Paddle/paddle/fluid/operators/sync_batch_norm_op.cu.h

/* 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. */

#pragma once

#include <algorithm>
#include <cfloat>
#include <cmath>
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/batch_norm_op.h"
#include "paddle/fluid/operators/norm_utils.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/nccl_helper.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;

template <typename T, int BlockDim, framework::DataLayout layout>
__global__ void KeLocalStats(const T *x, int N, int M, int C,
                             BatchNormParamType<T> *mean_var) {
  typedef cub::BlockReduce<BatchNormParamType<T>, BlockDim> BlockReduce;
  __shared__ typename BlockReduce::TempStorage temp_storage;
  for (int k = blockIdx.x; k < C; k += gridDim.x) {
    BatchNormParamType<T> x_sum = 0.;
    BatchNormParamType<T> x2_sum = 0.;
    for (int i = threadIdx.x; i < N * M; i += BlockDim) {
      int id = layout == framework::DataLayout::kNCHW
                   ? (i / M) * C * M + k * M + i % M
                   : i * C + k;
      auto x_in = static_cast<BatchNormParamType<T>>(x[id]);
      x_sum += x_in;
      x2_sum += x_in * x_in;
    }
    __syncthreads();
    auto out = BlockReduce(temp_storage).Reduce(x_sum, cub::Sum());
    __syncthreads();
    if (threadIdx.x == 0) {
      mean_var[k] = out / (N * M);
    }
    out = BlockReduce(temp_storage).Reduce(x2_sum, cub::Sum());
    __syncthreads();
    if (threadIdx.x == 0) {
      mean_var[k + C] = out / (N * M);
    }
  }
  if (blockIdx.x == 0 && threadIdx.x == 0) {
    mean_var[2 * C] = static_cast<BatchNormParamType<T>>(1.0);
  }
}

template <typename T>
__global__ void KeSyncAndMovingStats(
    BatchNormParamType<T> *means, BatchNormParamType<T> *variances,
    BatchNormParamType<T> *num_dev, const int C,
    const BatchNormParamType<T> momentum, const double epsilon,
    BatchNormParamType<T> *sv_mean_data, BatchNormParamType<T> *sv_inv_var_data,
    BatchNormParamType<T> *moving_means,
    BatchNormParamType<T> *moving_variances) {
  // sync stats across multi-devices
  int gid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  for (int i = gid; i < C; i += stride) {
    auto mean = means[i] / (*num_dev);
    auto var = variances[i] / (*num_dev);
    var = var - mean * mean;

    // sync stats
    sv_mean_data[i] = mean;
    sv_inv_var_data[i] = 1.0 / sqrt(var + epsilon);
    variances[i] = var;

    // moving stats
    moving_means[i] = moving_means[i] * momentum + mean * (1. - momentum);
    moving_variances[i] =
        moving_variances[i] * momentum + var * (1. - momentum);
  }
}

template <typename T, framework::DataLayout layout>
static __global__ void KeNormAffine(const T *x,
                                    const BatchNormParamType<T> *scale,
                                    const BatchNormParamType<T> *bias,
                                    const BatchNormParamType<T> *mean,
                                    const BatchNormParamType<T> *variance,
                                    const double epsilon, const int C,
                                    const int M, const int num, T *y) {
  int gid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  for (int i = gid; i < num; i += stride) {
    const int c = layout == framework::DataLayout::kNCHW ? (i / M) % C : i % C;
    auto x_i = static_cast<BatchNormParamType<T>>(x[i]);
    auto y_i =
        (x_i - mean[c]) / sqrt(variance[c] + epsilon) * scale[c] + bias[c];
    y[i] = static_cast<T>(y_i);
  }
}

template <typename DeviceContext, typename T>
void SyncBatchNormFunctor(const framework::ExecutionContext &ctx,
                          const DataLayout layout, const framework::Tensor *x,
                          framework::Tensor *y, const framework::Tensor *mean,
                          const framework::Tensor *variance,
                          framework::Tensor *mean_out,
                          framework::Tensor *variance_out,
                          framework::Tensor *saved_mean,
                          framework::Tensor *saved_variance, double epsilon,
                          const float momentum, const bool is_test,
                          const bool use_global_stats

