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batch_norm_kernel.cu
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batch_norm_kernel.cu
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// 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.
#ifdef __NVCC__
#include "cub/cub.cuh"
#endif
#ifdef __HIPCC__
#include <hipcub/hipcub.hpp>
namespace cub = hipcub;
#endif
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/operators/layout_utils.h"
#include "paddle/fluid/operators/norm_utils.cu.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/flags.h"
#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/phi/kernels/funcs/norm_utils.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, phi::DataLayout layout>
static __global__ void BNForwardInference(const T *x,
const BatchNormParamType<T> *mean,
const BatchNormParamType<T> *variance,
const BatchNormParamType<T> *scale,
const BatchNormParamType<T> *bias,
const int C,
const int N,
const int HxW,
const double epsilon,
T *y) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
int num = N * C * HxW;
for (int i = gid; i < num; i += stride) {
const int c = layout == phi::DataLayout::kNCHW ? i / HxW % C : i % C;
BatchNormParamType<T> x_sub_mean =
static_cast<BatchNormParamType<T>>(x[i]) - mean[c];
BatchNormParamType<T> inv_var = 1 / sqrt(variance[c] + epsilon);
y[i] = static_cast<T>(scale[c] * x_sub_mean * inv_var + bias[c]);
}
}
template <typename T, int BlockDim, phi::DataLayout layout>
static __global__ LAUNCH_BOUNDS(BlockDim) void BNForwardTraining(
const T *x,
const BatchNormParamType<T> *scale,
const BatchNormParamType<T> *bias,
const int C,
const int N,
const int HxW,
const double epsilon,
double exponentialAverageFactor,
T *y,
BatchNormParamType<T> *mean,
BatchNormParamType<T> *variance,
BatchNormParamType<T> *save_mean,
BatchNormParamType<T> *save_inv_variance) {
int outer_size = C;
int inner_size = N * HxW;
typedef cub::BlockReduce<BatchNormParamType<T>, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage mean_storage;
__shared__ typename BlockReduce::TempStorage variance_storeage;
__shared__ BatchNormParamType<T> mean_val;
__shared__ BatchNormParamType<T> variance_val;
__shared__ BatchNormParamType<T> inv_var_val;
for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
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;
variance_val = x_square_sum / inner_size - mean_val * mean_val;
inv_var_val = 1 / sqrt(variance_val + epsilon);
if (save_mean && save_inv_variance) {
save_mean[i] = mean_val;
save_inv_variance[i] = inv_var_val;
}
mean[i] = (1 - exponentialAverageFactor) * mean_val +
exponentialAverageFactor * mean[i];
variance[i] = (1 - exponentialAverageFactor) * variance_val +
exponentialAverageFactor * variance[i];
}
__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> x_sub_mean =
static_cast<BatchNormParamType<T>>(x[index]) - mean_val;
y[index] = scale[i] * x_sub_mean * inv_var_val + bias[i];
}
}
}
template <typename T, typename Context>
void BatchNormKernel(const Context &ctx,
const DenseTensor &x,
const DenseTensor &scale,
const DenseTensor &bias,
const DenseTensor &mean,
const DenseTensor &variance,
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,
DenseTensor *y,
DenseTensor *mean_out,
DenseTensor *variance_out,
DenseTensor *saved_mean,
DenseTensor *saved_variance,
DenseTensor *reserve_space) {
double epsilon = epsilon_f;
const bool trainable_stats = trainable_statistics;
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_layout_str);
bool test_mode = is_test && (!trainable_stats);
// Get the size for each dimension.
// NCHW [batch_size, in_channels, in_height, in_width]
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]",
x_dims.size()));
ctx.template Alloc<T>(y);
int N, C, H, W, D;
phi::funcs::ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
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 =
test_mode ||
(dtype == CUDNN_DATA_HALF && FLAGS_cudnn_batchnorm_spatial_persistent);
auto compute_format = fast_nhwc_batch_norm && data_layout == DataLayout::kNHWC
? DataLayout::kNHWC
: DataLayout::kNCHW;
#endif
DenseTensor transformed_x(x.type());
DenseTensor transformed_y(y->type());
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, y, &transformed_y);
} else {
transformed_x.ShareDataWith(x);
transformed_y.ShareDataWith(*y);
}
// ------------------- 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)
VLOG(3) << "Setting descriptors.";
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 * D * C, 1, W * D * C, D * C, C};
}
#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())));
// Note: PERSISTENT not implemented for inference
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenDeriveBNTensorDescriptor(
// bn_param_desc_, data_desc_, test_mode ? miopenBNSpatial : 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()));
// Note: PERSISTENT not implemented for inference
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_,
data_desc_,
test_mode ? CUDNN_BATCHNORM_SPATIAL : mode_));
#endif
auto handle = ctx.cudnn_handle();
// Now, depending on whether we are running test or not, we have two paths.
