elementwise_compute_test.cc

// Copyright (c) 2019 PaddlePaddle 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 "lite/kernels/fpga/elementwise_compute.h"
#include <gtest/gtest.h>
#include <string>
#include <vector>
#include "lite/backends/fpga/KD/float16.hpp"
#include "lite/core/op_registry.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace fpga {

using float16 = zynqmp::float16;

TEST(elementwise_add_fpga, retrive_op) {
  auto elementwise_add =
      KernelRegistry::Global()
          .Create<TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)>(
              "elementwise_add");
  ASSERT_FALSE(elementwise_add.empty());
  ASSERT_TRUE(elementwise_add.front());
}

TEST(elementwise_add_fpga, init) {
  ElementwiseAddCompute elementwise_add;
  ASSERT_EQ(elementwise_add.precision(), PRECISION(kFP16));
  ASSERT_EQ(elementwise_add.target(), TARGET(kFPGA));
}

template <typename dtype>
dtype sum(dtype d1, dtype d2) {
  return d1 + d2;
}
template <>
float16 sum<float16>(float16 d1, float16 d2) {
  return zynqmp::float_to_half(zynqmp::half_to_float(d1) +
                               zynqmp::half_to_float(d2));
}
template <typename dtype>
bool greater(dtype d1, dtype d2) {  // NOLINT
  return d1 > d2;
}
template <>
bool greater<float16>(float16 d1, float16 d2) {  // NOLINT
  return zynqmp::half_to_float(d1) > zynqmp::half_to_float(d2);
}
template <typename dtype>
dtype float_cast(float d) {
  return d;
}
template <>
float16 float_cast<float16>(float d) {
  return zynqmp::float_to_half(d);
}

template <typename dtype>
void elementwise_compute_ref(const operators::ElementwiseParam& param,
                             const std::string elt_type,
                             const std::string act_type) {
  const dtype* x_data = param.X->data<const dtype>();
  const dtype* y_data = param.Y->data<const dtype>();
  dtype* out_data = param.Out->mutable_data<dtype>();
  auto x_dims = param.X->dims();
  auto y_dims = param.Y->dims();
  int axis = param.axis;
  if (axis < 0) {
    axis = x_dims.size() - y_dims.size();
  }
  int batch = 1;
  int channels = 1;
  int num = 1;
  for (int i = 0; i < axis; ++i) {
    batch *= x_dims[i];
  }
  for (int i = 0; i < y_dims.size(); ++i) {
    channels *= y_dims[i];
  }
  for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
    num *= x_dims[i];
  }
  // do elementwise add/sub/max...
  if (elt_type == "add") {
    for (int i = 0; i < batch; ++i) {
      for (int j = 0; j < channels; ++j) {
        int offset = (i * channels + j) * num;
        const dtype* din_ptr = x_data + offset;
        const dtype diny_data = y_data[j];
        dtype* dout_ptr = out_data + offset;
        for (int k = 0; k < num; ++k) {
          *dout_ptr = sum(*din_ptr, diny_data);
          dout_ptr++;
          din_ptr++;
        }
      }
    }
  } else if (elt_type == "sub") {
    for (int i = 0; i < batch; ++i) {
      for (int j = 0; j < channels; ++j) {
        int offset = (i * channels + j) * num;
        const dtype* din_ptr = x_data + offset;
        const dtype diny_data = y_data[j];
        dtype* dout_ptr = out_data + offset;
        for (int k = 0; k < num; ++k) {
          *dout_ptr = *din_ptr - diny_data;
          dout_ptr++;
          din_ptr++;
        }
      }
    }
  } else {
    LOG(FATAL) << "unsupported Elementwise type: " << elt_type;
  }
  // do activation relu/sigmod...
  if (act_type.size() > 0) {
    if (act_type == "relu") {
      float16 zero = float_cast<float16>(0.0f);
      for (int i = 0; i < batch; ++i) {
        for (int j = 0; j < channels; ++j) {
          dtype* dout_ptr = out_data + (i * channels + j) * num;
          for (int k = 0; k < num; ++k) {
            *dout_ptr = greater(*dout_ptr, zero) ? *dout_ptr : zero;
            dout_ptr++;
          }
        }
      }
    } else {
      LOG(FATAL) << "unsupported Activation type: " << elt_type;
    }
  }
}

