Merge branch 'fc' into 'master'

Add FC op.

See merge request !249

mace/core/operator.cc

@@ -82,6 +82,7 @@ extern void Register_WinogradTransform(OperatorRegistry *op_registry);
 extern void Register_WinogradInverseTransform(OperatorRegistry *op_registry);
 extern void Register_Reshape(OperatorRegistry *op_registry);
 extern void Register_Eltwise(OperatorRegistry *op_registry);
+extern void Register_FullyConnected(OperatorRegistry *op_registry);
 
 OperatorRegistry::OperatorRegistry() {
   Register_Activation(this);
@@ -107,6 +108,7 @@ OperatorRegistry::OperatorRegistry() {
   Register_WinogradInverseTransform(this);
   Register_Reshape(this);
   Register_Eltwise(this);
+  Register_FullyConnected(this);
 }
 
 }  // namespace mace

mace/kernels/fully_connected.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_KERNELS_FULLY_CONNECTED_H_
+#define MACE_KERNELS_FULLY_CONNECTED_H_
+
+#include "mace/core/future.h"
+#include "mace/core/tensor.h"
+#include "mace/core/runtime/opencl/cl2_header.h"
+#include "mace/kernels/activation.h"
+
+namespace mace {
+namespace kernels {
+
+struct FullyConnectedBase {
+  FullyConnectedBase(const ActivationType activation,
+                     const float relux_max_limit,
+                     const float prelu_alpha)
+      : activation_(activation),
+        relux_max_limit_(relux_max_limit),
+        prelu_alpha_(prelu_alpha) {}
+
+  const ActivationType activation_;
+  const float relux_max_limit_;
+  const float prelu_alpha_;
+};
+
+template<DeviceType D, typename T>
+struct FullyConnectedFunctor : FullyConnectedBase {
+  FullyConnectedFunctor(const ActivationType activation,
+                        const float relux_max_limit,
+                        const float prelu_alpha) :
+      FullyConnectedBase(activation, relux_max_limit, prelu_alpha) {}
+
+  void operator()(const Tensor *input,
+                  const Tensor *weight,
+                  const Tensor *bias,
+                  Tensor *output,
+                  StatsFuture *future) {
+
+    std::vector<index_t> output_shape = {input->dim(0), 1, 1, weight->dim(0)};
+    output->Resize(output_shape);
+    const index_t N = output->dim(0);
+    const index_t input_size = weight->dim(1);
+    const index_t output_size = weight->dim(0);
+    Tensor::MappingGuard guard_input(input);
+    Tensor::MappingGuard guard_weight(weight);
+    Tensor::MappingGuard guard_bias(bias);
+    Tensor::MappingGuard guard_output(output);
+    const T *input_ptr = input->data<T>();
+    const T *weight_ptr = weight->data<T>();
+    const T *bias_ptr = bias == nullptr ? nullptr : bias->data<T>();
+    T *output_ptr = output->mutable_data<T>();
+
+#pragma omp parallel for collapse(2)
+    for (int i = 0; i < N; ++i) {
+      for (int out_idx = 0; out_idx < output_size; ++out_idx) {
+        T sum = 0;
+        if (bias_ptr != nullptr) sum = bias_ptr[out_idx];
+        index_t input_offset = i * input_size;
+        index_t weight_offset = out_idx * input_size;
+        for (int in_idx = 0; in_idx < input_size; ++in_idx) {
+          sum += input_ptr[input_offset] * weight_ptr[weight_offset];
+          input_offset++;
+          weight_offset++;
+        }
+        output_ptr[i * output_size + out_idx] = sum;
+      }
+    }
+
+    DoActivation(output_ptr, output_ptr, output->NumElements(), activation_,
+                 relux_max_limit_, prelu_alpha_);
+  }
+};
+
+template<typename T>
+struct FullyConnectedFunctor<DeviceType::OPENCL, T> : FullyConnectedBase {
+  FullyConnectedFunctor(const ActivationType activation,
+                        const float relux_max_limit,
+                        const float prelu_alpha) :
+      FullyConnectedBase(activation, relux_max_limit, prelu_alpha) {}
+
+  void operator()(const Tensor *input,
+                  const Tensor *weight,
+                  const Tensor *bias,
+                  Tensor *output,
+                  StatsFuture *future);
+
+  cl::Kernel kernel_;
+};
+
+}  //  namespace kernels
+}  //  namespace mace
+
+#endif  // MACE_KERNELS_FULLY_CONNECTED_H_

