!4001 add test case for opencl for pooling

Merge pull request !4001 from chenzhongming/master

mindspore/lite/test/CMakeLists.txt

@@ -289,7 +289,11 @@ if (SUPPORT_GPU)
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/matmul_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/concat_tests.cc
-            ${TEST_DIR}/ut/src/runtime/kernel/opencl/softmax_cl_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/softmax_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/arithmetic_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/max_pooling_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/utils_tests.cc
             )
 endif()
 

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

+/**
+ * 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 "common/common_test.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.h"
+
+namespace mindspore {
+
+void BoardcaseAdd(const float *a, const float b, float *c, const int size) {
+  for (int i = 0; i < size; i++) {
+    c[i] = a[i] + b;
+  }
+}
+
+void ElementAdd(const float *a, const float *b, float *c, const int size) {
+  for (int i = 0; i < size; i++) {
+    c[i] = a[i] + b[i];
+  }
+}
+
+bool DataCompare(const float *a, const float *b, const int size, const float accuracy = 1e-4) {
+  for (int i = 0; i < size; i++) {
+    auto diff = fabs(a[i] - b[i]);
+    if (diff > accuracy) {
+      MS_LOG(ERROR) << "compare failed at " << i << " exp " << a[i] << " bug got " << b[i];
+      return false;
+    }
+  }
+  return true;
+}
+
+void InitData(void *data, const int size) {
+  float *data_float = reinterpret_cast<float *>(data);
+  static unsigned int seed = 123;
+  for (int i = 0; i < size; i++) {
+    data_float[i] = static_cast<int>(rand_r(&seed)) % 100;
+  }
+}
+
+void LogData(void *data, const int size, const std::string prefix) {
+  std::cout << prefix;
+  float *data_float = reinterpret_cast<float *>(data);
+  for (int i = 0; i < size; i++) {
+    std::cout << data_float[i] << ",";
+  }
+  std::cout << std::endl;
+}
+
+void TestCase(const std::vector<int> &shape_a, const std::vector<int> &shape_b) {
+  std::cout << "TestCase" << std::endl;
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+
+  bool is_bias_add = shape_b.empty();
+  auto tensorType = schema::NodeType_ValueNode;
+
+  std::cout << "TestCase tensor" << std::endl;
+  lite::tensor::Tensor *tensor_a =
+    new lite::tensor::Tensor(kNumberTypeFloat32, shape_a, schema::Format_NHWC4, tensorType);
+  lite::tensor::Tensor *tensor_b =
+    new lite::tensor::Tensor(kNumberTypeFloat32, shape_b, schema::Format_NHWC4, tensorType);
+  lite::tensor::Tensor *tensor_c =
+    new lite::tensor::Tensor(kNumberTypeFloat32, shape_a, schema::Format_NHWC4, tensorType);
+  int64_t element_num = tensor_a->ElementsC4Num();
+  int64_t element_num_b = is_bias_add ? 1 : tensor_b->ElementsC4Num();
+
+  std::cout << "TestCase new data" << std::endl;
+  float *data_a = new float[element_num];
