Merge pull request #6601 from chengduoZH/profiling/cosine_op

chengduo · web-flow · commit f58fe6d3edb7 · 2018-01-02T10:08:10.000+08:00
Refine cos-sim-op
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
@@ -229,6 +229,7 @@ op_library(lstm_op DEPS sequence2batch lstm_compute)
 op_library(conv_transpose_op DEPS vol2col)
 op_library(gru_op DEPS sequence2batch gru_compute)
 op_library(recurrent_op DEPS executor)
+op_library(cos_sim_op DEPS cos_sim_functor)
 # FIXME(typhoonzero): save/load depends lodtensor serialization functions
 op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
@@ -13,19 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/cos_sim_functor.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/platform/for_range.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
 template <typename DeviceContext, typename T>
 class CosSimKernel : public framework::OpKernel<T> {
@@ -41,28 +37,25 @@ class CosSimKernel : public framework::OpKernel<T> {
     out_x_norm->mutable_data<T>(context.GetPlace());
     out_y_norm->mutable_data<T>(context.GetPlace());
 
-    // convert Tensor to Eigen Tensor
     int rows_x = in_x->dims()[0];
     int rows_y = in_y->dims()[0];
-    auto x = EigenMatrix<T>::Reshape(*in_x, 1);
-    auto y = EigenMatrix<T>::Reshape(*in_y, 1);
-    auto z = EigenVector<T>::Flatten(*out_z);
-    auto x_norm = EigenVector<T>::Flatten(*out_x_norm);
-    auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
 
-    // compute
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    auto row_along = Eigen::array<int, 1>({{1}});
-    x_norm.device(place) = x.square().sum(row_along).sqrt();
-    y_norm.device(place) = y.square().sum(row_along).sqrt();
+    int cols = framework::product(in_x->dims()) / rows_x;
+
     if (rows_x == rows_y) {
-      auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
-      z.device(place) = xy / x_norm / y_norm;
+      math::CosSimFunctor<T, true> functor(
+          in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
+          out_y_norm->data<T>(), out_z->data<T>(), cols);
+      platform::ForRange<DeviceContext> for_range(
+          static_cast<const DeviceContext&>(context.device_context()), rows_x);
+      for_range(functor);
     } else {
-      Eigen::DSizes<int, 2> bcast(rows_x, 1);
-      auto xy = (x * y.broadcast(bcast)).sum(row_along);
-      z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
+      math::CosSimFunctor<T, false> functor(
+          in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
+          out_y_norm->data<T>(), out_z->data<T>(), cols);
+      platform::ForRange<DeviceContext> for_range(
+          static_cast<const DeviceContext&>(context.device_context()), rows_x);
+      for_range(functor);
     }
   }
 };
@@ -81,62 +74,54 @@ class CosSimGradKernel : public framework::OpKernel<T> {
     auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
     auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
 
-    // convert Tensor to Eigen Tensor
-    auto x = EigenMatrix<T>::Reshape(*in_x, 1);
-    auto y = EigenMatrix<T>::Reshape(*in_y, 1);
-    auto z = EigenMatrix<T>::Reshape(*in_z, 1);
-    auto x_norm = EigenMatrix<T>::Reshape(*in_x_norm, 1);
-    auto y_norm = EigenMatrix<T>::Reshape(*in_y_norm, 1);
-    auto dz = EigenMatrix<T>::Reshape(*in_grad_z, 1);
-
     // compute gradident
     int rows_x = in_x->dims()[0];
     int rows_y = in_y->dims()[0];
     int cols = framework::product(in_x->dims()) / rows_x;
-    Eigen::DSizes<int, 2> bcast_cols(1, cols);
-    auto z_bcast = z.broadcast(bcast_cols);
-    auto dz_bcast = dz.broadcast(bcast_cols);
-    auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast_cols);
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
+
     if (rows_x == rows_y) {
-      auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_cols);
-      auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast_cols);
-      // compute dx
       if (out_grad_x) {
-        out_grad_x->mutable_data<T>(context.GetPlace());
-        auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
-        auto grad = y / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
-        dx.device(place) = dz_bcast * grad;
+        math::CosSimGradFunctor<T> functor(
+            in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
+            in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
+            out_grad_x->mutable_data<T>(context.GetPlace()), cols);
+        platform::ForRange<DeviceContext> for_range(
+            static_cast<const DeviceContext&>(context.device_context()),
+            rows_x);
+        for_range(functor);
       }
-      // compute dy
       if (out_grad_y) {
-        out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
-        auto grad = x / norm_prod_bcast - z_bcast * y / y_snorm_bcast;
-        dy.device(place) = dz_bcast * grad;
+        math::CosSimGradFunctor<T> functor(
+            in_y_norm->data<T>(), in_x_norm->data<T>(), in_y->data<T>(),
+            in_x->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
+            out_grad_y->mutable_data<T>(context.GetPlace()), cols);
+        platform::ForRange<DeviceContext> for_range(
+            static_cast<const DeviceContext&>(context.device_context()),
+            rows_x);
+        for_range(functor);
       }
     } else {
-      Eigen::DSizes<int, 2> bcast_rows(rows_x, 1);
-      Eigen::DSizes<int, 2> bcast_rows_cols(rows_x, cols);
-      auto y_bcast = y.broadcast(bcast_rows);
-      auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_rows_cols);
-      auto norm_prod_bcast = (x_norm * y_norm.eval().broadcast(bcast_rows))
-                                 .eval()
-                                 .broadcast(bcast_cols);
-      // compute dx
       if (out_grad_x) {
-        out_grad_x->mutable_data<T>(context.GetPlace());
-        auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
-        auto grad = y_bcast / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
-        dx.device(place) = dz_bcast * grad;
+        math::CosSimDxFunctor<T> functor(
+            in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
+            in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
+            out_grad_x->mutable_data<T>(context.GetPlace()), cols);
+        platform::ForRange<DeviceContext> for_range(
+            static_cast<const DeviceContext&>(context.device_context()),
+            rows_x);
+        for_range(functor);
       }
-      // compute dy
       if (out_grad_y) {
         out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenVector<T>::Flatten(*out_grad_y);
-        auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
-        dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
+        math::SetConstant<DeviceContext, T> set_zero;
+        auto& dev_ctx = context.template device_context<DeviceContext>();
+        set_zero(dev_ctx, out_grad_y, static_cast<T>(0));
+
+        math::CosSimDyFunctor<DeviceContext, T> functor;
+        functor(dev_ctx, in_x_norm->data<T>(), in_y_norm->data<T>(),
+                in_x->data<T>(), in_y->data<T>(), in_z->data<T>(),
+                in_grad_z->data<T>(), static_cast<size_t>(rows_x),
+                static_cast<size_t>(cols), out_grad_y->data<T>());
       }
     }
   }
diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
@@ -16,6 +16,7 @@ if(WITH_GPU)
     nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
     nv_library(unpooling SRCS unpooling.cc unpooling.cu DEPS device_context)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
+    nv_library(cos_sim_functor SRCS cos_sim_functor.cc cos_sim_functor.cu DEPS device_context)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@@ -30,6 +31,7 @@ else()
     cc_library(maxouting SRCS maxouting.cc DEPS device_context)
     cc_library(unpooling SRCS unpooling.cc DEPS device_context)
     cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
+    cc_library(cos_sim_functor SRCS cos_sim_functor.cc DEPS device_context)
 endif()
 
 cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
diff --git a/paddle/operators/math/cos_sim_functor.cc b/paddle/operators/math/cos_sim_functor.cc
@@ -0,0 +1,48 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/math/cos_sim_functor.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+struct CosSimDyFunctor<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& ctx, const T* x_norm,
+                  const T* y_norm, const T* x, const T* y, const T* z,
+                  const T* dz, const size_t rows, const size_t cols,
+                  T* dy) const {
+    for (size_t row_id = 0; row_id < rows; ++row_id) {
+      auto xy_norm_prod = x_norm[row_id] * y_norm[0];
+      auto dz_data = dz[row_id];
+      auto z_data = z[row_id];
+      auto* x_data = x + cols * row_id;
+      auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
+
+      auto y_norm_square = y_norm[0] * y_norm[0];
+      auto reciprocal_y_norm_square = 1 / y_norm_square;
+      for (size_t i = 0; i < cols; ++i) {
+        dy[i] += dz_data * (x_data[i] * reciprocal_xy_norm_prod -
+                            z_data * y[i] * reciprocal_y_norm_square);
+      }
+    }
+  }
+};
+
+template class CosSimDyFunctor<platform::CPUDeviceContext, float>;
+template class CosSimDyFunctor<platform::CPUDeviceContext, double>;
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/cos_sim_functor.cu b/paddle/operators/math/cos_sim_functor.cu
@@ -0,0 +1,64 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/math/cos_sim_functor.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+__global__ void CosSimDyKernel(const T* x_norm, const T* y_norm, const T* x,
+                               const T* y, const T* z, const T* dz,
+                               const size_t rows, const size_t cols, T* dy) {
+  int grid_size = blockDim.x * gridDim.x;
+  T y_norm_data = y_norm[0];
+  for (int row_id = blockIdx.x * blockDim.x + threadIdx.x; row_id < rows;
+       row_id += grid_size) {
+    T xy_norm_prod = x_norm[row_id] * y_norm_data;
+    T dz_data = dz[row_id];
+    T z_data = z[row_id];
+    const T* x_data = x + cols * row_id;
+    T reciprocal_xy_norm_prod = 1 / xy_norm_prod;
+
+    T y_norm_square = y_norm_data * y_norm_data;
+    T reciprocal_y_norm_square = 1 / y_norm_square;
+    for (size_t i = 0; i < cols; ++i) {
+      T dy_data = dz_data * (x_data[i] * reciprocal_xy_norm_prod -
+                             z_data * y[i] * reciprocal_y_norm_square);
+      platform::CudaAtomicAdd(dy + i, dy_data);
+    }
+  }
+}
+
+template <typename T>
+struct CosSimDyFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& ctx, const T* x_norm,
+                  const T* y_norm, const T* x, const T* y, const T* z,
+                  const T* dz, const size_t rows, const size_t cols,
+                  T* dy) const {
+    const int block_size = 512;
+    dim3 threads(block_size, 1);
+    dim3 grid(1, (rows + block_size - 1) / block_size);
+    CosSimDyKernel<T><<<grid, threads, 0, ctx.stream()>>>(
+        x_norm, y_norm, x, y, z, dz, rows, cols, dy);
+  }
+};
+
+template class CosSimDyFunctor<platform::CUDADeviceContext, float>;
+template class CosSimDyFunctor<platform::CUDADeviceContext, double>;
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/cos_sim_functor.h b/paddle/operators/math/cos_sim_functor.h
