[https://nvbugs/5531963][fix] cherry pick #7725

[QiJune]

cherry pick #7725

Summary by CodeRabbit

• Bug Fixes
  • Fixed an issue in the PyTorch backend where CUDA Graph defaults were applied even when no CUDA Graph configuration was provided. This prevented unintended adjustments to batch size and avoided potential errors when the configuration was absent. Users without a CUDA Graph setup will now see consistent behavior without unexpected modifications.

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[coderabbitai]

📝 Walkthrough

Walkthrough

Adjusts PyTorch backend logic in get_llm_args to update cuda_graph_config.max_batch_size only when cuda_graph_config is present/truthy. Previously it assumed presence and modified unconditionally when batch sizes and max_batch_size were unset. No public API changes.

Changes

Cohort / File(s)	Summary
PyTorch backend guard for cuda graph config tensorrt_llm/bench/dataclasses/configuration.py	Wraps in-place updates to updated_llm_args["cuda_graph_config"] behind a truthy check; sets max_batch_size from batch_sizes only if cuda_graph_config exists and both batch_sizes and max_batch_size were previously unset.

Sequence Diagram(s)

sequenceDiagram
  participant C as Caller
  participant CFG as configuration.get_llm_args
  C->>CFG: get_llm_args(params)
  alt backend == "pytorch"
    CFG->>CFG: check cuda_graph_config
    alt cuda_graph_config is truthy
      CFG->>CFG: if batch_sizes set and max_batch_size unset<br/>then set max_batch_size = max(batch_sizes)
    else cuda_graph_config absent/falsey
      Note over CFG: Skip cuda graph modifications
    end
  else other backend
    Note over CFG: Unchanged paths
  end
  CFG-->>C: updated_llm_args

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Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings, 1 inconclusive)

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 PR description currently contains only "cherry pick #7725" and the repository's template text but the required fields are not filled in. Key required information is missing or incomplete: a title following the template (ticket/type), a Description that explains what changed and why, and Test Coverage that lists relevant tests and how to run them, so reviewers cannot assess the change or its testing. Because these required sections are unpopulated, the description does not meet the repository's template requirements.	Please update the PR by adding a proper title per the template (include ticket/issue and [type]), a concise Description outlining the change and rationale, and a Test Coverage section listing relevant tests/commands; complete the PR checklist or mark items N/A and include the original PR/commit reference (#7725) and any cherry-pick notes to help reviewers validate the backport. Once those sections are populated with concrete details, re-request review.
Title Check	❓ Inconclusive	The title includes a valid NVBugs link and the [fix] type but only reads "cherry pick #7725" and does not summarize the actual code change; the repository template expects a short descriptive summary of the primary fix so reviewers can understand intent at a glance. Because the title is generic and only indicates a cherry-pick rather than describing the change, its suitability is inconclusive for review.	Please update the title to follow the repo template and add a concise summary of the change (for example: "[https://nvbugs/5531963][fix] Guard cuda_graph_config modification in PyTorch backend when absent") and then re-run the checks.

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[QiJune]

/bot run

[tensorrt-cicd]

PR_Github #19577 [ run ] triggered by Bot

[coderabbitai]

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)

85-86: Dict union operator breaks Python 3.8 compatibility

backend_cache_config | kv_cache_config requires Python 3.9+. Guidelines target Python 3.8+. Replace with unpack merge.

-        llm_args["kv_cache_config"] = backend_cache_config | kv_cache_config
+        llm_args["kv_cache_config"] = {**backend_cache_config, **kv_cache_config}

🧹 Nitpick comments (3)

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

103-103: Preserve absence semantics for cuda_graph_config

Avoid writing back a None value; keep the key absent if not present originally.

-            updated_llm_args["cuda_graph_config"] = cuda_graph_config
+            if cuda_graph_config is not None:
+                updated_llm_args["cuda_graph_config"] = cuda_graph_config

---

12-13: Type annotation mismatch and unnecessary class import

get_pytorch_perf_config() returns Dict[str, Any], but is annotated as PyTorchConfig. This also forces an otherwise-unused class import. Align the annotation and drop the import.

-from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
@@
-    def get_pytorch_perf_config(self) -> PyTorchConfig:
-        return self.pytorch_config
+    def get_pytorch_perf_config(self) -> Dict[str, Any]:
+        return self.pytorch_config

Also applies to: 128-130

---

1-1: Missing NVIDIA Apache-2.0 header

Per guidelines, add the NVIDIA Apache-2.0 copyright header with the current year.

+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

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📒 Files selected for processing (1)

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

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🧠 Learnings (3)

📓 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.

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.

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.

📚 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: 2025-08-26T09:37:10.463Z

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)

93-101: LGTM: Guarded max_batch_size default aligns with respecting user-provided batch sizes

The new guard only defaults max_batch_size when both batch_sizes and max_batch_size are unset, which respects explicit user choices (including empty lists). Please confirm that an explicit empty list for batch_sizes is intended to opt out of auto-defaulting.

[tensorrt-cicd]

PR_Github #19577 [ run ] completed with state SUCCESS
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