batch_norm_op.cu.cc

/* Copyright (c) 2016 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/operators/batch_norm_op.h"
#include <cfloat>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using DataLayout = framework::DataLayout;
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
template <typename T>
using BatchNormParamType = typename CudnnDataType<T>::BatchNormParamType;

void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout,
                  int *N, int *C, int *H, int *W, int *D) {
  *N = dims[0];
  if (dims.size() == 2) {
    *C = dims[1];
    *H = 1;
    *W = 1;
    *D = 1;
  } else {
    *C = data_layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
    *H = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
    *W = dims.size() > 3
             ? (data_layout == DataLayout::kNCHW ? dims[3] : dims[2])
             : 1;
    *D = dims.size() > 4
             ? (data_layout == DataLayout::kNCHW ? dims[4] : dims[3])
             : 1;
  }
}

template <typename T>
class BatchNormKernel<platform::CUDADeviceContext, T>
    : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                   "It must use CUDAPlace.");
    double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
    const float momentum = ctx.Attr<float>("momentum");
    const bool is_test = ctx.Attr<bool>("is_test");
    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
    const DataLayout data_layout =
        framework::StringToDataLayout(data_layout_str);

    // Get the size for each dimension.
    // NCHW [batch_size, in_channels, in_height, in_width]
    const auto *x = ctx.Input<Tensor>("X");
    const auto &x_dims = x->dims();
    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
                   "The Input dim size should be between 2 and 5");
    int N, C, H, W, D;
    ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);

    // ------------------- cudnn descriptors ---------------------
    cudnnTensorDescriptor_t data_desc_;
    cudnnTensorDescriptor_t bn_param_desc_;
    cudnnBatchNormMode_t mode_;

    CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
    CUDNN_ENFORCE(
        platform::dynload::cudnnCreateTensorDescriptor(&bn_param_desc_));

    if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
      LOG(ERROR) << "Provided epsilon is smaller than "
                 << "CUDNN_BN_MIN_EPSILON. Setting it to "
                 << "CUDNN_BN_MIN_EPSILON instead.";
    }
    epsilon = std::max(epsilon, CUDNN_BN_MIN_EPSILON);
#if CUDNN_VERSION_MIN(7, 0, 0)
    mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
#else
    mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif

    VLOG(1) << "Setting descriptors.";
    std::vector<int> dims;
    std::vector<int> strides;
    if (data_layout == DataLayout::kNCHW) {
      dims = {N, C, H, W, D};
      strides = {C * H * W * D, H * W * D, W * D, D, 1};
    } else {
      dims = {N, C, H, W, D};
      strides = {H * W * D * C, 1, W * D * C, D * C, C};
    }
    CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
        data_desc_, CudnnDataType<T>::type,
        x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
    // Note: PERSISTENT not implemented for inference
    CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
        bn_param_desc_, data_desc_, is_test ? CUDNN_BATCHNORM_SPATIAL : mode_));

    const auto *scale = ctx.Input<Tensor>("Scale");
    const auto *bias = ctx.Input<Tensor>("Bias");

    auto *y = ctx.Output<Tensor>("Y");
    auto *mean_out = ctx.Output<Tensor>("MeanOut");
    auto *variance_out = ctx.Output<Tensor>("VarianceOut");
    auto *saved_mean = ctx.Output<Tensor>("SavedMean");
    auto *saved_variance = ctx.Output<Tensor>("SavedVariance");

    // alloc memory
    y->mutable_data<T>(ctx.GetPlace());
    mean_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    variance_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    saved_mean->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    saved_variance->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());

    auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    math::SetConstant<platform::CUDADeviceContext, BatchNormParamType<T>>
        functor;
    functor(dev_ctx, saved_mean, static_cast<BatchNormParamType<T>>(0));
    functor(dev_ctx, saved_variance, static_cast<BatchNormParamType<T>>(0));

    auto handle = dev_ctx.cudnn_handle();

    // Now, depending on whether we are running test or not, we have two paths.
    if (is_test) {
      // only when test we use input to do computation.
      const auto *est_mean = ctx.Input<Tensor>("Mean");
      const auto *est_var = ctx.Input<Tensor>("Variance");
      // Run inference mode.
      PADDLE_ENFORCE_EQ(est_mean->dims().size(), 1UL);
      PADDLE_ENFORCE_EQ(est_var->dims().size(), 1UL);
      PADDLE_ENFORCE_EQ(est_mean->dims()[0], C);
      PADDLE_ENFORCE_EQ(est_var->dims()[0], C);

      CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationForwardInference(
          handle,
          // Note: PERSISTENT not implemented for inference
          CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
          CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
          data_desc_, y->template mutable_data<T>(ctx.GetPlace()),
          bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
          bias->template data<BatchNormParamType<T>>(),
          est_mean->template data<BatchNormParamType<T>>(),
          est_var->template data<BatchNormParamType<T>>(), epsilon));
    } else {
      // Run training mode.
      // obtain running mean and running inv var, and see if we need to
      // initialize them.
      double this_factor = 1. - momentum;

      CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationForwardTraining(
          handle, mode_, CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
          data_desc_, x->template data<T>(), data_desc_,
          y->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
          scale->template data<BatchNormParamType<T>>(),
          bias->template data<BatchNormParamType<T>>(), this_factor,
          mean_out->template mutable_data<BatchNormParamType<T>>(
              ctx.GetPlace()),
          variance_out->template mutable_data<BatchNormParamType<T>>(
              ctx.GetPlace()),
          epsilon, saved_mean->template mutable_data<BatchNormParamType<T>>(
                       ctx.GetPlace()),
          saved_variance->template mutable_data<BatchNormParamType<T>>(
              ctx.GetPlace())));
    }

    // clean when exit.
    CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
    CUDNN_ENFORCE(
        platform::dynload::cudnnDestroyTensorDescriptor(bn_param_desc_));
  }
};

template <typename T>
class BatchNormGradKernel<platform::CUDADeviceContext, T>
    : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                   "It must use CUDAPlace.");
    double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
    const DataLayout data_layout =
        framework::StringToDataLayout(data_layout_str);
    const auto *x = ctx.Input<Tensor>("X");
    const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
    const auto *scale = ctx.Input<Tensor>("Scale");

    const auto &x_dims = x->dims();

    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
                   "The Input dim size should be between 2 and 5");
    int N, C, H, W, D;
    ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);

    PADDLE_ENFORCE_EQ(scale->dims().size(), 1UL);
    PADDLE_ENFORCE_EQ(scale->dims()[0], C);

    // ------------------- cudnn descriptors ---------------------
    cudnnTensorDescriptor_t data_desc_;
    cudnnTensorDescriptor_t bn_param_desc_;
    cudnnBatchNormMode_t mode_;

    CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
    CUDNN_ENFORCE(
        platform::dynload::cudnnCreateTensorDescriptor(&bn_param_desc_));
    if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
      LOG(ERROR) << "Provided epsilon is smaller than "
                 << "CUDNN_BN_MIN_EPSILON. Setting it to "
                 << "CUDNN_BN_MIN_EPSILON instead.";
    }
    epsilon = std::max(epsilon, CUDNN_BN_MIN_EPSILON);
#if CUDNN_VERSION_MIN(7, 0, 0)
    mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
#else
    mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif

    std::vector<int> dims;
    std::vector<int> strides;
    if (data_layout == DataLayout::kNCHW) {
      dims = {N, C, H, W, D};
      strides = {C * H * W * D, H * W * D, W * D, D, 1};
    } else {
      dims = {N, C, H, W, D};
      strides = {H * W * C * D, 1, W * D * C, D * C, C};
    }
    CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
        data_desc_, CudnnDataType<T>::type,
        x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
    CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
        bn_param_desc_, data_desc_, mode_));

    // init output
    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));

    d_x->mutable_data<T>(ctx.GetPlace());
    d_scale->mutable_data<T>(ctx.GetPlace());
    d_bias->mutable_data<T>(ctx.GetPlace());

    const auto *saved_mean = ctx.Input<Tensor>("SavedMean");
    const auto *saved_var = ctx.Input<Tensor>("SavedVariance");
    const void *saved_mean_data = saved_mean->template data<T>();
    const void *saved_var_data = saved_var->template data<T>();

    auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
        dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
        CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
        CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
        data_desc_, d_y->template data<T>(), data_desc_,
        d_x->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
        scale->template data<T>(),
        d_scale->template mutable_data<T>(ctx.GetPlace()),
        d_bias->template mutable_data<T>(ctx.GetPlace()), epsilon,
        saved_mean_data, saved_var_data));

    // clean when exit.
    CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
    CUDNN_ENFORCE(
        platform::dynload::cudnnDestroyTensorDescriptor(bn_param_desc_));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
    batch_norm, ops::BatchNormKernel<plat::CUDADeviceContext, float>,
    ops::BatchNormKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
    batch_norm_grad, ops::BatchNormGradKernel<plat::CUDADeviceContext, float>);