Clean MatMul

paddle/fluid/operators/conv_op.h

@@ -161,6 +161,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
     math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
 
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     for (int i = 0; i < batch_size; i++) {
       Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
       Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
@@ -186,8 +187,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
         // gemm
         Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
         Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
-        math::matmul<DeviceContext, T>(dev_ctx, filter_slice, false, col_matrix,
-                                       false, T(1.0), &out_slice, T(0.0));
+        blas.MatMul(filter_slice, col_matrix, &out_slice);
       }
     }
   }
@@ -274,6 +274,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
 
     math::SetConstant<DeviceContext, T> set_zero;
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
 
     if (input_grad) {
       input_grad->mutable_data<T>(context.GetPlace());
@@ -303,9 +304,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
             col_matrix.ShareDataWith(in_grad_slice);
             col_matrix.Resize(col_matrix_shape);
           }
-          math::matmul<DeviceContext, T>(dev_ctx, filter_slice, true,
-                                         out_grad_slice, false, T(1.0),
-                                         &col_matrix, T(0.0));
+          blas.MatMul(filter_slice, true, out_grad_slice, false, &col_matrix);
 
           if (is_expand && data_dim == 2U) {
             col2im(dev_ctx, col, dilations, strides,
@@ -352,9 +351,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
           // gemm
           Tensor filter_grad_slice =
               filter_grad_.Slice(g * out_step, (g + 1) * out_step);
-          math::matmul<DeviceContext, T>(dev_ctx, out_grad_slice, false,
-                                         col_matrix, true, T(1.0),
-                                         &filter_grad_slice, T(1.0));
+          blas.MatMul(out_grad_slice, false, col_matrix, true,
+                      &filter_grad_slice);
         }
       }
     }

paddle/fluid/operators/conv_transpose_op.h

@@ -118,6 +118,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
     output->mutable_data<T>(context.GetPlace());
     math::SetConstant<DeviceContext, T> set_zero;
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     set_zero(dev_ctx, output, static_cast<T>(0));
 
     math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
@@ -134,9 +135,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
 
       // col_matrix = filter * input_batch
       // of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
-      math::matmul<DeviceContext, T>(dev_ctx, filter, true, input_batch, false,
-                                     static_cast<T>(1.0), &col_matrix,
-                                     static_cast<T>(0.0));
+      blas.MatMul(filter, true, input_batch, false, &col_matrix);
 
       if (data_dim == 2U) {
         // col2im: col_matrix -> dy
@@ -213,6 +212,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
     // im2col + gemm (similar to conv-forward)
     // input need to compute gradient
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     if (input_grad || filter_grad) {
       Tensor col;
       col.mutable_data<T>(col_shape, context.GetPlace());
@@ -267,9 +267,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
           // or
           // (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
           // d, h, w)
-          math::matmul<DeviceContext, T>(
-              dev_ctx, filter, false, col_matrix, false, static_cast<T>(1.0),
-              &input_grad_batch, static_cast<T>(0.0));
+          blas.MatMul(filter, false, col_matrix, false, &input_grad_batch);
         }
         if (filter_grad) {
           // input batch
@@ -279,9 +277,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
           // or
           // (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
           // k_h * k_w)
-          math::matmul<DeviceContext, T>(dev_ctx, in_batch, false, col_matrix,
-                                         true, static_cast<T>(1.0),
-                                         &filter_grad_, static_cast<T>(1.0));
+          blas.MatMul(in_batch, false, col_matrix, true, &filter_grad_);
         }
       }
     }

paddle/fluid/operators/lstm_op.h

@@ -114,6 +114,7 @@ class LSTMKernel : public framework::OpKernel<T> {
     auto cand_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("candidate_activation"));
 
