[None][fix] fix: resolve GPU memory imbalance in concurrent weight loading #6472
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Signed-off-by: Necofish <[email protected]>
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tensorrt_llm/_torch/models/modeling_utils.py(3 hunks)
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tensorrt_llm/_torch/models/modeling_utils.py
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Files:
tensorrt_llm/_torch/models/modeling_utils.py
🧠 Learnings (2)
📓 Common learnings
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
tensorrt_llm/_torch/models/modeling_utils.py (2)
Learnt from: amitz-nv
PR: #5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks is_adapter_in_cpu_cache() and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: moraxu
PR: #6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
🔇 Additional comments (1)
tensorrt_llm/_torch/models/modeling_utils.py (1)
16-16: LGTM: Import addition is correct and necessary.The import of
mpi_rankfromtensorrt_llm._utilsis properly placed and maintains the namespace convention as required by the coding guidelines.
| logger.info(f"Renamed weights with params_map: {params_map}") | ||
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| def load_single_module(name, module): | ||
| torch.cuda.set_device(mpi_rank()) |
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🛠️ Refactor suggestion
Consistent fix applied to v2 implementation - good practice.
This change mirrors the fix in _load_weights_impl and ensures both weight loading implementations handle GPU device context correctly during concurrent execution.
Consider consolidating the error handling by extracting the device setting logic into a helper function:
+def _set_cuda_device_for_worker():
+ """Set CUDA device to current MPI rank with error handling."""
+ try:
+ torch.cuda.set_device(mpi_rank())
+ except (RuntimeError, ValueError) as e:
+ logger.warning(f"Failed to set CUDA device to MPI rank {mpi_rank()}: {e}")
+ # Handle appropriately based on requirementsThen use this helper in both load_single_module functions to maintain consistency and reduce code duplication.
Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In tensorrt_llm/_torch/models/modeling_utils.py at line 878, the call to set the
CUDA device using torch.cuda.set_device(mpi_rank()) should be extracted into a
separate helper function that sets the device context. Refactor both
_load_weights_impl and the v2 load_single_module functions to call this helper
instead of duplicating the device setting logic, ensuring consistent GPU device
handling and reducing code duplication.
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this PR for solving issue: #6281 |
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karljang
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/bot run |
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PR_Github #21317 [ run ] triggered by Bot |
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@Nekofish-L , could you please fix the pre-commit check failure: https://github.com/NVIDIA/TensorRT-LLM/actions/runs/18487388759/job/52673370030 |
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PR_Github #21317 [ run ] completed with state |
Signed-off-by: Nekofish-L <[email protected]>
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Hi @karljang , the pre-commit check failure has been fixed. |
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/bot run |
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PR_Github #21376 [ run ] triggered by Bot |
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PR_Github #21376 [ run ] completed with state |
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Summary by CodeRabbit
Description
When using
run_concurrentlyfor parallel weight loading in multi-GPU environments, all worker threads were defaulting to GPU 0 due to missing CUDA device context. This caused severe memory imbalance where GPU 0 consumed significantly more memory than other GPUs during concurrent model loading.Test Coverage
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