[https://nvbugs/5451280][fix] Reduce memory fraction problem by warmu…

[liji-nv]

…p with large request

Summary by CodeRabbit

• Performance Improvements

  • Faster model warmup, reducing initial latency.
  • More efficient memory allocation for large-to-small workloads, improving throughput.
  • Enhanced performance when using piecewise CUDA graph execution.

• Reliability

  • More consistent warmup across varying batch sizes and sequence lengths.
  • Improved stability during context preparation under high request counts.

Description

Test Coverage

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[liji-nv]

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[tensorrt-cicd]

PR_Github #19954 [ run ] triggered by Bot

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📝 Walkthrough

Walkthrough

Introduces a generalized warmup routine with a new get_warmup_request parameter to choose between legacy and alternate context computations. Adds general_warmup utility and integrates it into PyTorch and piecewise CUDA-graph warmup flows, including a “most-requests” pass and optional reverse ordering. Updates control flow to use these routines and synchronizations.

Changes

Cohort / File(s)

Summary

Warmup strategy refactor tensorrt_llm/_torch/pyexecutor/model_engine.py

Added get_warmup_request(…, least_requrests: bool) alternate path for computing warmup sizes; introduced general_warmup(reverse=False) to run standardized warmup batches and sync; replaced prior imperative warmup loop with general_warmup under _torch_compile_enabled; expanded piecewise CUDA-graph warmup to include “most-requests” batch using least_requrests=False and a reverse general_warmup; added additional passes and CUDA synchronizations; minor naming typo retained in parameter name.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant User as Caller
  participant Engine as ModelEngine
  participant Warm as general_warmup()
  participant Req as get_warmup_request()
  participant Model as forward()
  participant CUDA as cuda.synchronize()

  Note over Engine: _torch_compile_enabled path
  User->>Engine: warmup()
  Engine->>Warm: general_warmup(reverse=false)
  Warm->>Req: (1,1) via least_requrests=true
  Req-->>Warm: batch
  Warm->>Model: forward(batch)
  Warm->>CUDA: synchronize
  Warm->>Req: (batch_size,batch_size)
  Req-->>Warm: batch
  Warm->>Model: forward(batch)
  Warm->>CUDA: synchronize
  Warm->>Req: (2,0)
  Req-->>Warm: batch
  Warm->>Model: forward(batch)
  Warm->>CUDA: synchronize
  Warm->>Req: (curr_max_num_tokens,0)
  Req-->>Warm: batch
  Warm->>Model: forward(batch)
  Warm->>CUDA: synchronize
  Warm-->>Engine: done

Loading

sequenceDiagram
  autonumber
  participant User as Caller
  participant Engine as ModelEngine
  participant Req as get_warmup_request()
  participant Warm as general_warmup()
  participant Model as forward()
  participant CUDA as cuda.synchronize()

  Note over Engine: piecewise_cuda_graph path
  User->>Engine: warmup()
  Engine->>Req: (num_tokens, 0, least_requrests=false)  %% "most-requests" batch
  Req-->>Engine: batch_max_reqs
  Engine->>Model: forward(batch_max_reqs)
  Engine->>CUDA: synchronize

  Note over Engine,Warm: Reverse pass to allocate large→small
  Engine->>Warm: general_warmup(reverse=true)
  Warm->>Req: (curr_max_num_tokens,0) ...
  loop predefined set (reversed)
    Req-->>Warm: batch
    Warm->>Model: forward(batch)
    Warm->>CUDA: synchronize
  end

  Note over Engine: Optional additional pass repeated per code
  Engine->>Model: forward(batch_max_reqs)
  Engine->>CUDA: synchronize
  Engine-->>User: warmup complete