// It is training mode when it's not reference AND not using pre-trained
// model.
bool training = !test_mode && !use_global_stats;
if (!training) {
// only when test we use input to do computation.
const auto *est_mean = &mean;
const auto *est_var = &variance;
// Run inference mode.
PADDLE_ENFORCE_EQ(
est_mean->dims().size(),
1UL,
phi::errors::InvalidArgument(
"The size of mean's dimensions must equal to 1."
"But received: the size of mean's dimensions mean is [%d],"
"the dimensions of mean is [%s].",
est_mean->dims().size(),
est_mean->dims()));
PADDLE_ENFORCE_EQ(
est_var->dims().size(),
1UL,
phi::errors::InvalidArgument(
"The size of variance's dimensions must equal to 1."
"But received: the size of variance's dimensions is [%d],"
"the dimensions of variance is [%s].",
est_var->dims().size(),
est_var->dims()));
PADDLE_ENFORCE_EQ(
est_mean->dims()[0],
C,
phi::errors::InvalidArgument(
"The first dimension of mean must equal to the number of "
"Channels, which is [%d]. But received: the first dimension"
"of mean is [%d], the dimensions of mean is [%s].",
C,
est_mean->dims()[0],
est_mean->dims()));
PADDLE_ENFORCE_EQ(
est_var->dims()[0],
C,
phi::errors::InvalidArgument(
"The first dimension of variance must equal to the number"
"of Channels, which is [%d]. But received: the first dimension of"
"variance is [%d], the dimensions of variance is [%s].",
C,
est_var->dims()[0],
est_var->dims()));
#ifdef PADDLE_WITH_HIP
const int block_size = 256;
const int grid_size = (N * C * H * W * D + block_size - 1) / block_size;
if (compute_format == DataLayout::kNCHW) {
BNForwardInference<T, DataLayout::kNCHW>
<<<grid_size, block_size, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
transformed_y.template data<T>());
} else {
BNForwardInference<T, DataLayout::kNHWC>
<<<grid_size, block_size, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
transformed_y.template data<T>());
}
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationForwardInference(
// handle, miopenBNSpatial,
// const_cast<void *>(
// static_cast<const void *>(CudnnDataType<T>::kOne())),
// const_cast<void *>(
// static_cast<const void *>(CudnnDataType<T>::kZero())),
// data_desc_,
// static_cast<const void *>(transformed_x.template data<T>()),
// data_desc_,
// static_cast<void *>(
// transformed_y.template mutable_data<T>(ctx.GetPlace())),
// bn_param_desc_,
// const_cast<void *>(static_cast<const void *>(
// scale->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// bias->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// est_mean->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// est_var->template data<BatchNormParamType<T>>())),
// epsilon));
#else
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::cudnnBatchNormalizationForwardInference(
handle,
// Note: PERSISTENT not implemented for inference
CUDNN_BATCHNORM_SPATIAL,
CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(),
data_desc_,
transformed_x.template data<T>(),
data_desc_,
ctx.template Alloc<T>(&transformed_y),
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));
#endif
} else {
// if MomentumTensor is set, use MomentumTensor value, momentum
// is only used in this training branch
// need to solve here
// if (ctx.HasInput("MomentumTensor")) {
// const auto *mom_tensor = MomentumTensor;
// DenseTensor mom_cpu;
// paddle::framework::TensorCopySync(*mom_tensor, platform::CPUPlace(),
// &mom_cpu);
// momentum = mom_cpu.data<float>()[0];
// }
// Run training mode.
