h @@ -91,7 +91,7 @@ template ; using LayoutB = cutlass::layout::ColumnMajor; static constexpr int AlignmentB = 128; - /* // Input C */ + /* // Input C: ElementC=void; per-N bias via LinCombPerColBias EVT. */ using ElementC = void; using LayoutC = cutlass::layout::RowMajor; static constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; @@ -131,7 +131,7 @@ struct DeviceGemmMXFP8xMXFP4GemmSm100 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder>::CollectiveOp; + cutlass::epilogue::fusion::LinCombPerColBias>::CollectiveOp; using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder typename Gemm::Arguments prepareGemmArgsSm100(void* D, void const* A, void const* B, void const* input_sf, - void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, dim3 prefered_cga, int XSM) + void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, dim3 prefered_cga, int XSM, + void const* bias = nullptr) { using Sm1xxBlkScaledConfig = typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig; using ElementA = typename Gemm::ElementA; @@ -186,6 +187,7 @@ typename Gemm::Arguments prepareGemmArgsSm100(void* D, void const* A, void const operator_args.mode = cutlass::gemm::GemmUniversalMode::kGemm; auto& fusion_args = operator_args.epilogue.thread; fusion_args.alpha_ptr = static_cast(global_sf); + fusion_args.bias_ptr = static_cast(bias); operator_args.problem_shape = cute::make_shape(m, n, k, batch_count); @@ -228,7 +230,7 @@ template ::value, cutlass::half_t, T>::type; @@ -243,7 +245,7 @@ size_t genericMXFP8xMXFP4GemmKernelLauncher(void* D, void const* A, void const* typename DeviceGemmMXFP8xMXFP4GemmSm100::Gemm; MXFP8xMXFP4GemmOperator gemm; auto args = prepareGemmArgsSm100(D, A, B, input_sf, weight_sf, global_sf, m, n, k, - batch_count, dim3(CGA_M{}, CGA_N{}, CGA_K{}), MXSMTypeAdapter::Scale); + batch_count, dim3(CGA_M{}, CGA_N{}, CGA_K{}), MXSMTypeAdapter::Scale, bias); /* // Check shared memory size; throw when SMEM exceeds */ int smem_size = int(sizeof(typename MXFP8xMXFP4GemmOperator::GemmKernel::SharedStorage)); static int mMaxSmemSize = tk::getMaxSharedMemoryPerBlockOptin(); diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm100.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm100.h index 277a16aa1b91..013dc4830312 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm100.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm100.h @@ -108,7 +108,7 @@ template size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, - size_t const workspaceBytes, cudaStream_t stream, int* occupancy) + size_t const workspaceBytes, cudaStream_t stream, int* occupancy, void const* bias = nullptr) { static_assert(always_false, "Kernel should be explicitly instantiated."); return 0; @@ -122,7 +122,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void cute::Int, cute::Int, cute::Int, cute::Int, XSM_>(void* D, void const* A, \ void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, \ int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, \ - cudaStream_t stream, int* occupancy) \ + cudaStream_t stream, int* occupancy, void const* bias) \ { \ throw std::runtime_error( \ "[TensorRT LLM Error][FP4 gemm Runner] TensorRT LLM is not compiled with support for this Architecture."); \ @@ -147,7 +147,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void using ElementB = ElementType; \ using LayoutB = cutlass::layout::ColumnMajor; \ static constexpr int AlignmentB = 128 / cutlass::sizeof_bits::value; \ - /* // Input C */ \ + /* // Input C: ElementC=void (no C-tile SMEM); per-N bias via LinCombPerColBias EVT below. */ \ using ElementC = void; \ using LayoutC = cutlass::layout::RowMajor; \ static constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; \ @@ -162,10 +162,12 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void using MainloopSchedule = SMTypeAdapter::MainloopSchedule; \ using MmaTileShape = cute::Shape::Scale>, cute::Int, \ cute::Int ? 3 : 1)>>; \ + /* D = alpha * Acc + bias[N]; bias_ptr=null resolves to null_default=0 (no-op). */ \ using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder>::CollectiveOp; \ + cutlass::epilogue::fusion::LinCombPerColBias>::CollectiveOp; \ \ using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder, LayoutA, AlignmentA, \ @@ -205,7 +207,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void typename Gemm::Arguments \ prepareGemmArgs_##ARCH_##_##T##_##CTA_M_##_##CTA_N_##_##CTA_K_##_##CGA_M_##_##CGA_N_##_##CGA_K_##XSM_(void* D, \ void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, \ - int n, int k, int batch_count) \ + int n, int k, int batch_count, void const* bias = nullptr) \ { \ using Sm1xxBlkScaledConfig = typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig; \ using ElementA = typename Gemm::ElementA; \ @@ -220,6 +222,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void operator_args.mode = cutlass::gemm::GemmUniversalMode::kGemm; \ auto& fusion_args = operator_args.epilogue.thread; \ fusion_args.alpha_ptr = static_cast(global_sf); \ + fusion_args.bias_ptr = static_cast(bias); \ \ operator_args.problem_shape = cute::make_shape(m, n, k, batch_count); \ \ @@ -263,7 +266,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void cute::Int, cute::Int, cute::Int, cute::Int, XSM_>(void* D, void const* A, \ void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, \ int batch_count, tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, \ - cudaStream_t stream, int* occupancy) \ + cudaStream_t stream, int* occupancy, void const* bias) \ { \ using ElementOutput__ = typename cutlass::platform::conditional::value, \ cutlass::half_t, T>::type; \ @@ -280,7 +283,7 @@ size_t genericFp4GemmKernelLauncher(void* D, void const* A, void const* B, void Fp4GemmOperator gemm; \ auto args \ = prepareGemmArgs_##ARCH_##_##T##_##CTA_M_##_##CTA_N_##_##CTA_K_##_##CGA_M_##_##CGA_N_##_##CGA_K_##XSM_< \ - Fp4GemmOperator>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count); \ + Fp4GemmOperator>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, bias); \ /* // Check shared memory size; throw when SMEM exceeds */ \ int smem_size = int(sizeof(typename Fp4GemmOperator::GemmKernel::SharedStorage)); \ static int mMaxSmemSize = tk::getMaxSharedMemoryPerBlockOptin(); \ diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm120.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm120.h index eaa3378acb0e..315d760bb417 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm120.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/fp4_gemm/nvfp4_nvfp4_gemm_template_sm120.h @@ -52,7 +52,7 @@ template , "Kernel should be explicitly instantiated."); return 0; @@ -66,7 +66,7 @@ size_t genericFp4GemmKernelLauncherSm120(void* D, void const* A, void const* B, cute::Int, cute::Int, cute::Int>(void* D, void const* A, void const* B, \ void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, \ tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, \ - int* occupancy) \ + int* occupancy, void const* bias) \ { \ throw std::runtime_error( \ "[TensorRT LLM Error][FP4 gemm Runner] TensorRT LLM is not compiled with support for this Architecture."); \ @@ -89,7 +89,7 @@ size_t genericFp4GemmKernelLauncherSm120(void* D, void const* A, void const* B, using ElementB = cutlass::float_e2m1_t; \ using LayoutB = cutlass::layout::ColumnMajor; \ static constexpr int AlignmentB = 16 * 8 / cutlass::sizeof_bits::value; \ - /* // Input C */ \ + /* // Input C: ElementC=void (no C-tile SMEM); per-N bias via LinCombPerColBias EVT. */ \ using ElementC = void; \ using LayoutC = cutlass::layout::ColumnMajor; \ using LayoutD = cutlass::layout::RowMajor; \ @@ -98,7 +98,7 @@ size_t genericFp4GemmKernelLauncherSm120(void* D, void const* A, void const* B, using ElementPairA = cutlass::nv_float4_t; \ using ElementPairB = cutlass::nv_float4_t; \ using SFType = cutlass::float_ue4m3_t; \ - using FusionOperation = cutlass::epilogue::fusion::LinearCombination; \ + using FusionOperation = cutlass::epilogue::fusion::LinCombPerColBias; \ using ElementCompute = float; \ using ElementAccumulator = float; \ using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder(global_sf); \ + fusion_args.bias_ptr = static_cast(bias); \ \ operator_args.problem_shape = cute::make_shape(m, n, k, batch_count); \ \ @@ -201,7 +202,7 @@ size_t genericFp4GemmKernelLauncherSm120(void* D, void const* A, void const* B, cute::Int, cute::Int, cute::Int>(void* D, void const* A, void const* B, \ void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, \ tkc::CutlassGemmConfig gemmConfig, char* workspace, const size_t workspaceBytes, cudaStream_t stream, \ - int* occupancy) \ + int* occupancy, void const* bias) \ { \ using ElementOutput__ = typename cutlass::platform::conditional::value, \ cutlass::half_t, T>::type; \ @@ -216,7 +217,7 @@ size_t genericFp4GemmKernelLauncherSm120(void* D, void const* A, void const* B, = DeviceGemmFp4GemmSm120_##T##_##CTA_M_##_##CTA_N_##_##CTA_K_##_##CGA_M_##_##CGA_N_##_##CGA_K_::Gemm; \ Fp4GemmOperator gemm; \ auto args = prepareGemmArgs_Sm120_##T##_##CTA_M_##_##CTA_N_##_##CTA_K_##_##CGA_M_##_##CGA_N_##_##CGA_K_< \ - Fp4GemmOperator>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count); \ + Fp4GemmOperator>(D, A, B, input_sf, weight_sf, global_sf, m, n, k, batch_count, bias); \ /* // Check shared memory size; throw when SMEM exceeds */ \ int smem_size = int(sizeof(typename Fp4GemmOperator::GemmKernel::SharedStorage)); \ static int mMaxSmemSize = tk::getMaxSharedMemoryPerBlockOptin(); \ diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/fp4_gemm.h b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/fp4_gemm.h index 944dbc0227da..b0e8a307df79 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/include/fp4_gemm.h +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/include/fp4_gemm.h @@ -54,7 +54,7 @@ class CutlassFp4GemmRunnerInterface virtual void gemm(void* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, - char* workspace, const size_t workspaceBytes, cudaStream_t stream) + char* workspace, const size_t workspaceBytes, cudaStream_t stream, void const* bias = nullptr) = 0; // Returns desired workspace size in bytes. @@ -78,7 +78,7 @@ class CutlassFp4GemmRunner : public virtual CutlassFp4GemmRunnerInterface void gemm(void* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, - char* workspace, const size_t workspaceBytes, cudaStream_t stream) override; + char* workspace, const size_t workspaceBytes, cudaStream_t stream, void const* bias = nullptr) override; // Returns desired workspace size in bytes. size_t getWorkspaceSize(int const m, int const n, int const k, int const batch_count) override; @@ -88,7 +88,8 @@ class CutlassFp4GemmRunner : public virtual CutlassFp4GemmRunnerInterface private: size_t dispatchToArch(T* D, void const* A, void const* B, void const* input_sf, void const* weight_sf, float const* global_sf, int m, int n, int k, int batch_count, tkc::CutlassGemmConfig gemmConfig, - char* workspace, const size_t workspaceBytes, cudaStream_t stream, int* occupancy = nullptr); + char* workspace, const size_t workspaceBytes, cudaStream_t stream, int* occupancy = nullptr, + void const* bias = nullptr); size_t getWorkspaceSizeImpl(int const m, int const n, int const k, int const batch_count); diff --git a/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.cu b/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.cu index 1d208a293b6c..8df4bf96b616 100644 --- a/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.cu +++ b/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.cu @@ -30,7 +30,7 @@ template __device__ void cudaCoreGemmImpl(InputType const* __restrict__ act, InputType const* __restrict__ weight, ScaleType const* __restrict__ scale_a, ScaleType const* __restrict__ scale_w, float const alpha, - OutputType* __restrict__ output, SizeType32 m, SizeType32 n, SizeType32 k) + OutputType* __restrict__ output, OutputType const* __restrict__ bias, SizeType32 m, SizeType32 n, SizeType32 k) { using VecType = int4; @@ -63,6 +63,7 @@ __device__ void cudaCoreGemmImpl(InputType const* __restrict__ act, InputType co act += tile_id_m * k / 2; weight += tile_id_n * k / 2; output += tile_id_m * n + tile_id_n; + OutputType const* __restrict__ bias_tile = (bias != nullptr) ? bias + tile_id_n : nullptr; scale_a += tile_id_m * k / nvfp4_scale_granularity; @@ -154,6 +155,10 @@ __device__ void cudaCoreGemmImpl(InputType const* __restrict__ act, InputType co { val += shmem[jj * TILE_M * TILE_N + ii]; } + if (bias_tile != nullptr) + { + val += static_cast(bias_tile[nid]); + } output[mid * n + nid] = static_cast(val); } @@ -166,13 +171,13 @@ template __global__ void cudaCoreGemmFp4(InputType const* __restrict__ act, InputType const* __restrict__ weight, ScaleType const* __restrict__ scale_a, ScaleType const* __restrict__ scale_w, float const* alpha_ptr, - OutputType* __restrict__ output, SizeType32 m, SizeType32 n, SizeType32 k) + OutputType* __restrict__ output, OutputType const* __restrict__ bias, SizeType32 m, SizeType32 n, SizeType32 k) { float alpha = alpha_ptr[0]; cudaCoreGemmImpl( reinterpret_cast(act), reinterpret_cast(weight), reinterpret_cast(scale_a), reinterpret_cast(scale_w), alpha, - reinterpret_cast(output), m, n, k); + reinterpret_cast(output), reinterpret_cast(bias), m, n, k); } template , reinterpret_cast(params.act), reinterpret_cast(params.weight), reinterpret_cast(params.scale_a), reinterpret_cast(params.scale_b), - params.alpha_ptr, reinterpret_cast(params.output), params.m, params.n, params.k)); + params.alpha_ptr, reinterpret_cast(params.output), + reinterpret_cast(params.bias), params.m, params.n, params.k)); } } else @@ -213,7 +219,8 @@ void cudaCoreGemmKernel(Params const& params, cudaStream_t stream) cudaCoreGemmFp4<<>>( reinterpret_cast(params.act), reinterpret_cast(params.weight), reinterpret_cast(params.scale_a), reinterpret_cast(params.scale_b), - params.alpha_ptr, reinterpret_cast(params.output), params.m, params.n, params.k); + params.alpha_ptr, reinterpret_cast(params.output), + reinterpret_cast(params.bias), params.m, params.n, params.k); } } } diff --git a/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.h b/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.h index d47d37c06aef..616f9d25c2bf 100644 --- a/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.h +++ b/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv/cudaCoreGemmNVFP4.h @@ -56,11 +56,13 @@ struct Params __nv_fp8_e4m3 const* scale_a; __nv_fp8_e4m3 const* scale_b; float const* alpha_ptr; + // Optional per-N bias broadcast: shape [n], same dtype as output. May be nullptr. + void const* bias; // used by torch flow Params(void const* _act, void const* _weight, void* _output, SizeType32 _m, SizeType32 _n, SizeType32 _k, __nv_fp8_e4m3 const* _scale_a, __nv_fp8_e4m3 const* _scale_b, cudaDataType_t _inputType, - cudaDataType_t _outputType, float const* _alpha_ptr) + cudaDataType_t _outputType, float const* _alpha_ptr, void const* _bias = nullptr) : act(_act) , weight(_weight) , output(_output) @@ -72,6 +74,7 @@ struct Params , scale_a(_scale_a) , scale_b(_scale_b) , alpha_ptr(_alpha_ptr) + , bias(_bias) { } }; diff --git a/cpp/tensorrt_llm/thop/cublasFp4ScaledMM.cpp b/cpp/tensorrt_llm/thop/cublasFp4ScaledMM.cpp index 760ca6e3dd70..a9ad46ad8f04 100644 --- a/cpp/tensorrt_llm/thop/cublasFp4ScaledMM.cpp +++ b/cpp/tensorrt_llm/thop/cublasFp4ScaledMM.cpp @@ -79,7 +79,8 @@ inline cudaDataType_t getCudaDataType(at::ScalarType dtype) } void cublas_fp4_gemm_caller(torch::Tensor& out, torch::Tensor const& a, torch::Tensor const& b, - torch::Tensor const& scale_a, torch::Tensor const& scale_b, torch::Tensor const& alpha) + torch::Tensor const& scale_a, torch::Tensor const& scale_b, torch::Tensor const& alpha, + c10::optional const& bias = c10::nullopt) { int32_t m = a.sizes()[0]; int32_t n = b.sizes()[0]; @@ -144,11 +145,26 @@ void cublas_fp4_gemm_caller(torch::Tensor& out, torch::Tensor const& a, torch::T // 3. Passing dimensions as (n, m, k) instead of (m, n, k) // 4. Swapping scaling factors to match (b_sf_ptr, a_sf_ptr) // Note: beta is always 0 and is managed internally by BlockScaleGemm + void const* bias_ptr = nullptr; + if (bias.has_value() && bias->defined()) + { + TLLM_CHECK_WITH_INFO(bias->is_cuda(), "bias must be a CUDA tensor for cuBLASLt epilogue"); + TLLM_CHECK_WITH_INFO( + bias->device() == out.device(), "bias must reside on the same CUDA device as the GEMM output"); + TLLM_CHECK_WITH_INFO(bias->is_contiguous(), "bias must be contiguous for cuBLASLt epilogue"); + TLLM_CHECK_WITH_INFO(bias->dim() == 1 && bias->size(0) == n, + "bias must be a 1-D tensor of shape [N] matching the GEMM output dim"); + TLLM_CHECK_WITH_INFO( + bias->scalar_type() == out.scalar_type(), "bias dtype must match output dtype for cuBLASLt epilogue"); + bias_ptr = bias->data_ptr(); + } + cublasWrapper->BlockScaleGemm(CUBLAS_OP_T, CUBLAS_OP_N, n, m, k, b_ptr, k, // B matrix (swapped to first position) a_ptr, k, // A matrix (swapped to second position) out_ptr, n, // Output: C[m, n] in row-major b_sf_ptr, a_sf_ptr, // Scaling factors (also swapped) - alpha_ptr); // Uses default algorithm (nullptr) + alpha_ptr, // Uses default algorithm (nullptr) + bias_ptr); // Optional bias } } // namespace @@ -176,10 +192,12 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder return static_cast(num_algos); } - // Run GEMM with specified tactic (-1 for default/best) + // Run GEMM with specified tactic (-1 for default/best). Optional `bias` + // is fused via CUBLASLT_EPILOGUE_BIAS. at::Tensor runGemm(at::Tensor const& mat1, at::Tensor const& mat2, at::Tensor const& mat1_scale, at::Tensor const& mat2_scale, at::Tensor const& alpha, int64_t output_buffer_kind, int64_t tactic, - c10::optional> group = c10::nullopt) const + c10::optional> group = c10::nullopt, + c10::optional bias = c10::nullopt) const { int m = mat1.size(0); int k_compressed = mat1.size(1); @@ -221,7 +239,8 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder // Execute GEMM (beta is always 0 and is managed internally) if (has_algo) { - cublas_fp4_gemm_caller_with_algo(out, mat1, mat2, mat1_scale, mat2_scale, alpha, *algo_ptr, mOutputDtype); + cublas_fp4_gemm_caller_with_algo( + out, mat1, mat2, mat1_scale, mat2_scale, alpha, *algo_ptr, mOutputDtype, bias); } else { @@ -230,7 +249,7 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder "CublasLtFP4GemmRunner: No valid algorithm found (tactic=%ld, available=%zu), falling back to default " "for shape (m=%d, n=%d, k=%d)", tactic, cache.heuristics.size(), m, n, k); - cublas_fp4_gemm_caller(out, mat1, mat2, mat1_scale, mat2_scale, alpha); + cublas_fp4_gemm_caller(out, mat1, mat2, mat1_scale, mat2_scale, alpha, bias); } return out; @@ -354,7 +373,8 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder // Helper function to run GEMM with a specific algorithm static void cublas_fp4_gemm_caller_with_algo(torch::Tensor& out, torch::Tensor const& a, torch::Tensor const& b, torch::Tensor const& scale_a, torch::Tensor const& scale_b, torch::Tensor const& alpha, - cublasLtMatmulAlgo_t const& algo, at::ScalarType output_dtype) + cublasLtMatmulAlgo_t const& algo, at::ScalarType output_dtype, + c10::optional const& bias = c10::nullopt) { int32_t m = a.sizes()[0]; int32_t n = b.sizes()[0]; @@ -409,6 +429,20 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder // 3. Passing dimensions as (n, m, k) instead of (m, n, k) // 4. Swapping scaling factors to match matrices (b_sf_ptr, a_sf_ptr) + void const* bias_ptr = nullptr; + if (bias.has_value() && bias->defined()) + { + TLLM_CHECK_WITH_INFO(bias->is_cuda(), "bias must be a CUDA tensor for cuBLASLt epilogue"); + TLLM_CHECK_WITH_INFO( + bias->device() == out.device(), "bias must reside on the same CUDA device as the GEMM output"); + TLLM_CHECK_WITH_INFO(bias->is_contiguous(), "bias must be contiguous for cuBLASLt epilogue"); + TLLM_CHECK_WITH_INFO(bias->dim() == 1 && bias->size(0) == n, + "bias must be a 1-D tensor of shape [N] matching the GEMM output dim"); + TLLM_CHECK_WITH_INFO( + bias->scalar_type() == out.scalar_type(), "bias dtype must match output dtype for cuBLASLt epilogue"); + bias_ptr = bias->data_ptr(); + } + // Use BlockScaleGemm with specified algorithm for autotuning // Note: beta is always 0 and is managed internally by BlockScaleGemm cublasWrapper->BlockScaleGemm(CUBLAS_OP_T, CUBLAS_OP_N, n, m, k, b_ptr, @@ -417,7 +451,8 @@ class CublasLtFP4GemmRunner : public torch::CustomClassHolder out_ptr, n, // Output: C[m, n] in row-major b_sf_ptr, a_sf_ptr, // Scaling factors (also swapped) alpha_ptr, // Alpha - &algo); // Use specified algorithm + &algo, // Use specified algorithm + bias_ptr); // Optional bias for CUBLASLT_EPILOGUE_BIAS } }; diff --git a/cpp/tensorrt_llm/thop/cudaNvfp4MM.cpp b/cpp/tensorrt_llm/thop/cudaNvfp4MM.cpp index b71270b1d08f..d98bfc53986e 100644 --- a/cpp/tensorrt_llm/thop/cudaNvfp4MM.cpp +++ b/cpp/tensorrt_llm/thop/cudaNvfp4MM.cpp @@ -36,7 +36,7 @@ namespace using tensorrt_llm::common::check; void cuda_core_nvfp4_gemm_caller(Tensor& out, Tensor const& a, Tensor const& b, Tensor const& scale_a, - Tensor const& scale_b, Tensor const& alpha, bool fast_acc = false) + Tensor const& scale_b, Tensor const& alpha, std::optional const& bias, bool fast_acc = false) { int32_t m = a.sizes()[0]; int32_t n = b.sizes()[0]; @@ -67,9 +67,22 @@ void cuda_core_nvfp4_gemm_caller(Tensor& out, Tensor const& a, Tensor const& b, cudaDataType_t alphaType = convert_torch_dtype(alpha.scalar_type()); TORCH_CHECK(alphaType == CUDA_R_32F); + void const* bias_ptr = nullptr; + if (bias.has_value()) + { + auto const& bias_tensor = *bias; + CHECK_TH_CUDA(bias_tensor); + TORCH_CHECK(bias_tensor.device() == out.device(), "bias must reside on the same CUDA device as the output"); + TORCH_CHECK(bias_tensor.is_contiguous(), "bias must be contiguous"); + TORCH_CHECK(bias_tensor.dim() == 1, "bias must be 1-D"); + TORCH_CHECK(bias_tensor.sizes()[0] == n, "bias size must equal n=", n); + TORCH_CHECK(bias_tensor.scalar_type() == out.scalar_type(), "bias dtype must match output dtype"); + bias_ptr = bias_tensor.data_ptr(); + } + tensorrt_llm::kernels::cuda_core_gemm_nvfp4::Params params(a_ptr, b_ptr, out_ptr, m, n, k, reinterpret_cast<__nv_fp8_e4m3 const*>(a_scale), reinterpret_cast<__nv_fp8_e4m3 const*>(b_scale), aType, - outType, reinterpret_cast(alpha_ptr)); + outType, reinterpret_cast(alpha_ptr), bias_ptr); bool dispatched = tensorrt_llm::kernels::cuda_core_gemm_nvfp4::cudaCoreGemmDispatcher(params, stream); TORCH_CHECK(dispatched, "Failed to dispatch cudaCoreGemmLauncher"); } @@ -94,12 +107,10 @@ Tensor& cuda_core_nvfp4_gemm_out(Tensor const& mat_a, Tensor const& mat_b, Tenso TORCH_CHECK(mat_a.sizes()[1] == mat_b.sizes()[1]); TORCH_CHECK(mat_b.sizes()[0] == out.sizes()[1]); - TORCH_CHECK(!bias.has_value(), "bias is not support yet"); - TORCH_CHECK(scale_a.dtype() == SF_DTYPE); TORCH_CHECK(scale_b.dtype() == SF_DTYPE); - cuda_core_nvfp4_gemm_caller(out, mat_a, mat_b, scale_a, scale_b, alpha, true); + cuda_core_nvfp4_gemm_caller(out, mat_a, mat_b, scale_a, scale_b, alpha, bias, true); return out; } diff --git a/cpp/tensorrt_llm/thop/fp4Gemm.cpp b/cpp/tensorrt_llm/thop/fp4Gemm.cpp index 98ceb358c590..f0066c714624 100644 --- a/cpp/tensorrt_llm/thop/fp4Gemm.cpp +++ b/cpp/tensorrt_llm/thop/fp4Gemm.cpp @@ -96,7 +96,7 @@ tkc::CutlassGemmConfig getDefaultGemmConfig(int64_t m, int64_t n, int64_t k, FP4 template void runGemm(at::Tensor& out, at::Tensor const& mat1, at::Tensor const& mat2, at::Tensor const& mat1Scale, at::Tensor const& mat2Scale, at::Tensor const& globalScale, int64_t m, int64_t n, int64_t k, int64_t batch_count, - tkc::CutlassGemmConfig const& gemmConfig, FP4GemmType fp4GemmType) + tkc::CutlassGemmConfig const& gemmConfig, FP4GemmType fp4GemmType, void const* bias_ptr = nullptr) { if (fp4GemmType == FP4GemmType::W4A8_MXFP4_MXFP8) { @@ -107,7 +107,8 @@ void runGemm(at::Tensor& out, at::Tensor const& mat1, at::Tensor const& mat2, at gemmRunner.gemm(out.data_ptr(), mat1.const_data_ptr(), mat2.const_data_ptr(), mat1Scale.const_data_ptr(), mat2Scale.const_data_ptr(), globalScale.data_ptr(), m, n, k, batch_count, gemmConfig, - reinterpret_cast(workspace.data_ptr()), wsBytes, at::cuda::getCurrentCUDAStream(mat1.get_device())); + reinterpret_cast(workspace.data_ptr()), wsBytes, at::cuda::getCurrentCUDAStream(mat1.get_device()), + bias_ptr); } else if (fp4GemmType == FP4GemmType::W4A4_NVFP4_NVFP4) { @@ -118,7 +119,8 @@ void runGemm(at::Tensor& out, at::Tensor const& mat1, at::Tensor const& mat2, at gemmRunner.gemm(out.data_ptr(), mat1.const_data_ptr(), mat2.const_data_ptr(), mat1Scale.const_data_ptr(), mat2Scale.const_data_ptr(), globalScale.data_ptr(), m, n, k, batch_count, gemmConfig, - reinterpret_cast(workspace.data_ptr()), wsBytes, at::cuda::getCurrentCUDAStream(mat1.get_device())); + reinterpret_cast(workspace.data_ptr()), wsBytes, at::cuda::getCurrentCUDAStream(mat1.get_device()), + bias_ptr); } } @@ -133,7 +135,8 @@ void runGemm(at::Tensor& out, at::Tensor const& mat1, at::Tensor const& mat2, at at::Tensor fp4_bmm_impl(at::Tensor const& mat1, at::Tensor const& mat2, at::Tensor const& mat1Scale, at::Tensor const& mat2Scale, at::Tensor const& globalScale, FP4GemmType fp4GemmType, std::optional out_dtype, int64_t output_buffer_kind, - tkc::CutlassGemmConfig const* maybe_config = nullptr, c10::optional> group = c10::nullopt) + tkc::CutlassGemmConfig const* maybe_config = nullptr, c10::optional> group = c10::nullopt, + std::optional const& bias = std::nullopt) { if (fp4GemmType == FP4GemmType::W4A8_MXFP4_MXFP8) { @@ -198,16 +201,31 @@ at::Tensor fp4_bmm_impl(at::Tensor const& mat1, at::Tensor const& mat2, at::Tens std::vector out_shape = mat1.dim() == 2 ? std::vector{m, n} : std::vector{b, m, n}; auto [out, _] = torch_ext::allocate_output( out_shape, out_dtype.value(), mat1.device(), static_cast(output_buffer_kind), group); + + void const* bias_ptr = nullptr; + if (bias.has_value()) + { + auto const& bias_tensor = *bias; + CHECK_TH_CUDA(bias_tensor); + TORCH_CHECK(bias_tensor.device() == out.device(), "bias must reside on the same CUDA device as the output"); + TORCH_CHECK(bias_tensor.is_contiguous(), "bias must be contiguous"); + TORCH_CHECK(bias_tensor.dim() == 1, "bias must be 1-D"); + TORCH_CHECK(bias_tensor.sizes()[0] == n, "bias size must equal n=", n); + TORCH_CHECK(bias_tensor.scalar_type() == out.scalar_type(), "bias dtype must match output dtype"); + bias_ptr = bias_tensor.const_data_ptr(); + } + switch (out_dtype.value()) { case at::ScalarType::Half: - runGemm(out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType); + runGemm(out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType, bias_ptr); break; case at::ScalarType::BFloat16: - runGemm<__nv_bfloat16>(out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType); + runGemm<__nv_bfloat16>( + out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType, bias_ptr); break; case at::ScalarType::Float: - runGemm(out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType); + runGemm(out, mat1, mat2, mat1Scale, mat2Scale, globalScale, m, n, k, b, config, fp4GemmType, bias_ptr); break; default: C10_THROW_ERROR(NotImplementedError, "out_dtype must be one of fp16/bf16/fp32."); } @@ -278,7 +296,8 @@ class FP4GemmRunner : public torch::CustomClassHolder at::Tensor runGemm(at::Tensor const& mat1, at::Tensor const& mat2, at::Tensor const& mat1Scale, at::Tensor const& mat2Scale, at::Tensor const& globalScale, int64_t output_buffer_kind, int64_t configIdx, - c10::optional> group = c10::nullopt) const + c10::optional> group = c10::nullopt, + std::optional const& bias = std::nullopt) const { tkc::CutlassGemmConfig const* config = nullptr; if (configIdx != -1) @@ -287,7 +306,7 @@ class FP4GemmRunner : public torch::CustomClassHolder config = &mConfigs.at(configIdx); } return fp4_bmm_impl(mat1, mat2, mat1Scale, mat2Scale, globalScale, mfp4GemmType, mOutputDtype, - output_buffer_kind, config, group); + output_buffer_kind, config, group, bias); } at::ScalarType getOutputDtype() const diff --git a/tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py b/tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py index 681cc3d3a0d1..efdfb0640c9e 100644 --- a/tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py @@ -529,6 +529,7 @@ def forward( self, inputs: List[torch.Tensor], tactic, + bias: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: """ @@ -542,6 +543,8 @@ def forward( inputs[3]: Weight scale tensor of shape (n, k//16), dtype: fp8. inputs[4]: Alpha scaling factor. dtype: float32. tactic: Tiling and cluster strategy, typically a tuple (mma_tiler_mn, cluster_shape_mn). + bias: Optional per-N bias [N]. Added post-GEMM inside the + custom op (native CuTeDSL epilogue fusion is a follow-up). Returns: torch.Tensor: Output tensor of shape (m, n), dtype: bf16. @@ -758,6 +761,12 @@ def forward( if swap_ab: c_tensor = c_tensor.permute(1, 0) + if bias is not None: + if bias.ndim != 1 or bias.shape[0] != c_tensor.shape[-1]: + raise ValueError( + f"bias must be a 1-D tensor of shape [N]={c_tensor.shape[-1]}, " + f"got shape {tuple(bias.shape)}") + c_tensor = c_tensor + bias return c_tensor # a/b: fp4, scale: fp8, output: bf16 diff --git a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py index 8189606adbc6..0170e0417112 100644 --- a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py @@ -558,9 +558,10 @@ def forward( self, inputs: List[torch.Tensor], tactic: int = -1, + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: mat1, mat2, mat1_scale, mat2_scale, global_scale = inputs - return self.fp4_gemm_runner.run_gemm( + out = self.fp4_gemm_runner.run_gemm( mat1, mat2, mat1_scale, @@ -569,7 +570,9 @@ def forward( self.output_buffer_kind, tactic, self.group, + bias, ) + return out class CublasLtFP4GemmRunner(TunableRunner): @@ -621,6 +624,7 @@ def forward( self, inputs: List[torch.Tensor], tactic: int = -1, + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: mat1, mat2, mat1_scale, mat2_scale, alpha = inputs result = self.cublaslt_runner.run_gemm( @@ -632,6 +636,7 @@ def forward( self.output_buffer_kind, tactic, self.group, + bias, ) return result @@ -688,6 +693,7 @@ def forward( self, inputs: List[torch.Tensor], tactic: int = -1, + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: act_fp4, weight, act_sf, weight_scale, alpha = inputs @@ -697,14 +703,13 @@ def forward( act_sf_unswizzled = torch.ops.trtllm.block_scale_interleave_reverse( act_sf.view((m + 128 - 1) // 128 * 128, -1)) - # Call CUDA Core NVFP4 GEMM result = torch.ops.trtllm.cuda_core_nvfp4_gemm( act_fp4, weight, scale_a=act_sf_unswizzled, scale_b=weight_scale, alpha=alpha, - bias=None, + bias=bias, out_dtype=self.output_dtype, output_buffer_kind=self.output_buffer_kind, group=self.group, @@ -714,16 +719,20 @@ def forward( @torch.library.custom_op("trtllm::nvfp4_gemm_cublaslt", mutates_args=()) def nvfp4_gemm_cublaslt( - act_fp4: torch.Tensor, - weight: torch.Tensor, - act_sf: torch.Tensor, - weight_scale: torch.Tensor, - alpha: torch.Tensor, - output_dtype: torch.dtype, - output_buffer_kind: int = int(BufferKind.DEFAULT), + act_fp4: torch.Tensor, + weight: torch.Tensor, + act_sf: torch.Tensor, + weight_scale: torch.Tensor, + alpha: torch.Tensor, + output_dtype: torch.dtype, + output_buffer_kind: int = int(BufferKind.DEFAULT), + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """cuBLASLt-based NVFP4 GEMM with heuristic-based auto-tuning. + Args: + bias: Optional per-N bias [N], fused via CUBLASLT_EPILOGUE_BIAS. + Note: This function is primarily used internally by nvfp4_gemm. Direct usage is discouraged. Consider using nvfp4_gemm instead @@ -742,24 +751,27 @@ def nvfp4_gemm_cublaslt( [nvfp4_gemm_runner], nvfp4_gemm_runner.tuning_config, [act_fp4, weight, act_sf, weight_scale, alpha], + bias=bias, ) result = nvfp4_gemm_runner( inputs=[act_fp4, weight, act_sf, weight_scale, alpha], - tactic=best_tactic) + tactic=best_tactic, + bias=bias) return result @nvfp4_gemm_cublaslt.register_fake def _( - act_fp4: torch.Tensor, - weight: torch.Tensor, - act_sf: torch.Tensor, - weight_scale: torch.Tensor, - alpha: torch.Tensor, - output_dtype: torch.dtype, - output_buffer_kind: int = int(BufferKind.DEFAULT), + act_fp4: torch.Tensor, + weight: torch.Tensor, + act_sf: torch.Tensor, + weight_scale: torch.Tensor, + alpha: torch.Tensor, + output_dtype: torch.dtype, + output_buffer_kind: int = int(BufferKind.DEFAULT), + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: return act_fp4.new_empty((act_fp4.size(0), weight.size(0)), dtype=output_dtype) @@ -767,16 +779,21 @@ def _( @torch.library.custom_op("trtllm::nvfp4_gemm_cutlass", mutates_args=()) def nvfp4_gemm_cutlass( - act_fp4: torch.Tensor, - weight: torch.Tensor, - act_sf: torch.Tensor, - weight_scale: torch.Tensor, - alpha: torch.Tensor, - output_dtype: torch.dtype, - output_buffer_kind: int = int(BufferKind.DEFAULT), + act_fp4: torch.Tensor, + weight: torch.Tensor, + act_sf: torch.Tensor, + weight_scale: torch.Tensor, + alpha: torch.Tensor, + output_dtype: torch.dtype, + output_buffer_kind: int = int(BufferKind.DEFAULT), + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """CUTLASS-based NVFP4 GEMM with auto-tuning. + Args: + bias: Optional per-N bias [N], fused via the CUTLASS + LinCombPerColBias epilogue. + Note: This function is primarily used internally by nvfp4_gemm. Direct usage is discouraged. Consider using nvfp4_gemm instead @@ -794,22 +811,25 @@ def nvfp4_gemm_cutlass( [nvfp4_gemm_runner], nvfp4_gemm_runner.tuning_config, [act_fp4, weight, act_sf, weight_scale, alpha], + bias=bias, ) return nvfp4_gemm_runner( inputs=[act_fp4, weight, act_sf, weight_scale, alpha], - tactic=best_tactic) + tactic=best_tactic, + bias=bias) @nvfp4_gemm_cutlass.register_fake def _( - act_fp4: torch.Tensor, - weight: torch.Tensor, - act_sf: torch.Tensor, - weight_scale: torch.Tensor, - alpha: torch.Tensor, - output_dtype: torch.dtype, - output_buffer_kind: int = int(BufferKind.DEFAULT), + act_fp4: torch.Tensor, + weight: torch.Tensor, + act_sf: torch.Tensor, + weight_scale: torch.Tensor, + alpha: torch.Tensor, + output_dtype: torch.dtype, + output_buffer_kind: int = int(BufferKind.DEFAULT), + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: return act_fp4.new_empty((act_fp4.size(0), weight.size(0)), dtype=output_dtype) @@ -961,6 +981,7 @@ def forward( tactic: Union[ Tuple, int] = -1, # tuple: (backend name, sub_tactic_id), or int: -1 for fallback + bias: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: # Handle fallback tactic on cache miss @@ -977,22 +998,27 @@ def forward( return CudaCoreNVFP4Runner(self.output_buffer_kind, self.output_dtype, group=self.group)(inputs, - tactic=sub_tactic) + tactic=sub_tactic, + bias=bias) elif backend == "cutlass": return FP4GemmRunner(fp4_utils.FP4GemmType.W4A4_NVFP4_NVFP4, self.output_buffer_kind, self.output_dtype, - group=self.group)(inputs, tactic=sub_tactic) + group=self.group)(inputs, + tactic=sub_tactic, + bias=bias) elif backend == "cublaslt": return CublasLtFP4GemmRunner(self.output_buffer_kind, self.output_dtype, group=self.group)(inputs, - tactic=sub_tactic) + tactic=sub_tactic, + bias=bias) elif backend == "cutedsl": return CuteDSLNVFP4BlackwellRunner(self.output_dtype, self.output_buffer_kind, self.group)(inputs, - tactic=sub_tactic) + tactic=sub_tactic, + bias=bias) else: raise ValueError(f"Invalid tactic: {tactic}") @@ -1008,6 +1034,7 @@ def nvfp4_gemm( output_buffer_kind: int = int(BufferKind.DEFAULT), allowed_backends: str = "cutlass,cublaslt,cuda_core", group: Optional[List[int]] = None, + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Unified NVFP4 GEMM with automatic backend selection. @@ -1077,6 +1104,7 @@ def nvfp4_gemm( NVFP4GemmUnifiedRunner. tuning_config, # All runners use the same tuning_config [act_fp4, weight, act_sf, weight_scale, alpha], + bias=bias, ) except IndexError as e: # Provide more helpful error message @@ -1091,6 +1119,7 @@ def nvfp4_gemm( return runner( inputs=[act_fp4, weight, act_sf, weight_scale, alpha], tactic=best_tactic, + bias=bias, ) @@ -1105,6 +1134,7 @@ def _( output_buffer_kind: int = int(BufferKind.DEFAULT), allowed_backends: str = "cutlass,cublaslt,cuda_core", group: Optional[List[int]] = None, + bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Fake implementation for torch.compile support.""" return act_fp4.new_empty((act_fp4.size(0), weight.size(0)), diff --git a/tensorrt_llm/_torch/modules/linear.py b/tensorrt_llm/_torch/modules/linear.py index 84a695b1f974..aae3f3a65ab2 100644 --- a/tensorrt_llm/_torch/modules/linear.py +++ b/tensorrt_llm/_torch/modules/linear.py @@ -1404,6 +1404,11 @@ def apply(self, module: Linear, input: torch.Tensor, group = (module.mapping.tp_group if output_buffer_kind == int(BufferKind.NCCL_WINDOW) and module.mapping is not None else None) + # Fuse bias inside the GEMM op when N is unpadded and the output is a + # plain buffer; otherwise fall back to post-op `out + bias` below. + fuse_bias_in_gemm = (bias is not None + and output_buffer_kind == int(BufferKind.DEFAULT) + and module.weight.shape[0] == module.out_features) output = torch.ops.trtllm.nvfp4_gemm( act_fp4, module.weight, @@ -1413,7 +1418,8 @@ def apply(self, module: Linear, input: torch.Tensor, module.dtype, output_buffer_kind=output_buffer_kind, allowed_backends=allowed_backends_str, - group=group) + group=group, + bias=bias if fuse_bias_in_gemm else None) # Take the dim of out_features if padded. Make sure the output is contiguous if output.shape[-1] > module.out_features: output = output[..., :module.out_features].contiguous() @@ -1421,7 +1427,7 @@ def apply(self, module: Linear, input: torch.Tensor, if original_shape is not None: output = output.reshape(*original_shape[:-1], output.shape[-1]) - if bias is not None: + if bias is not None and not fuse_bias_in_gemm: output = output + bias return output diff --git a/tests/unittest/_torch/thop/parallel/test_fp4_linear.py b/tests/unittest/_torch/thop/parallel/test_fp4_linear.py index a7647c277cf1..339131905b63 100644 --- a/tests/unittest/_torch/thop/parallel/test_fp4_linear.py +++ b/tests/unittest/_torch/thop/parallel/test_fp4_linear.py @@ -767,3 +767,53 @@ def test_fp4_linear_cuda_core(dtype, mnk): # nvfp4_gemm_perf_test(torch.bfloat16, m, 7168, 65792) # nvfp4_gemm_perf_test(torch.bfloat16, m, 227368, 2560, test_ref=False) # nvfp4_gemm_perf_test(torch.bfloat16, m, 2560, 113664) + + +def _make_nvfp4_inputs_for_bias_test(m, n, k, dtype=torch.bfloat16): + """Quantize random bf16 act+weight; return (act_fp4, weight_fp4, act_sf, weight_sf, alpha).""" + torch.manual_seed(0) + act = torch.randn(m, k, dtype=dtype, device="cuda") + weight = torch.randn(n, k, dtype=dtype, device="cuda") + act_gs = torch.tensor([1.0], dtype=torch.float32, device="cuda") + w_gs = torch.tensor([1.0], dtype=torch.float32, device="cuda") + act_fp4, act_sf = torch.ops.trtllm.fp4_quantize(act, act_gs, 16, False, + True) + w_fp4, w_sf = torch.ops.trtllm.fp4_quantize(weight, w_gs, 16, False, True) + alpha = (1.0 / act_gs * 1.0 / w_gs).to(torch.float32).reshape(()) + return act_fp4, w_fp4, act_sf, w_sf, alpha + + +@skip_pre_blackwell +@pytest.mark.parametrize("backend", ["cutlass", "cublaslt", "cuda_core"]) +@pytest.mark.parametrize("mnk", [(8, 4096, 4096), (252, 2048, 2048), + (1024, 4096, 4096), (4096, 6144, 4096)]) +def test_fp4_gemm_bias_per_backend(backend, mnk): + """Per-backend numerical parity: nvfp4_gemm(bias=B) ≈ nvfp4_gemm(bias=None) + B.""" + m, n, k = mnk + if backend == "cuda_core" and m > 8: + pytest.skip("cuda_core backend only supports M <= 8") + + act_fp4, w_fp4, act_sf, w_sf, alpha = _make_nvfp4_inputs_for_bias_test( + m, n, k) + bias = torch.randn(n, dtype=torch.bfloat16, device="cuda") * 0.5 + + out_no_bias = torch.ops.trtllm.nvfp4_gemm(act_fp4, + w_fp4, + act_sf, + w_sf, + alpha, + torch.bfloat16, + allowed_backends=backend) + ref = out_no_bias + bias + + out_fused = torch.ops.trtllm.nvfp4_gemm(act_fp4, + w_fp4, + act_sf, + w_sf, + alpha, + torch.bfloat16, + allowed_backends=backend, + bias=bias) + + # bf16 1-ULP at cast boundary (~0.0039) — fused vs gemm+add differs by ULP-level rounding. + torch.testing.assert_close(out_fused, ref, rtol=1e-2, atol=5e-3) From 2ee96cf153bbaf4132a2e677cd3898d8933fc6e1 Mon Sep 17 00:00:00 2001 From: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> Date: Tue, 9 Jun 2026 18:36:19 +0800 Subject: [PATCH 076/119] [https://nvbugs/6278380][unwaive] unwaive ad cases (#15148) Signed-off-by: Ivy Zhang <25222398+crazydemo@users.noreply.github.com> --- tests/integration/test_lists/waives.txt | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 2331769b8687..6a5179f2c5ea 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -14,10 +14,8 @@ accuracy/test_llm_api.py::TestMistralNemo12B::test_fp8 SKIP (https://nvbugs/5413 accuracy/test_llm_api_autodeploy.py::TestGemma4MoE::test_bf16 SKIP (https://nvbugs/6158397) accuracy/test_llm_api_autodeploy.py::TestGemmaE2B::test_gemma4_e2b_it SKIP (https://nvbugs/6194934) accuracy/test_llm_api_autodeploy.py::TestMiniMaxM2::test_finegrained_fp8 SKIP (https://nvbugs/6158397) -accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[deepseek-ai_DeepSeek-R1-0528-True] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_autodeploy.py::TestModelRegistryAccuracy::test_autodeploy_from_registry[nvidia_Llama-3.1-8B-Instruct-NVFP4-True] SKIP (https://nvbugs/6245279) accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_bf16_small[4] SKIP (https://nvbugs/6158397) -accuracy/test_llm_api_autodeploy.py::TestQwen3_5_397B_MoE::test_nvfp4[8] SKIP (https://nvbugs/6278380) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] SKIP (https://nvbugs/6191524) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput] SKIP (https://nvbugs/6084775) accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_mtp] SKIP (https://nvbugs/6029882) From 104b9d71bba5a7e34d555006630b477f256e4685 Mon Sep 17 00:00:00 2001 From: Eran Geva <19514940+MrGeva@users.noreply.github.com> Date: Tue, 9 Jun 2026 13:44:42 +0300 Subject: [PATCH 077/119] [https://nvbugs/6244474][fix] AutoDeploy: Remove llama perf test from CI (#15107) Signed-off-by: Eran Geva <19514940+MrGeva@users.noreply.github.com> --- tests/integration/test_lists/test-db/l0_h100.yml | 2 -- tests/integration/test_lists/waives.txt | 1 - 2 files changed, 3 deletions(-) diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index 4f568c96fbb5..5d73133cf6be 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -504,8 +504,6 @@ l0_h100: - examples/test_ad_speculative_decoding.py::test_eagle_wrapper_forward[2] - examples/test_ad_speculative_decoding.py::test_nemotron_mtp_model_with_weights - examples/test_ad_guided_decoding.py::test_autodeploy_guided_decoding_main_json - # ------------- AutoDeploy Perf Sanity --------------- - - perf/test_perf_sanity.py::test_e2e[aggr_upload-llama3_1_8b_fp8_ad_hopper-llama3_1_8b_ad_ws1_1k1k] TIMEOUT (120) - condition: ranges: system_gpu_count: diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index 6a5179f2c5ea..dcb280edabd4 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -326,7 +326,6 @@ perf/test_perf.py::test_perf[whisper_large_v3-bench-float16-input_output_len:128 perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_dep8_mtp1_8k1k] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_blackwell-v32_fp4_tep8_mtp3_8k1k] SKIP (https://nvbugs/6280721) perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_v32_fp4_grace_blackwell-v32_fp4_tep4_mtp3_8k1k] SKIP (https://nvbugs/6280721) -perf/test_perf_sanity.py::test_e2e[aggr_upload-llama3_1_8b_fp8_ad_hopper-llama3_1_8b_ad_ws1_1k1k] SKIP (https://nvbugs/6281573) perf/test_perf_sanity.py::test_e2e[aggr_upload-super_ad_blackwell-super_ad_ws1_1k1k] SKIP (https://nvbugs/6153575) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-r1-fp4_128k8k_con128_ctx1_pp8_gen1_dep16_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6215844) perf/test_perf_sanity.py::test_e2e[disagg_upload-e2e-gb200_deepseek-v32-fp4_1k1k_con2048_ctx1_dep4_gen1_dep4_eplb0_mtp1_ccb-NIXL] SKIP (https://nvbugs/6280649) From ba6ba1f6a0c460c1685c77bcf4aab2dd3d2e72b3 Mon Sep 17 00:00:00 2001 From: xingfei xi <95731198+xxi-nv@users.noreply.github.com> Date: Tue, 9 Jun 2026 19:49:02 +0800 Subject: [PATCH 078/119] [https://nvbugs/6212252][fix] Select CUTLASS MoE backend on non-Blackwell SMs in TestQwen3_5_35B_A3B::test_fp8 (#15081) Signed-off-by: xxi --- tests/integration/defs/accuracy/test_llm_api_pytorch.py | 9 ++++++++- tests/integration/test_lists/waives.txt | 1 - 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch.py b/tests/integration/defs/accuracy/test_llm_api_pytorch.py index a9d7bd7167fc..17daf7c6c4b8 100644 --- a/tests/integration/defs/accuracy/test_llm_api_pytorch.py +++ b/tests/integration/defs/accuracy/test_llm_api_pytorch.py @@ -6104,7 +6104,14 @@ def test_fp8(self, enable_block_reuse, mocker): kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.8, enable_block_reuse=enable_block_reuse) - moe_config = MoeConfig(backend='DEEPGEMM') + # DeepGEMM MoE kernels only support datacenter Blackwell (SM100/SM103). + # Fall back to the CUTLASS MoE backend (which supports FP8 block scales) + # on other architectures such as Hopper (SM90) and consumer Blackwell + # (SM120/SM121); otherwise the unsupported kernel trips a scale-factor + # dtype assertion at warmup. + moe_backend = "DEEPGEMM" if get_sm_version() in (100, + 103) else "CUTLASS" + moe_config = MoeConfig(backend=moe_backend) cuda_graph_config = CudaGraphConfig(enable_padding=True, max_batch_size=128) with LLM(model_dir, diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index dcb280edabd4..860224ff4280 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -140,7 +140,6 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_fp8[throughput_laten accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.5-fp8kv=False] SKIP (https://nvbugs/6260915) accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.5-fp8kv=True] SKIP (https://nvbugs/6248783) accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_4gpus[target_sparsity_0.9-fp8kv=False] SKIP (https://nvbugs/6260915) -accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_fp8[enable_block_reuse=False] SKIP (https://nvbugs/6212252) accuracy/test_llm_api_pytorch.py::TestQwen3_5_397B_A17B::test_nvfp4[tep4_cutedsl] SKIP (https://nvbugs/6255417) accuracy/test_llm_api_pytorch.py::TestQwen3_5_4B::test_bf16 SKIP (https://nvbugs/6283537) accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_off] SKIP (https://nvbugs/6212250) From d62085198c05da36c4e05481a9771bdc8d0a4e99 Mon Sep 17 00:00:00 2001 From: Zhenhua Wang <4936589+zhenhuaw-me@users.noreply.github.com> Date: Tue, 9 Jun 2026 21:19:29 +0800 Subject: [PATCH 079/119] [TRTLLM-13302][feat] Register NVIDIA Wan2.2-T2V quantized checkpoints (#15093) Signed-off-by: Zhenhua Wang Signed-off-by: Zhenhua Wang <4936589+zhenhuaw-me@users.noreply.github.com> --- .../visual_gen/models/cosmos3/pipeline_cosmos3.py | 2 +- .../_torch/visual_gen/models/wan/pipeline_wan.py | 2 ++ tensorrt_llm/visual_gen/visual_gen.py | 14 ++++++++++---- 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py index 2977bcb64a61..732dc5d4fa9d 100644 --- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py +++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/pipeline_cosmos3.py @@ -57,8 +57,8 @@ hf_ids=[ "nvidia/Cosmos3-Nano", "nvidia/Cosmos3-Super", - "nvidia/Cosmos3-Super-Text2Image", "nvidia/Cosmos3-Super-Image2Video", + "nvidia/Cosmos3-Super-Text2Image", ], doc="Cosmos3 Omnimodal world models.", ) diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py index 7442f581f958..1cda852093f1 100644 --- a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py +++ b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py @@ -85,6 +85,8 @@ "Wan-AI/Wan2.1-T2V-14B-Diffusers", "Wan-AI/Wan2.2-T2V-A14B-Diffusers", "Wan-AI/Wan2.2-TI2V-5B-Diffusers", + "nvidia/Wan2.2-T2V-A14B-Diffusers-FP8", + "nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4", ], doc="Wan 2.1 & 2.2 text-to-video family.", ) diff --git a/tensorrt_llm/visual_gen/visual_gen.py b/tensorrt_llm/visual_gen/visual_gen.py index c61acdbf190b..3073f708adf7 100644 --- a/tensorrt_llm/visual_gen/visual_gen.py +++ b/tensorrt_llm/visual_gen/visual_gen.py @@ -680,11 +680,17 @@ class VisualGen: def supported_models(cls) -> List[str]: """Return canonical HuggingFace model IDs of every registered pipeline. - Fine-tunes inherit the parent's Diffusers ``_class_name`` and dispatch - automatically without needing to appear in this list. The result is - a fresh list — mutating it does not affect the underlying registry. + The returned list is a *subset* of the variants each pipeline can + actually run. It typically contains the original official upstream + checkpoints and well-known optimized checkpoints (e.g. NVIDIA NVFP4 / + FP8 quantizations published on HuggingFace) that have been tested. + Other variants — community fine-tunes and quantizations not + enumerated here while some of them may run if no model architecture + changes. + + IDs are returned sorted alphabetically for stable. """ - return [hf_id for entry in PIPELINE_REGISTRY.values() for hf_id in entry.hf_ids] + return sorted(hf_id for entry in PIPELINE_REGISTRY.values() for hf_id in entry.hf_ids) @classmethod @set_api_status("prototype") From 484e6c96c318c8e4b2d8b98d9b9f54689ad8b42f Mon Sep 17 00:00:00 2001 From: Zhenhua Wang <4936589+zhenhuaw-me@users.noreply.github.com> Date: Tue, 9 Jun 2026 21:29:10 +0800 Subject: [PATCH 080/119] [None][chore] add VisualGen team as the codeowner of the VisualGen Attention (#15150) Signed-off-by: Zhenhua Wang <4936589+zhenhuaw-me@users.noreply.github.com> --- .github/CODEOWNERS | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index eb248da25149..3e34cf383e49 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -45,8 +45,8 @@ ## TensorRT-LLM Pytorch - VisualGen /tensorrt_llm/_torch/visual_gen @NVIDIA/trt-llm-torch-visual-gen-devs -/tensorrt_llm/_torch/visual_gen/attention_backend @NVIDIA/trt-llm-torch-attention-devs -/tensorrt_llm/_torch/visual_gen/modules/attention.py @NVIDIA/trt-llm-torch-attention-devs +/tensorrt_llm/_torch/visual_gen/attention_backend @NVIDIA/trt-llm-torch-attention-devs @NVIDIA/trt-llm-torch-visual-gen-devs +/tensorrt_llm/_torch/visual_gen/modules/attention.py @NVIDIA/trt-llm-torch-attention-devs @NVIDIA/trt-llm-torch-visual-gen-devs /tensorrt_llm/visual_gen @NVIDIA/trt-llm-llmapi-devs /tests/integration/defs/examples/test_visual_gen.py @NVIDIA/trt-llm-torch-visual-gen-devs /tests/integration/defs/visual_gen @NVIDIA/trt-llm-torch-visual-gen-devs From 487330e8a03351d9eea96da088c407ce20c5a439 Mon Sep 17 00:00:00 2001 From: Yihan Wang Date: Tue, 9 Jun 2026 21:57:02 +0800 Subject: [PATCH 081/119] [None][feat] Default on FlashInferTrtllmGenAttention (#14618) Signed-off-by: Yihan Wang --- cpp/tensorrt_llm/nanobind/thop/bindings.cpp | 21 +- .../thop/trtllmGenQKVProcessOp.cpp | 30 +- .../_torch/attention_backend/trtllm.py | 28 +- .../_torch/attention_backend/trtllm_gen.py | 1567 +++++++++-------- 4 files changed, 842 insertions(+), 804 deletions(-) diff --git a/cpp/tensorrt_llm/nanobind/thop/bindings.cpp b/cpp/tensorrt_llm/nanobind/thop/bindings.cpp index 993de4743193..08e1578bcfc1 100644 --- a/cpp/tensorrt_llm/nanobind/thop/bindings.cpp +++ b/cpp/tensorrt_llm/nanobind/thop/bindings.cpp @@ -34,11 +34,12 @@ namespace tensorrt_llm::nanobind::thop namespace { -nb::object optionalTensorToObject(std::optional const& tensor) +template +nb::object optionalToObject(std::optional const& value) { - if (tensor.has_value()) + if (value.has_value()) { - return nb::cast(*tensor); + return nb::cast(*value); } return nb::none(); } @@ -72,9 +73,9 @@ nb::tuple trtllmGenContextPreprocessBinding(torch::Tensor qkv_input, torch::Tens total_num_blocks, kv_factor, need_build_kv_cache_metadata); }(); - return nb::make_tuple(std::get<0>(result), optionalTensorToObject(std::get<1>(result)), - optionalTensorToObject(std::get<2>(result)), optionalTensorToObject(std::get<3>(result)), - optionalTensorToObject(std::get<4>(result)), optionalTensorToObject(std::get<5>(result)), std::get<6>(result), + return nb::make_tuple(std::get<0>(result), optionalToObject(std::get<1>(result)), + optionalToObject(std::get<2>(result)), optionalToObject(std::get<3>(result)), + optionalToObject(std::get<4>(result)), optionalToObject(std::get<5>(result)), std::get<6>(result), std::get<7>(result), std::get<8>(result), std::get<9>(result), std::get<10>(result), std::get<11>(result)); } @@ -108,9 +109,9 @@ nb::tuple trtllmGenGenerationPreprocessBinding(torch::Tensor qkv_input, torch::T need_build_kv_cache_metadata); }(); - return nb::make_tuple(std::get<0>(result), optionalTensorToObject(std::get<1>(result)), - optionalTensorToObject(std::get<2>(result)), optionalTensorToObject(std::get<3>(result)), std::get<4>(result), - std::get<5>(result), std::get<6>(result), optionalTensorToObject(std::get<7>(result)), std::get<8>(result), + return nb::make_tuple(std::get<0>(result), optionalToObject(std::get<1>(result)), + optionalToObject(std::get<2>(result)), optionalToObject(std::get<3>(result)), std::get<4>(result), + std::get<5>(result), std::get<6>(result), optionalToObject(std::get<7>(result)), std::get<8>(result), std::get<9>(result), std::get<10>(result), std::get<11>(result)); } @@ -305,7 +306,7 @@ void initBindings(nb::module_& m) auto const mapping = torch_ext::readKvCachePoolMapping(host_kv_cache_pool_mapping, layer_idx); blockTables = kv_cache_block_offsets.select(0, mapping.poolIndex).narrow(0, batch_start, batch_size); } - return nb::make_tuple(nb::cast(kvPool), nb::cast(blockTables), optionalTensorToObject(kvScalePool)); + return nb::make_tuple(nb::cast(kvPool), nb::cast(blockTables), optionalToObject(kvScalePool)); }, nb::arg("host_kv_cache_pool_pointers"), nb::arg("host_kv_cache_pool_mapping"), nb::arg("kv_cache_block_offsets"), nb::arg("layer_idx"), nb::arg("num_kv_heads"), nb::arg("tokens_per_block"), diff --git a/cpp/tensorrt_llm/thop/trtllmGenQKVProcessOp.cpp b/cpp/tensorrt_llm/thop/trtllmGenQKVProcessOp.cpp index b1401f8d4bec..9302e2a4978f 100644 --- a/cpp/tensorrt_llm/thop/trtllmGenQKVProcessOp.cpp +++ b/cpp/tensorrt_llm/thop/trtllmGenQKVProcessOp.cpp @@ -50,8 +50,6 @@ using tensorrt_llm::runtime::TorchUtils; namespace { -constexpr size_t kFlashinferTrtllmGenCounterWorkspaceSize = 8 * 1024 * 1024; - int64_t computeWindowLeft( int64_t const cyclicAttentionWindowSize, int64_t const maxKvLength, int64_t const attentionChunkSize) { @@ -81,22 +79,6 @@ cudaStream_t currentStreamFor(at::Tensor const& tensor) return at::cuda::getCurrentCUDAStream(tensor.get_device()).stream(); } -void zeroFlashinferTrtllmGenCounterWorkspaceAsync(at::Tensor const& workspace, cudaStream_t stream) -{ - // FlashInfer reserves the first 8 MiB of the trtllm-gen workspace for - // multi-CTA KV semaphores. The remaining scratch space is overwritten by - // the FMHA kernels and does not need to be cleared. - auto const workspaceBytes = static_cast(workspace.nbytes()); - auto const counterBytes = workspaceBytes < kFlashinferTrtllmGenCounterWorkspaceSize - ? workspaceBytes - : kFlashinferTrtllmGenCounterWorkspaceSize; - if (counterBytes == 0) - { - return; - } - check_cuda_error(cudaMemsetAsync(workspace.data_ptr(), 0, counterBytes, stream)); -} - struct WorkspaceAccessor { uint8_t* base{}; @@ -291,7 +273,7 @@ trtllmGenContextPreprocess(torch::Tensor qkv_input, torch::Tensor workspace, tor TORCH_CHECK(host_kv_cache_pool_mapping.has_value(), "host_kv_cache_pool_mapping is required."); TORCH_CHECK(kv_cache_block_offsets.has_value(), "kv_cache_block_offsets is required."); - bool const separateQKvOutput = paged_context_fmha; + bool const separateQKvOutput = paged_context_fmha || fp8_context_fmha; auto const qkvScalarType = qkv_input.scalar_type(); auto const qkvElementSize = static_cast(qkv_input.element_size()); auto const quantMode = tensorrt_llm::common::QuantMode(static_cast(kv_cache_quant_mode)); @@ -403,7 +385,7 @@ trtllmGenContextPreprocess(torch::Tensor qkv_input, torch::Tensor workspace, tor qkvParams.position_embedding_type = static_cast(position_embedding_type); qkvParams.position_shift_enabled = false; qkvParams.cache_type = cacheTypeFromQuantMode(quantMode); - qkvParams.separate_q_kv_output = paged_context_fmha; + qkvParams.separate_q_kv_output = separateQKvOutput; qkvParams.quantized_fp8_output = fp8_context_fmha; qkvParams.generation_phase = false; qkvParams.multi_processor_count = static_cast(multi_processor_count); @@ -454,8 +436,8 @@ trtllmGenContextPreprocess(torch::Tensor qkv_input, torch::Tensor workspace, tor qProcessed = qkv_input.slice(1, 0, num_heads * head_size).view({num_tokens, num_heads, head_size}); } - zeroFlashinferTrtllmGenCounterWorkspaceAsync(views.trtllmGenWorkspace, stream); - + // FlashInfer paged context launches trtllm-gen with multi-CTA-KV mode disabled, so it does not + // consume the counter slab reserved at the head of the workspace. auto const windowLeft = computeWindowLeft(cyclic_attention_window_size, max_past_kv_length, attention_chunk_size); return {qProcessed, kvPool, blockTables, kvScalePool, views.fmhaBmm1Scale, views.fmhaBmm2Scale, views.trtllmGenWorkspace, views.cuQSeqlens, views.cuKvSeqlens, input_seq_length, max_past_kv_length, @@ -481,6 +463,7 @@ void trtllmGenContextPostprocess(torch::Tensor qkv_input, torch::Tensor workspac auto const qkvScalarType = qkv_input.scalar_type(); auto const qkvElementSize = static_cast(qkv_input.element_size()); auto const quantMode = tensorrt_llm::common::QuantMode(static_cast(kv_cache_quant_mode)); + bool const separateQKvOutput = paged_context_fmha || fp8_context_fmha; auto const ptrs = [&] { auto const layout = TrtllmAttentionWorkspaceManager::buildContextLayout( @@ -548,7 +531,7 @@ void trtllmGenContextPostprocess(torch::Tensor qkv_input, torch::Tensor workspac qkvParams.position_embedding_type = static_cast(position_embedding_type); qkvParams.position_shift_enabled = false; qkvParams.cache_type = cacheTypeFromQuantMode(quantMode); - qkvParams.separate_q_kv_output = paged_context_fmha; + qkvParams.separate_q_kv_output = separateQKvOutput; qkvParams.quantized_fp8_output = fp8_context_fmha; qkvParams.generation_phase = false; qkvParams.multi_processor_count = static_cast(multi_processor_count); @@ -767,7 +750,6 @@ trtllmGenGenerationPreprocess(torch::Tensor qkv_input, torch::Tensor workspace, } auto qProcessed = views.qBuf.view({num_tokens, num_heads, head_size}); - zeroFlashinferTrtllmGenCounterWorkspaceAsync(views.trtllmGenWorkspace, stream); auto const windowLeft = computeWindowLeft(cyclic_attention_window_size, max_past_kv_length, attention_chunk_size); return {qProcessed, kvPool, blockTables, kvScalePool, views.bmm1Scale, views.bmm2Scale, views.trtllmGenWorkspace, diff --git a/tensorrt_llm/_torch/attention_backend/trtllm.py b/tensorrt_llm/_torch/attention_backend/trtllm.py index 216e2a95df5c..0f01774e9a49 100644 --- a/tensorrt_llm/_torch/attention_backend/trtllm.py +++ b/tensorrt_llm/_torch/attention_backend/trtllm.py @@ -29,7 +29,7 @@ # Enable TRTLLM-Gen attention backend by default. Set # TRTLLM_ENABLE_TRTLLM_GEN_ATTENTION=0 to force the thop.attention path. _TRTLLM_ENABLE_TRTLLM_GEN_ATTENTION = (os.environ.get( - "TRTLLM_ENABLE_TRTLLM_GEN_ATTENTION", "0") == "1") + "TRTLLM_ENABLE_TRTLLM_GEN_ATTENTION", "1") == "1") # ``AttentionForwardArgs`` fields that this backend does not consume. # Sync test (test_attention_op_sync.py) requires every other field to map to a @@ -1515,30 +1515,26 @@ def _run( assert metadata.kv_cache_manager is None assert metadata.num_contexts == metadata.num_seqs - helix_active = metadata.helix_position_offsets is not None - use_sage_attn = (forward_args.sage_attn_num_elts_per_blk_q > 0 - or forward_args.sage_attn_num_elts_per_blk_k > 0 - or forward_args.sage_attn_num_elts_per_blk_v > 0) - use_trtllm_gen = False if _TRTLLM_ENABLE_TRTLLM_GEN_ATTENTION: trtllm_gen_backend = self._get_trtllm_gen_backend() use_trtllm_gen = trtllm_gen_backend.is_supported( q, - metadata=metadata, - forward_args=forward_args, - mask_type=int(forward_args.mask_type), - active_helix=helix_active, - use_sage_attn=use_sage_attn, + k, + v, + attn=self, + meta=metadata, + fwd=forward_args, )[0] if use_trtllm_gen: - trtllm_gen_backend.attention( + trtllm_gen_backend.forward( q, - metadata=metadata, - forward_args=forward_args, - mask_type=int(forward_args.mask_type), - use_paged_context_fmha=metadata.use_paged_context_fmha, + k, + v, + attn=self, + meta=metadata, + fwd=forward_args, ) else: # Every kwarg sources from ``self`` / ``metadata`` / diff --git a/tensorrt_llm/_torch/attention_backend/trtllm_gen.py b/tensorrt_llm/_torch/attention_backend/trtllm_gen.py index a7fb8ccfa2fe..bbb6f22ffb1d 100644 --- a/tensorrt_llm/_torch/attention_backend/trtllm_gen.py +++ b/tensorrt_llm/_torch/attention_backend/trtllm_gen.py @@ -1,3 +1,18 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """ TrtLLM-Gen Attention Backend @@ -9,24 +24,22 @@ - QKV preprocessing & RoPE: C++ kernels via tensorrt_llm.bindings.internal.thop, same as thop.attention. Writes K/V to paged KV cache via pool pointers. - Attention: flashinfer trtllm-gen FMHA kernels, reading KV cache through - the KV cache manager carried by attention metadata. + the paged KV cache fields carried by FmhaParams. Entry points: FlashInferTrtllmGenAttention.is_supported() - Check if trtllm-gen can handle the given config. - FlashInferTrtllmGenAttention.attention() - Main attention method (called from TrtllmAttention.run). + FlashInferTrtllmGenAttention.forward() - Main attention method. Example: backend = FlashInferTrtllmGenAttention(attention_layer=...) - supported, reason = backend.is_supported( - q, metadata=..., forward_args=..., ...) + supported, reason = backend.is_supported(q, k, v, attn=..., meta=..., fwd=...) if supported: - backend.attention(q, metadata=..., forward_args=..., ...) + backend.forward(q, k, v, attn=..., meta=..., fwd=...) else: Fallback to thop.attention() """ import math -import weakref from dataclasses import dataclass from functools import lru_cache from typing import TYPE_CHECKING, List, Optional, Tuple @@ -39,12 +52,10 @@ import flashinfer from tensorrt_llm._torch.attention_backend.interface import AttentionForwardArgs, AttentionInputType -from tensorrt_llm._utils import get_sm_version, is_sm_100f +from tensorrt_llm._utils import get_sm_version, is_sm_100f, torch_dtype_to_binding from tensorrt_llm.bindings import DataType from tensorrt_llm.bindings.internal import thop from tensorrt_llm.functional import AttentionMaskType -from tensorrt_llm.logger import logger -from tensorrt_llm.models.modeling_utils import QuantConfig from tensorrt_llm.quantization.mode import QuantMode if TYPE_CHECKING: @@ -54,283 +65,163 @@ ) -class TrtllmGenSupportChecker: - """ - Validates if a configuration is supported by trtllm-gen backend. - - Implements all checks from the original C++ AttentionOp to determine - if trtllm-gen kernel can handle the attention computation. - """ - - # Supported data types - SUPPORTED_INPUT_DTYPES = {torch.float16, torch.bfloat16, torch.float8_e4m3fn} - SUPPORTED_KV_CACHE_DTYPES = { - DataType.HALF, - DataType.BF16, - DataType.FP8, - DataType.NVFP4, - } - SUPPORTED_OUT_DTYPES = {torch.float16, torch.bfloat16, torch.float8_e4m3fn} - - # Supported Q:KV:O dtype combinations for trtllm-gen kernels - # Format: (q_dtype: torch.dtype, kv_dtype: DataType, o_dtype: torch.dtype) - # Context phase supported combinations - SUPPORTED_DTYPE_COMBOS_CONTEXT = { - # e4m3:e4m3:e4m3 - (torch.float8_e4m3fn, DataType.FP8, torch.float8_e4m3fn), - # fp16:fp16:fp16 - (torch.float16, DataType.HALF, torch.float16), - # bf16:bf16:bf16 - (torch.bfloat16, DataType.BF16, torch.bfloat16), - # e4m3:e4m3:fp16 - (torch.float8_e4m3fn, DataType.FP8, torch.float16), - # e4m3:e4m3:bf16 - (torch.float8_e4m3fn, DataType.FP8, torch.bfloat16), - # e4m3:nvfp4:* - (torch.float8_e4m3fn, DataType.NVFP4, torch.float8_e4m3fn), - (torch.float8_e4m3fn, DataType.NVFP4, torch.float16), - (torch.float8_e4m3fn, DataType.NVFP4, torch.bfloat16), - } - - # Generation phase supported combinations (includes context + additional) - SUPPORTED_DTYPE_COMBOS_GENERATION = { - # All context combinations - (torch.float8_e4m3fn, DataType.FP8, torch.float8_e4m3fn), - (torch.float16, DataType.HALF, torch.float16), - (torch.bfloat16, DataType.BF16, torch.bfloat16), - (torch.float8_e4m3fn, DataType.FP8, torch.float16), - (torch.float8_e4m3fn, DataType.FP8, torch.bfloat16), - # Additional generation-only combinations - # bf16:e4m3:bf16 - (torch.bfloat16, DataType.FP8, torch.bfloat16), - # fp16:e4m3:fp16 - (torch.float16, DataType.FP8, torch.float16), - # e4m3:nvfp4:* - (torch.float8_e4m3fn, DataType.NVFP4, torch.float8_e4m3fn), - (torch.float8_e4m3fn, DataType.NVFP4, torch.float16), - (torch.float8_e4m3fn, DataType.NVFP4, torch.bfloat16), - } - - # Unsupported head sizes for context FMHA. - # 96 is excluded because trtllm-gen kernel library does not ship - # context kernels for headDim=96 (affects Phi-3 family models). - UNSUPPORTED_HEAD_SIZES_CONTEXT = {72, 80, 96} - - # Maximum heads ratio for generation. - MAX_HEADS_RATIO_GENERATION = 32 - - # Minimum tokens per block, tokens_per_block < 8 is not supported by TRTLLM-GEN kernels. - MIN_TOKENS_PER_BLOCK = 8 - - # Supported tokens_per_block values for trtllm-gen kernels - SUPPORTED_TOKENS_PER_BLOCK = {16, 32, 64} - - # MLA shapes accepted by FlashInfer's trtllm-gen wrapper/launcher. The decode API - # uses kv_lora_rank as headDimV and kv_lora_rank + qk_rope_head_dim as headDimQk. - SUPPORTED_MLA_GENERATION_HEAD_DIMS = { - (320, 256), - (576, 512), - } - - # Known FlashInfer package gap: this shape can pass the coarse checks but then fail - # in the TRTLLM-GEN launcher with "Missing TRTLLM-GEN kernel". - MISSING_MLA_GENERATION_KERNELS = { - (576, 512, 32), - } - - @classmethod - def _check_mla_generation_support( - cls, - head_size: int, - tokens_per_block: int, - kv_lora_rank: Optional[int], - qk_rope_head_dim: Optional[int], - ) -> Tuple[bool, str]: - missing_params = [ - name - for name, value in ( - ("kv_lora_rank", kv_lora_rank), - ("qk_rope_head_dim", qk_rope_head_dim), - ) - if value is None or value <= 0 - ] - if missing_params: - return ( - False, - f"[Generation][MLA] Missing required MLA parameter(s): {', '.join(missing_params)}.", - ) - - kv_rank = int(kv_lora_rank) - qk_rope_dim = int(qk_rope_head_dim) - head_dim_qk = kv_rank + qk_rope_dim - head_dim_v = kv_rank - if head_size != head_dim_qk: - return ( - False, - f"[Generation][MLA] head_size ({head_size}) must match " - f"kv_lora_rank + qk_rope_head_dim ({head_dim_qk}).", - ) - - if (head_dim_qk, head_dim_v) not in cls.SUPPORTED_MLA_GENERATION_HEAD_DIMS: - supported = sorted(cls.SUPPORTED_MLA_GENERATION_HEAD_DIMS) - return ( - False, - f"[Generation][MLA] Unsupported head dimensions: " - f"headDimQk={head_dim_qk}, headDimV={head_dim_v}. Supported: {supported}.", - ) - - if (head_dim_qk, head_dim_v, tokens_per_block) in cls.MISSING_MLA_GENERATION_KERNELS: - return ( - False, - f"[Generation][MLA] Missing TRTLLM-GEN decode kernel for " - f"headDimQk={head_dim_qk}, headDimV={head_dim_v}, " - f"tokens_per_block={tokens_per_block}.", - ) - - return True, "" - - @classmethod - def is_supported( - cls, - q_dtype: torch.dtype, - kv_cache_dtype: DataType, - num_heads: int, - num_kv_heads: int, - head_size: int, - attention_input_type: Optional[int] = None, - out_dtype: Optional[torch.dtype] = None, - mask_type: int = 1, - beam_width: int = 1, - tokens_per_block: Optional[int] = 64, - use_paged_kv_cache: bool = True, - is_mla_enable: bool = False, - kv_lora_rank: Optional[int] = None, - qk_rope_head_dim: Optional[int] = None, - cross_attention: bool = False, - is_spec_decoding: bool = False, - has_alibi: bool = False, - is_padded: bool = False, - position_shift_enabled: bool = False, - quant_config: Optional[QuantConfig] = None, - has_sparse_attention: bool = False, - has_skip_softmax_attention: bool = False, - ) -> Tuple[bool, str]: - if tokens_per_block is None: - tokens_per_block = 0 - has_context_phase = True - has_generation_phase = True - if attention_input_type is not None: - attn_input_type = AttentionInputType(attention_input_type) - has_context_phase = attn_input_type != AttentionInputType.generation_only - has_generation_phase = attn_input_type != AttentionInputType.context_only - - sm = get_sm_version() - if not is_sm_100f(sm): - return (False, f"trtllm-gen requires SM100 or SM103 (Blackwell). Current: SM{sm}.") - - if has_skip_softmax_attention: - return ( - False, - "Skip-softmax attention is not supported by trtllm-gen backend.", - ) +def _clear_multi_ctas_kv_counter_workspace( + fmha_workspace: torch.Tensor, + num_heads: int, + max_num_requests: int, + multi_processor_count: Optional[int], +) -> None: + counter_size = _get_multi_ctas_kv_counter_size( + num_heads, + max_num_requests, + multi_processor_count, + ) + fmha_workspace.narrow(0, 0, counter_size).zero_() - if has_sparse_attention: - return False, "Sparse attention is not supported by trtllm-gen backend." - if is_mla_enable and has_context_phase: - return False, ( - "MLA context and mixed phases fall back to thop.attention until " - "FlashInfer context support is ready." - ) - if cross_attention: - return False, "Cross attention is not supported by trtllm-gen backend." - if q_dtype not in cls.SUPPORTED_INPUT_DTYPES: - return False, f"Input dtype {q_dtype} not supported. Supported: FP16, BF16, FP8 (E4M3)." - if kv_cache_dtype not in cls.SUPPORTED_KV_CACHE_DTYPES: - return ( - False, - f"KV cache dtype {kv_cache_dtype} not supported. Supported: FP16, BF16, FP8, NVFP4.", +def _get_multi_ctas_kv_counter_size( + num_heads: int, + max_num_requests: int, + multi_processor_count: Optional[int], +) -> int: + return max(num_heads * max_num_requests, multi_processor_count or 0) + + +def _get_bmm1_scale_log2(bmm1_scale: torch.Tensor) -> torch.Tensor: + if bmm1_scale.numel() < 2: + raise RuntimeError("trtllm-gen bmm1_scale workspace must contain raw and log2 scales.") + return bmm1_scale.narrow(0, 1, 1) + + +def _trtllm_gen_batch_decode_with_kv_cache( + query: torch.Tensor, + kv_pool: torch.Tensor, + workspace_buffer: torch.Tensor, + block_tables: torch.Tensor, + seq_lens: torch.Tensor, + max_seq_len: int, + bmm1_scale: float | torch.Tensor, + bmm2_scale: float | torch.Tensor, + window_left: int, + out: torch.Tensor, + sinks: Optional[torch.Tensor], + enable_pdl: bool, + q_len_per_req: Optional[int], + max_q_len: Optional[int], + cum_seq_lens_q: Optional[torch.Tensor], + kv_scale_pool: Optional[torch.Tensor], + uses_shared_paged_kv_idx: bool, +) -> None: + if q_len_per_req is not None: + decode_max_q_len = q_len_per_req + batch_size = query.size(0) // q_len_per_req + else: + if max_q_len is None or cum_seq_lens_q is None: + raise RuntimeError( + "trtllm-gen multi-token generation requires max_q_len and cum_seq_lens_q." ) - if out_dtype is not None and out_dtype not in cls.SUPPORTED_OUT_DTYPES: - return False, f"Output dtype {out_dtype} not supported. Supported: FP16, BF16, FP8." + decode_max_q_len = max_q_len + batch_size = cum_seq_lens_q.size(0) - 1 - assert num_heads > 0, "num_heads must be positive." - assert num_kv_heads > 0, "num_kv_heads must be positive." - if num_heads % num_kv_heads != 0: - return ( - False, - f"num_heads ({num_heads}) must be divisible by num_kv_heads ({num_kv_heads}).", - ) + bmm1_scale_arg = ( + _get_bmm1_scale_log2(bmm1_scale) if isinstance(bmm1_scale, torch.Tensor) else bmm1_scale + ) - o_dtype = out_dtype if out_dtype is not None else q_dtype + run_func = flashinfer.decode.get_trtllm_gen_fmha_module().trtllm_paged_attention_decode + sm_count = flashinfer.decode.get_device_sm_count(query.device) + run_func( + out, + None, # out_scale_factor + query, + kv_pool, + kv_pool, + workspace_buffer, + block_tables, + seq_lens, + decode_max_q_len, + max_seq_len, + bmm1_scale_arg, + bmm2_scale, + -1.0, # o_sf_scale + -1, # o_sf_vec_size + 0, # o_sf_start_index + batch_size, + window_left, + 0, # sparse_mla_top_k + sm_count, + enable_pdl, + workspace_buffer.numel() * workspace_buffer.element_size(), + sinks, + cum_seq_lens_q, + kv_scale_pool, # k_block_scales + kv_scale_pool, # v_block_scales + None, # skip_softmax_threshold_scale_factor + uses_shared_paged_kv_idx, + None, # lse + 0, # lse_stride_tokens + 0, # lse_stride_heads + ) - check_context_phase = has_context_phase and not is_mla_enable - if check_context_phase: - if head_size in cls.UNSUPPORTED_HEAD_SIZES_CONTEXT: - return False, f"[Context] Head size {head_size} is not supported." - try: - if AttentionMaskType(mask_type) == AttentionMaskType.custom_mask: - return False, "[Context] Custom mask is not supported." - except ValueError: - return False, f"[Context] Invalid mask_type: {mask_type}." - if has_alibi: - return False, "[Context] ALiBi is not supported." - if is_padded: - return False, "[Context] Padded input is not supported." - if (q_dtype, kv_cache_dtype, o_dtype) not in cls.SUPPORTED_DTYPE_COMBOS_CONTEXT: - return False, ( - f"[Context] Unsupported dtype combination: Q={q_dtype}, KV={kv_cache_dtype}, O={o_dtype}." - ) - if has_generation_phase: - if beam_width != 1: - return ( - False, - f"[Generation] Beam search (beam_width={beam_width}) is not supported. Must be 1.", - ) - if position_shift_enabled: - return False, "[Generation] Position shift is not supported." - if tokens_per_block < cls.MIN_TOKENS_PER_BLOCK: - return ( - False, - f"[Generation] tokens_per_block ({tokens_per_block}) must be >= {cls.MIN_TOKENS_PER_BLOCK}.", - ) - heads_ratio = num_heads // num_kv_heads - if not is_mla_enable and heads_ratio > cls.MAX_HEADS_RATIO_GENERATION: - return ( - False, - f"[Generation] heads ratio ({heads_ratio}) exceeds maximum ({cls.MAX_HEADS_RATIO_GENERATION}).", - ) - if has_alibi: - return False, "[Generation] ALiBi is not supported." - if (q_dtype, kv_cache_dtype, o_dtype) not in cls.SUPPORTED_DTYPE_COMBOS_GENERATION: - return False, ( - f"[Generation] Unsupported dtype combination: Q={q_dtype}, KV={kv_cache_dtype}, O={o_dtype}." - ) - if is_mla_enable: - supported, reason = cls._check_mla_generation_support( - head_size=head_size, - tokens_per_block=tokens_per_block, - kv_lora_rank=kv_lora_rank, - qk_rope_head_dim=qk_rope_head_dim, - ) - if not supported: - return False, reason - - if use_paged_kv_cache: - if tokens_per_block <= 0: - return False, "tokens_per_block must be positive." - if tokens_per_block & (tokens_per_block - 1) != 0: - return False, f"tokens_per_block ({tokens_per_block}) must be power of 2." - if tokens_per_block not in cls.SUPPORTED_TOKENS_PER_BLOCK: - supported = sorted(cls.SUPPORTED_TOKENS_PER_BLOCK) - return ( - False, - f"tokens_per_block ({tokens_per_block}) is not supported " - f"by trtllm-gen kernels. Supported: {supported}.", - ) +def _trtllm_gen_batch_context_with_kv_cache( + query: torch.Tensor, + kv_pool: torch.Tensor, + workspace_buffer: torch.Tensor, + block_tables: torch.Tensor, + seq_lens: torch.Tensor, + max_q_len: int, + max_kv_len: int, + bmm1_scale: float | torch.Tensor, + bmm2_scale: float | torch.Tensor, + batch_size: int, + cum_seq_lens_q: torch.Tensor, + cum_seq_lens_kv: torch.Tensor, + window_left: int, + out: torch.Tensor, + sinks: Optional[torch.Tensor], + enable_pdl: bool, + kv_scale_pool: Optional[torch.Tensor], + uses_shared_paged_kv_idx: bool, +) -> None: + bmm1_scale_arg = ( + _get_bmm1_scale_log2(bmm1_scale) if isinstance(bmm1_scale, torch.Tensor) else bmm1_scale + ) - return True, "" + run_func = flashinfer.prefill.get_trtllm_gen_fmha_module().trtllm_paged_attention_context + sm_count = flashinfer.prefill.get_device_sm_count(query.device) + run_func( + out, + None, # out_scale_factor + query, + kv_pool, + kv_pool, + workspace_buffer, + block_tables, + seq_lens, + max_q_len, + max_kv_len, + bmm1_scale_arg, + bmm2_scale, + -1.0, # o_sf_scale + -1, # o_sf_vec_size + 0, # o_sf_start_index + batch_size, + window_left, + cum_seq_lens_q, + cum_seq_lens_kv, + sm_count, + enable_pdl, + workspace_buffer.numel() * workspace_buffer.element_size(), + sinks, + kv_scale_pool, # key_block_scales + kv_scale_pool, # value_block_scales + None, # skip_softmax_threshold_scale_factor + uses_shared_paged_kv_idx, + True, # causal + None, # lse + 0, # lse_stride_tokens + 0, # lse_stride_heads + ) @lru_cache(maxsize=128) @@ -460,24 +351,16 @@ def _get_workspace_size( @dataclass(slots=True) -class EnqueueParams: - """Per-call dynamic parameters for trtllm-gen attention. - - Layer-static properties (num_heads, head_size, rotary params, etc.) are - read directly from ``FlashInferTrtllmGenAttention`` cached attributes - to avoid redundant copies on every forward call. - """ - - forward: AttentionForwardArgs +class FmhaParams: + attn: "TrtllmAttention" + meta: "TrtllmAttentionMetadata" + fwd: AttentionForwardArgs + workspace: torch.Tensor attention_input: Optional[torch.Tensor] = None qkv_input: Optional[torch.Tensor] = None context_buf: Optional[torch.Tensor] = None - workspace: Optional[torch.Tensor] = None sequence_lengths: Optional[torch.Tensor] = None context_lengths: Optional[torch.Tensor] = None - kv_cache_block_offsets: Optional[torch.Tensor] = None - host_kv_cache_pool_pointers: Optional[torch.Tensor] = None - host_kv_cache_pool_mapping: Optional[torch.Tensor] = None input_seq_length: int = 0 max_past_kv_length: int = 0 max_attention_window_size: int = 0 @@ -485,22 +368,15 @@ class EnqueueParams: num_tokens: int = 0 seq_offset: int = 0 tokens_per_block: int = 64 - mask_type: int = 1 - kv_cache_quant_mode: int = 0 - layer_idx: int = 0 fp8_context_fmha: bool = False - paged_context_fmha: bool = False kv_factor: int = 0 total_num_blocks: int = 0 # Context-only fields batch_size: int = 0 # Generation-only fields - beam_width: int = 1 num_requests: int = 0 - predicted_tokens_per_seq: int = 1 spec_decoding_generation_lengths: Optional[torch.Tensor] = None spec_decoding_position_offsets: Optional[torch.Tensor] = None - spec_decoding_packed_mask: Optional[torch.Tensor] = None class FlashInferTrtllmGenAttention: @@ -515,12 +391,55 @@ class FlashInferTrtllmGenAttention: # block-table layout used by the fused preprocessing path. USE_SHARED_PAGED_KV_IDX = False + # Supported data types + SUPPORTED_INPUT_DTYPES = {torch.float16, torch.bfloat16, torch.float8_e4m3fn} + SUPPORTED_KV_CACHE_DTYPES = {DataType.HALF, DataType.BF16, DataType.FP8, DataType.NVFP4} + SUPPORTED_OUT_DTYPES = {torch.float16, torch.bfloat16, torch.float8_e4m3fn} + + # Supported Q:KV:O dtype combinations for trtllm-gen kernels + # Format: (q_dtype: torch.dtype, kv_dtype: DataType, o_dtype: torch.dtype) + SUPPORTED_DTYPE_COMBOS_CONTEXT = { + (torch.float8_e4m3fn, DataType.FP8, torch.float8_e4m3fn), + (torch.float16, DataType.HALF, torch.float16), + (torch.bfloat16, DataType.BF16, torch.bfloat16), + (torch.float8_e4m3fn, DataType.FP8, torch.float16), + (torch.float8_e4m3fn, DataType.FP8, torch.bfloat16), + # e4m3:nvfp4:* + (torch.float8_e4m3fn, DataType.NVFP4, torch.float8_e4m3fn), + (torch.float8_e4m3fn, DataType.NVFP4, torch.float16), + (torch.float8_e4m3fn, DataType.NVFP4, torch.bfloat16), + } + SUPPORTED_DTYPE_COMBOS_GENERATION = { + (torch.float8_e4m3fn, DataType.FP8, torch.float8_e4m3fn), + (torch.float16, DataType.HALF, torch.float16), + (torch.bfloat16, DataType.BF16, torch.bfloat16), + (torch.float8_e4m3fn, DataType.FP8, torch.float16), + (torch.float8_e4m3fn, DataType.FP8, torch.bfloat16), + (torch.bfloat16, DataType.FP8, torch.bfloat16), + (torch.float16, DataType.FP8, torch.float16), + # e4m3:nvfp4:* + (torch.float8_e4m3fn, DataType.NVFP4, torch.float8_e4m3fn), + (torch.float8_e4m3fn, DataType.NVFP4, torch.float16), + (torch.float8_e4m3fn, DataType.NVFP4, torch.bfloat16), + } + + # 96 is excluded because trtllm-gen does not ship context kernels for it. + UNSUPPORTED_HEAD_SIZES_CONTEXT = {72, 80, 96} + MAX_HEADS_RATIO_GENERATION = 32 + MIN_TOKENS_PER_BLOCK = 8 + SUPPORTED_TOKENS_PER_BLOCK = {16, 32, 64} + SUPPORTED_MLA_GENERATION_HEAD_DIMS = { + (320, 256), + (576, 512), + } + MISSING_MLA_GENERATION_KERNELS = { + (576, 512, 32), + } + def __init__( self, attention_layer: "TrtllmAttention", ): - self._attention_layer_ref = weakref.ref(attention_layer) - self._checker = TrtllmGenSupportChecker() self._layout = self.DEFAULT_KV_LAYOUT # Read once so the hot path is not sensitive to later environment changes. self._enable_pdl = get_env_enable_pdl() @@ -530,54 +449,7 @@ def __init__( f"trtllm-gen requires fused nanobind ops, missing: {', '.join(missing_ops)}." ) - # Cache layer-static properties to avoid repeated attribute lookups - # through the weakref on every layer forward call. - self._num_heads = attention_layer.num_heads - self._num_kv_heads = attention_layer.num_kv_heads - self._head_dim = attention_layer.head_dim - self._quant_mode = attention_layer.quant_mode - self._q_scaling = attention_layer.q_scaling - self._position_embedding_type = attention_layer.position_embedding_type - self._is_mla_enable = attention_layer.is_mla_enable - self._kv_lora_rank = attention_layer.kv_lora_rank or 0 - self._qk_nope_head_dim = attention_layer.qk_nope_head_dim or 0 - self._qk_rope_head_dim = attention_layer.qk_rope_head_dim or 0 - self._v_head_dim = attention_layer.v_head_dim - self._predicted_tokens_per_seq = attention_layer.predicted_tokens_per_seq - self._rotary_embedding_dim = attention_layer.rope_params.dim - self._rotary_embedding_base = attention_layer.rope_params.theta - self._rotary_embedding_scale_type = int(attention_layer.rope_params.scale_type) - self._rotary_embedding_scale = attention_layer.rope_params.scale - self._rotary_embedding_max_positions = attention_layer.rope_params.max_positions - self._bmm1_scale = 1.0 / (math.sqrt(self._head_dim) * self._q_scaling) - self._rotary_inv_freq = attention_layer.rotary_inv_freq - self._rotary_cos_sin = attention_layer.rotary_cos_sin - self._attention_chunk_size = ( - attention_layer.attention_chunk_size - if attention_layer.attention_chunk_size is not None - else 0 - ) - - # Static keyword args shared across preprocess / postprocess C++ calls. - # Built once to avoid dict construction on every forward call. - self._static_kw: dict[str, object] = dict( - num_heads=self._num_heads, - num_kv_heads=self._num_kv_heads, - head_size=self._head_dim, - rotary_embedding_dim=self._rotary_embedding_dim, - rotary_embedding_base=self._rotary_embedding_base, - rotary_embedding_scale_type=self._rotary_embedding_scale_type, - rotary_embedding_scale=self._rotary_embedding_scale, - rotary_embedding_max_positions=self._rotary_embedding_max_positions, - position_embedding_type=self._position_embedding_type, - bmm1_scale=self._bmm1_scale, - attention_chunk_size=self._attention_chunk_size, - ) - - # Cached