We want to sincerely thank our dedicated community for your contributions. This release is composed of 6,682 commits and 784 contributors since 2.0. More details can be found in the library updates blog. CPU inductor improvements, AVX512 support, scaled-dot-product-attention support) as well as a prototype release of torch.export, a sound full-graph capture mechanism, and torch.export-based quantization.Īlong with 2.1, we are also releasing a series of updates to the PyTorch domain libraries. In addition, this release offers numerous performance improvements (e.g. We are excited to announce the release of PyTorch® 2.1! PyTorch 2.1 offers automatic dynamic shape support in pile, for saving/loading distributed training jobs on multiple ranks in parallel, and pile support for the NumPy API. The #110961 contains all relevant pull requests related to this release as well as links to related issues. Verify flatbuffer module fields are initialized for mobile deserialization ( #109794).Fix circular import between Dynamo and einops ( #110575).Fix segfault in for non-contiguous inputs ( #111742).

Fix copy from mps to cpu device when storage_offset is non-zero ( #109557).

Fix the meta device implementation for nn.functional.scaled_dot_product_attention ( #110893).

Fix handling of non-contiguous bias_mask in torch.nn.functional.scaled_dot_product_attention ( #112673).

Continue if param not exist in sharded load in ( #109116).

Add planner argument to .load_sharded_optimizer_state_dict ( #111393).

Remove unnecessary slow code in .load_sharded_optimizer_state_dict ( #111687).

Fix the behavior of torch.new_zeros in onnx due to TorchScript behavior change ( #111694).

Ignore beartype=0.16.0 within the onnx package as it is incompatible ( #111861).

Fix missing CUDA initialization when calling FFT operations ( #110326).

Fix cuda driver API to load the appropriate.

Fix segfault in foreach_* operations when input list length does not match ( #112349).

Remove spurious warning in comparison ops ( #112170).

This release is meant to fix the following issues (regressions / silent correctness):

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