mace/kernels/opencl/activation_opencl.cc

@@ -54,7 +54,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
         tuning_key_prefix = "sigmoid_opencl_kernel_";
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation_;
     }
     kernel_ =

mace/kernels/opencl/batch_norm_opencl.cc

@@ -59,7 +59,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation_;
     }
 

mace/kernels/opencl/buffer_to_image.cc

@@ -48,6 +48,7 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()(Tensor *buffer,
       kernel_name = i2b_ ? "arg_image_to_buffer" : "arg_buffer_to_image";
       break;
     case IN_OUT_HEIGHT:
+    case WEIGHT_HEIGHT:
       kernel_name = i2b_ ? "in_out_height_image_to_buffer" : "in_out_height_buffer_to_image";
       break;
     case IN_OUT_WIDTH:
@@ -80,6 +81,10 @@ void BufferToImageFunctor<DeviceType::OPENCL, T>::operator()(Tensor *buffer,
   b2f_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(buffer->buffer())));
   if (type == ARGUMENT) {
     b2f_kernel.setArg(idx++, static_cast<uint32_t>(buffer->dim(0)));
+  } else if(type == WEIGHT_HEIGHT) {
+    b2f_kernel.setArg(idx++, static_cast<uint32_t>(buffer->dim(0)));
+    b2f_kernel.setArg(idx++, static_cast<uint32_t>(buffer->dim(1)));
+    b2f_kernel.setArg(idx++, 1);
   } else {
     b2f_kernel.setArg(idx++, static_cast<uint32_t>(buffer->dim(1)));
     b2f_kernel.setArg(idx++, static_cast<uint32_t>(buffer->dim(2)));

mace/kernels/opencl/cl/batch_norm.cl

@@ -9,8 +9,8 @@ __kernel void batch_norm(__read_only image2d_t input,
                          __private const float epsilon,
 #endif
                          __write_only image2d_t output,
-                         __private const DATA_TYPE relux_max_limit,
-                         __private const DATA_TYPE prelu_alpha) {
+                         __private const float relux_max_limit,
+                         __private const float prelu_alpha) {
   const int ch_blk = get_global_id(0);
   const int w = get_global_id(1);
   const int hb = get_global_id(2);

mace/kernels/opencl/cl/common.h

@@ -22,8 +22,8 @@ __constant sampler_t SAMPLER = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP |
 
 
 inline DATA_TYPE4 do_activation(DATA_TYPE4 in,
-                                __private const DATA_TYPE relux_max_limit,
-                                __private const DATA_TYPE prelu_alpha) {
+                                __private const float relux_max_limit,
+                                __private const float prelu_alpha) {
   DATA_TYPE4 out;
 #ifdef USE_RELU
   out = fmax(in, 0);

mace/kernels/opencl/cl/conv_2d.cl

@@ -6,8 +6,8 @@ __kernel void conv_2d(__read_only image2d_t input, /* [c%4 * w * c/4, h * b] */
     __read_only image2d_t bias, /* cout%4 * cout/4 */
 #endif
                       __write_only image2d_t output,
-                      __private const DATA_TYPE relux_max_limit,
-                      __private const DATA_TYPE prelu_alpha,
+                      __private const float relux_max_limit,
+                      __private const float prelu_alpha,
                       __private const int in_height,
                       __private const int in_width,
                       __private const int in_ch_blks,

mace/kernels/opencl/cl/conv_2d_1x1.cl

@@ -6,8 +6,8 @@ __kernel void conv_2d_1x1(__read_only image2d_t input, /* [c%4 * w * c/4, h * b]
                           __read_only image2d_t bias, /* cout%4 * cout/4 */
 #endif
                           __write_only image2d_t output,
-                          __private const DATA_TYPE relux_max_limit,
-                          __private const DATA_TYPE prelu_alpha,
+                          __private const float relux_max_limit,
+                          __private const float prelu_alpha,
                           __private const int in_height,
                           __private const int in_width,
                           __private const int in_ch_blks,

