Merge branch 'incubate/lite' of http://10.87.145.36/inference/paddlelite into hongming/arm-fix

paddle/fluid/lite/arm/math/elementwise.cc

@@ -41,15 +41,15 @@ void elementwise_add<float>(const float* dinx, const float* diny, float* dout,
     float32x4_t diny2 = vld1q_f32(diny_ptr + 8);
     float32x4_t diny3 = vld1q_f32(diny_ptr + 12);
 
-    float32x4_t vsum0 = vaddq_f32(dinx0, diny0);
-    float32x4_t vsum1 = vaddq_f32(dinx1, diny1);
-    float32x4_t vsum2 = vaddq_f32(dinx2, diny2);
-    float32x4_t vsum3 = vaddq_f32(dinx3, diny3);
+    dinx0 = vaddq_f32(dinx0, diny0);
+    dinx1 = vaddq_f32(dinx1, diny1);
+    dinx2 = vaddq_f32(dinx2, diny2);
+    dinx3 = vaddq_f32(dinx3, diny3);
 
-    vst1q_f32(dout_ptr, vsum0);
-    vst1q_f32(dout_ptr + 4, vsum1);
-    vst1q_f32(dout_ptr + 8, vsum2);
-    vst1q_f32(dout_ptr + 12, vsum3);
+    vst1q_f32(dout_ptr, dinx0);
+    vst1q_f32(dout_ptr + 4, dinx1);
+    vst1q_f32(dout_ptr + 8, dinx2);
+    vst1q_f32(dout_ptr + 12, dinx3);
   }
   if (remain > 0) {
     const float* dinx_ptr = dinx + (cnt << 4);
@@ -64,6 +64,69 @@ void elementwise_add<float>(const float* dinx, const float* diny, float* dout,
   }
 }
 
+template <>
+void elementwise_add_axis<float>(const float* dinx, const float* diny,
+                                 float* dout, int batch, int channels,
+                                 int num) {
+#pragma omp parallel for collapse(2)
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const float* din_ptr = dinx + offset;
+      const float diny_data = diny[j];
+      float* dout_ptr = dout + offset;
+
+      int cnt = num >> 4;
+      int remain = num % 16;
+      float32x4_t rb = vdupq_n_f32(diny_data);
+      for (int k = 0; k < cnt; ++k) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        float32x4_t din1 = vld1q_f32(din_ptr + 4);
+        float32x4_t din2 = vld1q_f32(din_ptr + 8);
+        float32x4_t din3 = vld1q_f32(din_ptr + 12);
+
+        din0 = vaddq_f32(din0, rb);
+        din1 = vaddq_f32(din1, rb);
+        din2 = vaddq_f32(din2, rb);
+        din3 = vaddq_f32(din3, rb);
+
+        vst1q_f32(dout_ptr, din0);
+        vst1q_f32(dout_ptr + 4, din1);
+        vst1q_f32(dout_ptr + 8, din2);
+        vst1q_f32(dout_ptr + 12, din3);
+        din_ptr += 16;
+        dout_ptr += 16;
+      }
+      if (remain >= 8) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        float32x4_t din1 = vld1q_f32(din_ptr + 4);
+        din0 = vaddq_f32(din0, rb);
+        din1 = vaddq_f32(din1, rb);
+        vst1q_f32(dout_ptr, din0);
+        vst1q_f32(dout_ptr + 4, din1);
+        din_ptr += 8;
+        dout_ptr += 8;
+        remain -= 8;
+      }
+      if (remain >= 4) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        din0 = vaddq_f32(din0, rb);
+        vst1q_f32(dout_ptr, din0);
+        din_ptr += 4;
+        dout_ptr += 4;
+        remain -= 4;
+      }
+      if (remain > 0) {
+        for (int p = 0; p < remain; p++) {
+          *dout_ptr = *din_ptr + diny_data;
+          dout_ptr++;
+          din_ptr++;
+        }
+      }
+    }
+  }
+}
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

paddle/fluid/lite/arm/math/elementwise.h

@@ -22,6 +22,10 @@ namespace math {
 template <typename T>
 void elementwise_add(const T* dinx, const T* diny, T* dout, int num);
 
+template <typename T>
+void elementwise_add_axis(const T* dinx, const T* diny, T* dout, int batch,
+                          int channels, int num);
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

paddle/fluid/lite/kernels/arm/conv_compute.cc

@@ -100,7 +100,7 @@ void ConvCompute::Run() {
 REGISTER_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::ConvCompute, def)
     .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
+    //  .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
@@ -108,7 +108,7 @@ REGISTER_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW,
 REGISTER_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::ConvCompute, def)
     .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
+    //    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();

paddle/fluid/lite/kernels/arm/elementwise_add_compute.cc

@@ -25,8 +25,31 @@ void ElementwiseAddCompute::Run() {
   const float* x_data = param.X->data<float>();
   const float* y_data = param.Y->data<float>();
   float* out_data = param.Out->mutable_data<float>();
-  int n = param.X->dims().production();
-  // lite::arm::math::elementwise_add(x_data, y_data, out_data, n);
+  int axis = param.axis;
+  auto x_dims = param.X->dims();
+  auto y_dims = param.Y->dims();
+  if (axis < 0) {
+    axis = x_dims.size() - y_dims.size();
+  }
+  if (x_dims.size() == y_dims.size()) {
+    lite::arm::math::elementwise_add(x_data, y_data, out_data,
+                                     x_dims.production());
+  } else {
+    int batch = 1;
+    int channels = 1;
+    int num = 1;
+    for (int i = 0; i < axis; ++i) {
+      batch *= x_dims[i];
+    }
+    for (int i = 0; i < y_dims.size(); ++i) {
+      channels *= y_dims[i];
+    }
+    for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
+      num *= x_dims[i];
+    }
+    lite::arm::math::elementwise_add_axis(x_data, y_data, out_data, batch,
+                                          channels, num);
+  }
 }
 
