[None][fix] Replace PYTORCH_CUDA_ALLOC_CONF with PYTORCH_ALLOC_CONF to fix deprecation warning

[jiaganc]

Summary by CodeRabbit

• Chores
  • Updated PyTorch configuration environment variable naming from PYTORCH_CUDA_ALLOC_CONF to PYTORCH_ALLOC_CONF across deployment configurations and documentation.

Description

Get warnings like:

[W1112 10:31:27.900539658 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

This PR should fix it.

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[coderabbitai]

📝 Walkthrough

Walkthrough

All changes involve renaming the PyTorch memory allocator configuration environment variable from PYTORCH_CUDA_ALLOC_CONF to PYTORCH_ALLOC_CONF across Docker configuration, deployment documentation, container orchestration, and Python utility code.

Changes

Cohort / File(s)	Summary
Container and Build Configuration docker/Dockerfile.multi, enroot/Makefile	Updated environment variable name from PYTORCH_CUDA_ALLOC_CONF to PYTORCH_ALLOC_CONF with value preservation (max_split_size_mb:8192 and garbage_collection_threshold:0.99999 respectively).
Documentation docs/source/deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.md	Updated troubleshooting tip to reference PYTORCH_ALLOC_CONF instead of PYTORCH_CUDA_ALLOC_CONF. Removed one additional connection-issues note.
Python Utility Code tensorrt_llm/_torch/pyexecutor/_util.py	Updated _adjust_torch_mem_fraction function to use PYTORCH_ALLOC_CONF environment variable and updated corresponding warning message.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

• Changes are homogeneous, repetitive variable renames across all files
• No logic modifications or behavioral changes
• Straightforward string replacements that can be quickly verified
• Low cognitive load; minimal reasoning required per file

Pre-merge checks and finishing touches

❌ Failed checks (1 inconclusive)

Check name	Status	Explanation	Resolution
Description check	❓ Inconclusive	The description provides the issue (deprecation warning) and mentions the fix, but lacks details on solution specifics, affected components, test coverage information, and leaves most checklist items unchecked.	Expand description with specific files changed, why this replacement is safe, how the change was tested, and complete the PR checklist items as applicable.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly summarizes the main change: replacing a deprecated PyTorch environment variable with its modern equivalent to fix warnings across multiple files.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing touches

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📜 Recent review details

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📥 Commits

Reviewing files that changed from the base of the PR and between a79c0df and 17777fd.

📒 Files selected for processing (4)

• docker/Dockerfile.multi (1 hunks)
• docs/source/deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.md (1 hunks)
• enroot/Makefile (1 hunks)
• tensorrt_llm/_torch/pyexecutor/_util.py (1 hunks)

🧰 Additional context used

🧠 Learnings (5)

📚 Learning: 2025-08-19T12:45:11.997Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 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/_util.py

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 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/_torch/pyexecutor/_util.py

📚 Learning: 2025-08-26T06:07:02.166Z

Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/_util.py

📚 Learning: 2025-08-20T15:04:42.885Z

Learnt from: dbari
Repo: NVIDIA/TensorRT-LLM PR: 7095
File: docker/Dockerfile.multi:168-168
Timestamp: 2025-08-20T15:04:42.885Z
Learning: In docker/Dockerfile.multi, wildcard COPY for benchmarks (${CPP_BUILD_DIR}/benchmarks/*Benchmark) is intentionally used instead of directory copy because the benchmarks directory contains various other build artifacts during C++ builds, and only specific benchmark executables should be copied to the final image.

Applied to files:

• docker/Dockerfile.multi

📚 Learning: 2025-08-21T00:16:56.457Z

Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.

Applied to files:

• docs/source/deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.md

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (4)

docker/Dockerfile.multi (1)

31-31: LGTM! Docker environment variable correctly updated.

The environment variable name has been properly updated to PYTORCH_ALLOC_CONF while preserving the same value (garbage_collection_threshold:0.99999). This ensures the Docker container uses the non-deprecated variable name.

enroot/Makefile (1)

42-42: LGTM! Makefile export correctly updated.

The exported environment variable has been properly renamed to PYTORCH_ALLOC_CONF while maintaining the same value. This ensures containers launched via srun use the non-deprecated environment variable.

docs/source/deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.md (1)

253-253: LGTM! Documentation correctly updated to reflect the new environment variable.

The troubleshooting tip now correctly references PYTORCH_ALLOC_CONF instead of the deprecated PYTORCH_CUDA_ALLOC_CONF, ensuring users follow current best practices. The link to PyTorch documentation provides additional context for users.

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

964-972: Verification complete: no deprecated variable references found in codebase.

The search across all file types found no remaining references to PYTORCH_CUDA_ALLOC_CONF, confirming that the changes in tensorrt_llm/_torch/pyexecutor/_util.py (lines 964-972) successfully complete the deprecation migration to PYTORCH_ALLOC_CONF.

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