+  float *data_b = new float[element_num_b];
+  float *data_c_cpu = new float[element_num];
+  float *data_c_ocl = new float[element_num];
+
+  InitData(data_a, element_num);
+  InitData(data_b, element_num_b);
+  memset(data_c_ocl, 0, sizeof(float) * element_num);
+
+  std::cout << "TestCase run cpu" << std::endl;
+  if (is_bias_add) {
+    BoardcaseAdd(data_a, static_cast<float *>(data_b)[0], data_c_cpu, element_num);
+  } else {
+    ElementAdd(data_a, data_b, data_c_cpu, element_num);
+  }
+
+  std::cout << "TestCase set data" << std::endl;
+  std::vector<lite::tensor::Tensor *> inputs = {tensor_a};
+  if (!is_bias_add) {
+    inputs.push_back(tensor_b);
+  } else {
+    tensor_b->MallocData();
+    memcpy(tensor_b->Data(), data_b, sizeof(float));
+  }
+  std::vector<lite::tensor::Tensor *> outputs = {tensor_c};
+
+  ArithmeticParameter *param = new ArithmeticParameter();
+  param->ndim_ = 4;
+  param->op_parameter_.type_ = PrimitiveType_Add;
+
+  std::vector<lite::tensor::Tensor *> arithmetic_inputs = {tensor_a, tensor_b};
+  lite::Context ctx;
+  auto *arith_kernel =
+    new kernel::ArithmeticOpenCLKernel(reinterpret_cast<OpParameter *>(param), arithmetic_inputs, outputs, &ctx);
+  arith_kernel->Init();
+
+  std::vector<kernel::LiteKernel *> kernels{arith_kernel};
+  auto *kernel = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  std::cout << "TestCase Init" << std::endl;
+  kernel->Init();
+
+  memcpy(inputs[0]->Data(), data_a, sizeof(float) * element_num);
+  if (!is_bias_add) {
+    memcpy(inputs[1]->Data(), data_b, sizeof(float) * element_num_b);
+  }
+
+  std::cout << "TestCase Run" << std::endl;
+  kernel->Run();
+
+  memcpy(data_c_ocl, outputs[0]->Data(), sizeof(float) * element_num);
+
+  // ocl_runtime->SyncCommandQueue();
+  LogData(data_a, 10, "Data A : ");
+  LogData(data_b, tensor_b->shape().empty() ? 1 : 10, "Data B : ");
+  LogData(data_c_cpu, 10, "Expect compute : ");
+  LogData(outputs[0]->Data(), 10, "OpenCL compute : ");
+  bool cmp = DataCompare(data_c_cpu, data_c_ocl, element_num);
+  MS_LOG(INFO) << "Compare " << (cmp ? "success!" : "failed!");
+  std::cout << "TestCase End" << std::endl;
+
+  // free
+  delete[] data_a;
+  delete[] data_b;
+  delete[] data_c_cpu;
+  delete[] data_c_ocl;
+
+  delete kernel;
+  delete arith_kernel;
+
+  for (auto tensor : inputs) {
+    delete tensor;
+  }
+  for (auto tensor : outputs) {
+    delete tensor;
+  }
+  lite::opencl::OpenCLRuntime::DeleteInstance();
+}
+
+class TestArithmeticOpenCL : public mindspore::Common {
+ public:
+  TestArithmeticOpenCL() {}
+};
+
+TEST_F(TestArithmeticOpenCL, AddElementwiseTest) {
+  const std::vector<int> &shape_a = {1, 32, 32, 4};
+  const std::vector<int> &shape_b = {1, 32, 32, 4};
+  TestCase(shape_a, shape_b);
+}
+
+// TEST_F(TestOpenCLKernel, AddBoardcaseTest) {
+//  const std::vector<int> &shape_a = {1, 4, 128, 128};
+//  const std::vector<int> &shape_b = {};
+//  TestCase(shape_a, shape_b);
+//}
+
+}  // namespace mindspore

