[None][feat] reuse cudagraph memory pool in normal forward flow #8095

HuiGao-NV · 2025-09-30T08:21:29Z

Summary by CodeRabbit

New Features
- Added controls to prefer a CUDA memory pool during graph capture/replay, with graceful fallback when unavailable.
- Introduced simple APIs to set/get a preferred memory pool and a context manager to apply it, improving memory efficiency and stability.
Refactor
- Reorganized CUDA graph capture/replay flow to integrate memory pool handling without changing existing usage.

Description

Test Coverage

PR Checklist

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PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
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HuiGao-NV · 2025-09-30T08:21:44Z

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coderabbitai · 2025-09-30T08:25:40Z

📝 Walkthrough

Walkthrough

Introduces a memory pool preference mechanism and integrates it into CUDA graph execution and tensor allocation. Adds pool accessors, a context manager to set preferred pools, an accessor in CUDAGraphRunner, updates model_engine forward to capture/replay under preferred pool, and modifies buffer allocation to try the pool before falling back.

Changes

Cohort / File(s)	Summary
Memory pool API `tensorrt_llm/_torch/utils.py`	Adds global pool management: set_mem_pool, get_graph_pool, and set_prefer_mem_pool context manager to temporarily prefer a given buffer pool.
CUDA graph runner accessor `tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py`	Adds CUDAGraphRunner.get_graph_pool() returning self.memory_pool; minor formatting only.
Model engine integration `tensorrt_llm/_torch/pyexecutor/model_engine.py`	Uses set_prefer_mem_pool around CUDA graph capture/replay; refactors capture flow via closures (capture_forward_fn, capture_postprocess_fn); adjusts control flow for capture vs replay vs eager fallback.
Memory-buffer allocation path `tensorrt_llm/_torch/memory_buffer_utils.py`	Attempts allocation within get_graph_pool context; on failure falls back to standard CUDA tensor allocation; logs pool allocation failure.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  actor User
  participant ModelEngine
  participant CUDAGraphRunner as GraphRunner
  participant Utils as Utils (pool API)
  participant CUDA as CUDA Runtime

  User->>ModelEngine: forward(inputs)
  alt Using CUDA graph
    ModelEngine->>GraphRunner: need_capture?
    alt Capture required
      ModelEngine->>GraphRunner: get_graph_pool()
      GraphRunner-->>ModelEngine: memory_pool
      ModelEngine->>Utils: set_prefer_mem_pool(memory_pool) [enter]
      activate Utils
      note over ModelEngine,GraphRunner: Define capture_forward_fn & capture_postprocess_fn
      ModelEngine->>GraphRunner: register(capture_forward_fn, capture_postprocess_fn)
      ModelEngine->>GraphRunner: capture_and_replay(inputs)
      GraphRunner->>CUDA: capture graph & allocate via pool
      GraphRunner-->>ModelEngine: outputs
      Utils-->>ModelEngine: restore previous pool [exit]
      deactivate Utils
    else Replay
      ModelEngine->>GraphRunner: get_graph_pool()
      GraphRunner-->>ModelEngine: memory_pool
      ModelEngine->>Utils: set_prefer_mem_pool(memory_pool) [enter]
      activate Utils
      ModelEngine->>GraphRunner: replay(inputs)
      GraphRunner->>CUDA: launch graph
      GraphRunner-->>ModelEngine: outputs
      Utils-->>ModelEngine: restore previous pool [exit]
      deactivate Utils
    end
  else Eager path
    ModelEngine->>CUDA: execute forward eagerly
  end

sequenceDiagram
  autonumber
  participant AllocCaller as Buffer Utils
  participant Utils as Utils (pool API)
  participant CUDA as CUDA Runtime

