pytorch

d6e3aed0 - add eigen blas for mobile build (#26508)

add eigen blas for mobile build (#26508) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26508 Enable BLAS for pytorch mobile build using Eigen BLAS. It's not most juicy optimization for typical mobile CV models as we are already using NNPACK/QNNPACK for most ops there. But it's nice to have good fallback implementation for other ops. Test Plan: - Create a simple matrix multiplication script model: ``` import torch class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.weights = torch.ones(1000, 1000) def forward(self, x): return torch.mm(x, self.weights) n = Net() module = torch.jit.trace_module(n, {'forward': torch.ones(1000, 1000)}) module.save('mm.pk') ``` - Before integrate with eigen blas: ``` adb shell 'cd /data/local/tmp; \ ./speed_benchmark_torch \ --model=mm.pk \ --input_dims="1000,1000" \ --input_type=float \ --warmup=5 \ --iter=5' Milliseconds per iter: 2218.52. ``` - After integrate with eigen blas: ``` adb shell 'cd /data/local/tmp; \ ./speed_benchmark_torch_eigen \ --model=mm.pk \ --input_dims="1000,1000" \ --input_type=float \ --warmup=5 \ --iter=5' Milliseconds per iter: 314.535. ``` - Improve MobileNetV2 single thread perf by ~5%: ``` adb shell 'cd /data/local/tmp; \ ./speed_benchmark_torch \ --model=mobilenetv2.pk \ --input_dims="1,3,224,224" \ --input_type=float \ --warmup=5 \ --iter=20 \ --print_output=false \ --caffe2_threadpool_force_inline=true' Milliseconds per iter: 367.055. adb shell 'cd /data/local/tmp; \ ./speed_benchmark_torch_eigen \ --model=mobilenetv2.pk \ --input_dims="1,3,224,224" \ --input_type=float \ --warmup=5 \ --iter=20 \ --print_output=false \ --caffe2_threadpool_force_inline=true' Milliseconds per iter: 348.77. ``` Differential Revision: D17489587 fbshipit-source-id: efe542db810a900f680da7ec7e60f215f58db66e