[None][chore] Fix error when running trtllm-bench without cuda graph.

[bobboli]

To run trtllm-bench without cuda graph, currently the method is to add cuda_graph_config: null to the llm api config yaml. However, that will cause cuda_graph_config.get throw error. Add guard if.

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Summary by CodeRabbit

• Bug Fixes
  • Improved stability for the PyTorch backend when CUDA graph settings are not provided, preventing runtime errors.
  • Ensures default max_batch_size is applied only when appropriate, maintaining expected behavior with existing batch size settings.
• Chores
  • Internal configuration handling refined for more robust defaults.
• Notes
  • No changes to the public API.

[bobboli]

/bot run

[tensorrt-cicd]

PR_Github #18611 [ run ] triggered by Bot

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📝 Walkthrough

Walkthrough

Adds a conditional guard to only mutate PyTorch backend cuda_graph_config when it is truthy, preserving existing logic for deriving max_batch_size from runtime settings when batch_sizes and max_batch_size are absent.

Changes

Cohort / File(s)	Summary
PyTorch backend cuda_graph_config guard tensorrt_llm/bench/dataclasses/configuration.py	Wraps cuda_graph_config mutation in a truthy check; defaults max_batch_size from runtime only if neither batch_sizes nor max_batch_size are provided; adjusts retrieval to use get("max_batch_size", None).

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

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❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description Check	⚠️ Warning	The PR description states the bug and the intended guard fix in a short paragraph but does not follow the repository's required template: it omits a populated "Test Coverage" section, does not fill the PR checklist, and lacks a clear "Description" header with repro steps or config example to validate the change, so the submission is under-documented for reviewers.	Please update the PR description to follow the repository template by adding a clear "Description" block with repro steps and the config example that triggers the error, a "Test Coverage" section listing new or affected tests (or justification if none), and complete the PR checklist so reviewers can validate and approve the change.
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.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The provided title "[None][chore] Fix error when running trtllm-bench without cuda graph" succinctly and accurately summarizes the primary change (guarding against a null cuda_graph_config) and follows the repository's "[ticket][type]" convention, so it is clear, specific, and not misleading.

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Actionable comments posted: 1

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tensorrt_llm/bench/dataclasses/configuration.py (1)

1-1: Add NVIDIA Apache-2.0 header (2025)

Prepend the NVIDIA Apache-2.0 header to tensorrt_llm/bench/dataclasses/configuration.py above the first line.

+# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 from __future__ import annotations

🧹 Nitpick comments (3)

tensorrt_llm/bench/dataclasses/configuration.py (3)

239-244: Honor configured kv_cache_reuse instead of hardcoding False.

ExecutorSettingsConfig.kv_cache_reuse is never used; the current code forces reuse off.

Apply:

     def get_kvcache_config(self) -> KvCacheConfig:
         return KvCacheConfig(
             free_gpu_memory_fraction=self.kv_cache_percent,
-            enable_block_reuse=False,
+            enable_block_reuse=self.kv_cache_reuse,
         )

---

183-191: Validation message vs. condition mismatch (>= vs. “equals”).

The check allows surplus GPUs (>=) but the error says “does not equal”. Either enforce equality or fix the message.

Apply one of:

• Enforce equality:

-        valid_world = bool(num_gpus >= parallel_world)
+        valid_world = (num_gpus == parallel_world)

• Or keep >= and clarify:

-            raise ValueError(
-                f"World configuration is invalid, TP * PP ({parallel_world})"
-                "does not equal the total number of available GPUs"
-                f"({num_gpus}).")
+            raise ValueError(
+                f"World configuration is invalid: total GPUs ({num_gpus}) "
+                f"is less than required TP*PP ({parallel_world}).")

---

129-131: Return a shallow copy to avoid aliasing external mutations.

Defensive copy prevents callers from mutating PerformanceOptions.pytorch_config indirectly.

-    def get_pytorch_perf_config(self) -> PyTorchConfig:
-        return self.pytorch_config
+    def get_pytorch_perf_config(self) -> PyTorchConfig:
+        return dict(self.pytorch_config)

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• tensorrt_llm/bench/dataclasses/configuration.py (1 hunks)

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🧠 Learnings (4)

📓 Common learnings

Learnt from: MrGeva
PR: NVIDIA/TensorRT-LLM#7219
File: tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py:162-168
Timestamp: 2025-09-04T07:33:10.618Z
Learning: When users explicitly provide cuda_graph_batch_sizes in TorchCudagraphCompiler, respect their choices and only sanitize the values (clamp, dedupe, sort) without forcing additional batch sizes like 1 or max_batch_size. Only add commonly-used batch sizes when falling back to the heuristic.

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM's bench configuration, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which is a Dict[str, Any] that can contain default values including `cuda_graph_config`, making the fallback `llm_args["cuda_graph_config"]` safe to use.

Applied to files:

• tensorrt_llm/bench/dataclasses/configuration.py

📚 Learning: 2025-09-04T07:33:10.618Z

Learnt from: MrGeva
PR: NVIDIA/TensorRT-LLM#7219
File: tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py:162-168
Timestamp: 2025-09-04T07:33:10.618Z
Learning: When users explicitly provide cuda_graph_batch_sizes in TorchCudagraphCompiler, respect their choices and only sanitize the values (clamp, dedupe, sort) without forcing additional batch sizes like 1 or max_batch_size. Only add commonly-used batch sizes when falling back to the heuristic.

Applied to files:

• tensorrt_llm/bench/dataclasses/configuration.py

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.

Applied to files:

• tensorrt_llm/bench/dataclasses/configuration.py

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🔇 Additional comments (1)

tensorrt_llm/bench/dataclasses/configuration.py (1)

91-92: Incorrect — no KeyError here: llm_args always supplies cuda_graph_config.

get_pytorch_perf_config() returns self.pytorch_config and the PyTorchConfig/LlmArgs definitions provide a default for cuda_graph_config (default_factory=CudaGraphConfig), so llm_args["cuda_graph_config"] will be present. See tensorrt_llm/bench/dataclasses/configuration.py:129 and tensorrt_llm/llmapi/llm_args.py:2197.

Likely an incorrect or invalid review comment.

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PR_Github #18611 [ run ] completed with state SUCCESS
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