+    auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
     for (size_t n = 0; n < num_batch; n++) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
@@ -129,9 +130,8 @@ class LSTMKernel : public framework::OpKernel<T> {
         int pre_h_start = static_cast<int>(batch_starts[n - 1]);
         int pre_h_end = pre_h_start + cur_batch_size;
         auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
-        math::matmul<DeviceContext, T>(device_ctx, pre_hidden_t, false, *weight,
-                                       false, static_cast<T>(1.0), &gate_t,
-                                       static_cast<T>(1.0));
+        blas.MatMul(pre_hidden_t, false, *weight, false, static_cast<T>(1.0),
+                    &gate_t, static_cast<T>(1.0));
       } else if (hidden_t0) {
         // If n == 0 and there is no initialized hidden state, that is to say
         // the H0 is zeros, the calculation W_h * H0 will be skiped.
@@ -143,9 +143,8 @@ class LSTMKernel : public framework::OpKernel<T> {
         Tensor ordered_h0;
         ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
                                            &ordered_h0, true);
-        math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false, *weight,
-                                       false, static_cast<T>(1.0), &gate_t,
-                                       static_cast<T>(1.0));
+        blas.MatMul(ordered_h0, false, *weight, false, static_cast<T>(1.0),
+                    &gate_t, static_cast<T>(1.0));
       }
 
       lstm_value.gate_value = gate_t.data<T>();
@@ -282,6 +281,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
 
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
+    auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
     for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
@@ -320,29 +320,25 @@ class LSTMGradKernel : public framework::OpKernel<T> {
         int pre_h_start = static_cast<int>(batch_starts[n - 1]);
         int pre_h_end = pre_h_start + cur_batch_size;
         auto pre_hidden_g = batch_hidden_g.Slice(pre_h_start, pre_h_end);
-        math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight, true,
-                                       static_cast<T>(1.0), &pre_hidden_g,
-                                       static_cast<T>(1.0));
+        blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
+                    &pre_hidden_g, static_cast<T>(1.0));
         if (weight_g) {
           /* backward weight */
           auto pre_hidden = batch_hidden.Slice(pre_h_start, pre_h_end);
-          math::matmul<DeviceContext, T>(device_ctx, pre_hidden, true, gate_g,
-                                         false, static_cast<T>(1.0), weight_g,
-                                         static_cast<T>(1.0));
+          blas.MatMul(pre_hidden, true, gate_g, false, static_cast<T>(1.0),
+                      weight_g, static_cast<T>(1.0));
         }
       } else {
         if (h0 && weight_g) {
           ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
                                              &ordered_h0, true);
-          math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true, gate_g,
-                                         false, static_cast<T>(1.0), weight_g,
-                                         static_cast<T>(1.0));
+          blas.MatMul(ordered_h0, true, gate_g, false, static_cast<T>(1.0),
+                      weight_g, static_cast<T>(1.0));
         }
         if (h0 && h0_g) {
           ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
-          math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
-                                         true, static_cast<T>(1.0),
-                                         &ordered_h0_g, static_cast<T>(0.0));
+          blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
+                      &ordered_h0_g, static_cast<T>(0.0));
         }
       }
     }

paddle/fluid/operators/lstmp_op.h

@@ -143,7 +143,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
     auto proj_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("proj_activation"));
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
-
+    auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
     for (size_t n = 0; n < num_batch; n++) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
@@ -160,9 +160,8 @@ class LSTMPKernel : public framework::OpKernel<T> {
         int pre_h_start = static_cast<int>(batch_starts[n - 1]);
         int pre_h_end = pre_h_start + cur_batch_size;
         auto pre_proj_t = batch_proj.Slice(pre_h_start, pre_h_end);
-        math::matmul<DeviceContext, T>(device_ctx, pre_proj_t, false, *weight,
-                                       false, static_cast<T>(1.0), &gate_t,
-                                       static_cast<T>(1.0));
+        blas.MatMul(pre_proj_t, false, *weight, false, static_cast<T>(1.0),
+                    &gate_t, static_cast<T>(1.0));
       } else if (hidden_t0) {
         // If n == 0 and there is no initialized hidden state, that is to say
         // the H0 is zeros, the calculation W_h * H0 will be skiped.
@@ -176,16 +175,14 @@ class LSTMPKernel : public framework::OpKernel<T> {
         ordered_proj0->mutable_data<T>(ctx.GetPlace());
         ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
                                            &ordered_h0, true);
-        math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false,
-                                       *proj_weight, false, static_cast<T>(1.0),
-                                       ordered_proj0, static_cast<T>(0.0));
+        blas.MatMul(ordered_h0, false, *proj_weight, false, static_cast<T>(1.0),
+                    ordered_proj0, static_cast<T>(0.0));
         if (proj_act != math::detail::ActivationType::kIdentity) {
           auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
           ActCompute(cell_act, place, proj0_dev, proj0_dev);
         }
-        math::matmul<DeviceContext, T>(device_ctx, *ordered_proj0, false,
-                                       *weight, false, static_cast<T>(1.0),
-                                       &gate_t, static_cast<T>(1.0));
+        blas.MatMul(*ordered_proj0, false, *weight, false, static_cast<T>(1.0),
+                    &gate_t, static_cast<T>(1.0));
       }
 
