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Commits on Dec 1, 2022

• [QNNPACK] Fix Memory Leak in QNNPACK QSoftmax Op (#89544)

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• Add non-reentrant checkpoint to composable APIs (#90015)

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• [UCC] Properly finalize unsuccessful collective posts (#89306)

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• Revert "[quant] Explictly set default quantized engine instead of relying on the order of supported_qengines (#89804)"

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• [doc] update dtensor readme (#89991)

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• triton supports devices < 7.0, not 6.0 (#90020)

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• [quant] Explictly set default quantized engine instead of relying on the order of supported_qengines (#89804)

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• [Vulkan][TCC] Add tests for quantized add, sub, mul and div (#89578)

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• [quant][be] Merge qconfig_mapping_utils.py in quantization and fx folders (#89979)

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• [aarch64] add sleef_arm dependency (#89988)

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• [chalf] relax tolerance : conv_transpose2d (#89993)

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• [tp] ufmt test/distributed/tensor (#89970)

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• [tp] umft distributed.tensor.parallel (#89969)

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• [dtensor] ufmt test/distributed/_tensor (#89968)

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• [dtensor] ufmt distributed._tensor (#89967)

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• Add `torch.compile` implementation (#89607)

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• Revert "Dynamo, FX, Inductor Progress Bars (#88384)" (#90018)

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• Editorial pass on Dyamo docs (#89921)

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• [ONNX] Supports scatter_add with different static shape of src and index (#89787)

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• [Quant][fx][bc-breaking] Rename fx/*patterns.py (#89872)

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• fix citation file in MANIFEST (#89994)

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• Remove DDP import (#89982)

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• Add error repro test for FSDP ignored modules with mixed precision (#89971)

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• add vjp test with non-contig inputs (#89375)

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• Type torch._dynamo.guards (#89919)

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• Guarantee symbol allocation for all sizes/strides/storage offset (#89879)

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• Add definitely_not_01 set to ShapeEnv. (#89871)

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• When dealing with dupe arguments, prefer leafifying if possible (#89896)

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• [FSDP][Dynamo] Define annotation attributes as globals (#89913)

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• remove torch.equal usages (#89527)

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• fix assert_close docstring (#89620)

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• document torch.testing.assert_allclose (#89526)

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• enable ufmt for torch/testing/*.py (#89525)

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• ad more error info for cublasLtMatmul (#89983)

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• Add manual meta implementations to quantize_per_tensor.tensor and co (#89958)

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