!4637 [MS][LITE][Develop] add prelu to opencl

Merge pull request !4637 from liuzhongkai/prelu

mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.cc

@@ -0,0 +1,130 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 <set>
+#include <vector>
+
+#include "src/kernel_registry.h"
+#include "include/errorcode.h"
+#include "src/runtime/kernel/opencl/kernel/prelu.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "src/runtime/kernel/opencl/cl/fp32/activation.cl.inc"
+#include "src/runtime/kernel/arm/nnacl/prelu_parameter.h"
+
+using mindspore::kernel::KERNEL_ARCH::kGPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_Prelu;
+
+namespace mindspore::kernel {
+
+int PReluOpenCLKernel::Init() {
+  if (in_tensors_[0]->shape().size() != 4) {
+    MS_LOG(ERROR) << "PRelu only support dim=4, but your dim=" << in_tensors_[0]->shape().size();
+    return RET_ERROR;
+  }
+  std::set<std::string> build_options;
+  std::string source = activation_source_fp32;
+  std::string program_name = "PRelu";
+  std::string kernel_name = "ReluScalar";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->LoadSource(program_name, source);
+  ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
+  ori_format_ = out_tensors_[0]->GetFormat();
+  out_tensors_[0]->SetFormat(schema::Format_NHWC4);
+  MS_LOG(DEBUG) << program_name << " init Done!";
+  return RET_OK;
+}
+
+int PReluOpenCLKernel::Run() {
+  MS_LOG(DEBUG) << op_parameter_->name_ << " Running!";
+  int N = in_tensors_[0]->shape()[0];
+  int H = in_tensors_[0]->shape()[1];
+  int W = in_tensors_[0]->shape()[2];
+  int C = in_tensors_[0]->shape()[3];
+  cl_int4 input_shape = {N, H, W, C};
+  if (in_tensors_[1]->ElementsNum() < 1) {
+    MS_LOG(ERROR) << "PRelu weight size must be greater than 1! But your weight size is "
+                  << in_tensors_[1]->ElementsNum();
+    return RET_ERROR;
+  }
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  int arg_idx = 0;
+  ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
+  ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
+  ocl_runtime->SetKernelArg(kernel_, arg_idx++, input_shape);
+  ocl_runtime->SetKernelArg(kernel_, arg_idx++, reinterpret_cast<float *>(in_tensors_[1]->Data())[0]);
+
+  std::vector<size_t> local = {1, 1};
+  std::vector<size_t> global = {static_cast<size_t>(H), static_cast<size_t>(W)};
+  auto ret = ocl_runtime->RunKernel(kernel_, global, local, nullptr);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Run kernel " << op_parameter_->name_ << " error.";
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+int PReluOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
+  int H = in_tensors_[0]->shape()[1];
+  int W = in_tensors_[0]->shape()[2];
+  int C = in_tensors_[0]->shape()[3];
+
+#ifdef ENABLE_FP16
+  size_t img_dtype = CL_HALF_FLOAT;
+#else
+  size_t img_dtype = CL_FLOAT;
+#endif
+
+  img_size->clear();
+  img_size->push_back(W * UP_DIV(C, C4NUM));
+  img_size->push_back(H);
+  img_size->push_back(img_dtype);
+  return RET_OK;
+}
+
+kernel::LiteKernel *OpenCLPReluKernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                             const std::vector<lite::tensor::Tensor *> &outputs,
+                                             OpParameter *opParameter, const lite::Context *ctx,
+                                             const kernel::KernelKey &desc, const lite::PrimitiveC *primitive) {
+  if (inputs.size() == 0) {
+    MS_LOG(ERROR) << "Input data size must be greater than 0, but your size is " << inputs.size();
+    return nullptr;
+  }
+  if (inputs[0]->shape()[0] > 1) {
+    MS_LOG(ERROR) << "Init PRelu kernel failed: Unsupported multi-batch.";
+    return nullptr;
+  }
+  auto *kernel = new (std::nothrow) PReluOpenCLKernel(reinterpret_cast<OpParameter *>(opParameter), inputs, outputs);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "kernel " << opParameter->name_ << "is nullptr.";
+    return nullptr;
+  }
+
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init PRelu kernel failed!";
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Prelu, OpenCLPReluKernelCreator)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.h

@@ -0,0 +1,46 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_PRELU_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_PRELU_H_
+
+#include <vector>
+#include <string>
+#include "src/ir/tensor.h"
+#include "src/runtime/kernel/opencl/opencl_kernel.h"
+#include "schema/model_generated.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+
+namespace mindspore::kernel {
+
+class PReluOpenCLKernel : public OpenCLKernel {
+ public:
+  explicit PReluOpenCLKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                             const std::vector<lite::tensor::Tensor *> &outputs)
+      : OpenCLKernel(parameter, inputs, outputs) {}
+  ~PReluOpenCLKernel() override{};
+
+  int Init() override;
+  int Run() override;
+  int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
+
+ private:
+  cl::Kernel kernel_;
+};
+
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_PRELU_H_

mindspore/lite/test/CMakeLists.txt

@@ -151,6 +151,7 @@ if (SUPPORT_GPU)
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/reshape.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/to_format.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/caffe_prelu.cc
+            ${LITE_DIR}/src/runtime/kernel/opencl/kernel/prelu.cc
             )
 endif()
 ### minddata lite
@@ -327,6 +328,7 @@ if (SUPPORT_GPU)
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/activation_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/to_format_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/caffe_prelu_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/prelu_tests.cc
             )
 endif()
 

mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc

@@ -0,0 +1,185 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 <iostream>
+#include <memory>
+
+#include "utils/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/src/common/file_utils.h"
+#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.h"
+#include "mindspore/lite/src/runtime/kernel/arm/nnacl/prelu_parameter.h"
+
+using mindspore::kernel::LiteKernel;
+using mindspore::kernel::PReluOpenCLKernel;
+using mindspore::kernel::SubGraphOpenCLKernel;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+
+namespace mindspore {
+class TestPReluOpenCL : public mindspore::CommonTest {};
+
+void LoadDataPRelu(void *dst, size_t dst_size, const std::string &file_path) {
+  if (file_path.empty()) {
+    memset(dst, 0x00, dst_size);
+  } else {
+    auto src_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(file_path.c_str(), &dst_size));
+    memcpy(dst, src_data, dst_size);
+  }
+}
+
+void CompareOutPRelu(lite::tensor::Tensor *output_tensor, const std::string &standard_answer_file) {
+  auto *output_data = reinterpret_cast<float *>(output_tensor->Data());
+  size_t output_size = output_tensor->Size();
+  auto expect_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(standard_answer_file.c_str(), &output_size));
+  constexpr float atol = 0.0002;
+  for (int i = 0; i < output_tensor->ElementsNum(); ++i) {
+    if (std::fabs(output_data[i] - expect_data[i]) > atol) {
+      printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
+      printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
+      printf("error at idx[%d] expect=%.3f output=%.3f\n\n\n", i, expect_data[i], output_data[i]);
+      return;
+    }
+  }
+  printf("compare success!\n");
+  printf("compare success!\n");
+  printf("compare success!\n\n\n");
+}
+
+TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
+  std::string in_file = "/data/local/tmp/in_data.bin";
+  std::string standard_answer_file = "/data/local/tmp/leaky_relu.bin";
+  MS_LOG(INFO) << "-------------------->> Begin test PRelu!";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->Init();
+  auto allocator = ocl_runtime->GetAllocator();
+
+  MS_LOG(INFO) << "Init tensors.";
+  std::vector<int> input_shape = {1, 4, 3, 8};
+
+  auto data_type = kNumberTypeFloat32;
+  auto tensor_type = schema::NodeType_ValueNode;
+  auto input_tensor =
+    new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensor_type);
+  if (input_tensor == nullptr) {
+    MS_LOG(ERROR) << "new input_tensor error!";
+    return;
+  }
+
+  auto output_tensor =
+    new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensor_type);
+  if (output_tensor == nullptr) {
+    MS_LOG(ERROR) << "new output_tensor error";
+    delete input_tensor;
+    return;
+  }
+
+  auto weight_tensor =
+    new (std::nothrow) lite::tensor::Tensor(data_type, std::vector<int>{1}, schema::Format_NHWC, tensor_type);
+  if (weight_tensor == nullptr) {
+    MS_LOG(ERROR) << "new weight_tensor error";
+    delete input_tensor;
+    delete output_tensor;
+    return;
+  }
+  std::vector<lite::tensor::Tensor *> inputs{input_tensor, weight_tensor};
+  std::vector<lite::tensor::Tensor *> outputs{output_tensor};
+
+  // freamework to do!!! allocate memory by hand
+  inputs[0]->MallocData(allocator);
+  inputs[1]->MallocData(allocator);
+
+  MS_LOG(INFO) << "initialize input data";
+  LoadDataPRelu(input_tensor->Data(), input_tensor->Size(), in_file);
+  auto weight_data = reinterpret_cast<float *>(weight_tensor->Data());
+  weight_data[0] = 0.3;
+  auto *input_data = reinterpret_cast<float *>(inputs[0]->Data());
+  PrintData("PRelu input data", input_data, inputs[0]->ElementsC4Num());
+
+  auto param = new (std::nothrow) PreluParameter();
+  if (param == nullptr) {
+    MS_LOG(ERROR) << "new PreluParameter error";
+    delete input_tensor;
+    delete output_tensor;
+    delete weight_tensor;
+    return;
+  }
+  auto prelu_kernel =
+    new (std::nothrow) kernel::PReluOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (prelu_kernel == nullptr) {
+    MS_LOG(ERROR) << "new PReluOpenCLKernel error";
+    delete input_tensor;
+    delete output_tensor;
+    delete weight_tensor;
+    delete param;
+    return;
+  }
+  auto ret = prelu_kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init prelu kernel error";
+    return;
+  }
+
+  MS_LOG(INFO) << "initialize sub_graph";
+  std::vector<kernel::LiteKernel *> kernels{prelu_kernel};
+  auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel({input_tensor}, outputs, kernels, kernels, kernels);
+  if (sub_graph == nullptr) {
+    MS_LOG(ERROR) << "Create kernel sub_graph error";
+    delete input_tensor;
+    delete output_tensor;
+    delete weight_tensor;
+    delete param;
+    delete prelu_kernel;
+    return;
+  }
+  ret = sub_graph->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init sub graph error";
+    delete input_tensor;
+    delete output_tensor;
+    delete weight_tensor;
+    delete param;
+    delete prelu_kernel;
+    delete sub_graph;
+    return;
+  }
+
+  ret = sub_graph->Run();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Run sub graph error";
+    delete input_tensor;
+    delete output_tensor;
+    delete weight_tensor;
+    delete param;
+    delete prelu_kernel;
+    delete sub_graph;
+    return;
+  }
+
+  MS_LOG(INFO) << "PRelu==================output data================";
+  auto *output_data = reinterpret_cast<float *>(outputs[0]->Data());
+  PrintData("output_data", output_data, outputs[0]->ElementsC4Num());
+  CompareOutPRelu(output_tensor, standard_answer_file);
+  delete input_tensor;
+  delete output_tensor;
+  delete weight_tensor;
+  delete param;
+  delete prelu_kernel;
+  delete sub_graph;
+  lite::opencl::OpenCLRuntime::DeleteInstance();
+}
+}  // namespace mindspore