       lstmp_value.gate_value = gate_t.data<T>();
@@ -196,9 +193,8 @@ class LSTMPKernel : public framework::OpKernel<T> {
           device_ctx, lstmp_value, frame_size, cur_batch_size, gate_act,
           cell_act, cand_act);
       lstmp_value.prev_state_value = lstmp_value.state_value;
-      math::matmul<DeviceContext, T>(device_ctx, hidden_t, false, *proj_weight,
-                                     false, static_cast<T>(1.0), &proj_t,
-                                     static_cast<T>(0.0));
+      blas.MatMul(hidden_t, false, *proj_weight, false, static_cast<T>(1.0),
+                  &proj_t, static_cast<T>(0.0));
       if (proj_act != math::detail::ActivationType::kIdentity) {
         auto proj_t_dev = EigenMatrix<T>::From(proj_t);
         ActCompute(cell_act, place, proj_t_dev, proj_t_dev);
@@ -361,6 +357,7 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
 
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
+    auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
     for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
@@ -375,15 +372,13 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       }
       /* hidden state backwarad */
       Tensor out_g = batch_hidden_g.Slice(bstart, bend);
-      math::matmul<DeviceContext, T>(device_ctx, proj_g, false, *proj_weight,
-                                     true, static_cast<T>(1.0), &out_g,
-                                     static_cast<T>(0.0));
+      blas.MatMul(proj_g, false, *proj_weight, true, static_cast<T>(1.0),
+                  &out_g, static_cast<T>(0.0));
       /* projection weight backward*/
       if (proj_weight_g) {
         Tensor hidden_t = batch_hidden->Slice(bstart, bend);
-        math::matmul<DeviceContext, T>(device_ctx, hidden_t, true, proj_g,
-                                       false, static_cast<T>(1.0),
-                                       proj_weight_g, static_cast<T>(1.0));
+        blas.MatMul(hidden_t, true, proj_g, false, static_cast<T>(1.0),
+                    proj_weight_g, static_cast<T>(1.0));
       }
 
       Tensor gate = batch_gate->Slice(bstart, bend);
@@ -419,24 +414,21 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
         int pre_h_start = static_cast<int>(batch_starts[n - 1]);
         int pre_h_end = pre_h_start + cur_batch_size;
         auto pre_proj_g = batch_proj_g.Slice(pre_h_start, pre_h_end);
-        math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight, true,
-                                       static_cast<T>(1.0), &pre_proj_g,
-                                       static_cast<T>(1.0));
+        blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
+                    &pre_proj_g, static_cast<T>(1.0));
         if (weight_g) {
           /* weight backward*/
           auto pre_proj = batch_proj.Slice(pre_h_start, pre_h_end);
-          math::matmul<DeviceContext, T>(device_ctx, pre_proj, true, gate_g,
-                                         false, static_cast<T>(1.0), weight_g,
-                                         static_cast<T>(1.0));
+          blas.MatMul(pre_proj, true, gate_g, false, static_cast<T>(1.0),
+                      weight_g, static_cast<T>(1.0));
         }
       } else {
         if (h0 && weight_g) {
           ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
                                              &ordered_h0, true);
           if (weight_g) {
-            math::matmul<DeviceContext, T>(device_ctx, *ordered_proj0, true,
-                                           gate_g, false, static_cast<T>(1.0),
-                                           weight_g, static_cast<T>(1.0));
+            blas.MatMul(*ordered_proj0, true, gate_g, false,
+                        static_cast<T>(1.0), weight_g, static_cast<T>(1.0));
           }
         }
         if (h0 && (h0_g || proj_weight_g)) {
@@ -444,9 +436,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
           Tensor proj0_g;
           proj0_g.Resize({in_dims[0], proj_weight->dims()[1]});
           proj0_g.mutable_data<T>(ctx.GetPlace());
-          math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
-                                         true, static_cast<T>(1.0), &proj0_g,
-                                         static_cast<T>(0.0));
+          blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
+                      &proj0_g, static_cast<T>(0.0));
           if (proj_act != math::detail::ActivationType::kIdentity) {
             auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
             auto proj0_g_dev = EigenMatrix<T>::From(proj0_g);
@@ -454,14 +445,12 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
                            proj0_g_dev);
           }
           if (h0_g) {
-            math::matmul<DeviceContext, T>(
-                device_ctx, proj0_g, false, *proj_weight, true,
-                static_cast<T>(1.0), &ordered_h0_g, static_cast<T>(0.0));
+            blas.MatMul(proj0_g, false, *proj_weight, true, static_cast<T>(1.0),
+                        &ordered_h0_g, static_cast<T>(0.0));
           }
           if (proj_weight_g) {
-            math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true,
-                                           proj0_g, false, static_cast<T>(1.0),
-                                           proj_weight_g, static_cast<T>(1.0));
+            blas.MatMul(ordered_h0, true, proj0_g, false, static_cast<T>(1.0),
+                        proj_weight_g, static_cast<T>(1.0));
           }
         }
       }

