pytorch

473d1939 - Use mkldnn copy for copy_ when self and src are Mkldnn layout (#54248)

Use mkldnn copy for copy_ when self and src are Mkldnn layout (#54248) Summary: Currently, when copy_ is called with Mkldnn layout, a RuntimeError is raised. **Environment** - CPU : Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz - PyTorch master(1772e26f6380d1) - build with USE_MKLDNN=1 **Sample code to reproduce:** ```python import torch x = torch.randn(4, 5, dtype=torch.float32) mkldnn_x = x.to_mkldnn() mkldnn_y = torch.randn(4, 5, dtype=torch.float32).to_mkldnn() mkldnn_y.copy_(mkldnn_x) print(x) print(mkldnn_y.to_dense()) ``` **Results:** Actual: ```sh Traceback (most recent call last): File "mkldnn_copy.py", line 6, in <module> mkldnn_y.copy_(mkldnn_x) RuntimeError: unsupported tensor layout: Mkldnn ``` Expected: ```sh # x tensor([[ 0.1276, -0.1179, 1.1970, 2.4836, 1.9059], [-1.9647, 0.8613, -0.5060, 0.1555, 0.3661], [-0.1560, -0.2133, 0.3414, -1.7095, -2.3431], [ 1.3291, 0.3083, 0.5523, -2.0577, -0.4740]]) # mkldnn_y tensor([[ 0.1276, -0.1179, 1.1970, 2.4836, 1.9059], [-1.9647, 0.8613, -0.5060, 0.1555, 0.3661], [-0.1560, -0.2133, 0.3414, -1.7095, -2.3431], [ 1.3291, 0.3083, 0.5523, -2.0577, -0.4740]]) ``` This is because `copy_` does not support Mkldnn layout. So I modified to call `copy_mkldnn_` in `copy_` when both `self` and `src` are Mkldnn layout. Pull Request resolved: https://github.com/pytorch/pytorch/pull/54248 Reviewed By: mrshenli Differential Revision: D27641352 Pulled By: ezyang fbshipit-source-id: 70a37cdacb4a40b250ca16f2f6ddb6b71ff52d90