Optimize int8 5x5 depthwise conv, add aarch64 macros to make compilation no problem

src/operators/kernel/arm/conv_kernel.cpp

@@ -31,12 +31,19 @@ bool ConvKernel<CPU, float>::Init(ConvParam<CPU> *param) {
   bool depth5x5 = conv5x5 && param->Groups() == param->Input()->dims()[1] &&
                   param->Input()->dims()[1] == param->Output()->dims()[1];
   if (param->Filter()->type() == typeid(int8_t)) {
+#ifndef __aarch64__
     if (depth3x3 && param->Strides()[0] < 3 &&
         param->Strides()[0] == param->Strides()[1]) {
       param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE3x3_INT8;
+    } else if (depth5x5 && param->Strides()[0] < 2 &&
+               param->Strides()[0] == param->Strides()[1]) {
+      param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE5x5_INT8;
     } else {
+#endif  // __aarch64__
       param->ExecMode() = ConvParam<CPU>::EXEC_GEMM_INT8;
+#ifndef __aarch64__
     }
+#endif  // __aarch64__
   } else {
     if (depth3x3 && param->Strides()[0] == param->Strides()[1] &&
         param->Strides()[0] == 1 && param->Paddings()[0] == 1 &&
@@ -50,10 +57,10 @@ bool ConvKernel<CPU, float>::Init(ConvParam<CPU> *param) {
                param->Strides()[0] == 2 && param->Paddings()[0] == 1 &&
                param->Paddings()[0] == param->Paddings()[1]) {
       param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE3x3S2P1_FLOAT;
+#ifndef __aarch64__
     } else if (depth5x5 && param->Strides()[0] == param->Strides()[1] &&
                param->Strides()[0] == 1) {
-      param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE5x5S1_FLOAT;
-#ifndef __aarch64__
+      param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE5x5_FLOAT;
     } else if (conv3x3 && param->Strides()[0] == param->Strides()[1] &&
                param->Dilations()[0] == param->Dilations()[1] &&
                param->Strides()[0] == 1 && param->Dilations()[0] == 1 &&
@@ -79,9 +86,14 @@ void ConvKernel<CPU, float>::Compute(const ConvParam<CPU> &param) {
     case ConvParam<CPU>::EXEC_GEMM_INT8:
       GemmConv<int8_t, int32_t>(param);
       break;
+#ifndef __aarch64__
     case ConvParam<CPU>::EXEC_DEPTHWISE3x3_INT8:
       DepthwiseConv3x3<int8_t, int32_t>(param);
       break;
+    case ConvParam<CPU>::EXEC_DEPTHWISE5x5_INT8:
+      DepthwiseConv5x5<int8_t, int32_t>(param);
+      break;
+#endif  // __aarch64__
     case ConvParam<CPU>::EXEC_DEPTHWISE3x3S1P1_FLOAT:
       math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
                                  nullptr, false);
@@ -94,13 +106,14 @@ void ConvKernel<CPU, float>::Compute(const ConvParam<CPU> &param) {
       math::DepthwiseConv3x3s2p0(param.Input(), param.Filter(), param.Output(),
                                  nullptr, false);
       break;
-    case ConvParam<CPU>::EXEC_DEPTHWISE5x5S1_FLOAT:
-      math::DepthwiseConv5x5S1<float, float>(*param.Input(), *param.Filter(),
-                                             param.Paddings(), param.Output());
+#ifndef __aarch64__
+    case ConvParam<CPU>::EXEC_DEPTHWISE5x5_FLOAT:
+      DepthwiseConv5x5<float, float>(param);
       break;
     case ConvParam<CPU>::EXEC_WINOGRAD3X3_FLOAT:
       WinogradConv3x3<8, 3>(param);
       break;
+#endif  // __aarch64__
     case ConvParam<CPU>::EXEC_GEMM_FLOAT:
       GemmConv<float, float>(param);
       break;

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

@@ -161,6 +161,7 @@ inline void WinogradConv3x3(const ConvParam<CPU> &param) {
   }
 }
 
