pytorch

abcd4eb9 - Optimize min and max(reduce_dim) performance on CPU (#34875)

Optimize min and max(reduce_dim) performance on CPU (#34875) Summary: This PR is about improve min and max(reduce_dim) performance on CPU. Test script: ``` import torch import torch.nn as nn import time torch.manual_seed(0) def _time(): return time.time() device = "cpu" torch.set_num_threads(1) #warm up for n in [10, 200]: # contiguous # input = torch.randn(n, n, n, requires_grad=False, device=device) # discontiguous input = torch.randn(n, 2*n, n, requires_grad=False, device=device)[:, :n, :] for dim in range(input.dim()): for i in range(1000): output = input.min(dim) #output = input.max(dim) for n in [10, 200]: # contiguous input # input = torch.randn(n, n, n, requires_grad=False, device=device) # discontiguous input = torch.randn(n, 2*n, n, requires_grad=False, device=device)[:, :n, :] for dim in range(input.dim()): fwd_t = 0 for i in range(10000): t1 = _time() output = input.min(dim) #output = input.max(dim) t2 = _time() fwd_t = fwd_t + (t2 -t1) fwd_avg = fwd_t / 10000 * 1000 print("size = (%d, %d, %d); reduce dim=%d; compute time is %.4f(ms)" % (n, n, n, dim, fwd_avg)) ``` Test device: **skx-8180**. ### Contiguous case. - num_threads = 56   | Bef(ms) | Bef(ms) | Bef(ms) | Aft(ms) | Aft(ms) | Aft(ms) -- | -- | -- | -- | -- | -- | -- size | dim=0 | dim=1 | dim=2 | dim=0 | dim=1 | dim=2 n=10 | 0.0243 | 0.0243 | 0.0244 | 0.0063 | 0.0065 | 0.0063 n=200 | 0.9615 | 0.9453 | 0.7772 | 0.2937 | 0.2675 | 0.2607 - num_threads = 1   | Bef(ms) | Bef(ms) | Bef(ms) | Aft(ms) | Aft(ms) | Aft(ms) -- | -- | -- | -- | -- | -- | -- size | dim=0 | dim=1 | dim=2 | dim=0 | dim=1 | dim=2 n=10 | 0.0126 | 0.0126 | 0.0114 | 0.0062 | 0.0065 | 0.0064 n=200 | 32.1276 | 33.3489 | 29.0757 | 8.0556 | 7.0188 | 6.5014 ### Discontiguous case. - num_threads = 56   | Bef(ms) | Bef(ms) | Bef(ms) | Aft(ms) | Aft(ms) | Aft(ms) -- | -- | -- | -- | -- | -- | -- size | dim=0 | dim=1 | dim=2 | dim=0 | dim=1 | dim=2 n=10 | 0.0106 | 0.0115 | 0.0131 | 0.0063 | 0.0066 | 0.0065 n=200 | 14.652 | 15.3496 | 9.8153 | 0.2946 | 0.2708 | 0.267 - num_threads = 1   | Bef(ms) | Bef(ms) | Bef(ms) | Aft(ms) | Aft(ms) | Aft(ms) -- | -- | -- | -- | -- | -- | -- size | dim=0 | dim=1 | dim=2 | dim=0 | dim=1 | dim=2 n=10 | 0.0108 | 0.0116 | 0.0132 | 0.0058 | 0.0062 | 0.0061 n=200 | 12.5132 | 13.0785 | 9.6738 | 8.3733 | 7.3051 | 6.4566 https://github.com/pytorch/pytorch/issues/24671 and https://github.com/pytorch/pytorch/issues/24672 are also fixed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/34875 Differential Revision: D20605596 Pulled By: ngimel fbshipit-source-id: 08fd4dacd1db63309123d7ec5942a4b8a0071896