paddlepaddle--paddle/paddle/phi/kernels/xpu/batch_norm_kernel.cc

// Copyright (c) 2022 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/phi/kernels/batch_norm_kernel.h"
#include "paddle/phi/kernels/full_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/norm_utils.h"

namespace phi {

template <typename T, typename Context>
void BatchNormKernel(const Context& dev_ctx,
                     const DenseTensor& x,
                     const DenseTensor& mean,
                     const DenseTensor& variance,
                     const optional<DenseTensor>& scale,
                     const optional<DenseTensor>& bias,
                     bool is_test,
                     float momentum,
                     float epsilon,
                     const std::string& data_layout_str,
                     bool use_global_stats,
                     bool trainable_statistics,
                     DenseTensor* y,
                     DenseTensor* mean_out,
                     DenseTensor* variance_out,
                     DenseTensor* saved_mean,
                     DenseTensor* saved_variance,
                     DenseTensor* reserve_space) {
  if (x.numel() == 0) {
    dev_ctx.template Alloc<T>(y);
    if (mean_out) dev_ctx.template Alloc<T>(mean_out);
    if (variance_out) dev_ctx.template Alloc<T>(variance_out);
    if (saved_mean) dev_ctx.template Alloc<T>(saved_mean);
    if (saved_variance) dev_ctx.template Alloc<T>(saved_variance);
    if (reserve_space) {
      reserve_space->Resize({0});
      dev_ctx.template Alloc<T>(reserve_space);
    }
    return;
  }
  using XPUType = typename XPUTypeTrait<T>::Type;
  bool test_mode = is_test && (!trainable_statistics);
  bool global_stats = test_mode || use_global_stats;
  const auto data_layout = StringToDataLayout(data_layout_str);
  PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
                    true,
                    common::errors::InvalidArgument(
                        "The 'data_layout' attribute must be NCHW or NHWC. "
                        "But received 'data_layout' is [%s].",
                        data_layout_str));

  const auto& x_dims = x.dims();
  PADDLE_ENFORCE_EQ(
      x_dims.size() >= 2 && x_dims.size() <= 5,
      true,
      common::errors::InvalidArgument(
          "The size of input's dimensions should be between 2 and 5"
          "But received: the size of input's dimensions is [%d]",
          x_dims.size()));

  int64_t N = -1, C = -1, H = -1, W = -1, D = -1;
  funcs::ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
  N = (N == 0) ? 1 : N;
  C = (C == 0) ? 1 : C;
  H = (H == 0) ? 1 : H;
  W = (W == 0) ? 1 : W;
  D = (D == 0) ? 1 : D;

  W = W * D;

  auto* Scale = scale.get_ptr();
  auto* Bias = bias.get_ptr();

  DenseTensor new_scale;
  DenseTensor new_bias;

  if (Scale) {
    new_scale = scale.get();
  } else {
    new_scale = phi::Full<T, Context>(dev_ctx, {C}, static_cast<T>(1));
  }

  if (Bias) {
    new_bias = bias.get();
  } else {
    new_bias = phi::Full<T, Context>(dev_ctx, {C}, static_cast<T>(0));
  }

  const auto* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
  const auto* scale_data = new_scale.data<float>();
  const auto* bias_data = new_bias.data<float>();

  // alloc memory
  auto* y_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(y));
  dev_ctx.template Alloc<float>(mean_out);
  dev_ctx.template Alloc<float>(variance_out);
  dev_ctx.template Alloc<float>(saved_mean);
  dev_ctx.template Alloc<float>(saved_variance);
  PADDLE_ENFORCE_LE(
      x_dims.size(),
      5,
      common::errors::InvalidArgument(
          "The size of input X's dimensions should be less than 6."
          "But received: the size of input X's dimensions is [%d]",
          x_dims.size()));

  bool is_nchw = data_layout_str == "NCHW";

  if (!global_stats) {
    auto* mean_out_data = mean_out->data<float>();
    auto* variance_out_data = variance_out->data<float>();
    auto* saved_mean_data = saved_mean->data<float>();
    auto* saved_variance_data = saved_variance->data<float>();

    int r = xpu::batch_norm<XPUType>(dev_ctx.x_context(),
                                     x_data,
                                     y_data,
                                     N,
                                     C,
                                     H,
                                     W,
                                     epsilon,
                                     momentum,
                                     scale_data,
                                     bias_data,
                                     saved_mean_data,
                                     saved_variance_data,
                                     mean_out_data,
                                     variance_out_data,
                                     is_nchw);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "batch_norm");
  } else {
    const auto* mean_data = mean.data<float>();
    const auto* variance_data = variance.data<float>();
    int r = xpu::batch_norm_infer<XPUType>(dev_ctx.x_context(),
                                           x_data,
                                           y_data,
                                           N,
                                           C,
                                           H,
                                           W,
                                           epsilon,
                                           scale_data,
                                           bias_data,
                                           mean_data,
                                           variance_data,
                                           is_nchw);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "batch_norm_infer");
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(
    batch_norm, XPU, ALL_LAYOUT, phi::BatchNormKernel, float, phi::float16) {
  kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
  kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
  kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32);
  kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32);
  kernel->OutputAt(5).SetDataType(phi::DataType::UINT8);
}