mace/kernels/opencl/cl/conv_2d_3x3.cl

@@ -6,8 +6,8 @@ __kernel void conv_2d_3x3(__read_only image2d_t input, /* [c%4 * w * c/4, h * b]
                           __read_only image2d_t bias, /* cout%4 * cout/4 */
 #endif
                           __write_only image2d_t output,
-                          __private const DATA_TYPE relux_max_limit,
-                          __private const DATA_TYPE prelu_alpha,
+                          __private const float relux_max_limit,
+                          __private const float prelu_alpha,
                           __private const int in_height,
                           __private const int in_width,
                           __private const int in_ch_blks,

mace/kernels/opencl/cl/depthwise_conv2d.cl

@@ -7,8 +7,8 @@ __kernel void depthwise_conv2d(__read_only image2d_t input, /* [c%4 * w * c/4, h
     __read_only image2d_t bias, /* cout%4 * cout/4 */
 #endif
                                __write_only image2d_t output,
-                               __private const DATA_TYPE relux_max_limit,
-                               __private const DATA_TYPE prelu_alpha,
+                               __private const float relux_max_limit,
+                               __private const float prelu_alpha,
                                __private const short in_height,
                                __private const short in_width,
                                __private const short in_ch_blks,

mace/kernels/opencl/cl/fully_connected.cl

+#include <common.h>
+
+// output = weight * input + bias
+__kernel void fully_connected(__read_only image2d_t input,
+                              __read_only image2d_t weight,
+#ifdef BIAS
+                              __read_only image2d_t bias,
+#endif
+                              __write_only image2d_t output,
+                              __private const int input_height,
+                              __private const int input_width,
+                              __private const int input_channel,
+                              __private const float relux_max_limit,
+                              __private const float prelu_alpha) {
+  const int batch_idx = get_global_id(0);
+  const int out_blk_idx = get_global_id(1);
+  const int input_chan_blk = (input_channel + 3) >> 2;
+
+  float4 input_value;
+  float4 w0, w1, w2, w3;
+
+#ifdef BIAS
+  DATA_TYPE4 result = READ_IMAGET(bias, SAMPLER, (int2)(out_blk_idx, 0));
+#else
+  DATA_TYPE4 result = (DATA_TYPE4)(0, 0, 0, 0);
+#endif
+
+  int2 input_coord = (int2)(0, mul24(batch_idx, input_height));
+  int weight_x = 0;
+  for (short h_idx = 0; h_idx < input_height; ++h_idx) {
+    for (short w_idx = 0; w_idx < input_width; ++w_idx) {
+      input_coord.x = w_idx;
+      weight_x = (h_idx * input_width + w_idx) * input_channel;
+#pragma unroll
+      for (short chan_idx = 0; chan_idx < input_chan_blk; ++chan_idx) {
+        input_value = READ_IMAGET(input, SAMPLER, input_coord);
+
+        w0 = READ_IMAGET(weight, SAMPLER, (int2)(weight_x++, out_blk_idx));
+        w1 = READ_IMAGET(weight, SAMPLER, (int2)(weight_x++, out_blk_idx));
+        w2 = READ_IMAGET(weight, SAMPLER, (int2)(weight_x++, out_blk_idx));
+        w3 = READ_IMAGET(weight, SAMPLER, (int2)(weight_x++, out_blk_idx));
+
+        result = mad(input_value.x, w0, result);
+        result = mad(input_value.y, w1, result);
+        result = mad(input_value.z, w2, result);
+        result = mad(input_value.w, w3, result);
+
+        input_coord.x += input_width;
+      }
+    }
+    input_coord.y++;
+  }
+
+#if defined(USE_RELU) || defined(USE_RELUX) || defined(USE_PRELU) || defined(USE_TANH) || defined(USE_SIGMOID)
+  result = do_activation(result, relux_max_limit, prelu_alpha);
+#endif
+  WRITE_IMAGET(output, (int2)(out_blk_idx, batch_idx), result);
+}

mace/kernels/opencl/cl/winograd_transform.cl

@@ -115,8 +115,8 @@ __kernel void winograd_inverse_transform_2x2(__read_only image2d_t input,
                                              __private const int out_width,
                                              __private const int round_hw,
                                              __private const int round_w,
-                                             __private const DATA_TYPE relux_max_limit,
-                                             __private const DATA_TYPE prelu_alpha) {
+                                             __private const float relux_max_limit,
+                                             __private const float prelu_alpha) {
   const int width_idx = get_global_id(0);
   const int height_idx = get_global_id(1);
   const int out_channel = get_global_size(1);