is_supported() result. None means not yet checked; - # a positive result is stable (model-static) and cached permanently. - self._support_result: Optional[Tuple[bool, str]] = None - # Lazily set on the first attention() call from the query device. + # Lazily set on the first forward() call from the query device. self._multi_processor_count: Optional[int] = None @property @@ -585,12 +457,6 @@ def layout(self) -> str: """KV cache layout.""" return self._layout - def _get_attention_layer(self) -> "TrtllmAttention": - attention_layer = self._attention_layer_ref() - if attention_layer is None: - raise RuntimeError("trtllm-gen attention layer has been destroyed.") - return attention_layer - def _get_kv_scale_params( self, forward_args: AttentionForwardArgs, @@ -616,76 +482,279 @@ def _get_kv_scale_params( return kv_scale_orig_quant, kv_scale_quant_orig + @staticmethod + def _get_kv_cache_dtype_and_total_blocks( + meta: "TrtllmAttentionMetadata", + is_mla_enable: bool, + ) -> Tuple[Optional[DataType], int]: + kv_cache_dtype = None + total_num_blocks = 0 + kv_cache_manager = meta.kv_cache_manager + if kv_cache_manager is not None: + kv_cache_dtype = kv_cache_manager.dtype + kv_factor = 1 if is_mla_enable else 2 + blocks_in_primary_pool = getattr(kv_cache_manager, "blocks_in_primary_pool", None) + if blocks_in_primary_pool is None: + blocks_per_window = getattr(kv_cache_manager, "blocks_per_window", None) + if blocks_per_window: + blocks_in_primary_pool = max( + int(primary) for primary, _ in blocks_per_window.values() + ) + if blocks_in_primary_pool is not None: + total_num_blocks = ( + int(blocks_in_primary_pool) * kv_cache_manager.num_local_layers * kv_factor + ) + return kv_cache_dtype, total_num_blocks + + @staticmethod + def _get_kv_factor(attn: "TrtllmAttention") -> int: + return 1 if attn.is_mla_enable else 2 + + @staticmethod + def _get_generation_out_head_size(attn: "TrtllmAttention") -> int: + kv_lora_rank = attn.kv_lora_rank or 0 + if attn.is_mla_enable and kv_lora_rank: + return kv_lora_rank + return attn.head_dim + + @staticmethod + def _get_context_out_head_size(attn: "TrtllmAttention") -> int: + if attn.is_mla_enable and attn.v_head_dim: + return attn.v_head_dim + return attn.head_dim + + @staticmethod + def _get_bmm1_scale(attn: "TrtllmAttention") -> float: + return 1.0 / (math.sqrt(attn.head_dim) * attn.q_scaling) + + @staticmethod + def _get_attention_chunk_size(attn: "TrtllmAttention") -> int: + return attn.attention_chunk_size if attn.attention_chunk_size is not None else 0 + + @classmethod + def _check_mla_generation_support( + cls, + head_size: int, + tokens_per_block: int, + kv_lora_rank: Optional[int], + qk_rope_head_dim: Optional[int], + ) -> Tuple[bool, str]: + missing_params = [ + name + for name, value in ( + ("kv_lora_rank", kv_lora_rank), + ("qk_rope_head_dim", qk_rope_head_dim), + ) + if value is None or value <= 0 + ] + if missing_params: + return ( + False, + "[Generation][MLA] Missing required MLA parameter(s): " + f"{', '.join(missing_params)}.", + ) + + kv_rank = int(kv_lora_rank) + qk_rope_dim = int(qk_rope_head_dim) + head_dim_qk = kv_rank + qk_rope_dim + head_dim_v = kv_rank + if head_size != head_dim_qk: + return ( + False, + f"[Generation][MLA] head_size ({head_size}) must match " + f"kv_lora_rank + qk_rope_head_dim ({head_dim_qk}).", + ) + + if (head_dim_qk, head_dim_v) not in cls.SUPPORTED_MLA_GENERATION_HEAD_DIMS: + supported = sorted(cls.SUPPORTED_MLA_GENERATION_HEAD_DIMS) + return ( + False, + f"[Generation][MLA] Unsupported head dimensions: " + f"headDimQk={head_dim_qk}, headDimV={head_dim_v}. Supported: {supported}.", + ) + + if (head_dim_qk, head_dim_v, tokens_per_block) in cls.MISSING_MLA_GENERATION_KERNELS: + return ( + False, + f"[Generation][MLA] Missing TRTLLM-GEN decode kernel for " + f"headDimQk={head_dim_qk}, headDimV={head_dim_v}, " + f"tokens_per_block={tokens_per_block}.", + ) + + return True, "" + def is_supported( self, q: torch.Tensor, - *, - metadata: "TrtllmAttentionMetadata", - forward_args: AttentionForwardArgs, - mask_type: int, - active_helix: bool, - use_sage_attn: bool, + k: Optional[torch.Tensor], + v: Optional[torch.Tensor], + attn: "TrtllmAttention", + meta: "TrtllmAttentionMetadata", + fwd: AttentionForwardArgs, ) -> Tuple[bool, str]: - if use_sage_attn: + is_mla_enable = attn.is_mla_enable + has_skip_softmax = ( + attn.skip_softmax_threshold_scale_factor_prefill is not None + or attn.skip_softmax_threshold_scale_factor_decode is not None + ) + if ( + fwd.sage_attn_num_elts_per_blk_q > 0 + or fwd.sage_attn_num_elts_per_blk_k > 0 + or fwd.sage_attn_num_elts_per_blk_v > 0 + ): return False, "trtllm-gen does not support sage attention." - if active_helix: + if meta.helix_position_offsets is not None: return False, "trtllm-gen does not support helix parallelism." - # Return cached positive result after the first supported call. - if self._support_result is not None: - return self._support_result + sparse_kv_indices = fwd.sparse.sparse_kv_indices + sparse_attn_indices = fwd.sparse.sparse_attn_indices + if ( + (sparse_kv_indices is not None and sparse_kv_indices.numel() > 0) + or (sparse_attn_indices is not None and sparse_attn_indices.numel() > 0) + or meta.num_sparse_topk > 0 + ): + return False, "trtllm-gen does not support sparse attention." + if has_skip_softmax: + return False, "trtllm-gen does not support skip-softmax attention." + if meta.use_spec_decoding and meta.is_spec_dec_tree: + return ( + False, + "FlashInfer trtllm-gen does not support spec-dec tree/custom masks.", + ) + if is_mla_enable and fwd.attention_input_type != AttentionInputType.generation_only: + return False, "trtllm-gen MLA supports generation-only attention." if not IS_FLASHINFER_AVAILABLE: return False, "flashinfer package is not installed." - kv_cache_manager = metadata.kv_cache_manager - if kv_cache_manager is None: - return False, "trtllm-gen requires a KVCacheManager." - use_paged_kv_cache = metadata.kv_cache_block_offsets is not None - if not use_paged_kv_cache: + if meta.kv_cache_block_offsets is None: return False, "trtllm-gen requires paged KV cache." - - output = forward_args.output + output = fwd.output if output is None: - return False, "trtllm-gen requires forward_args.output." + return False, "trtllm-gen requires output." - attention_layer = self._get_attention_layer() - sparse_attention_config = attention_layer.sparse_attention_config - has_skip_softmax_attention = ( - getattr(sparse_attention_config, "algorithm", None) == "skip_softmax" - ) - has_sparse_attention = ( - sparse_attention_config is not None and not has_skip_softmax_attention - ) + tokens_per_block = meta.tokens_per_block + if tokens_per_block is None: + tokens_per_block = 0 + + attn_input_type = fwd.attention_input_type + has_context_phase = attn_input_type != AttentionInputType.generation_only + has_generation_phase = attn_input_type != AttentionInputType.context_only q_dtype = q.dtype - if kv_cache_manager.dtype == DataType.NVFP4: - q_dtype = torch.float8_e4m3fn + o_dtype = output.dtype + + sm = get_sm_version() + if not is_sm_100f(sm): + return False, (f"trtllm-gen requires SM100 or SM103 (Blackwell). Current: SM{sm}.") - result = self._checker.is_supported( - q_dtype=q_dtype, - kv_cache_dtype=kv_cache_manager.dtype, - num_heads=self._num_heads, - num_kv_heads=self._num_kv_heads, - head_size=self._head_dim, - attention_input_type=int(forward_args.attention_input_type), - out_dtype=output.dtype, - mask_type=mask_type, - beam_width=metadata.beam_width, - tokens_per_block=metadata.tokens_per_block, - use_paged_kv_cache=use_paged_kv_cache, - is_mla_enable=self._is_mla_enable, - kv_lora_rank=self._kv_lora_rank, - qk_rope_head_dim=self._qk_rope_head_dim, - cross_attention=False, - is_spec_decoding=metadata.is_spec_decoding_enabled, - has_alibi=self._position_embedding_type in (4, 5), - is_padded=False, - position_shift_enabled=False, - quant_config=attention_layer.quant_config, - has_sparse_attention=has_sparse_attention, - has_skip_softmax_attention=has_skip_softmax_attention, + if q_dtype not in self.SUPPORTED_INPUT_DTYPES: + return False, ( + f"Input dtype {q_dtype} not supported. Supported: FP16, BF16, FP8 (E4M3)." + ) + + kv_cache_dtype, _ = self._get_kv_cache_dtype_and_total_blocks(meta, is_mla_enable) + if kv_cache_dtype is None: + kv_cache_dtype = torch_dtype_to_binding(q_dtype) + + is_fp8_out = output.dtype == torch.float8_e4m3fn + is_fp4_out = output.dtype == torch.uint8 + has_fp8_kv = kv_cache_dtype == DataType.FP8 + has_fp4_kv = kv_cache_dtype == DataType.NVFP4 + fp8_context_fmha = ( + is_fp8_out or is_fp4_out or has_fp4_kv or (has_fp8_kv and has_context_phase) ) - if result[0]: - self._support_result = result - return result + if has_fp4_kv or fp8_context_fmha: + q_dtype = torch.float8_e4m3fn + + if kv_cache_dtype not in self.SUPPORTED_KV_CACHE_DTYPES: + return False, ( + f"KV cache dtype {kv_cache_dtype} not supported. Supported: FP16, BF16, FP8, NVFP4." + ) + if o_dtype not in self.SUPPORTED_OUT_DTYPES: + return False, f"Output dtype {o_dtype} not supported. Supported: FP16, BF16, FP8." + + assert attn.num_heads > 0, "num_heads must be positive." + assert attn.num_kv_heads > 0, "num_kv_heads must be positive." + if attn.num_heads % attn.num_kv_heads != 0: + return ( + False, + f"num_heads ({attn.num_heads}) must be divisible by " + f"num_kv_heads ({attn.num_kv_heads}).", + ) + + has_alibi = attn.position_embedding_type in (4, 5) + check_context_phase = has_context_phase and not is_mla_enable + if check_context_phase: + if attn.head_dim in self.UNSUPPORTED_HEAD_SIZES_CONTEXT: + return False, f"[Context] Head size {attn.head_dim} is not supported." + try: + if AttentionMaskType(fwd.mask_type) == AttentionMaskType.custom_mask: + return False, "[Context] Custom mask is not supported." + except ValueError: + return False, f"[Context] Invalid mask_type: {fwd.mask_type}." + if has_alibi: + return False, "[Context] ALiBi is not supported." + if (q_dtype, kv_cache_dtype, o_dtype) not in self.SUPPORTED_DTYPE_COMBOS_CONTEXT: + return False, ( + f"[Context] Unsupported dtype combination: " + f"Q={q_dtype}, KV={kv_cache_dtype}, O={o_dtype}." + ) + + if has_generation_phase: + if meta.beam_width != 1: + return ( + False, + f"[Generation] Beam search (beam_width={meta.beam_width}) " + "is not supported. Must be 1.", + ) + sink_token_length = 0 + if sink_token_length != 0: + return ( + False, + f"[Generation] StreamingLLM " + f"(sink_token_length={sink_token_length}) is not supported.", + ) + if tokens_per_block < self.MIN_TOKENS_PER_BLOCK: + return ( + False, + f"[Generation] tokens_per_block ({tokens_per_block}) " + f"must be >= {self.MIN_TOKENS_PER_BLOCK}.", + ) + heads_ratio = attn.num_heads // attn.num_kv_heads + if not is_mla_enable and heads_ratio > self.MAX_HEADS_RATIO_GENERATION: + return ( + False, + f"[Generation] heads ratio ({heads_ratio}) exceeds maximum " + f"({self.MAX_HEADS_RATIO_GENERATION}).", + ) + if has_alibi: + return False, "[Generation] ALiBi is not supported." + if (q_dtype, kv_cache_dtype, o_dtype) not in self.SUPPORTED_DTYPE_COMBOS_GENERATION: + return False, ( + f"[Generation] Unsupported dtype combination: " + f"Q={q_dtype}, KV={kv_cache_dtype}, O={o_dtype}." + ) + if is_mla_enable: + supported, reason = self._check_mla_generation_support( + head_size=attn.head_dim, + tokens_per_block=tokens_per_block, + kv_lora_rank=attn.kv_lora_rank, + qk_rope_head_dim=attn.qk_rope_head_dim, + ) + if not supported: + return False, reason + + if tokens_per_block <= 0: + return False, "tokens_per_block must be positive." + if tokens_per_block & (tokens_per_block - 1) != 0: + return False, f"tokens_per_block ({tokens_per_block}) must be power of 2." + if tokens_per_block not in self.SUPPORTED_TOKENS_PER_BLOCK: + supported = sorted(self.SUPPORTED_TOKENS_PER_BLOCK) + return ( + False, + f"tokens_per_block ({tokens_per_block}) is not supported " + f"by trtllm-gen kernels. Supported: {supported}.", + ) + + return True, "" @staticmethod @lru_cache(maxsize=None) @@ -701,67 +770,56 @@ def _get_multi_processor_count(self, device: torch.device) -> int: device_index = torch.cuda.current_device() return self._get_multi_processor_count_for_device(device_index) - def attention( + def forward( self, q: torch.Tensor, - *, - metadata: "TrtllmAttentionMetadata", - forward_args: AttentionForwardArgs, - mask_type: int, - use_paged_context_fmha: bool, + k: Optional[torch.Tensor], + v: Optional[torch.Tensor], + attn: "TrtllmAttention", + meta: "TrtllmAttentionMetadata", + fwd: AttentionForwardArgs, ) -> None: - attention_layer = self._get_attention_layer() - layer_idx = attention_layer.get_local_layer_idx(metadata) - logger.debug(f"trtllm_gen_attention starts at layer {layer_idx}") - - output = forward_args.output + output = fwd.output if output is None: - raise RuntimeError("trtllm-gen attention requires forward_args.output.") + raise RuntimeError("trtllm-gen attention requires output.") + if meta.kv_cache_block_offsets is None: + raise RuntimeError("trtllm-gen attention requires paged KV cache.") - workspace = ( - metadata.workspace if not metadata.is_cuda_graph else metadata.cuda_graph_workspace - ) + workspace = meta.effective_workspace + if workspace is None: + workspace = torch.empty((0,), device=q.device, dtype=torch.int8) # Lazily cache the SM count from the first query tensor's device. if self._multi_processor_count is None: self._multi_processor_count = self._get_multi_processor_count(q.device) - # Use cached layer-static properties. - num_heads = self._num_heads - num_kv_heads = self._num_kv_heads - head_size = self._head_dim - quant_mode = self._quant_mode - is_mla_enable = self._is_mla_enable - kv_lora_rank = self._kv_lora_rank - v_head_dim = self._v_head_dim - - # Per-call dynamic values from metadata / forward_args. - tokens_per_block = metadata.tokens_per_block - max_num_requests = metadata.max_num_requests - max_context_length = min(metadata.max_seq_len - 1, metadata.max_num_tokens) - attention_window_size = forward_args.attention_window_size or metadata.max_seq_len - beam_width = metadata.beam_width - attention_input_type = int(forward_args.attention_input_type) + num_heads = attn.num_heads + num_kv_heads = attn.num_kv_heads + head_size = attn.head_dim + quant_mode = attn.quant_mode + is_mla_enable = attn.is_mla_enable + tokens_per_block = meta.tokens_per_block + max_num_requests = meta.max_num_requests + max_context_length = meta.max_context_length + attention_window_size = fwd.attention_window_size + beam_width = meta.beam_width + num_tokens = q.size(0) + attn_input_type = fwd.attention_input_type + is_gen_only = attn_input_type == AttentionInputType.generation_only is_fp8_out = output.dtype == torch.float8_e4m3fn is_fp4_out = output.dtype == torch.uint8 kv_cache_quant_mode = QuantMode(quant_mode) fp8_context_fmha = ( is_fp8_out or is_fp4_out - or ( - (kv_cache_quant_mode.has_fp8_kv_cache() or kv_cache_quant_mode.has_fp4_kv_cache()) - and use_paged_context_fmha - ) + or kv_cache_quant_mode.has_fp4_kv_cache() + or (kv_cache_quant_mode.has_fp8_kv_cache() and not is_gen_only) ) - num_tokens = q.size(0) - attn_input_type = AttentionInputType(attention_input_type) - is_gen_only = attn_input_type == AttentionInputType.generation_only - - num_contexts = metadata.num_contexts - num_ctx_tokens = metadata.num_ctx_tokens - num_generations = metadata.host_request_types_runtime.size(0) - num_contexts + num_contexts = meta.num_contexts + num_ctx_tokens = meta.num_ctx_tokens + num_generations = meta.num_generations num_gen_tokens = num_tokens if is_gen_only else num_tokens - num_ctx_tokens if num_gen_tokens < 0: raise RuntimeError( @@ -772,41 +830,35 @@ def attention( workspace_max_tokens = max(num_tokens, max_context_length) workspace_max_gen_tokens = max(num_gen_tokens, max_num_requests) required_workspace_size = _get_workspace_size( - q.dtype, - workspace_max_tokens, - workspace_max_gen_tokens, - num_heads, - num_kv_heads, - head_size, - max_num_requests, - self._rotary_embedding_dim, - fp8_context_fmha, - ) - - current_workspace_size = ( - workspace.numel() * workspace.element_size() if workspace is not None else 0 + dtype=q.dtype, + num_tokens=workspace_max_tokens, + num_gen_tokens=workspace_max_gen_tokens, + num_heads=num_heads, + num_kv_heads=num_kv_heads, + head_size=head_size, + max_num_requests=max_num_requests, + rotary_embedding_dim=attn.rope_dim, + fp8_context_fmha=fp8_context_fmha, ) + current_workspace_size = workspace.numel() * workspace.element_size() if current_workspace_size < required_workspace_size: - logger.warning( - f"Attention workspace size is not enough, increase the size from " - f"{current_workspace_size} bytes to {required_workspace_size} bytes" - ) - if workspace is None: - workspace = torch.zeros(required_workspace_size, device=q.device, dtype=torch.uint8) - else: - workspace.resize_(required_workspace_size) - workspace.zero_() - - if is_mla_enable and is_gen_only and kv_lora_rank: - out_head_size = kv_lora_rank - elif is_mla_enable and v_head_dim: - out_head_size = v_head_dim - else: - out_head_size = head_size + if meta.is_cuda_graph and torch.cuda.is_current_stream_capturing(): + raise RuntimeError( + "Attention CUDA graph workspace is smaller than the " + "required size for trtllm-gen." + ) + required_workspace_numel = math.ceil(required_workspace_size / workspace.element_size()) + workspace.resize_((required_workspace_numel,)) + + out_head_size = ( + self._get_generation_out_head_size(attn) + if is_gen_only + else self._get_context_out_head_size(attn) + ) out_tensor = output.view(num_tokens, num_heads, out_head_size) - cache_indirection = metadata.cache_indirection + cache_indirection = meta.cache_indirection max_attn_window_size = ( attention_window_size if beam_width == 1 @@ -817,42 +869,39 @@ def attention( ) ) cyclic_attn_window_size = attention_window_size - kv_factor, total_num_blocks = self._get_kv_cache_metadata(metadata, is_mla_enable) - params = EnqueueParams( - forward=forward_args, + tokens_per_block = tokens_per_block if tokens_per_block is not None else 64 + _, total_num_blocks = self._get_kv_cache_dtype_and_total_blocks(meta, is_mla_enable) + + params = FmhaParams( + attn=attn, + meta=meta, + fwd=fwd, workspace=workspace, max_attention_window_size=max_attn_window_size, cyclic_attention_window_size=cyclic_attn_window_size, - kv_cache_block_offsets=metadata.kv_cache_block_offsets, - host_kv_cache_pool_pointers=metadata.host_kv_cache_pool_pointers, - host_kv_cache_pool_mapping=metadata.host_kv_cache_pool_mapping, - tokens_per_block=tokens_per_block if tokens_per_block is not None else 64, - mask_type=mask_type, - kv_cache_quant_mode=quant_mode, - layer_idx=layer_idx, + tokens_per_block=tokens_per_block, fp8_context_fmha=fp8_context_fmha, - paged_context_fmha=use_paged_context_fmha, - kv_factor=kv_factor, + kv_factor=self._get_kv_factor(attn), total_num_blocks=total_num_blocks, ) - sequence_length = metadata.kv_lens_cuda_runtime - host_past_key_value_lengths = metadata.kv_lens_runtime - context_lengths = metadata.prompt_lens_cuda_runtime - host_context_lengths = metadata.prompt_lens_cpu_runtime + sequence_length = meta.kv_lens_cuda_runtime + host_past_key_value_lengths = meta.kv_lens_runtime if num_contexts > 0 and attn_input_type != AttentionInputType.generation_only: seq_offset = 0 token_offset = 0 num_seqs = num_contexts + context_lengths = meta.prompt_lens_cuda_runtime + host_context_lengths = meta.prompt_lens_cpu_runtime max_context_q_len = int(host_context_lengths[seq_offset : seq_offset + num_seqs].max()) max_past_kv_len = int( host_past_key_value_lengths[seq_offset : seq_offset + num_seqs].max() ) params.attention_input = q[token_offset : token_offset + num_ctx_tokens] - params.qkv_input = q[token_offset : token_offset + num_ctx_tokens] + params.qkv_input = params.attention_input params.context_buf = out_tensor[token_offset : token_offset + num_ctx_tokens] params.sequence_lengths = sequence_length[seq_offset:] params.context_lengths = context_lengths[seq_offset:] @@ -873,45 +922,32 @@ def attention( ) input_seq_length = num_gen_tokens // num_seqs if num_seqs > 0 else 1 - predicted_tokens_per_seq = self._predicted_tokens_per_seq + predicted_tokens_per_seq = attn.predicted_tokens_per_seq spec_gen_lengths = None spec_pos_offsets = None - spec_packed_mask = None - if ( - metadata.is_spec_decoding_enabled - and metadata.use_spec_decoding - and predicted_tokens_per_seq > 1 - ): - spec_gen_lengths = metadata.spec_decoding_generation_lengths - position_offsets_for_cpp = metadata.spec_decoding_position_offsets + if meta.is_spec_decoding_enabled and predicted_tokens_per_seq > 1: + spec_gen_lengths = meta.spec_decoding_generation_lengths + position_offsets_for_cpp = meta.spec_decoding_position_offsets_for_cpp if position_offsets_for_cpp is not None and position_offsets_for_cpp.dim() == 1: - position_offsets_for_cpp = position_offsets_for_cpp.view( - metadata.max_num_requests, -1 - ) + position_offsets_for_cpp = position_offsets_for_cpp.view(max_num_requests, -1) spec_pos_offsets = position_offsets_for_cpp - spec_packed_mask = metadata.spec_decoding_packed_mask params.attention_input = q[token_offset : token_offset + num_gen_tokens] - params.qkv_input = q[token_offset : token_offset + num_gen_tokens] + params.qkv_input = params.attention_input params.context_buf = out_tensor[token_offset : token_offset + num_gen_tokens] params.sequence_lengths = sequence_length[seq_offset:] - params.context_lengths = context_lengths[seq_offset:] params.max_past_kv_length = max_past_kv_len params.num_tokens = num_gen_tokens params.seq_offset = seq_offset params.input_seq_length = input_seq_length - params.beam_width = beam_width params.num_requests = num_seqs // beam_width - params.predicted_tokens_per_seq = predicted_tokens_per_seq params.spec_decoding_generation_lengths = spec_gen_lengths params.spec_decoding_position_offsets = spec_pos_offsets - params.spec_decoding_packed_mask = spec_packed_mask if is_mla_enable: self.run_mla_generation(params) else: self.run_generation(params) - - logger.debug(f"trtllm_gen_attention stops at layer {layer_idx}") + return @staticmethod def _compute_window_left( @@ -927,7 +963,8 @@ def _compute_window_left( """ if attention_chunk_size != 0 and cyclic_attention_window_size < max_kv_length: raise ValueError( - "Chunked-attention and sliding-window-attention should not be enabled at the same time." + "Chunked-attention and sliding-window-attention should not " + "be enabled at the same time." ) if 0 < cyclic_attention_window_size < max_kv_length: return cyclic_attention_window_size - 1 @@ -945,42 +982,16 @@ def _missing_fused_nanobind_ops() -> List[str]: ) return [op for op in required_ops if not hasattr(thop, op)] - def _get_kv_cache_metadata( - self, - metadata: "TrtllmAttentionMetadata", - is_mla_enable: bool, - ) -> Tuple[int, int]: - """Return (kv_factor, total_num_blocks) for building KV cache views.""" - kv_cache_manager = metadata.kv_cache_manager - if kv_cache_manager is None: - raise RuntimeError("trtllm-gen requires a KVCacheManager.") - - kv_factor = 1 if is_mla_enable else 2 - blocks_in_primary_pool = getattr(kv_cache_manager, "blocks_in_primary_pool", None) - if blocks_in_primary_pool is None: - blocks_per_window = getattr(kv_cache_manager, "blocks_per_window", None) - if blocks_per_window: - blocks_in_primary_pool = max( - int(primary) for primary, _ in blocks_per_window.values() - ) - if blocks_in_primary_pool is None: - raise RuntimeError( - "trtllm-gen could not determine blocks_in_primary_pool from the KVCacheManager." - ) - total_num_blocks = ( - int(blocks_in_primary_pool) * kv_cache_manager.num_local_layers * kv_factor - ) - return kv_factor, total_num_blocks - def run_context( self, - params: EnqueueParams, - ): - kv_scale_orig_quant, kv_scale_quant_orig = self._get_kv_scale_params( - params.forward, params.kv_cache_quant_mode - ) - attention_output_orig_quant = params.forward.out_scale - mrope_rotary_cos_sin = params.forward.mrope_rotary_cos_sin + params: FmhaParams, + ) -> None: + attn = params.attn + meta = params.meta + fwd = params.fwd + rope_params = attn.rope_params + bmm1_scale_static = self._get_bmm1_scale(attn) + attention_chunk_size = self._get_attention_chunk_size(attn) ( q_processed, @@ -996,118 +1007,137 @@ def run_context( max_kv_len, window_left, ) = thop.trtllm_gen_context_preprocess( - qkv_input=params.qkv_input, - workspace=params.workspace, - sequence_lengths=params.sequence_lengths, - context_lengths=params.context_lengths, - kv_cache_block_offsets=params.kv_cache_block_offsets, - host_kv_cache_pool_pointers=params.host_kv_cache_pool_pointers, - host_kv_cache_pool_mapping=params.host_kv_cache_pool_mapping, - kv_scale_orig_quant=kv_scale_orig_quant, - kv_scale_quant_orig=kv_scale_quant_orig, - attention_output_orig_quant=attention_output_orig_quant, - rotary_inv_freq=self._rotary_inv_freq, - rotary_cos_sin=self._rotary_cos_sin, - mrope_rotary_cos_sin=mrope_rotary_cos_sin, - layer_idx=params.layer_idx, - tokens_per_block=params.tokens_per_block, - mask_type=params.mask_type, - kv_cache_quant_mode=params.kv_cache_quant_mode, - max_attention_window_size=params.max_attention_window_size, - cyclic_attention_window_size=params.cyclic_attention_window_size, - num_tokens=params.num_tokens, - batch_size=params.batch_size, - input_seq_length=params.input_seq_length, - max_past_kv_length=params.max_past_kv_length, - bmm2_scale=1.0, - fp8_context_fmha=params.fp8_context_fmha, - paged_context_fmha=params.paged_context_fmha, - is_mla_enable=self._is_mla_enable, - total_num_blocks=params.total_num_blocks, - kv_factor=params.kv_factor, - need_build_kv_cache_metadata=True, - multi_processor_count=self._multi_processor_count, - **self._static_kw, + params.qkv_input, # qkv_input + params.workspace, # workspace + params.sequence_lengths, # sequence_lengths + params.context_lengths, # context_lengths + meta.kv_cache_block_offsets, # kv_cache_block_offsets + meta.host_kv_cache_pool_pointers, # host_kv_cache_pool_pointers + meta.host_kv_cache_pool_mapping, # host_kv_cache_pool_mapping + fwd.kv_scale_orig_quant, # kv_scale_orig_quant + fwd.kv_scale_quant_orig, # kv_scale_quant_orig + fwd.out_scale, # attention_output_orig_quant + attn.rotary_inv_freq, # rotary_inv_freq + attn.rotary_cos_sin, # rotary_cos_sin + fwd.mrope_rotary_cos_sin, # mrope_rotary_cos_sin + attn.local_layer_idx, # layer_idx + attn.num_heads, # num_heads + attn.num_kv_heads, # num_kv_heads + attn.head_dim, # head_size + params.tokens_per_block, # tokens_per_block + fwd.mask_type, # mask_type + attn.quant_mode, # kv_cache_quant_mode + params.max_attention_window_size, # max_attention_window_size + params.cyclic_attention_window_size, # cyclic_attention_window_size + params.num_tokens, # num_tokens + params.batch_size, # batch_size + params.input_seq_length, # input_seq_length + params.max_past_kv_length, # max_past_kv_length + rope_params.dim, # rotary_embedding_dim + rope_params.theta, # rotary_embedding_base + int(rope_params.scale_type), # rotary_embedding_scale_type + rope_params.scale, # rotary_embedding_scale + rope_params.max_positions, # rotary_embedding_max_positions + attn.position_embedding_type, # position_embedding_type + bmm1_scale_static, # bmm1_scale + 1.0, # bmm2_scale + attention_chunk_size, # attention_chunk_size + params.fp8_context_fmha, # fp8_context_fmha + meta.use_paged_context_fmha, # paged_context_fmha + attn.is_mla_enable, # is_mla_enable + self._multi_processor_count, # multi_processor_count + params.total_num_blocks, # total_num_blocks + params.kv_factor, # kv_factor + True, # need_build_kv_cache_metadata ) - # FlashInfer accepts a split K/V tuple; TensorRT-LLM stores both views - # in one flat paged KV pool, so both tuple entries intentionally alias. - kv_cache_sf = None - if kv_scale_pool is not None: - kv_cache_sf = (kv_scale_pool, kv_scale_pool) - - has_fp4_kv = QuantMode(params.kv_cache_quant_mode).has_fp4_kv_cache() - if has_fp4_kv: + has_fp4_kv = QuantMode(attn.quant_mode).has_fp4_kv_cache() + if has_fp4_kv and kv_scale_pool is None: + raise RuntimeError("trtllm-gen FP4 KV cache requires KV scale pool.") + if has_fp4_kv or params.fp8_context_fmha: q_processed = ( q_processed.view(torch.uint8) - .flatten()[: params.num_tokens * self._num_heads * self._head_dim] + .flatten()[: params.num_tokens * attn.num_heads * attn.head_dim] .view(torch.float8_e4m3fn) - .view(params.num_tokens, self._num_heads, self._head_dim) + .view(params.num_tokens, attn.num_heads, attn.head_dim) ) - ctx_bmm1_scale = bmm1_scale if has_fp4_kv and bmm1_scale is not None else self._bmm1_scale - ctx_bmm2_scale = bmm2_scale if has_fp4_kv and bmm2_scale is not None else 1.0 - - flashinfer.prefill.trtllm_batch_context_with_kv_cache( - query=q_processed, - kv_cache=(kv_pool, kv_pool), - workspace_buffer=fmha_workspace, - block_tables=block_tables, - seq_lens=params.sequence_lengths, - max_q_len=max_q_len, - max_kv_len=max_kv_len, - bmm1_scale=ctx_bmm1_scale, - bmm2_scale=ctx_bmm2_scale, - batch_size=params.batch_size, - cum_seq_lens_q=cu_q_seqlens, - cum_seq_lens_kv=cu_kv_seqlens, - window_left=window_left, - out=params.context_buf, - kv_layout=self._layout, - sinks=params.forward.attention_sinks, - uses_shared_paged_kv_idx=self.USE_SHARED_PAGED_KV_IDX, - kv_cache_sf=kv_cache_sf, - enable_pdl=self._enable_pdl, + ctx_bmm1_scale = ( + bmm1_scale if params.fp8_context_fmha and bmm1_scale is not None else bmm1_scale_static + ) + ctx_bmm2_scale = bmm2_scale if params.fp8_context_fmha and bmm2_scale is not None else 1.0 + + _trtllm_gen_batch_context_with_kv_cache( + q_processed, # query + kv_pool, # kv_pool + fmha_workspace, # workspace_buffer + block_tables, # block_tables + params.sequence_lengths, # seq_lens + max_q_len, # max_q_len + max_kv_len, # max_kv_len + ctx_bmm1_scale, # bmm1_scale + ctx_bmm2_scale, # bmm2_scale + params.batch_size, # batch_size + cu_q_seqlens, # cum_seq_lens_q + cu_kv_seqlens, # cum_seq_lens_kv + window_left, # window_left + params.context_buf, # out + fwd.attention_sinks, # sinks + self._enable_pdl, # enable_pdl + kv_scale_pool, # kv_scale_pool + self.USE_SHARED_PAGED_KV_IDX, # uses_shared_paged_kv_idx ) thop.trtllm_gen_context_postprocess( - qkv_input=params.qkv_input, - workspace=params.workspace, - sequence_lengths=params.sequence_lengths, - context_lengths=params.context_lengths, - kv_cache_block_offsets=params.kv_cache_block_offsets, - host_kv_cache_pool_pointers=params.host_kv_cache_pool_pointers, - host_kv_cache_pool_mapping=params.host_kv_cache_pool_mapping, - kv_scale_orig_quant=kv_scale_orig_quant, - kv_scale_quant_orig=kv_scale_quant_orig, - attention_output_orig_quant=attention_output_orig_quant, - rotary_cos_sin=self._rotary_cos_sin, - mrope_rotary_cos_sin=mrope_rotary_cos_sin, - layer_idx=params.layer_idx, - tokens_per_block=params.tokens_per_block, - mask_type=params.mask_type, - kv_cache_quant_mode=params.kv_cache_quant_mode, - max_attention_window_size=params.max_attention_window_size, - cyclic_attention_window_size=params.cyclic_attention_window_size, - num_tokens=params.num_tokens, - batch_size=params.batch_size, - input_seq_length=params.input_seq_length, - max_past_kv_length=params.max_past_kv_length, - fp8_context_fmha=params.fp8_context_fmha, - paged_context_fmha=params.paged_context_fmha, - is_mla_enable=self._is_mla_enable, - multi_processor_count=self._multi_processor_count, - **self._static_kw, + params.qkv_input, # qkv_input + params.workspace, # workspace + params.sequence_lengths, # sequence_lengths + params.context_lengths, # context_lengths + meta.kv_cache_block_offsets, # kv_cache_block_offsets + meta.host_kv_cache_pool_pointers, # host_kv_cache_pool_pointers + meta.host_kv_cache_pool_mapping, # host_kv_cache_pool_mapping + fwd.kv_scale_orig_quant, # kv_scale_orig_quant + fwd.kv_scale_quant_orig, # kv_scale_quant_orig + fwd.out_scale, # attention_output_orig_quant + attn.rotary_cos_sin, # rotary_cos_sin + fwd.mrope_rotary_cos_sin, # mrope_rotary_cos_sin + attn.local_layer_idx, # layer_idx + attn.num_heads, # num_heads + attn.num_kv_heads, # num_kv_heads + attn.head_dim, # head_size + params.tokens_per_block, # tokens_per_block + fwd.mask_type, # mask_type + attn.quant_mode, # kv_cache_quant_mode + params.max_attention_window_size, # max_attention_window_size + params.cyclic_attention_window_size, # cyclic_attention_window_size + params.num_tokens, # num_tokens + params.batch_size, # batch_size + params.input_seq_length, # input_seq_length + params.max_past_kv_length, # max_past_kv_length + rope_params.dim, # rotary_embedding_dim + rope_params.theta, # rotary_embedding_base + int(rope_params.scale_type), # rotary_embedding_scale_type + rope_params.scale, # rotary_embedding_scale + rope_params.max_positions, # rotary_embedding_max_positions + attn.position_embedding_type, # position_embedding_type + bmm1_scale_static, # bmm1_scale + params.fp8_context_fmha, # fp8_context_fmha + meta.use_paged_context_fmha, # paged_context_fmha + attn.is_mla_enable, # is_mla_enable + attention_chunk_size, # attention_chunk_size + self._multi_processor_count, # multi_processor_count ) def run_generation( self, - params: EnqueueParams, - ): - batch_beam = params.num_requests * params.beam_width - kv_scale_orig_quant, kv_scale_quant_orig = self._get_kv_scale_params( - params.forward, params.kv_cache_quant_mode - ) - attention_output_orig_quant = params.forward.out_scale + params: FmhaParams, + ) -> None: + attn = params.attn + meta = params.meta + fwd = params.fwd + rope_params = attn.rope_params + bmm1_scale_static = self._get_bmm1_scale(attn) + attention_chunk_size = self._get_attention_chunk_size(attn) + batch_beam = params.num_requests * meta.beam_width ( q_processed, kv_pool, @@ -1122,110 +1152,136 @@ def run_generation( window_left, is_multi_token_gen, ) = thop.trtllm_gen_generation_preprocess( - qkv_input=params.qkv_input, - workspace=params.workspace, - sequence_lengths=params.sequence_lengths, - spec_decoding_generation_lengths=params.spec_decoding_generation_lengths, - spec_decoding_position_offsets=params.spec_decoding_position_offsets, - kv_cache_block_offsets=params.kv_cache_block_offsets, - host_kv_cache_pool_pointers=params.host_kv_cache_pool_pointers, - host_kv_cache_pool_mapping=params.host_kv_cache_pool_mapping, - kv_scale_orig_quant=kv_scale_orig_quant, - kv_scale_quant_orig=kv_scale_quant_orig, - attention_output_orig_quant=attention_output_orig_quant, - rotary_inv_freq=self._rotary_inv_freq, - rotary_cos_sin=self._rotary_cos_sin, - layer_idx=params.layer_idx, - seq_offset=params.seq_offset, - tokens_per_block=params.tokens_per_block, - kv_cache_quant_mode=params.kv_cache_quant_mode, - max_attention_window_size=params.max_attention_window_size, - cyclic_attention_window_size=params.cyclic_attention_window_size, - num_tokens=params.num_tokens, - batch_beam=batch_beam, - input_seq_length=params.input_seq_length, - max_past_kv_length=params.max_past_kv_length, - bmm2_scale=1.0, - fp8_context_fmha=params.fp8_context_fmha, - predicted_tokens_per_seq=params.predicted_tokens_per_seq, - multi_processor_count=self._multi_processor_count, - total_num_blocks=params.total_num_blocks, - kv_factor=params.kv_factor, - need_build_kv_cache_metadata=True, - **self._static_kw, + params.qkv_input, # qkv_input + params.workspace, # workspace + params.sequence_lengths, # sequence_lengths + params.spec_decoding_generation_lengths, # spec_decoding_generation_lengths + params.spec_decoding_position_offsets, # spec_decoding_position_offsets + meta.kv_cache_block_offsets, # kv_cache_block_offsets + meta.host_kv_cache_pool_pointers, # host_kv_cache_pool_pointers + meta.host_kv_cache_pool_mapping, # host_kv_cache_pool_mapping + fwd.kv_scale_orig_quant, # kv_scale_orig_quant + fwd.kv_scale_quant_orig, # kv_scale_quant_orig + fwd.out_scale, # attention_output_orig_quant + attn.rotary_inv_freq, # rotary_inv_freq + attn.rotary_cos_sin, # rotary_cos_sin + attn.local_layer_idx, # layer_idx + params.seq_offset, # seq_offset + attn.num_heads, # num_heads + attn.num_kv_heads, # num_kv_heads + attn.head_dim, # head_size + params.tokens_per_block, # tokens_per_block + attn.quant_mode, # kv_cache_quant_mode + params.max_attention_window_size, # max_attention_window_size + params.cyclic_attention_window_size, # cyclic_attention_window_size + params.num_tokens, # num_tokens + batch_beam, # batch_beam + params.input_seq_length, # input_seq_length + params.max_past_kv_length, # max_past_kv_length + rope_params.dim, # rotary_embedding_dim + rope_params.theta, # rotary_embedding_base + int(rope_params.scale_type), # rotary_embedding_scale_type + rope_params.scale, # rotary_embedding_scale + rope_params.max_positions, # rotary_embedding_max_positions + attn.position_embedding_type, # position_embedding_type + bmm1_scale_static, # bmm1_scale + 1.0, # bmm2_scale + params.fp8_context_fmha, # fp8_context_fmha + attn.predicted_tokens_per_seq, # predicted_tokens_per_seq + attention_chunk_size, # attention_chunk_size + self._multi_processor_count, # multi_processor_count + params.total_num_blocks, # total_num_blocks + params.kv_factor, # kv_factor + True, # need_build_kv_cache_metadata + ) + + # FIXME: Flashinfer trtllm-gen API doesn't support a separate + # multi CTAs counter buffer. We have to clear a small buffer + # before trtllm_gen_batch_decode_with_kv_cache. + # + # We must also avoid clearing the workspace only when it is + # resized. The warmup phase may have already cached the workspace + # pointer; if the capture phase skips the zeroing step, the + # CUDA graph will not include the counter initialization. We + # have already verified—specifically in the context of the GPTOSS-20B + # test graph replay scenario—that this skipping logic is unsafe. + # + # https://github.com/flashinfer-ai/flashinfer/issues/3433 + _clear_multi_ctas_kv_counter_workspace( + fmha_workspace, attn.num_heads, meta.max_num_requests, self._multi_processor_count ) q_len_per_req = None if is_multi_token_gen else params.input_seq_length decode_max_q_len = max_q_len if is_multi_token_gen else None decode_cu_seqlens = cu_seqlens if is_multi_token_gen else None - # FlashInfer accepts a split K/V tuple; TensorRT-LLM stores both views - # in one flat paged KV pool, so both tuple entries intentionally alias. - kv_cache_sf = None - if kv_scale_pool is not None: - kv_cache_sf = (kv_scale_pool, kv_scale_pool) - - has_fp4_kv = QuantMode(params.kv_cache_quant_mode).has_fp4_kv_cache() - if has_fp4_kv: + + has_fp4_kv = QuantMode(attn.quant_mode).has_fp4_kv_cache() + if has_fp4_kv and kv_scale_pool is None: + raise RuntimeError("trtllm-gen FP4 KV cache requires KV scale pool.") + if has_fp4_kv or params.fp8_context_fmha: q_processed = ( q_processed.view(torch.uint8) - .flatten()[: params.num_tokens * self._num_heads * self._head_dim] + .flatten()[: params.num_tokens * attn.num_heads * attn.head_dim] .view(torch.float8_e4m3fn) - .view(params.num_tokens, self._num_heads, self._head_dim) + .view(params.num_tokens, attn.num_heads, attn.head_dim) ) - gen_bmm1_scale = bmm1_scale if has_fp4_kv else self._bmm1_scale - gen_bmm2_scale = bmm2_scale if has_fp4_kv else 1.0 - - flashinfer.decode.trtllm_batch_decode_with_kv_cache( - query=q_processed, - kv_cache=(kv_pool, kv_pool), - workspace_buffer=fmha_workspace, - block_tables=block_tables, - seq_lens=params.sequence_lengths, - max_seq_len=max_kv_len, - out=params.context_buf, - bmm1_scale=gen_bmm1_scale, - bmm2_scale=gen_bmm2_scale, - window_left=window_left, - kv_layout=self._layout, - sinks=params.forward.attention_sinks, - q_len_per_req=q_len_per_req, - max_q_len=decode_max_q_len, - cum_seq_lens_q=decode_cu_seqlens, - uses_shared_paged_kv_idx=self.USE_SHARED_PAGED_KV_IDX, - kv_cache_sf=kv_cache_sf, - enable_pdl=self._enable_pdl, - backend="trtllm-gen", + gen_bmm1_scale = ( + bmm1_scale if params.fp8_context_fmha and bmm1_scale is not None else bmm1_scale_static + ) + gen_bmm2_scale = bmm2_scale if params.fp8_context_fmha and bmm2_scale is not None else 1.0 + + _trtllm_gen_batch_decode_with_kv_cache( + q_processed, # query + kv_pool, # kv_pool + fmha_workspace, # workspace_buffer + block_tables, # block_tables + params.sequence_lengths, # seq_lens + max_kv_len, # max_seq_len + gen_bmm1_scale, # bmm1_scale + gen_bmm2_scale, # bmm2_scale + window_left, # window_left + params.context_buf, # out + fwd.attention_sinks, # sinks + self._enable_pdl, # enable_pdl + q_len_per_req, # q_len_per_req + decode_max_q_len, # max_q_len + decode_cu_seqlens, # cum_seq_lens_q + kv_scale_pool, # kv_scale_pool + self.USE_SHARED_PAGED_KV_IDX, # uses_shared_paged_kv_idx ) def run_mla_generation( self, - params: EnqueueParams, + params: FmhaParams, ) -> None: """MLA generation decode using flashinfer MLA kernel.""" + attn = params.attn + meta = params.meta + fwd = params.fwd if 0 < params.cyclic_attention_window_size < params.max_past_kv_length: raise NotImplementedError( "Sliding-window attention is not supported by MLA decode path." ) - if self._attention_chunk_size != 0: + if self._get_attention_chunk_size(attn) != 0: raise NotImplementedError("Chunked-attention is not supported