+#ifndef __aarch64__
 template <typename Itype, typename Otype>
 inline void DepthwiseConv3x3(const ConvParam<CPU> &param) {
   const Tensor *input = param.Input();
@@ -181,13 +182,33 @@ inline void DepthwiseConv3x3(const ConvParam<CPU> &param) {
       math::DepthwiseConv3x3S2<Itype, Otype>(in_batch, *filter, paddings,
                                              &out_batch);
     } else {
-      // math::DepthwiseConv3x3<Itype, Otype>(input_pad, *filter,
-      // &out_batch);
-      PADDLE_MOBILE_THROW_EXCEPTION(
-          "Depthwise conv with generic strides has not been implemented.");
+      GemmConv<Itype, Otype>(param);
     }
   }
 }
+#endif  // __aarch64__
+
+template <typename Itype, typename Otype>
+inline void DepthwiseConv5x5(const ConvParam<CPU> &param) {
+  const Tensor *input = param.Input();
+  const Tensor *filter = param.Filter();
+  const std::vector<int> &paddings = param.Paddings();
+  const std::vector<int> &strides = param.Strides();
+  const int batch_size = input->dims()[0];
+  Tensor *output = param.Output();
+  output->mutable_data<Otype>();
+
+  if (strides[0] == 1) {
+    for (int i = 0; i < batch_size; i++) {
+      Tensor in_batch = input->Slice(i, i + 1);
+      Tensor out_batch = output->Slice(i, i + 1);
+      math::DepthwiseConv5x5S1<Itype, Otype>(in_batch, *filter, paddings,
+                                             &out_batch);
+    }
+  } else {
+    GemmConv<Itype, Otype>(param);
+  }
+}
 
 }  // namespace operators
 }  // namespace paddle_mobile

src/operators/math/depthwise_conv5x5.cpp

@@ -16,7 +16,6 @@ limitations under the License. */
 
 #include "operators/math/depthwise_conv5x5.h"
 #include <arm_neon.h>
-#include <iostream>
 
 namespace paddle_mobile {
 namespace operators {

src/operators/math/gemm.cpp

@@ -3150,9 +3150,11 @@ void Gemm::SgemmWithPRelu(int m, int n, int k, const float *A, int lda,
 void Gemm::Sgemm_omp(int m, int n, int k, float alpha, const float *A, int lda,
                      const float *B, int ldb, float beta, float *C, int ldc,
                      bool relu, float *bias) {
+#ifndef __aarch64__
   if (m == 1 && bias == nullptr) {
     return VectorKernel(m, n, k, alpha, A, lda, B, ldb, beta, C, ldc, relu);
   }
+#endif  // __aarch64__
 #ifdef _OPENMP
   int max_threads = omp_get_max_threads();
 #else

src/operators/math/pooling2x2.cpp

@@ -19,6 +19,8 @@ limitations under the License. */
 #include <arm_neon.h>
 #include "operators/math/pooling.h"
 
+// TODO(hjchen2): Optimize Pooling2x2NormalRow and use inline assembly
+
 namespace paddle_mobile {
 namespace operators {
 namespace math {
@@ -60,7 +62,6 @@ struct Pooling2x2NormalRowLoadInput<P, 2> {
   }
 };
 
-// TODO(hjchen2): To optimize Pooling2x2NormalRow
 template <PoolingType P, int Stride>
 inline void Pooling2x2NormalRow(const float *input, const int h_output,
                                 const int input_h, const int input_w,

src/operators/op_param.h

@@ -424,10 +424,10 @@ class ConvParam : public OpParam {
     EXEC_DEPTHWISE3x3_FLOAT,
     EXEC_WINOGRAD3X3_FLOAT,
     EXEC_WINOGRAD5X5_FLOAT,
-    EXEC_DEPTHWISE5x5S1_FLOAT,
-    EXEC_DEPTHWISE5x5S2_FLOAT,
+    EXEC_DEPTHWISE5x5_FLOAT,
     EXEC_GEMM_INT8,
     EXEC_DEPTHWISE3x3_INT8,
+    EXEC_DEPTHWISE5x5_INT8,
   };
 
   ExecMode &ExecMode() const { return exec_mode_; }

test/operators/test_conv_op.cpp

@@ -165,14 +165,12 @@ int TestConvOp(int in_channels, int in_height, int in_width, int out_channels,
   auto filter = filter_var->template GetMutable<framework::LoDTensor>();
   SetupTensor<Itype>(filter, filter_shape, -20, 20);
 
