add caffe prelu in openclt push

mindspore/lite/src/runtime/kernel/opencl/CMakeLists.txt

@@ -11,4 +11,6 @@ set(OPENCL_KERNEL_SRC
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel/conv2d_transpose.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reshape.cc
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel/activation.cc
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel/caffe_prelu.cc
     )

mindspore/lite/src/runtime/kernel/opencl/cl/fp32/caffe_prelu.cl

@@ -0,0 +1,26 @@
+#pragma OPENCL EXTENSION cl_arm_printf : enable
+
+#define SLICES 4
+#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
+#define FLT4 float4
+#define READ_FLT4 read_imagef
+#define WRITE_FLT4 write_imagef
+__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+
+__kernel void CaffePRelu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
+                         __global float *alpha) {
+  int C = input_shape.w;  // channel size
+
+  int Y = get_global_id(0);  // height id
+  int X = get_global_id(1);  // weight id
+  for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
+    FLT4 in_c4 = READ_FLT4(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y));  // NHWC4: H WC
+    FLT4 tmp;
+    int index = num * 4;
+    tmp.x = in_c4.x * alpha[index];
+    tmp.y = in_c4.y * alpha[index + 1];
+    tmp.z = in_c4.z * alpha[index + 2];
+    tmp.w = in_c4.w * alpha[index + 3];
+    WRITE_FLT4(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp);  // NHWC4: H WC
+  }
+}

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

@@ -44,7 +44,6 @@ int ActivationOpenClKernel::Init() {
     MS_LOG(ERROR) << "Activate fun only support dim=4, but your dim=" << in_tensors_[0]->shape().size();
     return RET_ERROR;
   }
-  ori_format_ = out_tensors_[0]->GetFormat();
   std::string program_name = "";
   std::string kernel_name = "";
   std::string source = activation_source_fp32;
@@ -68,6 +67,8 @@ int ActivationOpenClKernel::Init() {
   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) << op_parameter_->name_ << " init Done!";
   return RET_OK;
 }

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

@@ -0,0 +1,150 @@
+/**
+ * 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 <string>
+#include <set>
+#include <vector>
+
+#include "src/kernel_registry.h"
+#include "include/errorcode.h"
+#include "src/runtime/kernel/opencl/kernel/caffe_prelu.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "src/runtime/kernel/opencl/cl/fp32/caffe_prelu.cl.inc"
+#include "src/runtime/kernel/arm/nnacl/caffeprelu.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_CaffePReLU;
+
+namespace mindspore::kernel {
+
+void CaffePReluOpenCLKernel::CaffeWeight() {
+  int C = in_tensors_[1]->shape()[0];
+  int div_ci = UP_DIV(C, C4NUM);
+  std::cout << div_ci << std::endl;
+  auto allocator = lite::opencl::OpenCLRuntime::GetInstance()->GetAllocator();
+  CaffeWeight_ = reinterpret_cast<FLOAT_t *>(allocator->Malloc(div_ci * C4NUM * sizeof(FLOAT_t)));
+  CaffeWeight_ = reinterpret_cast<FLOAT_t *>(allocator->MapBuffer(CaffeWeight_, CL_MAP_WRITE, nullptr, true));
+  memset(CaffeWeight_, 0x00, div_ci * C4NUM * sizeof(FLOAT_t));
+  auto origin_weight = reinterpret_cast<FLOAT_t *>(in_tensors_[1]->Data());
+  for (int i = 0; i < in_tensors_[1]->ElementsNum(); ++i) {
+    CaffeWeight_[i] = origin_weight[i];
+  }
+  allocator->UnmapBuffer(CaffeWeight_);
+}
+
+int CaffePReluOpenCLKernel::Init() {
+  if (in_tensors_[0]->shape().size() != 4) {
+    MS_LOG(ERROR) << "Caffe PRelu only support dim=4, but your dim=" << in_tensors_[0]->shape().size();
+    return RET_ERROR;
+  }
+  CaffeWeight();
+  std::set<std::string> build_options;
+  std::string source = caffe_prelu_source_fp32;
+  std::string program_name = "CaffePRelu";
+  std::string kernel_name = "CaffePRelu";
+  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 CaffePReluOpenCLKernel::Run() {
+  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};
+  int C_Weight = in_tensors_[1]->shape()[0];
+  if (UP_DIV(C_Weight, C4NUM) != UP_DIV(C, C4NUM)) {
+    MS_LOG(ERROR) << "CaffePRelu weight channel size:" << C_Weight
+                  << " must be equal with in_teneors channel size:" << C;
+    return RET_ERROR;
+  }
+
+  MS_LOG(DEBUG) << op_parameter_->name_ << " Running!";
+  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++, CaffeWeight_);
+  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 CaffePReluOpenCLKernel::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 *OpenCLCaffePReluKernelCreator(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::Primitive *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 CaffePRelu kernel failed: Unsupported multi-batch.";
+    return nullptr;
+  }
+  auto *kernel =
+    new (std::nothrow) CaffePReluOpenCLKernel(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 CaffePRelu kernel failed!";
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_CaffePReLU, OpenCLCaffePReluKernelCreator)
+}  // namespace mindspore::kernel