                          ) {
  const auto &x_dims = x->dims();
  PADDLE_ENFORCE_GE(x_dims.size(), 2,
                    platform::errors::InvalidArgument(
                        "The Input dim size should be larger than 1."));
  PADDLE_ENFORCE_LE(x_dims.size(), 5,
                    platform::errors::InvalidArgument(
                        "The Input dim size should be less than 6."));
  int N, C, H, W, D;
  ExtractNCWHD(x_dims, layout, &N, &C, &H, &W, &D);
  int x_numel = x->numel();

  const T *x_d = x->data<T>();
  const auto *s_d = ctx.Input<Tensor>("Scale")->data<BatchNormParamType<T>>();
  const auto *b_d = ctx.Input<Tensor>("Bias")->data<BatchNormParamType<T>>();

  T *y_d = y->mutable_data<T>(ctx.GetPlace());

  const BatchNormParamType<T> *mean_data = nullptr;
  const BatchNormParamType<T> *var_data = nullptr;

  auto &dev_ctx = ctx.cuda_device_context();
  auto stream = dev_ctx.stream();
  const int block = 512;
  int max_threads = dev_ctx.GetMaxPhysicalThreadCount();

  paddle::memory::AllocationPtr alloc_ptr{nullptr};

  if (is_test) {
    mean_data = mean->data<BatchNormParamType<T>>();
    var_data = variance->data<BatchNormParamType<T>>();
  } else {
    // x, x^2, 1, here 1 is used to calc device num
    // device num also can be got from platform::DeviceContextPool
    const int bytes = (C * 2 + 1) * sizeof(BatchNormParamType<T>);
    alloc_ptr = memory::Alloc(dev_ctx, bytes);

    auto *stats = reinterpret_cast<BatchNormParamType<T> *>(alloc_ptr->ptr());
    const int threads = 256;
    int grid = std::min(C, (max_threads + threads - 1) / threads);
    if (layout == framework::DataLayout::kNCHW) {
      KeLocalStats<T, threads,
                   framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
          x_d, N, H * W * D, C, stats);
    } else {
      KeLocalStats<T, threads,
                   framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
          x_d, N, H * W * D, C, stats);
    }

    Tensor c_g_st;
    auto *c_g_st_d = c_g_st.mutable_data<BatchNormParamType<T>>(
        {2 * C + 1}, platform::CPUPlace());
    auto gplace = BOOST_GET_CONST(platform::CUDAPlace, ctx.GetPlace());
    memory::Copy(platform::CPUPlace(), c_g_st_d, gplace, stats, bytes, 0);

#ifdef PADDLE_WITH_NCCL
    auto *comm = dev_ctx.nccl_comm();
    if (comm) {
      int dtype = platform::ToNCCLDataType(mean_out->type());
      // In-place operation
      PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclAllReduce(
          stats, stats, 2 * C + 1, static_cast<ncclDataType_t>(dtype), ncclSum,
          comm, stream));
    }
#endif

    auto *est_mean_data =
        mean_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    auto *est_var_data =
        variance_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());

    auto *sv_mean_data =
        saved_mean->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    auto *sv_inv_var_data =
        saved_variance->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());

    // Note, Input('Mean')/Input('Variance') share variable with
    // Output('MeanOut')/Output('VarianceOut')
    KeSyncAndMovingStats<T><<<(C + block - 1) / block, block, 0, stream>>>(
        stats, stats + C, stats + 2 * C, C, momentum, epsilon, sv_mean_data,
        sv_inv_var_data, est_mean_data, est_var_data);

    mean_data = sv_mean_data;
    var_data = stats + C;
  }

  int grid2 = (std::min(x_numel, max_threads) + block - 1) / block;
  if (layout == framework::DataLayout::kNCHW) {
    KeNormAffine<T, framework::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
        x_d, s_d, b_d, mean_data, var_data, epsilon, C, H * W * D, x_numel,
        y_d);
  } else {
    KeNormAffine<T, framework::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
        x_d, s_d, b_d, mean_data, var_data, epsilon, C, H * W * D, x_numel,
        y_d);
  }
}