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description Check	⚠️ Warning	The pull request description remains mostly as the unfilled template: the title is incomplete and does not follow the required “[JIRA ticket/NVBugs ID/GitHub issue/None][type] Summary” format or use the @coderabbitai shorthand, and the Description and Test Coverage sections contain only placeholder comments rather than real content. The PR Checklist is present but no specific verification or updates are documented. Overall, the description lacks the substantive information needed to understand the change.	Please replace the placeholder title with a properly formatted one following the ticket/type pattern or @coderabbitai shorthand, fill in the Description section with a concise explanation of the issue and the solution, list the relevant tests under Test Coverage, and confirm or update items in the PR Checklist to reflect the actual changes.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The title follows the required format by including the NVBugs ID and [fix] type. It succinctly describes the change by indicating the fix involves reducing memory fraction problems through warmup logic. This matches the primary adjustments in the pull request and uses concise, clear wording without additional noise.

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Actionable comments posted: 2

🧹 Nitpick comments (3)

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

769-794: Make warmup ordering deterministic and reduce log verbosity.

• Using a set results in non-deterministic iteration order; this can lead to inconsistent warmup patterns and memory behavior across runs.
• Prefer a list with fixed order; sort only when reverse=True.
• Consider downgrading logs to debug to avoid noisy INFO logs during warmup.

Apply this diff:

-        def general_warmup(reverse: bool = False):
-            warmup_requests = set([
-                (1, 1),  # Specialize for 1 token.
-                (self.batch_size,
-                 self.batch_size),  # max_batch_size, pure generation
-                (2, 0),  # Non-one, pure context
-                (curr_max_num_tokens, 0),  # max_num_tokens, pure context
-            ])
-            if reverse:
-                warmup_requests = sorted(list(warmup_requests), reverse=reverse)
+        def general_warmup(reverse: bool = False):
+            warmup_requests = [
+                (1, 1),  # Specialize for 1 token.
+                (self.batch_size, self.batch_size),  # max_batch_size, pure generation
+                (2, 0),  # Non-one, pure context
+                (curr_max_num_tokens, 0),  # max_num_tokens, pure context
+            ]
+            if reverse:
+                warmup_requests = sorted(warmup_requests, key=lambda x: (x[0], x[1]), reverse=True)
@@
-                    logger.info(
+                    logger.debug(
                         f"Run warmup with {warmup_num_tokens} tokens, include {warmup_num_gen_tokens} generation tokens"
                     )

---

807-807: Good consolidation.

Replacing the manual loop with general_warmup() improves reuse and readability. Note: the warmup_requests variable defined above (Lines 797–804) is now unused; consider removing it in a follow-up cleanup.

---

894-914: Finalize “most-requests” pass; fix comment typos and ensure clarity.

• Typo: “memory faction” → “memory fragmentation”.
• Clarify the comment text.

Apply this diff:

-            # When using piecewise cuda graph, the logits may suffer severe memory faction problem.
-            # When the num of requests is growing, the block allocated by torch cannot be reused.
-            # So after piecewise cuda graph capture, a request with most requests is triggered to makes
-            # sure that a large enough block is allocated and can be correctly reused.
+            # When using piecewise CUDA graph, logits allocations may suffer severe memory fragmentation.
+            # As the number of requests grows, blocks allocated by torch may not be reused efficiently.
+            # After piecewise CUDA graph capture, trigger a batch with the most requests to ensure
+            # a sufficiently large block is allocated and can be correctly reused.
@@
-                with release_batch(batch) as batch:
+                with release_batch(batch) as batch:
                     logger.info(
                         f"Run piecewise CUDA graph warmup for num tokens={num_tokens} with most requests"
                     )

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• tensorrt_llm/_torch/pyexecutor/model_engine.py (5 hunks)

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🧠 Learnings (1)

📚 Learning: 2025-08-19T12:45:11.997Z

Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/model_engine.py

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

tensorrt_llm/_torch/attention_backend/trtllm.py (2)

• max_seq_len (571-581)
• max_seq_len (584-588)

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

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tensorrt_llm/logger.py (1)

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