Merge pull request #14728 from JiabinYang/optimize_hs_op

Optimize hs op

paddle/fluid/operators/hierarchical_sigmoid_op.cc

@@ -158,6 +158,7 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
       ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
     }
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
   }
 
  protected:

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -185,7 +185,6 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
           ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));
       w_grad->set_rows(real_rows);
       // Build a map of id -> row_index to speed up finding the index of one id
-      w_grad->SyncIndex();
       w_grad->set_height(w.dims()[0]);
       auto* w_grad_value = w_grad->mutable_value();
       framework::DDim temp_dim(w.dims());

paddle/fluid/operators/math/matrix_bit_code.cc

@@ -89,6 +89,8 @@ template <typename T>
 void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
                                   const framework::Tensor& weight,
                                   const framework::Tensor& input) {
+  auto blas =
+      GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
   size_t num_samples = tmat->dims()[0];
   size_t tmat_width = tmat->dims()[1];
   size_t input_width = input.dims()[1];
@@ -99,13 +101,12 @@ void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
   for (size_t i = 0; i < num_samples; ++i) {
     auto code = code_table_->get_code(i);
     int code_length = code->get_length();
+    const T* input_row = input_value + input_width * i;
     for (int j = 0; j < code_length; ++j) {
       size_t index = code->calc_index(j);
+      const T* weight_row = weight_value + weight_width * index;
       T sum = static_cast<T>(0.0);
-      for (size_t k = 0; k < input_width; ++k) {
-        sum += weight_value[weight_width * index + k] *
-               input_value[input_width * i + k];
-      }
+      sum = blas.DOT(input_width, weight_row, input_row);
       tmat_value[i * tmat_width + j] += sum;
     }
   }
@@ -115,6 +116,8 @@ template <typename T>
 void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
                                             framework::Tensor* weight,
                                             const framework::Tensor& input) {
+  auto blas =
+      GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
   size_t num_samples = tmat.dims()[0];
   size_t input_width = input.dims()[1];
   size_t tmat_width = tmat.dims()[1];
@@ -122,16 +125,25 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
   auto tmat_value = tmat.data<T>();
   auto weight_value = weight->data<T>();
   auto input_value = input.data<T>();
+
+  std::unordered_map<int, std::vector<std::pair<T, const T*>>> ops;
+
   for (size_t i = 0; i < num_samples; ++i) {
     auto code = code_table_->get_code(i);
     int code_length = code->get_length();
+    const T* input_value_row = input_value + input_width * i;
+    const T* tmat_row = tmat_value + i * tmat_width;
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code->calc_index(j);
-
-      for (size_t k = 0; k < input_width; ++k) {
-        weight_value[weight_width * index + k] +=
-            tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
-      }
+      ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
+    }
+  }
+  for (auto& op : ops) {
+    auto& op_in_row = op.second;
+    for (auto& pair : op_in_row) {
+      auto& scale = pair.first;
+      auto* input_row = pair.second;
+      T* weight_row = weight_value + op.first * weight_width;
+      blas.AXPY(input_width, scale, input_row, weight_row);
     }
   }
 }
@@ -140,6 +152,8 @@ template <typename T>
 void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
                                             framework::SelectedRows* weight,
                                             const framework::Tensor& input) {
+  auto blas =
+      GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
   size_t num_samples = tmat.dims()[0];
   size_t input_width = input.dims()[1];
   size_t tmat_width = tmat.dims()[1];
@@ -147,17 +161,28 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
   auto tmat_value = tmat.data<T>();
   auto weight_value = weight->mutable_value()->data<T>();
   auto input_value = input.data<T>();
+
+  std::unordered_map<int, std::vector<std::pair<T, const T*>>> ops;
+  ops.reserve(weight->rows().size());
+
   for (size_t i = 0; i < num_samples; ++i) {
     auto code = code_table_->get_code(i);
     int code_length = code->get_length();
+    const T* input_value_row = input_value + input_width * i;
+    const T* tmat_row = tmat_value + i * tmat_width;
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code->calc_index(j);
-      for (size_t k = 0; k < input_width; ++k) {
-        int64_t row_index = weight->GetIndexFromId(static_cast<int64_t>(index));
-        weight_value[row_index * weight_width + k] +=
-            tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
-      }
+      ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
+    }
+  }
+
+  for (auto& row : weight->rows()) {
+    auto& op_in_row = ops[row];
+    for (auto& pair : op_in_row) {
+      auto& scale = pair.first;
+      auto* input_row = pair.second;
+      blas.AXPY(input_width, scale, input_row, weight_value);
     }
+    weight_value += weight_width;
   }
 }
 

paddle/fluid/operators/math/matrix_bit_code.h

@@ -13,10 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <unordered_map>
+#include <utility>
+#include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/platform/device_context.h"
 
 #if defined(_WIN32)