template <typename T, const int BlockDim, framework::DataLayout layout>
__global__ void KeBackwardLocalStats(const T *dy, const T *x,
                                     const BatchNormParamType<T> *means, int N,
                                     int M, int C,
                                     BatchNormParamType<T> *sum_dy_prod) {
  typedef cub::BlockReduce<BatchNormParamType<T>, BlockDim> BlockReduce;
  __shared__ typename BlockReduce::TempStorage temp_storage;
  for (int k = blockIdx.x; k < C; k += gridDim.x) {
    BatchNormParamType<T> sum1 = 0.;
    BatchNormParamType<T> sum2 = 0.;
    auto mean = means[k];
    for (int i = threadIdx.x; i < N * M; i += blockDim.x) {
      int id = layout == framework::DataLayout::kNCHW
                   ? (i / M) * C * M + k * M + i % M
                   : i * C + k;
      auto g = static_cast<BatchNormParamType<T>>(dy[id]);
      sum1 += g;
      auto x_i = static_cast<BatchNormParamType<T>>(x[id]);
      sum2 += g * (x_i - mean);
    }

    __syncthreads();
    auto out = BlockReduce(temp_storage).Reduce(sum1, cub::Sum());
    __syncthreads();
    if (threadIdx.x == 0) {
      sum_dy_prod[k] = out;
    }
    out = BlockReduce(temp_storage).Reduce(sum2, cub::Sum());
    __syncthreads();
    if (threadIdx.x == 0) {
      sum_dy_prod[k + C] = out;
    }
  }
  if (blockIdx.x == 0 && threadIdx.x == 0) {
    sum_dy_prod[2 * C] = 1.0;
  }
}

template <typename T, int BlockDim, framework::DataLayout layout>
static __global__ void KeBNBackwardScaleBias(
    const T *dy, const T *x, const BatchNormParamType<T> *mean,
    const BatchNormParamType<T> *inv_variance, const double epsilon,
    const int N, const int C, const int HxW, BatchNormParamType<T> *dscale,
    BatchNormParamType<T> *dbias) {
  const int outer_size = C;
  const int inner_size = N * HxW;
  typedef cub::BlockReduce<BatchNormParamType<T>, BlockDim> BlockReduce;
  __shared__ typename BlockReduce::TempStorage temp_storage;

  for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
    BatchNormParamType<T> ds_sum = 0.;
    BatchNormParamType<T> db_sum = 0.;

    auto inv_var_i = inv_variance[i];
    auto mean_i = mean[i];
    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int id = layout == framework::DataLayout::kNCHW
                         ? ((j / HxW) * C + i) * HxW + (j % HxW)
                         : j * outer_size + i;
      auto x_i = static_cast<BatchNormParamType<T>>(x[id]);
      auto dy_i = static_cast<BatchNormParamType<T>>(dy[id]);
      ds_sum += dy_i * (x_i - mean_i);
      db_sum += dy_i;
    }
    __syncthreads();
    auto os = BlockReduce(temp_storage).Reduce(ds_sum, cub::Sum());
    __syncthreads();
    auto ob = BlockReduce(temp_storage).Reduce(db_sum, cub::Sum());
    __syncthreads();
    if (threadIdx.x == 0) {
      dscale[i] = os * inv_var_i;
      dbias[i] = ob;
    }
    __syncthreads();
  }
}

template <typename T, framework::DataLayout layout>
static __global__ void KeBNRestoreData(T *x, const BatchNormParamType<T> *scale,
                                       const BatchNormParamType<T> *bias,
                                       const BatchNormParamType<T> *mean,
                                       const BatchNormParamType<T> *sv_inv,
                                       const double epsilon, int C, int M,
                                       int num, const T *y) {
  int gid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  for (int i = gid; i < num; i += stride) {
    const int c = layout == framework::DataLayout::kNCHW ? (i / M) % C : i % C;
    auto y_i = static_cast<BatchNormParamType<T>>(y[i]);
    auto x_i = (y_i - bias[c]) / scale[c] / sv_inv[c] + mean[c];
    x[i] = static_cast<T>(x_i);
  }
}