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

@@ -0,0 +1,49 @@
+/**
+ * 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_CAFFEPRELU_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_CAFFEPRELU_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 CaffePReluOpenCLKernel : public OpenCLKernel {
+ public:
+  explicit CaffePReluOpenCLKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                                  const std::vector<lite::tensor::Tensor *> &outputs)
+      : OpenCLKernel(parameter, inputs, outputs) {}
+  ~CaffePReluOpenCLKernel() override{};
+
+  int Init() override;
+  int Run() override;
+  int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
+  void CaffeWeight();
+
+ private:
+  cl::Kernel kernel_;
+  FLOAT_t *CaffeWeight_;
+};
+
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_CAFFEPRELU_H_

mindspore/lite/src/runtime/opencl/opencl_allocator.cc

@@ -122,7 +122,8 @@ void *OpenCLAllocator::Malloc(size_t size, const std::vector<size_t> &img_size)
   mem_buf->host_ptr_ = host_ptr;
   mem_buf->image_ptr_ = image_ptr;
   mem_buf->img_size = img_size;
-  MS_LOG(DEBUG) << "Malloc a new buffer. size: " << mem_buf->size_ << ", host addr: " << mem_buf->host_ptr_
+  std::string type_name = img_size.empty() ? "buffer" : "Image2D";
+  MS_LOG(DEBUG) << "Malloc a new " << type_name << ". size: " << mem_buf->size_ << ", host addr: " << mem_buf->host_ptr_
                 << ", device addr: " << mem_buf->device_ptr_ << ", image_addr: " << image_ptr;
   allocated_list_[host_ptr] = mem_buf.release();
   UnLock();

mindspore/lite/test/CMakeLists.txt

@@ -149,6 +149,7 @@ if (SUPPORT_GPU)
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/transpose.cc
             ${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
             )
 endif()
 ### minddata lite
@@ -322,15 +323,15 @@ if (SUPPORT_GPU)
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/transpose_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/convolution_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/activation_tests.cc
-            #${TEST_DIR}/ut/src/runtime/kernel/opencl/leakyrelu_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/to_format_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/caffe_prelu_tests.cc
             )
 endif()
 
 if (ENABLE_FP16)
     set(TEST_SRC
             ${TEST_SRC}
-)
+            ${TEST_DIR}/ut/src/runtime/kernel/arm/fp16/convolution_fp16_tests.cc)
 endif ()
 
 

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

@@ -128,7 +128,7 @@ int RunSubGraphOpenCLKernel(const std::vector<lite::tensor::Tensor *> &inputs,
   return RET_OK;
 }
 
-TEST_F(TestActivationOpenCL, LeakyReluFp32_dim4) {
+TEST_F(TestActivationOpenCL, ActivationFp32_dim4) {
   MS_LOG(INFO) << "Begin test:";
   auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
   ocl_runtime->Init();