by MLA decode path.") - batch_beam = params.num_requests * params.beam_width + batch_beam = params.num_requests * meta.beam_width if params.attention_input is None: raise RuntimeError("MLA generation requires attention_input.") - kv_cache, block_tables, _ = thop.build_trtllm_gen_kv_cache_metadata( - host_kv_cache_pool_pointers=params.host_kv_cache_pool_pointers, - host_kv_cache_pool_mapping=params.host_kv_cache_pool_mapping, - kv_cache_block_offsets=params.kv_cache_block_offsets, - layer_idx=params.layer_idx, - num_kv_heads=self._num_kv_heads, - tokens_per_block=params.tokens_per_block, - head_dim=self._head_dim, - kv_factor=params.kv_factor, - total_num_blocks=params.total_num_blocks, - kv_cache_quant_mode=params.kv_cache_quant_mode, - batch_start=params.seq_offset, - batch_size=batch_beam, - dtype=params.attention_input.dtype, + kv_cache, block_tables = thop.build_trtllm_gen_kv_cache_metadata( + meta.host_kv_cache_pool_pointers, # host_kv_cache_pool_pointers + meta.host_kv_cache_pool_mapping, # host_kv_cache_pool_mapping + meta.kv_cache_block_offsets, # kv_cache_block_offsets + attn.local_layer_idx, # layer_idx + attn.num_kv_heads, # num_kv_heads + params.tokens_per_block, # tokens_per_block + attn.head_dim, # head_dim + params.kv_factor, # kv_factor + params.total_num_blocks, # total_num_blocks + attn.quant_mode, # kv_cache_quant_mode + params.seq_offset, # batch_start + batch_beam, # batch_size + params.attention_input.dtype, # dtype ) pages_per_superblock = 128 // params.tokens_per_block @@ -1236,31 +1292,34 @@ def run_mla_generation( pad = pages_per_superblock - remainder block_tables = torch.nn.functional.pad(block_tables, (0, pad), value=0) - kv_lora_rank = self._kv_lora_rank - qk_nope_head_dim = self._qk_nope_head_dim - qk_rope_head_dim = self._qk_rope_head_dim + kv_lora_rank = attn.kv_lora_rank or 0 + qk_nope_head_dim = attn.qk_nope_head_dim or 0 + qk_rope_head_dim = attn.qk_rope_head_dim or 0 mla_head_dim_qk = kv_lora_rank + qk_rope_head_dim q_len_per_req = params.num_tokens // batch_beam if batch_beam > 0 else 1 - query = params.qkv_input.view(batch_beam, q_len_per_req, self._num_heads, mla_head_dim_qk) + query = params.qkv_input.view(batch_beam, q_len_per_req, attn.num_heads, mla_head_dim_qk) - bmm1_scale = 1.0 / (self._q_scaling * math.sqrt(qk_nope_head_dim + qk_rope_head_dim)) + bmm1_scale = 1.0 / (attn.q_scaling * math.sqrt(qk_nope_head_dim + qk_rope_head_dim)) flashinfer.mla.trtllm_batch_decode_with_kv_cache_mla( - query=query, - kv_cache=kv_cache, - workspace_buffer=params.workspace.view(-1, 4), - qk_nope_head_dim=qk_nope_head_dim, - kv_lora_rank=kv_lora_rank, - qk_rope_head_dim=qk_rope_head_dim, - block_tables=block_tables, - seq_lens=params.sequence_lengths, - max_seq_len=params.max_past_kv_length, - out=params.context_buf.view(batch_beam, q_len_per_req, self._num_heads, kv_lora_rank), - bmm1_scale=bmm1_scale, - bmm2_scale=1.0, - sinks=params.forward.attention_sinks, - uses_shared_paged_kv_idx=self.USE_SHARED_PAGED_KV_IDX, - enable_pdl=self._enable_pdl, - backend="trtllm-gen", + query, # query + kv_cache, # kv_cache + params.workspace.view(-1, 4), # workspace_buffer + qk_nope_head_dim, # qk_nope_head_dim + kv_lora_rank, # kv_lora_rank + qk_rope_head_dim, # qk_rope_head_dim + block_tables, # block_tables + params.sequence_lengths, # seq_lens + params.max_past_kv_length, # max_seq_len + 0, # sparse_mla_top_k + params.context_buf.view(batch_beam, q_len_per_req, attn.num_heads, kv_lora_rank), # out + bmm1_scale, # bmm1_scale + 1.0, # bmm2_scale + fwd.attention_sinks, # sinks + None, # skip_softmax_threshold_scale_factor + self._enable_pdl, # enable_pdl + "trtllm-gen", # backend + True, # is_var_seq + self.USE_SHARED_PAGED_KV_IDX, # uses_shared_paged_kv_idx ) From 58fbfb983fb733295e1ccc591b3d40a755254c2d Mon Sep 17 00:00:00 2001 From: Yuanjing Xue <197832395+yuanjingx87@users.noreply.github.com> Date: Tue, 9 Jun 2026 08:52:36 -0700 Subject: [PATCH 082/119] [None][infra] Test DFW with BSL branch (#14597) Signed-off-by: Yuanjing Xue <197832395+yuanjingx87@users.noreply.github.com> --- jenkins/L0_Test.groovy | 105 ++++++++++++------ .../scripts/cbts/rules/out_of_scope_rule.py | 2 + scripts/generate_duration.py | 14 ++- .../integration/defs/.test_durations_aws_dfw | 61 ++++++++++ 4 files changed, 144 insertions(+), 38 deletions(-) create mode 100644 tests/integration/defs/.test_durations_aws_dfw diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index a9504ad0d47a..590d5b27171e 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -300,7 +300,7 @@ def runIsolatedTests(preprocessedLists, testCmdLine, llmSrc, stageName) { return rerunFailed // Return the updated value } -def processShardTestList(llmSrc, testDBList, splitId, splits, perfMode=false) { +def processShardTestList(llmSrc, testDBList, splitId, splits, perfMode=false, durationsPath="") { // Preprocess testDBList to extract ISOLATION markers echo "Preprocessing testDBList to extract ISOLATION markers..." @@ -367,8 +367,11 @@ def processShardTestList(llmSrc, testDBList, splitId, splits, perfMode=false) { "--test-list=${cleanedTestDBList}", "--quiet", "--splits ${splits}", - "--group ${splitId}" + "--group ${splitId}", ] + if (durationsPath) { + testListCmd += ["--durations-path ${durationsPath}"] + } try { // First execute the pytest command and check if it succeeds @@ -582,7 +585,7 @@ def cleanUpNodeResources(def pipeline, SlurmCluster cluster, String clusterName, } } -def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, skipInstallWheel=false, cpver="cp312", String postTag="") +def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, skipInstallWheel=false, cpver="cp312", String postTag="", boolean useClusterDurations=false) { SlurmPartition partition = SlurmConfig.resolvePlatform(platform) SlurmCluster cluster = SlurmConfig.clusterConfig[partition.clusterName] @@ -801,7 +804,7 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, } else { throw new Exception("Unsupported container runtime: ${cluster.containerRuntime}") } - executeLLMTestOnSlurm(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, slurmRunner, postTag) + executeLLMTestOnSlurm(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, slurmRunner, postTag, useClusterDurations) } finally { stage("Clean Up Slurm Resource") { // Workaround to handle the interruption during clean up SLURM resources @@ -816,12 +819,12 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, } } -def executeLLMTestOnSlurm(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", runner, String postTag="") +def executeLLMTestOnSlurm(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", runner, String postTag="", boolean useClusterDurations=false) { runner { // TODO: refactor the finallyRunner to reuse within slurm or nonslurm job. cacheErrorAndUploadResult(stageName, { - runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, false, postTag) + runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, false, postTag, useClusterDurations) }, { // If the execution test list is null, remove the test result xml sh """ @@ -980,7 +983,7 @@ def getMountListForSlurmTest(SlurmCluster cluster, boolean useSbatch = false) return mounts } -def runLLMTestlistWithSbatch(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, nodeCount=1, skipInstallWheel=false, cpver="cp312", String postTag="") +def runLLMTestlistWithSbatch(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, nodeCount=1, skipInstallWheel=false, cpver="cp312", String postTag="", boolean useClusterDurations=false) { SlurmPartition partition = SlurmConfig.resolvePlatform(platform) SlurmCluster cluster = SlurmConfig.clusterConfig[partition.clusterName] @@ -1064,7 +1067,8 @@ def runLLMTestlistWithSbatch(pipeline, platform, testList, config=VANILLA_CONFIG // line is "Mako options:", maybe we can make it more generic, which // if the line cannot be split by "=", just ignore that line. def makoOptsJson = transformMakoArgsToJson(["Mako options:"] + makoArgs) - def testListPathLocal = renderTestDB(pipeline, testList, llmSrcLocal, stageName, makoOptsJson) + String clusterNameForDurations = useClusterDurations ? partition.clusterName.replaceAll('[^a-zA-Z0-9]', '_') : null + def testListPathLocal = renderTestDB(pipeline, testList, llmSrcLocal, stageName, makoOptsJson, clusterNameForDurations) Utils.copyFileToRemoteHost( pipeline, remote, @@ -1111,6 +1115,12 @@ def runLLMTestlistWithSbatch(pipeline, platform, testList, config=VANILLA_CONFIG pytestUtil = "$llmSrcNode/tensorrt_llm/llmapi/trtllm-llmapi-launch" } + def clusterDurationsArgsNode = [] + if (useClusterDurations) { + def clusterKey = partition.clusterName.replaceAll('[^a-zA-Z0-9]', '_') + def clusterDurationsPathNode = "${llmSrcNode}/tests/integration/defs/.test_durations_${clusterKey}" + clusterDurationsArgsNode = ["--durations-path ${clusterDurationsPathNode}"] + } def pytestCommand = getPytestBaseCommandLine( llmSrcNode, stageName, @@ -1124,7 +1134,8 @@ def runLLMTestlistWithSbatch(pipeline, platform, testList, config=VANILLA_CONFIG "--test-list=$testListPathNode", "--splitting-algorithm least_duration", "--splits $splits", - "--group $splitId" + "--group $splitId", + *clusterDurationsArgsNode, ] ).join(" ") @@ -1513,7 +1524,7 @@ def _cbtsMaybeCollapseSplits(stageName, splitId, splits) { return [skip: false, splits: 1, splitId: 1] } -def runLLMTestlistOnSlurm(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, nodeCount=1, runWithSbatch=false, skipInstallWheel=false, cpver="cp312", String outerAttemptTag="") +def runLLMTestlistOnSlurm(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, gpuCount=1, nodeCount=1, runWithSbatch=false, skipInstallWheel=false, cpver="cp312", String outerAttemptTag="", boolean useClusterDurations=false) { def collapse = _cbtsMaybeCollapseSplits(stageName, splitId, splits) if (collapse.skip) { @@ -1548,9 +1559,9 @@ def runLLMTestlistOnSlurm(pipeline, platform, testList, config=VANILLA_CONFIG, p def postTag = "${outerAttemptTag}${innerSuffix}" if (nodeCount > 1 || runWithSbatch) { - runLLMTestlistWithSbatch(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, gpuCount, nodeCount, skipInstallWheel, cpver, postTag) + runLLMTestlistWithSbatch(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, gpuCount, nodeCount, skipInstallWheel, cpver, postTag, useClusterDurations) } else { - runLLMTestlistWithAgent(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, gpuCount, skipInstallWheel, cpver, postTag) + runLLMTestlistWithAgent(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, gpuCount, skipInstallWheel, cpver, postTag, useClusterDurations) } // Job succeeded @@ -1712,9 +1723,9 @@ class GlobalState { static final int MAX_PORT = 32000 // Maximum port number to avoid system ports } -def recordRenderedStageAttemptEstimate(pipeline, String llmSrc, String testListPath, String stageName, def renderedTestCount) +def recordRenderedStageAttemptEstimate(pipeline, String llmSrc, String testListPath, String stageName, def renderedTestCount, String clusterName=null) { - def estimate = trtllm_utils.estimateRenderedStageAttemptMillis(pipeline, llmSrc, testListPath, stageName, renderedTestCount) + def estimate = trtllm_utils.estimateRenderedStageAttemptMillis(pipeline, llmSrc, testListPath, stageName, renderedTestCount, clusterName) if (estimate.error) { echo "[CI-BUDGET] ${stageName}: failed to read .test_durations; using count-based estimate. Error: ${estimate.error}" } @@ -2481,7 +2492,7 @@ def getMakoArgsFromStageName(stageName, parseSysinfo=false) { return makoArgs } -def renderTestDB(pipeline, testContext, llmSrc, stageName, preDefinedMakoOpts=null) { +def renderTestDB(pipeline, testContext, llmSrc, stageName, preDefinedMakoOpts=null, String clusterName=null) { def makoOpts = preDefinedMakoOpts if (!makoOpts) { @@ -2543,7 +2554,7 @@ def renderTestDB(pipeline, testContext, llmSrc, stageName, preDefinedMakoOpts=nu def testDBLabel = (cbts != null && cbts.test_db_dir_override) ? "CBTS-narrowed [${cbts.scope}]" : "source" echo "renderTestDB: stage=${stageName} context=${testContext} test-db=${testDBLabel} dir=${testDBPath} -> ${testCount} tests" sh(script: "cat ${testList}") - recordRenderedStageAttemptEstimate(pipeline, llmSrc, testList, stageName, testCount) + recordRenderedStageAttemptEstimate(pipeline, llmSrc, testList, stageName, testCount, clusterName) return testList } @@ -3053,7 +3064,7 @@ def priorAttemptTags(String postTag) { return priors } -def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", typeCheck=false, String postTag="") +def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", typeCheck=false, String postTag="", boolean useClusterDurations=false) { // Step 1: create LLM_ROOT dir and clean up the workspace def llmRootConfig = "${LLM_ROOT}${config}" @@ -3203,7 +3214,23 @@ def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CO def noRegularTests = false def noIsolateTests = false def rerunFailed = false - def testDBList = renderTestDB(pipeline, testList, llmSrc, stageName) + + // When useClusterDurations is set, use a per-cluster durations file keyed on + // partition.clusterName (e.g. "aws-dfw", "dlcluster"). This lets each cluster + // build its own timing baseline so sharding is not skewed by timings collected + // on different hardware. Falls back to the shared .test_durations when unset. + def clusterDurationsArgs = [] + def clusterDurationsPath = "" + String clusterNameForDurations = null + if (useClusterDurations) { + def partition = SlurmConfig.resolvePlatform(platform) + def clusterKey = partition.clusterName.replaceAll('[^a-zA-Z0-9]', '_') + clusterNameForDurations = clusterKey + clusterDurationsPath = "${llmSrc}/tests/integration/defs/.test_durations_${clusterKey}" + clusterDurationsArgs = ["--durations-path ${clusterDurationsPath}"] + } + + def testDBList = renderTestDB(pipeline, testList, llmSrc, stageName, null, clusterNameForDurations) // Download and Merge waives.txt mergeWaivesTxt(pipeline, llmSrc, stageName) @@ -3214,7 +3241,7 @@ def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CO } // Process shard test list and create separate files for regular and isolate tests - def preprocessedLists = processShardTestList(llmSrc, testDBList, splitId, splits, perfMode) + def preprocessedLists = processShardTestList(llmSrc, testDBList, splitId, splits, perfMode, clusterDurationsPath) // Test Coverage def TRTLLM_WHL_PATH = sh(returnStdout: true, script: "pip3 show tensorrt_llm | grep Location | cut -d ' ' -f 2").replaceAll("\\s","") @@ -3248,7 +3275,7 @@ def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CO TRTLLM_WHL_PATH, coverageConfigFile, "", // pytestUtil - [], // extraArgs + clusterDurationsArgs, containerPortStart, containerPortNum ) @@ -3420,7 +3447,7 @@ def runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config=VANILLA_CO // composed with an attempt tag by the helper) and `isFinalAttempt` (so this // function's `cacheErrorAndUploadResult` can suppress synthetic stage-fail XML // and junit() for intermediate retryable failures). -def runLLMTestlistOnPlatform(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", postTag="", typeCheck=false, boolean isFinalAttempt=true, Map retryContext=null) +def runLLMTestlistOnPlatform(pipeline, platform, testList, config=VANILLA_CONFIG, perfMode=false, stageName="Undefined", splitId=1, splits=1, skipInstallWheel=false, cpver="cp312", postTag="", typeCheck=false, boolean isFinalAttempt=true, Map retryContext=null, boolean useClusterDurations=false) { def collapse = _cbtsMaybeCollapseSplits(stageName, splitId, splits) if (collapse.skip) { @@ -3430,7 +3457,7 @@ def runLLMTestlistOnPlatform(pipeline, platform, testList, config=VANILLA_CONFIG splitId = collapse.splitId cacheErrorAndUploadResult(stageName, { - runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, typeCheck, postTag) + runLLMTestlistOnPlatformImpl(pipeline, platform, testList, config, perfMode, stageName, splitId, splits, skipInstallWheel, cpver, typeCheck, postTag, useClusterDurations) }, { if (testFilter[(DEBUG_MODE)]) { try { @@ -3925,11 +3952,11 @@ def runKubernetesPodWithInfraRetry(Map opts = [:], pipeline, podSpec, containerN } } -def buildStageConfigs(stageName, platform, testlist, testCount, gpuCount, nodeCount, runWithSbatch=false) { +def buildStageConfigs(stageName, platform, testlist, testCount, gpuCount, nodeCount, runWithSbatch=false, useClusterDurations=false) { def configs = [:] for (int k = 1; k <= testCount; k++) { def key = "${stageName}-${k}" - configs[key] = [platform, testlist, k, testCount, gpuCount, nodeCount, runWithSbatch] + configs[key] = [platform, testlist, k, testCount, gpuCount, nodeCount, runWithSbatch, useClusterDurations] } return configs } @@ -4151,11 +4178,15 @@ def launchTestJobs(pipeline, testFilter) fullSet += SBSATestConfigs.keySet() SBSASlurmTestConfigs = [ - "GB200-4_GPUs-PyTorch-1": ["auto:gb200-x4", "l0_gb200_multi_gpus", 1, 4, 4], - "GB200-4_GPUs-PyTorch-2": ["auto:gb200-x4", "l0_gb200_multi_gpus", 2, 4, 4], - "GB200-4_GPUs-PyTorch-3": ["auto:gb200-x4", "l0_gb200_multi_gpus", 3, 4, 4], - "GB200-4_GPUs-PyTorch-4": ["auto:gb200-x4", "l0_gb200_multi_gpus", 4, 4, 4], - "GB200-4_GPUs-PyTorch-Post-Merge-1": ["auto:gb200-x4", "l0_gb200_multi_gpus", 1, 1, 4], + // [platform, testList, splitId, splits, gpuCount, nodeCount?, runWithSbatch?, useClusterDurations?] + // useClusterDurations=true: record actual test times so each cluster builds its own + // .test_durations_ baseline for load-balanced sharding. + "GB200-4_GPUs-PyTorch-1": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 1, 5, 4, 1, false, true], + "GB200-4_GPUs-PyTorch-2": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 2, 5, 4, 1, false, true], + "GB200-4_GPUs-PyTorch-3": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 3, 5, 4, 1, false, true], + "GB200-4_GPUs-PyTorch-4": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 4, 5, 4, 1, false, true], + "GB200-4_GPUs-PyTorch-5": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 5, 5, 4, 1, false, true], + "GB200-4_GPUs-PyTorch-Post-Merge-1": ["auto:gb200-x4-split", "l0_gb200_multi_gpus", 1, 1, 4, 1, false, true], "GB10-PyTorch-Post-Merge-1": ["gb10x-single", "l0_gb10", 1, 1], "GB300-PyTorch-1": ["auto:gb300-x4", "l0_gb300", 1, 1], "GB300-4_GPUs-PyTorch-Post-Merge-1": ["auto:gb300-x4", "l0_gb300_multi_gpus", 1, 3, 4], @@ -4180,11 +4211,11 @@ def launchTestJobs(pipeline, testFilter) multiNodesSBSAConfigs = [ // Each testcase uses 8 GPUs and 2 nodes. // https://nvbugs/5598863 (uncorrectable NVLink error detected during the execution) may not exist in OCI machines. - "GB200-8_GPUs-2_Nodes-PyTorch-1": ["auto:gb200-flex", "l0_gb200_multi_nodes", 1, 2, 8, 2], - "GB200-8_GPUs-2_Nodes-PyTorch-2": ["auto:gb200-flex", "l0_gb200_multi_nodes", 2, 2, 8, 2], - "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-1": ["auto:gb200-flex", "l0_gb200_multi_nodes", 1, 3, 8, 2], - "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-2": ["auto:gb200-flex", "l0_gb200_multi_nodes", 2, 3, 8, 2], - "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-3": ["auto:gb200-flex", "l0_gb200_multi_nodes", 3, 3, 8, 2], + "GB200-8_GPUs-2_Nodes-PyTorch-1": ["auto:gb200-flex-split", "l0_gb200_multi_nodes", 1, 2, 8, 2], + "GB200-8_GPUs-2_Nodes-PyTorch-2": ["auto:gb200-flex-split", "l0_gb200_multi_nodes", 2, 2, 8, 2], + "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-1": ["auto:gb200-flex-split", "l0_gb200_multi_nodes", 1, 3, 8, 2], + "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-2": ["auto:gb200-flex-split", "l0_gb200_multi_nodes", 2, 3, 8, 2], + "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-3": ["auto:gb200-flex-split", "l0_gb200_multi_nodes", 3, 3, 8, 2], ] // PerfSanity post-merge aggregated // 2 Nodes @@ -4343,7 +4374,7 @@ def launchTestJobs(pipeline, testFilter) if (env.targetArch == AARCH64_TRIPLE) { parallelJobs = SBSATestConfigs.collectEntries{key, values -> [key, [createKubernetesPodConfig(LLM_DOCKER_IMAGE, values[0], "arm64"), { attemptTag, isFinalAttempt, retryContext = null -> - runLLMTestlistOnPlatform(pipeline, values[0], values[1], LINUX_AARCH64_CONFIG, false, key, values[2], values[3], false, "cp312", attemptTag, false, isFinalAttempt, retryContext) + runLLMTestlistOnPlatform(pipeline, values[0], values[1], LINUX_AARCH64_CONFIG, false, key, values[2], values[3], false, "cp312", attemptTag, false, isFinalAttempt, retryContext, values[4] ?: false) }]]} // Add SBSA Slurm jobs @@ -4362,7 +4393,7 @@ def launchTestJobs(pipeline, testFilter) if (key.contains("llvm")) { config = LLVM_CONFIG } - runLLMTestlistOnSlurm(pipeline, values[0], values[1], config, key.contains("-Perf-"), key, values[2], values[3], values[4] ?: 1, values[5] ?: 1, values[6] ?: false, false, "cp312", attemptTag) + runLLMTestlistOnSlurm(pipeline, values[0], values[1], config, key.contains("-Perf-"), key, values[2], values[3], values[4] ?: 1, values[5] ?: 1, values[6] ?: false, false, "cp312", attemptTag, values[7] ?: false) }, [singleAttempt: true]]]} parallelJobs += parallelSlurmJobs @@ -4376,7 +4407,7 @@ def launchTestJobs(pipeline, testFilter) if (key.contains("llvm")) { config = LLVM_CONFIG } - runLLMTestlistOnSlurm(pipeline, values[0], values[1], config, key.contains("-Perf-"), key, values[2], values[3], values[4] ?: 1, values[5] ?: 2, values[6] ?: false, false, "cp312", attemptTag) + runLLMTestlistOnSlurm(pipeline, values[0], values[1], config, key.contains("-Perf-"), key, values[2], values[3], values[4] ?: 1, values[5] ?: 2, values[6] ?: false, false, "cp312", attemptTag, values[7] ?: false) }, [singleAttempt: true]]]} parallelJobs += parallelMultiNodesSBSAJobs diff --git a/jenkins/scripts/cbts/rules/out_of_scope_rule.py b/jenkins/scripts/cbts/rules/out_of_scope_rule.py index 28b233c8ce93..7017abc65ad1 100644 --- a/jenkins/scripts/cbts/rules/out_of_scope_rule.py +++ b/jenkins/scripts/cbts/rules/out_of_scope_rule.py @@ -38,6 +38,8 @@ # consumed by any L0 pipeline. # - tests/integration/defs/.test_durations : pytest-split timing cache; # used at runtime, doesn't affect test selection. +# - tests/integration/defs/.test_durations_* : per-cluster timing caches +# (e.g. .test_durations_aws_dfw); same rationale as above. # - tests/integration/defs/agg_unit_mem_df.csv : per-(gpu, case) # pytest-xdist parallel_factor table consumed by test_unittests.py; # tunes worker count only, no impact on which tests run or their diff --git a/scripts/generate_duration.py b/scripts/generate_duration.py index 10d69978f572..fec429c305e6 100644 --- a/scripts/generate_duration.py +++ b/scripts/generate_duration.py @@ -10,6 +10,13 @@ type=str, default="new_test_duration.json", help="Path to the output duration file (default: new_test_duration.json)") +parser.add_argument( + "--cluster", + type=str, + default=None, + help="Cluster name (e.g. 'aws_dfw'). When set, writes " + "tests/integration/defs/.test_durations_ relative to the " + "repo root instead of --duration-file.") args = parser.parse_args() # Define the directory containing the test result folders @@ -17,7 +24,12 @@ # Define the output file paths FULL_RESULT_LOG = "full_result.log" -NEW_TEST_DURATION = args.duration_file +if args.cluster: + _repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + NEW_TEST_DURATION = os.path.join(_repo_root, "tests", "integration", "defs", + f".test_durations_{args.cluster}") +else: + NEW_TEST_DURATION = args.duration_file # Step 1: Prepare full_result.log with open(FULL_RESULT_LOG, 'w') as full_result_file: diff --git a/tests/integration/defs/.test_durations_aws_dfw b/tests/integration/defs/.test_durations_aws_dfw new file mode 100644 index 000000000000..5eb61f0475c0 --- /dev/null +++ b/tests/integration/defs/.test_durations_aws_dfw @@ -0,0 +1,61 @@ +{ + 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"test_unittests.py::test_unittests_v2[unittest/_torch/modules/moe/test_moe_comm.py::TestMoEComm::test_moe_comm_postquant]": 211.54122115299106 +} From 451dbb8b2cdbc2256379ac4c9a1cc7a17ceb9994 Mon Sep 17 00:00:00 2001 From: "Wang, Xiao" <24860335+xwang233@users.noreply.github.com> Date: Tue, 9 Jun 2026 08:57:20 -0700 Subject: [PATCH 083/119] [TRTLLM-12214][perf] customMoeRoutingKernel: lower BLOCK_SIZE to 128, raise maxNumBlocks (#14590) Signed-off-by: Xiao Wang <24860335+xwang233@users.noreply.github.com> --- .../kernels/customMoeRoutingKernels.cu | 4 +- .../_torch/modules/test_moe_routing.py | 48 +++++++++++++++++++ 2 files changed, 50 insertions(+), 2 deletions(-) diff --git a/cpp/tensorrt_llm/kernels/customMoeRoutingKernels.cu b/cpp/tensorrt_llm/kernels/customMoeRoutingKernels.cu index c32b9662f832..58ccb4ac8ea8 100644 --- a/cpp/tensorrt_llm/kernels/customMoeRoutingKernels.cu +++ b/cpp/tensorrt_llm/kernels/customMoeRoutingKernels.cu @@ -39,7 +39,7 @@ static constexpr int WARP_SIZE = 32; // Default block size for kernels with small MaxNumExperts (<=128). // Large-expert variants (256/384/512) use a smaller block (see pickBlockSize) // to reduce register-file pressure and permit higher SM occupancy. -static constexpr int DEFAULT_BLOCK_SIZE = 1024; +static constexpr int DEFAULT_BLOCK_SIZE = 128; static constexpr int LARGE_BLOCK_SIZE = 256; template @@ -240,7 +240,7 @@ void invokeCustomMoeRouting(InputT* routerLogits, OutputT* topkValues, IdxT* top int64_t const numExperts, int64_t const topK, cudaStream_t const stream) { - const uint32_t maxNumBlocks = 1024; + const uint32_t maxNumBlocks = 8192; uint32_t maxNumExperts = nextPowerOfTwo(numExperts) < 32 ? 32 : nextPowerOfTwo(numExperts); uint32_t maxNumTopExperts = nextPowerOfTwo(topK); diff --git a/tests/unittest/_torch/modules/test_moe_routing.py b/tests/unittest/_torch/modules/test_moe_routing.py index 04ab38e4b736..fa456b462cb1 100644 --- a/tests/unittest/_torch/modules/test_moe_routing.py +++ b/tests/unittest/_torch/modules/test_moe_routing.py @@ -151,6 +151,54 @@ def test_default_moe_routing(top_k): reference_scales[2, reference_indices[2, i]]) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA") +@pytest.mark.parametrize("num_tokens, num_experts, top_k", [ + (32, 128, 8), + (1, 128, 8), + (8192, 128, 8), +]) +def test_default_moe_routing_decode_anchor(num_tokens, num_experts, top_k): + """Numerics check for the ``default_moe_routing_op`` (softmax-before-topk) + path backed by ``customMoeRoutingKernel``. + + This exercises the exact shapes the Qwen3-235B-A22B + EAGLE3 dyntree + decode path runs through (decode anchor num_tokens=32, num_experts=128, + top_k=8; plus the single-token and prefill corners). The kernel is + per-warp independent — each warp owns one token and computes the full + softmax + top-k for it — so its output is bit-identical regardless of the + block geometry. Lowering ``BLOCK_SIZE`` (1024 -> 128) and raising + ``maxNumBlocks`` (1024 -> 8192) only changes how warps are tiled across + SMs, never the per-token arithmetic; this test guards that invariant. + """ + routing = DefaultMoeRoutingMethod(top_k=top_k) + + # Unique logits per row so top-k selection has no tie ambiguity. + router_logits = gen_unique_logits(num_tokens, num_experts, torch.float32) + + indices, scales = routing.apply(router_logits) + assert indices.shape == (num_tokens, top_k) + assert scales.shape == (num_tokens, top_k) + assert indices.dtype == torch.int32 + assert scales.dtype == torch.float32 + + # Reference: softmax over all experts, then take the top-k. + probs = F.softmax(router_logits.float(), dim=1) + ref_scales, ref_indices = probs.topk(top_k, dim=1) + + # Compare the selected expert set (order-independent) and the gathered + # softmax weights aligned by sorted expert id. + idx_sorted, perm = torch.sort(indices, dim=1) + ref_idx_sorted, ref_perm = torch.sort(ref_indices.to(torch.int32), dim=1) + assert torch.equal(idx_sorted.cpu(), ref_idx_sorted.cpu()) + + scales_sorted = torch.gather(scales, 1, perm) + ref_scales_sorted = torch.gather(ref_scales, 1, ref_perm) + torch.testing.assert_close(scales_sorted, + ref_scales_sorted, + rtol=1e-3, + atol=1e-3) + + @pytest.mark.parametrize("top_k", [1, 2, 3]) def test_renormalize_moe_routing(top_k): routing = RenormalizeMoeRoutingMethod(top_k=top_k) From f0ba8c721ec243bfc66c979e38b41c0fe96d866a Mon Sep 17 00:00:00 2001 From: "Wang, Xiao" <24860335+xwang233@users.noreply.github.com> Date: Tue, 9 Jun 2026 09:01:51 -0700 Subject: [PATCH 084/119] [TRTLLM-12214][perf] DeepGemmFusedMoE: skip redundant data expand via fused expand+quant Triton kernel (#14591) Signed-off-by: Xiao Wang <24860335+xwang233@users.noreply.github.com> --- cpp/tensorrt_llm/thop/moeUtilOp.cpp | 33 +-- .../_torch/custom_ops/cpp_custom_ops.py | 1 + .../modules/fused_moe/fused_moe_deepgemm.py | 231 +++++++++++++++++- .../modules/fused_moe/ops/moe_op_deepgemm.py | 30 ++- .../test_lists/test-db/l0_b200.yml | 1 + .../test_lists/test-db/l0_b300.yml | 1 + .../test_lists/test-db/l0_h100.yml | 1 + .../test_deepgemm_fused_expand_quant.py | 201 +++++++++++++++ .../test_deepgemm_fused_gather_finalize.py | 2 +- 9 files changed, 465 insertions(+), 36 deletions(-) create mode 100644 tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py diff --git a/cpp/tensorrt_llm/thop/moeUtilOp.cpp b/cpp/tensorrt_llm/thop/moeUtilOp.cpp index c11fe1703bf8..e5496a89cdb0 100644 --- a/cpp/tensorrt_llm/thop/moeUtilOp.cpp +++ b/cpp/tensorrt_llm/thop/moeUtilOp.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022-2025, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -48,7 +48,7 @@ void runPermute(void const* input_activations_void, void const* input_sf_void, i int* blocked_expert_counts_, int* blocked_expert_counts_cumsum_, int* blocked_row_to_unpermuted_row_, cutlass_kernels::MOEParallelismConfig parallelism_config, bool use_lora, kernels::LoraParams& lora_params, bool use_fp8_block_scaling, bool min_latency_mode, cutlass_kernels::MoeMinLatencyParams& min_latency_params, - cudaStream_t stream) + bool skip_data_expand, cudaStream_t stream) { TLLM_CHECK_WITH_INFO(experts_per_token * full_num_experts <= std::numeric_limits::max(), "experts_per_token * num_experts is too large"); @@ -79,14 +79,17 @@ void runPermute(void const* input_activations_void, void const* input_sf_void, i } sync_check_cuda_error(stream); - using ExpandedActivationsType = T; - float const* token_topk_unpermuted_scales = token_final_scales; - cutlass_kernels::expandInputRowsKernelLauncher(input_activations, - reinterpret_cast(permuted_data_), token_topk_unpermuted_scales, - permuted_token_final_scales_, permuted_row_to_unpermuted_row_, num_rows, hidden_size, experts_per_token, - num_experts_per_node, quant_params, /*use_per_expert_act_scale*/ false, expert_first_token_offset_, - /* fc1_fp4_act_scale_ */ nullptr, input_sf, true, /* prequant_scales */ nullptr, stream); - sync_check_cuda_error(stream); + if (!skip_data_expand) + { + using ExpandedActivationsType = T; + float const* token_topk_unpermuted_scales = token_final_scales; + cutlass_kernels::expandInputRowsKernelLauncher(input_activations, + reinterpret_cast(permuted_data_), token_topk_unpermuted_scales, + permuted_token_final_scales_, permuted_row_to_unpermuted_row_, num_rows, hidden_size, experts_per_token, + num_experts_per_node, quant_params, /*use_per_expert_act_scale*/ false, expert_first_token_offset_, + /* fc1_fp4_act_scale_ */ nullptr, input_sf, true, /* prequant_scales */ nullptr, stream); + sync_check_cuda_error(stream); + } } std::tuple moe_permute_op( @@ -95,7 +98,7 @@ std::tuple> quant_scales, torch::optional input_sf, int64_t const num_experts_on_rank, int64_t const tp_size, int64_t const tp_rank, int64_t const ep_size, int64_t const ep_rank, int64_t const cluster_size, - int64_t const cluster_rank, bool min_latency_mode, bool use_fp8_block_scaling) + int64_t const cluster_rank, bool min_latency_mode, bool use_fp8_block_scaling, bool skip_data_expand) { TORCH_CHECK(cluster_size == 1 && cluster_rank == 0, "smart_router is supported in min_latency mode"); TORCH_CHECK(min_latency_mode == false, "min_latency_mode is not supported now"); @@ -178,7 +181,7 @@ std::tuple(blocked_expert_counts_tensor.data_ptr()), static_cast(blocked_expert_counts_cumsum_tensor.data_ptr()), static_cast(blocked_row_to_unpermuted_row_tensor.data_ptr()), parallelism_config, /*use_lora*/ false, - lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, stream); + lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, skip_data_expand, stream); break; case torch::kBFloat16: runPermute<__nv_bfloat16>(input.const_data_ptr(), @@ -199,7 +202,7 @@ std::tuple(blocked_expert_counts_tensor.data_ptr()), static_cast(blocked_expert_counts_cumsum_tensor.data_ptr()), static_cast(blocked_row_to_unpermuted_row_tensor.data_ptr()), parallelism_config, /*use_lora*/ false, - lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, stream); + lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, skip_data_expand, stream); break; case torch::kHalf: runPermute(input.const_data_ptr(), input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr, @@ -219,7 +222,7 @@ std::tuple(blocked_expert_counts_tensor.data_ptr()), static_cast(blocked_expert_counts_cumsum_tensor.data_ptr()), static_cast(blocked_row_to_unpermuted_row_tensor.data_ptr()), parallelism_config, /*use_lora*/ false, - lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, stream); + lora_params, use_fp8_block_scaling, min_latency_mode, min_latency_params, skip_data_expand, stream); break; default: throw std::invalid_argument( @@ -339,7 +342,7 @@ TORCH_LIBRARY_FRAGMENT(trtllm, m) "moe_permute_op(Tensor input, Tensor token_selected_experts, Tensor? token_final_scales, Tensor " "fc1_expert_weights, Tensor fc2_expert_weights, Tensor[]? quant_scales, Tensor? input_sf, int " "num_experts_on_rank, int tp_size, int tp_rank, int ep_size, int ep_rank, int cluster_size, int cluster_rank, " - "bool min_latency_mode, bool use_fp8_block_scaling)" + "bool min_latency_mode, bool use_fp8_block_scaling, bool skip_data_expand=False)" "-> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)"); m.def( "moe_finalize_scale_op(Tensor gemm2_output, Tensor? biases, Tensor unpermuted_final_scales, Tensor " diff --git a/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py b/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py index 30ec7bfaa358..b09d87d3b864 100644 --- a/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/cpp_custom_ops.py @@ -680,6 +680,7 @@ def _( cluster_rank: int, min_latency_mode: bool, use_fp8_block_scaling: bool, + skip_data_expand: bool = False, ): experts_per_token = token_selected_experts.shape[1] diff --git a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py index e5286f2b7887..0b775ecc4edc 100644 --- a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py +++ b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py @@ -177,6 +177,198 @@ def masked_index_copy_group_quant_fp8( return output_s +@triton.jit +def _fused_expand_group_quant_fp8( + # Source input (original hidden states before expansion) + source_input_ptr, + # Permutation mapping: expanded_idx -> unpermuted expanded idx + perm_to_unperm_ptr, + # Output pointers + out_q_ptr, + out_s_ptr, + # Expert offset metadata + start_offsets_ptr, + row_indices_ptr, + # Dimensions + row_size, + col_size, + dim_size, + group_size, + # Output scale factor size + aligned_col, + aligned_dim, + # Parameters + num_source_tokens, + eps, + fp8_max, + # Block size + BLOCK: tl.constexpr, + NUM_STAGE: tl.constexpr, +): + """Fused expand + group quantize FP8 kernel. + + Combines expandInputRowsKernel and _masked_index_copy_group_quant_fp8 + into a single pass. Instead of reading from an intermediate expanded + buffer, this kernel reads directly from the original (compact) input + using the permutation map to find the source row. + + The permuted_row_to_unpermuted_row mapping encodes the original expanded + index as: unpermuted_idx = k_rank * num_source_tokens + token_id. + Therefore: source_row = unpermuted_idx % num_source_tokens. + """ + group_block = tl.program_id(0) + token_block = tl.program_id(1) + token_block_num = tl.num_programs(1) + + # calculate group and element offsets + num_tokens = tl.load(start_offsets_ptr + row_size) + elem_offsets = group_block * group_size * 4 + tl.arange(0, BLOCK) + output_s_offs = out_s_ptr + group_block * aligned_col + + # process tokens + for token_index in tl.range(token_block, + num_tokens, + token_block_num, + num_stages=NUM_STAGE): + # load indices for output placement + row_idx = tl.load(row_indices_ptr + token_index) + start_offset = tl.load(start_offsets_ptr + row_idx) + idx = row_idx * col_size + token_index - start_offset + idx_s = row_idx * aligned_dim * aligned_col + token_index - start_offset + + # Compute source row: unpermuted_idx = k_rank * num_source_tokens + token_id + unpermuted_idx = tl.load(perm_to_unperm_ptr + token_index) + source_row = unpermuted_idx % num_source_tokens + + output_s_int32 = 0 + for group_index in tl.range(4): + # load input data directly from original (compact) source + dim_offset = elem_offsets + group_index * group_size + valid = dim_offset < dim_size + input_data = tl.load(source_input_ptr + source_row * dim_size + + dim_offset, + mask=valid, + other=0.0) + # quantization (identical to _masked_index_copy_group_quant_fp8) + _absmax = tl.maximum(tl.max(tl.abs(input_data)), eps) + output_s = _absmax / fp8_max + output_s = tl.exp2(tl.ceil(tl.log2(tl.abs(output_s)))) + output_q = tl.clamp(input_data / output_s, -fp8_max, + fp8_max).to(out_q_ptr.dtype.element_ty) + output_s = output_s.to(tl.int32, bitcast=True) >> 23 + output_s_int32 += output_s << (group_index * 8) + + # store quantized values + tl.store(out_q_ptr + idx * dim_size + dim_offset, + output_q, + mask=valid) + tl.store(output_s_offs + idx_s, output_s_int32) + + +def fused_expand_group_quant_fp8( + output: torch.Tensor, + output_s: torch.Tensor, + source_input: torch.Tensor, + perm_to_unperm: torch.Tensor, + start_offsets: torch.Tensor, + row_indices: torch.Tensor, + experts_per_token: int, + group_size: int, + eps: float = 1e-10, +): + """Fused expand + group quantize FP8. + + Instead of reading from the expanded intermediate buffer (permuted_data), + this reads directly from the original input using the permutation map. + This eliminates the 3.5MB intermediate buffer read, replacing it with + indirect reads from the 448KB source (which fits in L2 cache). + + The permutation map encodes: unpermuted_idx = k_rank * num_tokens + token_id. + To recover the source row: source_row = unpermuted_idx % num_tokens. + + Args: + output: Pre-allocated FP8 output [num_experts, col_size, dim_size] + output_s: Pre-allocated scale output + source_input: Original input hidden states [num_tokens, dim_size] + perm_to_unperm: Mapping from expanded idx to unpermuted expanded idx + start_offsets: Expert first token offsets [num_experts + 1] + row_indices: Token-to-expert map [num_expanded_tokens] + experts_per_token: Number of experts per token (top_k) + group_size: Quantization group size (128) + eps: Epsilon for numerical stability + """ + assert ( + source_input.shape[-1] % group_size == 0 + ), "the last dimension of `source_input` cannot be divisible by `group_size`" + assert source_input.is_contiguous(), "`source_input` is not contiguous" + assert source_input.ndim == 2, "source_input must be a 2D tensor" + assert output.ndim == 3, "Output must be a 3D tensor, [row, col, dim]" + assert start_offsets.shape[ + 0] == output.shape[0] + 1, "Start offsets must be (num_experts + 1)" + + row_size = output.shape[0] + col_size = output.shape[1] + dim_size = output.shape[2] + + alignment = 4 + scale_dim = (dim_size + group_size - 1) // group_size + padded_dim_size = (scale_dim + alignment - 1) // alignment * alignment + padded_col_size = (col_size + alignment - 1) // alignment * alignment + + # get block/grid/stage/warp - use num_expanded_tokens for workload sizing + num_expanded_tokens = perm_to_unperm.shape[0] + num_groups = (dim_size + group_size - 1) // group_size + BLOCK = group_size + if num_expanded_tokens <= 1000 or col_size <= 256: # Small workload + TOKEN_BLOCK_NUM = 256 + NUM_STAGES = 4 + num_warps = 2 + elif num_expanded_tokens <= 10000 or col_size <= 2048: # Medium workload + TOKEN_BLOCK_NUM = 1024 + NUM_STAGES = 2 + num_warps = 1 + else: # Large workload + TOKEN_BLOCK_NUM = 2048 + NUM_STAGES = 2 + num_warps = 1 + grid = ( + (num_groups + 3) // 4, + TOKEN_BLOCK_NUM, + ) + + # FP8 quantization parameters + finfo = torch.finfo(torch.float8_e4m3fn) + fp8_max = finfo.max + + # num_source_tokens is the number of original (compact) input tokens + # The perm_to_unperm map encodes: value = k_rank * num_source_tokens + token_id + # So source_row = value % num_source_tokens + num_source_tokens = source_input.shape[0] + + _fused_expand_group_quant_fp8[grid]( + source_input, + perm_to_unperm, + output, + output_s, + start_offsets, + row_indices, + row_size, + col_size, + dim_size, + group_size, + padded_col_size, + padded_dim_size // 4, + num_source_tokens, + eps, + fp8_max, + BLOCK=BLOCK, + NUM_STAGE=NUM_STAGES, + num_warps=num_warps, + ) + output_s = output_s.transpose(1, 2)[:, :col_size, :] + return output_s + + @triton.jit def masked_index_gather_kernel(output_ptr, input_ptr, start_offsets_ptr, row_indices_ptr, row_size, col_size, dim_size, @@ -418,12 +610,17 @@ def _preprocess_after_permute_kernel( @nvtx_range("[DG] preprocess_after_permute") def preprocess_after_permute(expert_first_token_offset_tensor, - permuted_data_tensor): + num_permuted_tokens): """ Python wrapper that launches a single fused kernel to get the token-to-expert map and the number of tokens per expert. + + Only the number of permuted (expanded) tokens is needed here, not the + permuted activations themselves. Callers that run moe_permute_op with + skip_data_expand=True leave permuted_data_tensor uninitialized, so the count + must come from a populated tensor (e.g. permuted_row_to_unpermuted_row_tensor.shape[0]). """ - total_tokens = permuted_data_tensor.shape[0] + total_tokens = num_permuted_tokens num_experts = expert_first_token_offset_tensor.shape[0] - 1 # create output tensors @@ -770,13 +967,20 @@ def run_moe( assert token_selected_experts is not None assert token_final_scales is not None - # Permutation + # Permutation. + # skip_data_expand=True computes the permutation maps but skips the + # data-copy step (expandInputRowsKernel), so permuted_data_tensor and + # permuted_token_final_scales_tensor are returned with UNINITIALIZED + # contents (still full-size, just never written). The fused expand+quant + # kernel re-derives the activations from x via + # permuted_row_to_unpermuted_row_tensor instead, so all unused outputs are + # discarded with `_`. ( permuted_row_to_unpermuted_row_tensor, - permuted_token_selected_experts_tensor, - permuted_data_tensor, + _, # permuted_token_selected_experts_tensor (unused) + _, # permuted_data_tensor (uninitialized under skip_data_expand) expert_first_token_offset_tensor, - permuted_token_final_scales_tensor, + _, # permuted_token_final_scales_tensor (uninitialized under skip_data_expand) unpermuted_row_to_permuted_row_tensor, ) = torch.ops.trtllm.moe_permute_op( x, @@ -795,14 +999,19 @@ def run_moe( cluster_rank=self.cluster_rank, min_latency_mode=False, use_fp8_block_scaling=True, + skip_data_expand=True, ) - if permuted_data_tensor.numel() == 0: + # permuted_row_to_unpermuted_row_tensor has one entry per permuted + # (expanded) token, so its length is the expanded token count. Use it + # instead of the uninitialized permuted_data_tensor. + num_permuted_tokens = permuted_row_to_unpermuted_row_tensor.shape[0] + if num_permuted_tokens == 0: return torch.zeros_like(x) # Preprocess after permute masked_m, token_to_expert_map = preprocess_after_permute( - expert_first_token_offset_tensor, permuted_data_tensor) + expert_first_token_offset_tensor, num_permuted_tokens) expected_m = (token_selected_experts.numel() + self.expert_size_per_partition - @@ -821,12 +1030,14 @@ def run_moe( self.expert_size_per_partition, scale_k_padded // 4, m_padded) - act_input_sf = masked_index_copy_group_quant_fp8( + act_input_sf = fused_expand_group_quant_fp8( act_input_fp8, act_input_sf, - permuted_data_tensor, + x, + permuted_row_to_unpermuted_row_tensor, expert_first_token_offset_tensor, token_to_expert_map, + experts_per_token=token_selected_experts.shape[1], group_size=128) # Grouped gemm 1 diff --git a/tensorrt_llm/_torch/modules/fused_moe/ops/moe_op_deepgemm.py b/tensorrt_llm/_torch/modules/fused_moe/ops/moe_op_deepgemm.py index 3d8c1ea5787b..ab8936374530 100644 --- a/tensorrt_llm/_torch/modules/fused_moe/ops/moe_op_deepgemm.py +++ b/tensorrt_llm/_torch/modules/fused_moe/ops/moe_op_deepgemm.