paddle/fluid/operators/math/blas_impl.cu.h

@@ -61,12 +61,10 @@ struct CUBlas<platform::float16> {
 
 template <>
 template <typename T>
-void Blas<platform::CUDADeviceContext>::GEMM(const CBLAS_TRANSPOSE transA,
-                                             const CBLAS_TRANSPOSE transB,
-                                             const int M, const int N,
-                                             const int K, const T alpha,
-                                             const T *A, const T *B,
-                                             const T beta, T *C) const {
+void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
+                                             CBLAS_TRANSPOSE transB, int M,
+                                             int N, int K, T alpha, const T *A,
+                                             const T *B, T beta, T *C) const {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -83,10 +81,10 @@ void Blas<platform::CUDADeviceContext>::GEMM(const CBLAS_TRANSPOSE transA,
 template <>
 template <>
 inline void Blas<platform::CUDADeviceContext>::GEMM(
-    const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, const int M,
-    const int N, const int K, const platform::float16 alpha,
-    const platform::float16 *A, const platform::float16 *B,
-    const platform::float16 beta, platform::float16 *C) const {
+    CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N, int K,
+    platform::float16 alpha, const platform::float16 *A,
+    const platform::float16 *B, platform::float16 beta,
+    platform::float16 *C) const {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -134,14 +132,14 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
 
 template <>
 template <typename T>
-void Blas<platform::CUDADeviceContext>::GEMM(
-    const bool transA, const bool transB, const int M, const int N, const int K,
-    const T alpha, const T *A, const int lda, const T *B, const int ldb,
-    const T beta, T *C, const int ldc) const {
+void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
+                                             int N, int K, T alpha, const T *A,
+                                             int lda, const T *B, int ldb,
+                                             T beta, T *C, int ldc) const {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
-  cublasOperation_t cuTransA = transA == false ? CUBLAS_OP_N : CUBLAS_OP_T;
-  cublasOperation_t cuTransB = transB == false ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
+  cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
   CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha,
                   B, ldb, A, lda, &beta, C, ldc);
 }

paddle/fluid/operators/math/blas_impl.h

@@ -45,12 +45,10 @@ struct CBlas<platform::float16> {
 
 template <>
 template <typename T>
-void Blas<platform::CPUDeviceContext>::GEMM(const CBLAS_TRANSPOSE transA,
-                                            const CBLAS_TRANSPOSE transB,
-                                            const int M, const int N,
-                                            const int K, const T alpha,
-                                            const T *A, const T *B,
-                                            const T beta, T *C) const {
+void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
+                                            CBLAS_TRANSPOSE transB, int M,
+                                            int N, int K, T alpha, const T *A,
+                                            const T *B, T beta, T *C) const {
   int lda = (transA == CblasNoTrans) ? K : M;
   int ldb = (transB == CblasNoTrans) ? N : K;
   int ldc = N;
@@ -60,15 +58,41 @@ void Blas<platform::CPUDeviceContext>::GEMM(const CBLAS_TRANSPOSE transA,
 
 template <>
 template <typename T>
-void Blas<platform::CPUDeviceContext>::GEMM(
-    const bool transA, const bool transB, const int M, const int N, const int K,
-    const T alpha, const T *A, const int lda, const T *B, const int ldb,
-    const T beta, T *C, const int ldc) const {
+void Blas<platform::CPUDeviceContext>::GEMM(bool transA, bool transB, int M,
+                                            int N, int K, T alpha, const T *A,
+                                            int lda, const T *B, int ldb,
+                                            T beta, T *C, int ldc) const {
   CBlas<T>::GEMM(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
                  transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
                  lda, B, ldb, beta, C, ldc);
 }
 
