{"action":"created","comment":{"url":"https://api.github.com/repos/pytorch/pytorch/pulls/comments/218908701","pull_request_review_id":156953031,"id":218908701,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIxODkwODcwMQ==","diff_hunk":"@@ -43,6 +43,205 @@ Tensor & rrelu_(Tensor & self, Scalar lower, Scalar upper, bool training, Genera\n   return at::rrelu_with_noise_(self, self.type().tensor(), lower, upper, training, generator);\n }\n \n+// -----------------------------------\n+// prelu forward\n+// -----------------------------------\n+template <typename scalar_t>\n+void prelu_cpu_kernel(\n+  Tensor& result,\n+  const Tensor& input,\n+  const Tensor& weight,\n+  int64_t input_dim0_size,\n+  int64_t channel_size,\n+  int64_t input_stride0,\n+  int64_t input_stride1) {\n+\n+  int64_t i, j, k;\n+  auto result_data = result.data<scalar_t>();\n+  auto input_data = input.data<scalar_t>();\n+  auto weight_data = weight.data<scalar_t>();\n+\n+  // multiply values at each channel with weight[channel_index]\n+  #pragma omp parallel for private(i,j,k) if (input.numel() > 10000)\n+  for (i = 0; i < input_dim0_size; i++) {\n+    for (j = 0; j < channel_size; j++) {\n+      for (k = 0; k < input_stride1; k++) {\n+        int64_t pos = i * input_stride0 + j * input_stride1 + k;\n+        result_data[pos] = (input_data[pos] > 0) ? input_data[pos] : weight_data[j] * input_data[pos];","path":"aten/src/ATen/native/Activation.cpp","position":36,"original_position":28,"commit_id":"b247a8075436e025210d5d30a3a40c4f534828a1","original_commit_id":"b9909afa90444c1634d1d12d74fa93ec5f912ff8","user":{"login":"weiyangfb","id":38509346,"node_id":"MDQ6VXNlcjM4NTA5MzQ2","avatar_url":"https://avatars1.githubusercontent.com/u/38509346?v=4","gravatar_id":"","url":"https://api.github.com/users/weiyangfb","html_url":"https://github.com/weiyangfb","followers_url":"https://api.github.com/users/weiyangfb/followers","following_url":"https://api.github.com/users/weiyangfb/following{/other_user}","gists_url":"https://api.github.com/users/weiyangfb/gists{/gist_id}","starred_url":"https://api.github.com/users/weiyangfb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/weiyangfb/subscriptions","organizations_url":"https://api.github.com/users/weiyangfb/orgs","repos_url":"https://api.github.com/users/weiyangfb/repos","events_url":"https://api.github.com/users/weiyangfb/events{/privacy}","received_events_url":"https://api.github.com/users/weiyangfb/received_events","type":"User","site_admin":false},"body":"Thanks a lot! Indeed I should've cached the input_data! I will compress the nested loops into just one loop later on","created_at":"2018-09-19T18:10:23Z","updated_at":"2018-09-19T18:10:24Z","html_url":"https://github.com/pytorch/pytorch/pull/11758#discussion_r218908701","pull_request_url":"https://api.github.com/repos/pytorch/pytorch/pulls/11758","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/pytorch/pytorch/pulls/comments/218908701"},"html":{"href":"https://github.com/pytorch/pytorch/pull/11758#discussion_r218908701"},"pull_request":{"href":"https://api.github.com/repos/pytorch/pytorch/pulls/11758"}},"in_reply_to_id":218888490},"pull_request":{"url":"https://api.github.com/repos/pytorch/pytorch/pulls/11758","id":216040664,"node_id":"MDExOlB1bGxSZXF1ZXN0MjE2MDQwNjY0","html_url":"https://github.com/pytorch/pytorch/pull/11758","diff_url":"https://github.com/pytorch/pytorch/pull/11758.diff","patch_url":"https://github.com/pytorch/pytorch/pull/11758.patch","issue_url":"https://api.github.com/repos/pytorch/pytorch/issues/11758","number":11758,"state":"open","locked":false,"title":"migrate PReLU to