Feature: Add capability API for get performance of CPU, GPU.

mace/libmace/capability.cc

+// Copyright 2019 The MACE 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 <map>
+#include <mutex>  // NOLINT(build/c++11)
+#include <string>
+#include <vector>
+
+#include "mace/ops/conv_pool_2d_util.h"
+#include "mace/public/mace.h"
+
+namespace mace {
+namespace capability {
+
+const Capability kCPUDefaultCapability = {
+    PerformanceInfo {1.f},  // float32_performance
+    PerformanceInfo {.6f},  // quantized8_performance tested with mobilenet-v2
+    true,  // supported
+};
+
+const Capability kGPUDefaultCapability = {
+    PerformanceInfo {1.f},  // float32_performance
+    PerformanceInfo {-1.f},  // quantized8_performance
+    false,  // supported
+};
+
+const Capability kDSPDefaultCapability = {
+    PerformanceInfo {-1.f},  // float32_performance
+    PerformanceInfo {1.f},  // quantized8_performance
+    false,  // supported
+};
+
+class BMNet {
+ public:
+  static BMNet *Get();
+  MaceStatus Run(DeviceType device,
+                 float *exec_time);
+
+ private:
+  BMNet();
+  void SetUp();
+
+  std::string AddExpandedConv(
+      const std::string &blk_name,
+      const std::string &input_name,
+      const std::vector<int> &dw_strides,
+      int input_channel,
+      int output_channel,
+      const std::vector<std::vector<int>> &feature_map_shapes,
+      bool has_expand = false,
+      bool has_residual = false);
+  void AddConv(
+      const std::string &conv_type,
+      const std::string &op_name,
+      const std::string &input_name,
+      const std::string &filter_name,
+      const std::string &output_name,
+      const std::vector<int> &strides,
+      const std::vector<int64_t> &filter_shape,
+      const std::vector<int64_t> &output_shape,
+      bool has_relu6 = true,
+      bool has_bias = true,
+      int padding_type = 1);
+  void AddEltwise(const std::string &op_name,
+                  const std::vector<std::string> &inputs,
+                  const std::string &output,
+                  const std::vector<int64_t> &output_shape,
+                  int type = 0);
+
+  void AddIntArg(OperatorDef *op_def, const std::string &name, int value);
+  void AddIntsArg(OperatorDef *op_def,
+                  const std::string &name,
+                  const std::vector<int> &values);
+  void AddStringArg(OperatorDef *op_def,
+                    const std::string &name,
+                    const std::string &value);
+  void AddFloatArg(OperatorDef *op_def, const std::string &name, float value);
+  void AddTensor(const std::string &name,
+                 const std::vector<int64_t> &shape,
+                 int64_t offset,
+                 int64_t data_size);
+
+ private:
+  // the size of model's weight, used as offset when set up model
+  int64_t weight_size_;
+  NetDef net_;
+  std::vector<unsigned char> weight_;
+  std::vector<std::string> input_names_;
+  std::vector<std::vector<int64_t>> input_shapes_;
+  std::vector<std::string> output_names_;
+  std::vector<std::vector<int64_t>> output_shapes_;
+  std::mutex run_mutex_;
+};
+
+BMNet* BMNet::Get() {
+  static BMNet net;
+  return &net;
+}
+
+BMNet::BMNet() : weight_size_(0) {
+  SetUp();
+}
+
+void BMNet::SetUp() {
+  // Use subnet of mobilenet-v2 as the workload
+  input_names_.push_back("Input");
+  input_shapes_.push_back({1, 224, 224, 3});
+  std::string blk_name = "Conv";
+  std::string op_output_name = blk_name + "/Conv2D";
+  AddConv("Conv2D", blk_name + "/Conv2D",
+          "Input", blk_name + "/filter", op_output_name,
+          {2, 2}, {32, 3, 3, 3}, {1, 112, 112, 32});
+  op_output_name = AddExpandedConv("ExpandedConv", op_output_name, {1, 1},
+      32, 16, {{112, 112}, {112, 112}});