+template <typename DeviceContext>
+template <typename T>
+void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a, bool trans_a,
+                                 const framework::Tensor &mat_b, bool trans_b,
+                                 T alpha, framework::Tensor *mat_out,
+                                 T beta) const {
+  auto dim_a = mat_a.dims();
+  auto dim_b = mat_b.dims();
+  auto dim_out = mat_out->dims();
+  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
+                 "The input and output of matmul be matrix");
+  PADDLE_ENFORCE(
+      mat_a.place() == mat_b.place() && mat_a.place() == mat_out->place(),
+      "The places of matrices must be same");
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = !trans_a ? dim_a[1] : dim_a[0];
+
+  CBLAS_TRANSPOSE transA = !trans_a ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = !trans_b ? CblasNoTrans : CblasTrans;
+
+  this->GEMM(transA, transB, M, N, K, alpha, mat_a.data<T>(), mat_b.data<T>(),
+             beta, mat_out->data<T>());
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/math_function.cc

@@ -24,73 +24,6 @@ namespace math {
 
 using float16 = paddle::platform::float16;
 
-template <>
-void matmul<platform::CPUDeviceContext, float16>(
-    const platform::CPUDeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, float16 alpha,
-    framework::Tensor* matrix_out, float16 beta) {
-  PADDLE_THROW("float16 matmul not supported on CPU");
-}
-
-template <>
-void matmul<platform::CPUDeviceContext, float>(
-    const platform::CPUDeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, float alpha,
-    framework::Tensor* matrix_out, float beta) {
-  auto dim_a = matrix_a.dims();
-  auto dim_b = matrix_b.dims();
-  auto dim_out = matrix_out->dims();
-  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
-                 "The input and output of matmul be matrix");
-
-  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
-                     platform::is_cpu_place(matrix_b.place()) &&
-                     platform::is_cpu_place(matrix_out->place()),
-                 "Matrix must all be in CPUPlace");
-
-  int M = dim_out[0];
-  int N = dim_out[1];
-  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
-
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-  Blas<platform::CPUDeviceContext>(context).GEMM(
-      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
-      matrix_b.data<float>(), beta, matrix_out->data<float>());
-}
-
-template <>
-void matmul<platform::CPUDeviceContext, double>(
-    const platform::CPUDeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, double alpha,
-    framework::Tensor* matrix_out, double beta) {
-  auto dim_a = matrix_a.dims();
-  auto dim_b = matrix_b.dims();
-  auto dim_out = matrix_out->dims();
-  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
-                 "The input and output of matmul be matrix");
-
-  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
-                     platform::is_cpu_place(matrix_b.place()) &&
-                     platform::is_cpu_place(matrix_out->place()),
-                 "Matrix must all be in CPUPlace");
-
-  int M = dim_out[0];
-  int N = dim_out[1];
-  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
-
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-  Blas<platform::CPUDeviceContext>(context).GEMM(
-      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
-      matrix_b.data<double>(), beta, matrix_out->data<double>());
-}
-
 template <>
 void batched_gemm<platform::CPUDeviceContext, float16>(
     const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,

paddle/fluid/operators/math/math_function.cu

@@ -25,93 +25,6 @@ namespace math {
 
 using float16 = paddle::platform::float16;
 