mace/kernels/opencl/conv_2d_opencl_1x1.cc

@@ -66,7 +66,7 @@ extern void Conv2dOpenclK1x1(cl::Kernel *kernel,
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation;
     }
 

mace/kernels/opencl/conv_2d_opencl_3x3.cc

@@ -61,7 +61,7 @@ extern void Conv2dOpenclK3x3(cl::Kernel *kernel,
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation;
     }
 

mace/kernels/opencl/conv_2d_opencl_general.cc

@@ -61,7 +61,7 @@ extern void Conv2dOpencl(cl::Kernel *kernel,
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation;
     }
 

mace/kernels/opencl/depthwise_conv_opencl.cc

@@ -78,7 +78,7 @@ void DepthwiseConv2d(cl::Kernel *kernel,
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation;
     }
 

mace/kernels/opencl/fully_connected_opencl.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/kernels/fully_connected.h"
+#include "mace/core/runtime/opencl/opencl_runtime.h"
+#include "mace/kernels/opencl/helper.h"
+#include "mace/utils/tuner.h"
+
+namespace mace {
+namespace kernels {
+
+template<typename T>
+void FullyConnectedFunctor<DeviceType::OPENCL, T>::operator()(
+    const Tensor *input,
+    const Tensor *weight,
+    const Tensor *bias,
+    Tensor *output,
+    StatsFuture *future) {
+
+  std::vector<index_t> output_shape = {input->dim(0), 1, 1, weight->dim(0)};
+  std::vector<size_t> output_image_shape;
+  CalImage2DShape(output_shape, BufferType::IN_OUT_CHANNEL, output_image_shape);
+  output->ResizeImage(output_shape, output_image_shape);
+
+  const index_t batch = output->dim(0);
+  const index_t output_size = output->dim(3);
+
+  const index_t output_blocks = RoundUpDiv4(output_size);
+
+  if (kernel_.get() == nullptr) {
+    auto runtime = OpenCLRuntime::Global();
+    std::set<std::string> built_options;
+    auto dt = DataTypeToEnum<T>::value;
+    std::string kernel_name = MACE_OBFUSCATE_SYMBOL("fully_connected");
+    built_options.emplace("-Dfully_connected=" + kernel_name);
+    built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
+    built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
+    if (bias != nullptr) {
+      built_options.emplace("-DBIAS");
+    }
+    switch (activation_) {
+      case NOOP:
+        break;
+      case RELU:
+        built_options.emplace("-DUSE_RELU");
+        break;
+      case RELUX:
+        built_options.emplace("-DUSE_RELUX");
+        break;
+      case PRELU:
+        built_options.emplace("-DUSE_PRELU");
+        break;
+      case TANH:
+        built_options.emplace("-DUSE_TANH");
+        break;
+      case SIGMOID:
+        built_options.emplace("-DUSE_SIGMOID");
+        break;
+      default:
+        LOG(FATAL) << "Unknown activation type: " << activation_;
+    }
+    kernel_ = runtime->BuildKernel("fully_connected", kernel_name, built_options);
+
+    uint32_t idx = 0;
+    kernel_.setArg(idx++,
+                   *(static_cast<const cl::Image2D *>(input->buffer())));
+    kernel_.setArg(idx++,
+                   *(static_cast<const cl::Image2D *>(weight->buffer())));
+    if (bias != nullptr) {
+      kernel_.setArg(idx++,
+                     *(static_cast<const cl::Image2D *>(bias->buffer())));
+    }
+    kernel_.setArg(idx++,
+                   *(static_cast<const cl::Image2D *>(output->buffer())));
+    kernel_.setArg(idx++, static_cast<int>(input->dim(1)));
+    kernel_.setArg(idx++, static_cast<int>(input->dim(2)));
+    kernel_.setArg(idx++, static_cast<int>(input->dim(3)));
+    // FIXME handle flexable data type: half not supported
+    kernel_.setArg(idx++, relux_max_limit_);
+    kernel_.setArg(idx++, prelu_alpha_);
+  }
+
+  const uint32_t gws[2] = {
+      static_cast<uint32_t>(batch),
+      static_cast<uint32_t>(output_blocks),
+  };
+  const std::vector<uint32_t> lws = {16, 64, 1};
+  std::stringstream ss;
+  ss << "fc_opencl_kernel_"
+     << output->dim(0) << "_"
+     << output->dim(1) << "_"
+     << output->dim(2) << "_"
+     << output->dim(3);
+  TuningOrRun2DKernel(kernel_, ss.str(), gws, lws, future);
+
+};
+
+template
+struct FullyConnectedFunctor<DeviceType::OPENCL, float>;
+
+template
+struct FullyConnectedFunctor<DeviceType::OPENCL, half>;
+
+}  // namespace kernels
+}  //  namespace mace