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -146,7 +146,7 @@ def compute_moe( """ # Import necessary functions for DeepGemm - from ..fused_moe_deepgemm import (masked_index_copy_group_quant_fp8, + from ..fused_moe_deepgemm import (fused_expand_group_quant_fp8, preprocess_after_permute, set_strides, triton_masked_index_gather) @@ -169,13 +169,17 @@ def compute_moe( intermediate_size = module.intermediate_size hidden_size = x.shape[1] - # Permute the data for expert-parallel processing + # Permute the data for expert-parallel processing. + # Unlike DeepGemmFusedMoE (which fuses gather+finalize and never touches + # permuted_data_tensor), this op reuses permuted_data_tensor as a + # write-before-read scratch buffer in the gather+finalize tail below, so + # it is kept; only the genuinely unused outputs are discarded with `_`. ( permuted_row_to_unpermuted_row_tensor, - permuted_token_selected_experts_tensor, + _, # permuted_token_selected_experts_tensor (unused) permuted_data_tensor, expert_first_token_offset_tensor, - permuted_token_final_scales_tensor, + _, # permuted_token_final_scales_tensor (uninitialized under skip_data_expand) unpermuted_row_to_permuted_row_tensor, ) = torch.ops.trtllm.moe_permute_op( x, @@ -194,14 +198,18 @@ def compute_moe( cluster_rank=cluster_rank, min_latency_mode=min_latency_mode, use_fp8_block_scaling=True, # Always use block scaling for DeepGemm + skip_data_expand=True, ) - if permuted_data_tensor.numel() == 0: + # Take the expanded-token count from the populated permutation map (one + # entry per permuted token) rather than the uninitialized data tensor. + num_permuted_tokens = permuted_row_to_unpermuted_row_tensor.shape[0] + if num_permuted_tokens == 0: return torch.zeros_like(x) # Preprocess for masked operations masked_m, token_to_expert_map = preprocess_after_permute( - expert_first_token_offset_tensor, permuted_data_tensor) + expert_first_token_offset_tensor, num_permuted_tokens) expected_m = (token_selected_slots.numel() + expert_size_per_partition - 1) // expert_size_per_partition @@ -222,13 +230,15 @@ def compute_moe( expert_size_per_partition, scale_k_padded // 4, m_padded) - # Quantize and copy input with masking - act_input_sf = masked_index_copy_group_quant_fp8( + # Fused expand + quantize (reads from original input via perm map) + act_input_sf = fused_expand_group_quant_fp8( act_input_fp8, act_input_sf, - permuted_data_tensor, + x, + permuted_row_to_unpermuted_row_tensor, expert_first_token_offset_tensor, token_to_expert_map, + experts_per_token=token_selected_slots.shape[1], group_size=128) # First grouped GEMM (w3 and w1) diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index 5ac2ad5f115f..a55992be4273 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -106,6 +106,7 @@ l0_b200: - unittest/_torch/modules/test_moe_routing.py - unittest/_torch/modules/test_moe_host_sharer.py - unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py + - unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py # ------------- legacy MoE tests --------------- - unittest/_torch/modules/test_fused_moe.py # ------------- MoE: test_moe_backend (by backend) --------------- diff --git a/tests/integration/test_lists/test-db/l0_b300.yml b/tests/integration/test_lists/test-db/l0_b300.yml index 515dd979da6d..b9b3c226aa00 100644 --- a/tests/integration/test_lists/test-db/l0_b300.yml +++ b/tests/integration/test_lists/test-db/l0_b300.yml @@ -33,6 +33,7 @@ l0_b300: - unittest/_torch/modules/test_moe_load_balancer.py - unittest/_torch/modules/test_moe_routing.py - unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py + - unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py # ------------- legacy MoE tests --------------- - unittest/_torch/modules/test_fused_moe.py # ------------- MoE: test_moe_backend (by backend) --------------- diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index 5d73133cf6be..3bc3d1685aab 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -36,6 +36,7 @@ l0_h100: - unittest/_torch/modules/test_moe_routing.py - unittest/_torch/modules/test_moe_host_sharer.py - unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py + - unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py # ------------- legacy MoE tests --------------- - unittest/_torch/modules/test_fused_moe.py # ------------- MoE: test_moe_backend (by backend) --------------- diff --git a/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py b/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py new file mode 100644 index 000000000000..e1558d0aab4a --- /dev/null +++ b/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py @@ -0,0 +1,201 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Differential correctness test for ``fused_expand_group_quant_fp8``. + +The DeepGemm pre-GEMM pipeline used to be:: + + moe_permute_op(...) # writes the expanded buffer + masked_index_copy_group_quant_fp8(expanded) # re-reads it, group-quants FP8 + +``fused_expand_group_quant_fp8`` collapses both: instead of re-reading the +expanded (topk-materialized) buffer, it reads the *compact* source activations +directly via the permutation map, re-deriving the source row analytically +(``source_row = perm_to_unperm[i] % num_source_tokens``), and applies the +identical group-wise FP8 quantization. + +Because the eliminated "expand" step is a pure data copy (no arithmetic) and the +new kernel runs the exact same quant op-sequence over the same input group, the +result is **bit-identical** -- there is no floating-point reordering, and the +outputs are FP8 bytes + packed int32 scales, so there is no FMA/rounding +ambiguity. This test therefore asserts strict equality. + +To make the "expanded buffer == compact source, reordered" invariant exact, the +source ``x`` is **fp32**: ``moe_permute_op`` produces an fp32 expanded buffer, so +the old kernel (reading the fp32 expanded buffer) and the new kernel (reading +fp32 ``x``) load identical values. This is precisely the off-by-one-prone index +encoding the kernel relies on (``unpermuted_idx = k_rank * num_tokens + +token_id``, k_rank slow), so the test directly guards it against the real maps. + +Run as:: + + pytest tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_expand_quant.py -v +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import pytest +import torch + +import tensorrt_llm # noqa: F401 (registers torch.ops.trtllm.*) +from tensorrt_llm._torch.modules.fused_moe.fused_moe_deepgemm import ( + fused_expand_group_quant_fp8, + masked_index_copy_group_quant_fp8, + preprocess_after_permute, +) +from tensorrt_llm._utils import get_sm_version + +skip_unsupported = pytest.mark.skipif( + not torch.cuda.is_available() or get_sm_version() < 90, + reason="Requires CUDA SM90+ (DeepGemm MoE pre-GEMM kernels)", +) + +GROUP_SIZE = 128 + + +@dataclass(frozen=True) +class ExpandQuantShape: + name: str + num_source_tokens: int + hidden: int + num_experts: int + top_k: int + + +# Anchors mirror the PR-2 microbench sweep (study-plan note): the model anchor +# (32 x 8 x 4096 x 128), the small corner, and a medium batch. ``hidden`` is a +# multiple of the 128 quant group size (required by both kernels). +SHAPES = [ + ExpandQuantShape("anchor", num_source_tokens=32, hidden=4096, num_experts=128, top_k=8), + ExpandQuantShape("anchor_h7168", num_source_tokens=32, hidden=7168, num_experts=128, top_k=8), + ExpandQuantShape("small", num_source_tokens=1, hidden=512, num_experts=8, top_k=4), + ExpandQuantShape("medium_batch", num_source_tokens=64, hidden=7168, num_experts=128, top_k=8), + ExpandQuantShape("topk4", num_source_tokens=16, hidden=4096, num_experts=64, top_k=4), +] + + +def _ceil_div(a: int, b: int) -> int: + return (a + b - 1) // b + + +def _align(a: int, b: int) -> int: + return _ceil_div(a, b) * b + + +def _alloc_outputs(num_experts: int, m_max: int, hidden: int, *, device: str): + """Allocate the (output_q, output_s) buffer pair the way DeepGemm forward() + does, zero-initialized so untouched padding compares equal across runs.""" + output_q = torch.zeros((num_experts, m_max, hidden), dtype=torch.float8_e4m3fn, device=device) + m_padded = _align(m_max, 4) + scale_k = _ceil_div(hidden, GROUP_SIZE) + scale_k_padded = _align(scale_k, 4) + output_s = torch.zeros( + (num_experts, scale_k_padded // 4, m_padded), dtype=torch.int32, device=device + ) + return output_q, output_s + + +@skip_unsupported +@pytest.mark.parametrize("shape", SHAPES, ids=lambda s: s.name) +def test_fused_expand_quant_matches_unfused(shape: ExpandQuantShape) -> None: + device = "cuda" + gen = torch.Generator(device=device).manual_seed(1234) + + num_rows = shape.num_source_tokens + hidden = shape.hidden + num_experts = shape.num_experts + top_k = shape.top_k + + tp_size, tp_rank, ep_size, ep_rank = 1, 0, 1, 0 + cluster_size, cluster_rank = 1, 0 + num_experts_per_node = num_experts + + # Per-row distinct top-k experts with positive normalized weights. + logits = torch.randn((num_rows, num_experts), device=device, dtype=torch.float32, generator=gen) + topk_vals, topk_ids = logits.topk(top_k, dim=-1) + token_selected_experts = topk_ids.to(torch.int32) + token_final_scales = torch.softmax(topk_vals, dim=-1).to(torch.float32) + + # fp32 source: makes the expanded buffer an exact copy of x, so the old and + # new kernels read bit-identical values (see module docstring). + x = torch.randn((num_rows, hidden), device=device, dtype=torch.float32, generator=gen) + + # Real permutation maps. skip_data_expand defaults to False here, so the + # expanded buffer (permuted_data_tensor) IS populated -- it is the input the + # old (baseline) kernel reads from. + ( + permuted_row_to_unpermuted_row_tensor, + _permuted_token_selected_experts_tensor, + permuted_data_tensor, + expert_first_token_offset_tensor, + _permuted_token_final_scales_tensor, + _unpermuted_row_to_permuted_row_tensor, + ) = torch.ops.trtllm.moe_permute_op( + x, + token_selected_experts, + token_final_scales, + None, # fc1_expert_weights + None, # fc2_expert_weights + None, # quant_scales + input_sf=None, + num_experts_on_rank=num_experts_per_node, + tp_size=tp_size, + tp_rank=tp_rank, + ep_size=ep_size, + ep_rank=ep_rank, + cluster_size=cluster_size, + cluster_rank=cluster_rank, + min_latency_mode=False, + use_fp8_block_scaling=False, + ) + + num_expanded = num_rows * top_k + assert permuted_data_tensor.shape[0] == num_expanded + # The expanded buffer must equal x reordered for the bit-exact premise to + # hold; moe_permute_op produces it in fp32. + assert permuted_data_tensor.dtype == torch.float32 + + _masked_m, token_to_expert_map = preprocess_after_permute( + expert_first_token_offset_tensor, permuted_data_tensor.shape[0] + ) + + m_max = _align(num_rows, 128) + + # ---- Old path: quantize from the pre-expanded buffer ---- + out_q_old, out_s_old = _alloc_outputs(num_experts, m_max, hidden, device=device) + masked_index_copy_group_quant_fp8( + out_q_old, + out_s_old, + permuted_data_tensor, + expert_first_token_offset_tensor, + token_to_expert_map, + group_size=GROUP_SIZE, + ) + + # ---- New path: fused expand + quant, reading x via the perm map ---- + out_q_new, out_s_new = _alloc_outputs(num_experts, m_max, hidden, device=device) + fused_expand_group_quant_fp8( + out_q_new, + out_s_new, + x, + permuted_row_to_unpermuted_row_tensor, + expert_first_token_offset_tensor, + token_to_expert_map, + experts_per_token=top_k, + group_size=GROUP_SIZE, + ) + + # Strict bit-exact: FP8 quantized bytes and packed int32 scales must match + # exactly. Compare FP8 via its int8 byte view (torch.equal does not operate + # on float8 dtypes directly). + q_equal = torch.equal(out_q_old.view(torch.int8), out_q_new.view(torch.int8)) + s_equal = torch.equal(out_s_old, out_s_new) + if not (q_equal and s_equal): + q_mismatch = (out_q_old.view(torch.int8) != out_q_new.view(torch.int8)).sum().item() + s_mismatch = (out_s_old != out_s_new).sum().item() + pytest.fail( + f"[{shape.name}] fused expand+quant differs from baseline: " + f"{q_mismatch} FP8 byte mismatches, " + f"{s_mismatch} int32 scale mismatches" + ) diff --git a/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py b/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py index 446187f7a845..7b224cdd44a9 100644 --- a/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py +++ b/tests/unittest/_torch/modules/fused_moe/test_deepgemm_fused_gather_finalize.py @@ -140,7 +140,7 @@ def test_fused_gather_finalize_matches_unfused(shape: GatherFinalizeShape) -> No assert permuted_data_tensor.shape[0] == num_expanded _masked_m, token_to_expert_map = preprocess_after_permute( - expert_first_token_offset_tensor, permuted_data_tensor + expert_first_token_offset_tensor, permuted_data_tensor.shape[0] ) # Synthesize the expert GEMM output h3: [num_experts, max_tokens_per_expert, From 736dc22fd62d02aa4b11c5544618372e3cd1298c Mon Sep 17 00:00:00 2001 From: Chien-Chun Hung <2679986+chienchunhung@users.noreply.github.com> Date: Tue, 9 Jun 2026 09:29:02 -0700 Subject: [PATCH 085/119] [TRTLLM-12648][test] implement disagg cancellation load thread (#15124) Signed-off-by: Chien-Chun Hung <2679986+chienchunhung@users.noreply.github.com> --- .../defs/stress_test/disagg_cancel/README.md | 36 ++- .../configs/marathon_cpp_v1_deepseek.yaml | 2 +- .../defs/stress_test/disagg_cancel/harness.py | 254 +++++++++++++++- .../test_disagg_cancel_stress.py | 19 +- .../disagg_cancel/test_load_thread.py | 276 ++++++++++++++++++ 5 files changed, 546 insertions(+), 41 deletions(-) create mode 100644 tests/integration/defs/stress_test/disagg_cancel/test_load_thread.py diff --git a/tests/integration/defs/stress_test/disagg_cancel/README.md b/tests/integration/defs/stress_test/disagg_cancel/README.md index a0b4415f13f0..8cc9650a8bf3 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/README.md +++ b/tests/integration/defs/stress_test/disagg_cancel/README.md @@ -1,7 +1,7 @@ # Disaggregated Cancellation Stress-Test Suite Marathon-style stress tests that gate regressions of the bug class -fixed by [PR #13713](https://github.com/NVIDIA/TensorRT-LLM/pull/13713) +fixed by (cleanup / lifetime / quiescence invariants in the disagg KV transceiver under heavy mid-flight cancellation). @@ -9,7 +9,7 @@ transceiver under heavy mid-flight cancellation). |---|---| | **Tracked by** | [TRTLLM-12648](https://jirasw.nvidia.com/browse/TRTLLM-12648), [TRTLLM-12721](https://jirasw.nvidia.com/browse/TRTLLM-12721) | | **Bug it gates** | NVBug 6104831 (disaggregated permanent wedge) | -| **Fix it gates** | [PR #13713](https://github.com/NVIDIA/TensorRT-LLM/pull/13713) | +| **Fix it gates** | | ## Status @@ -22,12 +22,12 @@ land incrementally: | `metrics_thread` | Implemented — `trtllm_kv_cache_utilization` scraper | | `injector_thread` | Implemented — SIGSTOP/SIGCONT/SIGKILL + respawn | | `canary_thread` | Implemented — greedy canaries + token-equivalence | -| `load_thread` | Stub | +| `load_thread` | Implemented — duration-bounded steady/burst cancellation load | Component-level coverage: `test_log_scanner.py`, `test_metrics_thread.py`, -`test_injector.py`, `test_canary.py`. The parametrized marathon pytest -still runs a lifecycle smoke until `setup()` launches a real cluster -and the remaining thread (`load`) is wired. +`test_injector.py`, `test_canary.py`, `test_load_thread.py`. The +parametrized marathon pytest still runs a lifecycle smoke until +`setup()` launches a real cluster. ## File layout @@ -41,6 +41,7 @@ tests/integration/defs/stress_test/disagg_cancel/ ├── test_metrics_thread.py (metrics_thread unit tests) ├── test_injector.py (injector unit tests) ├── test_canary.py (canary_thread unit tests) +├── test_load_thread.py (load_thread unit tests) └── configs/ ├── README.md (YAML schema + how to add a config) ├── marathon_cpp_v1_deepseek.yaml @@ -61,7 +62,7 @@ Future additions: The marathons are **not** registered in pre-merge CI. They are run nightly / weekly via `tests/integration/test_lists/qa/llm_function_stress.txt` (wiring -lands together with the load-thread implementation). +lands with the explicit CI-registration change). ### Unit tests (no GPU, no cluster) @@ -77,21 +78,26 @@ cd /path/to/TensorRT-LLM export PYTHONPATH=tests/integration/defs:tests/integration/defs/disaggregated -# Step 4 — canary thread (greedy canaries + token-equivalence) +# Steps 1-2 — log scanner + metrics (optional sanity) python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ - tests/integration/defs/stress_test/disagg_cancel/test_canary.py -v + tests/integration/defs/stress_test/disagg_cancel/test_log_scanner.py \ + tests/integration/defs/stress_test/disagg_cancel/test_metrics_thread.py -v # Step 3 — injector thread (SIGSTOP / SIGCONT / SIGKILL + respawn) python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ tests/integration/defs/stress_test/disagg_cancel/test_injector.py -v -# Steps 1-2 — log scanner + metrics (optional sanity) +# Step 4 — canary thread (greedy canaries + token-equivalence) python3 -m pytest -c /dev/null -o addopts= \ --confcutdir=tests/integration/defs/stress_test \ - tests/integration/defs/stress_test/disagg_cancel/test_log_scanner.py \ - tests/integration/defs/stress_test/disagg_cancel/test_metrics_thread.py -v + tests/integration/defs/stress_test/disagg_cancel/test_canary.py -v + +# Step 5 — load thread (steady/burst wrapper around cancel stress load) +python3 -m pytest -c /dev/null -o addopts= \ + --confcutdir=tests/integration/defs/stress_test \ + tests/integration/defs/stress_test/disagg_cancel/test_load_thread.py -v # Marathon YAML parse/validate (includes stress_config.injections schedule) python3 -m pytest -c /dev/null -o addopts= \ @@ -99,7 +105,7 @@ python3 -m pytest -c /dev/null -o addopts= \ tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py::test_all_marathon_yamls_parse_and_validate -v ``` -All three together: +All component tests together: ```bash python3 -m pytest -c /dev/null -o addopts= \ @@ -125,7 +131,7 @@ pytest -sv tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_s immediately because no workers are registered via `bind_tracked_workers()`. -### Local marathon (after `setup()` + load/canary land) +### Local marathon (after `setup()` lands) Once `setup()` launches a real 3P3D cluster and registers workers, the full 2-hour marathon runs via the same pytest entry point. For @@ -171,7 +177,7 @@ For now, when the skeleton test fails: ## Cross-references -- [PR #13713](https://github.com/NVIDIA/TensorRT-LLM/pull/13713) — +- — the bug fix this suite gates regressions against. - [TRTLLM-12648](https://jirasw.nvidia.com/browse/TRTLLM-12648), [TRTLLM-12721](https://jirasw.nvidia.com/browse/TRTLLM-12721) — diff --git a/tests/integration/defs/stress_test/disagg_cancel/configs/marathon_cpp_v1_deepseek.yaml b/tests/integration/defs/stress_test/disagg_cancel/configs/marathon_cpp_v1_deepseek.yaml index 6f8258b1c5aa..7764035ce05f 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/configs/marathon_cpp_v1_deepseek.yaml +++ b/tests/integration/defs/stress_test/disagg_cancel/configs/marathon_cpp_v1_deepseek.yaml @@ -2,7 +2,7 @@ # # Exercises the C++-backed disagg path (BindKvCacheTransceiver + V1 # KVCacheManager + NIXL backend) — the configuration NVBug 6104831 -# was filed against and that PR #13713 stabilizes. Schema +# was filed against and that the in-flight cancellation fix stabilizes. Schema # documentation lives in ../README.md. hostname: localhost diff --git a/tests/integration/defs/stress_test/disagg_cancel/harness.py b/tests/integration/defs/stress_test/disagg_cancel/harness.py index 6b925f26c932..d85b9d88b936 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/harness.py +++ b/tests/integration/defs/stress_test/disagg_cancel/harness.py @@ -58,7 +58,7 @@ # simply aren't passed to the constructor, so the field defaults # apply automatically and are not duplicated here. _STRESS_CONFIG_COERCERS: dict[str, Callable[[Any], Any]] = { - "duration_min": int, + "duration_min": float, "kv_cache_manager": str, "transceiver": str, "base_concurrency": int, @@ -76,7 +76,7 @@ class StressConfig: pass them around without re-parsing. """ - duration_min: int = 120 + duration_min: float = 120.0 kv_cache_manager: str = "v1" # v1 | v2 (v2 + CPP is invalid) transceiver: str = "cpp" # cpp | python base_concurrency: int = 64 @@ -703,6 +703,111 @@ def _tokens_equivalent(returned: Optional[list[int]], reference: Optional[list[i return list(returned) == list(reference) +# --------------------------------------------------------------------------- +# Load-thread helpers +# --------------------------------------------------------------------------- + + +def _parse_token_range(raw: Any, default: tuple[int, int], label: str) -> tuple[int, int]: + """Parse a YAML ``input_length`` mapping into a `(min_tokens, max_tokens)` tuple.""" + if raw is None: + return default + if not isinstance(raw, dict): + raise ValueError(f"{label} must be a mapping with min_tokens/max_tokens, got {raw!r}") + try: + min_tokens = int(raw.get("min_tokens", default[0])) + max_tokens = int(raw.get("max_tokens", default[1])) + except (TypeError, ValueError) as exc: + raise ValueError(f"{label} min_tokens/max_tokens must be integers: {raw!r}") from exc + if min_tokens <= 0 or max_tokens < min_tokens: + raise ValueError( + f"{label} must satisfy 0 < min_tokens <= max_tokens, got {min_tokens}/{max_tokens}" + ) + return min_tokens, max_tokens + + +def _parse_cancel_after_range(raw: Any) -> tuple[float, float]: + """Parse optional ``cancel_after_range`` config; default to existing test values.""" + default = (0.01, 0.1) + if raw is None: + return default + if not isinstance(raw, dict): + raise ValueError(f"cancel_after_range must be a mapping, got {raw!r}") + try: + min_s = float(raw.get("min_s", raw.get("min", default[0]))) + max_s = float(raw.get("max_s", raw.get("max", default[1]))) + except (TypeError, ValueError) as exc: + raise ValueError(f"cancel_after_range min/max must be numbers: {raw!r}") from exc + if min_s < 0.0 or max_s < min_s: + raise ValueError(f"cancel_after_range must satisfy 0 <= min <= max, got {min_s}/{max_s}") + return min_s, max_s + + +def _load_iteration_shape(config: StressConfig, elapsed_s: float) -> dict[str, Any]: + """Return the load shape that should run at ``elapsed_s``. + + Bursts start after the first full ``bursts.interval_min`` period, + then repeat every interval. This keeps the marathon from starting + immediately in burst mode and preserves a steady-state baseline at + T+0. + """ + steady_prompt_range = _parse_token_range( + config.raw.get("input_length"), (4096, 12288), "stress_config.input_length" + ) + if config.base_concurrency <= 0: + raise ValueError( + f"stress_config.base_concurrency must be positive, got {config.base_concurrency}" + ) + shape: dict[str, Any] = { + "mode": "steady", + "requests_per_burst": config.base_concurrency, + "prompt_len_range": steady_prompt_range, + } + + bursts = config.raw.get("bursts") + if bursts is None: + return shape + if not isinstance(bursts, dict): + raise ValueError("stress_config.bursts must be a mapping") + + try: + interval_s = float(bursts.get("interval_min", 0.0)) * 60.0 + duration_s = float(bursts.get("duration_s", 0.0)) + except (TypeError, ValueError) as exc: + raise ValueError("stress_config.bursts interval_min/duration_s must be numbers") from exc + + if interval_s <= 0.0: + raise ValueError( + f"stress_config.bursts.interval_min must be positive, got {bursts.get('interval_min')!r}" + ) + if duration_s <= 0.0: + raise ValueError( + f"stress_config.bursts.duration_s must be positive, got {bursts.get('duration_s')!r}" + ) + if elapsed_s < interval_s: + return shape + + offset_s = elapsed_s % interval_s + if offset_s >= duration_s: + return shape + + try: + requests_per_burst = int(bursts.get("concurrency", config.base_concurrency)) + except (TypeError, ValueError) as exc: + raise ValueError("stress_config.bursts.concurrency must be an integer") from exc + if requests_per_burst <= 0: + raise ValueError( + f"stress_config.bursts.concurrency must be positive, got {requests_per_burst}" + ) + return { + "mode": "burst", + "requests_per_burst": requests_per_burst, + "prompt_len_range": _parse_token_range( + bursts.get("input_length"), steady_prompt_range, "stress_config.bursts.input_length" + ), + } + + # --------------------------------------------------------------------------- # Harness # --------------------------------------------------------------------------- @@ -742,6 +847,8 @@ def __init__( injector_poll_interval_s: float = 1.0, canary_request_timeout_s: float = 10.0, canary_interval_s: Optional[float] = None, + load_duration_s: Optional[float] = None, + load_iteration_pause_s: float = 0.05, ) -> None: """Construct a marathon harness. @@ -773,6 +880,14 @@ def __init__( gap between requests. `None` derives from `canary.rate_per_min` (`60 / rate_per_min`); tests pass a small value. + load_duration_s: Optional override (seconds) for the load + loop duration. `None` derives from `duration_min`; + tests pass a small value. + load_iteration_pause_s: Minimum pause between load + generator calls. Keeps the wrapper from busy-spinning + when the injected load runner returns immediately in + unit tests; the production generator already spends + most of its time in HTTP requests. Raises: ValueError: If the YAML is malformed or its @@ -794,6 +909,8 @@ def __init__( self._injector_poll_interval_s: float = injector_poll_interval_s self._canary_request_timeout_s: float = canary_request_timeout_s self._canary_interval_s: Optional[float] = canary_interval_s + self._load_duration_s: Optional[float] = load_duration_s + self._load_iteration_pause_s: float = load_iteration_pause_s # Cluster + worker tracking (populated by setup()). self._cluster: Any = None # tuple returned by setup_disagg_cluster @@ -818,6 +935,7 @@ def __init__( self._canary_records: list[dict[str, Any]] = [] self._kv_utilization_samples: list[dict[str, Any]] = [] self._injection_events: list[dict[str, Any]] = [] + self._load_records: list[dict[str, Any]] = [] # ------------------------------------------------------------------ # Lifecycle @@ -872,10 +990,9 @@ def bind_server_endpoint(self, server_url: str, model_name: str) -> None: def start(self) -> None: """Spawn the five worker threads. Returns immediately. - Stub stage: each thread body is a no-op that returns - immediately. The load-thread stub signals ``stop_event`` on - exit so the lifecycle smoke ``start() -> wait_until_done() -> - stop()`` completes cleanly without waiting out the + If ``setup()`` has not bound a live server endpoint yet, the + load thread warns and signals ``stop_event`` so the lifecycle + smoke still completes cleanly without waiting out the ``wait_until_done`` timeout. """ self._marathon_start_monotonic = time.monotonic() @@ -1008,6 +1125,7 @@ def collect_results(self) -> dict[str, Any]: for the caller to mutate without affecting the harness): - ``canary_records``: per-canary request outcomes. + - ``load_records``: per-load-generator call outcomes. - ``kv_utilization_samples``: timestamped KV-cache utilization scrapes from the metrics thread. - ``injection_events``: SIGSTOP / SIGCONT / SIGKILL @@ -1018,6 +1136,7 @@ def collect_results(self) -> dict[str, Any]: """ return { "canary_records": list(self._canary_records), + "load_records": list(self._load_records), "kv_utilization_samples": list(self._kv_utilization_samples), "injection_events": list(self._injection_events), "failure_reason": self.failure_reason, @@ -1030,17 +1149,120 @@ def collect_results(self) -> dict[str, Any]: def _load_thread_body(self) -> None: """Wrap ``run_cancel_stress_test`` in a duration-bounded loop. - Stub: no-op that immediately signals end-of-marathon via - ``stop_event``. The real implementation loops until either - ``duration_min`` elapses or ``stop_event`` is set, calling - ``run_cancel_stress_test`` repeatedly; at end-of-marathon it - sets ``stop_event`` so the other four threads wind down. - Setting ``stop_event`` here in the stub preserves that - downstream contract and lets ``wait_until_done`` return - cleanly from the lifecycle smoke. + Loops until ``duration_min`` elapses or a stop/fail-fast + event is set. Each loop iteration picks the current steady + or burst load shape from ``stress_config``, runs one burst of + the existing disagg cancellation load generator, and appends + a record to ``_load_records`` for later correlation with + canary/metrics/injection observations. + + At normal end-of-marathon, the load thread sets + ``stop_event`` so the other four threads wind down. + """ + if not self._server_url: + logger.warning("[load_thread] no server endpoint bound (setup() not wired); exiting") + self.stop_event.set() + return + + duration_s = ( + self._load_duration_s + if self._load_duration_s is not None + else float(self.config.duration_min) * 60.0 + ) + if duration_s <= 0.0: + logger.info("[load_thread] non-positive duration %.3fs; exiting", duration_s) + self.stop_event.set() + return + + try: + cancel_after_range = _parse_cancel_after_range( + self.config.raw.get("cancel_after_range") + ) + except ValueError as exc: + self.mark_failed(f"load_thread config error: {exc}") + return + + deadline = time.monotonic() + duration_s + logger.info( + "[load_thread] running for %.1fs against %s (base_concurrency=%d)", + duration_s, + self._server_url, + self.config.base_concurrency, + ) + + try: + while ( + time.monotonic() < deadline + and not self.stop_event.is_set() + and not self.failed_event.is_set() + ): + iteration_start = time.monotonic() + elapsed_s = iteration_start - self._marathon_start_monotonic + try: + shape = _load_iteration_shape(self.config, elapsed_s) + except ValueError as exc: + self.mark_failed(f"load_thread config error: {exc}") + break + + record: dict[str, Any] = { + "timestamp": time.time(), + "elapsed_s": elapsed_s, + "mode": shape["mode"], + "num_bursts": 1, + "requests_per_burst": shape["requests_per_burst"], + "prompt_len_range": shape["prompt_len_range"], + "cancel_after_range": cancel_after_range, + "success": False, + "error": None, + } + try: + self._run_cancel_stress_iteration( + server_url=self._server_url, + num_bursts=1, + requests_per_burst=shape["requests_per_burst"], + prompt_len_range=shape["prompt_len_range"], + cancel_after_range=cancel_after_range, + ) + record["success"] = True + except Exception as exc: + record["error"] = f"{type(exc).__name__}: {exc}" + self.mark_failed(f"load_thread runner failed: {record['error']}") + break + finally: + record["duration_s"] = time.monotonic() - iteration_start + self._load_records.append(record) + + pause_s = min(self._load_iteration_pause_s, max(0.0, deadline - time.monotonic())) + if pause_s > 0.0: + self.stop_event.wait(timeout=pause_s) + finally: + if not self.failed_event.is_set(): + logger.info("[load_thread] completed; signalling stop_event") + self.stop_event.set() + + def _run_cancel_stress_iteration( + self, + *, + server_url: str, + num_bursts: int, + requests_per_burst: int, + prompt_len_range: tuple[int, int], + cancel_after_range: tuple[float, float], + ) -> None: + """Run one call to the shared disaggregated cancellation load generator. + + Kept as a method so unit tests can monkeypatch it without + importing the heavyweight disaggregated integration module. """ - logger.debug("[load_thread] stub — exiting and signalling stop_event") - self.stop_event.set() + from test_disaggregated import run_cancel_stress_test + + run_cancel_stress_test( + server_url, + num_bursts=num_bursts, + requests_per_burst=requests_per_burst, + prompt_len_range=prompt_len_range, + cancel_after_range=cancel_after_range, + ) def _canary_thread_body(self) -> None: """Send greedy canaries and append per-request records to `_canary_records`. diff --git a/tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py b/tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py index 7065e0a53ab8..36d5b607d3bb 100644 --- a/tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py +++ b/tests/integration/defs/stress_test/disagg_cancel/test_disagg_cancel_stress.py @@ -61,7 +61,8 @@ def test_disagg_cancellation_marathon(config_filename: str) -> None: Current scope: only what the already-implemented thread bodies can contribute. The marathon entry point exists; the marathon - *content* lands incrementally as each thread body is wired up: + *content* lands incrementally as setup / pass-criteria wiring is + completed: - lifecycle plumbing (setup -> start -> wait -> stop -> collect_results, fail-fast event propagation, dict-shape @@ -71,7 +72,7 @@ def test_disagg_cancellation_marathon(config_filename: str) -> None: (component-level coverage in ``test_log_scanner.py``). Marathon pass criteria not yet enforced here (will land alongside - their owning thread bodies in follow-up changes): canary error + their owning result aggregation in follow-up changes): canary error rate, recovery time after each injection, KV-cache utilization growth bound, injection-schedule completeness, sustained load throughput. Until those land, this test passes trivially after @@ -89,13 +90,12 @@ def test_disagg_cancellation_marathon(config_filename: str) -> None: try: harness.setup() harness.start() - # Skeleton stage: stub threads exit immediately; the - # load-thread stub signals ``stop_event`` on exit so this - # returns cleanly (True) almost instantly. Once the - # duration-bounded load thread is wired up, the timeout - # becomes ``stress_config.duration_min`` plus a safety - # margin, and ``clean`` reports whether the marathon ran to - # completion without tripping fail-fast. + # setup() is still a stub, so no server endpoint is bound. + # The load thread exits and signals ``stop_event`` on that + # no-endpoint path, which lets this lifecycle smoke complete + # almost instantly. Once setup launches a real cluster, the + # timeout becomes ``stress_config.duration_min`` plus a safety + # margin. clean = harness.wait_until_done(timeout_s=10.0) assert clean is True, ( f"wait_until_done did not return cleanly; failure_reason={harness.failure_reason!r}" @@ -108,6 +108,7 @@ def test_disagg_cancellation_marathon(config_filename: str) -> None: # collector returns the expected shape so future commits can # extend in place. assert "canary_records" in results + assert "load_records" in results assert "kv_utilization_samples" in results assert "injection_events" in results assert results["failure_reason"] is None, ( diff --git a/tests/integration/defs/stress_test/disagg_cancel/test_load_thread.py b/tests/integration/defs/stress_test/disagg_cancel/test_load_thread.py new file mode 100644 index 000000000000..56ebc2315e31 --- /dev/null +++ b/tests/integration/defs/stress_test/disagg_cancel/test_load_thread.py @@ -0,0 +1,276 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Unit tests for ``DisaggCancellationStressHarness._load_thread_body``. + +The load thread is tested with a monkeypatched +``_run_cancel_stress_iteration`` so these tests do not import the +heavy disaggregated integration module, start a server, or require +GPU/model resources. +""" + +from __future__ import annotations + +import textwrap +import threading +import time +from pathlib import Path +from typing import Any + +import pytest + +from .harness import ( + DisaggCancellationStressHarness, + StressConfig, + _load_iteration_shape, + _parse_cancel_after_range, + _parse_token_range, +) + + +def _write_load_yaml( + tmp_path: Path, + *, + extra_stress_config: str = "", +) -> Path: + """Write a minimal valid marathon YAML for load-thread tests.""" + yaml_path = tmp_path / "stress.yaml" + content = textwrap.dedent( + """\ + hostname: localhost + model: dummy + backend: pytorch + context_servers: {} + generation_servers: {} + stress_config: + duration_min: 1 + kv_cache_manager: v1 + transceiver: cpp + base_concurrency: 4 + input_length: + distribution: uniform + min_tokens: 11 + max_tokens: 22 + """ + ) + if extra_stress_config: + content += textwrap.indent(textwrap.dedent(extra_stress_config).strip(), " ") + "\n" + yaml_path.write_text(content) + return yaml_path + + +def _make_harness( + tmp_path: Path, + *, + extra_stress_config: str = "", + load_duration_s: float = 0.05, +) -> DisaggCancellationStressHarness: + """Construct a load-thread harness with a short test duration.""" + h = DisaggCancellationStressHarness( + _write_load_yaml(tmp_path, extra_stress_config=extra_stress_config), + load_duration_s=load_duration_s, + load_iteration_pause_s=0.005, + ) + h.bind_server_endpoint("http://127.0.0.1:8000", "test-model") + h._marathon_start_monotonic = time.monotonic() + return h + + +def _run_load_thread(h: DisaggCancellationStressHarness, timeout_s: float = 2.0) -> None: + """Run the load thread to self-exit and assert it joined.""" + thread = threading.Thread(target=h._load_thread_body, name="test-load", daemon=True) + thread.start() + thread.join(timeout=timeout_s) + assert not thread.is_alive(), "load thread did not exit within timeout" + + +def test_parse_token_range_defaults_and_validates() -> None: + assert _parse_token_range(None, (1, 2), "input") == (1, 2) + assert _parse_token_range({"min_tokens": "3", "max_tokens": 5}, (1, 2), "input") == ( + 3, + 5, + ) + with pytest.raises(ValueError, match="min_tokens <= max_tokens"): + _parse_token_range({"min_tokens": 8, "max_tokens": 7}, (1, 2), "input") + + +def test_parse_cancel_after_range_defaults_and_validates() -> None: + assert _parse_cancel_after_range(None) == pytest.approx((0.01, 0.1)) + assert _parse_cancel_after_range({"min_s": 0.2, "max_s": 0.4}) == pytest.approx((0.2, 0.4)) + with pytest.raises(ValueError, match="0 <= min <= max"): + _parse_cancel_after_range({"min_s": 0.5, "max_s": 0.4}) + + +def test_load_iteration_shape_switches_from_steady_to_burst(tmp_path: Path) -> None: + yaml_path = _write_load_yaml( + tmp_path, + extra_stress_config=textwrap.dedent( + """\ + bursts: + interval_min: 1 + concurrency: 9 + duration_s: 10 + input_length: + min_tokens: 33 + max_tokens: 