-template <>
-void matmul<platform::CUDADeviceContext, float16>(
-    const platform::CUDADeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, float16 alpha,
-    framework::Tensor* matrix_out, float16 beta) {
-  auto dim_a = matrix_a.dims();
-  auto dim_b = matrix_b.dims();
-  auto dim_out = matrix_out->dims();
-  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
-                 "The input and output of matmul be matrix");
-
-  PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
-                     platform::is_gpu_place(matrix_b.place()) &&
-                     platform::is_gpu_place(matrix_out->place()),
-                 "Matrix must all be in CUDAPlace");
-
-  int M = dim_out[0];
-  int N = dim_out[1];
-  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
-
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-  Blas<platform::CUDADeviceContext>(context).GEMM(
-      transA, transB, M, N, K, alpha, matrix_a.data<float16>(),
-      matrix_b.data<float16>(), beta, matrix_out->data<float16>());
-}
-
-template <>
-void matmul<platform::CUDADeviceContext, float>(
-    const platform::CUDADeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, float alpha,
-    framework::Tensor* matrix_out, float beta) {
-  auto dim_a = matrix_a.dims();
-  auto dim_b = matrix_b.dims();
-  auto dim_out = matrix_out->dims();
-  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
-                 "The input and output of matmul be matrix");
-
-  PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
-                     platform::is_gpu_place(matrix_b.place()) &&
-                     platform::is_gpu_place(matrix_out->place()),
-                 "Matrix must all be in CUDAPlace");
-
-  int M = dim_out[0];
-  int N = dim_out[1];
-  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
-
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-  Blas<platform::CUDADeviceContext>(context).GEMM(
-      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
-      matrix_b.data<float>(), beta, matrix_out->data<float>());
-}
-
-template <>
-void matmul<platform::CUDADeviceContext, double>(
-    const platform::CUDADeviceContext& context,
-    const framework::Tensor& matrix_a, bool trans_a,
-    const framework::Tensor& matrix_b, bool trans_b, double alpha,
-    framework::Tensor* matrix_out, double beta) {
-  auto dim_a = matrix_a.dims();
-  auto dim_b = matrix_b.dims();
-  auto dim_out = matrix_out->dims();
-  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
-                 "The input and output of matmul be matrix");
-
-  PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
-                     platform::is_gpu_place(matrix_b.place()) &&
-                     platform::is_gpu_place(matrix_out->place()),
-                 "Matrix must all be in CUDAPlace");
-
-  int M = dim_out[0];
-  int N = dim_out[1];
-  int K = (trans_a == false) ? dim_a[1] : dim_a[0];
-
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-  Blas<platform::CUDADeviceContext>(context).GEMM(
-      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
-      matrix_b.data<double>(), beta, matrix_out->data<double>());
-}
-
 template <>
 void batched_gemm<platform::CUDADeviceContext, float16>(
     const platform::CUDADeviceContext& context, const CBLAS_TRANSPOSE transA,

paddle/fluid/operators/math/math_function.h

@@ -64,14 +64,31 @@ class Blas {
   explicit Blas(const DeviceContext& context) : context_(context) {}
 
   template <typename T>
-  void GEMM(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
-            const int M, const int N, const int K, const T alpha, const T* A,
-            const T* B, const T beta, T* C) const;
+  void GEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N, int K,
+            T alpha, const T* A, const T* B, T beta, T* C) const;
 
   template <typename T>
-  void GEMM(const bool transA, const bool transB, const int M, const int N,
-            const int K, const T alpha, const T* A, const int lda, const T* B,
-            const int ldb, const T beta, T* C, const int ldc) const;
+  void GEMM(bool transA, bool transB, int M, int N, int K, T alpha, const T* A,
+            int lda, const T* B, int ldb, T beta, T* C, int ldc) const;
+
+  template <typename T>
+  void MatMul(const framework::Tensor& mat_a, bool trans_a,
+              const framework::Tensor& mat_b, bool trans_b, T alpha,
+              framework::Tensor* mat_out, T beta) const;
+
+  template <typename T>
+  void MatMul(const framework::Tensor& mat_a, bool trans_a,
+              const framework::Tensor& mat_b, bool trans_b,
+              framework::Tensor* mat_out) const {
+    MatMul(mat_a, trans_a, mat_b, trans_b, static_cast<T>(1.0), mat_out,
+           static_cast<T>(0.0));
+  }
+
+  template <typename T>
+  void MatMul(const framework::Tensor& mat_a, const framework::Tensor& mat_b,
+              framework::Tensor* mat_out) const {
+    this->template MatMul<T>(mat_a, false, mat_b, false, mat_out);
+  }
 
  private:
   const DeviceContext& context_;
@@ -86,6 +103,11 @@ class BlasT : private Blas<DeviceContext> {
   void GEMM(ARGS... args) const {
     static_cast<const Blas<DeviceContext>*>(this)->template GEMM<T>(args...);
   }
+
+  template <typename... ARGS>
+  void MatMul(ARGS... args) const {
+    static_cast<const Blas<DeviceContext>*>(this)->template MatMul<T>(args...);
+  }
 };
 
 template <typename DeviceContext, typename T>
@@ -100,12 +122,6 @@ inline BlasT<DeviceContext, T> GetBlas(const DeviceContext& dev_ctx) {
   return BlasT<DeviceContext, T>(dev_ctx);
 }
 
