batch_norm_grad_kernel.cu 38.6 KB
Newer Older
H
hong 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
// 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/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/batch_norm_kernel.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"

#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"

#include "paddle/fluid/operators/norm_utils.cu.h"
23
#include "paddle/phi/kernels/funcs/norm_utils.h"
H
hong 已提交
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311

#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/operators/layout_utils.h"
#include "paddle/fluid/platform/enforce.h"

#include "paddle/fluid/platform/flags.h"
#include "paddle/phi/kernels/gpu/batch_norm_utils.h"

#ifdef __HIPCC__
#define LAUNCH_BOUNDS(BlockDim) __launch_bounds__(BlockDim)
#else
#define LAUNCH_BOUNDS(BlockDim)
#endif

DECLARE_bool(cudnn_batchnorm_spatial_persistent);
namespace phi {

template <typename T>
using CudnnDataType = paddle::platform::CudnnDataType<T>;
template <typename T>
using BatchNormParamType = typename CudnnDataType<T>::BatchNormParamType;

template <typename T, int BlockDim, phi::DataLayout layout>
static __global__ LAUNCH_BOUNDS(BlockDim) void KeBNBackwardScaleBias(
    const T *dy,
    const T *x,
    const BatchNormParamType<T> *mean,
    const BatchNormParamType<T> *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 ds_storage;
  __shared__ typename BlockReduce::TempStorage db_storage;

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

    BatchNormParamType<T> inv_var_i = 1.0 / sqrt(variance[i] + epsilon);
    BatchNormParamType<T> mean_i = mean[i];
    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int index = layout == phi::DataLayout::kNCHW
                            ? (j / HxW * C + i) * HxW + j % HxW
                            : j * outer_size + i;
      ds_sum += static_cast<BatchNormParamType<T>>(dy[index]) *
                (static_cast<BatchNormParamType<T>>(x[index]) - mean_i);
      db_sum += static_cast<BatchNormParamType<T>>(dy[index]);
    }
    ds_sum = BlockReduce(ds_storage).Reduce(ds_sum, cub::Sum());
    db_sum = BlockReduce(db_storage).Reduce(db_sum, cub::Sum());
    if (threadIdx.x == 0) {
      dscale[i] = ds_sum * inv_var_i;
      dbias[i] = db_sum;
    }
    __syncthreads();
  }
}

template <typename T, phi::DataLayout layout>
static __global__ void KeBNBackwardData(const T *dy,
                                        const BatchNormParamType<T> *scale,
                                        const BatchNormParamType<T> *variance,
                                        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;
  for (int i = gid; i < num; i += stride) {
    const int c = layout == phi::DataLayout::kNCHW ? i / HxW % C : i % C;
    BatchNormParamType<T> inv_var = 1.0 / sqrt(variance[c] + epsilon);
    dx[i] = static_cast<T>(static_cast<BatchNormParamType<T>>(dy[i]) *
                           scale[c] * inv_var);
  }
}

template <typename T>
static __global__ void KeBNRestoreData(const phi::DataLayout layout,
                                       T *x,
                                       const BatchNormParamType<T> *scale,
                                       const BatchNormParamType<T> *bias,
                                       const BatchNormParamType<T> *mean,
                                       const BatchNormParamType<T> *variance,
                                       double epsilon,
                                       int C,
                                       int M,
                                       const 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 == phi::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] / variance[c] + mean[c];
    x[i] = static_cast<T>(x_i);
  }
}

template <typename T>
class InplaceHelper {
 public:
  void operator()(const phi::DataLayout layout,
                  T *x,
                  const BatchNormParamType<T> *scale,
                  const BatchNormParamType<T> *bias,
                  const BatchNormParamType<T> *mean,
                  const BatchNormParamType<T> *variance,
                  double epsilon,
                  int C,
                  int M,
                  const int num,
                  const T *y,
                  int grid2,
                  const int block,
                  const gpuStream_t &stream) {
    PADDLE_ENFORCE_EQ(x,
                      y,
                      phi::errors::InvalidArgument(
                          "X and Y should be inplaced in inplace mode"));
    KeBNRestoreData<<<grid2, block, 0, stream>>>(
        layout, x, scale, bias, mean, variance, epsilon, C, M, num, y);
  }
};

template <typename T, int BlockDim, phi::DataLayout layout>
static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackward(
    const T *dy,
    const T *x,
    const BatchNormParamType<T> *scale,
    const BatchNormParamType<T> *saved_mean,
    const BatchNormParamType<T> *saved_inv_variance,
    const int C,
    const int N,
    const int HxW,
    const double epsilon,
    T *dx,
    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 ds_storage;
  __shared__ typename BlockReduce::TempStorage db_storage;
  __shared__ typename BlockReduce::TempStorage mean_storage;
  __shared__ typename BlockReduce::TempStorage variance_storeage;
  __shared__ BatchNormParamType<T> inv_var_val;
  __shared__ BatchNormParamType<T> mean_val;
  __shared__ BatchNormParamType<T> dscale_val;
  __shared__ BatchNormParamType<T> dbias_val;

