From 213ec37d6ad84c3774f1a5e203566dc47a1b63da Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 25 Oct 2018 16:18:04 +0200
Subject: [PATCH] MKLDNN elementwise_add: simple initial implementation of the
 operator for MKLDNN format

---
 .../operators/elementwise_mul_mkldnn_op.cc    | 99 +++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 paddle/fluid/operators/elementwise_mul_mkldnn_op.cc

diff --git a/paddle/fluid/operators/elementwise_mul_mkldnn_op.cc b/paddle/fluid/operators/elementwise_mul_mkldnn_op.cc
new file mode 100644
index 000000000..22289ab41
--- /dev/null
+++ b/paddle/fluid/operators/elementwise_mul_mkldnn_op.cc
@@ -0,0 +1,99 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise_op_function.h"
+
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::DataLayout;
+
+template <typename T>
+class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    int axis = ctx.Attr<int>("axis");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    const T* x_data = x->data<T>();
+    const T* y_data = y->data<T>();
+    T* z_data = z->mutable_data<T>(ctx.GetPlace());
+
+    auto x_dims = x->dims();
+    auto y_dims_untrimmed = y->dims();
+
+    if (x_dims != y_dims_untrimmed) {
+      int pre, n, post;
+      get_mid_dims(x_dims, y_dims_untrimmed, axis, &pre, &n, &post);
+
+      if (post == 1) {
+        PADDLE_THROW("Not implemented when post is 1");
+      } else {
+        // Just check whether it works for RE-Resnext.
+
+        PADDLE_ENFORCE_EQ(x_dims.size(), 4, "X should have 4 dimensions");
+
+        int n = x_dims[0];
+        int c = x_dims[1];
+        int h = x_dims[2];
+        int w = x_dims[3];
+
+        PADDLE_ENFORCE(y_dims_untrimmed[0] == n && y_dims_untrimmed[1] == c,
+                       "Y should be in nc format");
+
+        constexpr int simd_width = 16;
+        int C = c / simd_width;
+
+        for (int ni = 0; ni < n; ni++) {
+          for (int ci = 0; ci < C; ci++) {
+            for (int hi = 0; hi < h; hi++) {
+              for (int wi = 0; wi < w; wi++) {
+                auto ptr_x = x_data + ni * C * h * w * simd_width +
+                             ci * h * w * simd_width + hi * w * simd_width +
+                             wi * simd_width;
+                auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
+
+                auto ptr_z = z_data + ni * C * h * w * simd_width +
+                             ci * h * w * simd_width + hi * w * simd_width +
+                             wi * simd_width;
+
+                for (int i = 0; i < simd_width; i++) {
+                  ptr_z[i] = ptr_x[i] * ptr_y[i];
+                }
+              }
+            }
+          }
+        }
+      }
+
+      z->set_layout(DataLayout::kMKLDNN);
+      z->set_format(x->format());
+    } else {
+      PADDLE_THROW("Not implemented when dims are equal");
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(elementwise_mul, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::ElementwiseMulMKLDNNKernel<float>)
-- 
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