mace/kernels/opencl/helper.cc

@@ -73,6 +73,15 @@ void CalInOutWidthImageShape(const std::vector<index_t> &shape, /* NHWC */
   image_shape[1] = shape[0] * shape[1];
 }
 
+// [W, (H + 3) / 4]
+void CalWeightHeightImageShape(const std::vector<index_t> &shape, /* HW */
+                               std::vector<size_t> &image_shape) {
+  MACE_CHECK(shape.size() == 2);
+  image_shape.resize(2);
+  image_shape[0] = shape[1];
+  image_shape[1] = RoundUpDiv4(shape[0]);
+}
+
 void CalImage2DShape(const std::vector<index_t> &shape, /* NHWC */
                      const BufferType type,
                      std::vector<size_t> &image_shape) {
@@ -98,6 +107,9 @@ void CalImage2DShape(const std::vector<index_t> &shape, /* NHWC */
     case WINOGRAD_FILTER:
       CalWinogradFilterImageShape(shape, image_shape);
       break;
+    case WEIGHT_HEIGHT:
+      CalWeightHeightImageShape(shape, image_shape);
+      break;
     default:
       LOG(FATAL) << "Mace not supported yet.";
   }

mace/kernels/opencl/helper.h

@@ -24,6 +24,7 @@ enum BufferType {
   IN_OUT_WIDTH = 4,
   WINOGRAD_FILTER = 5,
   DW_CONV2D_FILTER = 6,
+  WEIGHT_HEIGHT = 7,
 };
 
 void CalImage2DShape(const std::vector<index_t> &shape, /* NHWC */

mace/kernels/opencl/winograd_transform.cc

@@ -101,7 +101,7 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()(const Te
       case SIGMOID:
         built_options.emplace("-DUSE_SIGMOID");
         break;
-      defeult:
+      default:
         LOG(FATAL) << "Unknown activation type: " << activation_;
     }
 

mace/ops/fully_connected.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/ops/fully_connected.h"
+
+namespace mace {
+
+void Register_FullyConnected(OperatorRegistry *op_registry) {
+  REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC")
+                                     .Device(DeviceType::CPU)
+                                     .TypeConstraint<float>("T")
+                                     .Build(),
+                    FullyConnectedOp<DeviceType::CPU, float>);
+
+  REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC")
+                                     .Device(DeviceType::OPENCL)
+                                     .TypeConstraint<float>("T")
+                                     .Build(),
+                    FullyConnectedOp<DeviceType::OPENCL, float>);
+
+  REGISTER_OPERATOR(op_registry, OpKeyBuilder("FC")
+                                     .Device(DeviceType::OPENCL)
+                                     .TypeConstraint<half>("T")
+                                     .Build(),
+                    FullyConnectedOp<DeviceType::OPENCL, half>);
+}
+
+}  //  namespace mace