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

+/**
+ * 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/pooling2d.h"
+
+namespace mindspore {
+
+class TestAvgPoolingOpenCL : public mindspore::Common {};
+
+void InitAvgPoolingParam(PoolingParameter *param) {
+  param->input_batch_ = 1;
+  param->input_h_ = 2;
+  param->input_w_ = 2;
+  param->input_channel_ = 4;
+
+  param->output_batch_ = 1;
+  param->output_h_ = 1;
+  param->output_w_ = 1;
+  param->output_channel_ = 4;
+
+  param->window_h_ = 2;
+  param->window_w_ = 2;
+
+  param->stride_h_ = 2;
+  param->stride_w_ = 2;
+
+  param->pad_u_ = 0;
+  param->pad_d_ = 0;
+  param->pad_l_ = 0;
+  param->pad_r_ = 0;
+
+  param->max_pooling_ = false;
+  param->avg_pooling_ = true;
+}
+
+TEST_F(TestAvgPoolingOpenCL, AvgPoolFp32) {
+  MS_LOG(INFO) << "start TEST_F TestPoolingOpenCL";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->Init();
+
+  MS_LOG(INFO) << "create PoolingParameter";
+  auto param = new PoolingParameter();
+  InitAvgPoolingParam(param);
+
+  MS_LOG(INFO) << "create Tensors";
+  std::vector<int> shape_in = {
+    param->input_batch_,
+    param->input_h_,
+    param->input_w_,
+    param->input_channel_,
+  };
+  std::vector<int> shape_out = {
+    param->output_batch_,
+    param->output_h_,
+    param->output_w_,
+    param->output_channel_,
+  };
+  auto data_type = kNumberTypeFloat32;
+  auto tensorType = schema::NodeType_ValueNode;
+  lite::tensor::Tensor *tensor_in = new lite::tensor::Tensor(data_type, shape_in, schema::Format_NHWC, tensorType);
+  lite::tensor::Tensor *tensor_out = new lite::tensor::Tensor(data_type, shape_out, schema::Format_NHWC, tensorType);
+  std::vector<lite::tensor::Tensor *> inputs{tensor_in};
+  std::vector<lite::tensor::Tensor *> outputs{tensor_out};
+
+  MS_LOG(INFO) << "create OpenCL Kernel";
+  auto *pooling_kernel = new kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  pooling_kernel->Init();
+  std::vector<kernel::LiteKernel *> kernels{pooling_kernel};
+
+  MS_LOG(INFO) << "create SubGraphOpenCLKernel";
+  auto *pGraph = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  pGraph->Init();
+
+  MS_LOG(INFO) << "initialize data";
+  std::vector<lite::tensor::Tensor *> tensor_map = {tensor_in};
+  for (auto &tensor_file : tensor_map) {
+    auto tensor = tensor_file;
+    size_t size = tensor->Size();
+    const float data[16] = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f,
+                            0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};
+    memcpy(tensor->Data(), data, size);
+  }
+
+  MS_LOG(INFO) << "pGraph->Run()";
+  pGraph->Run();
+
+  MS_LOG(INFO) << "==================output data=================";
+  float *output_data = reinterpret_cast<float *>(tensor_out->Data());
+  printf("output:");
+  for (int i = 0; i < 4; i++) {
+    printf("%.3f ", output_data[i]);
+  }
+  printf("\n");
+  size_t output_size = tensor_out->Size();
+  float expect[4] = {2.0f, 3.0f, 4.0f, 5.0f};
+
+  for (int i = 0; i < tensor_out->ElementsNum(); ++i)
+    if (std::fabs(output_data[i] - expect[i]) > 1e-5) {
+      printf("idx[%d] except=%.3f output=%.3f, ", i, expect[i], output_data[i]);
+    }
+  printf("test all close OK!\n");
+  lite::CompareOutputData(output_data, expect, 4);
+}
+
+}  // namespace mindspore