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

    if (saved_mean && saved_inv_variance) {
      if (threadIdx.x == 0) {
        inv_var_val = saved_inv_variance[i];
        mean_val = saved_mean[i];
      }
    } else {
      BatchNormParamType<T> x_sum = static_cast<BatchNormParamType<T>>(0);
      BatchNormParamType<T> x_square_sum =
          static_cast<BatchNormParamType<T>>(0);

      for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
        const int index = layout == phi::DataLayout::kNCHW
                              ? (j / HxW * C + i) * HxW + j % HxW
                              : j * outer_size + i;
        BatchNormParamType<T> x_i =
            static_cast<BatchNormParamType<T>>(x[index]);
        x_sum += x_i;
        x_square_sum += x_i * x_i;
      }
      x_sum = BlockReduce(mean_storage).Reduce(x_sum, cub::Sum());
      x_square_sum =
          BlockReduce(variance_storeage).Reduce(x_square_sum, cub::Sum());
      if (threadIdx.x == 0) {
        mean_val = x_sum / inner_size;
        inv_var_val =
            1 / sqrt(x_square_sum / inner_size - mean_val * mean_val + epsilon);
      }
    }
    __syncthreads();

    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int index = layout == phi::DataLayout::kNCHW
                            ? (j / HxW * C + i) * HxW + j % HxW
                            : j * outer_size + i;
      BatchNormParamType<T> dy_i =
          static_cast<BatchNormParamType<T>>(dy[index]);
      ds_sum +=
          dy_i * (static_cast<BatchNormParamType<T>>(x[index]) - mean_val);
      db_sum += dy_i;
    }
    ds_sum = BlockReduce(ds_storage).Reduce(ds_sum, cub::Sum());
    db_sum = BlockReduce(db_storage).Reduce(db_sum, cub::Sum());
    if (threadIdx.x == 0) {
      dscale_val = ds_sum * inv_var_val;
      dbias_val = db_sum;
      dscale[i] = dscale_val;
      dbias[i] = dbias_val;
    }
    __syncthreads();

    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int index = layout == phi::DataLayout::kNCHW
                            ? (j / HxW * C + i) * HxW + j % HxW
                            : j * outer_size + i;
      dx[index] = scale[i] * inv_var_val *
                  (static_cast<BatchNormParamType<T>>(dy[index]) -
                   dbias_val / static_cast<BatchNormParamType<T>>(inner_size) -
                   (static_cast<BatchNormParamType<T>>(x[index]) - mean_val) *
                       inv_var_val * dscale_val / inner_size);
    }
  }
}

template <typename T, int BlockDim, phi::DataLayout layout>
static __global__ LAUNCH_BOUNDS(BlockDim) void BNBackwardData(
    const T *dy,
    const BatchNormParamType<T> *scale,
    const BatchNormParamType<T> *mean,
    const T *x,
    const BatchNormParamType<T> *variance,
    const int C,
    const int N,
    const int HxW,
    T *dx) {
  const int outer_size = C;
  const int inner_size = N * HxW;
  typedef cub::BlockReduce<BatchNormParamType<T>, BlockDim> BlockReduce;
  __shared__ typename BlockReduce::TempStorage dy_storage;
  __shared__ typename BlockReduce::TempStorage dy_x_sub_mean_storage;
  __shared__ BatchNormParamType<T> dy_sum_val;
  __shared__ BatchNormParamType<T> dy_x_sub_mean_sum_val;

  for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
    BatchNormParamType<T> inv_var_i = variance[i];
    BatchNormParamType<T> mean_i = mean[i];
    BatchNormParamType<T> dy_sum = static_cast<BatchNormParamType<T>>(0);
    BatchNormParamType<T> dy_x_sub_mean_sum =
        static_cast<BatchNormParamType<T>>(0);
    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int index = layout == phi::DataLayout::kNCHW
                            ? (j / HxW * C + i) * HxW + j % HxW
                            : j * outer_size + i;
      BatchNormParamType<T> dy_i =
          static_cast<BatchNormParamType<T>>(dy[index]);
      dy_sum += dy_i;
      dy_x_sub_mean_sum +=
          dy_i * (static_cast<BatchNormParamType<T>>(x[index]) - mean_i);
    }

