conv_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 "paddle/fluid/lite/kernels/arm/conv_compute.h"
#include <gtest/gtest.h>
#include <limits>
#include <memory>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/arm/math/type_trans.h"
#include "paddle/fluid/lite/core/op_registry.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace arm {

static int get_rand(int start, int end) {
  int i = rand();  // NOLINT
  i = (i % (end - start)) + start;
  return i;
}

template <typename Dtype1, typename Dtype2>
static void conv_basic(const Dtype1* din, Dtype2* dout, int num, int chout,
                       int hout, int wout, int chin, int hin, int win,
                       const Dtype1* weights, const Dtype2* bias, int group,
                       int kernel_w, int kernel_h, int stride_w, int stride_h,
                       int dila_w, int dila_h, int pad_w, int pad_h,
                       bool flag_bias, bool flag_relu) {
  Dtype2 beta = 0;
  auto src_data = din;
  auto dst_data_ref = dout;
  auto weights_data = weights;
  auto with_bias = flag_bias;
  auto bias_data = bias;

  int in_num = num;
  int out_channels = chout;
  int out_h = hout;
  int out_w = wout;

  int in_channel = chin;
  int in_h = hin;
  int in_w = win;
  int out_c_group = out_channels / group;
  int in_c_group = in_channel / group;

  for (int n = 0; n < in_num; ++n) {
    for (int g = 0; g < group; ++g) {
      for (int oc = 0; oc < out_c_group; ++oc) {
        for (int oh = 0; oh < out_h; ++oh) {
          for (int ow = 0; ow < out_w; ++ow) {
            int out_idx = n * group * out_c_group * out_h * out_w +
                          g * out_c_group * out_h * out_w + oc * out_h * out_w +
                          oh * out_w + ow;
            Dtype2 bias_d =
                with_bias ? (bias_data[g * out_c_group + oc]) : (Dtype2)0;
            dst_data_ref[out_idx] = bias_d;  // + dst_data_ref[out_idx] * beta;
            for (int ic = 0; ic < in_c_group; ++ic) {
              for (int kh = 0; kh < kernel_h; ++kh) {
                for (int kw = 0; kw < kernel_w; ++kw) {
                  int iw = ow * stride_w - pad_w + kw * (dila_w);
                  int ih = oh * stride_h - pad_h + kh * (dila_h);
                  if (iw < 0 || iw >= in_w) continue;
                  if (ih < 0 || ih >= in_h) continue;

                  int iidx = n * in_channel * in_h * in_w +
                             g * in_c_group * in_h * in_w + ic * in_h * in_w +
                             ih * in_w + iw;
                  int widx =
                      g * out_c_group * in_c_group * kernel_h * kernel_w +
                      oc * in_c_group * kernel_h * kernel_w +
                      ic * kernel_h * kernel_w + kh * kernel_w + kw;

                  dst_data_ref[out_idx] += src_data[iidx] * weights_data[widx];
                }
              }
            }
            if (flag_relu) {
              dst_data_ref[out_idx] = dst_data_ref[out_idx] > (Dtype2)0
                                          ? dst_data_ref[out_idx]
                                          : (Dtype2)0;
            }
          }
        }
      }
    }
  }
}

template <typename Dtype1, typename Dtype2>
void conv_compute_ref(const operators::ConvParam& param) {
  const Dtype1* din = param.x->data<Dtype1>();
  Dtype2* dout = param.output->mutable_data<Dtype2>();

  int num = param.x->dims()[0];
  int chout = param.output->dims()[1];
  int hout = param.output->dims()[2];
  int wout = param.output->dims()[3];

  int chin = param.x->dims()[1];
  int hin = param.x->dims()[2];
  int win = param.x->dims()[3];

  const Dtype1* weights = param.filter->mutable_data<Dtype1>();
  Dtype2* bias = nullptr;
  if (param.bias != nullptr) {
    bias = param.bias->mutable_data<Dtype2>();
  }

  int group = param.groups;
  int kernel_w = param.filter->dims()[2];
  int kernel_h = param.filter->dims()[3];
  int stride_w = param.strides[0];
  int stride_h = param.strides[1];
  int dila_w = param.dilations[0];
  int dila_h = param.dilations[1];

  int pad_w = param.paddings[0];
  int pad_h = param.paddings[1];
  bool flag_bias = (param.bias != nullptr);
  bool flag_relu = param.fuse_relu;

  conv_basic(din, dout, num, chout, hout, wout, chin, hin, win, weights, bias,
             group, kernel_w, kernel_h, stride_w, stride_h, dila_w, dila_h,
             pad_w, pad_h, flag_bias, flag_relu);
}

