PyTorch
Data & MLTensors and Dynamic neural networks in Python with strong GPU acceleration
Release History
v2.13.0Breaking1 fix9 featuresPyTorch 2.13 introduces significant performance features like FlexAttention on MPS and the CuTeDSL backend for Inductor, alongside major internal cleanups including the removal of Bazel support and named tensors. This release contains several breaking changes, particularly around C++ APIs and Python interpreter version support.
v2.12.13 fixesThis release focuses on fixing regressions and silent correctness issues, primarily related to GPU operations using Triton and memory access bugs.
v2.12.0Breaking6 featuresPyTorch 2.12 introduces significant performance improvements, notably in batched linalg.eigh on CUDA and fused Adagrad optimization. This release also enforces stricter build requirements, including C++20 and CUDA 12.6 for source builds, and updates distributed functional API usage within torch.compile.
v2.11.0Breaking5 featuresPyTorch 2.11 introduces major highlights like Differentiable Collectives and FlexAttention updates, but enforces breaking changes by moving PyPI wheels to CUDA 13.0 and modifying APIs for variable length attention and hub loading.
v2.10.0Breaking14 featuresPyTorch 2.10 introduces Python 3.14 support for torch.compile, new features like combo-kernels fusion and LocalTensor for distributed debugging, and removes several deprecated or legacy functionalities across ONNX, Dataloader, and nn modules.
v2.9.1Breaking12 fixes3 featuresThis maintenance release addresses critical regressions in PyTorch 2.9.0, specifically fixing memory issues in 3D convolutions, Inductor compilation bugs for Gemma/vLLM, and various distributed and numeric stability fixes.
v2.9.0Breaking1 fix7 featuresPyTorch 2.9.0 introduces Python 3.10 as the minimum requirement, defaults the ONNX exporter to the Dynamo-based pipeline, and adds support for symmetric memory and FlexAttention on new hardware.
v2.8.0Breaking3 fixes10 featuresPyTorch 2.8.0 introduces high-performance quantized LLM inference on Intel CPUs, SYCL support for CPP extensions, and stricter validation for autograd and torch.compile. It includes significant breaking changes regarding CUDA architecture support and internal configuration renames.
v2.7.1Breaking16 fixes3 featuresThis maintenance release focuses on fixing regressions and silent correctness issues across torch.compile, Distributed, and Flex Attention, while also improving wheel sizes and platform-specific compatibility for MacOS, Windows, and XPU.
v2.7.0Breaking1 fix9 featuresPyTorch 2.7.0 introduces Blackwell support and FlexAttention optimizations while enforcing stricter C++ API visibility and Python limited API compliance. It marks a significant shift in ONNX and Export workflows by deprecating legacy capture methods in favor of the unified torch.export API.
v2.6.0Breaking10 featuresPyTorch 2.6 introduces Python 3.13 support for torch.compile, FP16 support for X86 CPUs, and new AOTInductor packaging APIs. It includes a significant security change making torch.load use weights_only=True by default and deprecates the official Anaconda channel.
Common Errors
ProcessRaisedException3 reportsProcessRaisedException in PyTorch often arises from issues within multiprocessing contexts, specifically related to CUDA device handling or argument mismatches during distributed operations or within TorchInductor. Ensure CUDA devices are correctly initialized and visible to all processes, and verify that all function/class calls within multiprocessing conform to the expected argument count and types as defined by PyTorch or TorchInductor APIs, paying special attention to distributed configurations.
RuntimeError2 reportsThis error typically occurs in pytorch. Check the example issues for common solutions.
InductorError2 reportsInductorError in PyTorch often arises from unsupported operations or improper handling of dynamic shapes within the Inductor compiler. To fix it, try simplifying the model by rewriting problematic operations using supported PyTorch functions or explicitly materializing dynamic shapes with `torch.compile(dynamic=False)` or `aten.detach` to help Inductor better trace the computation. If the issue persists, file a bug report with a minimal reproducible example for the PyTorch team to investigate.
NotImplementedError2 reportsThis error typically occurs in pytorch. Check the example issues for common solutions.
FileNotFoundError2 reportsFileNotFoundError in PyTorch tests usually means the test is trying to access a file that doesn't exist at the expected location. This often happens when a test depends on data files not being correctly created or copied during the build/test process or when the test assumes a specific working directory. To fix it, ensure any required data files are created/copied to the correct location before the test runs, and explicitly set the working directory if necessary using `os.chdir()` or by providing the full path to the file.
OpCheckError2 reportsOpCheckError usually arises when a custom operator's forward and backward implementations produce results with incorrect data types, shapes, or values compared to the expected output based on the OpInfo specification. To fix it, carefully review the custom operator's forward and backward functions, ensuring that the output tensor's data type, shape, and values match the expected behavior defined in the OpInfo, and use `torch.testing.assert_close` for numerical comparisons in your test.
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