    dy_sum = BlockReduce(dy_storage).Reduce(dy_sum, cub::Sum());
    dy_x_sub_mean_sum = BlockReduce(dy_x_sub_mean_storage)
                            .Reduce(dy_x_sub_mean_sum, cub::Sum());

    if (threadIdx.x == 0) {
      dy_sum_val = dy_sum;
      dy_x_sub_mean_sum_val = dy_x_sub_mean_sum;
    }
    __syncthreads();
    for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
      const int index = layout == phi::DataLayout::kNCHW
                            ? (j / HxW * C + i) * HxW + j % HxW
                            : j * outer_size + i;
      dx[index] =
          (static_cast<BatchNormParamType<T>>(dy[index]) -
           dy_sum_val / static_cast<BatchNormParamType<T>>(inner_size) -
           (static_cast<BatchNormParamType<T>>(x[index]) - mean_i) *
               dy_x_sub_mean_sum_val * inv_var_i * inv_var_i / inner_size) *
          scale[i] * inv_var_i;
    }
  }
}

template <typename T, typename Context>
void BatchNormGradRawKernel(const Context &ctx,
                            const DenseTensor &x,
                            const DenseTensor &scale,
                            const DenseTensor &bias,
H
hong 已提交
312 313
                            paddle::optional<const DenseTensor &> mean,
                            paddle::optional<const DenseTensor &> variance,
H
hong 已提交
314 315 316
                            const DenseTensor &saved_mean,
                            const DenseTensor &saved_variance,
                            paddle::optional<const DenseTensor &> reserve_space,
H
hong 已提交
317
                            const DenseTensor &y_grad,
H
hong 已提交
318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353
                            float momentum,
                            float epsilon_f,
                            const std::string &data_layout_str,
                            bool is_test,
                            bool use_global_stats,
                            bool trainable_statistics,
                            bool fuse_with_relu,
                            bool is_inplace,
                            DenseTensor *x_grad,
                            DenseTensor *scale_grad,
                            DenseTensor *bias_grad) {
  double epsilon = static_cast<double>(epsilon_f);

  const DataLayout data_layout =
      paddle::framework::StringToDataLayout(data_layout_str);

  const auto *d_y = &y_grad;

  auto *d_x = x_grad;
  auto *d_scale = scale_grad;
  auto *d_bias = bias_grad;

  use_global_stats = is_test || use_global_stats;

  const auto &x_dims = x.dims();

  PADDLE_ENFORCE_EQ(
      x_dims.size() >= 2 && x_dims.size() <= 5,
      true,
      phi::errors::InvalidArgument(
          "The size of input's dimensions should be between 2 and 5."
          "But received: the size of input's dimensions is [%d],"
          "the dimensions of input is [%s]",
          x_dims.size(),
          x_dims));
  int N, C, H, W, D;
354
  phi::funcs::ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
H
hong 已提交
355 356 357 358 359 360 361

  // init output
  if (d_x) {
    ctx.template Alloc<T>(d_x);
  }

  if (d_scale && d_bias) {
H
hong 已提交
362 363
    ctx.template Alloc<BatchNormParamType<T>>(d_scale);
    ctx.template Alloc<BatchNormParamType<T>>(d_bias);
H
hong 已提交
364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571
  }

  PADDLE_ENFORCE_EQ(
      scale.dims().size(),
      1UL,
      phi::errors::InvalidArgument(
          "The size of scale's dimensions must equal to 1. But received: "
          "the size of scale's dimensions is [%d], the dimensions of scale "
          "is [%s].",
          scale.dims().size(),
          scale.dims()));
  PADDLE_ENFORCE_EQ(
      scale.dims()[0],
      C,
      phi::errors::InvalidArgument(
          "The first dimension of scale must equal to Channels[%d]. But "
          "received: the first dimension of scale is [%d]",
          C,
          scale.dims()[0]));

  auto dtype = paddle::platform::CudnnDataType<T>::type;
#ifdef PADDLE_WITH_HIP
  auto compute_format =
      data_layout == DataLayout::kNHWC ? DataLayout::kNHWC : DataLayout::kNCHW;