TEST(elementwise_add, compute) {
  ElementwiseAddCompute elementwise_add;
  operators::ElementwiseParam param;
  lite::Tensor x, y, output, output_ref;

  for (auto n : {1}) {
    for (auto c : {8}) {
      for (auto h : {8}) {
        for (auto w : {8}) {
          for (auto axis : {0}) {
            for (auto yd : {std::vector<int64_t>({n, c, h, w})}) {
              auto x_dim = DDim(std::vector<int64_t>({n, c, h, w}));
              auto y_dim = DDim(yd);
              int axis_t = axis < 0 ? x_dim.size() - y_dim.size() : axis;

              if (axis_t + y_dim.size() > 4) continue;
              bool flag = false;
              for (int i = 0; i < y_dim.size(); i++) {
                if (x_dim[i + axis_t] != y_dim[i]) flag = true;
              }
              if (flag) continue;

              x.Resize(x_dim);
              y.Resize(y_dim);
              output.Resize(x_dim);
              output_ref.Resize(x_dim);
              auto* x_data = x.mutable_data<float16>(TARGET(kFPGA));
              auto* y_data = y.mutable_data<float16>(TARGET(kFPGA));
              auto* output_data = output.mutable_data<float16>(TARGET(kFPGA));
              auto* output_ref_data =
                  output_ref.mutable_data<float16>(TARGET(kFPGA));
              for (int i = 0; i < x_dim.production(); i++) {
                x_data[i] = zynqmp::float_to_half(i);
              }
              for (int i = 0; i < y_dim.production(); i++) {
                y_data[i] = zynqmp::float_to_half(i);
              }
              param.X = &x;
              param.Y = &y;
              param.axis = axis;
              param.Out = &output;
              elementwise_add.SetParam(param);
              elementwise_add.PrepareForRun();
              elementwise_add.Run();
              param.Out = &output_ref;

              elementwise_compute_ref<float16>(param, "add", "");
              for (int i = 0; i < output.dims().production(); i++) {
                EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
              }
            }
          }
        }
      }
    }
  }
}

TEST(fusion_elementwise_add_activation_fpga, retrive_op) {
  auto fusion_elementwise_add_activation =
      KernelRegistry::Global()
          .Create<TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)>(
              "fusion_elementwise_add_activation");
  ASSERT_FALSE(fusion_elementwise_add_activation.empty());
  ASSERT_TRUE(fusion_elementwise_add_activation.front());
}

TEST(fusion_elementwise_add_activation_fpga, init) {
  ElementwiseAddActivationCompute fusion_elementwise_add_activation;
  ASSERT_EQ(fusion_elementwise_add_activation.precision(), PRECISION(kFP16));
  ASSERT_EQ(fusion_elementwise_add_activation.target(), TARGET(kFPGA));
}

TEST(fusion_elementwise_add_activation_fpga, compute) {
  ElementwiseAddActivationCompute fusion_elementwise_add_activation;
  operators::FusionElementwiseActivationParam param;
  lite::Tensor x, y, output, output_ref;

  for (auto act_type : {"relu"}) {
    for (auto n : {1}) {
      for (auto c : {8}) {
        for (auto h : {8}) {
          for (auto w : {8}) {
            for (auto axis : {0}) {
              for (auto yd : {std::vector<int64_t>({n, c, h, w})}) {
                auto x_dim = DDim(std::vector<int64_t>({n, c, h, w}));
                auto y_dim = DDim(yd);
                int axis_t = axis < 0 ? x_dim.size() - y_dim.size() : axis;

                if (axis_t + y_dim.size() > 4) continue;
                bool flag = false;
                for (int i = 0; i < y_dim.size(); i++) {
                  if (x_dim[i + axis_t] != y_dim[i]) flag = true;
                }
                if (flag) continue;

                x.Resize(x_dim);
                y.Resize(y_dim);
                output.Resize(x_dim);
                output_ref.Resize(x_dim);
                auto* x_data = x.mutable_data<float16>(TARGET(kFPGA));
                auto* y_data = y.mutable_data<float16>(TARGET(kFPGA));
                auto* output_data = output.mutable_data<float16>(TARGET(kFPGA));
                auto* output_ref_data =
                    output_ref.mutable_data<float16>(TARGET(kFPGA));
                for (int i = 0; i < x_dim.production(); i++) {
                  float sign = i % 3 == 0 ? -1.0f : 1.0f;
                  x_data[i] = zynqmp::float_to_half(i * sign);
                }
                for (int i = 0; i < y_dim.production(); i++) {
                  float sign = i % 2 == 0 ? 0.5f : -0.5f;
                  y_data[i] = zynqmp::float_to_half(i * sign);
                }
                param.X = &x;
                param.Y = &y;
                param.axis = axis;
                param.Out = &output;
                param.act_type = act_type;
                fusion_elementwise_add_activation.SetParam(param);
                fusion_elementwise_add_activation.PrepareForRun();
                fusion_elementwise_add_activation.Run();
                param.Out = &output_ref;
                elementwise_compute_ref<float16>(param, "add", act_type);
                for (int i = 0; i < output.dims().production(); i++) {
                  EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
                }
              }
            }
          }
        }
      }
    }
  }
}

}  // namespace fpga
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

USE_LITE_KERNEL(elementwise_add, kFPGA, kFP16, kNHWC, def);
USE_LITE_KERNEL(fusion_elementwise_add_activation, kFPGA, kFP16, kNHWC, def);