mace/ops/fully_connected.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_OPS_FULLY_CONNECTED_H_
+#define MACE_OPS_FULLY_CONNECTED_H_
+
+#include "mace/core/operator.h"
+#include "mace/kernels/fully_connected.h"
+
+namespace mace {
+
+template <DeviceType D, class T>
+class FullyConnectedOp : public Operator<D, T> {
+ public:
+  FullyConnectedOp(const OperatorDef &operator_def, Workspace *ws)
+      : Operator<D, T>(operator_def, ws),
+        functor_(
+            kernels::StringToActivationType(
+                OperatorBase::GetSingleArgument<std::string>("activation",
+                                                             "NOOP")),
+            OperatorBase::GetSingleArgument<float>("max_limit", 0.0f),
+            OperatorBase::GetSingleArgument<float>("alpha", 0.0f)) {}
+
+  bool Run(StatsFuture *future) override {
+    const Tensor *input = this->Input(INPUT);
+    const Tensor *weight = this->Input(WEIGHT);
+    const Tensor *bias = this->InputSize() >= 3 ? this->Input(BIAS) : nullptr;
+    Tensor *output = this->Output(OUTPUT);
+
+    const index_t input_size = input->dim(1) * input->dim(2) * input->dim(3);
+    MACE_CHECK(input_size == weight->dim(1) && weight->dim(0) == bias->dim(0))
+        << "The size of Input, Weight and Bias don't match.";
+
+    functor_(input, weight, bias, output, future);
+    return true;
+  }
+
+ private:
+  kernels::FullyConnectedFunctor<D, T> functor_;
+
+ protected:
+  OP_INPUT_TAGS(INPUT, WEIGHT, BIAS);
+  OP_OUTPUT_TAGS(OUTPUT);
+};
+
+}  // namespace mace
+
+#endif  // MACE_OPS_FULLY_CONNECTED_H_

mace/ops/fully_connected_benchmark.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include <string>
+#include "mace/core/operator.h"
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+template <DeviceType D, typename T>
+static void FCBenchmark(
+    int iters, int batch, int height, int width, int channel, int out_channel) {
+  mace::testing::StopTiming();
+
+  OpsTestNet net;
+
+  // Add input data
+  net.AddRandomInput<D, float>("Input", {batch, height, width, channel});
+  net.AddRandomInput<D, float>("Weight", {out_channel, height * width * channel});
+  net.AddRandomInput<D, float>("Bias", {out_channel});
+
+  if (D == DeviceType::OPENCL) {
+    BufferToImage<D, T>(net, "Input", "InputImage",
+                            kernels::BufferType::IN_OUT_CHANNEL);
+    BufferToImage<D, T>(net, "Weight", "WeightImage",
+                            kernels::BufferType::WEIGHT_HEIGHT);
+    BufferToImage<D, T>(net, "Bias", "BiasImage",
+                            kernels::BufferType::ARGUMENT);
+
+    OpDefBuilder("FC", "FullyConnectedTest")
+        .Input("InputImage")
+        .Input("WeightImage")
+        .Input("BiasImage")
+        .Output("OutputImage")
+        .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
+        .Finalize(net.NewOperatorDef());
+  } else {
+    OpDefBuilder("FC", "FullyConnectedTest")
+        .Input("Input")
+        .Input("Weight")
+        .Input("Bias")
+        .Output("Output")
+        .Finalize(net.NewOperatorDef());
+  }
+
+  // Warm-up
+  for (int i = 0; i < 5; ++i) {
+    net.RunOp(D);
+  }
+  net.Sync();
+
+  mace::testing::StartTiming();
+  while (iters--) {
+    net.RunOp(D);
+  }
+  net.Sync();
+}
+
+#define BM_FC_MACRO(N, H, W, C, OC, TYPE, DEVICE)                              \
+  static void BM_FC_##N##_##H##_##W##_##C##_##OC##_##TYPE##_##DEVICE(int iters) { \
+    const int64_t macc = static_cast<int64_t>(iters) * N * C * H * W * OC + OC;  \
+    const int64_t tot = static_cast<int64_t>(iters) * (N + OC) * C * H * W + OC; \
+    mace::testing::MaccProcessed(macc);                                          \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                          \
+    FCBenchmark<DEVICE, TYPE>(iters, N, H, W, C, OC);                            \
+  }                                                                              \
+  BENCHMARK(BM_FC_##N##_##H##_##W##_##C##_##OC##_##TYPE##_##DEVICE)
+
+#define BM_FC(N, H, W, C, OC)                 \
+  BM_FC_MACRO(N, H, W, C, OC, float, CPU);    \
+  BM_FC_MACRO(N, H, W, C, OC, float, OPENCL); \
+  BM_FC_MACRO(N, H, W, C, OC, half, OPENCL);
+
+BM_FC(1, 16, 16, 32, 32);
+BM_FC(1, 8, 8, 32, 1000);
+}  // namespace mace

