[None][test] add e2e key model perf test

tests/integration/defs/perf/_model_paths.py

@@ -46,6 +46,7 @@
     "gemma_3_12b_it_fp4": "gemma/gemma-3-12b-it-fp4",
     "gemma_3_1b_it": "gemma/gemma-3-1b-it",
     "gemma_4_26b_a4b_nvfp4": "gemma/nvidia-Gemma-4-26B-A4B-NVFP4",
+    "gemma_4_31b_it_nvfp4": "gemma/nvidia-Gemma-4-31B-IT-NVFP4",
     "deepseek_r1_fp8": "DeepSeek-R1/DeepSeek-R1",
     "deepseek_r1_nvfp4": "DeepSeek-R1/DeepSeek-R1-FP4",
     "deepseek_r1_0528_fp8": "DeepSeek-R1/DeepSeek-R1-0528/",
@@ -120,6 +121,11 @@
     "deepseek_v4_pro_dspark": "DeepSeek-V4-Pro-DSpark",
     # GLM-5 FP8 (MoE)
     "glm_5_fp8": "GLM-5-FP8",
+    # GLM-5.2 NVFP4 (MoE, MLA + DSA on the DeepSeek-V3.2 code path)
+    "glm_5.2_fp4": "GLM-5.2-NVFP4",
+    # MiniMax-M3 NVFP4: MXFP8 base layers with NVFP4 routed experts
+    # (the MXFP8 checkpoint is registered as "minimax_m3_mxfp8" above).
+    "minimax_m3_fp4": "MiniMax-M3-NVFP4",
     # Kimi K2.5 NVFP4 (~1T MoE multimodal)
     "kimi_k2.5_fp4": "Kimi-K2.5-NVFP4",
     # Keys below are sanity-side aliases; some point to the same weights as