// TODO(wangran16): wait for MIOpen to improve the performance of BN
// HIP do not support compute format of NHWC
// auto compute_format = DataLayout::kNCHW;
#else
  const bool fast_nhwc_batch_norm = dtype == CUDNN_DATA_HALF &&
                                    FLAGS_cudnn_batchnorm_spatial_persistent &&
                                    (reserve_space.get_ptr() != nullptr);
  auto compute_format = fast_nhwc_batch_norm && data_layout == DataLayout::kNHWC
                            ? DataLayout::kNHWC
                            : DataLayout::kNCHW;
#endif

  DenseTensor transformed_x(x.type());
  DenseTensor transformed_d_y(d_y->type());
  DenseTensor transformed_d_x;
  if (data_layout == DataLayout::kNHWC && compute_format == DataLayout::kNCHW &&
      x_dims.size() > 2) {
    VLOG(3) << "Transform input tensor from NHWC to NCHW.";
    ResizeToChannelFirst<Context, T>(ctx, &x, &transformed_x);
    TransToChannelFirst<Context, T>(ctx, &x, &transformed_x);
    ResizeToChannelFirst<Context, T>(ctx, d_y, &transformed_d_y);
    TransToChannelFirst<Context, T>(ctx, d_y, &transformed_d_y);
    if (d_x) {
      ResizeToChannelFirst<Context, T>(ctx, d_x, &transformed_d_x);
    }
  } else {
    transformed_x.ShareDataWith(x);
    transformed_d_y.ShareDataWith(*d_y);
    if (d_x) {
      transformed_d_x.ShareDataWith(*d_x);
    }
  }

  std::vector<int> dims;
  std::vector<int> strides;
  if (compute_format == 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 int num = transformed_x.numel();
#ifdef HIPCC
  const int block = 256;
#else
  const int block = 512;
#endif
  int max_threads = ctx.GetMaxPhysicalThreadCount();
  const int max_blocks = std::max(max_threads / block, 1);
  int grid1 = (num + block - 1) / block;
  int grid2 = std::min(C, max_blocks);
  auto stream = ctx.stream();
  InplaceHelper<T> inplace_functor;

  if (!use_global_stats) {
    if ((N * H * W * D) == 1) {
      if (d_x) {
        paddle::framework::TensorCopy(*d_y, ctx.GetPlace(), d_x);
      }
      phi::funcs::SetConstant<Context, BatchNormParamType<T>> functor;
      functor(ctx, d_scale, static_cast<BatchNormParamType<T>>(0));
      functor(ctx, d_bias, static_cast<BatchNormParamType<T>>(0));
      return;
    }

// ------------------- cudnn descriptors ---------------------
#ifdef PADDLE_WITH_HIP
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// miopenTensorDescriptor_t data_desc_;
// miopenTensorDescriptor_t bn_param_desc_;
// miopenBatchNormMode_t mode_;

// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenCreateTensorDescriptor(&data_desc_));
// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenCreateTensorDescriptor(&bn_param_desc_));
#else
    cudnnTensorDescriptor_t data_desc_;
    cudnnTensorDescriptor_t bn_param_desc_;
    cudnnBatchNormMode_t mode_;

    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnCreateTensorDescriptor(
            &bn_param_desc_));
#endif
    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);
#ifdef PADDLE_WITH_HIP
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// mode_ = miopenBNSpatial;
#elif CUDNN_VERSION_MIN(7, 0, 1)
    if (FLAGS_cudnn_batchnorm_spatial_persistent) {
      mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
    } else if (H == 1 && W == 1) {
      mode_ = CUDNN_BATCHNORM_PER_ACTIVATION;
    } else {
      mode_ = CUDNN_BATCHNORM_SPATIAL;
    }
#else
    if (H == 1 && W == 1) {
      mode_ = CUDNN_BATCHNORM_PER_ACTIVATION;
    } else {
      mode_ = CUDNN_BATCHNORM_SPATIAL;
    }
#endif  // CUDNN_VERSION_MIN(7, 0, 1)

