wehub-resource-sync
257 lines
9.7 KiB
C++
257 lines
9.7 KiB
C++
/* Copyright (c) 2020 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. */
|
|
|
|
#pragma once
|
|
|
|
#include <vector>
|
|
|
|
#include "paddle/phi/backends/context_pool.h"
|
|
#include "paddle/phi/backends/dynload/cudnn.h"
|
|
#include "paddle/phi/backends/gpu/forwards.h"
|
|
#include "paddle/phi/backends/gpu/gpu_dnn.h"
|
|
#include "paddle/phi/core/dense_tensor.h"
|
|
|
|
namespace phi {
|
|
|
|
class ScopedRNNBase {
|
|
public:
|
|
ScopedRNNBase(int seq_length,
|
|
int batch_size,
|
|
int input_size,
|
|
int hidden_size,
|
|
int num_layers,
|
|
float dropout_prob,
|
|
int seed,
|
|
int weight_numel,
|
|
bool initialized,
|
|
bool is_bidirec)
|
|
: seq_length_(seq_length),
|
|
batch_size_(batch_size),
|
|
input_size_(input_size),
|
|
hidden_size_(hidden_size),
|
|
num_layers_(num_layers),
|
|
dropout_prob_(dropout_prob),
|
|
seed_(seed),
|
|
weight_numel_(weight_numel),
|
|
initialized_(initialized),
|
|
is_bidirec_(is_bidirec) {}
|
|
|
|
template <typename T>
|
|
void Create(const cudnnHandle_t& handle,
|
|
const Place& place,
|
|
const std::vector<int>& sequence_length,
|
|
size_t* workspace_size,
|
|
size_t* reserve_size,
|
|
DenseTensor* dropout_state) {
|
|
int numDirections = is_bidirec_ ? 2 : 1;
|
|
cudnnDataType_t cudnn_type = backends::gpu::CudnnDataType<T>::type;
|
|
|
|
// ------------------- cudnn x, y descriptors ---------------------
|
|
std::vector<int> dims_x = {batch_size_, input_size_, 1};
|
|
std::vector<int> strides_x = {input_size_, 1, 1};
|
|
std::vector<int> dims_y = {batch_size_, hidden_size_ * numDirections, 1};
|
|
std::vector<int> strides_y = {hidden_size_ * numDirections, 1, 1};
|
|
for (int i = 0; i < seq_length_; ++i) {
|
|
x_descs_.emplace_back(x_desc_.descriptor<T>(dims_x, strides_x));
|
|
y_descs_.emplace_back(y_desc_.descriptor<T>(dims_y, strides_y));
|
|
}
|
|
|
|
#if CUDNN_VERSION >= 90000
|
|
auto seqlen_is_empty = sequence_length.empty();
|
|
if (seqlen_is_empty) {
|
|
std::vector<int> seqlen_array(batch_size_);
|
|
for (int i = 0; i < batch_size_; ++i) {
|
|
seqlen_array[i] = seq_length_;
|
|
}
|
|
x_seq_desc_.descriptor<T>(
|
|
seq_length_, batch_size_, input_size_, true, seqlen_array);
|
|
y_seq_desc_.descriptor<T>(seq_length_,
|
|
batch_size_,
|
|
hidden_size_ * numDirections,
|
|
true,
|
|
seqlen_array);
|
|
} else {
|
|
x_seq_desc_.descriptor<T>(
|
|
seq_length_, batch_size_, input_size_, true, sequence_length);
|
|
y_seq_desc_.descriptor<T>(seq_length_,
|
|
batch_size_,
|
|
hidden_size_ * numDirections,
|
|
true,
|
|
sequence_length);
|
|
}
|
|
#elif CUDNN_VERSION >= 7201
|
|
if (!sequence_length.empty()) {
|
|
x_seq_desc_.descriptor<T>(
|
|
seq_length_, batch_size_, input_size_, true, sequence_length);
|
|
y_seq_desc_.descriptor<T>(seq_length_,
|
|
batch_size_,
|
|
hidden_size_ * numDirections,