// obtain running mean and running inv var, and there is no need
// to initialize them.
ctx.template Alloc<BatchNormParamType<T>>(mean_out);
ctx.template Alloc<BatchNormParamType<T>>(variance_out);
ctx.template Alloc<BatchNormParamType<T>>(saved_mean);
ctx.template Alloc<BatchNormParamType<T>>(saved_variance);
if ((N * H * W * D) == 1) {
// Only 1 element in normalization dimension,
// skip the batch norm calculation, let y = x.
paddle::framework::TensorCopy(x, ctx.GetPlace(), y);
} else {
double this_factor = 1. - momentum;
#ifdef PADDLE_WITH_HIP
const int num = transformed_x.numel();
const int block = 256;
const int max_threads = ctx.GetMaxPhysicalThreadCount();
const int max_blocks = std::max(max_threads / block, 1);
const int grid = std::min(C, max_blocks);
if (compute_format == DataLayout::kNCHW) {
BNForwardTraining<T, block, DataLayout::kNCHW>
<<<grid, block, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
this_factor,
transformed_y.template data<T>(),
mean_out->template data<BatchNormParamType<T>>(),
variance_out->template data<BatchNormParamType<T>>(),
saved_mean->template data<BatchNormParamType<T>>(),
saved_variance->template data<BatchNormParamType<T>>());
} else {
BNForwardTraining<T, block, DataLayout::kNHWC>
<<<grid, block, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
this_factor,
transformed_y.template data<T>(),
mean_out->template data<BatchNormParamType<T>>(),
variance_out->template data<BatchNormParamType<T>>(),
saved_mean->template data<BatchNormParamType<T>>(),
saved_variance->template data<BatchNormParamType<T>>());
}
// TODO(wangran16): wait for MIOpen to improve the performance of BN
// PADDLE_ENFORCE_GPU_SUCCESS(
// platform::dynload::miopenBatchNormalizationForwardTraining(
// handle, mode_, const_cast<void *>(static_cast<const void *>(
// CudnnDataType<T>::kOne())),
// const_cast<void *>(
// static_cast<const void *>(CudnnDataType<T>::kZero())),
// data_desc_,
// static_cast<const void *>(transformed_x.template data<T>()),
// data_desc_,
// static_cast<void *>(
// transformed_y.template mutable_data<T>(ctx.GetPlace())),
// bn_param_desc_,
// const_cast<void *>(static_cast<const void *>(
// scale->template data<BatchNormParamType<T>>())),
// const_cast<void *>(static_cast<const void *>(
// bias->template data<BatchNormParamType<T>>())),
// this_factor,
// static_cast<void *>(
// mean_out->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(variance_out->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace())),
// epsilon,
// static_cast<void *>(
// saved_mean->template mutable_data<BatchNormParamType<T>>(
// ctx.GetPlace())),
// static_cast<void *>(saved_variance->template mutable_data<
// BatchNormParamType<T>>(ctx.GetPlace()))));
#else
// CUDNN PER_ACTIVATION mode only support small batch size
const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
const bool use_native_kernel =
(x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD);
if (use_native_kernel) {
const int block = 512;
const int max_threads = ctx.GetMaxPhysicalThreadCount();
const int max_blocks = std::max(max_threads / block, 1);
const int grid = std::min(C, max_blocks);
if (compute_format == DataLayout::kNCHW) {
BNForwardTraining<T, block, DataLayout::kNCHW>
<<<grid, block, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
this_factor,
transformed_y.template data<T>(),
mean_out->template data<BatchNormParamType<T>>(),
variance_out->template data<BatchNormParamType<T>>(),
saved_mean->template data<BatchNormParamType<T>>(),
saved_variance->template data<BatchNormParamType<T>>());
} else {
BNForwardTraining<T, block, DataLayout::kNHWC>
<<<grid, block, 0, ctx.stream()>>>(
transformed_x.template data<T>(),
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
C,
N,
H * W * D,
epsilon,
this_factor,
transformed_y.template data<T>(),
mean_out->template data<BatchNormParamType<T>>(),
variance_out->template data<BatchNormParamType<T>>(),
saved_mean->template data<BatchNormParamType<T>>(),
saved_variance->template data<BatchNormParamType<T>>());
}
} else {
#if CUDNN_VERSION_MIN(7, 4, 1)
size_t workspace_size = 0;
size_t reserve_space_size = 0;
void *reserve_space_ptr = nullptr;
void *workspace_ptr = nullptr;
DenseTensor workspace_tensor;
DenseTensor reserve_space_tensor;
// Create reserve space and workspace for batch norm.