+  op_output_name = AddExpandedConv("ExpandedConv1", op_output_name, {2, 2},
+      16, 24, {{112, 112}, {56, 56}, {56, 56}}, true);
+  op_output_name = AddExpandedConv("ExpandedConv2", op_output_name, {1, 1},
+      24, 24, {{56, 56}, {56, 56}, {56, 56}}, true, true);
+  op_output_name = AddExpandedConv("ExpandedConv3", op_output_name, {2, 2},
+      24, 32, {{56, 56}, {28, 28}, {28, 28}}, true);
+  op_output_name = AddExpandedConv("ExpandedConv4", op_output_name, {1, 1},
+      32, 32, {{28, 28}, {28, 28}, {28, 28}}, true, true);
+  op_output_name = AddExpandedConv("ExpandedConv5", op_output_name, {1, 1},
+      32, 32, {{28, 28}, {28, 28}, {28, 28}}, true, true);
+  op_output_name = AddExpandedConv("ExpandedConv6", op_output_name, {2, 2},
+      32, 64, {{28, 28}, {14, 14}, {14, 14}}, true);
+  op_output_name = AddExpandedConv("ExpandedConv7", op_output_name, {1, 1},
+      64, 64, {{14, 14}, {14, 14}, {14, 14}}, true, true);
+  output_names_.push_back(op_output_name);
+  output_shapes_.push_back({1, 14, 14, 64});
+
+  // Add input and output information
+  for (size_t i = 0; i < input_names_.size(); ++i) {
+    InputInfo *info = net_.add_input_info();
+    info->set_name(input_names_[i]);
+    for (auto d : input_shapes_[i]) {
+      info->add_dims(static_cast<int>(d));
+    }
+  }
+  for (auto output_name : output_names_) {
+    OutputInfo *info = net_.add_output_info();
+    info->set_name(output_name);
+  }
+  // allocate weight data
+  weight_.resize(weight_size_, 0);
+}
+
+void BMNet::AddIntArg(mace::OperatorDef *op_def,
+                      const std::string &name,
+                      int value) {
+  auto arg = op_def->add_arg();
+  arg->set_name(name);
+  arg->set_i(value);
+}
+
+void BMNet::AddIntsArg(mace::OperatorDef *op_def,
+                       const std::string &name,
+                       const std::vector<int> &values) {
+  auto arg = op_def->add_arg();
+  arg->set_name(name);
+  for (auto value : values) {
+    arg->add_ints(value);
+  }
+}
+
+void BMNet::AddStringArg(mace::OperatorDef *op_def,
+                         const std::string &name,
+                         const std::string &value) {
+  auto arg = op_def->add_arg();
+  arg->set_name(name);
+  arg->set_s(value);
+}
+
+void BMNet::AddFloatArg(mace::OperatorDef *op_def,
+                        const std::string &name,
+                        float value) {
+  auto arg = op_def->add_arg();
+  arg->set_name(name);
+  arg->set_f(value);
+}
+
+void BMNet::AddTensor(const std::string &name,
+                      const std::vector<int64_t> &shape,
+                      int64_t offset,
+                      int64_t data_size) {
+  ConstTensor *tensor_ptr = net_.add_tensors();
+  tensor_ptr->set_name(name);
+  tensor_ptr->mutable_dims()->Reserve(shape.size());
+  for (auto dim : shape) {
+    tensor_ptr->add_dims(dim);
+  }
+  tensor_ptr->set_offset(offset);
+  tensor_ptr->set_data_size(data_size);
+  tensor_ptr->set_data_type(DT_HALF);
+}
+
+void BMNet::AddConv(const std::string &conv_type,
+                    const std::string &op_name,
+                    const std::string &input_name,
+                    const std::string &filter_name,
+                    const std::string &output_name,
+                    const std::vector<int> &strides,
+                    const std::vector<int64_t> &filter_shape,
+                    const std::vector<int64_t> &output_shape,
+                    bool has_relu6,
+                    bool has_bias,
+                    int padding_type) {
+  const int kAlignPad = 4;
+  auto op_def = net_.add_op();
+
+  op_def->set_name(op_name);
+  op_def->set_type(conv_type);
+  op_def->add_input(input_name);
+  op_def->add_input(filter_name);
