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Commits on Mar 13, 2020

• Make spmmfp32 and spmmi8 benchmark consistent with tests (pytorch#315)

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Commits on Feb 24, 2020

• Output formatting of results

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Commits on Jan 17, 2020

• Update readme with link to wiki page

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• Bug fixes in default path for fp16 (pytorch#251)

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Commits on Jan 13, 2020

• Cmake changes for MSVC build (pytorch#242)

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• Allocate memory instead of using variable length arrays (pytorch#241)

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• More fixes related to MSVC build (pytorch#240)

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• fix perf regression in resnet50 and resnet101 (pytorch#239)

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• Fix multi-instance benchmarking for fp16 (pytorch#246)

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Commits on Jan 10, 2020

• attributes not at the right place

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Commits on Jan 3, 2020

• Handle remainder loop with masked SIMD instructions (pytorch#235)

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Commits on Jan 2, 2020

• Adding missing copyright message

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Commits on Dec 20, 2019

• Using import/export correctly

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Commits on Dec 18, 2019

• Avoid GNU compiler specific C++ extension

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Commits on Dec 17, 2019

• Streamline usage of attributes (pytorch#222)

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• Cast instead of reinterpret_cast in recently added code

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Commits on Dec 12, 2019

• Use cast intrinsics

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• Suitable declaration for cblas_gemm_compute

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• Explicit template parameters

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• Things like iota/accumulate are defined in numeric header

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• fbgemm API decorator only needed at declaration

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• Aligned memory allocation that works with MSVC as well

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Commits on Dec 4, 2019

• fix test build

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• Vector and scalar path should produce the same result

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Commits on Nov 27, 2019

• Update asmjit for OSS

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• Optimized im2col for more shapes

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Commits on Nov 5, 2019

• Relax constraints for optimzied fbgemmGConv

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Commits on Nov 4, 2019

• fix a bug in GConv for batch sizes > 1

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• Update gcc requirement to 5+ as c++11 constructs such as is_trivially_copyable don't exist pre-gcc 5 (pytorch#165)

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Commits on Nov 1, 2019

• fix shared lib build

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Commits on Oct 30, 2019

• 3D groupwise conv using 2d kernels

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Commits on Oct 29, 2019

• Refactor dispatching of output processing

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Commits on Oct 23, 2019

• Minor possible bug fix when int32 number can't be reprsented exactly in float

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• Packing/unpacking of weights for 3D conv

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• Better groupwise conv for small number of channels per group (pytorch#145)

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