add arm kernel and unit test for relu op

paddle/fluid/lite/arm/math/CMakeLists.txt

@@ -32,6 +32,7 @@ cc_library(math_arm SRCS
     conv_winograd_3x3.cc
     conv_winograd.cc
     split.cc
+    activation.cc
     DEPS ${lite_kernel_deps} eigen3 framework_proto_lite)
   # TODO(TJ): fix me do not deps proto
  

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+namespace paddle {
+namespace lite {
+namespace arm {
+namespace math {
+
+template <typename T>
+void act_relu(const T* din, T* dout, int size, int threads);
+
+template <typename T>
+void act_relu_neg(const T* din, T* dout, int size, const float negative_slope,
+                  int threads);
+
+template <typename T>
+void act_clipped_relu(const T* din, T* dout, int size, const float coef,
+                      int threads);
+
+template <typename T>
+void act_prelu(const T* din, T* dout, int outer_size, int channel_size,
+               int inner_size, bool channel_shared, float* channel_slope,
+               int threads);
+
+template <typename T>
+void act_sigmoid(const T* din, T* dout, int size, int threads);
+
+template <typename T>
+void act_tanh(const T* din, T* dout, int size, int threads);
+
+template <typename T>
+void act_swish(const T* din, T* dout, int size, const float coef, int threads);
+
+}  // namespace math
+}  // namespace arm
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/arm/CMakeLists.txt

@@ -5,7 +5,7 @@ endif()
 message(STATUS "compile with lite ARM kernels")
 
 cc_library(fc_compute_arm SRCS fc_compute.cc DEPS ${lite_kernel_deps} math_arm)
-cc_library(relu_compute_arm SRCS relu_compute.cc DEPS ${lite_kernel_deps})
+cc_library(activation_compute_arm SRCS activation_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(mul_compute_arm SRCS mul_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(scale_compute_arm SRCS scale_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(softmax_compute_arm SRCS softmax_compute.cc DEPS ${lite_kernel_deps} math_arm)
@@ -16,6 +16,7 @@ cc_library(pool_compute_arm SRCS pool_compute.cc DEPS ${lite_kernel_deps} math_a
 cc_library(split_compute_arm SRCS split_compute.cc DEPS ${lite_kernel_deps} math_arm)
 
 lite_cc_test(test_fc_compute_arm SRCS fc_compute_test.cc DEPS fc_compute_arm math_arm)
+lite_cc_test(test_activation_compute_arm SRCS activation_compute_test.cc DEPS activation_compute_arm)
 lite_cc_test(test_scale_compute_arm SRCS scale_compute_test.cc DEPS scale_compute_arm)
 lite_cc_test(test_softmax_compute_arm SRCS softmax_compute_test.cc DEPS softmax_compute_arm)
 lite_cc_test(test_conv_compute_arm SRCS conv_compute_test.cc DEPS conv_compute_arm)
@@ -27,7 +28,7 @@ lite_cc_test(test_split_compute_arm SRCS split_compute_test.cc DEPS split_comput
 
 set(arm_kernels
     fc_compute_arm
-    relu_compute_arm
+    activation_compute_arm
     mul_compute_arm
     scale_compute_arm
     softmax_compute_arm

paddle/fluid/lite/kernels/arm/relu_compute.h → paddle/fluid/lite/kernels/arm/activation_compute.cc

@@ -12,31 +12,23 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#pragma once
-#include <algorithm>
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/kernels/arm/activation_compute.h"
+#include "paddle/fluid/lite/arm/math/funcs.h"
 
 namespace paddle {
 namespace lite {
 namespace kernels {
 namespace arm {
 
-class ReluCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
- public:
-  void Run() override {
-    auto& param = Param<operators::ReluParam>();
-    auto n = param.input->dims().production();
-    const float* input = param.input->data<float>();
-    float* output = param.output->mutable_data<float>();
-    for (int i = 0; i < n; i++) {
-      output[i] = std::max(0.f, input[i]);
-    }
-  }
-
-  TargetType target() const override { return TARGET(kARM); }
-  PrecisionType precision() const override { return PRECISION(kFloat); }
-};
+void ReluCompute::Run() {
+  auto& param = this->Param<param_t>();
+  auto& ctx = this->ctx_->template As<ARMContext>();
+  auto x_dims = param.X->dims();
+  auto x_data = param.X->data<float>();
+  auto output_data = param.Out->mutable_data<float>();
+  lite::arm::math::act_relu<float>(x_data, output_data, x_dims.production(),
+                                   ctx.threads());
+}
 
 }  // namespace arm
 }  // namespace kernels

paddle/fluid/lite/kernels/arm/relu_compute.cc → paddle/fluid/lite/kernels/arm/activation_compute.h

@@ -12,4 +12,26 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/lite/kernels/arm/relu_compute.h"
+#pragma once
+#include <algorithm>
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+class ReluCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ActivationParam;
+
+  void Run() override;
+
+  virtual ~ReluCompute() = default;
+};
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/kernels/arm/activation_compute.h"
+#include <gtest/gtest.h>
+#include <memory>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+template <typename dtype>
+void activation_compute_ref(const operators::ActivationParam& param) {
+  auto x_data = param.X->data<dtype>();
+  auto output_data = param.Out->mutable_data<dtype>();
+  DDim x_dims = param.X->dims();
+  DDim output_dims = param.Out->dims();
+  ASSERT_EQ(x_dims.data(), output_dims.data());
+  for (int i = 0; i < output_dims.production(); i++) {
+    output_data[i] = std::max(0.f, x_data[i]);
+  }
+}
+
+TEST(activation_arm, retrive_op) {
+  auto activation =
+      KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>("relu");
+  ASSERT_FALSE(activation.empty());
+  ASSERT_TRUE(activation.front());
+}
+
+TEST(activation_arm, init) {
+  ReluCompute activation;
+  ASSERT_EQ(activation.precision(), PRECISION(kFloat));
+  ASSERT_EQ(activation.target(), TARGET(kARM));
+}
+
+TEST(activation_arm, compute) {
+  DeviceInfo::Init();
+  for (auto n : {1, 2}) {
+    for (auto c : {6, 32 /*, 128*/}) {
+      for (auto h : {9, 18 /*, 56 , 112, 224, 512*/}) {
+        for (auto w : {9, 18 /*, 56, 112, 224, 512*/}) {
+          Tensor x;
+          Tensor output;
+          Tensor output_ref;
+          // set the dims of input, output, ref output tensors
+          x.Resize({n, c, h, w});
+          output.Resize({n, c, h, w});
+          output_ref.Resize({n, c, h, w});
+          // initialize the data of input tensors
+          auto* x_data = x.mutable_data<float>();
+          auto* output_data = output.mutable_data<float>();
+          for (int i = 0; i < x.dims().production(); i++) {
+            float sign = i % 3 == 0 ? -1.0f : 1.0f;
+            x_data[i] = sign * static_cast<float>(i % 128) * 0.013f;
+          }
+          // prepare kernel params and run
+          ReluCompute activation;
+          std::unique_ptr<KernelContext> ctx(new KernelContext);
+          ctx->As<ARMContext>();
+          activation.SetContext(std::move(ctx));
+          operators::ActivationParam param;
+          param.X = &x;
+          param.Out = &output;
+          activation.SetParam(param);
+          activation.Launch();
+          // invoking ref implementation and compare results
+          param.Out = &output_ref;
+          activation_compute_ref<float>(param);
+          auto* output_ref_data = output_ref.mutable_data<float>();
+          for (int i = 0; i < output.dims().production(); i++) {
+            EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);