/
dropout_op_cudnn.cc
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/
dropout_op_cudnn.cc
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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 CuDNNDropoutOp final : public Operator<CUDAContext> {
public:
USE_OPERATOR_FUNCTIONS(CUDAContext);
explicit CuDNNDropoutOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<CUDAContext>(operator_def, ws),
cudnn_wrapper_(&context_),
ratio_(OperatorBase::GetSingleArgument<float>("ratio", 0.5)),
is_test_(OperatorBase::GetSingleArgument<int>(OpSchema::Arg_IsTest, 0)),
states_initialized_(false),
random_seed_(operator_def.device_option().random_seed()) {
CAFFE_ENFORCE_GE(ratio_, 0);
CAFFE_ENFORCE_LT(ratio_, 1);
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(cudnnCreateDropoutDescriptor(&dropout_desc_));
CUDNN_ENFORCE(cudnnDropoutGetStatesSize(
cudnn_wrapper_.inline_cudnn_handle(),
reinterpret_cast<size_t*>(&states_size_in_bytes_)));
if (!is_test_) {
scratch_blob_ = ws->CreateBlob(scratch_blob_name(operator_def.output(1)));
CAFFE_ENFORCE(scratch_blob_);
}
}
~CuDNNDropoutOp() noexcept override {
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(cudnnDestroyDropoutDescriptor(dropout_desc_));
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
static string scratch_blob_name(string mask_blob_name) {
return "cudnn_dropout_scratch_" + mask_blob_name;
}
protected:
CuDNNWrapper cudnn_wrapper_;
cudnnTensorDescriptor_t data_desc_;
cudnnDropoutDescriptor_t dropout_desc_;
vector<int64_t> cudnn_input_dims_;
float ratio_;
bool is_test_;
Blob* scratch_blob_ = nullptr;
size_t states_size_in_bytes_, reserve_space_size_in_bytes_;
// Input: X, Output: Y, mask_and_states
// track whether states have been initialized - only needs to happen once
bool states_initialized_;
// random seed
unsigned long long random_seed_;
};
class CuDNNDropoutGradientOp final : public Operator<CUDAContext> {
public:
USE_OPERATOR_FUNCTIONS(CUDAContext);
explicit CuDNNDropoutGradientOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<CUDAContext>(operator_def, ws),
cudnn_wrapper_(&context_),
ratio_(OperatorBase::GetSingleArgument<float>("ratio", 0.5)),
is_test_(OperatorBase::GetSingleArgument<int>(OpSchema::Arg_IsTest, 0)),
states_initialized_(false),
random_seed_(operator_def.device_option().random_seed()) {
CAFFE_ENFORCE_GE(ratio_, 0);
CAFFE_ENFORCE_LT(ratio_, 1);
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(cudnnCreateDropoutDescriptor(&dropout_desc_));
CUDNN_ENFORCE(cudnnDropoutGetStatesSize(
cudnn_wrapper_.inline_cudnn_handle(),
reinterpret_cast<size_t*>(&states_size_in_bytes_)));
// Share scratch with the forward op
scratch_blob_ =
ws->GetBlob(CuDNNDropoutOp::scratch_blob_name(operator_def.input(1)));
CAFFE_ENFORCE(scratch_blob_);
}
~CuDNNDropoutGradientOp() noexcept override {
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(cudnnDestroyDropoutDescriptor(dropout_desc_));
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
protected:
CuDNNWrapper cudnn_wrapper_;
cudnnTensorDescriptor_t data_desc_;
cudnnDropoutDescriptor_t dropout_desc_;
vector<int64_t> cudnn_input_dims_;
Blob* scratch_blob_;
float ratio_;
bool is_test_;
size_t states_size_in_bytes_, reserve_space_size_in_bytes_;
// Input: dY, mask_and_states, Output: dX
// only need to initialize states once (size is static)
bool states_initialized_;
unsigned long long random_seed_;
};
template <typename T, typename M>