ATen","user":{"login":"weiyangfb","id":38509346,"node_id":"MDQ6VXNlcjM4NTA5MzQ2","avatar_url":"https://avatars1.githubusercontent.com/u/38509346?v=4","gravatar_id":"","url":"https://api.github.com/users/weiyangfb","html_url":"https://github.com/weiyangfb","followers_url":"https://api.github.com/users/weiyangfb/followers","following_url":"https://api.github.com/users/weiyangfb/following{/other_user}","gists_url":"https://api.github.com/users/weiyangfb/gists{/gist_id}","starred_url":"https://api.github.com/users/weiyangfb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/weiyangfb/subscriptions","organizations_url":"https://api.github.com/users/weiyangfb/orgs","repos_url":"https://api.github.com/users/weiyangfb/repos","events_url":"https://api.github.com/users/weiyangfb/events{/privacy}","received_events_url":"https://api.github.com/users/weiyangfb/received_events","type":"User","site_admin":false},"body":"- fixes https://github.com/pytorch/pytorch/issues/10723\r\n- migrate PReLU to ATen and deprecate legacy PReLU\r\n- performance:\r\n\r\nCPU with weight.numel() = 1\r\n```\r\n# === previous ===\r\n>>> m = nn.PReLU()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True)\r\n>>> %timeit y = m(x)\r\n100 loops, best of 3: 9.91 ms per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit y.backward(retain_graph=True)\r\n10 loops, best of 3: 26.3 ms per loop\r\n\r\n# === current ====\r\n>>> m = nn.PReLU()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True)\r\n>>> %timeit y = m(x)\r\n1000 loops, best of 3: 641 µs per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit y.backward(retain_graph=True)\r\n100 loops, best of 3: 2.56 ms per loop\r\n```\r\n\r\nCPU with weight.numel() = channels\r\n```\r\n# === previous ===\r\n>>> m = nn.PReLU(100)\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True)\r\n>>> %timeit y = m(x)\r\n1000 loops, best of 3: 1.22 ms per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit y.backward(retain_graph=True)\r\n10 loops, best of 3: 17.1 ms per loop\r\n\r\n# === current ====\r\n>>> m = nn.PReLU(100)\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True)\r\n>>> %timeit y = m(x)\r\n100 loops, best of 3: 2.36 ms per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit y.backward(retain_graph=True)\r\n100 loops, best of 3: 4.68 ms per loop\r\n```\r\n\r\nCUDA with weight.numel() = 1\r\n```\r\n# === previous ===\r\n>>> m = nn.PReLU().cuda()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()\r\n>>> %timeit torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();\r\n10000 loops, best of 3: 169 µs per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();\r\n100 loops, best of 3: 2.86 ms per loop\r\n\r\n# === current ====\r\n>>> m = nn.PReLU().cuda()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()\r\n>>> %timeit torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();\r\n1000 loops, best of 3: 240 µs per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();\r\n100 loops, best of 3: 3.26 ms per loop\r\n```\r\n\r\nCUDA with weight.numel() = channel\r\n```\r\n# === previous ===\r\n>>> m = nn.PReLU(100).cuda()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()\r\n>>> %timeit torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();\r\n10000 loops, best of 3: 168 µs per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();\r\n100 loops, best of 3: 2.86 ms per loop\r\n\r\n# === current ====\r\n>>> m = nn.PReLU(100).cuda()\r\n>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()\r\n>>> %timeit torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();\r\n10000 loops, best of 3: 188 µs per loop\r\n\r\n>>> y = m(x).sum()\r\n>>> %timeit torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();\r\n100 loops, best of 3: 3.86 ms per loop\r\n```\r\n\r\nThe huge performance regression in CPU when weight.numel() = 1 is addressed by replacing at::CPU_tensor_apply* with parallelized kernels.\r\n\r\n@ezyang @SsnL @zou3519  @soumith 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