mace/ops/fully_connected_test.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include <fstream>
+#include "mace/core/operator.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+
+class FullyConnectedOpTest : public OpsTestBase {};
+
+template<DeviceType D>
+void Simple(const std::vector<index_t> &input_shape,
+            const std::vector<float> &input_value,
+            const std::vector<index_t> &weight_shape,
+            const std::vector<float> &weight_value,
+            const std::vector<index_t> &bias_shape,
+            const std::vector<float> &bias_value,
+            const std::vector<index_t> &output_shape,
+            const std::vector<float> &output_value) {
+  OpsTestNet net;
+
+  // Add input data
+  net.AddInputFromArray<D, float>("Input", input_shape, input_value);
+  net.AddInputFromArray<D, float>("Weight", weight_shape, weight_value);
+  net.AddInputFromArray<D, float>("Bias", bias_shape, bias_value);
+
+  if (D == DeviceType::OPENCL) {
+    BufferToImage<D, float>(net, "Input", "InputImage",
+                            kernels::BufferType::IN_OUT_CHANNEL);
+    BufferToImage<D, float>(net, "Weight", "WeightImage",
+                            kernels::BufferType::WEIGHT_HEIGHT);
+    BufferToImage<D, float>(net, "Bias", "BiasImage",
+                            kernels::BufferType::ARGUMENT);
+
+    OpDefBuilder("FC", "FullyConnectedTest")
+        .Input("InputImage")
+        .Input("WeightImage")
+        .Input("BiasImage")
+        .Output("OutputImage")
+        .Finalize(net.NewOperatorDef());
+    // Run
+    net.RunOp(D);
+
+    // Transfer output
+    ImageToBuffer<D, float>(net, "OutputImage", "Output",
+                            kernels::BufferType::IN_OUT_CHANNEL);
+  } else {
+    OpDefBuilder("FC", "FullyConnectedTest")
+        .Input("Input")
+        .Input("Weight")
+        .Input("Bias")
+        .Output("Output")
+        .Finalize(net.NewOperatorDef());
+    // Run
+    net.RunOp(D);
+  }
+
+  // Check
+  auto expected =
+      CreateTensor<float>(output_shape, output_value);
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
+}
+
+TEST_F(FullyConnectedOpTest, SimpleCPU) {
+  Simple<DeviceType::CPU>({1, 2, 2, 2},
+                          {1, 2, 3, 4, 5, 6, 7, 8},
+                          {1, 8},
+                          {1, 2, 3, 4, 5, 6, 7, 8},
+                          {1}, {2},
+                          {1, 1, 1, 1}, {206});
+  Simple<DeviceType::CPU>({1, 1, 2, 5},
+                          {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+                          {2, 10},
+                          {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+                           10, 20, 30, 40, 50, 60, 70, 80, 90, 100},
+                          {2}, {2, 3},
+                          {1, 1, 1, 2}, {387, 3853});
+  Simple<DeviceType::CPU>({1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6},
+                          {5, 6},
+                          {1, 2, 3, 4, 5, 6,
+                           10, 20, 30, 40, 50, 60,
+                           1, 2, 3, 4, 5, 6,
+                           10, 20, 30, 40, 50, 60,
+                           1, 2, 3, 4, 5, 6},
+                          {5}, {1, 2, 3, 4, 5},
+                          {1, 1, 1, 5}, {92, 912, 94, 914, 96});
+}
+
+TEST_F(FullyConnectedOpTest, SimpleCPUWithBatch) {
+  Simple<DeviceType::CPU>({2, 1, 2, 2},
+                          {1, 2, 3, 4, 5, 6, 7, 8},
+                          {1, 4},
+                          {1, 2, 3, 4},
+                          {1}, {2},
+                          {2, 1, 1, 1}, {32, 72});
+}
+
+TEST_F(FullyConnectedOpTest, SimpleOPENCL) {
+  Simple<DeviceType::OPENCL>({1, 2, 2, 2},
+                             {1, 2, 3, 4, 5, 6, 7, 8},
+                             {1, 8},
+                             {1, 2, 3, 4, 5, 6, 7, 8},
+                             {1}, {2},
+                             {1, 1, 1, 1}, {206});
+  Simple<DeviceType::OPENCL>({1, 1, 2, 5},