// Create tensor for each batchnorm op, it will be used in the
// backward. Thus this tensor shouldn't be temp.
// auto *reserve_space = ctx.Output<Tensor>("ReserveSpace");
if (reserve_space == nullptr) {
reserve_space = &reserve_space_tensor;
}
PADDLE_ENFORCE_NOT_NULL(
reserve_space,
phi::errors::NotFound(
"The argument ReserveSpace of batch_norm op is not found."));
// --------------- cudnn batchnorm workspace ---------------
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(
/*handle=*/handle,
/*mode=*/mode_,
/*bnIps=*/CUDNN_BATCHNORM_OPS_BN,
/*xDesc=*/data_desc_,
/*zDesc=*/nullptr,
/*yDesc=*/data_desc_,
/*bnScaleBiasMeanVarDesc=*/bn_param_desc_,
/*activationDesc=*/nullptr,
/*sizeInBytes=*/&workspace_size));
// -------------- cudnn batchnorm reserve space --------------
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::
cudnnGetBatchNormalizationTrainingExReserveSpaceSize(
/*handle=*/handle,
/*mode=*/mode_,
/*bnOps=*/CUDNN_BATCHNORM_OPS_BN,
/*activationDesc=*/nullptr,
/*xDesc=*/data_desc_,
/*sizeInBytes=*/&reserve_space_size));
reserve_space->Resize({static_cast<int64_t>(reserve_space_size)});
reserve_space_ptr =
static_cast<void *>(ctx.template Alloc<uint8_t>(reserve_space));
workspace_tensor.Resize({static_cast<int64_t>(workspace_size)});
workspace_ptr =
static_cast<void *>(ctx.template Alloc<uint8_t>(&workspace_tensor));
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::cudnnBatchNormalizationForwardTrainingEx(
handle,
mode_,
CUDNN_BATCHNORM_OPS_BN,
CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(),
data_desc_,
transformed_x.template data<T>(),
nullptr,
nullptr,
data_desc_,
transformed_y.template data<T>(),
bn_param_desc_,
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
this_factor,
ctx.template Alloc<BatchNormParamType<T>>(mean_out),
ctx.template Alloc<BatchNormParamType<T>>(variance_out),
epsilon,
ctx.template Alloc<BatchNormParamType<T>>(saved_mean),
ctx.template Alloc<BatchNormParamType<T>>(saved_variance),
nullptr,
workspace_ptr,
workspace_size,
reserve_space_ptr,
reserve_space_size));
#else
PADDLE_ENFORCE_GPU_SUCCESS(
paddle::platform::dynload::cudnnBatchNormalizationForwardTraining(
handle,
mode_,
CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(),
data_desc_,
transformed_x.template data<T>(),
data_desc_,
ctx.template Alloc<T>(&transformed_y),
bn_param_desc_,
scale.template data<BatchNormParamType<T>>(),
bias.template data<BatchNormParamType<T>>(),
this_factor,
ctx.template Alloc<BatchNormParamType<T>>(mean_out),
ctx.template Alloc<BatchNormParamType<T>>(variance_out),
epsilon,
ctx.template Alloc<BatchNormParamType<T>>(saved_mean),
ctx.template Alloc<BatchNormParamType<T>>(saved_variance)));
#endif // CUDNN_VERSION_MIN(7, 4, 1)
}
#endif
}
}
if (data_layout == DataLayout::kNHWC && compute_format == DataLayout::kNCHW &&
x_dims.size() > 2) {
VLOG(3) << "Transform batchnorm output from NCHW to NHWC";
TransToChannelLast<Context, T>(ctx, &transformed_y, y);
}
#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
}
} // namespace phi
#ifdef PADDLE_WITH_HIP
PD_REGISTER_KERNEL(batch_norm,
GPU,
ALL_LAYOUT,
phi::BatchNormKernel,
float,
phi::dtype::float16) {}
#else
PD_REGISTER_KERNEL(batch_norm,
GPU,
ALL_LAYOUT,
phi::BatchNormKernel,
float,
double,
phi::dtype::float16) {
if (kernel_key.dtype() == phi::DataType::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);
}
}
#endif