Use mkl

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -150,19 +150,27 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
                                                        label.data<int64_t>()));
     }
 
-    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
-    auto pre_out_mat = EigenMatrix<T>::From(pre_out);
-    auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
-    auto out_grad_mat = EigenMatrix<T>::From(out_grad);
+    // softrelu derivative
 
-    Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
 
-    // softrelu derivative
-    pre_out_grad_mat.device(place) =
-        static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
+    auto* pre_out_grad_data = pre_out_grad.data<T>();
+    auto* pre_out_data = pre_out.data<T>();
+    auto n = pre_out.numel();
+    blas.VEXP(n, pre_out_data, pre_out_grad_data);
+    blas.VINV(n, pre_out_grad_data, pre_out_grad_data);
+    for (int64_t i = 0; i < n; ++i) {
+      pre_out_grad_data[i] = 1.0 - pre_out_grad_data[i];
+    }
     bit_code->Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
-    pre_out_grad_mat.device(place) =
-        pre_out_grad_mat * out_grad_mat.broadcast(bcast);
+    auto* out_grad_data = out_grad.data<T>();
+
+    int64_t dim0 = pre_out_grad.dims()[0];
+    int64_t dim1 = pre_out_grad.dims()[1];
+    for (int64_t i = 0; i < dim0; ++i) {
+      T tmp = out_grad_data[i];
+      blas.SCAL(dim1, tmp, pre_out_grad_data + i * dim1);
+    }
     // TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
     // be consistent with the clipping in forward.
 

paddle/fluid/operators/math/blas.h

@@ -181,6 +181,9 @@ class Blas {
               const framework::Tensor& mat_b, const MatDescriptor& dim_b,
               T alpha, framework::Tensor* mat_out, T beta) const;
 
+  template <typename T>
+  void VINV(int n, const T* a, T* y) const;
+
  private:
   const DeviceContext& context_;
 };
@@ -282,6 +285,11 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template BatchedGEMM<T>(args...);
   }
 
+  template <typename... ARGS>
+  void VINV(ARGS... args) const {
+    Base()->template VINV<T>(args...);
+  }
+
  private:
   const Blas<DeviceContext>* Base() const {
     return static_cast<const Blas<DeviceContext>*>(this);

paddle/fluid/operators/math/blas_impl.h

@@ -118,6 +118,11 @@ struct CBlas<float> {
   static void VPOW(ARGS... args) {
     platform::dynload::vsPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vsInv(args...);
+  }
 };
 
 template <>
@@ -213,6 +218,11 @@ struct CBlas<double> {
   static void VPOW(ARGS... args) {
     platform::dynload::vdPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vdInv(args...);
+  }
 };
 
 #else
@@ -603,6 +613,17 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
         dim_a.stride_, dim_b.stride_);
   }
 }
+template <typename DeviceContext>
+template <typename T>
+void Blas<DeviceContext>::VINV(int n, const T *a, T *y) const {
+#ifdef PADDLE_WITH_MKLML
+  CBlas<T>::VINV(n, a, y);
+#else
+  for (int i = 0; i < n; ++i) {
+    y[i] = 1.0 / a[i];
+  }
+#endif
+}
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/platform/dynload/mklml.h

@@ -82,6 +82,8 @@ extern void* mklml_dso_handle;
   __macro(vdSqr);                   \
   __macro(vsPowx);                  \
   __macro(vdPowx);                  \
+  __macro(vsInv);                   \
+  __macro(vdInv);                   \
   __macro(MKL_Set_Num_Threads)
 
 MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);