[https://nvbugs/5629833][fix] Don't fill tensors #9296
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📝 WalkthroughWalkthroughChanged CUDA memory buffer allocation from Changes
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tensorrt_llm/_torch/memory_buffer_utils.py (1)
93-106: Behavior change: buffers from shared pool are now uninitialized; fallback still zero‑initsSwitching to
torch.emptyhere means new buffers from the shared pool no longer have guaranteed zero contents, while the fallback allocation from the default pool still usestorch.zeros. This introduces (1) a semantic change vs. previous behavior and (2) different initialization semantics depending on which pool is used. Please confirm that all call sites fully overwrite the returned buffer before any read, and consider aligning the fallback path for consistency and perf (or documenting the assumption that contents are undefined on first use).If you want consistent behavior, you could apply:
except Exception as ex: # Need to check if this is an OOM exception logger.debug( f"Exception happened to create tensor from given memory pool: {str(ex)}" ) # if exception happens during allocating memory from shared pool, retry # to allocate from default pool - new_buffer_tensor = torch.zeros((required_memory_size, ), - device='cuda', - dtype=torch.uint8) + new_buffer_tensor = torch.empty((required_memory_size, ), + device='cuda', + dtype=torch.uint8)
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tensorrt_llm/_torch/memory_buffer_utils.py(1 hunks)
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🧠 Learnings (2)
📓 Common learnings
Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
📚 Learning: 2025-08-08T04:10:19.038Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6728
File: cpp/tensorrt_llm/plugins/mixtureOfExperts/mixtureOfExpertsPlugin.cpp:966-966
Timestamp: 2025-08-08T04:10:19.038Z
Learning: TensorRT plugins currently don't support padding functionality, and TensorRT is not getting new features (in maintenance mode). This means that duplicating parameters like mExpertHiddenSize in function calls, even with TODO comments, can be acceptable as pragmatic solutions within these constraints.
Applied to files:
tensorrt_llm/_torch/memory_buffer_utils.py
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