+  int64_t filter_size = std::accumulate(filter_shape.begin(),
+      filter_shape.end(), 1, std::multiplies<int64_t>());
+  AddTensor(filter_name, filter_shape, weight_size_, filter_size);
+  weight_size_ += filter_size * sizeof(half);
+  if (weight_size_ % kAlignPad != 0) {
+    weight_size_ += kAlignPad - (weight_size_ % kAlignPad);
+  }
+  if (has_bias) {
+    std::string bias_tensor_name = op_name + "_bias:0";
+    op_def->add_input(bias_tensor_name);
+    AddTensor(bias_tensor_name, {output_shape[3]},
+              weight_size_, output_shape[3]);
+    weight_size_ += output_shape[3] * sizeof(half);
+    if (weight_size_ % kAlignPad != 0) {
+      weight_size_ += kAlignPad - (weight_size_ % kAlignPad);
+    }
+  }
+  op_def->add_output(output_name);
+  AddIntsArg(op_def, "strides", strides);
+  AddIntArg(op_def, "padding", padding_type);
+  AddIntArg(op_def, "T", DT_HALF);
+  AddIntArg(op_def, "data_format", 1);
+  if (has_relu6) {
+    AddStringArg(op_def, "activation", "RELUX");
+    AddFloatArg(op_def, "max_limit", 6);
+  }
+  OutputShape *shape = op_def->add_output_shape();
+  for (auto dim : output_shape) {
+    shape->add_dims(dim);
+  }
+}
+
+void BMNet::AddEltwise(const std::string &op_name,
+                       const std::vector<std::string> &inputs,
+                       const std::string &output,
+                       const std::vector<int64_t> &output_shape,
+                       int type) {
+  auto op_def = net_.add_op();
+
+  op_def->set_name(op_name);
+  op_def->set_type("Eltwise");
+  for (auto input : inputs) {
+    op_def->add_input(input);
+  }
+  op_def->add_output(output);
+  AddIntArg(op_def, "type", type);
+  AddIntArg(op_def, "T", DT_HALF);
+  AddIntArg(op_def, "data_format", 1);
+  OutputShape *shape = op_def->add_output_shape();
+  for (auto dim : output_shape) {
+    shape->add_dims(dim);
+  }
+}
+
+std::string BMNet::AddExpandedConv(
+    const std::string &blk_name,
+    const std::string &input_name,
+    const std::vector<int> &dw_strides,
+    int input_channel,
+    int output_channel,
+    const std::vector<std::vector<int>> &feature_map_shapes,
+    bool has_expand,
+    bool has_residual) {
+  int expand_scale = 6;
+  std::string middle_input_name = input_name;
+  std::string middle_output_name;
+  std::string filter_name;
+  int expand_out_channel = input_channel;
+  int feature_map_idx = 0;
+  if (has_expand) {
+    std::string expand_conv2d_name = blk_name + "/expand/Conv2D";
+    filter_name = expand_conv2d_name + "/filter";
+    middle_output_name = expand_conv2d_name + ":0";
+    expand_out_channel = input_channel * expand_scale;
+    AddConv("Conv2D", expand_conv2d_name, middle_input_name, filter_name,
+            middle_output_name, {1, 1},
+            {expand_out_channel, input_channel, 1, 1},
+            {1, feature_map_shapes[feature_map_idx][0],
+             feature_map_shapes[feature_map_idx][1], expand_out_channel});
+    feature_map_idx += 1;
+    middle_input_name = middle_output_name;
+  }
+
+  std::string dw_conv2d_name = blk_name + "/depthwise/depthwise";
+  filter_name = dw_conv2d_name + "/filter";
+  middle_output_name = dw_conv2d_name + ":0";
+  AddConv("DepthwiseConv2d", dw_conv2d_name, middle_input_name, filter_name,
+          middle_output_name, dw_strides, {1, expand_out_channel, 3, 3},
+          {1, feature_map_shapes[feature_map_idx][0],
+           feature_map_shapes[feature_map_idx][1], expand_out_channel});
+  feature_map_idx += 1;
+  middle_input_name = middle_output_name;
+
+  std::string project_conv2d_name = blk_name + "/project/conv2d";
+  filter_name = project_conv2d_name + "/filter";
+  middle_output_name = project_conv2d_name + ":0";
+  AddConv("Conv2D", project_conv2d_name, middle_input_name, filter_name,