tests/integration/defs/perf/pytorch_model_config.py

@@ -142,6 +142,156 @@ def get_model_yaml_config(model_label: str,
                 },
             }
         },
+        # DeepSeek V4 Pro throughput knobs, from
+        # examples/configs/curated/deepseek-v4-pro-throughput.yaml (ADP + EP,
+        # small per-rank batch). MTP-1 matches the checkpoint's
+        # num_nextn_predict_layers and the curated throughput recipe.
+        {
+            'patterns': [
+                'deepseek_v4_pro_fp4-bench-pytorch-float4-maxbs:32-maxnt:8448',
+            ],
+            'config': {
+                'enable_attention_dp': True,
+                'enable_lm_head_tp_in_adp': True,
+                'attention_dp_config': {
+                    'enable_balance': True,
+                },
+                'moe_config': {
+                    'backend': 'TRTLLM',
+                    'use_low_precision_moe_combine': True,
+                },
+                'max_seq_len': 9256,
+                'kv_cache_config': {
+                    'dtype': 'fp8',
+                    'enable_block_reuse': False,
+                    'free_gpu_memory_fraction': 0.6,
+                    'tokens_per_block': 128,
+                },
+                'cuda_graph_config': {
+                    'enable_padding': True,
+                    'batch_sizes': [1, 2, 4, 8, 16, 24, 32],
+                },
+                'speculative_config': {
+                    'decoding_type': 'MTP',
+                    'max_draft_len': 1,
+                },
+                'stream_interval': 100,
+                'num_postprocess_workers': 4,
+            }
+        },
+        # DeepSeek V4 Pro latency knobs, from
+        # examples/configs/curated/deepseek-v4-pro-latency.yaml (pure TP, no
+        # attention DP, deeper MTP, larger KV fraction).
+        {
+            'patterns': [
+                'deepseek_v4_pro_fp4-bench-pytorch-float4-maxbs:128-maxnt:8448',
+            ],
+            'config': {
+                'enable_attention_dp': False,
+                'enable_lm_head_tp_in_adp': False,
+                'moe_config': {
+                    'backend': 'TRTLLM',
+                    'use_low_precision_moe_combine': True,
+                },
+                'max_seq_len': 9256,
+                'kv_cache_config': {
+                    'dtype': 'fp8',
+                    'enable_block_reuse': False,
+                    'free_gpu_memory_fraction': 0.9,
+                    'tokens_per_block': 128,
+                },
+                'cuda_graph_config': {
+                    'enable_padding':
+                    True,
+                    'batch_sizes': [
+                        1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96,
+                        104, 112, 120, 128
+                    ],
+                },
+                'speculative_config': {
+                    'decoding_type': 'MTP',
+                    'max_draft_len': 3,
+                },
+                'stream_interval': 100,
+                'num_postprocess_workers': 4,
+            }
+        },
+        # GLM-5.2 NVFP4 reuses the DeepSeek-V3.2 MLA + DSA path with
+        # cross-layer indexer sharing; NVFP4 weights run on the CuteDSL MoE
+        # backend (see accuracy/test_llm_api_pytorch.py::TestGLM52).
+        # Spec decoding is intentionally left off so the sweep measures kernel
+        # time rather than MTP acceptance rate.
+        {
+            'patterns': ['glm_5.2_fp4'],
+            'config': {
+                'enable_attention_dp': True,
+                'enable_chunked_prefill': True,
+                'moe_config': {
+                    'backend': 'CUTEDSL',
+                },
+                'max_seq_len': 10240,
+                'kv_cache_config': {
+                    'free_gpu_memory_fraction': 0.7,
+                },
+                'cuda_graph_config': {
+                    'enable_padding': True,
+                    'max_batch_size': 128,
+                },
+            }
+        },
+        # MiniMax-M3 NVFP4 (MXFP8 base layers with NVFP4 routed experts, run on
+        # the CUTLASS MoE backend). Scoped to the NVFP4 checkpoint only; the
+        # MXFP8 checkpoint has its own block below and must not inherit the
+        # 'msa' implementation.
+        # The block-sparse attention path is mandatory (there is no dense
+        # fallback), it does not support KV-cache block reuse, and max_seq_len
+        # must be capped just above ISL+OSL -- left at the checkpoint default
+        # (1M) the CUDA-graph warmup decode allocates gigabyte-scale temporaries
+        # and capture fails with cudaErrorStreamCaptureUnsupported. Every NVFP4
+        # case keeps ISL+OSL <= 2048. See
+        # docs/source/deployment-guide/deployment-guide-for-minimax-m3-on-trtllm.md
+        {
+            'patterns': ['minimax_m3_fp4'],
+            'config': {
+                'trust_remote_code': True,
+                'enable_attention_dp': True,
+                'max_seq_len': 2560,
+                'moe_config': {
+                    'backend': 'CUTLASS',
+                },
+                'sparse_attention_config': {
+                    'algorithm': 'minimax_m3',
+                    # 'msa' is the fmha_sm100 path that the MiniMax-M3 perf
+                    # work targets ('triton' is the reference implementation);
+                    # it requires SM100/SM103, so these cases are Blackwell-only.
+                    'implementation': 'msa',
+                },
+                'kv_cache_config': {
+                    'enable_block_reuse': False,
+                    'free_gpu_memory_fraction': 0.6,
+                    # The MSA path runs an FP8 KV cache.
+                    'dtype': 'fp8',
+                },
+                'stream_interval': 10,
+                'num_postprocess_workers': 4,
+            }
+        },
+        # Gemma 4 NVFP4: VSWA (1024-token sliding window) plus per-layer
+        # head_dim 256/512. The FLASHINFER backend is selected by the model's
+        # own get_model_defaults(), so it is not repeated here. KV-cache reuse
+        # is disabled to match the validated accuracy configuration.
+        {
+            'patterns': ['gemma_4_'],
+            'config': {
+                'enable_chunked_prefill': True,
+                'kv_cache_config': {
+                    'dtype': 'fp8',
+                    'enable_block_reuse': False,
+                    'enable_partial_reuse': False,
+                    'free_gpu_memory_fraction': 0.6,
+                },
+            }
+        },
         # DeepSeek R1 models with MTP speculative decoding
         {
             'patterns': [

tests/integration/defs/perf/test_perf.py

@@ -61,6 +61,7 @@
     "llama_v3.1_nemotron_ultra_253b_fp8",
     "kimi_k2_nvfp4",
     "kimi_k2.5_fp4",
+    "minimax_m3_fp4",
     "nemotron_3_super_120b_nvfp4",
     "nemotron_3_super_120b_nvfp4_mtp",
     "nemotron_3_ultra_550b_nvfp4",