template <typename T, framework::DataLayout layout>
static __global__ void KeBNBackwardData(
    const T *dy, const T *x, const BatchNormParamType<T> *gamma,
    const BatchNormParamType<T> *mean,
    const BatchNormParamType<T> *inv_variance,
    const BatchNormParamType<T> *g_sum_dy,
    const BatchNormParamType<T> *g_sum_dy_prod,
    const BatchNormParamType<T> *num_dev, const double epsilon, const int C,
    const int HxW, const int num, T *dx) {
  int gid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  auto scale = static_cast<BatchNormParamType<T>>(C) / num;
  auto dev_num = num_dev[0];
  for (int i = gid; i < num; i += stride) {
    const int c = layout == framework::DataLayout::kNCHW ? i / HxW % C : i % C;
    auto inv_var = inv_variance[c];
    auto s_d = gamma[c];
    auto gvar =
        -(g_sum_dy_prod[c] / dev_num) * s_d * inv_var * (inv_var * inv_var);
    auto gmean = -(g_sum_dy[c] / dev_num) * s_d * inv_var;

    auto x_i = static_cast<BatchNormParamType<T>>(x[i]);
    auto dy_i = static_cast<BatchNormParamType<T>>(dy[i]);
    auto dx_i =
        dy_i * s_d * inv_var + gmean * scale + gvar * scale * (x_i - mean[c]);
    dx[i] = static_cast<T>(dx_i);
  }
}

template <typename DeviceContext, typename T>
void SyncBatchNormGradFunctor(
    const framework::ExecutionContext &ctx, const DataLayout layout,
    const framework::Tensor *scale, const framework::Tensor *bias,
    framework::Tensor *d_x, const framework::Tensor *d_y,
    framework::Tensor *d_scale, framework::Tensor *d_bias,
    const framework::Tensor *mean, const framework::Tensor *variance,
    const double epsilon) {
  // sync_batch_norm with inplace as false will take X as grad input, which
  // is same as cuDNN batch_norm backward calculation, batch_norm
  // with inplace as true only take Y as input and X should be calculate
  // by inverse operation of batch_norm on Y
  const Tensor *x;
  bool is_inplace;
  if (ctx.HasInput("Y")) {
    x = ctx.Input<Tensor>("Y");
    is_inplace = true;
  } else {
    x = ctx.Input<Tensor>("X");
    is_inplace = false;
  }

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

  PADDLE_ENFORCE_GE(x_dims.size(), 2,
                    platform::errors::InvalidArgument(
                        "The Input X dim size should be larger than 1."));
  PADDLE_ENFORCE_LE(x_dims.size(), 5,
                    platform::errors::InvalidArgument(
                        "The Input X dim size should be less than 6."));

  int N, C, H, W, D;
  ExtractNCWHD(x_dims, layout, &N, &C, &H, &W, &D);
  PADDLE_ENFORCE_EQ(scale->dims()[0], C,
                    platform::errors::InvalidArgument(
                        "Expected first dim for input parameter(scale) of "
                        "OP(sync_batch_norm) be (%d), but given (%d).",
                        C, scale->dims()[0]));

  d_x->mutable_data<T>(ctx.GetPlace());
  if (d_scale && d_bias) {
    d_scale->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
    d_bias->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
  }
  PADDLE_ENFORCE_EQ(scale->dims().size(), 1UL,
                    platform::errors::InvalidArgument(
                        "Expected rank for input parameter(scale) of "
                        "OP(sync_batch_norm) be (1), but given (%d).",
                        scale->dims().size()));

  std::vector<int> dims;
  std::vector<int> strides;
  if (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};
  }
  const T *x_d = x->data<T>();
  auto px = *x;
  const T *dy_d = d_y->data<T>();