+          middle_output_name, {1, 1},
+          {output_channel, expand_out_channel, 1, 1},
+          {1, feature_map_shapes[feature_map_idx][0],
+           feature_map_shapes[feature_map_idx][1], output_channel},
+          false);
+  middle_input_name = middle_output_name;
+  if (has_residual) {
+    std::string eltwise_name = blk_name + "/add";
+    middle_output_name = eltwise_name + ":0";
+    AddEltwise(eltwise_name, {input_name, middle_input_name},
+               middle_output_name,
+               {1, feature_map_shapes[2][0], feature_map_shapes[2][1],
+                output_channel});
+  }
+
+  return middle_output_name;
+}
+
+MaceStatus BMNet::Run(DeviceType device,
+                      float *exec_time) {
+  std::lock_guard<std::mutex> lock(run_mutex_);
+  MaceStatus status;
+  MaceEngineConfig config(device);
+  config.SetCPUThreadPolicy(-1, CPUAffinityPolicy::AFFINITY_BIG_ONLY);
+  if (device == DeviceType::GPU) {
+    config.SetGPUHints(GPUPerfHint::PERF_HIGH, GPUPriorityHint::PRIORITY_LOW);
+  }
+  MaceEngine engine(config);
+
+  status = engine.Init(&net_, input_names_, output_names_, weight_.data());
+  if (status != MaceStatus::MACE_SUCCESS) {
+    return status;
+  }
+
+  const size_t input_count = input_names_.size();
+  const size_t output_count = output_names_.size();
+
+  std::map<std::string, mace::MaceTensor> inputs;
+  std::map<std::string, mace::MaceTensor> outputs;
+  for (size_t i = 0; i < input_count; ++i) {
+    int64_t input_size =
+        std::accumulate(input_shapes_[i].begin(), input_shapes_[i].end(), 1,
+                        std::multiplies<int64_t>());
+    auto buffer_in = std::shared_ptr<float>(new float[input_size],
+                                            std::default_delete<float[]>());
+    inputs[input_names_[i]] = mace::MaceTensor(input_shapes_[i], buffer_in);
+  }
+
+  for (size_t i = 0; i < output_count; ++i) {
+    int64_t output_size =
+        std::accumulate(output_shapes_[i].begin(), output_shapes_[i].end(), 1,
+                        std::multiplies<int64_t>());
+    auto buffer_out = std::shared_ptr<float>(new float[output_size],
+                                             std::default_delete<float[]>());
+    outputs[output_names_[i]] = mace::MaceTensor(output_shapes_[i], buffer_out);
+  }
+
+  // warm up
+  status = engine.Run(inputs, &outputs);
+  if (status != MaceStatus::MACE_SUCCESS) {
+    return status;
+  }
+
+  int round = 10;
+  int64_t duration = 0;
+  for (int i = 0; i < round; ++i) {
+    int64_t start = NowMicros();
+    status = engine.Run(inputs, &outputs);
+    duration += NowMicros() - start;
+    if (status != MaceStatus::MACE_SUCCESS) {
+      return status;
+    }
+  }
+  *exec_time = duration / 1000.0f / round;
+  return MaceStatus::MACE_SUCCESS;
+}
+}  // namespace capability
+
+Capability GetCapability(DeviceType device_type, float cpu_float32_exec_time) {
+  Capability capability;
+  if (device_type == DeviceType::HEXAGON) {
+    return capability::kDSPDefaultCapability;
+  } else if (device_type == DeviceType::CPU) {
+    capability = capability::kCPUDefaultCapability;
+  } else if (device_type == DeviceType::GPU) {
+    capability = capability::kGPUDefaultCapability;
+  } else {
+    LOG(FATAL) << "No support the device " << device_type;
+  }
+  capability::BMNet *net = capability::BMNet::Get();
+  float exec_time;
+  MaceStatus status = net->Run(device_type, &exec_time);
+  if (status == MaceStatus::MACE_SUCCESS) {
+    capability.float32_performance.exec_time =
+        exec_time / cpu_float32_exec_time;
+    capability.supported = true;
+  } else {
+    capability.supported = false;
+  }
+  return capability;
+}
+}  // namespace mace

