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This pull request correctly updates the code to use torch.set_float32_matmul_precision instead of the deprecated torch.backends.cuda.matmul.fp32_precision. The changes in the modified files are accurate. However, the fix is incomplete as another usage of the deprecated API was missed in a different file. I've added a specific comment pointing out the location for a complete fix. Additionally, the pull request title and description are misleading and should be updated to reflect that this change applies a fix, rather than reverting one.
njhill
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- [Misc] Implement `TokenizerLike.convert_tokens_to_ids` (vllm-project/vllm#31796) [INFERENG-4151](https://issues.redhat.com/browse/INFERENG-4151) - [Bug] Revert torch warning fix (vllm-project/vllm#31585) [INFERENG-4152](https://issues.redhat.com/browse/INFERENG-4152) - [Bug] Fix AttributeError: `ColumnParallelLinear` object has no attribute `weight_scale_inv` (vllm-project/vllm#30823) [INFERENG-4153](https://issues.redhat.com/browse/INFERENG-4153) - Avoid `opencv-python-headless==4.13.0.90`, it's broken. See opencv/opencv-python#1183 - [Bugfix] Handle mistral tokenizer in get_hf_processor (vllm-project/vllm#31817) [INFERENG-4151](https://issues.redhat.com/browse/INFERENG-4151) - [Bugfix] Fix Whisper/encoder-decoder GPU memory leak vllm-project/vllm#32789 - [Model] Handle `trust_remote_code` for transformers backend (vllm-project/vllm#32194) (fixes GHSA-2pc9-4j83-qjmr) - [Bugfix] CUDA: fix segfault by bumping numba to `numba==0.63.1` ([AIPCC-9384](https://issues.redhat.com/browse/AIPCC-9384)) - [Bugfix] pin `mistral_common==1.8.5` to avoid crash with Voxtral ([INFERENG-4154](https://issues.redhat.com/browse/INFERENG-4154)) - [Bugfix] fix tokenizer loading for mistral models (vllm-project/vllm#33175) [INFERENG-4151](https://issues.redhat.com/browse/INFERENG-4151)
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Purpose
Fixes #31579
In 2.9.0, there was a warning in https://github.com/pytorch/pytorch/blob/0fabc3ba44823f257e70ce397d989c8de5e362c1/aten/src/ATen/Context.cpp#L79
But seems already be removed in PyTorch 2.9.1.
I think we can revert the fix now