  auto &dev_ctx = ctx.cuda_device_context();
  auto stream = dev_ctx.stream();

  const auto *saved_mean = mean->data<BatchNormParamType<T>>();
  const auto *saved_inv_var = variance->data<BatchNormParamType<T>>();
  const int bytes = (C * 2 + 1) * sizeof(BatchNormParamType<T>);
  auto alloc_ptr = memory::Alloc(dev_ctx, bytes);
  auto *stats = reinterpret_cast<BatchNormParamType<T> *>(alloc_ptr->ptr());

  const int block = 512;
  const int threads = 256;
  int x_numel = x->numel();
  int fsize = H * W * D;
  int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
  int grid = std::min(C, (max_threads + threads - 1) / threads);
  int grid2 = (std::min(x_numel, max_threads) + block - 1) / block;

  if (is_inplace) {
    if (layout == framework::DataLayout::kNCHW) {
      KeBNRestoreData<
          T, framework::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
          px.mutable_data<T>(ctx.GetPlace()),
          scale->data<BatchNormParamType<T>>(),
          bias->data<BatchNormParamType<T>>(), saved_mean, saved_inv_var,
          epsilon, C, H * W * D, x_numel, x->data<T>());
    } else {
      KeBNRestoreData<
          T, framework::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
          px.mutable_data<T>(ctx.GetPlace()),
          scale->data<BatchNormParamType<T>>(),
          bias->data<BatchNormParamType<T>>(), saved_mean, saved_inv_var,
          epsilon, C, H * W * D, x_numel, x->data<T>());
    }
  }

  if (layout == framework::DataLayout::kNCHW) {
    KeBackwardLocalStats<
        T, threads, framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
        dy_d, x_d, saved_mean, N, fsize, C, stats);
  } else {
    KeBackwardLocalStats<
        T, threads, framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
        dy_d, x_d, saved_mean, N, fsize, C, stats);
  }

#ifdef PADDLE_WITH_NCCL
  auto *comm = dev_ctx.nccl_comm();
  if (comm) {
    int dtype = platform::ToNCCLDataType(scale->type());
    // In-place operation
    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclAllReduce(
        stats, stats, 2 * C + 1, static_cast<ncclDataType_t>(dtype), ncclSum,
        comm, stream));
  }
#endif

  if (layout == framework::DataLayout::kNCHW) {
    if (d_scale && d_bias) {
      KeBNBackwardScaleBias<
          T, threads,
          framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
          dy_d, x_d, saved_mean, saved_inv_var, epsilon, N, C, fsize,
          d_scale->data<BatchNormParamType<T>>(),
          d_bias->data<BatchNormParamType<T>>());
    }
    if (d_x) {
      KeBNBackwardData<
          T, framework::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
          dy_d, x_d, scale->data<BatchNormParamType<T>>(), saved_mean,
          saved_inv_var, stats, stats + C, stats + 2 * C, epsilon, C, fsize,
          x->numel(), d_x->data<T>());
    }
  } else {
    if (d_scale && d_bias) {
      KeBNBackwardScaleBias<
          T, threads,
          framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
          dy_d, x_d, saved_mean, saved_inv_var, epsilon, N, C, fsize,
          d_scale->data<BatchNormParamType<T>>(),
          d_bias->data<BatchNormParamType<T>>());
    }
    if (d_x) {
      KeBNBackwardData<
          T, framework::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
          dy_d, x_d, scale->data<BatchNormParamType<T>>(), saved_mean,
          saved_inv_var, stats, stats + C, stats + 2 * C, epsilon, C, fsize,
          x->numel(), d_x->data<T>());
    }
  }
}

template <typename DeviceContext, typename T>
class SyncBatchNormKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override;
};

// Deriving the Gradient for the Backward Pass of Batch Normalization
// https://kevinzakka.github.io/2016/09/14/batch_normalization/
template <typename DeviceContext, typename T>
class SyncBatchNormGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override;
};

}  // namespace operators
}  // namespace paddle