mace/public/mace.h

@@ -100,7 +100,44 @@ class RunMetadata {
   std::vector<OperatorStats> op_stats;
 };
 
-const char *MaceVersion();
+/// Consistent with Android NNAPI
+struct PerformanceInfo {
+  // Time of executing some workload.
+  // negative value for unsupported.
+  float exec_time;
+};
+
+struct Capability {
+  // Performance of running with float32 data type
+  // run time of the workload for CPU device,
+  // ratio of run time to execute same workload compared to the time the CPU
+  // execute same workload.
+  PerformanceInfo float32_performance;
+
+  // Performance of running with quantized-8 data type
+  // ratio compared with float32_performance
+  PerformanceInfo quantized8_performance;
+
+  // support or not
+  bool supported;
+};
+
+/// Get Devices Capacity
+///
+/// The float32_performance of CPU and GPU is tested using the workload of
+/// first 8 layer of mobilenet-v2 which contain Conv(1x1, 3x3),
+/// DepthwiseConv(3x3) and ElementWise Ops.
+/// The quantized8_performance is just a arbitrary value tested
+/// using mobilenet-v2 offline
+/// Actually, It's hard to test the precise performance, the result could be
+/// more accurate when your model is like with mobilenet-v2, otherwise the
+/// value is just a reference.
+///
+/// \return capability of the device
+MACE_API Capability GetCapability(DeviceType device_type,
+                                  float cpu_float32_exec_time = 1.f);
+
+MACE_API const char *MaceVersion();
 
 class MaceStatus {
  public:

mace/test/BUILD

@@ -120,3 +120,35 @@ cc_test(
         "@gtest//:gtest_main",
     ],
 )
+
+cc_test(
+    name = "device_capability_api_test",
+    testonly = 1,
+    srcs = ["device_capability_api_test.cc"],
+    copts = [
+        "-Werror",
+        "-Wextra",
+        "-Wno-missing-field-initializers",
+    ] + if_openmp_enabled([
+        "-fopenmp"
+    ]) + if_neon_enabled([
+        "-DMACE_ENABLE_NEON",
+    ]) + if_android_armv7([
+        "-mfpu=neon",
+    ]) + if_android_armv7([
+        "-mfloat-abi=softfp",
+    ]) + if_opencl_enabled([
+        "-DMACE_ENABLE_OPENCL",
+    ]) + if_quantize_enabled([
+        "-DMACE_ENABLE_QUANTIZE",
+    ]) + if_hexagon_enabled([
+        "-DMACE_ENABLE_HEXAGON",
+    ]),
+    linkopts = ["-fopenmp"],
+    linkstatic = 1,
+    deps = [
+        "//mace/ops:test",
+        "//mace/libmace:libmace",
+        "@gtest//:gtest_main",
+    ],
+)

mace/test/device_capability_api_test.cc

+// Copyright 2018 The MACE 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 "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace test {
+
+TEST(MaceDeviceCapalibityAPITest, Capability) {
+  auto cpu_capability = GetCapability(DeviceType::CPU);
+  LOG(INFO) << "CPU: float32 "
+            << cpu_capability.float32_performance.exec_time
+            << " vs quantized8 "
+            << cpu_capability.quantized8_performance.exec_time;
+  auto gpu_capability = GetCapability(
+      DeviceType::GPU, cpu_capability.float32_performance.exec_time);
+  if (gpu_capability.supported) {
+    LOG(INFO) << "GPU: float32 "
+              << gpu_capability.float32_performance.exec_time
+              << " vs quantized8 "
+              << gpu_capability.quantized8_performance.exec_time;
+  }
+}
+
+}  // namespace test
+}  // namespace mace