fix: restrict PyTorch memory usage to avoid OOMs

Signed-off-by: ixlmar <[email protected]>

Author: ixlmar

docker/Dockerfile.multi

@@ -67,6 +67,9 @@ RUN bash ./install_mpi4py.sh && rm install_mpi4py.sh
 ARG TORCH_INSTALL_TYPE="skip"
 COPY docker/common/install_pytorch.sh install_pytorch.sh
 RUN bash ./install_pytorch.sh $TORCH_INSTALL_TYPE && rm install_pytorch.sh
+#
+# NB: PyTorch requires this to be < 1.0
+ENV PYTORCH_CUDA_ALLOC_CONF="garbage_collection_threshold:0.99999"
 
 # Install OpenCV with FFMPEG support
 RUN pip3 uninstall -y opencv && rm -rf /usr/local/lib/python3*/dist-packages/cv2/

tensorrt_llm/_torch/pyexecutor/_util.py

@@ -1,3 +1,4 @@
+import os
 import random
 from collections.abc import Iterable
 from typing import Dict, List, Optional
@@ -18,6 +19,7 @@
 
 from ..model_config import ModelConfig
 from ..speculative import get_spec_decoder
+from .config import PyTorchConfig
 from .config_utils import is_mla, is_nemotron_hybrid
 from .kv_cache_transceiver import AttentionTypeCpp, create_kv_cache_transceiver
 from .llm_request import ExecutorResponse
@@ -676,3 +678,45 @@ def _try_infer_num_experts(model_config: ModelConfig) -> int:
         return 1
 
     return num_experts
+
+
+def _adjust_torch_mem_fraction(pytorch_backend_config: PyTorchConfig):
+    # FIXME: PyTorch only uses the garbage_collection_threshold setting
+    #        if a memory fraction is set, cf.
+    #   https://github.com/pytorch/pytorch/blob/cd995bfb2aac8891465809be3ce29543bd524287/c10/cuda/CUDACachingAllocator.cpp#L1357
+    logger.debug("Setting PyTorch memory fraction to 1.0")
+    torch.cuda.set_per_process_memory_fraction(1.0)
+
+    # FIXME: As soon as
+    #     torch.cuda._set_allocator_settings (added in PyTorch 2.8.0-rc1)
+    #   or a similar API is available, the warning below should be removed
+    #   and the allocator GC threshold be set via the new API instead.
+    torch_allocator_config = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
+    torch_mem_threshold_advised = (
+        torch.cuda.get_allocator_backend() == "native"
+        and "expandable_segments:True" not in torch_allocator_config)
+    torch_mem_threshold_set = "garbage_collection_threshold:" in torch_allocator_config
+    if torch_mem_threshold_advised and not torch_mem_threshold_set:
+        logger.warning(
+            "It is recommended to incl. 'garbage_collection_threshold:0.???' or 'backend:cudaMallocAsync'"
+            " or 'expandable_segments:True' in PYTORCH_CUDA_ALLOC_CONF.")
+
+    # NOTE: Even if a memory threshold was not set (cf. warning above), setting a memory
+    #       fraction < 1.0 is beneficial, because
+    #         https://github.com/pytorch/pytorch/blob/5228986c395dc79f90d2a2b991deea1eef188260/c10/cuda/CUDACachingAllocator.cpp#L2719
+    #       and
+    #         https://github.com/pytorch/pytorch/blob/5228986c395dc79f90d2a2b991deea1eef188260/c10/cuda/CUDACachingAllocator.cpp#L1240
+    #       lead PyTorch to release all unused memory before hitting the set fraction. This
+    #       still mitigates OOM, although at a higher performance impact, because it
+    #       effectively resets the allocator cache.
+    if not pytorch_backend_config._limit_torch_cuda_mem_fraction:
+        return
+    mem_reserved = torch.cuda.memory_reserved()
+    mem_free, mem_total = torch.cuda.mem_get_info()
+    safety_margin = 32 * 1024**2
+    mem_torch_max = mem_free + mem_reserved - safety_margin
+    mem_torch_fraction = mem_torch_max / mem_total
+    logger.info(
+        f"Setting PyTorch memory fraction to {mem_torch_fraction} ({mem_torch_max / 1024**3} GiB)"
+    )
+    torch.cuda.set_per_process_memory_fraction(mem_torch_fraction)

tensorrt_llm/_torch/pyexecutor/config.py

@@ -87,6 +87,11 @@ class PyTorchConfig:
     enable_min_latency: bool = False
     allreduce_strategy: str = "AUTO"
 
+    # If true, adjust PyTorch CUDA memory fraction to correspond to the
+    # total GPU memory minus the statically allocated engine memory.
+    # If false, set the PyTorch CUDA memory fraction to 1.0.
+    _limit_torch_cuda_mem_fraction: bool = True
+
 
 EXETENDED_EXECUTOR_CONFIG_FIELDS = [
     'backend',

tensorrt_llm/_torch/pyexecutor/py_executor_creator.py

@@ -20,8 +20,8 @@
 from ..attention_backend.interface import AttentionRuntimeFeatures
 from ..distributed import MPIDist
 from ..speculative import NGramConfig, get_spec_resource_manager
-from ._util import (KvCacheCreator, create_py_executor_instance,
-                    instantiate_sampler, is_mla)
+from ._util import (KvCacheCreator, _adjust_torch_mem_fraction,
+                    create_py_executor_instance, instantiate_sampler, is_mla)
 from .config import PyTorchConfig
 from .config_utils import is_mla
 from .model_engine import PyTorchModelEngine
@@ -372,5 +372,7 @@ def create_py_executor(
                 draft_model_engine, False, sampler, lora_config,
                 garbage_collection_gen0_threshold)
 
+    _adjust_torch_mem_fraction(executor_config.pytorch_backend_config)
+
     py_executor.start_worker()
     return py_executor

tests/integration/test_lists/waives.txt

@@ -403,8 +403,6 @@ accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_auto_dtype
 test_e2e.py::test_openai_multi_chat_example SKIP (https://nvbugs/5236980)
 test_e2e.py::test_ptp_quickstart_advanced[Llama3.1-70B-FP8-llama-3.1-model/Llama-3.1-70B-Instruct-FP8] SKIP (https://nvbugs/5318059)
 test_e2e.py::test_ptp_quickstart_advanced_ngram[Llama-3.1-8B-Instruct-llama-3.1-model/Llama-3.1-8B-Instruct] SKIP (https://nvbugspro.nvidia.com/bug/5324239)
-accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp2pp2-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5318143)
-accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp2pp2-attn_backend=TRTLLM-torch_compile=True] SKIP (https://nvbugs/5318143)
 test_e2e.py::test_ptp_quickstart_advanced[Nemotron-H-8B-Nemotron-H-8B-Base-8K] SKIP (https://nvbugs/5325284)
 disaggregated/test_disaggregated.py::test_disaggregated_single_gpu_with_mpirun[TinyLlama-1.1B-Chat-v1.0] SKIP (https://nvbugs/5328160)
 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/5328495)