  AllocCaller->>Utils: get_graph_pool()
  Utils-->>AllocCaller: pool or None
  alt Pool available
    AllocCaller->>CUDA: try allocate in pool
    alt Success
      CUDA-->>AllocCaller: pooled tensor
    else Exception
      AllocCaller->>AllocCaller: log debug pool alloc failure
      AllocCaller->>CUDA: standard allocation
      CUDA-->>AllocCaller: tensor
    end
  else No pool
    AllocCaller->>CUDA: standard allocation
    CUDA-->>AllocCaller: tensor
  end

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Pre-merge checks and finishing touches

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✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The title follows the repository’s required format with “[None][feat]” and succinctly summarizes the primary change—reusing the CUDA graph memory pool during regular forward execution—and directly reflects the main modification introduced by this pull request. It is specific enough for teammates to grasp the intent without unnecessary detail or noise.

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📥 Commits

Reviewing files that changed from the base of the PR and between 948b8b9 and e29858a.

📒 Files selected for processing (4)

tensorrt_llm/_torch/memory_buffer_utils.py (2 hunks)
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (2 hunks)
tensorrt_llm/_torch/pyexecutor/model_engine.py (2 hunks)
tensorrt_llm/_torch/utils.py (1 hunks)

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tensorrt_llm/_torch/utils.py
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py

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tensorrt_llm/_torch/memory_buffer_utils.py
tensorrt_llm/_torch/pyexecutor/model_engine.py
tensorrt_llm/_torch/utils.py
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py

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tensorrt_llm/_torch/pyexecutor/model_engine.py
tensorrt_llm/_torch/utils.py
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py

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tensorrt_llm/_torch/memory_buffer_utils.py (3)

tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)

get_graph_pool (197-198)

tensorrt_llm/_torch/utils.py (1)

get_graph_pool (325-327)

tensorrt_llm/logger.py (1)

debug (144-145)

tensorrt_llm/_torch/pyexecutor/model_engine.py (3)

tensorrt_llm/_torch/utils.py (4)

set_prefer_mem_pool (331-337)

set_torch_compiling (33-35)

with_model_extra_attrs (67-77)

get_graph_pool (325-327)

tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (4)

get_graph_pool (197-198)

needs_capture (193-195)

capture (200-270)

replay (272-306)

tensorrt_llm/_torch/modules/fused_moe/moe_load_balancer.py (1)

MoeLoadBalancerIterContext (995-1034)

tensorrt_llm/_torch/utils.py (1)

tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)

get_graph_pool (197-198)

tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)

tensorrt_llm/_torch/utils.py (1)

get_graph_pool (325-327)

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tensorrt_llm/_torch/memory_buffer_utils.py

93-93: Do not catch blind exception: Exception

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96-96: f-string without any placeholders

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🔇 Additional comments (3)

tensorrt_llm/_torch/pyexecutor/model_engine.py (3)

51-52: LGTM!

The imports are correctly added and used within the file.

2190-2190: LGTM!

The memory pool context manager correctly wraps the CUDA graph execution flow, ensuring consistent memory allocation during graph capture and replay operations.

2197-2218: No additional MoeLoadBalancerIterContext needed for replay after capture
The iteration context is correctly applied inside capture_forward_fn during graph capture, so wrapping the subsequent replay() call is unnecessary.

Likely an incorrect or invalid review comment.

tensorrt_llm/_torch/memory_buffer_utils.py

tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py

tensorrt_llm/_torch/utils.py

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…IA#8095) Signed-off-by: Hui Gao <[email protected]>

…IA#8095) Signed-off-by: Hui Gao <[email protected]> Signed-off-by: yufeiwu-nv <[email protected]>

…IA#8095) Signed-off-by: Hui Gao <[email protected]>

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Reuse memory_pool

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govind-ramnarayan pushed a commit to nv-auto-deploy/TensorRT-LLM that referenced this pull request Oct 21, 2025

[None][feat] reuse cudagraph memory pool in normal forward flow (NVID…

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…IA#8095) Signed-off-by: Hui Gao <[email protected]>

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[None][feat] reuse cudagraph memory pool in normal forward flow #8095

[None][feat] reuse cudagraph memory pool in normal forward flow #8095

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