bool CuDNNDropoutOp::DoRunWithType() {
const auto& X = Input(0);
auto* Y = Output(0);
auto size_prod = 1;
for (auto dim : X.sizes()) {
size_prod *= dim;
}
// now actually run the computation
if (is_test_) {
if (Y != &X) {
context_.CopySameDevice<T>(
X.numel(), X.template data<T>(), Y->template mutable_data<T>());
}
return true;
} else {
// Reshape tensor descriptors if necessary
if (X.sizes() != cudnn_input_dims_) {
CAFFE_ENFORCE(scratch_blob_);
Tensor* states = BlobGetMutableTensor(scratch_blob_, CUDA);
cudnn_input_dims_ = X.sizes().vec();
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc_,
GetCudnnTensorFormat(StorageOrder::NCHW),
cudnnTypeWrapper<T>::type,
size_prod,
1,
1,
1));
// get the reserve space we need
CUDNN_ENFORCE(cudnnDropoutGetReserveSpaceSize(
data_desc_, &reserve_space_size_in_bytes_));
states->Resize(states_size_in_bytes_);
if (!states_initialized_) {
// set the dropout descriptor (note: need to allocate the states data
// before acquiring the mutex)
uint8_t* states_data = states->template mutable_data<uint8_t>();
{
// Need to protect as clashes with NCCL
std::lock_guard<std::mutex> lk(CUDAContext::mutex());
CUDNN_ENFORCE(cudnnSetDropoutDescriptor(
dropout_desc_,
cudnn_wrapper_.inline_cudnn_handle(),
ratio_,
states_data,
states_size_in_bytes_,
random_seed_
));
}
states_initialized_ = true;
}
}
auto* mask = Output(
1,
{static_cast<int64_t>(reserve_space_size_in_bytes_)},
at::dtype<uint8_t>());
CUDNN_ENFORCE(cudnnDropoutForward(
cudnn_wrapper_.inline_cudnn_handle(),
dropout_desc_,
data_desc_,
X.template data<T>(),
data_desc_,
Y->template mutable_data<T>(),
mask->template mutable_data<uint8_t>(),
reserve_space_size_in_bytes_));
}
return true;
}
bool CuDNNDropoutOp::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>();
}
return false;
}
template <typename T, typename M>
bool CuDNNDropoutGradientOp::DoRunWithType() {
const auto& dY = Input(0);
const auto& mask = Input(1);
const Tensor& states = scratch_blob_->Get<Tensor>();
auto* dX = Output(0);
auto size_prod = 1;
for (auto dim : dY.sizes()) {
size_prod *= dim;
}
if (!states_initialized_) {
// set the dropout descriptor
{
// Need to protect as clashes with NCCL
std::lock_guard<std::mutex> lk(CUDAContext::mutex());
CUDNN_ENFORCE(cudnnRestoreDropoutDescriptor(
dropout_desc_,
cudnn_wrapper_.inline_cudnn_handle(),
ratio_,
const_cast<uint8_t*>(states.data<uint8_t>()),
states_size_in_bytes_,
random_seed_
));
}
states_initialized_ = true;
}
if (dY.sizes() != cudnn_input_dims_) {
cudnn_input_dims_ = dY.sizes().vec();
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc_,
GetCudnnTensorFormat(StorageOrder::NCHW),
cudnnTypeWrapper<T>::type,
size_prod,
1,
1,
1));
// get the reserve space we need
CUDNN_ENFORCE(cudnnDropoutGetReserveSpaceSize(
data_desc_, &reserve_space_size_in_bytes_));
}
// run the computation
void* mask_data = const_cast<void*>(mask.raw_data());
CUDNN_ENFORCE(cudnnDropoutBackward(
cudnn_wrapper_.inline_cudnn_handle(),
dropout_desc_,
data_desc_,
dY.data<T>(),
data_desc_,
dX->template mutable_data<T>(),
mask_data,
reserve_space_size_in_bytes_));
return true;
}
bool CuDNNDropoutGradientOp::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& dY = Input(0);
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>();
}
return false;
}
namespace {
REGISTER_CUDNN_OPERATOR(Dropout, CuDNNDropoutOp);
REGISTER_CUDNN_OPERATOR(DropoutGrad, CuDNNDropoutGradientOp);
}
}; // namespace caffe2