TEST(conv_arm, retrive_op) {
  auto conv = KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>(
      "conv2d");
  ASSERT_FALSE(conv.empty());
  ASSERT_TRUE(conv.front());
}

TEST(conv_arm_int8, retrive_op) {
  auto conv =
      KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kInt8)>("conv2d");
  ASSERT_FALSE(conv.empty());
  ASSERT_TRUE(conv.front());
}

TEST(conv_arm, init) {
  ConvCompute conv;
  ASSERT_EQ(conv.precision(), PRECISION(kFloat));
  ASSERT_EQ(conv.target(), TARGET(kARM));
}

TEST(conv_arm_int8, init) {
  ConvComputeInt8<PRECISION(kFloat)> float_out;
  ASSERT_EQ(float_out.precision(), PRECISION(kInt8));
  ASSERT_EQ(float_out.target(), TARGET(kARM));
  ConvComputeInt8<PRECISION(kInt8)> int8_out;
  ASSERT_EQ(float_out.precision(), PRECISION(kInt8));
  ASSERT_EQ(float_out.target(), TARGET(kARM));
}

TEST(conv_arm_int8, int8_int32) {
  DeviceInfo::Init();
  for (auto n : {2}) {
    for (auto ic : {6}) {
      for (auto oc : {6}) {
        for (auto ih : {9}) {
          for (auto iw : {9}) {
            for (auto flag_bias : {false, true}) {
              for (auto flag_relu : {false, true}) {
                for (auto depthwise : {false, /*true*/}) {
                  for (auto dilation : {1}) {
                    for (auto stride : {1}) {
                      for (auto padding : {0}) {
                        for (auto ks : {1}) {
                          int group = 1;
                          if (depthwise) {  // depthwise convolution ?
                            group = oc = ic;
                          }

                          const int dks = dilation * (ks - 1) + 1;
                          int oh = (ih + 2 * padding - dks) / stride + 1;
                          int ow = (iw + 2 * padding - dks) / stride + 1;
                          std::vector<int64_t> input_shape = {n, ic, ih, iw};
                          std::vector<int64_t> filter_shape = {oc, ic / group,
                                                               ks, ks};
                          std::vector<int64_t> output_shape({n, oc, oh, ow});

                          Tensor input_int8;
                          Tensor filter_int8;
                          Tensor output_int32, output_int32_ref;

                          input_int8.Resize(input_shape);
                          filter_int8.Resize(filter_shape);
                          output_int32.Resize(output_shape);
                          output_int32_ref.Resize(output_shape);

                          int8_t* input_int8_data =
                              input_int8.mutable_data<int8_t>();
                          int8_t* filter_int8_data =
                              filter_int8.mutable_data<int8_t>();
                          for (int i = 0; i < input_int8.dims().production();
                               i++) {
                            input_int8_data[i] = i % 10 * (i % 3 - 1);
                          }
                          for (int i = 0; i < filter_int8.dims().production();
                               i++) {
                            filter_int8_data[i] = i % 10 * (i % 3 - 1);
                          }

                          operators::ConvParam param;
                          param.x = &input_int8;
                          param.filter = &filter_int8;
                          param.bias = nullptr;
                          param.fuse_relu = false;
                          param.paddings = std::vector<int>({padding, padding});
                          param.strides = std::vector<int>({stride, stride});
                          param.dilations =
                              std::vector<int>({dilation, dilation});
                          param.groups = group;
                          param.output = &output_int32_ref;
                          conv_compute_ref<int8_t, int>(param);

                          param.output = &output_int32;
                          std::unique_ptr<KernelContext> ctx(new KernelContext);
                          lite::arm::math::GemmLikeConvInt8<PRECISION(kInt32)>
                              int8gemm_int32;
                          int8gemm_int32.init(param, &ctx->As<ARMContext>());
                          int8gemm_int32.create(param, &ctx->As<ARMContext>());
                          int8gemm_int32.run(param);

