| commit | 16b9307fb5f580c33cdaeaf7b34086af101f16a0 | [log] [tgz] |
|---|---|---|
| author | benoitjacob <benoitjacob@google.com> | Thu Apr 16 21:47:25 2020 -0700 |
| committer | Copybara-Service <copybara-worker@google.com> | Thu Apr 16 22:06:19 2020 -0700 |
| tree | 8d1e59cd68cd5a426682696585ef329052a302fd | |
| parent | 9f53ba413e6fc879236dcaa3e008915973d67a4f [diff] |
Use ruy's newly curated default set of paths. This: - on all CPU architecturesremoves the reference code path, which is never used (the need for a portable non-simd fallback is already filled by the 'standard c++' path). - on x86, with --define=tflite_with_ruy=true (non-default), this removes a couple of experimental paths, keeping only AVX2 and AVX512 paths. - on x86 for per-channel-quantized ops where ruy is unconditionally used (no alternativee implementation), this enables AVX2 and AVX512 paths. This should fix slowness issues for such per-channel-quantized ops on x86. PiperOrigin-RevId: 306981662
This is not an officially supported Google product.
ruy is a matrix multiplication library. Its focus is to cover the matrix multiplication needs of neural network inference engines. Its initial user has been TensorFlow Lite, where it is used by default on the ARM CPU architecture.
ruy supports both floating-point and 8bit-integer-quantized matrices.
ruy is designed to achieve maximal performance not just on very large sizes, as is the focus of many established libraries, but on whatever are the actual sizes and shapes of matrices most critical in current TensorFlow Lite applications. This often means quite small sizes, e.g. 100x100 or even 50x50, and all sorts of rectangular shapes.
ruy is currently only optimized for the ARM architectures (both 64-bit and 32-bit code). Optimization for the Intel x86 architecture is in progress.
ruy is currently optimized only for the following combination of storage orders: LHS = row-major, RHS = column-major, destination = column-major. All other combinations of storage orders fall back to slow reference code at the moment.