提交 56b723c4 编写于 作者: Q Qiao Longfei 提交者: GitHub

Cudnn batch norm op (#5067)

* init cudnn batch norm op

* rename batch_norm_cudnn_op.cc batch_norm_op.cu

* correct name style

* add ExtractNCWHD, simplify code

* fix ExtractNCWHD

* use CUDNN_ENFORCE instead of PADDLE_ENFORCE
上级 629cbdae
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/batch_norm_op.h"
#include <cfloat>
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
void ExtractNCWHD(const framework::DDim &dims,
const TensorFormat &tensor_format, int *N, int *C, int *H,
int *W, int *D) {
*N = dims[0];
*C = tensor_format == TensorFormat::NCHW ? dims[1] : dims[dims.size() - 1];
*H = tensor_format == TensorFormat::NCHW ? dims[2] : dims[1];
*W = dims.size() > 3
? (tensor_format == TensorFormat::NCHW ? dims[3] : dims[2])
: 1;
*D = dims.size() > 4
? (tensor_format == TensorFormat::NCHW ? dims[4] : dims[3])
: 1;
}
template <typename T>
class BatchNormKernel<platform::GPUPlace, 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 GPUPlace.");
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 tensor_format_str =
ctx.Attr<std::string>("tensor_format");
const TensorFormat tensor_format = StringToTensorFormat(tensor_format_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() >= 3 && x_dims.size() <= 5,
"The Input dim size should be between 3 and 5");
int N, C, H, W, D;
ExtractNCWHD(x_dims, tensor_format, &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 (tensor_format == TensorFormat::NCHW) {
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()));
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_, data_desc_, 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<T>(ctx.GetPlace());
variance_out->mutable_data<T>(ctx.GetPlace());
saved_mean->mutable_data<T>(ctx.GetPlace());
saved_variance->mutable_data<T>(ctx.GetPlace());
math::SetConstant<platform::GPUPlace, T> functor;
functor(ctx.device_context(), saved_mean, 0);
functor(ctx.device_context(), saved_variance, 0);
// FIXME(qiao) should not set zero self
functor(ctx.device_context(), mean_out, 0);
functor(ctx.device_context(), variance_out, 0);
auto handle = ctx.cuda_device_context().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<T>(), bias->template data<T>(),
est_mean->template data<T>(), est_var->template data<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<T>(), bias->template data<T>(), this_factor,
mean_out->template mutable_data<T>(ctx.GetPlace()),
variance_out->template mutable_data<T>(ctx.GetPlace()), epsilon,
saved_mean->template mutable_data<T>(ctx.GetPlace()),
saved_variance->template mutable_data<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::GPUPlace, 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 GPUPlace.");
double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
const std::string tensor_format_str =
ctx.Attr<std::string>("tensor_format");
const TensorFormat tensor_format = StringToTensorFormat(tensor_format_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() >= 3 && x_dims.size() <= 5,
"The Input dim size should be between 3 and 5");
int N, C, H, W, D;
ExtractNCWHD(x_dims, tensor_format, &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 = {N, C, H, W, D};
std::vector<int> 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>();
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
ctx.cuda_device_context().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;
REGISTER_OP_GPU_KERNEL(batch_norm,
ops::BatchNormKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
batch_norm_grad,
ops::BatchNormGradKernel<paddle::platform::GPUPlace, float>);
...@@ -22,6 +22,47 @@ limitations under the License. */ ...@@ -22,6 +22,47 @@ limitations under the License. */
namespace paddle { namespace paddle {
namespace platform { namespace platform {
inline const char* cudnnGetErrorString(cudnnStatus_t status) {
switch (status) {
case CUDNN_STATUS_SUCCESS:
return "CUDNN_STATUS_SUCCESS";
case CUDNN_STATUS_NOT_INITIALIZED:
return "CUDNN_STATUS_NOT_INITIALIZED";
case CUDNN_STATUS_ALLOC_FAILED:
return "CUDNN_STATUS_ALLOC_FAILED";
case CUDNN_STATUS_BAD_PARAM:
return "CUDNN_STATUS_BAD_PARAM";
case CUDNN_STATUS_INTERNAL_ERROR:
return "CUDNN_STATUS_INTERNAL_ERROR";
case CUDNN_STATUS_INVALID_VALUE:
return "CUDNN_STATUS_INVALID_VALUE";
case CUDNN_STATUS_ARCH_MISMATCH:
return "CUDNN_STATUS_ARCH_MISMATCH";
case CUDNN_STATUS_MAPPING_ERROR:
return "CUDNN_STATUS_MAPPING_ERROR";
case CUDNN_STATUS_EXECUTION_FAILED:
return "CUDNN_STATUS_EXECUTION_FAILED";
case CUDNN_STATUS_NOT_SUPPORTED:
return "CUDNN_STATUS_NOT_SUPPORTED";
case CUDNN_STATUS_LICENSE_ERROR:
return "CUDNN_STATUS_LICENSE_ERROR";
default:
return "Unknown cudnn error number";
}
}
#define CUDNN_VERSION_MIN(major, minor, patch) \
(CUDNN_VERSION >= ((major)*1000 + (minor)*100 + (patch)))
#define CUDNN_ENFORCE(condition) \
do { \
cudnnStatus_t status = condition; \
if (status != CUDNN_STATUS_SUCCESS) { \
VLOG(1) << ::paddle::platform::cudnnGetErrorString(status); \
PADDLE_THROW("cuDNN call failed"); \
} \
} while (false)
enum class DataLayout { enum class DataLayout {
kNHWC, kNHWC,
kNCHW, kNCHW,
...@@ -40,12 +81,30 @@ template <> ...@@ -40,12 +81,30 @@ template <>
class CudnnDataType<float> { class CudnnDataType<float> {
public: public:
static const cudnnDataType_t type = CUDNN_DATA_FLOAT; static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
typedef const float ScalingParamType;
static ScalingParamType* kOne() {
static ScalingParamType v = 1.0;
return &v;
}
static ScalingParamType* kZero() {
static ScalingParamType v = 0.0;
return &v;
}
}; };
template <> template <>
class CudnnDataType<double> { class CudnnDataType<double> {
public: public:
static const cudnnDataType_t type = CUDNN_DATA_DOUBLE; static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
typedef const double ScalingParamType;
static ScalingParamType* kOne() {
static ScalingParamType v = 1.0;
return &v;
}
static ScalingParamType* kZero() {
static ScalingParamType v = 0.0;
return &v;
}
}; };
inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) { inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
......
...@@ -83,6 +83,7 @@ extern void* cudnn_dso_handle; ...@@ -83,6 +83,7 @@ extern void* cudnn_dso_handle;
__macro(cudnnDestroyConvolutionDescriptor); \ __macro(cudnnDestroyConvolutionDescriptor); \
__macro(cudnnSetConvolutionNdDescriptor); \ __macro(cudnnSetConvolutionNdDescriptor); \
__macro(cudnnGetConvolutionNdDescriptor); \ __macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreate); \ __macro(cudnnCreate); \
__macro(cudnnDestroy); \ __macro(cudnnDestroy); \
__macro(cudnnSetStream); \ __macro(cudnnSetStream); \
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册