                          int* output_int32_data =
                              output_int32.mutable_data<int>();
                          int* output_int32_ref_data =
                              output_int32_ref.mutable_data<int>();

                          for (int i = 0; i < output_int32.dims().production();
                               i++) {
                            EXPECT_NEAR(output_int32_data[i],
                                        output_int32_ref_data[i], 1e-3);
                          }
                        }
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

TEST(conv_arm_int8, int8_fp32) {
  DeviceInfo::Init();
  for (auto n : {2}) {
    for (auto ic : {6}) {
      for (auto oc : {6}) {
        for (auto ih : {9}) {
          for (auto iw : {9}) {
            for (auto flag_bias : {false, true}) {
              for (auto flag_relu : {false, true}) {
                for (auto depthwise : {false, /*true*/}) {
                  for (auto dilation : {1}) {
                    for (auto stride : {1}) {
                      for (auto padding : {0}) {
                        for (auto ks : {1}) {
                          int group = 1;
                          if (depthwise) {  // depthwise convolution ?
                            group = oc = ic;
                          }

                          LOG(INFO) << "flag_bias: " << flag_bias;

                          const int dks = dilation * (ks - 1) + 1;
                          int oh = (ih + 2 * padding - dks) / stride + 1;
                          int ow = (iw + 2 * padding - dks) / stride + 1;
                          std::vector<int64_t> input_shape = {n, ic, ih, iw};
                          std::vector<int64_t> filter_shape = {oc, ic / group,
                                                               ks, ks};
                          std::vector<int64_t> bias_shape({1, oc, 1, 1});
                          std::vector<int64_t> output_shape({n, oc, oh, ow});

                          Tensor input_fp32, input_int8;
                          Tensor filter_fp32, filter_int8;
                          Tensor bias_fp32, bias_int32;
                          Tensor output_int32_ref, output_int32;
                          Tensor output_fp32_ref, output_fp32;
                          Tensor output_int8_ref, output_int8;

                          input_fp32.Resize(input_shape);
                          input_int8.Resize(input_shape);
                          filter_fp32.Resize(filter_shape);
                          filter_int8.Resize(filter_shape);
                          bias_fp32.Resize(bias_shape);
                          bias_int32.Resize(bias_shape);
                          output_int32.Resize(output_shape);
                          output_int32_ref.Resize(output_shape);
                          output_fp32_ref.Resize(output_shape);
                          output_fp32.Resize(output_shape);
                          output_int8_ref.Resize(output_shape);
                          output_int8.Resize(output_shape);

                          float* input_fp32_data =
                              input_fp32.mutable_data<float>();
                          int8_t* input_int8_data =
                              input_int8.mutable_data<int8_t>();

                          float* filter_fp32_data =
                              filter_fp32.mutable_data<float>();
                          int8_t* filter_int8_data =
                              filter_int8.mutable_data<int8_t>();

                          float* bias_fp32_data =
                              bias_fp32.mutable_data<float>();
                          int* bias_int32_data = bias_int32.mutable_data<int>();

                          for (int i = 0; i < input_fp32.dims().production();
                               i++) {
                            input_fp32_data[i] = i % 10 * (i % 3 - 1);
                          }
                          for (int i = 0; i < filter_fp32.dims().production();
                               i++) {
                            filter_fp32_data[i] = i % 10 * (i % 3 - 1);
                          }
                          for (int i = 0; i < bias_fp32.dims().production();
                               i++) {
                            bias_fp32_data[i] = i % 10 * (i % 3 - 1);
                          }

                          std::vector<float> in_scale;
                          lite::arm::math::get_tensor_scale<PRECISION(kFloat)>(
                              input_fp32, &in_scale, -1, 127.f);
                          lite::arm::math::trans_tensor_fp32_to_int8(
                              &input_fp32, &input_int8, in_scale[0]);

                          std::vector<float> w_scale;
                          lite::arm::math::get_tensor_scale<PRECISION(kFloat)>(
                              filter_fp32, &w_scale, -1, 127.f);
                          int axis_size = oc;
                          int inner_size = ic / group * ks * ks;
                          w_scale = lite::arm::math::get_tensor_scale_n(
                              filter_fp32_data, axis_size, inner_size, 127.f);
                          lite::arm::math::fp32_to_int8(
                              filter_fp32_data, filter_int8_data,
                              w_scale.data(), axis_size, 1, inner_size);