-  for (int i = 0; i < input->numel(); ++i) {
-    DLOG << "input[" << i
-         << "] = " << static_cast<int>(input->data<int8_t>()[i]);
-  }
-  for (int i = 0; i < filter->numel(); ++i) {
-    DLOG << "filter[" << i
-         << "] = " << static_cast<int>(filter->data<int8_t>()[i]);
-  }
+  //  for (int i = 0; i < input->numel(); ++i) {
+  //    DLOG << "input[" << i << "] = " << float(input->data<Itype>()[i]);
+  //  }
+  //  for (int i = 0; i < filter->numel(); ++i) {
+  //    DLOG << "filter[" << i << "] = " << float(filter->data<Itype>()[i]);
+  //  }
 
   auto output_var = scope.get()->Var("output");
   framework::AttributeMap attrs;
@@ -198,18 +196,12 @@ int TestConvOp(int in_channels, int in_height, int in_width, int out_channels,
   //                     (ts_end.tv_nsec - ts_begin.tv_nsec) / 1e6;
   //  LOG(kLOG_INFO) << "elapsed: " << elapsed / 10.0 << " ms";
 
-  int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
-  int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
-  int output_h = (input_h + 2 * pad_h - kernel_extent_h) / stride_h + 1;
-  int output_w = (input_w + 2 * pad_w - kernel_extent_w) / stride_w + 1;
-  auto output_shape = framework::make_ddim(
-      std::vector<int>({batch_size, output_c, output_h, output_w}));
+  // compare results
+  auto *output = output_var->template Get<framework::LoDTensor>();
   framework::Tensor output_cmp;
-  output_cmp.mutable_data<Otype>(output_shape);
+  output_cmp.mutable_data<Otype>(output->dims());
   conv2d<Itype, Otype>(input, filter, attrs, &output_cmp);
 
-  // compare results
-  auto output = output_var->template Get<framework::LoDTensor>();
   const Otype *output_data = output->data<Otype>();
   Otype *output_cmp_data = output_cmp.data<Otype>();
   for (int i = 0; i < output->numel(); ++i) {
@@ -285,96 +277,39 @@ int main(int argc, char *argv[]) {
   paddle_mobile::TestConvOp<int8_t, int32_t, 3, 5, 2>(
       in_channels, in_height, in_width, out_channels, groups);
 
-  //  // kernel = 7, pad = 0, stride = 2
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=2";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 2>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 1, stride = 2
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=2";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 2>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 3, stride = 2
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=2";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 2>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 1, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 3, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 5, stride = 3
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=5, stride=3";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 5, 3>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 7, pad = 3, stride = 4
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=4";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 4>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 3, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=0, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 3, 0, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 3, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=0, stride=1";
-  //  paddle_mobile::TestConvOp<float, float, 3, 0, 1>(in_channels, in_height,
-  //                                                   in_width, out_channels,
-  //                                                   groups);
-  //  // kernel = 3, pad = 1, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=1, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 3, 1, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 3, pad = 1, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=1, stride=1";
-  //  paddle_mobile::TestConvOp<float, float, 3, 1, 1>(in_channels, in_height,
-  //                                                   in_width, out_channels,
-  //                                                   groups);
-  //  // kernel = 5, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=0, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 0, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 5, pad = 0, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=0, stride=1";
-  //  paddle_mobile::TestConvOp<float, float, 5, 0, 1>(in_channels, in_height,
-  //                                                   in_width, out_channels,
-  //                                                   groups);
-  //  // kernel = 5, pad = 2, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=2, stride=1";
-  //  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 2, 1>(in_channels,
-  //  in_height,
-  //                                                      in_width,
-  //                                                      out_channels, groups);
-  //  // kernel = 5, pad = 2, stride = 1
-  //  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=2, stride=1";
-  //  paddle_mobile::TestConvOp<float, float, 5, 2, 1>(in_channels, in_height,
-  //                                                   in_width, out_channels,
-  //                                                   groups);
+  // kernel = 5, pad = 0, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=0, stride=1";
+  paddle_mobile::TestConvOp<float, float, 5, 0, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 1, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=1, stride=1";
+  paddle_mobile::TestConvOp<float, float, 5, 1, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 2, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=2, stride=1";
+  paddle_mobile::TestConvOp<float, float, 5, 2, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 5, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=5, stride=1";
+  paddle_mobile::TestConvOp<float, float, 5, 5, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+
+  // kernel = 5, pad = 0, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=0, stride=1";
+  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 0, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 1, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=1, stride=1";
+  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 1, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 2, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=2, stride=1";
+  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 2, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+  // kernel = 5, pad = 5, stride = 1
+  LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=5, stride=1";
+  paddle_mobile::TestConvOp<int8_t, int32_t, 5, 5, 1>(
+      in_channels, in_height, in_width, out_channels, groups);
+
+  return 0;
 }