|
|
true,
|
|
sequence_length);
|
|
}
|
|
#endif
|
|
|
|
// ------------------- cudnn hx, hy, cx, cy descriptors----------
|
|
std::vector<int> dims_hx = {
|
|
num_layers_ * numDirections, batch_size_, hidden_size_};
|
|
std::vector<int> strides_hx = {hidden_size_ * batch_size_, hidden_size_, 1};
|
|
init_h_desc_.descriptor<T>(dims_hx, strides_hx);
|
|
init_c_desc_.descriptor<T>(dims_hx, strides_hx);
|
|
last_h_desc_.descriptor<T>(dims_hx, strides_hx);
|
|
last_c_desc_.descriptor<T>(dims_hx, strides_hx);
|
|
|
|
// ------------------- cudnn dropout descriptors ---------------------
|
|
size_t state_size;
|
|
if (!initialized_) {
|
|
PADDLE_ENFORCE_GPU_SUCCESS(
|
|
dynload::cudnnDropoutGetStatesSize(handle, &state_size));
|
|
DeviceContextPool& pool = DeviceContextPool::Instance();
|
|
auto* dev_ctx = reinterpret_cast<GPUContext*>(pool.Get(place));
|
|
dropout_state->Resize({static_cast<int64_t>(state_size)});
|
|
dev_ctx->template Alloc<uint8_t>(dropout_state);
|
|
}
|
|
dropout_desc_.descriptor(handle,
|
|
place,
|
|
initialized_,
|
|
dropout_prob_,
|
|
dropout_state,
|
|
seed_,
|
|
state_size);
|
|
|
|
// ------------------- cudnn rnn descriptors ---------------------
|
|
#if CUDNN_VERSION >= 90000
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnSetRNNDescriptor_v8(
|
|
rnn_desc_.desc(),
|
|
CUDNN_RNN_ALGO_STANDARD,
|
|
CUDNN_LSTM,
|
|
CUDNN_RNN_DOUBLE_BIAS,
|
|
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL,
|
|
CUDNN_LINEAR_INPUT,
|
|
cudnn_type,
|
|
cudnn_type,
|
|
CUDNN_DEFAULT_MATH,
|
|
input_size_,
|
|
hidden_size_,
|
|
hidden_size_,
|
|
num_layers_,
|
|
dropout_desc_.desc(),
|
|
seqlen_is_empty ? CUDNN_RNN_PADDED_IO_DISABLED
|
|
: CUDNN_RNN_PADDED_IO_ENABLED));
|
|
#else
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnSetRNNDescriptor_v6(
|
|
handle,
|
|
rnn_desc_.desc(),
|
|
hidden_size_,
|
|
num_layers_,
|
|
dropout_desc_.desc(),
|
|
CUDNN_LINEAR_INPUT,
|
|
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL,
|
|
CUDNN_LSTM,
|
|
CUDNN_RNN_ALGO_STANDARD,
|
|
cudnn_type));
|
|
#endif
|
|
|
|
#if CUDNN_VERSION < 90000 && CUDNN_VERSION >= 7201
|
|
if (!sequence_length.empty()) {
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnSetRNNPaddingMode(
|
|
rnn_desc_.desc(), CUDNN_RNN_PADDED_IO_ENABLED));
|
|
}
|
|
#endif
|
|
|
|
// ------------------- cudnn weights_size ---------------------
|
|
#if CUDNN_VERSION >= 90000
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnGetRNNWeightSpaceSize(
|
|
handle, rnn_desc_.desc(), &weights_size_));
|
|
#else
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnGetRNNParamsSize(
|
|
handle, rnn_desc_.desc(), x_descs_[0], &weights_size_, cudnn_type));
|
|
#endif
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
weights_size_,
|
|
sizeof(T) * weight_numel_,
|
|
common::errors::InvalidArgument(
|
|
"The cudnn lstm and setting weight size should be same."));