                          // lite::arm::math::trans_fp32_bias_to_int32_basic(&bias_fp32,
                          // &bias_int32, in_scale[0], w_scale);
                          for (int i = 0; i < bias_int32.dims().production();
                               i++) {
                            bias_int32_data[i] = 1;
                          }

                          operators::ConvParam param;
                          param.x = &input_int8;
                          param.filter = &filter_int8;
                          if (flag_bias) {
                            param.bias = &bias_int32;
                          } else {
                            param.bias = nullptr;
                          }
                          param.fuse_relu = false;
                          param.paddings = std::vector<int>({padding, padding});
                          param.strides = std::vector<int>({stride, stride});
                          param.dilations =
                              std::vector<int>({dilation, dilation});
                          param.groups = group;
                          param.output = &output_int32_ref;
                          conv_compute_ref<int8_t, int>(param);

                          int* output_int32_ref_data =
                              output_int32_ref.mutable_data<int>();

                          // ============ int8gemm_int32 ============
                          /*
                          param.output = &output_int32;
                          std::unique_ptr<KernelContext> ctx_int32(
                              new KernelContext);
                          lite::arm::math::GemmLikeConvInt8<PRECISION(kInt32)>
                              int8gemm_int32;
                          int8gemm_int32.init(param,
                                              &ctx_int32->As<ARMContext>());
                          int8gemm_int32.create(param,
                                                &ctx_int32->As<ARMContext>());
                          int8gemm_int32.run(param);
                          int* output_int32_data =
                              output_int32.mutable_data<int>();
                          for (int i = 0; i < output_int32.dims().production();
                               i++) {
                            EXPECT_NEAR(output_int32_data[i],
                                        output_int32_ref_data[i], 1e-3);
                          }
                          */
                          // ============ int8gemm_int8 ============
                          int8_t* output_int8_ref_data =
                              output_int8_ref.mutable_data<int8_t>();
                          lite::arm::math::trans_tensor_int32_to_int8(
                              &output_int32_ref, &output_int8_ref, in_scale[0],
                              1, w_scale);
                          param.output = &output_int8;
                          param.input_scale = in_scale[0];
                          param.output_scale = 1;
                          param.weight_scale = w_scale;
                          std::unique_ptr<KernelContext> ctx_int8(
                              new KernelContext);
                          lite::arm::math::GemmLikeConvInt8<PRECISION(kInt8)>
                              int8gemm_int8;
                          int8gemm_int8.init(param,
                                             &ctx_int8->As<ARMContext>());
                          int8gemm_int8.create(param,
                                               &ctx_int8->As<ARMContext>());
                          int8gemm_int8.run(param);
                          int8_t* output_int8_data =
                              output_int8.mutable_data<int8_t>();
                          for (int i = 0; i < output_int8.dims().production();
                               i++) {
                            EXPECT_NEAR(output_int8_data[i],
                                        output_int8_ref_data[i], 1e-3);
                          }

                          // ============ int8gemm_float32 ============
                          float* output_fp32_ref_data =
                              output_fp32_ref.mutable_data<float>();
                          lite::arm::math::trans_tensor_int32_to_fp32(
                              &output_int32_ref, &output_fp32_ref, in_scale[0],
                              w_scale);
                          param.output = &output_fp32;
                          param.input_scale = in_scale[0];
                          param.output_scale = 1;
                          param.weight_scale = w_scale;
                          std::unique_ptr<KernelContext> ctx_fp32(
                              new KernelContext);
                          lite::arm::math::GemmLikeConvInt8<PRECISION(kFloat)>
                              int8gemm_fp32;
                          int8gemm_fp32.init(param,
                                             &ctx_fp32->As<ARMContext>());
                          int8gemm_fp32.create(param,
                                               &ctx_fp32->As<ARMContext>());
                          int8gemm_fp32.run(param);
                          float* output_fp32_data =
                              output_fp32.mutable_data<float>();
                          for (int i = 0; i < output_fp32.dims().production();
                               i++) {
                            EXPECT_NEAR(output_fp32_data[i],
                                        output_fp32_ref_data[i], 1e-3);
                          }
                        }
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