 }  // namespace arm

paddle/fluid/lite/kernels/arm/elementwise_add_compute_test.cc

@@ -41,40 +41,97 @@ void elementwise_add_compute_ref(const operators::ElementwiseParam& param) {
   const dtype* x_data = param.X->data<const dtype>();
   const dtype* y_data = param.Y->data<const dtype>();
   dtype* out_data = param.Out->mutable_data<dtype>();
-  DDim dim = param.X->dims();
-  ASSERT_EQ(dim.data(), param.Out->dims().data());
-  for (int i = 0; i < dim.production(); i++) {
-    out_data[i] = x_data[i] + y_data[i];
+  auto x_dims = param.X->dims();
+  auto y_dims = param.Y->dims();
+  int axis = param.axis;
+  if (axis < 0) {
+    axis = x_dims.size() - y_dims.size();
+  }
+  int batch = 1;
+  int channels = 1;
+  int num = 1;
+  for (int i = 0; i < axis; ++i) {
+    batch *= x_dims[i];
+  }
+  for (int i = 0; i < y_dims.size(); ++i) {
+    channels *= y_dims[i];
+  }
+  for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
+    num *= x_dims[i];
+  }
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const dtype* din_ptr = x_data + offset;
+      const dtype diny_data = y_data[j];
+      dtype* dout_ptr = out_data + offset;
+      for (int k = 0; k < num; ++k) {
+        *dout_ptr = *din_ptr + diny_data;
+        dout_ptr++;
+        din_ptr++;
+      }
+    }
   }
 }
 
 TEST(elementwise_add, compute) {
   ElementwiseAddCompute elementwise_add;
   operators::ElementwiseParam param;
+  lite::Tensor x, y, output, output_ref;
 
-  lite::Tensor x, y, out, out_ref;
-  x.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  y.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  out.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  out_ref.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  auto* x_data = x.mutable_data<float>();
-  auto* y_data = y.mutable_data<float>();
-  auto* out_data = out.mutable_data<float>();
-  auto* out_ref_data = out_ref.mutable_data<float>();
-  for (int i = 0; i < x.dims().production(); i++) {
-    x_data[i] = y_data[i] = i;
-  }
+  for (auto n : {1, 3, 4, 11}) {
+    for (auto c : {1, 3, 4, 11}) {
+      for (auto h : {1, 3, 4, 11}) {
+        for (auto w : {1, 3, 4, 11}) {
+          for (auto axis : {-1, 0, 1, 2, 3}) {
+            for (auto yd :
+                 {std::vector<int64_t>({n}), std::vector<int64_t>({c}),
+                  std::vector<int64_t>({h}), std::vector<int64_t>({w}),
+                  std::vector<int64_t>({n, c}), std::vector<int64_t>({c, h}),
+                  std::vector<int64_t>({h, w}), std::vector<int64_t>({n, c, h}),
+                  std::vector<int64_t>({c, h, w}),
+                  std::vector<int64_t>({n, c, h, w})}) {
+              auto x_dim = DDim(std::vector<int64_t>({n, c, h, w}));
+              auto y_dim = DDim(yd);
+              int axis_t = axis < 0 ? x_dim.size() - y_dim.size() : axis;
 
-  param.X = &x;
-  param.Y = &y;
-  param.Out = &out;
-  elementwise_add.SetParam(param);
-  elementwise_add.Run();
+              if (axis_t + y_dim.size() > 4) continue;
+              bool flag = false;
+              for (int i = 0; i < y_dim.size(); i++) {
+                if (x_dim[i + axis_t] != y_dim[i]) flag = true;
+              }
+              if (flag) continue;
 
-  param.Out = &out_ref;
-  elementwise_add_compute_ref<float>(param);
-  for (int i = 0; i < out.dims().production(); i++) {
-    EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-5);
+              x.Resize(x_dim);
+              y.Resize(y_dim);
+              output.Resize(x_dim);
+              output_ref.Resize(x_dim);
+              auto* x_data = x.mutable_data<float>();
+              auto* y_data = y.mutable_data<float>();
+              auto* output_data = output.mutable_data<float>();
+              auto* output_ref_data = output_ref.mutable_data<float>();
+              for (int i = 0; i < x_dim.production(); i++) {
+                x_data[i] = i;
+              }
+              for (int i = 0; i < y_dim.production(); i++) {
+                y_data[i] = i;
+              }
+              param.X = &x;
+              param.Y = &y;
+              param.axis = axis;
+              param.Out = &output;
+              elementwise_add.SetParam(param);
+              elementwise_add.Run();
+              param.Out = &output_ref;
+              elementwise_add_compute_ref<float>(param);
+              for (int i = 0; i < output.dims().production(); i++) {
+                EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
+              }
+            }
+          }
+        }
+      }
+    }
   }
 }