|
|
// ------------------- cudnn weight descriptors ---------------------
|
|
DataLayout layout = DataLayout::NCHW;
|
|
int dim_tmp = weights_size_ / sizeof(T);
|
|
std::vector<int> dim_w = {dim_tmp, 1, 1};
|
|
weight_desc_.descriptor<T>(layout, dim_w);
|
|
// ------------------- cudnn workspace, reserve size ---------------------
|
|
#if CUDNN_VERSION >= 90000
|
|
PADDLE_ENFORCE_GPU_SUCCESS(
|
|
dynload::cudnnGetRNNTempSpaceSizes(handle,
|
|
rnn_desc_.desc(),
|
|
CUDNN_FWD_MODE_TRAINING,
|
|
x_seq_desc_.desc(),
|
|
workspace_size,
|
|
reserve_size));
|
|
#else
|
|
PADDLE_ENFORCE_GPU_SUCCESS(
|
|
dynload::cudnnGetRNNWorkspaceSize(handle,
|
|
rnn_desc_.desc(),
|
|
seq_length_,
|
|
x_descs_.data(),
|
|
workspace_size));
|
|
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnGetRNNTrainingReserveSize(
|
|
handle, rnn_desc_.desc(), seq_length_, x_descs_.data(), reserve_size));
|
|
#endif
|
|
}
|
|
cudnnTensorDescriptor_t* x_descs() { return x_descs_.data(); }
|
|
cudnnTensorDescriptor_t* y_descs() { return y_descs_.data(); }
|
|
#if CUDNN_VERSION >= 7201
|
|
cudnnRNNDataDescriptor_t x_seq_desc() { return x_seq_desc_.desc(); }
|
|
cudnnRNNDataDescriptor_t y_seq_desc() { return y_seq_desc_.desc(); }
|
|
#endif
|
|
cudnnTensorDescriptor_t init_h_desc() { return init_h_desc_.desc(); }
|
|
cudnnTensorDescriptor_t init_c_desc() { return init_c_desc_.desc(); }
|
|
cudnnTensorDescriptor_t last_h_desc() { return last_h_desc_.desc(); }
|
|
cudnnTensorDescriptor_t last_c_desc() { return last_c_desc_.desc(); }
|
|
cudnnRNNDescriptor_t rnn_desc() { return rnn_desc_.desc(); }
|
|
cudnnDropoutDescriptor_t dropout_desc() { return dropout_desc_.desc(); }
|
|
cudnnFilterDescriptor_t weight_desc() { return weight_desc_.desc(); }
|
|
size_t weights_size() { return weights_size_; }
|
|
|
|
private:
|
|
int seq_length_;
|
|
int batch_size_;
|
|
int input_size_;
|
|
int hidden_size_;
|
|
int num_layers_;
|
|
float dropout_prob_;
|
|
int seed_;
|
|
int weight_numel_;
|
|
bool initialized_;
|
|
bool is_bidirec_;
|
|
size_t weights_size_;
|
|
std::vector<cudnnTensorDescriptor_t> x_descs_;
|
|
std::vector<cudnnTensorDescriptor_t> y_descs_;
|
|
|
|
backends::gpu::ScopedTensorDescriptor x_desc_;
|
|
backends::gpu::ScopedTensorDescriptor y_desc_;
|
|
#if CUDNN_VERSION >= 7201
|
|
backends::gpu::ScopedRNNTensorDescriptor x_seq_desc_;
|
|
backends::gpu::ScopedRNNTensorDescriptor y_seq_desc_;
|
|
#endif
|
|
backends::gpu::ScopedTensorDescriptor init_h_desc_;
|
|
backends::gpu::ScopedTensorDescriptor init_c_desc_;
|
|
backends::gpu::ScopedTensorDescriptor last_h_desc_;
|
|
backends::gpu::ScopedTensorDescriptor last_c_desc_;
|
|
backends::gpu::ScopedDropoutDescriptor dropout_desc_;
|
|
backends::gpu::ScopedFilterDescriptor weight_desc_;
|
|
backends::gpu::ScopedRNNDescriptor rnn_desc_;
|
|
};
|
|
|
|
} // namespace phi
|