-// matrix multiply with continuous memory
-template <typename DeviceContext, typename T>
-void matmul(const DeviceContext& context, const framework::Tensor& matrix_a,
-            bool trans_a, const framework::Tensor& matrix_b, bool trans_b,
-            T alpha, framework::Tensor* matrix_out, T beta);
-
 // Batched gemm
 template <typename DeviceContext, typename T>
 void batched_gemm(const DeviceContext& context, const CBLAS_TRANSPOSE transA,

paddle/fluid/operators/math/math_function_test.cu

@@ -23,6 +23,13 @@ void fill_fp16_data(paddle::platform::float16* in_ptr, size_t size,
   }
 }
 
+template <typename T>
+inline paddle::operators::math::BlasT<paddle::platform::CUDADeviceContext, T>
+GetBlas(const paddle::platform::CUDADeviceContext& context) {
+  return paddle::operators::math::GetBlas<paddle::platform::CUDADeviceContext,
+                                          T>(context);
+}
+
 TEST(math_function, notrans_mul_trans_fp32) {
   paddle::framework::Tensor input1;
   paddle::framework::Tensor input1_gpu;
@@ -42,9 +49,8 @@ TEST(math_function, notrans_mul_trans_fp32) {
   paddle::framework::TensorCopySync(input1, gpu_place, &input2_gpu);
 
   out_gpu.mutable_data<float>({2, 2}, gpu_place);
-
-  paddle::operators::math::matmul<paddle::platform::CUDADeviceContext, float>(
-      context, input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0);
+  GetBlas<float>(context).MatMul(input1_gpu, false, input2_gpu, true, 1,
+                                 &out_gpu, 0);
 
   paddle::framework::TensorCopySync(out_gpu, cpu_place, &out);
 
@@ -81,10 +87,9 @@ TEST(math_function, notrans_mul_trans_fp16) {
 
   out_gpu.mutable_data<paddle::platform::float16>({2, 2}, gpu_place);
 
-  paddle::operators::math::matmul<paddle::platform::CUDADeviceContext,
-                                  paddle::platform::float16>(
-      context, input1_gpu, false, input2_gpu, true,
-      paddle::platform::float16(1), &out_gpu, paddle::platform::float16(0));
+  GetBlas<paddle::platform::float16>(context).MatMul(
+      input1_gpu, false, input2_gpu, true, paddle::platform::float16(1),
+      &out_gpu, paddle::platform::float16(0));
 
   paddle::framework::TensorCopySync(out_gpu, cpu_place, &out);
 
@@ -116,8 +121,8 @@ TEST(math_function, trans_mul_notrans_fp32) {
 
   out_gpu.mutable_data<float>({3, 3}, gpu_place);
 
-  paddle::operators::math::matmul<paddle::platform::CUDADeviceContext, float>(
-      context, input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0);
+  GetBlas<float>(context).MatMul(input1_gpu, true, input2_gpu, false, 1,
+                                 &out_gpu, 0);
 
   paddle::framework::TensorCopySync(out_gpu, cpu_place, &out);
 
@@ -159,10 +164,9 @@ TEST(math_function, trans_mul_notrans_fp16) {
 
   out_gpu.mutable_data<paddle::platform::float16>({3, 3}, gpu_place);
 
-  paddle::operators::math::matmul<paddle::platform::CUDADeviceContext,
-                                  paddle::platform::float16>(
-      context, input1_gpu, true, input2_gpu, false,
-      paddle::platform::float16(1), &out_gpu, paddle::platform::float16(0));
+  GetBlas<paddle::platform::float16>(context).MatMul(
+      input1_gpu, true, input2_gpu, false, paddle::platform::float16(1),
+      &out_gpu, paddle::platform::float16(0));
 
   paddle::framework::TensorCopySync(out_gpu, cpu_place, &out);
 
@@ -179,13 +183,6 @@ TEST(math_function, trans_mul_notrans_fp16) {
   EXPECT_EQ(static_cast<float>(out_ptr[8]), 29);
 }
 