tests/integration/test_lists/qa/llm_perf_core.yml

@@ -209,6 +209,24 @@ llm_perf_core:
   #deepseek_v4_flash (post-trained, FP4+FP8 mixed, 4-GPU)
   - perf/test_perf.py::test_perf[deepseek_v4_flash-bench-pytorch-float4-maxbs:128-maxnt:4096-kv_frac:0.5-input_output_len:8192,1024-ep:4-tp:4-gpus:4] TIMEOUT(120)
   - perf/test_perf.py::test_perf[deepseek_v4_flash-bench-pytorch-float4-maxbs:128-maxnt:4096-kv_frac:0.5-input_output_len:1000,2000-ep:4-tp:4-gpus:4] TIMEOUT(120)
+  #minimax_m3_fp4 (block-sparse MoE, MXFP8 base + NVFP4 experts 233G, 4-GPU ep=4)
+  #mirrors accuracy/test_llm_api_pytorch.py::TestMiniMaxM3::test_nvfp4[use_msa=True] (tp=ep=4).
+  #ISL+OSL is kept <= 2048 because the M3 sparse path caps max_seq_len for CUDA-graph capture.
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:128-maxnt:4096-input_output_len:1024,1024-ep:4-tp:4-gpus:4] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:1-input_output_len:1024,1024-reqs:10-con:1-ep:4-tp:4-gpus:4] #min_latency
+  #gemma_4_26b_a4b_nvfp4 (Gemma4 MoE 26B-A4B NVFP4 18G, 1-GPU; VSWA sliding_window=1024)
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-input_output_len:128,128]
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-input_output_len:500,2000]
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-input_output_len:2000,500]
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-input_output_len:8000,1000]
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-maxbs:1-input_output_len:8000,1000-reqs:10-con:1] #min_latency
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-float4-input_output_len:8000,1000-con:250] #max_throughput
+  #gemma_4_31b_it_nvfp4 (Gemma4 dense 31B NVFP4 31G, 1-GPU)
+  - perf/test_perf.py::test_perf[gemma_4_31b_it_nvfp4-bench-pytorch-float4-input_output_len:128,128]
+  - perf/test_perf.py::test_perf[gemma_4_31b_it_nvfp4-bench-pytorch-float4-input_output_len:500,2000]
+  - perf/test_perf.py::test_perf[gemma_4_31b_it_nvfp4-bench-pytorch-float4-input_output_len:2000,500]
+  - perf/test_perf.py::test_perf[gemma_4_31b_it_nvfp4-bench-pytorch-float4-maxbs:1-input_output_len:8000,1000-reqs:10-con:1] #min_latency
+  - perf/test_perf.py::test_perf[gemma_4_31b_it_nvfp4-bench-pytorch-float4-input_output_len:8000,1000-con:250] #max_throughput
   #llama_v3.1_nemotron_ultra_253b_fp8 (nemotron-nas FP8 241G, 4-GPU)
   - perf/test_perf.py::test_perf[llama_v3.1_nemotron_ultra_253b_fp8-bench-pytorch-float8-input_output_len:128,128-tp:4-gpus:4]
   - perf/test_perf.py::test_perf[llama_v3.1_nemotron_ultra_253b_fp8-bench-pytorch-float8-input_output_len:500,2000-tp:4-gpus:4]
@@ -272,6 +290,12 @@ llm_perf_core:
   #deepseek_v4_flash (post-trained, FP4+FP8 mixed, 8-GPU)
   - perf/test_perf.py::test_perf[deepseek_v4_flash-bench-pytorch-float4-maxbs:128-maxnt:4096-kv_frac:0.5-input_output_len:8192,1024-ep:8-tp:8-gpus:8] TIMEOUT(120)
   - perf/test_perf.py::test_perf[deepseek_v4_flash-bench-pytorch-float4-maxbs:128-maxnt:4096-kv_frac:0.5-input_output_len:1000,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  #deepseek_v4_pro_fp4 (1.6T MoE, sparse MLA + mHC, FP4+FP8 mixed, 8-GPU)
+  #knobs from examples/configs/curated/deepseek-v4-pro-{throughput,latency}.yaml;
+  #8K/1K is the fixed-shape workload used in tech_blog26 for DeepSeek-V4 Pro.