44 + """ + ), + ) + cfg = StressConfig.from_yaml_path(yaml_path) + + steady = _load_iteration_shape(cfg, elapsed_s=30) + assert steady["mode"] == "steady" + assert steady["requests_per_burst"] == 4 + assert steady["prompt_len_range"] == (11, 22) + + burst = _load_iteration_shape(cfg, elapsed_s=61) + assert burst["mode"] == "burst" + assert burst["requests_per_burst"] == 9 + assert burst["prompt_len_range"] == (33, 44) + + +@pytest.mark.parametrize( + ("burst_config", "match"), + [ + ( + "interval_min: 0\nconcurrency: 9\nduration_s: 10\n", + "bursts.interval_min must be positive", + ), + ( + "interval_min: 1\nconcurrency: 9\nduration_s: 0\n", + "bursts.duration_s must be positive", + ), + ], +) +def test_load_iteration_shape_rejects_invalid_burst_timing( + tmp_path: Path, burst_config: str, match: str +) -> None: + yaml_path = _write_load_yaml( + tmp_path, + extra_stress_config="bursts:\n" + textwrap.indent(burst_config, " "), + ) + cfg = StressConfig.from_yaml_path(yaml_path) + + with pytest.raises(ValueError, match=match): + _load_iteration_shape(cfg, elapsed_s=61) + + +def test_load_thread_without_server_endpoint_exits_and_signals_stop(tmp_path: Path) -> None: + h = DisaggCancellationStressHarness( + _write_load_yaml(tmp_path), + load_duration_s=0.05, + load_iteration_pause_s=0.005, + ) + + _run_load_thread(h) + + assert h.stop_event.is_set() + assert h._load_records == [] + + +def test_load_thread_runs_steady_iterations_and_records_results( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + h = _make_harness(tmp_path) + calls: list[dict[str, Any]] = [] + + def fake_runner(**kwargs: Any) -> None: + calls.append(kwargs) + time.sleep(0.002) + + monkeypatch.setattr(h, "_run_cancel_stress_iteration", fake_runner) + + _run_load_thread(h) + + assert h.stop_event.is_set() + assert not h.failed_event.is_set() + assert len(calls) >= 1 + assert len(h._load_records) == len(calls) + assert all(record["mode"] == "steady" for record in h._load_records) + assert calls[0]["server_url"] == "http://127.0.0.1:8000" + assert calls[0]["num_bursts"] == 1 + assert calls[0]["requests_per_burst"] == 4 + assert calls[0]["prompt_len_range"] == (11, 22) + + +def test_load_thread_uses_burst_shape_inside_burst_window( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + h = _make_harness( + tmp_path, + extra_stress_config=textwrap.dedent( + """\ + bursts: + interval_min: 0.001 + concurrency: 9 + duration_s: 0.04 + input_length: + min_tokens: 33 + max_tokens: 44 + """ + ), + load_duration_s=0.12, + ) + + def fake_runner(**_kwargs: Any) -> None: + time.sleep(0.004) + + monkeypatch.setattr(h, "_run_cancel_stress_iteration", fake_runner) + + _run_load_thread(h) + + modes = {record["mode"] for record in h._load_records} + assert modes == {"steady", "burst"} + burst_records = [record for record in h._load_records if record["mode"] == "burst"] + assert burst_records + assert all(record["requests_per_burst"] == 9 for record in burst_records) + assert all(record["prompt_len_range"] == (33, 44) for record in burst_records) + + +def test_load_thread_observes_stop_event_after_runner_returns( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + h = _make_harness(tmp_path, load_duration_s=10.0) + calls: list[dict[str, Any]] = [] + + def fake_runner(**kwargs: Any) -> None: + calls.append(kwargs) + h.stop_event.set() + + monkeypatch.setattr(h, "_run_cancel_stress_iteration", fake_runner) + + _run_load_thread(h) + + assert len(calls) == 1 + assert len(h._load_records) == 1 + assert h._load_records[0]["success"] is True + + +def test_load_thread_runner_exception_trips_fail_fast( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + h = _make_harness(tmp_path, load_duration_s=10.0) + + def fake_runner(**_kwargs: Any) -> None: + raise RuntimeError("boom") + + monkeypatch.setattr(h, "_run_cancel_stress_iteration", fake_runner) + + _run_load_thread(h) + + assert h.failed_event.is_set() + assert h.failure_reason == "load_thread runner failed: RuntimeError: boom" + assert len(h._load_records) == 1 + assert h._load_records[0]["success"] is False + assert h._load_records[0]["error"] == "RuntimeError: boom" From f1d39ea8a8f3570344dbe41aa60b4bb01be5aca3 Mon Sep 17 00:00:00 2001 From: RuQing Xu <7891482+xrq-phys@users.noreply.github.com> Date: Wed, 10 Jun 2026 01:32:28 +0900 Subject: [PATCH 086/119] [None][fix] Fix regression from SageAttention kernel: Use static scheduler (#15047) Signed-off-by: Ruqing Xu <7891482+xrq-phys@users.noreply.github.com> Signed-off-by: RuQing Xu <7891482+xrq-phys@users.noreply.github.com> Co-authored-by: Zhenhua Wang <4936589+zhenhuaw-me@users.noreply.github.com> --- .../kernels/trtllmGenKernels/fmha/fmhaKernels.h | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h index aa90a39146e6..7e5e41769198 100644 --- a/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h +++ b/cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h @@ -449,6 +449,13 @@ class TllmGenFmhaKernel FmhaAutoTuner autoTuner(options, optionsFromArgs, params.mMultiProcessorCount); std::tie(options, optionsFromArgs, ctaDim) = autoTuner.selectKernel(); + // Overwrite AutoTuner decision: SageAttention with SfsPV is known to cause regression to persistent scheduler. + // Remove this overwritten once we refresh the cubin kernels that containing the related fix. + if (mNumEltsPerSageAttnBlkP + mNumEltsPerSageAttnBlkV > 0) + { + options.mTileScheduler = TileScheduler::Static; + } + // Check if the options are valid or not. checkFmhaOptions(options, optionsFromArgs); // Update the options if needed. From 680c6c4c582c6e0619e24c42686350f0f423ca42 Mon Sep 17 00:00:00 2001 From: Venky <23023424+venkywonka@users.noreply.github.com> Date: Tue, 9 Jun 2026 09:38:16 -0700 Subject: [PATCH 087/119] [TRTLLM-12467][feat] EPD improvements (#13864) Signed-off-by: venkywonka <23023424+venkywonka@users.noreply.github.com> Signed-off-by: Venky Ganesh <23023424+venkywonka@users.noreply.github.com> --- .../trtllm-model-onboard-multimodal/SKILL.md | 8 +- docs/source/models/supported-models.md | 3 +- .../_torch/auto_deploy/shim/ad_executor.py | 2 + .../_torch/models/modeling_exaone4_5.py | 8 +- .../_torch/models/modeling_gemma3vl.py | 13 +- .../_torch/models/modeling_gemma4mm.py | 16 +- .../_torch/models/modeling_kimi_k25.py | 22 +- .../_torch/models/modeling_llava_next.py | 51 ++- .../_torch/models/modeling_mistral.py | 5 +- .../models/modeling_multimodal_utils.py | 48 ++- .../_torch/models/modeling_nemotron_nano.py | 193 +++++++++- tensorrt_llm/_torch/models/modeling_phi4mm.py | 15 +- .../_torch/models/modeling_qwen2vl.py | 50 ++- .../_torch/models/modeling_qwen3vl.py | 226 +++++++---- .../_torch/models/modeling_qwen3vl_moe.py | 4 +- .../_torch/models/modeling_step3p7vl.py | 4 +- tensorrt_llm/_torch/models/modeling_utils.py | 7 +- tensorrt_llm/_torch/pyexecutor/_util.py | 24 +- tensorrt_llm/_torch/pyexecutor/llm_request.py | 68 +++- .../_torch/pyexecutor/model_engine.py | 92 +++-- .../_torch/pyexecutor/resource_manager.py | 2 + tensorrt_llm/_torch/pyexecutor/sampler.py | 89 ++++- tensorrt_llm/executor/base_worker.py | 20 +- tensorrt_llm/executor/result.py | 1 - tensorrt_llm/inputs/multimodal.py | 364 +++++++++++++----- tensorrt_llm/inputs/registry.py | 12 +- tensorrt_llm/llmapi/llm.py | 45 ++- tensorrt_llm/serve/openai_protocol.py | 4 + .../defs/accuracy/references/videomme.yaml | 3 + .../accuracy/test_epd_disagg_multimodal.py | 288 ++++++++++++++ .../test_lists/qa/llm_function_core.txt | 3 + .../test_lists/test-db/l0_b200.yml | 2 + .../test_lists/test-db/l0_h100.yml | 3 +- .../test_modeling_nemotron_nano_v2_vl.py | 168 ++++++++ .../multimodal/test_find_num_image_tokens.py | 5 +- .../multimodal/test_multimodal_runtime.py | 29 +- .../multimodal/test_qwen3vl_disagg_prompt.py | 143 +++++++ .../test_multimodal_embedding_lengths.py | 272 +++++++++++++ .../auto_deploy/singlegpu/shim/test_engine.py | 3 + tests/unittest/inputs/test_multimodal.py | 171 +++++++- 40 files changed, 2094 insertions(+), 392 deletions(-) create mode 100644 tests/integration/defs/accuracy/test_epd_disagg_multimodal.py create mode 100644 tests/unittest/_torch/multimodal/test_qwen3vl_disagg_prompt.py create mode 100644 tests/unittest/_torch/pyexecutor/test_multimodal_embedding_lengths.py diff --git a/.claude/skills/trtllm-model-onboard-multimodal/SKILL.md b/.claude/skills/trtllm-model-onboard-multimodal/SKILL.md index 6cbf73dad66a..bef01b2e670b 100644 --- a/.claude/skills/trtllm-model-onboard-multimodal/SKILL.md +++ b/.claude/skills/trtllm-model-onboard-multimodal/SKILL.md @@ -83,7 +83,7 @@ metadata: When `@support_multimodal_disaggregated` is set and the deployment uses `TLLM_MULTIMODAL_DISAGGREGATED=1`: - **Encoder worker:** runs as a standalone `MultimodalEncoder` (`mm_encoder_only=True`). It executes only the multimodal encoder and ships `mm_embeddings` (+ mRoPE position ids/deltas) to prefill+decode workers as shared-tensor handles. -- **Prefill+decode worker:** the model's `__init__` skips constructing `self.mm_encoder` when `_is_disagg()` is true; the input processor's `_attach_multimodal_embeddings_impl()` override binds the encoder handles into the request (the base `attach_multimodal_embeddings` wrapper detokenizes tokenized inputs for non-fast-path VLMs, then delegates to your impl). For context-only requests, the engine re-clones mrope tensors so IPC handles outlive the encoder worker's freed memory — replicate that pattern for any new GPU-resident mm tensors. +- **Prefill+decode worker:** the model's `__init__` skips constructing `self.mm_encoder` when `_is_mm_disagg()` is true; the input processor's `attach_multimodal_embeddings()` override binds the encoder handles into the request. For context-only requests, the engine re-clones mrope tensors so IPC handles outlive the encoder worker's freed memory — replicate that pattern for any new GPU-resident mm tensors. ### Templates to study @@ -229,7 +229,7 @@ class {Name}Model(PreTrainedModel): if hasattr(self, "llm"): return # idempotency guard — re-entry from `post_config` etc. - if not _is_disagg(): + if not _is_mm_disagg(): self.mm_encoder = {Name}VisionModel(model_config) else: self.mm_encoder = None @@ -269,7 +269,7 @@ class {Name}Model(PreTrainedModel): multimodal_params = kwargs.get("multimodal_params", []) mm_embeds = [] - if len(multimodal_params) > 0 and not _is_disagg(): + if len(multimodal_params) > 0 and not _is_mm_disagg(): mm_embeds = get_multimodal_embeddings( encoder_forward_fn=self.mm_encoder.forward, multimodal_params=multimodal_params[:num_context_requests], @@ -341,7 +341,7 @@ class {Name}Model(PreTrainedModel): ... ```python def load_weights(self, weights, weight_mapper): - if not _is_disagg(): + if not _is_mm_disagg(): self.mm_encoder.load_weights(weights) # Release mmap pages backing the encoder weights as soon as we're done. if hasattr(weights, "mark_consumed"): diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 406086e4c07d..3bfbece3a549 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -81,6 +81,7 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl [^7]: Text-only support via the [AutoDeploy](../features/auto_deploy/auto-deploy.md) backend. [^8]: Supports text and image inputs. The vision tower runs in BF16 even when the text decoder is quantized (FP8 block-scale or NVFP4). The text decoder is also usable standalone (text-only) via the `Step3p5ForCausalLM` architecture. [^9]: Audio modality only supported on E2B/E4B variants. +[^10]: Audio requires a checkpoint with a `sound_config` and is supported only on the full (non-disaggregated) model path, not the EPD disaggregated path. # Multimodal Feature Support Matrix (PyTorch Backend) @@ -94,7 +95,7 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl | `LlavaNextForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | L + I | | `Llama4ForConditionalGeneration` | Yes | Yes | No | Yes | Yes | No | Yes | No | L + I | | `Mistral3ForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | L + I | -| `NemotronH_Nano_VL_V2` | Yes | Yes | Yes | Yes | Yes | N/A | Yes | No | L + I + V | +| `NemotronH_Nano_VL_V2` | Yes | Yes | Yes | Yes | Yes | N/A | Yes | Yes | L + I + V + A [^10] | | `Phi4MMForCausalLM` | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | L + I + A | | `Qwen2VLForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | L + I + V | | `Qwen2_5_VLForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | L + I + V | diff --git a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py index 743c24042909..285a8b6fe0dd 100644 --- a/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py +++ b/tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py @@ -66,6 +66,8 @@ { "layout_metadata", "mm_bidirectional_blocks", + "multimodal_embedding", + "multimodal_embedding_lengths", "special_token_offsets", "multimodal_embed_mask_cumsum", } diff --git a/tensorrt_llm/_torch/models/modeling_exaone4_5.py b/tensorrt_llm/_torch/models/modeling_exaone4_5.py index 1506c4fce23e..a617f7c5d34c 100644 --- a/tensorrt_llm/_torch/models/modeling_exaone4_5.py +++ b/tensorrt_llm/_torch/models/modeling_exaone4_5.py @@ -9,7 +9,7 @@ from transformers.models.auto import CONFIG_MAPPING from tensorrt_llm._torch.models.checkpoints.base_weight_mapper import BaseWeightMapper -from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_disagg +from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_mm_disagg from ...inputs import ( ContentFormat, @@ -198,7 +198,7 @@ def __init__( llm_model_config.pretrained_config = llm_model_config.pretrained_config.text_config self.llm = AutoModelForCausalLM.from_config(llm_model_config) - if not _is_disagg(): + if not _is_mm_disagg(): mm_encoder_config = copy.deepcopy(model_config) self.mm_encoder = Exaone4_5_VisionModel(mm_encoder_config, Qwen2_5_VisionModel) else: @@ -231,7 +231,7 @@ def forward( mm_multimodal_params = self._get_requests_with_mm_data(multimodal_params) if len(mm_multimodal_params) > 0: - if not _is_disagg(): + if not _is_mm_disagg(): mm_embeds = get_multimodal_embeddings( encoder_forward_fn=self.mm_encoder.forward, multimodal_params=mm_multimodal_params, @@ -262,6 +262,6 @@ def forward( def load_weights(self, weights, weight_mapper: BaseWeightMapper): assert isinstance(weight_mapper, Exaone4_5HfWeightMapper) weights = weight_mapper.preprocess_weights(weights) - if not _is_disagg(): + if not _is_mm_disagg(): self.mm_encoder.load_weights(weights) self.llm.load_weights(weights, weight_mapper) diff --git a/tensorrt_llm/_torch/models/modeling_gemma3vl.py b/tensorrt_llm/_torch/models/modeling_gemma3vl.py index 5f327c82c026..58303b01e5ae 100644 --- a/tensorrt_llm/_torch/models/modeling_gemma3vl.py +++ b/tensorrt_llm/_torch/models/modeling_gemma3vl.py @@ -1,6 +1,5 @@ import copy import dataclasses -import os from typing import List, Optional, Tuple import torch @@ -23,17 +22,11 @@ from ..modules.linear import Linear from ..modules.rms_norm import RMSNorm from .modeling_gemma3 import Gemma3ForCausalLM -from .modeling_multimodal_utils import fuse_input_embeds +from .modeling_multimodal_utils import (_MULTIMODAL_ENV_NAME, _is_mm_disagg, + fuse_input_embeds) from .modeling_siglip import SiglipVisionModel from .modeling_utils import ModelConfig, filter_weights, register_auto_model -_MULTIMODAL_ENV_NAME = "TLLM_MULTIMODAL_DISAGGREGATED" - - -# Make this a runtime lookup rather than a module-wide constant for easier unit testing. -def _is_disagg() -> bool: - return os.getenv(_MULTIMODAL_ENV_NAME, "0") == "1" - class Gemma3InputProcessor(BaseMultimodalInputProcessor, BaseMultimodalDummyInputsBuilder): @@ -185,7 +178,7 @@ def forward(self, vision_outputs: torch.Tensor): class Gemma3VLM(PreTrainedModel): def __init__(self, model_config: ModelConfig[Gemma3Config]): - if _is_disagg(): + if _is_mm_disagg(): raise NotImplementedError( "Gemma3VLM does not support disaggregated inference yet. Please unset " f"the {_MULTIMODAL_ENV_NAME} environment variable, or set it to '0'." diff --git a/tensorrt_llm/_torch/models/modeling_gemma4mm.py b/tensorrt_llm/_torch/models/modeling_gemma4mm.py index 66ef1b03cfcc..38e33d11069a 100644 --- a/tensorrt_llm/_torch/models/modeling_gemma4mm.py +++ b/tensorrt_llm/_torch/models/modeling_gemma4mm.py @@ -23,7 +23,6 @@ import copy import dataclasses import math -import os from typing import Dict, List, Optional, Tuple import torch @@ -51,7 +50,12 @@ from .modeling_gemma4 import Gemma4ForCausalLM from .modeling_gemma4_audio import Gemma4AudioModel from .modeling_gemma4_vision import Gemma4VisionModel -from .modeling_multimodal_utils import find_input_mm_embeds, fuse_input_embeds +from .modeling_multimodal_utils import ( + _MULTIMODAL_ENV_NAME, + _is_mm_disagg, + find_input_mm_embeds, + fuse_input_embeds, +) from .modeling_utils import ModelConfig, filter_weights, register_auto_model _MIN_TRANSFORMERS_FOR_GEMMA4 = "5.5.0" @@ -69,12 +73,6 @@ PreTrainedModel, ) -_MULTIMODAL_ENV_NAME = "TLLM_MULTIMODAL_DISAGGREGATED" - - -def _is_disagg() -> bool: - return os.getenv(_MULTIMODAL_ENV_NAME, "0") == "1" - class RMSNormNoScale(nn.Module): """RMSNorm without learnable scale (for multimodal embedder pre-projection).""" @@ -602,7 +600,7 @@ def _check_and_adjust_experts_implementation(self, *args, **kwargs): return None def __init__(self, model_config: ModelConfig[Gemma4Config]): - if _is_disagg(): + if _is_mm_disagg(): raise NotImplementedError( "Gemma4ForConditionalGeneration does not support " "disaggregated inference yet. Please unset the " diff --git a/tensorrt_llm/_torch/models/modeling_kimi_k25.py b/tensorrt_llm/_torch/models/modeling_kimi_k25.py index a00eb05615b8..7a85d98ce27f 100644 --- a/tensorrt_llm/_torch/models/modeling_kimi_k25.py +++ b/tensorrt_llm/_torch/models/modeling_kimi_k25.py @@ -49,7 +49,7 @@ PreTrainedTokenizerBase, ) -from tensorrt_llm.inputs.multimodal import MultimodalParams +from tensorrt_llm.inputs.multimodal import DisaggPrefillMultimodalInputs, MultimodalParams from tensorrt_llm.mapping import Mapping from ..._utils import prefer_pinned @@ -1411,12 +1411,12 @@ def call_with_text_prompt( "multimodal_data": multimodal_data, } - def get_prompt_token_ids( + def build_disagg_prefill_multimodal_inputs( self, inputs: TextPrompt, mm_handles: List[Dict[str, Any]], - ) -> Tuple[List[int], List[int], List[int]]: - """Build token IDs with multimodal placeholders expanded for disaggregated serving. + ) -> DisaggPrefillMultimodalInputs: + """Build disaggregated prefill inputs from multimodal embedding handles. Args: inputs: Text prompt input container. @@ -1424,7 +1424,9 @@ def get_prompt_token_ids( context phase, each containing ``tensor_size``. Returns: - Tuple of (expanded_ids, mm_token_lengths, mm_token_offsets). + DisaggPrefillMultimodalInputs containing expanded token IDs, + prompt-side MM positions/lengths, exact runs, and encoder-output + embedding lengths. """ text_prompt = inputs.get("prompt") if not text_prompt: @@ -1472,7 +1474,15 @@ def get_prompt_token_ids( expanded_ids[write_pos] = input_ids[read_pos] write_pos += 1 - return (expanded_ids.to(torch.int32).tolist(), mm_token_length, mm_token_offsets) + return DisaggPrefillMultimodalInputs( + prompt_token_ids=expanded_ids.to(torch.int32).tolist(), + multimodal_lengths=mm_token_length, + multimodal_positions=mm_token_offsets, + multimodal_embedding_lengths=[mm_handle["tensor_size"][0] for mm_handle in mm_handles], + multimodal_item_run_cu_offsets=list(range(len(mm_token_length) + 1)), + multimodal_run_positions=mm_token_offsets, + multimodal_run_lengths=mm_token_length, + ) # --------------------------------------------------------------------------- diff --git a/tensorrt_llm/_torch/models/modeling_llava_next.py b/tensorrt_llm/_torch/models/modeling_llava_next.py index 910bc3c82a18..5b09b28a577c 100644 --- a/tensorrt_llm/_torch/models/modeling_llava_next.py +++ b/tensorrt_llm/_torch/models/modeling_llava_next.py @@ -1,5 +1,4 @@ import copy -import os from typing import Any, ClassVar, Dict, List, Optional, Tuple, Union import numpy as np @@ -15,7 +14,9 @@ BaseWeightMapper from tensorrt_llm._torch.models.checkpoints.hf.llava_next_weight_mapper import \ LlavaNextHfWeightMapper -from tensorrt_llm.inputs.multimodal import MultimodalParams +from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_mm_disagg +from tensorrt_llm.inputs.multimodal import (DisaggPrefillMultimodalInputs, + MultimodalParams) from ...inputs import (BaseMultimodalDummyInputsBuilder, BaseMultimodalInputProcessor, ContentFormat, @@ -29,11 +30,10 @@ from .modeling_auto import AutoModelForCausalLM from .modeling_clip import CLIPVisionModel from .modeling_multimodal_utils import (find_input_mm_embeds, fuse_input_embeds, + get_attached_multimodal_embeddings, get_multimodal_embeddings) from .modeling_utils import register_auto_model, register_vision_encoder -DISAGG = os.getenv('TLLM_MULTIMODAL_DISAGGREGATED', '0') == '1' - class LlavaNextInputProcessor(BaseMultimodalInputProcessor, BaseMultimodalDummyInputsBuilder): @@ -111,7 +111,8 @@ def _expand_image_placeholders_in_token_ids( num_mm_tokens_per_placeholder: List[int], ) -> Tuple[List[int], List[int], List[int]]: """ - Shared logic (called by expand_prompt_token_ids_for_mm and get_prompt_token_ids): + Shared logic (called by expand_prompt_token_ids_for_mm and + build_disagg_prefill_multimodal_inputs): replace each image placeholder token in prompt_token_ids with placeholder_id repeated num_mm_tokens_per_placeholder[i] times. @@ -268,12 +269,11 @@ def _postprocess( mm_features = mm_features.view(-1, mm_features.shape[-1]) return fused_input_ids, mm_features - def get_prompt_token_ids( - self, inputs: Union[TextPrompt, TokensPrompt], - mm_handles: List[Dict[str, - Any]]) -> Tuple[List[int], List[int], List[int]]: + def build_disagg_prefill_multimodal_inputs( + self, inputs: Union[TextPrompt, TokensPrompt], + mm_handles: List[Dict[str, Any]]) -> DisaggPrefillMultimodalInputs: """ - Build input token ids with multimodal placeholders expanded to the number of MM tokens. + Build disaggregated prefill inputs from multimodal embedding handles. Uses an already tokenized prompt or tokenizes the txt prompt first. @@ -282,10 +282,9 @@ def get_prompt_token_ids( mm_handles: List of multimodal embedding handles. Returns: - Tuple[List[int], List[int], List[int]]: - - expanded_ids: token ids with each image token expanded to a placeholder repeated per MM token - - mm_token_length: per-image MM token lengths - - mm_token_offsets: start offsets (positions) for each image's MM tokens within expanded_ids + DisaggPrefillMultimodalInputs containing expanded token IDs, + prompt-side MM positions/lengths, exact runs, and encoder-output + embedding lengths. """ # TODO: Move this function to the base input processor class when extending for more models text_prompt = inputs.get("prompt") @@ -327,7 +326,18 @@ def get_prompt_token_ids( f"({mm_token_length[-1] + mm_token_offsets[-1]}) should be less " f"than or equal to final_length ({final_length})") - return expanded_ids, mm_token_length, mm_token_offsets + return DisaggPrefillMultimodalInputs( + prompt_token_ids=expanded_ids, + multimodal_lengths=mm_token_length, + multimodal_positions=mm_token_offsets, + multimodal_embedding_lengths=[ + mm_handle["tensor_size"][0] for mm_handle in mm_handles + ], + multimodal_item_run_cu_offsets=list(range(len(mm_token_length) + + 1)), + multimodal_run_positions=mm_token_offsets, + multimodal_run_lengths=mm_token_length, + ) def _attach_multimodal_embeddings_impl( self, inputs: TextPrompt, @@ -619,7 +629,7 @@ def __init__(self, model_config: ModelConfig[PretrainedConfig], *args, super().__init__(config) if hasattr(self, "llm"): return - if not DISAGG: + if not _is_mm_disagg(): self.mm_encoder = LlavaNextVisionModel(model_config) else: self.mm_encoder = None @@ -694,15 +704,14 @@ def forward( multimodal_params = kwargs.get("multimodal_params", []) mm_embeds = [] if len(multimodal_params) > 0: - if not DISAGG: + if self.mm_encoder is not None: mm_embeds = get_multimodal_embeddings( encoder_forward_fn=self.mm_encoder.forward, multimodal_params=multimodal_params[:num_context_requests]) else: - raise NotImplementedError( - "LlavaNextModel does not support disaggregated inference yet. Please unset " - f"the TLLM_MULTIMODAL_DISAGGREGATED environment variable, or set it to '0'." - ) + # E/P prefill: encoder already ran; use attached embeddings. + mm_embeds = get_attached_multimodal_embeddings( + multimodal_params[:num_context_requests]) mm_embeds = find_input_mm_embeds( mm_embeds, multimodal_params[:num_context_requests]) input_ids, inputs_embeds = fuse_input_embeds( diff --git a/tensorrt_llm/_torch/models/modeling_mistral.py b/tensorrt_llm/_torch/models/modeling_mistral.py index 7de6f2523d5f..7391920cf41c 100644 --- a/tensorrt_llm/_torch/models/modeling_mistral.py +++ b/tensorrt_llm/_torch/models/modeling_mistral.py @@ -25,7 +25,7 @@ from tensorrt_llm._torch.models.modeling_multimodal_mixin import ( MultimodalEncoderOutput, MultimodalModelMixin, PreparedLlmInputs) from tensorrt_llm._torch.models.modeling_multimodal_utils import ( - _MULTIMODAL_ENV_NAME, _is_disagg) + _MULTIMODAL_ENV_NAME, _is_mm_disagg) from tensorrt_llm._torch.models.modeling_utils import (DecoderModel, DecoderModelForCausalLM, _load_weights_impl, @@ -567,7 +567,8 @@ def __init__( self, model_config: ModelConfig[Mistral3Config], ): - if _is_disagg(): + # No MM E/P handoff here yet. Fail before partial model setup. + if _is_mm_disagg(): raise NotImplementedError( "Mistral3VLM does not support disaggregated inference yet. Please unset " f"the {_MULTIMODAL_ENV_NAME} environment variable, or set it to '0'." diff --git a/tensorrt_llm/_torch/models/modeling_multimodal_utils.py b/tensorrt_llm/_torch/models/modeling_multimodal_utils.py index 5615f33c1ec2..81d35a6132b9 100644 --- a/tensorrt_llm/_torch/models/modeling_multimodal_utils.py +++ b/tensorrt_llm/_torch/models/modeling_multimodal_utils.py @@ -35,10 +35,18 @@ # Make this a runtime lookup rather than a module-wide constant for easier unit testing. -def _is_disagg() -> bool: +# MM E/P split flag. Not generic disaggregated serving. +def _is_mm_disagg() -> bool: return os.getenv(_MULTIMODAL_ENV_NAME, "0") == "1" +def has_raw_multimodal_payload(param: MultimodalParams) -> bool: + multimodal_data = param.multimodal_data or {} + modality_type = multimodal_data.get("modality_type") + return (modality_type in ("image", "video", "audio") + and multimodal_data.get(modality_type) is not None) + + # Processor *output* keys that transformers 5.x's # ``ProcessorMixin._merge_kwargs`` strictly rejects when they leak into # ``output_kwargs[]`` and reach ``validate_typed_dict``. They @@ -270,6 +278,34 @@ def get_multimodal_embeddings( return [all_embeddings] +def get_attached_multimodal_embeddings( + multimodal_params: List[MultimodalParams]) -> List[torch.Tensor]: + """Gather embeddings already stored on MultimodalParams. + + Use this on E/P prefill workers and cached-only paths. The encoder already + ran somewhere else. This only makes the tensor list that + find_input_mm_embeds slices. + """ + attached_embeddings = [] + for param in multimodal_params: + embeds = param.multimodal_data.get("multimodal_embedding") + # No attached embedding for this request. + if embeds is None: + continue + # Some paths stash chunks. Slicer expects one tensor. + if isinstance(embeds, list): + embeds = torch.cat(embeds, dim=0) + param.multimodal_data["multimodal_embedding"] = embeds + if not isinstance(embeds, torch.Tensor): + raise TypeError("multimodal_embedding must be a torch.Tensor") + attached_embeddings.append(embeds) + + if not attached_embeddings: + return [] + # Match get_multimodal_embeddings output: one concatenated tensor. + return [torch.cat(attached_embeddings, dim=0)] + + def find_input_mm_embeds( mm_embeds: List[torch.Tensor], multimodal_params: List[MultimodalParams]) -> List[torch.Tensor]: @@ -291,10 +327,15 @@ def find_input_mm_embeds( Note: - Supports both individual batching (len(mm_embeds) == len(multimodal_params)) and pre-concatenated batching (len(mm_embeds) == 1) + - Call get_attached_multimodal_embeddings before this helper when + embeddings are already attached to multimodal_params. - Handles chunked prefill by considering chunk boundaries and current chunk tokens - Example: if a request has 8 MM embed rows, 2 cached rows, and 3 rows in the current chunk, this keeps rows [2:5]. """ + if not isinstance(mm_embeds, list): + raise TypeError("mm_embeds must be a list") + # Current support two batching modes: # 1. Pre-concatenated mm_embeds for each batch, i.e., len(mm_embeds) == 1 # 2. Individual mm_embeds for each multimodal param, i.e., len(mm_embeds) == len(multimodal_params) @@ -317,6 +358,11 @@ def find_input_mm_embeds( ) return [] + if not mm_embeds: + raise ValueError( + "No multimodal embeddings were provided or cached for active multimodal tokens." + ) + if total_mm_tokens == sum(mm_embed.shape[0] for mm_embed in mm_embeds): return mm_embeds diff --git a/tensorrt_llm/_torch/models/modeling_nemotron_nano.py b/tensorrt_llm/_torch/models/modeling_nemotron_nano.py index c81407e77809..1ab1be554094 100644 --- a/tensorrt_llm/_torch/models/modeling_nemotron_nano.py +++ b/tensorrt_llm/_torch/models/modeling_nemotron_nano.py @@ -1,7 +1,6 @@ # Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. import copy import math -import os import re from dataclasses import dataclass from typing import Any, ClassVar, Dict, List, Optional, Sequence, Tuple, Union @@ -14,7 +13,15 @@ from PIL import Image from tensorrt_llm._torch.models.checkpoints import NemotronHHfWeightMapper -from tensorrt_llm.inputs.multimodal import MultimodalParams +from tensorrt_llm.inputs.multimodal import ( + DisaggPrefillMultimodalInputs, + MultimodalParams, + _as_cpu_tensor, + _compute_mm_masks, + _find_mm_token_runs_from_mask, + _find_mm_token_start_pos_from_masks, + find_mm_token_lengths, +) from ...inputs import ( AudioData, @@ -24,10 +31,12 @@ MultimodalPlaceholderMetadata, MultimodalPlaceholderPlacement, TextPrompt, + TokensPrompt, compute_retained_tokens_count, compute_retained_tokens_from_tubelet_budget, compute_retention_mask, register_input_processor, + support_multimodal_disaggregated, ) from ...logger import logger from ...sampling_params import SamplingParams @@ -35,13 +44,16 @@ from ..model_config import ModelConfig from .modeling_auto import AutoModelForCausalLM from .modeling_multimodal_utils import ( + _is_mm_disagg, find_input_mm_embeds, fuse_input_embeds, + get_attached_multimodal_embeddings, get_multimodal_embeddings, + has_raw_multimodal_payload, ) from .modeling_parakeet import ParakeetExtractor, ProjectedParakeet from .modeling_radio import RADIOVisionModel, calc_seq_lens -from .modeling_utils import register_auto_model +from .modeling_utils import register_auto_model, register_vision_encoder # Set max_num_tiles to 1 for video modality, to match the training behavior. VIDEO_MAX_NUM_TILES = 1 @@ -390,10 +402,6 @@ def stack(images: List[torch.Tensor], patch_size: int) -> torch.Tensor: # Make this a runtime lookup rather than a module-wide constant for easier unit testing. -def _is_disagg() -> bool: - return os.getenv("TLLM_MULTIMODAL_DISAGGREGATED", "0") == "1" - - class SquaredReLU(nn.Module): def forward(self, x): return torch.pow(torch.nn.functional.relu(x), 2) @@ -406,6 +414,7 @@ class NanoV2VLVisionEncoder(transformers.PreTrainedModel): def __init__(self, model_config: ModelConfig[transformers.PretrainedConfig]): config = model_config.pretrained_config super().__init__(config) + self.model_config = model_config self.image_size = config.force_image_size self.patch_size = config.patch_size self.num_image_token = int( @@ -878,6 +887,39 @@ def _video_tubelet_geometry(self, t: int, T: int, ih: int, iw: int) -> Tuple[int return num_tubelets, wh +class NanoV2VLMultimodalEncoder(NanoV2VLVisionEncoder): + """EPD-only encoder wrapper for Nano VL image/video handoff. + + Full Nano V3 can support more modalities through the full model path. + This wrapper is only for the mm_encoder_only EPD worker. It returns one + vision embedding tensor for image/video inputs and does not run Nano audio + or video-audio interleave logic. + """ + + def __init__(self, model_config: ModelConfig[transformers.PretrainedConfig], *args, **kwargs): + super().__init__(model_config) + + def forward(self, multimodal_params: List[MultimodalParams]) -> List[torch.Tensor]: + for param in multimodal_params: + modality_type = param.multimodal_data["modality_type"] + if modality_type == "audio": + # EPD encoder-only handoff does not own the Nano audio encoder. + raise NotImplementedError( + "NanoV2VL MultimodalEncoder currently supports image/video inputs, not audio." + ) + audio_data = param.multimodal_data[modality_type].get("audio") + if audio_data is not None: + # TODO(TRTLLM-13129): Add audio support for encoder handoff. + raise NotImplementedError( + "NanoV2VL MultimodalEncoder does not yet encode audio extracted from video." + ) + + mm_embeddings, _ = super().forward(multimodal_params) + if not mm_embeddings: + return [] + return [torch.cat(mm_embeddings, dim=0)] + + class NanoV2VLInputProcessor(BaseMultimodalInputProcessor, BaseMultimodalDummyInputsBuilder): supports_token_id_mm_expansion: ClassVar[bool] = True @@ -2229,6 +2271,93 @@ def call_with_text_prompt( "multimodal_data": multimodal_data, } + def build_disagg_prefill_multimodal_inputs( + self, inputs: Union[TextPrompt, TokensPrompt], mm_handles: List[Dict[str, Any]] + ) -> DisaggPrefillMultimodalInputs: + text_prompt = inputs.get("prompt") + prompt_token_ids = inputs.get("prompt_token_ids") + if prompt_token_ids is None and not text_prompt: + raise ValueError("Either prompt_token_ids or text prompt is required") + if not isinstance(mm_handles, list): + raise TypeError("mm_handles must be a list") + + mm_data = inputs.get("multi_modal_data") or {} + if not mm_data: + raise ValueError("multi_modal_data is required for NanoV2VL multimodal handoff") + modalities = [name for name, value in mm_data.items() if value is not None] + if len(modalities) != 1: + raise ValueError( + "NanoV2VL multimodal handoff supports exactly one modality per request" + ) + if modalities[0] == "audio": + raise NotImplementedError( + "NanoV2VL multimodal handoff does not support audio-only inputs" + ) + + num_mm_tokens_by_key = find_mm_token_lengths(mm_data, self) + num_mm_tokens = [length for lengths in num_mm_tokens_by_key.values() for length in lengths] + if len(num_mm_tokens) != len(mm_handles): + raise RuntimeError( + f"Expected {len(num_mm_tokens)} multimodal handles, got {len(mm_handles)}." + ) + + expected_hidden_size = self.config.llm_config.hidden_size + multimodal_embedding_lengths: List[int] = [] + for i, mm_handle in enumerate(mm_handles): + tensor_size = mm_handle["tensor_size"] + if len(tensor_size) != 2: + raise RuntimeError( + f"Expected multimodal embedding {i} to be rank 2, got tensor_size={tensor_size}." + ) + if tensor_size[1] != expected_hidden_size: + raise RuntimeError( + f"Expected multimodal embedding {i} to have hidden size " + f"{expected_hidden_size}, got {tensor_size[1]}." + ) + multimodal_embedding_lengths.append(tensor_size[0]) + + if prompt_token_ids is None: + prompt_token_ids = self.tokenizer.encode(text_prompt, add_special_tokens=False) + prompt_token_ids = list(prompt_token_ids) + + expanded_ids, _ = self.expand_prompt_token_ids_for_mm( + prompt_token_ids, + num_mm_tokens, + hf_processor_mm_kwargs=inputs.get("mm_processor_kwargs"), + mm_data=mm_data, + ) + + input_ids_tensor = _as_cpu_tensor(expanded_ids) + mm_mask, embed_mask, special_mask = _compute_mm_masks( + input_ids_tensor, + vocab_size=self.get_vocab_size(), + mm_token_ids=self.get_mm_token_ids(), + mm_special_token_ids=self.get_mm_special_token_ids(), + ) + if int(embed_mask.sum().item()) != sum(multimodal_embedding_lengths): + raise RuntimeError( + "Multimodal embedding length mismatch: " + f"prompt has {int(embed_mask.sum().item())} embedding slots, " + f"handles provide {sum(multimodal_embedding_lengths)}." + ) + mm_token_offsets, special_token_offsets = _find_mm_token_start_pos_from_masks( + mm_mask, special_mask, num_mm_tokens + ) + item_run_cu_offsets, run_positions, run_lengths = _find_mm_token_runs_from_mask( + mm_mask, num_mm_tokens + ) + + return DisaggPrefillMultimodalInputs( + prompt_token_ids=expanded_ids, + multimodal_lengths=num_mm_tokens, + multimodal_positions=mm_token_offsets, + multimodal_embedding_lengths=multimodal_embedding_lengths, + multimodal_item_run_cu_offsets=item_run_cu_offsets, + multimodal_run_positions=run_positions, + multimodal_run_lengths=run_lengths, + special_token_offsets=special_token_offsets, + ) + def _prepare_audio_features( self, text: str, @@ -2423,6 +2552,8 @@ def _resample_audios( ) +@support_multimodal_disaggregated +@register_vision_encoder(NanoV2VLMultimodalEncoder) @register_auto_model("NemotronH_Nano_Omni_Reasoning_V3") @register_auto_model("NemotronH_Nano_VL_V2") @register_input_processor( @@ -2439,9 +2570,6 @@ class NemotronH_Nano_VL_V2(transformers.PreTrainedModel): _supports_flash_attn = True def __init__(self, model_config: ModelConfig): - if _is_disagg(): - raise ValueError("NanoV2VL does not support disaggregated inference yet.") - config = model_config.pretrained_config super().