gemm bn relu v1

src/operators/fusion_conv_add.h

@@ -68,11 +68,11 @@ class FusionConvAddOp : public framework::OperatorWithKernel<
 
 #ifdef PADDLE_MOBILE_CPU
 
-#ifndef CONV_ADD_REGISTER
-static framework::FusionOpRegistrar convadd_registrar(
-    new FusionConvAddMatcher());
-#define CONV_ADD_REGISTER
-#endif
+//#ifndef CONV_ADD_REGISTER
+//static framework::FusionOpRegistrar convadd_registrar(
+//    new FusionConvAddMatcher());
+//#define CONV_ADD_REGISTER
+//#endif
 
 #endif
 

src/operators/fusion_conv_add_bn_relu_op.h

@@ -79,11 +79,11 @@ class FusionConvAddBNReluOp
 
 #ifdef PADDLE_MOBILE_CPU
 
-//#ifndef FUSION_CONV_ADD_BN_RELU_REGISTER
-// static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
-//    new FusionConvAddBNReluMatcher());
-//#define FUSION_CONV_ADD_BN_RELU_REGISTER
-//#endif
+#ifndef FUSION_CONV_ADD_BN_RELU_REGISTER
+ static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
+    new FusionConvAddBNReluMatcher());
+#define FUSION_CONV_ADD_BN_RELU_REGISTER
+#endif
 
 #endif
 

src/operators/kernel/central-arm-func/conv_add_arm_func.h

@@ -125,10 +125,10 @@ void ConvAddCompute(const FusionConvAddParam &param) {
              param.Input()->dims()[1] == param.Output()->dims()[1] &&
              param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
              param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
-    //    math::DepthwiseConv3x3(param.Input(), param.Strides(),
-    //    param.Paddings(),
-    //                           param.Filter(), param.Bias(), param.Output(),
-    //                           false);
+//        math::DepthwiseConv3x3(param.Input(), param.Strides(),
+//        param.Paddings(),
+//                               param.Filter(), param.Bias(), param.Output(),
+//                               false);
 
     math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(), param.Output(),
                                  *param.Bias(), true);

src/operators/kernel/central-arm-func/conv_add_bn_relu_func.h

@@ -106,22 +106,25 @@ void ConvAddBNReluBasic(const FusionConvAddBNReluParam &param) {
       // 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<float>(filter_slice, false, col_matrix, false,
+//      math::matmul<float>(filter_slice, false, col_matrix, false,
+//                          static_cast<float>(1), &out_slice,
+//                          static_cast<float>(0));
+        math::matmulWithBn<float>(filter_slice, false, col_matrix, false,
                           static_cast<float>(1), &out_slice,
-                          static_cast<float>(0));
+                          static_cast<float>(0),true, &new_scale,&new_bias);
     }
   }
   /// todo : use neon in special case instead of 2for(300ms)
-  auto output_ptr = output->data<float>();
-  for (int c = 0; c < output_matrix_shape[0]; c++) {
-    int start = c * output_matrix_shape[1];
-    for (int j = 0; j < output_matrix_shape[1]; j++) {
-      output_ptr[start + j] =
-          output_ptr[start + j] * new_scale_ptr[c] + new_bias_ptr[c];
-      output_ptr[start + j] =
-          output_ptr[start + j] < 0 ? 0 : output_ptr[start + j];
-    }
-  }
+//  auto output_ptr = output->data<float>();
+//  for (int c = 0; c < output_matrix_shape[0]; c++) {
+//    int start = c * output_matrix_shape[1];
+//    for (int j = 0; j < output_matrix_shape[1]; j++) {
+//      output_ptr[start + j] =
+//          output_ptr[start + j] * new_scale_ptr[c] + new_bias_ptr[c];
+//      output_ptr[start + j] =
+//          output_ptr[start + j] < 0 ? 0 : output_ptr[start + j];
+//    }
+//  }
 }
 template <typename P>
 void ConvAddBNReluCompute(const FusionConvAddBNReluParam &param) {

src/operators/math/depthwise_conv_3x3.cpp

@@ -1036,8 +1036,7 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
 
   float32x4_t vbias = vdupq_n_f32(0.0);
 
-  float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1],
-      input_buff_top[w_times + 1];
+  float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1];
   float32x4_t elewise_res0, elewise_res1, elewise_res2, res3;
   int out2in_mid;
   float32x4_t zero = vdupq_n_f32(0.0);

src/operators/math/math_function.h

@@ -30,7 +30,7 @@ void matmul(const framework::Tensor &matrix_a, bool trans_a,
 template <typename T>
 void matmulWithBn(const framework::Tensor &matrix_a, bool trans_a,
                   const framework::Tensor &matrix_b, bool trans_b, T alpha,
-                  framework::Tensor *matrix_out, T beta, bool relu,
+                  framework::Tensor *matrix_out, T beta, bool relu = true,
                   framework::Tensor *new_scale, framework::Tensor *new_bias);
 }  // namespace math
 }  // namespace operators

test/common/test_gemm.cpp

@@ -52,8 +52,8 @@ int main() {
   }
 
   auto time1 = time();
-  paddle_mobile::operators::math::sgemm(m, n, k, 0.9, a, lda, b, ldb, 0.3, c,
-                                        ldc);
+//  paddle_mobile::operators::math::Sgemm(m, n, k, 0.9, a, lda, b, ldb, 0.3, c,
+//                                        ldc);
   auto time2 = time();
   DLOG << "gemm cost :" << time_diff(time1, time2) << "ms\n";
   for (int i = 0; i < m * n; ++i) {