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

+/**
+ * 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 <memory>
+#include "utils/log_adapter.h"
+#include "common/common_test.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/pooling2d.h"
+#include "mindspore/lite/test/ut/src/runtime/kernel/opencl/utils_tests.h"
+
+namespace mindspore {
+
+class TestMaxPoolingOpenCL : public mindspore::Common {};
+
+void InitParameter(PoolingParameter *param) {
+  param->window_h_ = 2;
+  param->window_w_ = 2;
+  param->stride_h_ = 2;
+  param->stride_w_ = 2;
+  param->pad_u_ = 0;
+  param->pad_d_ = 0;
+  param->pad_l_ = 0;
+  param->pad_r_ = 0;
+  param->avg_pooling_ = false;
+  param->max_pooling_ = true;
+}
+
+TEST_F(TestMaxPoolingOpenCL, MaxPool_1_32_512_96) {
+  MS_LOG(INFO) << "ocl runtime";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->Init();
+
+  MS_LOG(INFO) << "PoolingParameter";
+  auto param = new PoolingParameter;
+  InitParameter(param);
+
+  // define tensor
+  MS_LOG(INFO) << "define tensor";
+  std::vector<int> input_shape = {1, 16, 256, 192};
+  std::vector<int> output_shape = {1, 8, 128, 192};
+  auto data_type = kNumberTypeFloat32;
+  auto tensorType = schema::NodeType_ValueNode;
+  auto input_tensor = new lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensorType);
+  auto output_tensor = new lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensorType);
+  std::vector<lite::tensor::Tensor *> inputs{input_tensor};
+  std::vector<lite::tensor::Tensor *> outputs{output_tensor};
+
+  // run
+  auto *pooling_kernel = new kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  pooling_kernel->Init();
+  std::vector<kernel::LiteKernel *> kernels{pooling_kernel};
+  auto *pGraph = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  pGraph->Init();
+
+  // load data
+  MS_LOG(INFO) << "load data";
+  std::string input_file = "maxpool_in.bin";
+  std::string expect_file = "maxpool_out.bin";
+  LoadTestData(input_tensor->Data(), input_tensor->Size(), input_file);
+  auto *input_data = reinterpret_cast<float *>(input_tensor->Data());
+  printf("input[0:10]:");
+  for (int i = 0; i < 10; i++) {
+    printf("[%d]:%.3f ", i, input_data[i]);
+  }
+  printf("\n");
+
+  pGraph->Run();
+
+  MS_LOG(INFO) << "compare result";
+  CompareOutput(output_tensor, expect_file);
+}
+
+}  // namespace mindspore

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

+/**
+ * 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 "utils/log_adapter.h"
+#include "mindspore/lite/src/common/file_utils.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "mindspore/lite/test/ut/src/runtime/kernel/opencl/utils_tests.h"
+
+namespace mindspore {
+
+void LoadTestData(void *dst, size_t dst_size, const std::string &file_path) {
+  if (file_path.empty()) {
+    memset(dst, dst_size, dst_size);
+  } else {
+    memcpy(dst, reinterpret_cast<const void *>(dst_size), dst_size);
+  }
+}
+
+void CompareOutput(lite::tensor::Tensor *output_tensor, const std::string &file_path) {
+  float *output_data = reinterpret_cast<float *>(output_tensor->Data());
+  size_t output_size = output_tensor->Size();
+  float *expect_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(file_path.c_str(), &output_size));
+
+  printf("output[0:10]:");
+  for (int i = 0; i < 10; i++) {
+    printf("[%d]:%.3f ", i, output_data[i]);
+  }
+  printf("\n");
+  printf("expect[0:10]:");
+  for (int i = 0; i < 10; i++) {
+    printf("[%d]:%.3f ", i, expect_data[i]);
+  }
+  printf("\n");
+
+  constexpr float atol = 1e-5;
+  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", i, expect_data[i], output_data[i]);
+      return;
+    }
+  }
+  printf("compare success!\n");
+  printf("compare success!\n");
+  printf("compare success!\n\n\n");
+}
+
+}  // namespace mindspore

mindspore/lite/test/ut/src/runtime/kernel/opencl/utils_tests.h

+/**
+ * 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 <iostream>
+#include "tests/ut/cpp/common/common_test.h"
+#include "utils/log_adapter.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"
+
+#ifndef TESTS_UT_OPENCL_KERNEL_TESTS_UTILS_H_
+#define TESTS_UT_OPENCL_KERNEL_TESTS_UTILS_H_
+
+namespace mindspore {
+
+void LoadTestData(void *dst, size_t dst_size, const std::string &file_path);
+
+void CompareOutput(lite::tensor::Tensor *output_tensor, const std::string &file_path);
+
+}  // namespace mindspore
+
+#endif  // TESTS_UT_OPENCL_KERNEL_TESTS_UTILS_H_

mindspore/train/quant/quant.py

@@ -446,7 +446,7 @@ def export(network, *inputs, file_name, mean=127.5, std_dev=127.5, file_format='
               Ascend model.
             - BINARY: Binary format for model. An intermidiate representation format for models.
     """
-    supported_device = ["Ascend"]
+    supported_device = ["Ascend", "GPU"]
     supported_formats = ['GEIR', 'BINARY']
 
     mean = validator.check_type("mean", mean, (int, float))