haozhe.zhu 5a2db5152d allow to use bf16 as fp32 internal precision for mkldnn conv (#126050) ... Allow to use `BF16` as the internal computation data types by `torch.backends.mkldnn.conv.fp32_precision="bf16"` ### TestPlan python test/test_mkldnn.py -k conv ### Benchmarking FP32 conv2d vs. BF16 internal computation conv2d on SPR Single core: Input | fp32 ms | bf16 internal ms | Speed up -- | -- | -- | -- IC: 64, OC: 256, kernel: 1, stride: 1, N: 256, H: 56, W: 56, G: 1, pad: 0 | 185.5071 | 83.4749 | 2.22 IC: 128, OC: 512, kernel: 1, stride: 1, N: 256, H: 28, W: 28, G: 1, pad: 0 | 194.7558 | 79.1683| 2.46 IC: 256, OC: 256, kernel: 3, stride: 1, N: 1, H: 16, W: 16, G: 1, pad: 0 | 1.9213 | 1.3690 | 1.40 56 cores: Input | fp32 ms | bf16 internal ms | Speed up -- | -- | -- | -- IC: 64, OC: 256, kernel: 1, stride: 1, N: 256, H: 28, W: 28, G: 1, pad: 0 | 6.5804 | 7.4349 | 0.89 IC: 128, OC: 512, kernel: 1, stride: 1, N: 256, H: 28, W: 28, G: 1, pad: 0 | 4.9940 | 3.8093 | 1.31 IC: 256, OC: 1024, kernel: 1, stride: 1, N: 256, H: 14, W: 14, G: 1, pad: 0 | 8.8359 | 5.5802 | 1.58 IC: 1024, OC: 256, kernel: 1, stride: 1, N: 256, H: 14, W: 14, G: 1, pad: 0 | 16.5800 | 9.2367 | 1.80 IC: 256, OC: 256, kernel: 3, stride: 1, N: 1, H: 16, W: 16, G: 1, pad: 0 | 79.5436 | 38.3861 | 2.07 Pull Request resolved: https://github.com/pytorch/pytorch/pull/126050 Approved by: https://github.com/jgong5, https://github.com/jansel Co-authored-by: Jiang, Yanbing <yanbing.jiang@intel.com>		2025-07-02 01:31:23 +00:00
..
_internal	allow to use bf16 as fp32 internal precision for mkldnn conv (#126050)	2025-07-02 01:31:23 +00:00
__init__.py
_comparison.py	[Torch] Fix error message formatting in fp8 comparison logic (#153647)	2025-05-27 02:51:05 +00:00
_creation.py	PEP585 update - torch/testing (#145200)	2025-01-20 22:42:42 +00:00
_utils.py	Fix code descriptions in the test package. (#148145)	2025-03-04 19:14:41 +00:00