__init__(config) @@ -2493,10 +2621,12 @@ def load_weights(self, weights): # to be the LLM-only config and no longer has vision_config / # sound_config / force_image_size / etc. mm_pretrained = self._mm_model_config.pretrained_config - if self.vision_encoder is None and not _is_disagg(): + # Normal workers own encoders. MM E/P handoff uses attached embeddings. + is_multimodal_encoder_worker = not _is_mm_disagg() + if self.vision_encoder is None and is_multimodal_encoder_worker: self.vision_encoder = NanoV2VLVisionEncoder(self._mm_model_config).eval().to("cuda") sound_config = getattr(mm_pretrained, "sound_config", None) - if self.sound_encoder is None and sound_config is not None: + if self.sound_encoder is None and sound_config is not None and is_multimodal_encoder_worker: self.sound_encoder = ( ProjectedParakeet( sound_config, @@ -2681,6 +2811,24 @@ def _validate_evs_context_batch( "multimodal context chunks form a contiguous input_ids prefix." ) + def _check_encoders_exist(self, raw_ctx_params: List[MultimodalParams]) -> None: + """Check encoders needed by raw inputs exist. + + Raw image/video needs vision encoder; raw audio needs sound encoder. + Encoder-only EPD worker may have only one. Reject early with clear + message, not deep encoder failure. + """ + needs_vision_encoder = any( + param.multimodal_data["modality_type"] in ("image", "video") for param in raw_ctx_params + ) + if needs_vision_encoder and self.vision_encoder is None: + raise ValueError("Raw image/video inputs require a local NanoV2VL vision encoder.") + needs_sound_encoder = any( + param.multimodal_data["modality_type"] == "audio" for param in raw_ctx_params + ) + if needs_sound_encoder and self.sound_encoder is None: + raise ValueError("Raw audio inputs require a local NanoV2VL sound encoder.") + def merge_evs_mm_embeds( self, num_tokens_in_videos: List[int], @@ -2996,16 +3144,27 @@ def forward( ctx_params = multimodal_params[:num_context_requests] if self.video_pruning_rate > 0: self._validate_evs_context_batch(ctx_params, num_context_requests) - if not _is_disagg(): + raw_ctx_params = [param for param in ctx_params if has_raw_multimodal_payload(param)] + # Raw image/video/audio tensors: run local encoder. + if raw_ctx_params: + self._check_encoders_exist(raw_ctx_params) mm_embedding = get_multimodal_embeddings( encoder_forward_fn=self._encode_multimodal, multimodal_params=ctx_params, ) + # E/P prefill: encoder already ran; use attached embeddings. else: - raise NotImplementedError( - "Nano-V2-VLM does not support disaggregated inference yet. Please unset " - "the TLLM_MULTIMODAL_DISAGGREGATED environment variable, or set it to '0'." - ) + if self.video_pruning_rate > 0 and any( + param.has_content() and param.multimodal_data.get("modality_type") == "video" + for param in ctx_params + ): + # TODO(TRTLLM-12534): Carry EVS retained-token counts through + # encoder handoff before enabling video pruning for E/P. + raise ValueError( + "EVS video pruning is not supported with attached " + "multimodal embeddings yet." + ) + mm_embedding = get_attached_multimodal_embeddings(ctx_params) # Adjust input_ids in videos if EVS is applied. if self.video_pruning_rate > 0: # Retrieve per-video count stashed by `_encode_multimodal`. diff --git a/tensorrt_llm/_torch/models/modeling_phi4mm.py b/tensorrt_llm/_torch/models/modeling_phi4mm.py index abcc0c1f5ff2..df9afdf34508 100644 --- a/tensorrt_llm/_torch/models/modeling_phi4mm.py +++ b/tensorrt_llm/_torch/models/modeling_phi4mm.py @@ -42,7 +42,8 @@ from ..attention_backend import AttentionMetadata from ..model_config import ModelConfig from .modeling_auto import AutoModelForCausalLM -from .modeling_multimodal_utils import (find_input_mm_embeds, fuse_input_embeds, +from .modeling_multimodal_utils import (_is_mm_disagg, find_input_mm_embeds, + fuse_input_embeds, get_multimodal_embeddings) from .modeling_utils import register_auto_model @@ -73,10 +74,6 @@ def _is_torch_compile() -> bool: return os.getenv("TLLM_MULTIMODAL_ENCODER_TORCH_COMPILE", "0") == "1" -def _is_disagg() -> bool: - return os.getenv("TLLM_MULTIMODAL_DISAGGREGATED", "0") == "1" - - # Load the Phi4MM classes from HuggingFace Phi-4-multimodal-instruct repo. # Remove this function by using the transformers version of Phi4Multimodal when weights/configs are converted to transformers format. def _load_phi4mm_classes(local_path): @@ -957,7 +954,7 @@ class Phi4MMForCausalLM(transformers.PreTrainedModel): _supports_flash_attn = True def __init__(self, model_config: ModelConfig): - if _is_disagg(): + if _is_mm_disagg(): raise ValueError( "Phi4MM does not support disaggregated inference yet.") @@ -968,7 +965,7 @@ def __init__(self, model_config: ModelConfig): if hasattr(self, "llm"): return - if not _is_disagg(): + if not _is_mm_disagg(): _load_phi4mm_classes(config._name_or_path) self.hf_phi4mm_model = HFPhi4MultimodalEncoder(config).eval() @@ -989,7 +986,7 @@ def __init__(self, model_config: ModelConfig): def load_weights(self, weights): # Load weights into HFPhi4MultimodalEncoder. - if not _is_disagg(): + if not _is_mm_disagg(): filtered_weights = {} for k, v in weights.items(): # Skip image_embed head weights since we set it as NoOp. @@ -1076,7 +1073,7 @@ def forward( multimodal_params = kwargs.get("multimodal_params", []) mm_embedding = [] if len(multimodal_params) > 0: - if not _is_disagg(): + if not _is_mm_disagg(): encoder_kwargs = { "mm_token_ids": self.mm_token_ids, } diff --git a/tensorrt_llm/_torch/models/modeling_qwen2vl.py b/tensorrt_llm/_torch/models/modeling_qwen2vl.py index 7db60f085dfd..05fa290d105c 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen2vl.py +++ b/tensorrt_llm/_torch/models/modeling_qwen2vl.py @@ -22,12 +22,13 @@ BaseWeightMapper from tensorrt_llm._torch.models.checkpoints.hf.qwen2vl_weight_mapper import \ Qwen2VLHfWeightMapper -from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_disagg +from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_mm_disagg from tensorrt_llm._torch.modules.attention import Attention from tensorrt_llm._torch.modules.linear import Linear, TensorParallelMode from tensorrt_llm._torch.modules.rms_norm import RMSNorm from tensorrt_llm.functional import PositionEmbeddingType -from tensorrt_llm.inputs.multimodal import MultimodalParams +from tensorrt_llm.inputs.multimodal import (DisaggPrefillMultimodalInputs, + MultimodalParams) from ..._utils import nvtx_range, prefer_pinned from ...inputs import (BaseMultimodalDummyInputsBuilder, @@ -55,6 +56,7 @@ from .modeling_auto import AutoModelForCausalLM from .modeling_multimodal_utils import (bypass_processor_output_validation, find_input_mm_embeds, fuse_input_embeds, + get_attached_multimodal_embeddings, get_multimodal_embeddings) from .modeling_utils import (ModelConfig, QuantConfig, _load_weights_impl, filter_weights, register_auto_model, @@ -1075,7 +1077,8 @@ def __init__( llm_model_config.pretrained_config.architectures = ["Qwen2ForCausalLM"] self.llm = AutoModelForCausalLM.from_config(llm_model_config) - if not _is_disagg(): + # Normal worker owns encoder. MM E/P prefill worker gets attached embeddings. + if not _is_mm_disagg(): mm_encoder_config = copy.deepcopy(model_config) self.mm_encoder = Qwen2VisionModelBase( mm_encoder_config, kwargs.get('vision_model_class', None)) @@ -1191,7 +1194,8 @@ def forward( mm_multimodal_params = self._get_requests_with_mm_data( multimodal_params) if len(mm_multimodal_params) > 0: - if not _is_disagg(): + # Local encoder present: raw pixels/videos become embeddings here. + if self.mm_encoder is not None: mm_embeds = get_multimodal_embeddings( encoder_forward_fn=self.mm_encoder.forward, multimodal_params=mm_multimodal_params) @@ -1200,6 +1204,10 @@ def forward( "Qwen2VLModel does not support disaggregated inference yet. Please unset " f"the TLLM_MULTIMODAL_DISAGGREGATED environment variable, or set it to '0'." ) + # E/P prefill: encoder already ran; use attached embeddings. + else: + mm_embeds = get_attached_multimodal_embeddings( + mm_multimodal_params) mm_embeds = find_input_mm_embeds(mm_embeds, mm_multimodal_params) if not self.model_config.pretrained_config.disable_fuse_rope: @@ -1267,7 +1275,7 @@ def multimodal_data_device_paths(self) -> List[str]: ] def load_weights(self, weights, weight_mapper: BaseWeightMapper): - if not _is_disagg(): + if self.mm_encoder is not None: self.mm_encoder.load_weights(weights) self.llm.load_weights(weights, weight_mapper) @@ -1275,22 +1283,20 @@ def load_weights(self, weights, weight_mapper: BaseWeightMapper): class Qwen2_5VLInputProcessorBase(Qwen2VLInputProcessorBase): - def get_prompt_token_ids( - self, inputs: TextPrompt, - mm_handles: List[Dict[str, - Any]]) -> Tuple[List[int], List[int], List[int]]: + def build_disagg_prefill_multimodal_inputs( + self, inputs: TextPrompt, + mm_handles: List[Dict[str, Any]]) -> DisaggPrefillMultimodalInputs: """ - Build input token ids with multimodal placeholders expanded to the number of MM tokens. + Build disaggregated prefill inputs from multimodal embedding handles. Args: inputs: Text prompt input container. Must contain a non-empty prompt string. mm_handles: List of multimodal embedding handles. Returns: - Tuple[List[int], List[int], List[int]]: - - expanded_ids: token ids with each image token expanded to a placeholder repeated per MM token - - mm_token_length: per-image MM token lengths - - mm_token_offsets: start offsets (positions) for each image's MM tokens within expanded_ids + DisaggPrefillMultimodalInputs containing expanded token IDs, + prompt-side MM positions/lengths, exact runs, and encoder-output + embedding lengths. """ # TODO: Move this function to the base input processor class when extending for more models text_prompt = inputs.get("prompt") @@ -1347,8 +1353,18 @@ def get_prompt_token_ids( assert write_pos == final_length, f"Write position mismatch: {write_pos} != {final_length}" assert mm_token_length[-1] + mm_token_offsets[ -1] <= final_length, f"mm_token_length[-1] + mm_token_offsets[-1] ({mm_token_length[-1] + mm_token_offsets[-1]}) should be less than or equal to final_length ({final_length})" - return expanded_ids.to( - torch.int32).tolist(), mm_token_length, mm_token_offsets + return DisaggPrefillMultimodalInputs( + prompt_token_ids=expanded_ids.to(torch.int32).tolist(), + multimodal_lengths=mm_token_length, + multimodal_positions=mm_token_offsets, + multimodal_embedding_lengths=[ + mm_handle["tensor_size"][0] for mm_handle in mm_handles + ], + multimodal_item_run_cu_offsets=list(range(len(mm_token_length) + + 1)), + multimodal_run_positions=mm_token_offsets, + multimodal_run_lengths=mm_token_length, + ) @support_multimodal_disaggregated @@ -1387,7 +1403,7 @@ def load_weights(self, weights, weight_mapper: BaseWeightMapper): if isinstance(weight_mapper, Qwen2VLHfWeightMapper): weights = weight_mapper.preprocess_weights(weights) - if not _is_disagg(): + if self.mm_encoder is not None: self.mm_encoder.load_weights(weights) self.llm.load_weights(weights) diff --git a/tensorrt_llm/_torch/models/modeling_qwen3vl.py b/tensorrt_llm/_torch/models/modeling_qwen3vl.py index 38205de69687..f2553a2e2b0d 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3vl.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3vl.py @@ -13,7 +13,7 @@ Qwen3VLVisionPatchEmbed as HFQwen3VLVisionPatchEmbed, ) -from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_disagg +from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_mm_disagg from tensorrt_llm.functional import PositionEmbeddingType from tensorrt_llm.mapping import Mapping @@ -29,7 +29,7 @@ register_input_processor, support_multimodal_disaggregated, ) -from ...inputs.multimodal import MultimodalParams +from ...inputs.multimodal import DisaggPrefillMultimodalInputs, MultimodalParams from ...logger import logger from ...sampling_params import SamplingParams from ..attention_backend import AttentionMetadata @@ -46,6 +46,7 @@ bypass_processor_output_validation, find_input_mm_embeds, fuse_input_embeds, + get_attached_multimodal_embeddings, get_multimodal_embeddings, ) from .modeling_qwen2vl import Qwen2_5_VLVisionAttention @@ -59,6 +60,99 @@ ) +def _expand_prompt_token_ids_for_mm_handoff( + input_ids: torch.Tensor, + mm_handles: List[Dict[str, Any]], + *, + image_token_id: int, + video_token_id: int, + vision_start_token_id: int, + placeholder_id: int, +) -> DisaggPrefillMultimodalInputs: + """Expand Qwen3-VL image/video placeholders and emit sparse MM layout. + + Qwen handoff has one coarse or token per item. + This helper expands that one token to the number of embedding rows in the + handoff handle, then returns the sparse layout metadata. + + Agg gets this expansion from Qwen's HF processor taking raw images/videos + as inputs. Reusing that would be wasteful here, hence this helper that + expands based on the embedding handles row count. + + """ + placeholder_positions = [ + pos + for pos, token in enumerate(input_ids.tolist()) + if token in (image_token_id, video_token_id) + ] + if len(placeholder_positions) != len(mm_handles): + raise ValueError( + "Number of multimodal placeholders must match number of mm_handles: " + f"placeholders={len(placeholder_positions)}, " + f"mm_handles={len(mm_handles)}" + ) + + total_mm_embed_tokens = sum(mm_handle["tensor_size"][0] for mm_handle in mm_handles) + final_length = len(input_ids) - len(placeholder_positions) + total_mm_embed_tokens + expanded_ids = torch.empty(final_length, dtype=input_ids.dtype) + + mm_token_lengths: List[int] = [] + mm_token_offsets: List[int] = [] + item_types: List[int] = [] + item_run_cu_offsets: List[int] = [0] + run_positions: List[int] = [] + run_lengths: List[int] = [] + multimodal_embedding_lengths: List[int] = [] + special_token_offsets: List[int] = [] + + write_pos = 0 + mm_handle_idx = 0 + flat_mm_offset = 0 + for read_pos, token_id in enumerate(input_ids.tolist()): + if token_id not in (image_token_id, video_token_id): + expanded_ids[write_pos] = token_id + write_pos += 1 + continue + + mm_token_num = mm_handles[mm_handle_idx]["tensor_size"][0] + has_leading_special = ( + read_pos > 0 and int(input_ids[read_pos - 1].item()) == vision_start_token_id + ) + run_start = write_pos - 1 if has_leading_special else write_pos + prompt_mm_length = mm_token_num + int(has_leading_special) + + expanded_ids[write_pos : write_pos + mm_token_num] = placeholder_id + mm_token_offsets.append(run_start) + mm_token_lengths.append(prompt_mm_length) + multimodal_embedding_lengths.append(mm_token_num) + item_types.append(0 if token_id == image_token_id else 1) + run_positions.append(run_start) + run_lengths.append(prompt_mm_length) + item_run_cu_offsets.append(len(run_positions)) + + if has_leading_special: + special_token_offsets.append(flat_mm_offset) + + write_pos += mm_token_num + flat_mm_offset += prompt_mm_length + mm_handle_idx += 1 + + if write_pos != final_length: + raise RuntimeError(f"Write position mismatch: {write_pos} != {final_length}") + + return DisaggPrefillMultimodalInputs( + prompt_token_ids=expanded_ids.to(torch.int32).tolist(), + multimodal_lengths=mm_token_lengths, + multimodal_positions=mm_token_offsets, + multimodal_embedding_lengths=multimodal_embedding_lengths, + multimodal_item_run_cu_offsets=item_run_cu_offsets, + multimodal_run_positions=run_positions, + multimodal_run_lengths=run_lengths, + special_token_offsets=special_token_offsets, + item_types=item_types, + ) + + class Qwen3VLInputProcessorBase(BaseMultimodalInputProcessor, BaseMultimodalDummyInputsBuilder): def __init__( self, @@ -264,31 +358,16 @@ def get_num_tokens_per_video( video_grid_thw: Optional[torch.Tensor] = None, **kwargs, ) -> int: + if video_grid_thw is None: + raise ValueError( + "Qwen3-VL video token count requires processor-produced video_grid_thw" + ) + merge = self.config.vision_config.spatial_merge_size - if video_grid_thw is not None: - t, h, w = (int(x) for x in video_grid_thw) - return t * (h // merge) * (w // merge) - - # Must run the full processor: HF's Qwen3VLProcessor._get_num_multimodal_tokens - # (what the base class default delegates to) raises on video-only calls - # and returns a wrong-formula fallback that would break chunked prefill. - do_rescale = not (video and isinstance(video[0], torch.Tensor)) - processed = self._processor( - text=["<|vision_start|><|video_pad|><|vision_end|>"], - videos=[video], - padding=True, - do_rescale=do_rescale, - return_tensors="pt", - **kwargs, + token_counts = ( + video_grid_thw[:, 0] * (video_grid_thw[:, 1] // merge) * (video_grid_thw[:, 2] // merge) ) - vgt = processed.get("video_grid_thw") - if vgt is None or len(vgt) == 0: - raise RuntimeError( - "get_num_tokens_per_video: HF processor returned no " - "video_grid_thw for the provided video." - ) - t, h, w = (int(x) for x in vgt[0].tolist()) - return t * (h // merge) * (w // merge) + return int(token_counts.sum().item()) def _preprocess( self, text: Dict[str, Any], mm_data: Dict[str, Any], mm_processor_kwargs: Dict[str, Any] @@ -396,21 +475,20 @@ def call_with_text_prompt( "multimodal_data": multimodal_data, } - def get_prompt_token_ids( + def build_disagg_prefill_multimodal_inputs( self, inputs: TextPrompt, mm_handles: List[Dict[str, Any]] - ) -> Tuple[List[int], List[int], List[int]]: + ) -> DisaggPrefillMultimodalInputs: """ - Build input token ids with multimodal placeholders expanded to the number of MM tokens. + Build disaggregated prefill inputs from multimodal embedding handles. Args: inputs: Text prompt input container. Must contain a non-empty prompt string. mm_handles: List of multimodal embedding handles. Returns: - Tuple[List[int], List[int], List[int]]: - - expanded_ids: token ids with each image token expanded to a placeholder repeated per MM token - - mm_token_length: per-image MM token lengths - - mm_token_offsets: start offsets (positions) for each image's MM tokens within expanded_ids + DisaggPrefillMultimodalInputs containing expanded token IDs, + prompt-side MM positions/lengths, exact runs, and encoder-output + embedding lengths. """ # TODO: Move this function to the base input processor class when extending for more models text_prompt = inputs.get("prompt") @@ -433,44 +511,14 @@ def get_prompt_token_ids( input_ids = self.tokenizer(text_prompt, return_tensors="pt").input_ids[0] - # TODO: what about `video_token_id`? - image_token_index = self.config.image_token_id - - image_mask = input_ids == image_token_index - image_positions = torch.where(image_mask)[0] - num_images = len(image_positions) - assert num_images == len(mm_handles), "Number of images must match number of mm_handles" - total_mm_tokens = sum(mm_handle["tensor_size"][0] for mm_handle in mm_handles) - final_length = len(input_ids) - num_images + total_mm_tokens - # Create output tensor - expanded_ids = torch.empty(final_length, dtype=input_ids.dtype) - placeholder_id = self.tllm_multimodal_token_id - - # Fill the expanded sequence - write_pos = 0 - image_cnt = 0 - mm_token_length = [] - mm_token_offsets = [] - for read_pos in range(len(input_ids)): - if input_ids[read_pos] == image_token_index: - # Replace with placeholder id - mm_token_num = mm_handles[image_cnt]["tensor_size"][0] - expanded_ids[write_pos : write_pos + mm_token_num] = placeholder_id - mm_token_offsets.append(write_pos) - mm_token_length.append(mm_token_num) - write_pos += mm_token_num - image_cnt += 1 - else: - # Copy text token as-is - expanded_ids[write_pos] = input_ids[read_pos] - write_pos += 1 - - assert write_pos == final_length, f"Write position mismatch: {write_pos} != {final_length}" - assert mm_token_length[-1] + mm_token_offsets[-1] <= final_length, ( - f"mm_token_length[-1] + mm_token_offsets[-1] ({mm_token_length[-1] + mm_token_offsets[-1]}) should be less " - f"than or equal to final_length ({final_length})" + return _expand_prompt_token_ids_for_mm_handoff( + input_ids, + mm_handles, + image_token_id=self.config.image_token_id, + video_token_id=self.config.video_token_id, + vision_start_token_id=self.config.vision_start_token_id, + placeholder_id=self.tllm_multimodal_token_id, ) - return expanded_ids.to(torch.int32).tolist(), mm_token_length, mm_token_offsets class Qwen3VLVisionAttention(Qwen2_5_VLVisionAttention): @@ -1058,7 +1106,9 @@ def __init__( # Qwen3ForCausalLM. self.llm = AutoModelForCausalLM.from_config(llm_model_config) - if not _is_disagg(): + self.mm_encoder = None + # Normal workers own the encoder. MM E/P handoff uses attached embeddings. + if not _is_mm_disagg(): self.mm_encoder = Qwen3VisionModelBase( copy.deepcopy(model_config), kwargs.get("vision_model_class", None) ).eval() @@ -1188,18 +1238,31 @@ def forward( # NOTE: Qwen*-VL series has mrope_config even on the text-only prompts, # so we need to separate the mm_multimodal_params from the text-only prompts. - mm_multimodal_params = self._get_requests_with_mm_data(multimodal_params) + mm_multimodal_params, has_raw_image_or_video_data = self._get_requests_with_mm_data( + multimodal_params + ) if len(mm_multimodal_params) > 0: - if not _is_disagg(): + # Raw image/video tensors: run local encoder. + if has_raw_image_or_video_data and self.mm_encoder is not None: mm_embeds = get_multimodal_embeddings( encoder_forward_fn=self.mm_encoder.forward, multimodal_params=mm_multimodal_params, ) + # Raw image/video tensors on a worker with no encoder: bad route. + elif has_raw_image_or_video_data: + raise ValueError( + "Raw multimodal inputs require a local multimodal encoder on this " + "worker, or multimodal_embedding handles from an encoder handoff." + ) + # support_mm_disagg is only set in subclasses of Qwen3VLModelBase that support EPD elif not getattr(self, "support_mm_disagg", False): raise NotImplementedError( f"{type(self)} does not support disaggregated inference yet. Please unset " "the TLLM_MULTIMODAL_DISAGGREGATED environment variable, or set it to '0'." ) + # E/P prefill: encoder already ran; use attached embeddings. + else: + mm_embeds = get_attached_multimodal_embeddings(mm_multimodal_params) mm_embeds = find_input_mm_embeds(mm_embeds, mm_multimodal_params) if self.use_deepstack: @@ -1239,19 +1302,22 @@ def forward( def _get_requests_with_mm_data(self, multimodal_params): mm_multimodal_params = [] + # TODO: This returns one batch-wide "has raw pixels/video" flag. That is + # safe only when a batch is all raw-MM or all attached embeddings. If a + # scheduler can mix both, split raw requests from attached-embedding + # requests and merge outputs back by request index. + has_raw_image_or_video_data = False for multimodal_param in multimodal_params: data = multimodal_param.multimodal_data - if ( - # The first 2 conditions check whether there is input on which inference should be run. + has_raw_data = ( data.get("image", {}).get("pixel_values") is not None or data.get("video", {}).get("pixel_values_videos") is not None - # This condition corresponds to when the embeddings are already populated, as is e.g. - # the case in EPD disagg in the prefill worker. - or data.get("multimodal_embedding") is not None - ): + ) + has_raw_image_or_video_data |= has_raw_data + if has_raw_data or data.get("multimodal_embedding") is not None: mm_multimodal_params.append(multimodal_param) - return mm_multimodal_params + return mm_multimodal_params, has_raw_image_or_video_data @support_multimodal_disaggregated @@ -1284,7 +1350,7 @@ def multimodal_data_device_paths(self) -> List[str]: return ["image.pixel_values", "video.pixel_values_videos", "multimodal_embedding"] def load_weights(self, weights: Dict[str, torch.Tensor], weight_mapper: BaseWeightMapper): - if not _is_disagg(): + if self.mm_encoder is not None: self.mm_encoder.load_weights(weights) weight_mapper = Qwen3VLHfWeightMapper() diff --git a/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py b/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py index 4736960cf594..e26a9c317cc2 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py @@ -3,8 +3,6 @@ import torch from transformers import PretrainedConfig -from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_disagg - from ...inputs import ( ContentFormat, MultimodalPlaceholderMetadata, @@ -64,7 +62,7 @@ def multimodal_data_device_paths(self) -> List[str]: ] def load_weights(self, weights: Dict[str, torch.Tensor], weight_mapper: BaseWeightMapper): - if not _is_disagg(): + if self.mm_encoder is not None: self.mm_encoder.load_weights(weights) weight_mapper = Qwen3VLMoeHfWeightMapper() diff --git a/tensorrt_llm/_torch/models/modeling_step3p7vl.py b/tensorrt_llm/_torch/models/modeling_step3p7vl.py index 7f4027b309f4..9f74b634244d 100644 --- a/tensorrt_llm/_torch/models/modeling_step3p7vl.py +++ b/tensorrt_llm/_torch/models/modeling_step3p7vl.py @@ -57,7 +57,7 @@ from ..modules.layer_norm import LayerNorm from ..speculative import SpecMetadata from .modeling_multimodal_utils import ( - _is_disagg, + _is_mm_disagg, find_input_mm_embeds, fuse_input_embeds, get_multimodal_embeddings, @@ -946,7 +946,7 @@ def load_weights( allow_partial_loading: bool = False, ): """Split vision/text weights and delegate to the inner LM loader.""" - if self.mm_encoder is None and not _is_disagg() and hasattr(weights, "items"): + if self.mm_encoder is None and not _is_mm_disagg() and hasattr(weights, "items"): # Construct the vision tower here, outside MetaInitMode, so its # PerceptionEncoder / HF submodules allocate real tensors. Move it # straight to CUDA (model_loader already ran model.to("cuda") for diff --git a/tensorrt_llm/_torch/models/modeling_utils.py b/tensorrt_llm/_torch/models/modeling_utils.py index 69256521d364..03dbf36d0bc0 100755 --- a/tensorrt_llm/_torch/models/modeling_utils.py +++ b/tensorrt_llm/_torch/models/modeling_utils.py @@ -755,12 +755,13 @@ class SomeVLModel(...): """ def wrapper(model_cls: Type[nn.Module]) -> Type[nn.Module]: + registered = False for arch_name, registered_cls in MODEL_CLASS_MAPPING.items(): - if registered_cls.__name__ == model_cls.__name__: + if registered_cls is model_cls: MODEL_CLASS_VISION_ENCODER_MAPPING[arch_name] = ( vision_encoder_cls, vlm_base_model) - break - else: + registered = True + if not registered: raise ValueError( f"register_vision_encoder: model class {model_cls.__name__} is not registered " f"via register_auto_model; decorator order must ensure registration occurs first." diff --git a/tensorrt_llm/_torch/pyexecutor/_util.py b/tensorrt_llm/_torch/pyexecutor/_util.py index 31b0c8d28328..1e96f88621b5 100644 --- a/tensorrt_llm/_torch/pyexecutor/_util.py +++ b/tensorrt_llm/_torch/pyexecutor/_util.py @@ -7,6 +7,7 @@ import tensorrt_llm import tensorrt_llm.bindings.executor as trtllm +from tensorrt_llm._torch.models.modeling_multimodal_utils import _is_mm_disagg from tensorrt_llm._torch.models.modeling_utils import \ MODEL_CLASS_VISION_ENCODER_MAPPING from tensorrt_llm._utils import (confidential_compute_enabled, get_sm_version, @@ -392,18 +393,8 @@ def _create_dummy_mm_context_request( multimodal_input = extra_processed_inputs.get( 'multimodal_input') multimodal_data = extra_processed_inputs.get('multimodal_data') - req_mm_input = trtllm.MultimodalInput( - multimodal_hashes=multimodal_input.multimodal_hashes, - multimodal_positions=multimodal_input.multimodal_positions, - multimodal_lengths=multimodal_input.multimodal_lengths, - multimodal_uuids=multimodal_input.multimodal_uuids, - multimodal_item_run_cu_offsets=multimodal_input. - multimodal_item_run_cu_offsets, - multimodal_run_positions=multimodal_input. - multimodal_run_positions, - multimodal_run_lengths=multimodal_input. - multimodal_run_lengths, - ) if multimodal_input else None + req_mm_input = multimodal_input.to_binding( + trtllm) if multimodal_input else None request = trtllm.Request(prompt_token_ids, max_tokens=1, @@ -445,9 +436,12 @@ def _create_dummy_mm_context_request( def _create_dummy_context_requests( self, input_seq_len: int) -> List[trtllm.Request]: requests = [] - if hasattr(self._model_engine.model, - "original_arch") and MODEL_CLASS_VISION_ENCODER_MAPPING.get( - self._model_engine.model.original_arch, None): + # Disaggregated workers receive multimodal embeddings instead of raw + # pixel inputs, so capacity probing must use the text-only fallback. + if (not _is_mm_disagg() + and hasattr(self._model_engine.model, "original_arch") + and MODEL_CLASS_VISION_ENCODER_MAPPING.get( + self._model_engine.model.original_arch, None)): requests = self._create_dummy_mm_context_request(input_seq_len) # if succeed profiling with multimodal requests then return, otherwise profile # with default case diff --git a/tensorrt_llm/_torch/pyexecutor/llm_request.py b/tensorrt_llm/_torch/pyexecutor/llm_request.py index 79cff4fde8af..16db68c79f9d 100644 --- a/tensorrt_llm/_torch/pyexecutor/llm_request.py +++ b/tensorrt_llm/_torch/pyexecutor/llm_request.py @@ -395,21 +395,18 @@ def append_log_probs(self, self._log_probs.append(log_probs, cum_log_probs) def append_mm_embeddings(self, mm_embeddings: torch.Tensor, - multimodal_lengths: List[int]): + mm_embedding_lengths: List[int]): """Split concatenated embeddings by per-item lengths and create handles. Args: mm_embeddings: Concatenated multimodal embeddings tensor of shape [total_tokens, hidden_dim]. - multimodal_lengths: Current per-item split lengths. + mm_embedding_lengths: Per-item encoder-output embedding lengths. """ - # TODO(TRTLLM-12175): callers currently pass request.multimodal_lengths, - # a prompt-side MM-token count that may include non-embedding - # special/framing tokens. This split needs per-item encoder-output - # embedding lengths instead. - split_embeddings = torch.split(mm_embeddings, multimodal_lengths, dim=0) + split_embeddings = torch.split(mm_embeddings, + mm_embedding_lengths, + dim=0) - # Create a SharedTensorContainer handle for each split self._mm_embeddings = [ SharedTensorContainer.from_tensor(emb).dump_to_dict() for emb in split_embeddings @@ -421,10 +418,10 @@ def set_mrope_position( mrope_position_ids: torch.Tensor, mrope_position_deltas: torch.Tensor, ): - self._mrope_position_ids = (SharedTensorContainer.from_tensor( - mrope_position_ids).dump_to_dict()) - self._mrope_position_deltas = (SharedTensorContainer.from_tensor( - mrope_position_deltas).dump_to_dict()) + self._mrope_position_ids = SharedTensorContainer.from_tensor( + mrope_position_ids).dump_to_dict() + self._mrope_position_deltas = SharedTensorContainer.from_tensor( + mrope_position_deltas).dump_to_dict() self.diff.mrope_position_ids = self._mrope_position_ids self.diff.mrope_position_deltas = self._mrope_position_deltas @@ -660,7 +657,8 @@ def __init__( return_perf_metrics=return_perf_metrics, stop_words_list=torch.tensor(stop_words_list, dtype=torch.int32) if stop_words_list else None, - **kwargs) + **kwargs, + ) self.py_client_id = client_id self.py_request_id = self.request_id self.py_llm_request_type = self.llm_request_type @@ -941,6 +939,50 @@ def convert_wordlist(word_list) -> List[List[int]]: return [tokens, offsets] +def _validate_optional_int_list(values: Any, + field_name: str) -> Optional[List[int]]: + if values is None: + return None + if not isinstance(values, list): + raise TypeError(f"{field_name} must be a list") + if not all(isinstance(value, int) for value in values): + raise TypeError(f"{field_name} must contain only integers") + return values + + +def get_multimodal_embedding_lengths( + request: LlmRequest) -> Optional[List[int]]: + """Return explicit per-item encoder-output lengths for a multimodal request.""" + py_multimodal_data = request.py_multimodal_data + if py_multimodal_data is not None and not isinstance( + py_multimodal_data, dict): + raise TypeError("py_multimodal_data must be a dict") + # `multimodal_embedding_lengths` is Python-side layout metadata, not a + # nanobind request field, so validate the flat handoff contract here. + multimodal_embedding_lengths = _validate_optional_int_list( + py_multimodal_data.get("multimodal_embedding_lengths") + if py_multimodal_data is not None else None, + "multimodal_embedding_lengths") + if multimodal_embedding_lengths is None: + return None + + if any(length < 0 for length in multimodal_embedding_lengths): + raise ValueError("multimodal_embedding_lengths must be non-negative") + multimodal_lengths = request.multimodal_lengths + if multimodal_lengths is not None: + if len(multimodal_embedding_lengths) != len(multimodal_lengths): + raise ValueError("multimodal_embedding_lengths length must match " + "multimodal_lengths") + for item_idx, (embedding_length, prompt_length) in enumerate( + zip(multimodal_embedding_lengths, multimodal_lengths)): + if embedding_length > prompt_length: + raise ValueError( + f"multimodal_embedding_lengths[{item_idx}] exceeds " + f"multimodal_lengths[{item_idx}]") + + return multimodal_embedding_lengths + + def executor_request_to_llm_request( req_id: int, executor_request: ExecutorRequest, diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py index 789e2155da97..8c0ee9d0edf4 100644 --- a/tensorrt_llm/_torch/pyexecutor/model_engine.py +++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py @@ -20,6 +20,7 @@ from tensorrt_llm.bindings.internal.runtime import TaskLayerModuleConfig from tensorrt_llm.inputs.multimodal import (MultimodalParams, MultimodalRuntimeData, + _has_mm_payload_keys, check_mm_embed_cumsum_if_needed) from tensorrt_llm.inputs.registry import (create_input_processor, create_input_processor_with_hash) @@ -67,7 +68,8 @@ EncoderCUDAGraphRunnerConfig) from .guided_decoder import CapturableGuidedDecoder from .layerwise_nvtx_marker import LayerwiseNvtxMarker -from .llm_request import LlmRequest, get_draft_token_length +from .llm_request import (LlmRequest, get_draft_token_length, + get_multimodal_embedding_lengths) from .mamba_cache_manager import MambaHybridCacheManager from .model_loader import ModelLoader, _construct_checkpoint_loader from .resource_manager import (BaseResourceManager, KVCacheManager, @@ -4694,41 +4696,63 @@ def _forward_step_mm_encoder_only( multimodal_params = inputs.get("multimodal_params", []) if not multimodal_params or len(multimodal_params) == 0: # Return empty embeddings if no multimodal data - return {'mm_embeddings': []} - # TODO(TRTLLM-12175): split encoder outputs by explicit per-request - # encoder-output embedding lengths. multimodal_lengths is a - # prompt-side MM-token count and may include non-embedding - # special/framing tokens. - if getattr(scheduled_requests.context_requests[0], 'multimodal_lengths', - None) is None: - multimodal_chunks = None - else: - multimodal_chunks = [ - sum(request.multimodal_lengths) - for request in scheduled_requests.context_requests - if request.multimodal_lengths is not None - ] + return { + 'mm_embeddings': [], + 'mm_embedding_request_indices': [], + 'mm_embedding_lengths': [], + } + # Some ctx requests carry only mrope metadata (no actual vision + # content). Skip them so the encoder only runs on real image payloads. + mm_context_requests = [(request_idx, request) for request_idx, request + in enumerate(scheduled_requests.context_requests) + if request.py_multimodal_data is not None] + if len(mm_context_requests) != len(multimodal_params): + raise ValueError( + "mm_encoder_only expects one multimodal payload per context " + "request carrying py_multimodal_data") + mm_request_indices_with_payload = [] + mm_params_with_payload = [] + mm_embedding_lengths = [] + for (request_idx, + request), multimodal_param in zip(mm_context_requests, + multimodal_params): + if not _has_mm_payload_keys(request.py_multimodal_data): + # mrope-only warmup request (no actual vision content) -> skip. + continue + multimodal_embedding_lengths = get_multimodal_embedding_lengths( + request) + if multimodal_embedding_lengths is None: + # Vision payload keys present but no pre-computed embedding + # lengths — skip to avoid a downstream sum(None) TypeError. + continue + mm_request_indices_with_payload.append(request_idx) + mm_params_with_payload.append(multimodal_param) + mm_embedding_lengths.append(multimodal_embedding_lengths) + if not mm_params_with_payload: + return { + 'mm_embeddings': [], + 'mm_embedding_request_indices': [], + 'mm_embedding_lengths': [], + } # For mm_encoder_only mode, we only run the vision encoder part # The model should be a vision encoder (e.g., Qwen2VisionModelBase) - mm_embeddings = self.model.forward(multimodal_params) + mm_embeddings = self.model.forward(mm_params_with_payload) assert len( mm_embeddings ) == 1, "mm_embeddings should be a 1-element list, mix modality (video+image) is not supported" - if multimodal_chunks is None or len(multimodal_chunks) != len( - multimodal_params): - mm_embeddings = list( - torch.chunk(mm_embeddings[0], - scheduled_requests.num_context_requests, - dim=0)) - else: - mm_embeddings = list( - torch.split(mm_embeddings[0], multimodal_chunks, dim=0)) + split_lengths = [sum(lengths) for lengths in mm_embedding_lengths] + mm_embeddings = list(torch.split(mm_embeddings[0], split_lengths, + dim=0)) + if len(mm_embeddings) != len(mm_embedding_lengths): + raise ValueError( + "mm_encoder_only produced an embedding batch that does not " + "match mm_embedding_lengths") # Extract mrope position data from multimodal_params if available mrope_position_ids_list = [] mrope_position_deltas_list = [] - for multimodal_param in multimodal_params: + for multimodal_param in mm_params_with_payload: mrope_config = multimodal_param.multimodal_data.get( 'mrope_config', {}) mrope_position_ids = mrope_config.get('mrope_position_ids') @@ -4738,7 +4762,21 @@ def _forward_step_mm_encoder_only( if mrope_position_deltas is not None: mrope_position_deltas_list.append(mrope_position_deltas) - result = {'mm_embeddings': mm_embeddings, 'logits': None} + # mrope lists must align 1:1 with multimodal_params (or be empty); + # the sampler indexes them by per-MM-result position into mm_embeddings. + assert (len(mrope_position_ids_list) == len(mrope_position_deltas_list) + and len(mrope_position_ids_list) + in (0, len(mm_params_with_payload))), ( + f"mrope alignment: got {len(mrope_position_ids_list)} ids, " + f"{len(mrope_position_deltas_list)} deltas, " + f"{len(mm_params_with_payload)} mm params") + + result = { + 'mm_embeddings': mm_embeddings, + 'logits': None, + 'mm_embedding_request_indices': mm_request_indices_with_payload, + 'mm_embedding_lengths': mm_embedding_lengths, + } if mrope_position_ids_list: result['mrope_position_ids'] = mrope_position_ids_list if mrope_position_deltas_list: diff --git a/tensorrt_llm/_torch/pyexecutor/resource_manager.py b/tensorrt_llm/_torch/pyexecutor/resource_manager.py index f4bc390060e3..fbe5bfe0d56d 100644 --- a/tensorrt_llm/_torch/pyexecutor/resource_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/resource_manager.py @@ -161,6 +161,8 @@ def _ensure_int64_cpu_tensor( def _resolve_multimodal_run_metadata( req: LlmRequest) -> Optional[_MmRunMetadata]: + # TODO(perf): cache per request; block-reuse invokes this once per block, + # repeatedly rebuilding identical tensors for the same request metadata. # Worked example for one logical multimodal item split by text: # # prompt index: 0 1 2 3 4 5 diff --git a/tensorrt_llm/_torch/pyexecutor/sampler.py b/tensorrt_llm/_torch/pyexecutor/sampler.py index fc8e76b923e1..3c823888c363 100644 --- a/tensorrt_llm/_torch/pyexecutor/sampler.py +++ b/tensorrt_llm/_torch/pyexecutor/sampler.py @@ -291,9 +291,65 @@ def is_generation_model(self) -> bool: @dataclass(kw_only=True) class MultimodalResult: mm_embeddings: List[torch.Tensor] + # needed to torch.split the mm_embeddings into item-wise chunks + mm_embedding_lengths: List[List[int]] + # needed when requests mix text-only and multimodal ones + mm_embedding_request_indices: List[int] + # number of context requests in the batch + num_context_requests: int # Can be used to include e.g. `mrope_position_ids`, etc. extra_data: Optional[Dict[str, Any]] = None + def __post_init__(self) -> None: + num_embeddings = len(self.mm_embeddings) + num_lengths = len(self.mm_embedding_lengths) + if num_lengths != num_embeddings: + raise ValueError( + "mm_embedding_lengths batch size does not match mm_embeddings: " + f"{num_lengths} != {num_embeddings}" + ) + num_request_indices = len(self.mm_embedding_request_indices) + if num_request_indices != num_embeddings: + raise ValueError( + "mm_embedding_request_indices batch size does not match " + f"mm_embeddings: {num_request_indices} != {num_embeddings}" + ) + for result_index, (mm_embedding, mm_embedding_lengths) in enumerate( + zip(self.mm_embeddings, self.mm_embedding_lengths, strict=True) + ): + actual_rows = len(mm_embedding) + expected_rows = sum(mm_embedding_lengths) + if actual_rows != expected_rows: + raise ValueError( + f"mm_embedding shape mismatch for result {result_index}: " + f"{actual_rows} != {expected_rows}" + ) + for request_index in self.mm_embedding_request_indices: + if request_index < 0 or request_index >= self.num_context_requests: + raise ValueError( + "mm_embedding_request_indices contains an invalid request " + f"index: {request_index} not in [0, {self.num_context_requests})" + ) + + @classmethod + def from_model_outputs( + cls, model_outputs: Dict[str, Any], num_context_requests: int + ) -> "MultimodalResult": + result_keys = { + "mm_embeddings", + "mm_embedding_lengths", + "mm_embedding_request_indices", + } + return cls( + mm_embeddings=model_outputs["mm_embeddings"], + mm_embedding_lengths=model_outputs["mm_embedding_lengths"], + mm_embedding_request_indices=model_outputs["mm_embedding_request_indices"], + num_context_requests=num_context_requests, + extra_data={ + key: value for key, value in model_outputs.items() if key not in result_keys + }, + ) + @dataclass(kw_only=True) class SampleStateWithMMResult(SampleState[SampleStateTensors, SampleStateTensors]): @@ -336,11 +392,10 @@ def sample_async( resource_manager: Optional[ResourceManager] = None, ) -> SampleState: # from model_outputs to MultimodalResult - data = MultimodalResult( - mm_embeddings=model_outputs.pop("mm_embeddings"), - extra_data={**model_outputs}, - ) assert not scheduled_requests.generation_requests + data = MultimodalResult.from_model_outputs( + model_outputs, scheduled_requests.num_context_requests + ) return self.SampleState(requests=scheduled_requests.context_requests, data=data) @override @@ -356,26 +411,28 @@ def update_requests( extra_data = state.data.extra_data or {} mrope_position_ids = extra_data.get("mrope_position_ids", None) mrope_position_deltas = extra_data.get("mrope_position_deltas", None) - for i, (request, mm_embedding) in enumerate(zip(requests, mm_embeddings)): + for request in requests: request.state = LlmRequestState.GENERATION_COMPLETE # NOTE: This is a hack: set finish reason manually and set the beam 0 request.set_finished_reason(FinishReason.LENGTH, 0) - assert request.multimodal_lengths is not None - # TODO(TRTLLM-12175): request.multimodal_lengths is a - # prompt-side MM-token count and may include non-embedding - # special/framing tokens. This validation needs per-item - # encoder-output embedding lengths instead. - if len(mm_embedding) != sum(request.multimodal_lengths): - raise ValueError( - f"mm_embedding shape mismatch: {len(mm_embedding)} != {sum(request.multimodal_lengths)}" - ) - request.py_result.append_mm_embeddings(mm_embedding, request.multimodal_lengths) + request_indices = state.data.mm_embedding_request_indices + for result_index, (request_index, mm_embedding) in enumerate( + zip(request_indices, mm_embeddings, strict=True) + ): + request = requests[request_index] + mm_embedding_lengths = state.data.mm_embedding_lengths[result_index] + + request.py_result.append_mm_embeddings(mm_embedding, mm_embedding_lengths) # Store mrope data if available if mrope_position_ids is not None and mrope_position_deltas is not None: + mrope_index = ( + request_index if len(mrope_position_ids) == len(requests) else result_index + ) request.py_result.set_mrope_position( - mrope_position_ids[i], mrope_position_deltas[i] + mrope_position_ids[mrope_index], + mrope_position_deltas[mrope_index], ) @override diff --git a/tensorrt_llm/executor/base_worker.py b/tensorrt_llm/executor/base_worker.py index 18e8a182f210..2c6b19cb247a 100644 --- a/tensorrt_llm/executor/base_worker.py +++ b/tensorrt_llm/executor/base_worker.py @@ -468,21 +468,8 @@ def _enqueue_request(self, if request.multimodal_params is not None and request.multimodal_params.has_content( ): if request.multimodal_params.multimodal_input is not None: - multimodal_input = tllm.MultimodalInput( - multimodal_hashes=request.multimodal_params. - multimodal_input.multimodal_hashes, - multimodal_positions=request.multimodal_params. - multimodal_input.multimodal_positions, - multimodal_lengths=request.multimodal_params. - multimodal_input.multimodal_lengths, - multimodal_uuids=request.multimodal_params.multimodal_input. - multimodal_uuids, - multimodal_item_run_cu_offsets=request.multimodal_params. - multimodal_input.multimodal_item_run_cu_offsets, - multimodal_run_positions=request.multimodal_params. - multimodal_input.multimodal_run_positions, - multimodal_run_lengths=request.multimodal_params. - multimodal_input.multimodal_run_lengths) + multimodal_input = request.multimodal_params.multimodal_input.to_binding( + tllm) # NOTE: Setting to None here to avoid sending multimodal_input again through the 'py_multimodal_data' field request.multimodal_params.multimodal_input = None @@ -622,7 +609,8 @@ def _deduce_max_tokens(request: GenerationRequest, executor_request.py_disaggregated_params = request.disaggregated_params if self._is_pytorch_backend and request.multimodal_params is not None: if request.multimodal_params.multimodal_data is not None: - # NOTE: Deserialize SharedTensor handle to actual tensor + # Resolve SharedTensorContainer dicts inside multimodal_data, including + # E/P handoff embedding handles parked under "multimodal_embedding". request.multimodal_params.to_tensor("multimodal_data") executor_request.py_multimodal_data = request.multimodal_params.multimodal_data diff --git a/tensorrt_llm/executor/result.py b/tensorrt_llm/executor/result.py index 2649f7aa5516..8cae95503b4b 100644 --- a/tensorrt_llm/executor/result.py +++ b/tensorrt_llm/executor/result.py @@ -546,7 +546,6 @@ def _handle_response(self, if hasattr(response_result, "mm_embedding_handles" ) and response_result.mm_embedding_handles is not None: - # mm_embedding_handles is a list of handles (one per multimodal item). mm_embedding_handles = response_result.mm_embedding_handles if self._disaggregated_params is not None: self._disaggregated_params.multimodal_embedding_handles = mm_embedding_handles diff --git a/tensorrt_llm/inputs/multimodal.py b/tensorrt_llm/inputs/multimodal.py index f19c0af72007..35c7e7413341 100644 --- a/tensorrt_llm/inputs/multimodal.py +++ b/tensorrt_llm/inputs/multimodal.py @@ -23,6 +23,131 @@ _HASH_SCHEME_TAG = b"trtllm.mm.hash.v1" +def _validate_int_list(values: Any, field_name: str) -> None: + """Boundary metadata must be owned Python list[int]. No tensor/tuple.""" + if not isinstance(values, list): + raise TypeError(f"{field_name} must be a list") + if not all(isinstance(value, int) for value in values): + raise TypeError(f"{field_name} must contain only integers") + + +def _validate_multimodal_positions_and_lengths( + multimodal_positions: List[int], + multimodal_lengths: List[int], + expected_num_items: int, + expected_num_items_name: str, +) -> None: + """Validate one prompt span per MM item. + + expected_num_items is owner count: hashes for MultimodalInput, + embedding lengths for E/P handoff. Positions are prompt offsets. Lengths + are prompt token counts. + """ + _validate_int_list(multimodal_positions, "multimodal_positions") + _validate_int_list(multimodal_lengths, "multimodal_lengths") + + if len(multimodal_positions) != len(multimodal_lengths): + raise ValueError(f"Position and length arrays must match in size: " + f"positions={len(multimodal_positions)}, " + f"lengths={len(multimodal_lengths)}") + if len(multimodal_positions) != expected_num_items: + raise ValueError( + f"{expected_num_items_name}, multimodal_positions, and " + "multimodal_lengths must all have the same length") + + if any(position < 0 for position in multimodal_positions): + raise ValueError("multimodal_positions must be non-negative") + if any(length <= 0 for length in multimodal_lengths): + raise ValueError("multimodal_lengths must be positive") + + +def _validate_multimodal_runs( + num_items: int, + multimodal_lengths: List[int], + multimodal_item_run_cu_offsets: Optional[List[int]], + multimodal_run_positions: Optional[List[int]], + multimodal_run_lengths: Optional[List[int]], + item_count_name: str, +) -> None: + """Validate exact runs when they are present. + + Either no run fields, or all three. Offsets length is num_items + 1. + Runs for each item must sum to multimodal_lengths[i]. Values must fit + int32 for executor/KV-cache code. + """ + run_fields = ( + multimodal_item_run_cu_offsets, + multimodal_run_positions, + multimodal_run_lengths, + ) + if all(field is None for field in run_fields): + return + if any(field is None for field in run_fields): + raise ValueError( + "multimodal_item_run_cu_offsets, multimodal_run_positions, " + "and multimodal_run_lengths must be provided together") + + assert multimodal_item_run_cu_offsets is not None + assert multimodal_run_positions is not None + assert multimodal_run_lengths is not None + + for field_name, values in ( + ("multimodal_item_run_cu_offsets", multimodal_item_run_cu_offsets), + ("multimodal_run_positions", multimodal_run_positions), + ("multimodal_run_lengths", multimodal_run_lengths), + ): + _validate_int_list(values, field_name) + if any(value > _INT32_MAX for value in values): + raise ValueError(f"{field_name} values must fit in int32") + + if len(multimodal_item_run_cu_offsets) != num_items + 1: + raise ValueError("multimodal_item_run_cu_offsets length must be " + f"len({item_count_name}) + 1") + if multimodal_item_run_cu_offsets[0] != 0: + raise ValueError("multimodal_item_run_cu_offsets must start at 0") + if len(multimodal_run_positions) != len(multimodal_run_lengths): + raise ValueError( + "multimodal_run_positions and multimodal_run_lengths must " + "have the same length") + if multimodal_item_run_cu_offsets[-1] != len(multimodal_run_positions): + raise ValueError( + "multimodal_item_run_cu_offsets[-1] must equal the number of " + "flat multimodal runs") + if not all(multimodal_item_run_cu_offsets[i] <= + multimodal_item_run_cu_offsets[i + 1] + for i in range(len(multimodal_item_run_cu_offsets) - 1)): + raise ValueError( + "multimodal_item_run_cu_offsets must be non-decreasing") + if any(pos < 0 for pos in multimodal_run_positions): + raise ValueError("multimodal_run_positions must be non-negative") + if any(length <= 0 for length in multimodal_run_lengths): + raise ValueError("multimodal_run_lengths must be positive") + for run_idx, (position, length) in enumerate( + zip(multimodal_run_positions, multimodal_run_lengths)): + if position + length > _INT32_MAX: + raise ValueError( + f"multimodal run {run_idx} end position exceeds int32 " + f"range: position={position}, length={length}, " + f"max={_INT32_MAX}") + + for item_idx, expected_length in enumerate(multimodal_lengths): + run_begin = multimodal_item_run_cu_offsets[item_idx] + run_end = multimodal_item_run_cu_offsets[item_idx + 1] + actual_length = sum(multimodal_run_lengths[run_begin:run_end]) + if actual_length != expected_length: + raise ValueError( + f"multimodal run lengths for item {item_idx} sum to " + f"{actual_length}, expected {expected_length}") + item_positions = multimodal_run_positions[run_begin:run_end] + item_lengths = multimodal_run_lengths[run_begin:run_end] + for prev_pos, prev_len, pos in zip(item_positions, item_lengths, + item_positions[1:]): + if pos < prev_pos + prev_len: + raise ValueError( + "multimodal runs must be ordered and non-overlapping " + "within each item") + + def strip_mm_data_for_generation(mm_data: Dict[str, Any]) -> None: """Clear `mm_data` in place, retaining only `mrope_config.mrope_position_deltas`. @@ -123,23 +248,12 @@ def __post_init__(self): f"All hash arrays must have the same length, got lengths: {hash_lengths}" ) - # Check that positions and lengths are valid - if not all(isinstance(x, int) for x in self.multimodal_positions): - raise TypeError("multimodal_positions must contain only integers") - - if not all(isinstance(x, int) for x in self.multimodal_lengths): - raise TypeError("multimodal_lengths must contain only integers") - - # Check position and length arrays match in size - if len(self.multimodal_positions) != len(self.multimodal_lengths): - raise ValueError( - f"Position and length arrays must match in size: " - f"positions={len(self.multimodal_positions)}, lengths={len(self.multimodal_lengths)}" - ) - if len(self.multimodal_hashes) != len(self.multimodal_positions): - raise ValueError( - "multimodal_hashes, multimodal_positions, and multimodal_lengths " - "must all have the same length") + _validate_multimodal_positions_and_lengths( + self.multimodal_positions, + self.multimodal_lengths, + len(self.multimodal_hashes), + "multimodal_hashes", + ) # Validate multimodal_uuids if provided if self.multimodal_uuids is not None: @@ -155,90 +269,14 @@ def __post_init__(self): f"multimodal_uuids[{i}] must be a string or None, got {type(uuid)}" ) - self._validate_multimodal_runs() - - def _validate_multimodal_runs(self) -> None: - run_fields = ( + _validate_multimodal_runs( + len(self.multimodal_hashes), + self.multimodal_lengths, self.multimodal_item_run_cu_offsets, self.multimodal_run_positions, self.multimodal_run_lengths, + "multimodal_hashes", ) - if all(field is None for field in run_fields): - return - if any(field is None for field in run_fields): - raise ValueError( - "multimodal_item_run_cu_offsets, multimodal_run_positions, " - "and multimodal_run_lengths must be provided together") - - assert self.multimodal_item_run_cu_offsets is not None - assert self.multimodal_run_positions is not None - assert self.multimodal_run_lengths is not None - - if len(self.multimodal_item_run_cu_offsets) != len( - self.multimodal_hashes) + 1: - raise ValueError("multimodal_item_run_cu_offsets length must be " - "len(multimodal_hashes) + 1") - if self.multimodal_item_run_cu_offsets[0] != 0: - raise ValueError("multimodal_item_run_cu_offsets must start at 0") - if len(self.multimodal_run_positions) != len( - self.multimodal_run_lengths): - raise ValueError( - "multimodal_run_positions and multimodal_run_lengths must " - "have the same length") - if self.multimodal_item_run_cu_offsets[-1] != len( - self.multimodal_run_positions): - raise ValueError( - "multimodal_item_run_cu_offsets[-1] must equal the number of " - "flat multimodal runs") - - for field_name, values in ( - ("multimodal_item_run_cu_offsets", - self.multimodal_item_run_cu_offsets), - ("multimodal_run_positions", self.multimodal_run_positions), - ("multimodal_run_lengths", self.multimodal_run_lengths), - ): - if not isinstance(values, list): - raise TypeError(f"{field_name} must be a list") - if not all(isinstance(x, int) for x in values): - raise TypeError(f"{field_name} must contain only integers") - if any(value > _INT32_MAX for value in values): - raise ValueError(f"{field_name} values must fit in int32") - - if not all( - self.multimodal_item_run_cu_offsets[i] <= - self.multimodal_item_run_cu_offsets[i + 1] - for i in range(len(self.multimodal_item_run_cu_offsets) - 1)): - raise ValueError( - "multimodal_item_run_cu_offsets must be non-decreasing") - if any(pos < 0 for pos in self.multimodal_run_positions): - raise ValueError("multimodal_run_positions must be non-negative") - if any(length <= 0 for length in self.multimodal_run_lengths): - raise ValueError("multimodal_run_lengths must be positive") - for run_idx, (position, length) in enumerate( - zip(self.multimodal_run_positions, - self.multimodal_run_lengths)): - if position + length > _INT32_MAX: - raise ValueError( - f"multimodal run {run_idx} end position exceeds int32 " - f"range: position={position}, length={length}, " - f"max={_INT32_MAX}") - - for item_idx, expected_length in enumerate(self.multimodal_lengths): - run_begin = self.multimodal_item_run_cu_offsets[item_idx] - run_end = self.multimodal_item_run_cu_offsets[item_idx + 1] - actual_length = sum(self.multimodal_run_lengths[run_begin:run_end]) - if actual_length != expected_length: - raise ValueError( - f"multimodal run lengths for item {item_idx} sum to " - f"{actual_length}, expected {expected_length}") - item_positions = self.multimodal_run_positions[run_begin:run_end] - item_lengths = self.multimodal_run_lengths[run_begin:run_end] - for prev_pos, prev_len, pos in zip(item_positions, item_lengths, - item_positions[1:]): - if pos < prev_pos + prev_len: - raise ValueError( - "multimodal runs must be ordered and non-overlapping " - "within each item") @classmethod def from_components( @@ -267,6 +305,87 @@ def to_tensor(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: torch.tensor(self.multimodal_positions, dtype=torch.int32), torch.tensor(self.multimodal_lengths, dtype=torch.int32)) + def run_metadata(self) -> Dict[str, List[int]]: + metadata = {} + if self.multimodal_item_run_cu_offsets is not None: + metadata[ + "multimodal_item_run_cu_offsets"] = self.multimodal_item_run_cu_offsets + if self.multimodal_run_positions is not None: + metadata["multimodal_run_positions"] = self.multimodal_run_positions + if self.multimodal_run_lengths is not None: + metadata["multimodal_run_lengths"] = self.multimodal_run_lengths + return metadata + + def to_binding(self, executor_module: Any) -> Any: + kwargs = dict(multimodal_hashes=self.multimodal_hashes, + multimodal_positions=self.multimodal_positions, + multimodal_lengths=self.multimodal_lengths, + multimodal_uuids=self.multimodal_uuids) + kwargs.update(self.run_metadata()) + return executor_module.MultimodalInput(**kwargs) + + +@dataclass +class DisaggPrefillMultimodalInputs: + """Typed multimodal metadata returned by E/P disagg prefill processors.""" + + prompt_token_ids: List[int] + multimodal_lengths: List[int] + multimodal_positions: List[int] + multimodal_embedding_lengths: List[int] + multimodal_item_run_cu_offsets: Optional[List[int]] = None + multimodal_run_positions: Optional[List[int]] = None + multimodal_run_lengths: Optional[List[int]] = None + special_token_offsets: Optional[List[int]] = None + item_types: Optional[List[int]] = None + + def __post_init__(self) -> None: + _validate_int_list(self.prompt_token_ids, "prompt_token_ids") + _validate_int_list(self.multimodal_embedding_lengths, + "multimodal_embedding_lengths") + _validate_multimodal_positions_and_lengths( + self.multimodal_positions, + self.multimodal_lengths, + len(self.multimodal_embedding_lengths), + "multimodal_embedding_lengths", + ) + + if any(length <= 0 for length in self.multimodal_embedding_lengths): + raise ValueError("multimodal_embedding_lengths must be positive") + + _validate_multimodal_runs( + len(self.multimodal_lengths), + self.multimodal_lengths, + self.multimodal_item_run_cu_offsets, + self.multimodal_run_positions, + self.multimodal_run_lengths, + "multimodal_lengths", + ) + self._validate_optional_metadata() + + def _validate_optional_metadata(self) -> None: + if self.special_token_offsets is not None: + _validate_int_list(self.special_token_offsets, + "special_token_offsets") + if any(offset < 0 for offset in self.special_token_offsets): + raise ValueError("special_token_offsets must be non-negative") + if self.item_types is not None: + _validate_int_list(self.item_types, "item_types") + if len(self.item_types) != len(self.multimodal_lengths): + raise ValueError("item_types length must match " + "multimodal_lengths") + + def to_multimodal_input(self, + mm_hashes: List[List[int]]) -> MultimodalInput: + return MultimodalInput.from_components( + mm_hashes, + self.multimodal_positions, + self.multimodal_lengths, + mm_item_run_cu_offsets=self.multimodal_item_run_cu_offsets, + mm_run_positions=self.multimodal_run_positions, + mm_run_lengths=self.multimodal_run_lengths, + ) + @dataclass class MultimodalRuntimeData: @@ -329,6 +448,7 @@ def __post_init__(self): # Extend only after auditing each key's consumers. _CPU_ONLY_MULTIMODAL_DATA_KEYS = frozenset({ "multimodal_embed_mask_cumsum", + "multimodal_embedding_lengths", }) @@ -354,7 +474,10 @@ class MultimodalParams: "mrope_rotary_cos_sin": torch.Tensor, # Rotary embeddings (Qwen2/2.5-VL) "mrope_position_deltas": torch.Tensor, # Position deltas (Qwen2/2.5-VL) }, - "multimodal_embedding": torch.Tensor, # Pre-computed vision embeddings + "multimodal_embedding": torch.Tensor | List[SharedTensor handle dict], + # Pre-computed embeddings. In E/P handoff this may temporarily hold + # SharedTensorContainer dicts; BaseWorker restores them to tensors with + # to_tensor("multimodal_data") before PyTorch forward. "image": { "pixel_values": torch.Tensor, "image_height": torch.Tensor | List[int], @@ -827,6 +950,13 @@ def find_mm_token_lengths( mm_video_dict = (multimodal_data or {}).get("video") or {} video_grid_thw = mm_video_dict.get("video_grid_thw") + if video_grid_thw is not None: + video_grid_thw = torch.as_tensor(video_grid_thw) + assert video_grid_thw.device.type == "cpu", ( + "video_grid_thw must be CPU-resident when computing " + f"multimodal metadata, got {video_grid_thw.device}.") + if video_grid_thw.ndim != 2 or video_grid_thw.shape[-1] != 3: + raise ValueError("video_grid_thw must have shape [num_segments, 3]") for modality, items in mm_items.items(): if not hasattr(input_processor, f"get_num_tokens_per_{modality}"): @@ -836,12 +966,14 @@ def find_mm_token_lengths( video_grid_thw_for_items = None if modality == "video" and video_grid_thw is not None: - if len(video_grid_thw) == len(items): + if len(items) == 1: + video_grid_thw_for_items = video_grid_thw + elif video_grid_thw.shape[0] == len(items): video_grid_thw_for_items = video_grid_thw else: logger.warning( "find_mm_token_lengths: video_grid_thw row count " - f"({len(video_grid_thw)}) does not match number of " + f"({video_grid_thw.shape[0]}) does not match number of " f"videos in mm_data ({len(items)}); falling back to " "per-item recompute without video_grid_thw.") @@ -872,8 +1004,9 @@ def find_mm_token_lengths( # metadata route. Keep this for now: Qwen3-VL needs the # processor-produced video_grid_thw for correct video token # counts. - call_kwargs["video_grid_thw"] = video_grid_thw_for_items[ - idx] + call_kwargs["video_grid_thw"] = ( + video_grid_thw_for_items if len(items) == 1 else + video_grid_thw_for_items[idx:idx + 1]) num_tokens = input_processor.get_num_tokens_per_video( **call_kwargs) modality_token_lengths.append(num_tokens) @@ -896,6 +1029,7 @@ def find_mm_token_lengths( _MM_METADATA_ONLY_KEYS = frozenset({ "mrope_config", "multimodal_embed_mask_cumsum", + "multimodal_embedding_lengths", "special_token_offsets", "layout_metadata", }) @@ -1132,6 +1266,32 @@ def _find_mm_token_runs_from_mask( return item_run_cu_offsets, run_positions, run_lengths +def _find_mm_embedding_lengths_from_masks( + mm_mask: torch.Tensor, + embed_mask: torch.Tensor, + num_mm_tokens: List[int], +) -> List[int]: + """Compute embedding-slot counts per logical multimodal item.""" + if not torch.any(mm_mask): + return [] + + mm_positions = torch.where(mm_mask)[0] + lengths_t = torch.tensor(num_mm_tokens) + assert mm_positions.numel() == lengths_t.sum().item(), ( + f"Number of multimodal tokens ({mm_positions.numel()}) does not match " + f"sum of per-unit lengths ({lengths_t.sum().item()}): " + f"num_mm_tokens={num_mm_tokens}") + + embedding_lengths: List[int] = [] + offset = 0 + for item_length in num_mm_tokens: + item_positions = mm_positions[offset:offset + item_length] + offset += item_length + embedding_lengths.append(int(embed_mask[item_positions].sum().item())) + + return embedding_lengths + + def validate_mm_inputs(prompt_token_ids: Union[torch.Tensor, List[int], np.ndarray], mm_hashes: List[List[int]], start_positions: List[int], diff --git a/tensorrt_llm/inputs/registry.py b/tensorrt_llm/inputs/registry.py index 801e27b977eb..e9ed709010ba 100644 --- a/tensorrt_llm/inputs/registry.py +++ b/tensorrt_llm/inputs/registry.py @@ -20,6 +20,7 @@ from .content_format import ContentFormat from .data import TextPrompt from .multimodal import (MultimodalInput, _as_cpu_tensor, _compute_mm_masks, + _find_mm_embedding_lengths_from_masks, _find_mm_token_runs_from_mask, _find_mm_token_start_pos_from_masks, apply_mm_hashes, default_hasher, find_mm_token_lengths, @@ -818,10 +819,11 @@ def support_multimodal_disaggregated(model_cls: Type[nn.Module]): raise TypeError( f"{processor_cls.__name__} must inherit from BaseMultimodalInputProcessor to support multimodal disagg" ) - method = getattr(processor_cls, "get_prompt_token_ids", None) + method = getattr(processor_cls, "build_disagg_prefill_multimodal_inputs", + None) if method is None or not callable(method): raise TypeError( - f"{processor_cls.__name__} must implement a callable method `get_prompt_token_ids` to support multimodal disagg" + f"{processor_cls.__name__} must implement a callable method `build_disagg_prefill_multimodal_inputs` to support multimodal disagg" ) setattr(processor_cls, "support_mm_disagg", True) @@ -1116,6 +1118,7 @@ def multimodal_hashing_process( if input_ids_tensor.numel() == 0: start_positions, start_special_token_positions = [], [] item_run_cu_offsets, run_positions, run_lengths = [0], [], [] + multimodal_embedding_lengths = [] else: mm_mask, embed_mask, special_mask = _compute_mm_masks( input_ids_tensor, @@ -1131,6 +1134,11 @@ def multimodal_hashing_process( num_mm_tokens)) item_run_cu_offsets, run_positions, run_lengths = ( _find_mm_token_runs_from_mask(mm_mask, num_mm_tokens)) + multimodal_embedding_lengths = ( + _find_mm_embedding_lengths_from_masks(mm_mask, embed_mask, + num_mm_tokens)) + extra_processed_inputs["multimodal_data"][ + "multimodal_embedding_lengths"] = multimodal_embedding_lengths # Store special token offsets if available if len(start_special_token_positions ) > 0 and mm_special_token_ids is not None: diff --git a/tensorrt_llm/llmapi/llm.py b/tensorrt_llm/llmapi/llm.py index 3f387d97863e..d8c471d8624e 100644 --- a/tensorrt_llm/llmapi/llm.py +++ b/tensorrt_llm/llmapi/llm.py @@ -17,7 +17,8 @@ from transformers import PreTrainedTokenizerBase from tensorrt_llm._utils import mpi_disabled -from tensorrt_llm.inputs.multimodal import MultimodalInput, MultimodalParams +from tensorrt_llm.inputs.multimodal import (DisaggPrefillMultimodalInputs, + MultimodalParams) from tensorrt_llm.inputs.registry import BaseMultimodalInputProcessor from tensorrt_llm.llmapi import tracing from tensorrt_llm.metrics.enums import MetricNames @@ -566,13 +567,29 @@ def _preprocess( # This branch is applicable for Encode --> Prefill handoff scenario, # in E/P/D/ and E/PD settings. Prefill worker executes this code path. if is_mm_disagg: + if self.args.backend == "_autodeploy": + raise ValueError( + "Multimodal disaggregated inference (encode -> prefill " + "embedding handoff) is not supported with the AutoDeploy " + "backend. AutoDeploy runs the multimodal encoder in-prefill " + "on raw inputs and does not consume precomputed multimodal " + "embeddings.") if not getattr(self.input_processor, "support_mm_disagg", False): raise ValueError( "Multimodal disaggregated inference is not supported for this model" ) mm_handles = disaggregated_params.multimodal_embedding_handles - prompt_token_ids, mm_token_length, mm_token_positions = self.input_processor.get_prompt_token_ids( - inputs, mm_handles) + # TODO(TRTLLM-12869): Pass encoder-side MM layout through + # DisaggregatedParams so prefill does not rebuild prompt tokens, + # positions, lengths, runs, special offsets, and cumsum here. + disagg_mm_inputs = ( + self.input_processor.build_disagg_prefill_multimodal_inputs( + inputs, mm_handles)) + if not isinstance(disagg_mm_inputs, DisaggPrefillMultimodalInputs): + raise TypeError( + "build_disagg_prefill_multimodal_inputs must return " + "DisaggPrefillMultimodalInputs") + prompt_token_ids = disagg_mm_inputs.prompt_token_ids prompt = inputs.get("prompt", None) query_token_ids = inputs.get("query_token_ids", None) if is_gen_only: @@ -581,9 +598,25 @@ def _preprocess( ) else: mm_hashes = disaggregated_params.multimodal_hashes - multimodal_input = MultimodalInput.from_components( - mm_hashes, mm_token_positions, mm_token_length) - multimodal_data = {"multimodal_embedding": mm_handles} + multimodal_input = disagg_mm_inputs.to_multimodal_input( + mm_hashes) + # E/P handoff carries SharedTensorContainer dicts. Park them under the + # embedding key so BaseWorker's recursive to_tensor("multimodal_data") + # restores local tensor views before PyTorch forward. Until then this + # key holds handles, not tensors. + multimodal_data = { + "multimodal_embedding": + mm_handles, + "multimodal_embedding_lengths": + (disagg_mm_inputs.multimodal_embedding_lengths), + } + if disagg_mm_inputs.special_token_offsets is not None: + multimodal_data["special_token_offsets"] = ( + disagg_mm_inputs.special_token_offsets) + if disagg_mm_inputs.item_types is not None: + multimodal_data["layout_metadata"] = { + "item_types": disagg_mm_inputs.item_types + } if disaggregated_params.mrope_position_ids_handle is not None: # NOTE: `PyTorchModelEngine` assumes both are present when using mrope. assert disaggregated_params.mrope_position_deltas_handle is not None diff --git a/tensorrt_llm/serve/openai_protocol.py b/tensorrt_llm/serve/openai_protocol.py index 96316d2eb27d..59cc2f2d7295 100644 --- a/tensorrt_llm/serve/openai_protocol.py +++ b/tensorrt_llm/serve/openai_protocol.py @@ -158,6 +158,10 @@ class DisaggregatedParams(OpenAIBaseModel): schedule_style: Optional[DisaggScheduleStyle] = None conversation_id: Optional[str] = None ctx_usage: Optional[UsageInfo] = None + # TODO(TRTLLM-12407): Multimodal E/PD over trtllm-serve needs these protocol fields too: + # encoder embedding handles, multimodal hashes, and optional mRoPE handles. + # Add them here and in to_disaggregated_params()/to_llm_disaggregated_params() + # before routing MM encoder -> context -> generation through OpenAI protocol. class ErrorResponse(OpenAIBaseModel): diff --git a/tests/integration/defs/accuracy/references/videomme.yaml b/tests/integration/defs/accuracy/references/videomme.yaml index 2bee39c8c2b6..186971bc05b7 100644 --- a/tests/integration/defs/accuracy/references/videomme.yaml +++ b/tests/integration/defs/accuracy/references/videomme.yaml @@ -3,6 +3,9 @@ # Initial Video-MME short-shard guardrail for E2E video QA. Update these values # after collecting stable model baselines on the generated 300-question shard. +Qwen/Qwen3-VL-2B-Instruct: + - accuracy: 54.5 + num_samples: 300 nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8: - quant_algo: FP8 kv_cache_quant_algo: FP8 diff --git a/tests/integration/defs/accuracy/test_epd_disagg_multimodal.py b/tests/integration/defs/accuracy/test_epd_disagg_multimodal.py new file mode 100644 index 000000000000..20659f9b7ca9 --- /dev/null +++ b/tests/integration/defs/accuracy/test_epd_disagg_multimodal.py @@ -0,0 +1,288 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""VideoMME accuracy over llmapi encode / prefill-decode (E/PD) disaggregation. + +Separated from test_disaggregated_serving.py: the EPD-multimodal path uses an +in-process MultimodalEncoder plus a combined prefill/decode LLM, which is a +different mechanism from the trtllm-serve subprocess disaggregation exercised by +the other tests in that file. +""" + +# NOTE: +# The encoder and PD are resident on the same physical GPU in the current test +# harness. Placing them on different physical GPUs silently corrupts the +# embeddings (garbage output, no error raised) in TRT-LLM's current state because +# the consumer (PD worker) rebuilds the encoder's embedding from a CUDA-IPC handle +# that currently never copies the tensor onto the PD's own compute device. +# Real cross-GPU E/PD therefore requires a real cross-device transfer +# (CPU staging or NIXL/RDMA) that is currently not natively supported in TRT-LLM. + +import contextlib +import os +from dataclasses import dataclass +from typing import Any, Dict, Iterator, Mapping, Optional, Protocol +from unittest import mock + +import pytest + +from tensorrt_llm import LLM, MultimodalEncoder +from tensorrt_llm.llmapi import KvCacheConfig, RequestOutput, SamplingParams +from tensorrt_llm.quantization import QuantAlgo + +from ..conftest import llm_models_root, skip_pre_blackwell, skip_pre_hopper +from .accuracy_core import LlmapiAccuracyTestHarness, VideoMME +from .test_disaggregated_serving import DEFAULT_TEST_TIMEOUT, MyThreadPoolExecutor + + +class VideoMMECompatibleLLM(Protocol): + """LLM surface consumed by the VideoMME evaluator.""" + + args: Any + model: str + _hf_model_dir: str + tokenizer: Any + input_processor: Any + + def generate_async( + self, + inputs: Dict[str, Any], + sampling_params: Optional[SamplingParams] = None, + streaming: bool = False, + ) -> Any: ... + + +class _MultimodalEncoderPDAdapter: + """Adapter that runs VideoMME dict inputs through llmapi E/PD.""" + + def __init__( + self, encoder: MultimodalEncoder, pd_llm: LLM, thread_pool: MyThreadPoolExecutor + ) -> None: + self._encoder = encoder + self._pd_llm = pd_llm + self._thread_pool = thread_pool + self.args = pd_llm.args + self.model = pd_llm._hf_model_dir + self._hf_model_dir = pd_llm._hf_model_dir + self.tokenizer = pd_llm.tokenizer + self.input_processor = pd_llm.input_processor + + def _generate( + self, inputs: Dict[str, Any], sampling_params: Optional[SamplingParams], streaming: bool + ) -> RequestOutput: + if not isinstance(inputs, dict): + raise TypeError(f"Unsupported E/PD request input type: {type(inputs)}") + + encoder_output = self._encoder.generate_async(inputs).result() + disaggregated_params = encoder_output.disaggregated_params + if disaggregated_params is None: + raise RuntimeError("Multimodal encoder did not return disaggregated params.") + if disaggregated_params.multimodal_embedding_handles is None: + raise RuntimeError("Multimodal encoder did not return embedding handles.") + + disaggregated_params.request_type = "context_and_generation" + return self._pd_llm.generate_async( + inputs, + sampling_params=sampling_params, + streaming=streaming, + disaggregated_params=disaggregated_params, + ).result() + + def generate_async( + self, + inputs: Dict[str, Any], + sampling_params: Optional[SamplingParams] = None, + streaming: bool = False, + ): + future = self._thread_pool.submit(self._generate, inputs, sampling_params, streaming) + self._thread_pool.futures.append(future) + return future + + +@contextlib.contextmanager +def launch_multimodal_encoder_pd_llm( + encoder_llm_config: Dict[str, Any], + pd_llm_config: Dict[str, Any], + model_name: str, + max_workers: int = 16, +) -> Iterator[VideoMMECompatibleLLM]: + """Launch separate encoder and combined prefill/decode llmapi instances.""" + with contextlib.ExitStack() as stack: + stack.enter_context(mock.patch.dict(os.environ, {"TLLM_MULTIMODAL_DISAGGREGATED": "1"})) + thread_pool = stack.enter_context(MyThreadPoolExecutor(max_workers=max_workers)) + encoder = MultimodalEncoder(model=model_name, **encoder_llm_config) + pd_llm = LLM(model=model_name, **pd_llm_config) + with encoder, pd_llm: + yield _MultimodalEncoderPDAdapter(encoder, pd_llm, thread_pool) + + +@dataclass(frozen=True) +class EPDVariant: + """Immutable per-variant config for a VideoMME E/PD run.""" + + model_name: str + model_path: str + encoder_config: Mapping[str, Any] + pd_config: Mapping[str, Any] + expected_quant_algo: Optional[QuantAlgo] + max_workers: int + + @classmethod + def _build( + cls, + *, + model_name: str, + model_path: str, + kv_cache_config: KvCacheConfig, + max_batch_size: int, + expected_quant_algo: Optional[QuantAlgo], + max_num_tokens: int = 512, + attn_backend: Optional[str] = None, + max_workers: Optional[int] = None, + ) -> "EPDVariant": + """Fill shared encoder/PD defaults for one variant. + + Optional overrides are applied before construction so the frozen + instance never needs post-hoc mutation. + """ + # Optional attn_backend override, applied to both configs via a spread + # so the frozen instance never needs post-hoc mutation. + attn_override = {"attn_backend": attn_backend} if attn_backend is not None else {} + encoder_config = { + "trust_remote_code": True, + "max_batch_size": max_batch_size, + "cuda_graph_config": None, + **attn_override, + } + pd_config = { + "backend": "pytorch", + "disable_overlap_scheduler": True, + "trust_remote_code": True, + "kv_cache_config": kv_cache_config, + "enable_chunked_prefill": True, + "max_num_tokens": max_num_tokens, + "max_batch_size": max_batch_size, + "cuda_graph_config": None, + **attn_override, + } + + return cls( + model_name=model_name, + model_path=model_path, + encoder_config=encoder_config, + pd_config=pd_config, + expected_quant_algo=expected_quant_algo, + max_workers=max_workers if max_workers is not None else VideoMME.MAX_BATCH_SIZE, + ) + + @classmethod + def qwen3vl_2b(cls) -> "EPDVariant": + return cls._build( + model_name="Qwen/Qwen3-VL-2B-Instruct", + model_path=f"{llm_models_root()}/Qwen3/Qwen3-VL-2B-Instruct", + kv_cache_config=KvCacheConfig( + free_gpu_memory_fraction=0.8, + enable_block_reuse=False, + dtype="auto", + ), + max_batch_size=16, + expected_quant_algo=None, + max_workers=16, + attn_backend="VANILLA", + # Qwen3-VL VideoMME prompts can exceed 1024 tokens after visual + # expansion; avoid splitting a single context across vanilla + # SDPA chunks in the E/P handoff path. + max_num_tokens=2048, + ) + + @classmethod + def nano_omni_fp8(cls) -> "EPDVariant": + return cls._build( + model_name="nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", + model_path=f"{llm_models_root()}/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", + kv_cache_config=KvCacheConfig( + free_gpu_memory_fraction=0.8, + mamba_ssm_cache_dtype="float32", + enable_block_reuse=False, + dtype="fp8", + ), + max_batch_size=64, + expected_quant_algo=QuantAlgo.FP8, + ) + + @classmethod + def nano_omni_nvfp4(cls) -> "EPDVariant": + return cls._build( + model_name="nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", + model_path=f"{llm_models_root()}/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", + kv_cache_config=KvCacheConfig( + free_gpu_memory_fraction=0.8, + mamba_ssm_cache_dtype="float32", + enable_block_reuse=False, + dtype="fp8", + ), + max_batch_size=128, + expected_quant_algo=QuantAlgo.MIXED_PRECISION, + ) + + +class TestVideoMMEEPD(LlmapiAccuracyTestHarness): + """VideoMME accuracy over llmapi encode / prefill-decode (E/PD) disaggregation.""" + + SAMPLING_PARAMS = SamplingParams( + max_tokens=VideoMME.MAX_OUTPUT_LEN, + truncate_prompt_tokens=VideoMME.MAX_INPUT_LEN, + temperature=0.0, + top_k=1, + ) + + # Identical across all variants today; lifted to a class constant to mirror + # agg no_thinking_evaluator_kwargs. + NO_THINKING_EVALUATOR_KWARGS = { + "chat_template_kwargs": { + "enable_thinking": False, + }, + } + + def _launch_epd(self, variant: EPDVariant): + """Context manager: encoder + combined PD llmapi.""" + return launch_multimodal_encoder_pd_llm( + variant.encoder_config, + variant.pd_config, + variant.model_path, + max_workers=variant.max_workers, + ) + + def _run_videomme(self, llm, variant: EPDVariant) -> None: + actual_quant_algo = ( + llm.args.quant_config.quant_algo if llm.args.quant_config is not None else None + ) + assert actual_quant_algo == variant.expected_quant_algo + VideoMME(variant.model_name).evaluate( + llm, + sampling_params=self.SAMPLING_PARAMS, + extra_evaluator_kwargs=self.NO_THINKING_EVALUATOR_KWARGS, + ) + + @pytest.mark.timeout(DEFAULT_TEST_TIMEOUT) + @skip_pre_hopper + @pytest.mark.skip_less_device_memory(80000) + @pytest.mark.parametrize( + "variant", + [ + pytest.param( + EPDVariant.qwen3vl_2b(), marks=skip_pre_blackwell, id="qwen3vl_2b_instruct" + ), + pytest.param( + EPDVariant.nano_omni_fp8(), marks=skip_pre_hopper, id="nemotron_nano_v3_omni_fp8" + ), + pytest.param( + EPDVariant.nano_omni_nvfp4(), + marks=skip_pre_blackwell, + id="nemotron_nano_v3_omni_nvfp4", + ), + ], + ) + def test_disaggregated_videomme(self, variant: EPDVariant) -> None: + """Run VideoMME shard through a model-specific llmapi E/PD config.""" + with self._launch_epd(variant) as llm: + self._run_videomme(llm, variant) diff --git a/tests/integration/test_lists/qa/llm_function_core.txt b/tests/integration/test_lists/qa/llm_function_core.txt index be5a78639f9f..5bed3dfa9db5 100644 --- a/tests/integration/test_lists/qa/llm_function_core.txt +++ b/tests/integration/test_lists/qa/llm_function_core.txt @@ -98,6 +98,9 @@ accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_chunked_prefill accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_gen_first[noadp-ctx_tp2pp1-gen_tp1pp1] accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_gen_first[adp-ctx_tp2pp1-gen_tp2pp1] accuracy/test_disaggregated_serving.py::TestQwen3_8B::test_nixl_backend +accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[qwen3vl_2b_instruct] +accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[nemotron_nano_v3_omni_fp8] +accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[nemotron_nano_v3_omni_nvfp4] accuracy/test_disaggregated_serving.py::TestQwen3NextInstruct::test_auto_dtype[use_py_transceiver=False] accuracy/test_disaggregated_serving.py::TestQwen3NextInstruct::test_auto_dtype[use_py_transceiver=True] accuracy/test_dwdp_disaggregated_serving.py::TestDwdpDeepSeekV3Lite::test_dwdp_accuracy diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index a55992be4273..0e25ea8f8f52 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -74,6 +74,8 @@ l0_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_6_27B::test_fp8 - accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp1_block_reuse-cutlass] - accuracy/test_llm_api_pytorch_multimodal.py::TestNanoV3Omni::test_auto_dtype[nvfp4] + - accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[qwen3vl_2b_instruct] + - accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[nemotron_nano_v3_omni_nvfp4] - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_on] - accuracy/test_llm_api_pytorch.py::TestQwen3_5_35B_A3B::test_bf16_mtp[mtp_off] - accuracy/test_llm_api_pytorch.py::TestQwen3_5_9B::test_bf16[mtp_on] diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index 3bc3d1685aab..d1aa81437a6f 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -43,7 +43,7 @@ l0_h100: - unittest/_torch/modules/moe/test_moe_backend.py::test_moe_backend -k "CUTLASS" # ------------- MoE: test_single_gpu (by backend) --------------- - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "CUTLASS" - - unittest/_torch/multimodal + - unittest/_torch/multimodal -k "not nemotron_nano_v2_vl_fp8" - unittest/_torch/sampler - unittest/_torch/speculative/test_eagle3.py - unittest/_torch/speculative/hw_agnostic @@ -123,6 +123,7 @@ l0_h100: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_dummy_load_format - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized - accuracy/test_llm_api_pytorch_multimodal.py::TestNanoV3Omni::test_auto_dtype[fp8] + - accuracy/test_epd_disagg_multimodal.py::TestVideoMMEEPD::test_disaggregated_videomme[nemotron_nano_v3_omni_fp8] - accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales[latency] - accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales_early_first_token_response - accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_dummy_load_format diff --git a/tests/unittest/_torch/modeling/test_modeling_nemotron_nano_v2_vl.py b/tests/unittest/_torch/modeling/test_modeling_nemotron_nano_v2_vl.py index 3b89fe8ca204..f215fc208ebe 100644 --- a/tests/unittest/_torch/modeling/test_modeling_nemotron_nano_v2_vl.py +++ b/tests/unittest/_torch/modeling/test_modeling_nemotron_nano_v2_vl.py @@ -3,6 +3,7 @@ import os from pathlib import Path +from types import SimpleNamespace from unittest import mock from unittest.mock import MagicMock @@ -14,15 +15,19 @@ from test_modeling_nemotron_h import extract_decode_logprobs from tensorrt_llm import LLM +from tensorrt_llm._torch.models import modeling_nemotron_nano as nemotron_nano from tensorrt_llm._torch.models.modeling_multimodal_utils import get_multimodal_embeddings from tensorrt_llm._torch.models.modeling_nemotron_nano import ( NanoV2VLInputProcessor, + NanoV2VLMultimodalEncoder, NanoV2VLVisionEncoder, NemotronH_Nano_VL_V2, ) from tensorrt_llm._torch.models.modeling_parakeet import ProjectedParakeet +from tensorrt_llm._torch.models.modeling_utils import MODEL_CLASS_VISION_ENCODER_MAPPING from tensorrt_llm.inputs import ( AudioData, + VideoData, create_input_processor, create_input_processor_with_hash, default_multimodal_input_loader, @@ -36,6 +41,169 @@ MODEL_PATH = str(os.path.join(llm_models_root(), "NVIDIA-Nemotron-Nano-12B-v2-VL-BF16")) +def _make_minimal_nano_model_config(): + llm_config = SimpleNamespace(vocab_size=128) + pretrained_config = SimpleNamespace( + llm_config=llm_config, + torch_dtype=torch.bfloat16, + img_context_token_id=20, + video_context_token_id=21, + sound_context_token_id=None, + sound_config=None, + ) + return SimpleNamespace( + pretrained_config=pretrained_config, + quant_config=SimpleNamespace(exclude_modules=None), + quant_config_dict=None, + video_pruning_rate=None, + ) + + +def test_nemotron_nano_registers_native_multimodal_epd_components(): + """Native Nano VL/Omni classes advertise MM EPD support.""" + for arch in ("NemotronH_Nano_VL_V2", "NemotronH_Nano_Omni_Reasoning_V3"): + vision_encoder_cls, vlm_base_model = MODEL_CLASS_VISION_ENCODER_MAPPING[arch] + assert vision_encoder_cls is NanoV2VLMultimodalEncoder + assert vlm_base_model is None + assert NanoV2VLInputProcessor.support_mm_disagg is True + assert NemotronH_Nano_VL_V2.support_mm_disagg is True + + +def _assert_nano_video_handoff(handoff): + """Shared assertions for the EPD video handoff: split runs stay grouped under one MM item.""" + assert handoff.prompt_token_ids == [101, 30, 20, 20, 31, 55, 30, 20, 20, 31, 102] + assert handoff.multimodal_lengths == [8] + assert handoff.multimodal_positions == [1] + assert handoff.multimodal_embedding_lengths == [4] + assert handoff.multimodal_item_run_cu_offsets == [0, 2] + assert handoff.multimodal_run_positions == [1, 6] + assert handoff.multimodal_run_lengths == [4, 4] + assert handoff.special_token_offsets == [0, 3, 4, 7] + + +@pytest.mark.parametrize( + "input_field, input_value, asserts_encode_not_called", + [ + # Detokenized prompt text path: the tokenizer may encode the prompt. + ("prompt", "Question