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#include "caffe2/core/context_gpu.h"
#include "caffe2/core/cudnn_wrappers.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/types.h"
namespace caffe2 {
class CuDNNLRNOp final : public Operator<CUDAContext> {
public:
USE_OPERATOR_FUNCTIONS(CUDAContext);
template <class... Args>
explicit CuDNNLRNOp(Args&&... args)
: Operator<CUDAContext>(std::forward<Args>(args)...),
cudnn_wrapper_(&context_),
size_(OperatorBase::GetSingleArgument<int>("size", 0)),
alpha_(OperatorBase::GetSingleArgument<float>("alpha", 0)),
beta_(OperatorBase::GetSingleArgument<float>("beta", 0)),
bias_(OperatorBase::GetSingleArgument<float>("bias", 1)) {
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(cudnnCreateLRNDescriptor(&norm_desc_));
CUDNN_ENFORCE(
cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, bias_));
}
~CuDNNLRNOp() override {
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(cudnnDestroyLRNDescriptor(norm_desc_));
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
protected:
CuDNNWrapper cudnn_wrapper_;
cudnnTensorDescriptor_t data_desc_;
cudnnLRNDescriptor_t norm_desc_;
vector<int64_t> cudnn_input_dims_;
const int size_;
const float alpha_;
const float beta_;
const float bias_;
// Input: X, Output: Y
};
class CuDNNLRNGradientOp final : public Operator<CUDAContext> {
public:
USE_OPERATOR_FUNCTIONS(CUDAContext);
template <class... Args>
explicit CuDNNLRNGradientOp(Args&&... args)
: Operator<CUDAContext>(std::forward<Args>(args)...),
cudnn_wrapper_(&context_),
size_(OperatorBase::GetSingleArgument<int>("size", 0)),
alpha_(OperatorBase::GetSingleArgument<float>("alpha", 0)),
beta_(OperatorBase::GetSingleArgument<float>("beta", 0)),
bias_(OperatorBase::GetSingleArgument<float>("bias", 1)) {
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(cudnnCreateLRNDescriptor(&norm_desc_));
CUDNN_ENFORCE(
cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, bias_));
}
~CuDNNLRNGradientOp() override {
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(cudnnDestroyLRNDescriptor(norm_desc_));
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
protected:
CuDNNWrapper cudnn_wrapper_;
cudnnTensorDescriptor_t data_desc_;
cudnnLRNDescriptor_t norm_desc_;
vector<int64_t> cudnn_input_dims_;
const int size_;
const float alpha_;
const float beta_;
const float bias_;
// Input: X, Y, dY
// Output: dX
};
template <typename T, typename M>
bool CuDNNLRNOp::DoRunWithType() {
const auto& X = Input(0);
auto* Y = Output(0);
// Reshape tensor descriptors if necessary
if (X.sizes() != cudnn_input_dims_) {
VLOG(1) << "Setting descriptors";
cudnn_input_dims_ = X.sizes().vec();
int C = 1, H = 1, W = 1;
// Normal 4-dimensional tensors for images.
C = X.dim32(1);
H = X.dim32(2);
W = X.dim32(3);
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc_,
GetCudnnTensorFormat(StorageOrder::NCHW),
cudnnTypeWrapper<T>::type,
X.dim32(0),
C,
H,
W));
}
// now actually run the computation
CUDNN_ENFORCE(cudnnLRNCrossChannelForward(
cudnn_wrapper_.inline_cudnn_handle(),
norm_desc_,
CUDNN_LRN_CROSS_CHANNEL_DIM1,
cudnnTypeWrapper<T>::kOne(),
data_desc_,
X.template data<T>(),
cudnnTypeWrapper<T>::kZero(),
data_desc_,
Y->template mutable_data<T>()));
return true;
}
bool CuDNNLRNOp::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& X = Input(0);
auto* Y = Output(0);
Y->ResizeLike(X);
if (X.IsType<float>()) {
return DoRunWithType<float, float>();
} else if (X.IsType<at::Half>()) {
return DoRunWithType<at::Half, float>();
} else {
CAFFE_THROW("Unsupported input type");
}
return false;
}
template <typename T, typename M>
bool CuDNNLRNGradientOp::DoRunWithType() {
const auto& X = Input(0);
const auto& Y = Input(1);
const auto& dY = Input(2);
auto* dX = Output(0);
if (dY.sizes() != cudnn_input_dims_) {
VLOG(1) << "Setting descriptors";
cudnn_input_dims_ = dY.sizes().vec();
int C = 1, H = 1, W = 1;
// Normal 4-dimensional tensors for images.
C = dY.dim32(1);
H = dY.dim32(2);
W = dY.dim32(3);
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc_,
GetCudnnTensorFormat(StorageOrder::NCHW),
cudnnTypeWrapper<T>::type,
dY.dim32(0),
C,
H,
W));
}
// run the computation
CUDNN_ENFORCE(cudnnLRNCrossChannelBackward(
cudnn_wrapper_.inline_cudnn_handle(),
norm_desc_,
CUDNN_LRN_CROSS_CHANNEL_DIM1,
cudnnTypeWrapper<T>::kOne(),
data_desc_,
Y.template data<T>(),
data_desc_,
dY.template data<T>(),
data_desc_,
X.template data<T>(),
cudnnTypeWrapper<T>::kZero(),
data_desc_,
dX->template mutable_data<T>()));
return true;
}
bool CuDNNLRNGradientOp::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& dY = Input(2);
auto* dX = Output(0);
dX->ResizeLike(dY);
if (dY.IsType<float>()) {
return DoRunWithType<float, float>();
} else if (dY.IsType<at::Half>()) {
return DoRunWithType<at::Half, float>();
} else {
CAFFE_THROW("Unsupported input type");
}
return false;
}
namespace {
REGISTER_CUDNN_OPERATOR(LRN, CuDNNLRNOp);
REGISTER_CUDNN_OPERATOR(LRNGradient, CuDNNLRNGradientOp);
}
}; // namespace caffe2
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