#ifdef PADDLE_WITH_HIP
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::miopenSetTensorDescriptor(
//     data_desc_, CudnnDataType<T>::type,
//     x_dims.size() > 3 ? x_dims.size() : 4, const_cast<int *>(dims.data()),
//     const_cast<int *>(strides.data())));
// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenDeriveBNTensorDescriptor(bn_param_desc_,
//                                                       data_desc_, mode_));
#else
    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnSetTensorNdDescriptor(
            data_desc_,
            CudnnDataType<T>::type,
            x_dims.size() > 3 ? x_dims.size() : 4,
            dims.data(),
            strides.data()));
    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnDeriveBNTensorDescriptor(
            bn_param_desc_, data_desc_, mode_));
#endif

    const auto *saved_mean_data =
        saved_mean.template data<BatchNormParamType<T>>();
    const auto *saved_var_data =
        saved_variance.template data<BatchNormParamType<T>>();

    if (is_inplace) {
      inplace_functor(compute_format,
                      transformed_x.data<T>(),
                      scale.template data<BatchNormParamType<T>>(),
                      bias.template data<BatchNormParamType<T>>(),
                      saved_mean_data,
                      saved_var_data,
                      epsilon,
                      C,
                      H * W * D,
                      num,
                      transformed_x.data<T>(),
                      grid2,
                      block,
                      stream);
    }

    // This branch calls CUDNN APIs
    if (d_x && d_scale && d_bias) {
      bool called = false;
#if CUDNN_VERSION_MIN(7, 4, 1)
      called = true;
      size_t workspace_size = 0;
      void *workspace_ptr = nullptr;
      DenseTensor workspace_tensor;
      auto reserve_space_size = reserve_space->memory_size();
      // --------------- cudnn batchnorm workspace ---------------
      PADDLE_ENFORCE_GPU_SUCCESS(
          paddle::platform::dynload::
              cudnnGetBatchNormalizationBackwardExWorkspaceSize(
                  /*handle=*/ctx.cudnn_handle(),
                  /*mode=*/mode_,
                  /*bnIps=*/CUDNN_BATCHNORM_OPS_BN,
                  /*xDesc=*/data_desc_,
                  /*yDesc=*/data_desc_,
                  /*dyDesc=*/data_desc_,
                  /*dzDesc=*/nullptr,
                  /*dxDesc=*/data_desc_,
                  /*bnScaleBiasMeanVarDesc=*/bn_param_desc_,
                  /*activationDesc=*/nullptr,
                  /*sizeInBytes=*/&workspace_size));

H
hong 已提交
572
      workspace_tensor.Resize({static_cast<int64_t>(workspace_size)});
H
hong 已提交
573 574
      workspace_ptr =
          static_cast<void *>(ctx.template Alloc<uint8_t>(&workspace_tensor));
H
hong 已提交
575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597

      PADDLE_ENFORCE_GPU_SUCCESS(
          paddle::platform::dynload::cudnnBatchNormalizationBackwardEx(
              /*handle=*/ctx.cudnn_handle(),
              /*mode=*/mode_,
              /*bnOps=*/CUDNN_BATCHNORM_OPS_BN,
              /*alphaDataDiff=*/CudnnDataType<T>::kOne(),
              /*betaDataDiff=*/CudnnDataType<T>::kZero(),
              /*alphaParamDiff=*/CudnnDataType<T>::kOne(),
              /*betaParamDiff=*/CudnnDataType<T>::kZero(),
              /*xDesc=*/data_desc_,
              /*xData=*/transformed_x.template data<T>(),
              /*yDesc=*/nullptr,
              /*yData=*/nullptr,
              /*dyDesc=*/data_desc_,
              /*dyData=*/transformed_d_y.template data<T>(),
              /*dzDesc=*/nullptr,
              /*dzData=*/nullptr,
              /*dxDesc=*/data_desc_,
              /*dxData=*/ctx.template Alloc<T>(&transformed_d_x),
              /*dBnScaleBiasDesc=*/bn_param_desc_,
              /*bnScaleData=*/scale.template data<BatchNormParamType<T>>(),
              /*bnBiasData=*/nullptr,
H
hong 已提交
598 599 600
              /*dBnScaleData=*/ctx.template Alloc<BatchNormParamType<T>>(
                  d_scale),
              /*dBnBiasData=*/ctx.template Alloc<BatchNormParamType<T>>(d_bias),
H
hong 已提交
601 602 603 604 605 606
              /*epsilon=*/epsilon,
              /*savedMean=*/saved_mean_data,
              /*savedInvVariance=*/saved_var_data,
              /*activationDesc=*/nullptr,
              /*workspace=*/workspace_ptr,
              /*workSpaceSizeInBytes=*/workspace_size,
H
hong 已提交
607 608
              /*reserveSpace=*/const_cast<uint8_t *>(
                  reserve_space->template data<uint8_t>()),
H
hong 已提交
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626
              /*reserveSpaceSizeInBytes=*/reserve_space_size));
#endif  // CUDNN_VERSION_MIN(7, 4, 1)
      if (!called) {
#ifdef PADDLE_WITH_HIP
        if (compute_format == DataLayout::kNCHW) {
          BNBackward<T,
                     block,
                     DataLayout::kNCHW><<<grid2, block, 0, ctx.stream()>>>(
              transformed_d_y.template data<T>(),
              transformed_x.template data<T>(),
              scale.template data<BatchNormParamType<T>>(),
              saved_mean_data,
              saved_var_data,
              C,
              N,
              H * W * D,
              epsilon,
              transformed_d_x.template data<T>(),
H
hong 已提交
627 628
              ctx.template Alloc<BatchNormParamType<T>>(d_scale),
              ctx.template Alloc<BatchNormParamType<T>>(d_bias));
H
hong 已提交
629 630 631 632 633 634 635 636 637 638 639 640 641 642
        } else {
          BNBackward<T,
                     block,
                     DataLayout::kNHWC><<<grid2, block, 0, ctx.stream()>>>(
              transformed_d_y.template data<T>(),
              transformed_x.template data<T>(),
              scale.template data<BatchNormParamType<T>>(),
              saved_mean_data,
              saved_var_data,
              C,
              N,
              H * W * D,
              epsilon,
              transformed_d_x.template data<T>(),
H
hong 已提交
643 644
              ctx.template Alloc<BatchNormParamType<T>>(d_scale),
              ctx.template Alloc<BatchNormParamType<T>>(d_bias));
H
hong 已提交
645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678
        }

// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenBatchNormalizationBackward(
//         dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
//         CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
//         CudnnDataType<T>::kZero(), data_desc_,
//         transformed_x.template data<T>(), data_desc_,
//         transformed_d_y.template data<T>(), data_desc_,
//         transformed_d_x.template mutable_data<T>(ctx.GetPlace()),
//         bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
//         d_scale->template mutable_data<BatchNormParamType<T>>(
//             ctx.GetPlace()),
//         d_bias->template mutable_data<BatchNormParamType<T>>(
//             ctx.GetPlace()),
//         epsilon, saved_mean_data, saved_var_data));
#else
        PADDLE_ENFORCE_GPU_SUCCESS(
            paddle::platform::dynload::cudnnBatchNormalizationBackward(
                ctx.cudnn_handle(),
                mode_,
                CudnnDataType<T>::kOne(),
                CudnnDataType<T>::kZero(),
                CudnnDataType<T>::kOne(),
                CudnnDataType<T>::kZero(),
                data_desc_,
                transformed_x.template data<T>(),
                data_desc_,
                transformed_d_y.template data<T>(),
                data_desc_,
                ctx.template Alloc<T>(&transformed_d_x),
                bn_param_desc_,
                scale.template data<BatchNormParamType<T>>(),
H
hong 已提交
679 680
                ctx.template Alloc<BatchNormParamType<T>>(d_scale),
                ctx.template Alloc<BatchNormParamType<T>>(d_bias),
H
hong 已提交
681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869
                epsilon,
                saved_mean_data,
                saved_var_data));
#endif
      }

      if (data_layout == DataLayout::kNHWC &&
          compute_format == DataLayout::kNCHW) {
        VLOG(3) << "Transform batchnorm output from NCHW to NHWC";
        TransToChannelLast<Context, T>(ctx, &transformed_d_x, d_x);
      }
    } else {
      // This branch call CUDA kernels
      if (compute_format == DataLayout::kNCHW) {
        if (d_x) {
          BNBackwardData<
              T,
              block,
              phi::DataLayout::kNCHW><<<grid2, block, 0, ctx.stream()>>>(
              d_y->data<T>(),
              scale.data<BatchNormParamType<T>>(),
              saved_mean_data,
              x.data<T>(),
              saved_var_data,
              C,
              N,
              H * W * D,
              d_x->data<T>());
        }
        if (d_scale && d_bias) {
          KeBNBackwardScaleBias<
              T,
              block,
              phi::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
              d_y->data<T>(),
              x.data<T>(),
              saved_mean_data,
              saved_var_data,
              epsilon,
              N,
              C,
              H * W * D,
              d_scale->data<BatchNormParamType<T>>(),
              d_bias->data<BatchNormParamType<T>>());
        }
      } else {
        if (d_x) {
          BNBackwardData<
              T,
              block,
              phi::DataLayout::kNHWC><<<grid2, block, 0, ctx.stream()>>>(
              d_y->data<T>(),
              scale.data<BatchNormParamType<T>>(),
              saved_mean_data,
              x.data<T>(),
              saved_var_data,
              C,
              N,
              H * W * D,
              d_x->data<T>());
        }
        if (d_scale && d_bias) {
          KeBNBackwardScaleBias<
              T,
              block,
              phi::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
              d_y->data<T>(),
              x.data<T>(),
              saved_mean_data,
              saved_var_data,
              epsilon,
              N,
              C,
              H * W * D,
              d_scale->data<BatchNormParamType<T>>(),
              d_bias->data<BatchNormParamType<T>>());
        }
      }
    }