-template <typename T>
-inline paddle::operators::math::BlasT<paddle::platform::CUDADeviceContext, T>
-GetBlas(const paddle::platform::CUDADeviceContext& context) {
-  return paddle::operators::math::GetBlas<paddle::platform::CUDADeviceContext,
-                                          T>(context);
-}
-
 TEST(math_function, gemm_notrans_cublas_fp32) {
   paddle::framework::Tensor input1;
   paddle::framework::Tensor input2;

paddle/fluid/operators/mul_op.h

@@ -46,9 +46,10 @@ class MulKernel : public framework::OpKernel<T> {
     if (z_dim.size() != 2) {
       z->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
     }
-    math::matmul<DeviceContext, T>(
-        context.template device_context<DeviceContext>(), x_matrix, false,
-        y_matrix, false, static_cast<T>(1), z, static_cast<T>(0));
+
+    auto blas = math::GetBlas<DeviceContext, T>(context);
+
+    blas.MatMul(x_matrix, y_matrix, z);
     if (z_dim.size() != 2) {
       z->Resize(z_dim);
     }
@@ -79,6 +80,7 @@ class MulGradKernel : public framework::OpKernel<T> {
     Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     Tensor* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     if (dx) {
       dx->mutable_data<T>(ctx.GetPlace());
       Tensor dx_matrix = dx->dims().size() > 2
@@ -86,8 +88,7 @@ class MulGradKernel : public framework::OpKernel<T> {
                              : *dx;
 
       // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
-      math::matmul<DeviceContext, T>(dev_ctx, dout_mat, false, y_matrix, true,
-                                     1, &dx_matrix, 0);
+      blas.MatMul(dout_mat, false, y_matrix, true, &dx_matrix);
     }
     if (dy) {
       dy->mutable_data<T>(ctx.GetPlace());
@@ -95,8 +96,7 @@ class MulGradKernel : public framework::OpKernel<T> {
                              ? framework::ReshapeToMatrix(*dy, y_num_col_dims)
                              : *dy;
       // dy = x' * dout. dy K x N, dout : M x N, x : M x K
-      math::matmul<DeviceContext, T>(dev_ctx, x_matrix, true, dout_mat, false,
-                                     1, &dy_matrix, 0);
+      blas.MatMul(x_matrix, true, dout_mat, false, &dy_matrix);
     }
   }
 };

paddle/fluid/operators/sequence_conv_op.h

@@ -58,17 +58,15 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     // Because if padding_trainable is false, padding data should be zeros.
     math::SetConstant<DeviceContext, T> set_zero;
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     set_zero(dev_ctx, &col, static_cast<T>(0));
-
     math::ContextProjectFunctor<DeviceContext, T> seq_project_functor;
 
     seq_project_functor(dev_ctx, *in, *padding_data, padding_trainable,
                         context_start, context_length, context_stride, up_pad,
                         down_pad, &col);
 
-    math::matmul<DeviceContext, T>(dev_ctx, col, false, filter, false,
-                                   static_cast<T>(1.0), out,
-                                   static_cast<T>(0.0));
+    blas.MatMul(col, filter, out);
   }
 };
 
@@ -99,6 +97,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
 
     math::SetConstant<DeviceContext, T> set_zero;
     auto& dev_ctx = context.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     // use col_shape in the im2col calculation
     framework::DDim col_shape = {in->dims()[0],
                                  sequence_width * context_length};
@@ -108,8 +107,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
       col.mutable_data<T>(col_shape, context.GetPlace());
       // Because if padding_trainable is false, padding data should be zeros.
       set_zero(dev_ctx, &col, static_cast<T>(0));
-      math::matmul<DeviceContext, T>(dev_ctx, *out_g, false, *filter, true,
-                                     T(1.0), &col, T(1.0));
+      blas.MatMul(*out_g, false, *filter, true, &col);
     }
     math::ContextProjectFunctor<DeviceContext, T> seq_project_functor;
     math::ContextProjectGradFunctor<DeviceContext, T> seq_project_grad_functor;
@@ -150,8 +148,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
                           context_start, context_length, context_stride, up_pad,
                           down_pad, &col);
 
-      math::matmul<DeviceContext, T>(dev_ctx, col, true, out_grad, false,
-                                     T(1.0), &filter_grad, T(1.0));
+      blas.MatMul(col, true, out_grad, false, &filter_grad);
     }
   }
 };