+  - perf/test_perf.py::test_perf[deepseek_v4_pro_fp4-bench-pytorch-float4-maxbs:32-maxnt:8448-kv_frac:0.6-input_output_len:8192,1024-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[deepseek_v4_pro_fp4-bench-pytorch-float4-maxbs:32-maxnt:8448-kv_frac:0.6-input_output_len:1000,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[deepseek_v4_pro_fp4-bench-pytorch-float4-maxbs:128-maxnt:8448-input_output_len:8192,1024-reqs:10-con:1-ep:8-tp:8-gpus:8] TIMEOUT(120) #min_latency
   #deepseek_v4_pro_dspark (DSpark speculative decoding, FP4+FP8 mixed, 8-GPU)
   - perf/test_perf.py::test_perf[deepseek_v4_pro_dspark-bench-pytorch-float4-maxbs:128-maxnt:9216-kv_frac:0.5-input_output_len:8192,1024-ep:8-tp:8-gpus:8] TIMEOUT(120)
   - perf/test_perf.py::test_perf[deepseek_v4_pro_dspark-bench-pytorch-float4-maxbs:128-maxnt:4096-kv_frac:0.5-input_output_len:1000,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
@@ -291,6 +315,24 @@ llm_perf_core:
   - perf/test_perf.py::test_perf[glm_5_fp8-bench-pytorch-float8-input_output_len:1000,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
   - perf/test_perf.py::test_perf[glm_5_fp8-bench-pytorch-float8-maxbs:1-input_output_len:8000,1000-reqs:10-con:1-ep:8-tp:8-gpus:8] #min_latency
   - perf/test_perf.py::test_perf[glm_5_fp8-bench-pytorch-float8-maxbs:512-maxnt:2048-kv_frac:0.6-input_output_len:8000,1000-con:512-ep:8-tp:8-gpus:8] TIMEOUT(120) #max_throughput
+  #glm_5.2_fp4 (MoE NVFP4 433G, MLA + DSA on the DeepSeek-V3.2 path, 8-GPU ep=8)
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-input_output_len:128,128-ep:8-tp:8-gpus:8]
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-input_output_len:500,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-input_output_len:8000,1000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-input_output_len:1000,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-maxbs:1-input_output_len:8000,1000-reqs:10-con:1-ep:8-tp:8-gpus:8] #min_latency
+  - perf/test_perf.py::test_perf[glm_5.2_fp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.6-input_output_len:8000,1000-con:512-ep:8-tp:8-gpus:8] TIMEOUT(120) #max_throughput
+  #minimax_m3_fp4 (block-sparse MoE, 8-GPU ep=8 + attention DP, matching
+  #docs/source/deployment-guide/deployment-guide-for-minimax-m3-on-trtllm.md).
+  #The MXFP8 checkpoint is already covered at 4-GPU in condition 6.
+  #ISL+OSL stays <= 2048: the M3 sparse path needs max_seq_len capped just above it
+  #or CUDA-graph capture fails on gigabyte-scale warmup temporaries.
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:256-maxnt:8192-input_output_len:1024,1024-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:256-maxnt:8192-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:256-maxnt:8192-input_output_len:500,2000-ep:8-tp:8-gpus:8] TIMEOUT(120)
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:1-input_output_len:1024,1024-reqs:10-con:1-ep:8-tp:8-gpus:8] #min_latency
+  - perf/test_perf.py::test_perf[minimax_m3_fp4-bench-pytorch-float4-maxbs:256-maxnt:8192-input_output_len:1024,1024-con:256-ep:8-tp:8-gpus:8] TIMEOUT(120) #max_throughput
   #deepseek_v3.2_fp4 (FP4 389G, 8-GPU ep=8)
   - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:10000-con:3072-ep:8-tp:8-gpus:8] TIMEOUT(120) #max throughput test
   - perf/test_perf.py::test_perf[deepseek_v3.2_fp4-bench-pytorch-float4-input_output_len:128,128-ep:8-tp:8-gpus:8]