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

@@ -0,0 +1,155 @@
+/**
+ * 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 "utils/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/src/common/file_utils.h"
+#include "src/runtime/kernel/arm/nnacl/pack.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/caffe_prelu.h"
+#include "mindspore/lite/src/runtime/kernel/arm/nnacl/caffeprelu.h"
+
+using mindspore::kernel::CaffePReluOpenCLKernel;
+using mindspore::kernel::LiteKernel;
+using mindspore::kernel::SubGraphOpenCLKernel;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+
+namespace mindspore {
+class TestCaffePReluOpenCL : public mindspore::CommonTest {};
+
+void LoadDataCaffePRelu(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 CompareOutCaffePRelu(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->ElementsC4Num();
+  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->ElementsC4Num(); ++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");
+}
+
+void printf_tensor_caffeprelu(mindspore::lite::tensor::Tensor *in_data, int size) {
+  auto input_data = reinterpret_cast<float *>(in_data->Data());
+  for (int i = 0; i < size; ++i) {
+    printf("%f ", input_data[i]);
+  }
+  printf("\n");
+  MS_LOG(INFO) << "Print tensor done";
+}
+
+void printf_float(float *data, int num = 0) {
+  float *temp = data;
+  for (int i = 0; i < num; ++i) {
+    std::cout << *temp << " ";
+    temp++;
+  }
+  std::cout << std::endl;
+}
+
+TEST_F(TestCaffePReluOpenCL, CaffePReluFp32_dim4) {
+  std::string in_file = "/data/local/tmp/in_data.bin";
+  std::string weight_file = "/data/local/tmp/weight_data.bin";
+  std::string standard_answer_file = "/data/local/tmp/caffeprelu.bin";
+  MS_LOG(INFO) << "CaffePRelu Begin test:";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->Init();
+  auto allocator = ocl_runtime->GetAllocator();
+
+  MS_LOG(INFO) << "CaffePRelu init tensors.";
+
+  std::vector<int> input_shape = {1, 4, 3, 9};
+  std::vector<int> output_shape = {1, 4, 3, 9};
+  auto data_type = kNumberTypeFloat32;
+  auto tensor_type = schema::NodeType_ValueNode;
+  auto *input_tensor = new lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
+  auto *output_tensor = new lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
+  auto *weight_tensor =
+    new lite::tensor::Tensor(data_type, std::vector<int>{input_shape[3]}, schema::Format_NHWC, tensor_type);
+  std::vector<lite::tensor::Tensor *> inputs{input_tensor, weight_tensor};
+  std::vector<lite::tensor::Tensor *> outputs{output_tensor};
+  std::cout << input_tensor->ElementsNum() << std::endl;
+  std::cout << input_tensor->ElementsC4Num() << std::endl;
+  // freamework to do!!! allocate memory by hand
+  inputs[0]->MallocData(allocator);
+  inputs[1]->MallocData(allocator);
+  std::cout << input_tensor->Size() << std::endl;
+  LoadDataCaffePRelu(input_tensor->Data(), input_tensor->Size(), in_file);
+  MS_LOG(INFO) << "CaffePRelu==================input data================";
+  printf_tensor_caffeprelu(inputs[0], input_tensor->ElementsNum());
+
+  LoadDataCaffePRelu(weight_tensor->Data(), weight_tensor->Size(), weight_file);
+  MS_LOG(INFO) << "CaffePRelu==================weight data================";
+  printf_tensor_caffeprelu(inputs[1], weight_tensor->ElementsNum());
+
+  auto param = new CaffePReluParameter();
+  param->channel_num_ = input_shape[3];
+  auto *caffeprelu_kernel =
+    new (std::nothrow) kernel::CaffePReluOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (caffeprelu_kernel == nullptr) {
+    MS_LOG(ERROR) << "Create caffe prelu kernel error.";
+    return;
+  }
+
+  auto ret = caffeprelu_kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "caffeprelu_kernel init error.";
+    return;
+  }
+
+  MS_LOG(INFO) << "initialize sub_graph";
+  std::vector<kernel::LiteKernel *> kernels{caffeprelu_kernel};
+  auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel({input_tensor}, outputs, kernels, kernels, kernels);
+  if (sub_graph == nullptr) {
+    MS_LOG(ERROR) << "Create sub_graph kernel error.";
+    return;
+  }
+  ret = sub_graph->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "sub_graph init error.";
+    return;
+  }
+  MS_LOG(INFO) << "Sub graph begin running!";
+  ret = sub_graph->Run();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "sub_graph run error.";
+    return;
+  }
+
+  MS_LOG(INFO) << "CaffePRelu==================output data================";
+  printf_tensor_caffeprelu(outputs[0], output_tensor->ElementsC4Num());
+  std::cout << "output date size:" << output_tensor->Size() << std::endl;
+  CompareOutCaffePRelu(output_tensor, standard_answer_file);
+}
+}  // namespace mindspore