#ifdef PADDLE_WITH_HIP
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// clean when exit.
// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenDestroyTensorDescriptor(data_desc_));
// PADDLE_ENFORCE_GPU_SUCCESS(
//     platform::dynload::miopenDestroyTensorDescriptor(bn_param_desc_));
#else
    // clean when exit.
    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
    PADDLE_ENFORCE_GPU_SUCCESS(
        paddle::platform::dynload::cudnnDestroyTensorDescriptor(
            bn_param_desc_));
#endif
  } else {
    const auto *running_mean = mean.get_ptr();
    const auto *running_var = variance.get_ptr();

    const auto *running_mean_data =
        running_mean->template data<BatchNormParamType<T>>();
    const auto *running_var_data =
        running_var->template data<BatchNormParamType<T>>();

    if (is_inplace) {
      auto px = x;
      inplace_functor(data_layout,
                      ctx.template Alloc<T>(&px),
                      scale.template data<BatchNormParamType<T>>(),
                      bias.template data<BatchNormParamType<T>>(),
                      running_mean_data,
                      running_var_data,
                      epsilon,
                      C,
                      H * W * D,
                      num,
                      x.data<T>(),
                      grid2,
                      block,
                      stream);
    }

    if (compute_format == DataLayout::kNCHW) {
      if (d_x) {
        KeBNBackwardData<T,
                         phi::DataLayout::kNCHW><<<grid1, block, 0, stream>>>(
            d_y->data<T>(),
            scale.data<BatchNormParamType<T>>(),
            running_var_data,
            epsilon,
            C,
            H * W,
            num,
            d_x->data<T>());
      }
      if (d_scale && d_bias) {
        KeBNBackwardScaleBias<
            T,
            block,
            phi::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
            d_y->data<T>(),
            x.data<T>(),
            running_mean_data,
            running_var_data,
            epsilon,
            N,
            C,
            H * W * D,
            d_scale->data<BatchNormParamType<T>>(),
            d_bias->data<BatchNormParamType<T>>());
      }
    } else {
      if (d_x) {
        KeBNBackwardData<T,
                         phi::DataLayout::kNHWC><<<grid1, block, 0, stream>>>(
            d_y->data<T>(),
            scale.data<BatchNormParamType<T>>(),
            running_var_data,
            epsilon,
            C,
            H * W,
            num,
            d_x->data<T>());
      }
      if (d_scale && d_bias) {
        KeBNBackwardScaleBias<
            T,
            block,
            phi::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
            d_y->data<T>(),
            x.data<T>(),
            running_mean_data,
            running_var_data,
            epsilon,
            N,
            C,
            H * W * D,
            d_scale->data<BatchNormParamType<T>>(),
            d_bias->data<BatchNormParamType<T>>());
      }
    }
  }
}

template <typename T, typename Context>
void BatchNormGradKernel(const Context &dev_ctx,
                         const DenseTensor &x,
                         const DenseTensor &scale,
                         const DenseTensor &bias,
H
hong 已提交
870 871
                         paddle::optional<const DenseTensor &> mean,
                         paddle::optional<const DenseTensor &> variance,
H
hong 已提交
872 873 874
                         const DenseTensor &saved_mean,
                         const DenseTensor &saved_variance,
                         paddle::optional<const DenseTensor &> reserve_space,
H
hong 已提交
875
                         const DenseTensor &y_grad,
H
hong 已提交
876 877 878 879 880 881 882 883 884 885 886 887 888 889
                         float momentum,
                         float epsilon,
                         const std::string &data_layout,
                         bool is_test,
                         bool use_global_stats,
                         bool trainable_statistics,
                         bool fuse_with_relu,
                         DenseTensor *x_grad,
                         DenseTensor *scale_grad,
                         DenseTensor *bias_grad) {
  BatchNormGradRawKernel<T, Context>(dev_ctx,
                                     x,
                                     scale,
                                     bias,
H
hong 已提交
890 891
                                     mean,
                                     variance,
H
hong 已提交
892 893 894
                                     saved_mean,
                                     saved_variance,
                                     reserve_space,
H
hong 已提交
895
                                     y_grad,
H
hong 已提交
896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914
                                     momentum,
                                     epsilon,
                                     data_layout,
                                     is_test,
                                     use_global_stats,
                                     trainable_statistics,
                                     fuse_with_relu,
                                     false,
                                     x_grad,
                                     scale_grad,
                                     bias_grad);
}