TEST(conv_arm, compute) {
  DeviceInfo::Init();
#if 1
  for (auto n : {2}) {
    for (auto ic : {6}) {
      for (auto oc : {6}) {
        for (auto ih : {9}) {
          for (auto iw : {9}) {
            for (auto flag_bias : {false, true}) {
              for (auto flag_relu : {false, true}) {
                for (auto depthwise : {false, true}) {
                  for (auto dilation : {1}) {
                    for (auto stride : {1, 2}) {
                      for (auto padding : {0, 1, 2}) {
                        for (auto ks : {1, 3, 5}) {
#else
  for (auto n : {1, 2}) {
    for (auto ic : {6, 32 /*, 128*/}) {
      for (auto oc : {6, 32 /*, 128*/}) {
        for (auto ih : {9, 18 /*, 56 , 112, 224, 512*/}) {
          for (auto iw : {9, 18 /*, 56, 112, 224, 512*/}) {
            for (auto flag_bias : {false, true}) {
              for (auto flag_relu : {false, true}) {
                for (auto depthwise : {false, true}) {
                  for (auto dilation : {1, 2}) {
                    for (auto stride : {1, 2}) {
                      for (auto padding : {0, 1, 2}) {
                        for (auto ks : {1, 3, 5}) {
#endif
                          int group = 1;
                          if (depthwise) {  // depthwise convolution ?
                            group = oc = ic;
                          }
                          // get input, filter and output shape
                          std::vector<int64_t> input_shape = {n, ic, ih, iw};
                          std::vector<int64_t> filter_shape = {oc, ic / group,
                                                               ks, ks};
                          const int dks = dilation * (ks - 1) + 1;
                          int oh = (ih + 2 * padding - dks) / stride + 1;
                          int ow = (iw + 2 * padding - dks) / stride + 1;
                          std::vector<int64_t> output_shape({n, oc, oh, ow});
                          // resize input, filter and output
                          Tensor input;
                          Tensor filter;
                          Tensor bias;
                          Tensor output;
                          Tensor output_ref;
                          input.Resize(input_shape);
                          filter.Resize(filter_shape);
                          output.Resize(output_shape);
                          output_ref.Resize(output_shape);
                          VLOG(3) << "input: " << input.dims();
                          VLOG(3) << "filter: " << filter.dims()
                                  << " padding:" << padding
                                  << " stride:" << stride
                                  << " dilation:" << dilation;
                          VLOG(3) << "output: " << output.dims();
                          auto* input_data = input.mutable_data<float>();
                          auto* filter_data = filter.mutable_data<float>();
                          auto* output_data = output.mutable_data<float>();
                          for (int i = 0; i < input.dims().production(); i++) {
                            float sign = i % 3 == 0 ? -1.0f : 1.0f;
                            input_data[i] = sign * static_cast<float>(i % 128);
                          }
                          for (int i = 0; i < filter.dims().production(); i++) {
                            filter_data[i] =
                                i * 0.001f /
                                static_cast<float>(filter.dims().production());
                          }
                          // prepare kernel params and run
                          ConvCompute conv;
                          std::unique_ptr<KernelContext> ctx(new KernelContext);
                          ctx->As<ARMContext>();
                          conv.SetContext(std::move(ctx));
                          operators::ConvParam param;
                          param.x = &input;
                          param.filter = &filter;
                          param.output = &output;
                          param.bias = nullptr;
                          if (flag_bias) {
                            bias.Resize({oc});
                            auto* bias_data = bias.mutable_data<float>();
                            for (int i = 0; i < bias.dims().production(); i++) {
                              bias_data[i] = static_cast<float>(i);
                            }
                            param.bias = &bias;
                          }
                          param.fuse_relu = flag_relu;
                          param.paddings = std::vector<int>({padding, padding});
                          param.strides = std::vector<int>({stride, stride});
                          param.dilations =
                              std::vector<int>({dilation, dilation});
                          param.groups = group;
                          conv.SetParam(param);
                          conv.Launch();
                          // invoking ref implementation and compare results
                          param.output = &output_ref;
                          conv_compute_ref<float, float>(param);
                          auto* output_ref_data =
                              output_ref.mutable_data<float>();
                          for (int i = 0; i < output.dims().production(); i++) {
                            EXPECT_NEAR(output_data[i], output_ref_data[i],
                                        1e-3);
                          }
                        }
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

}  // namespace arm
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

USE_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW, def);
USE_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW, def);