+                             {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+                             {2, 10},
+                             {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+                              10, 20, 30, 40, 50, 60, 70, 80, 90, 100},
+                             {2}, {2, 3},
+                             {1, 1, 1, 2}, {387, 3853});
+  Simple<DeviceType::OPENCL>({1, 1, 2, 3},
+                             {1, 2, 3, 4, 5, 6},
+                             {5, 6},
+                             {1, 2, 3, 4, 5, 6,
+                              10, 20, 30, 40, 50, 60,
+                              1, 2, 3, 4, 5, 6,
+                              10, 20, 30, 40, 50, 60,
+                              1, 2, 3, 4, 5, 6},
+                             {5}, {1, 2, 3, 4, 5},
+                             {1, 1, 1, 5}, {92, 912, 94, 914, 96});
+}
+
+TEST_F(FullyConnectedOpTest, SimpleGPUWithBatch) {
+  Simple<DeviceType::OPENCL>({2, 1, 2, 2},
+                             {1, 2, 3, 4, 5, 6, 7, 8},
+                             {1, 4},
+                             {1, 2, 3, 4},
+                             {1}, {2},
+                             {2, 1, 1, 1}, {32, 72});
+}
+
+template<typename T>
+void Complex(const index_t batch,
+             const index_t height,
+             const index_t width,
+             const index_t channels,
+             const index_t out_channel) {
+  srand(time(NULL));
+
+  // Construct graph
+  OpsTestNet net;
+  OpDefBuilder("FC", "FullyConnectedTest")
+      .Input("Input")
+      .Input("Weight")
+      .Input("Bias")
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+
+  // Add input data
+  net.AddRandomInput<DeviceType::OPENCL, float>(
+      "Input", {batch, height, width, channels});
+  net.AddRandomInput<DeviceType::OPENCL, float>(
+      "Weight", {out_channel, height * width * channels});
+  net.AddRandomInput<DeviceType::OPENCL, float>(
+      "Bias", {out_channel});
+
+  // run cpu
+  net.RunOp();
+
+  // Check
+  Tensor expected;
+  expected.Copy(*net.GetOutput("Output"));
+
+  // Run on opencl
+  BufferToImage<DeviceType::OPENCL, T>(net, "Input", "InputImage",
+                                       kernels::BufferType::IN_OUT_CHANNEL);
+  BufferToImage<DeviceType::OPENCL, T>(net, "Weight", "WeightImage",
+                                       kernels::BufferType::WEIGHT_HEIGHT);
+  BufferToImage<DeviceType::OPENCL, float>(net, "Bias", "BiasImage",
+                                           kernels::BufferType::ARGUMENT);
+
+  OpDefBuilder("FC", "FullyConnectedTest")
+      .Input("InputImage")
+      .Input("WeightImage")
+      .Input("BiasImage")
+      .Output("OutputImage")
+      .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
+      .Finalize(net.NewOperatorDef());
+
+  // Run on opencl
+  net.RunOp(DeviceType::OPENCL);
+
+  ImageToBuffer<DeviceType::OPENCL, float>(net, "OutputImage", "OPENCLOutput",
+                                           kernels::BufferType::IN_OUT_CHANNEL);
+  if (DataTypeToEnum<T>::value == DataType::DT_HALF) {
+    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1);
+  } else {
+    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-3);
+  }
+}
+
+TEST_F(FullyConnectedOpTest, OPENCLAlignedWithoutBatch) {
+  Complex<float>(1, 16, 16, 32, 16);
+  Complex<float>(1, 16, 32, 32, 32);
+}
+TEST_F(FullyConnectedOpTest, OPENCLUnAlignedWithoutBatch) {
+  Complex<float>(1, 13, 11, 11, 17);
+  Complex<float>(1, 23, 29, 23, 113);
+}
+TEST_F(FullyConnectedOpTest, OPENCLUnAlignedWithBatch) {
+  Complex<float>(16, 11, 13, 23, 17);
+  Complex<float>(31, 13, 11, 29, 113);
+}
+TEST_F(FullyConnectedOpTest, OPENCLHalfAlignedWithoutBatch) {
+  Complex<half>(1, 16, 16, 32, 16);
+  Complex<half>(1, 16, 32, 32, 32);
+}
+TEST_F(FullyConnectedOpTest, OPENCLHalfUnAlignedWithBatch) {
+  Complex<half>(2, 11, 13, 61, 17);
+  Complex<half>(16, 13, 12, 31, 113);
+  Complex<half>(31, 21, 11, 23, 103);
+}
+
+}
+