template <typename T, typename Context>
void BatchNormDoubleGradKernel(const Context &ctx,
                               const DenseTensor &x,
                               const DenseTensor &scale,
                               paddle::optional<const DenseTensor &> mean,
                               paddle::optional<const DenseTensor &> variance,
915 916 917 918 919 920
                               const DenseTensor &saved_mean,
                               const DenseTensor &saved_variance,
                               const DenseTensor &y_grad,
                               const DenseTensor &x_grad_grad,
                               const DenseTensor &scale_grad_grad,
                               const DenseTensor &bias_grad_grad,
H
hong 已提交
921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990
                               float momentum,
                               float epsilon,
                               const std::string &data_layout_str,
                               bool is_test,
                               bool use_global_stats,
                               bool trainable_statistics,
                               bool fuse_with_relu,
                               DenseTensor *x_grad,
                               DenseTensor *scale_grad,
                               DenseTensor *y_grad_grad) {
  PADDLE_ENFORCE_EQ(is_test,
                    false,
                    phi::errors::InvalidArgument(
                        "`is_test = True` CANNOT be used in train program. If "
                        "you want to use global status in pre_train model, "
                        "please set `use_global_stats = True`"));

  const DataLayout data_layout =
      paddle::framework::StringToDataLayout(data_layout_str);

  const DenseTensor *running_mean = nullptr;
  const DenseTensor *running_variance = nullptr;
  if (use_global_stats) {
    running_mean = mean.get_ptr();
    running_variance = variance.get_ptr();
  }
  paddle::operators::NormDoubleGradFunctor<Context, T>(ctx,
                                                       data_layout,
                                                       &x,
                                                       &scale,
                                                       &y_grad,
                                                       &saved_mean,
                                                       &saved_variance,
                                                       running_mean,
                                                       running_variance,
                                                       epsilon,
                                                       use_global_stats,
                                                       &x_grad_grad,
                                                       &scale_grad_grad,
                                                       &bias_grad_grad,
                                                       x_grad,
                                                       scale_grad,
                                                       y_grad_grad);
}

}  // namespace phi

#ifdef PADDLE_WITH_HIP
PD_REGISTER_KERNEL(batch_norm_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormGradKernel,
                   float,
                   phi::dtype::float16) {}

PD_REGISTER_KERNEL(batch_norm_grad_raw,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormGradRawKernel,
                   float,
                   phi::dtype::float16) {}
#else
PD_REGISTER_KERNEL(batch_norm_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormGradKernel,
                   float,
                   double,
                   phi::dtype::float16) {
  if (kernel_key.dtype() == phi::DataType::FLOAT16) {
991 992 993
    kernel->OutputAt(0).SetDataType(phi::DataType::FLOAT32);  // x_grad
    kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);  // scale_grad
    kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);  // bias_grad
H
hong 已提交
994 995 996 997 998 999 1000 1001 1002 1003 1004
  }
}

PD_REGISTER_KERNEL(batch_norm_grad_raw,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormGradRawKernel,
                   float,
                   double,
                   phi::dtype::float16) {
  if (kernel_key.dtype() == phi::DataType::FLOAT16) {
1005 1006 1007
    kernel->OutputAt(0).SetDataType(phi::DataType::FLOAT32);  // x_grad
    kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);  // scale_grad
    kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);  // bias_grad
H
hong 已提交
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027
  }
}

#endif

#ifdef PADDLE_WITH_HIP
PD_REGISTER_KERNEL(batch_norm_grad_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormDoubleGradKernel,
                   float,
                   double) {}
#else
PD_REGISTER_KERNEL(batch_norm_grad_grad,
                   GPU,
                   ALL_LAYOUT,
                   phi::BatchNormDoubleGradKernel,
                   float,
                   double) {}
#endif