chore: import upstream snapshot with attribution
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wehub-resource-sync
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <vector>
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/backends/dynload/miopen.h"
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#include "paddle/phi/backends/gpu/forwards.h"
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#include "paddle/phi/backends/gpu/gpu_dnn.h"
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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class ScopedRNNBase {
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public:
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ScopedRNNBase(int seq_length,
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int batch_size,
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int input_size,
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int hidden_size,
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int num_layers,
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float dropout_prob,
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int seed,
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int weight_numel,
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bool initialized,
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bool is_bidirec)
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: seq_length_(seq_length),
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batch_size_(batch_size),
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input_size_(input_size),
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hidden_size_(hidden_size),
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num_layers_(num_layers),
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dropout_prob_(dropout_prob),
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seed_(seed),
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weight_numel_(weight_numel),
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initialized_(initialized),
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is_bidirec_(is_bidirec) {}
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template <typename T>
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void Create(const miopenHandle_t& handle,
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const Place& place,
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const std::vector<int>& sequence_length,
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size_t* workspace_size,
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size_t* reserve_size,
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DenseTensor* dropout_state) {
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int numDirections = is_bidirec_ ? 2 : 1;
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miopenDataType_t miopen_type = backends::gpu::CudnnDataType<T>::type;
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// ------------------- miopen x, y descriptors ---------------------
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std::vector<int> dims_x = {batch_size_, input_size_, 1};
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std::vector<int> strides_x = {input_size_, 1, 1};
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std::vector<int> dims_y = {batch_size_, hidden_size_ * numDirections, 1};
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std::vector<int> strides_y = {hidden_size_ * numDirections, 1, 1};
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for (int i = 0; i < seq_length_; ++i) {
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x_descs_.emplace_back(x_desc_.descriptor<T>(dims_x, strides_x));
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y_descs_.emplace_back(y_desc_.descriptor<T>(dims_y, strides_y));
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}
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// ------------------- miopen hx, hy, cx, cy descriptors----------
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std::vector<int> dims_hx = {
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num_layers_ * numDirections, batch_size_, hidden_size_};
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std::vector<int> strides_hx = {hidden_size_ * batch_size_, hidden_size_, 1};
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init_h_desc_.descriptor<T>(dims_hx, strides_hx);
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init_c_desc_.descriptor<T>(dims_hx, strides_hx);
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last_h_desc_.descriptor<T>(dims_hx, strides_hx);
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last_c_desc_.descriptor<T>(dims_hx, strides_hx);
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// ------------------- miopen dropout descriptors ---------------------
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size_t state_size;
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if (!initialized_) {
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PADDLE_ENFORCE_GPU_SUCCESS(
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dynload::miopenDropoutGetStatesSize(handle, &state_size));
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DeviceContextPool& pool = DeviceContextPool::Instance();
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auto* dev_ctx = reinterpret_cast<GPUContext*>(pool.Get(place));
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dropout_state->Resize({static_cast<int64_t>(state_size)});
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dev_ctx->template Alloc<uint8_t>(dropout_state);
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}
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dropout_desc_.descriptor(handle,
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place,
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initialized_,
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dropout_prob_,
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dropout_state,
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seed_,
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state_size);
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// ------------------- miopen rnn descriptors ---------------------
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PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenSetRNNDescriptor_V2(
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rnn_desc_.desc(),
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hidden_size_,
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num_layers_,
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dropout_desc_.desc(),
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miopenRNNlinear,
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is_bidirec_ ? miopenRNNbidirection : miopenRNNunidirection,
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miopenLSTM,
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miopenRNNwithBias,
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miopenRNNdefault,
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miopen_type));
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// ------------------- miopen weights_size ---------------------
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size_t weights_size_;
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PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenGetRNNParamsSize(
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handle, rnn_desc_.desc(), x_descs_[0], &weights_size_, miopen_type));
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PADDLE_ENFORCE_EQ(
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weights_size_,
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sizeof(T) * weight_numel_,
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common::errors::InvalidArgument(
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"The miopen lstm and setting weight size should be same."));
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// ------------------- miopen weight descriptors ---------------------
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DataLayout layout = DataLayout::NCHW;
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int dim_tmp = weights_size_ / sizeof(T);
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std::vector<int> dim_w = {dim_tmp, 1, 1};
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weight_desc_.descriptor<T>(layout, dim_w);
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// ------------------- miopen workspace, reserve size ---------------------
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PADDLE_ENFORCE_GPU_SUCCESS(
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dynload::miopenGetRNNWorkspaceSize(handle,
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rnn_desc_.desc(),
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seq_length_,
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x_descs_.data(),
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workspace_size));
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PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenGetRNNTrainingReserveSize(
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handle, rnn_desc_.desc(), seq_length_, x_descs_.data(), reserve_size));
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}
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miopenTensorDescriptor_t* x_descs() { return x_descs_.data(); }
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miopenTensorDescriptor_t* y_descs() { return y_descs_.data(); }
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miopenTensorDescriptor_t init_h_desc() { return init_h_desc_.desc(); }
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miopenTensorDescriptor_t init_c_desc() { return init_c_desc_.desc(); }
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miopenTensorDescriptor_t last_h_desc() { return last_h_desc_.desc(); }
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miopenTensorDescriptor_t last_c_desc() { return last_c_desc_.desc(); }
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miopenRNNDescriptor_t rnn_desc() { return rnn_desc_.desc(); }
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miopenDropoutDescriptor_t dropout_desc() { return dropout_desc_.desc(); }
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miopenTensorDescriptor_t weight_desc() { return weight_desc_.desc(); }
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private:
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int seq_length_;
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int batch_size_;
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int input_size_;
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int hidden_size_;
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int num_layers_;
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float dropout_prob_;
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int seed_;
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int weight_numel_;
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bool initialized_;
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bool is_bidirec_;
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std::vector<miopenTensorDescriptor_t> x_descs_;
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std::vector<miopenTensorDescriptor_t> y_descs_;
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backends::gpu::ScopedTensorDescriptor x_desc_;
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backends::gpu::ScopedTensorDescriptor y_desc_;
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backends::gpu::ScopedTensorDescriptor init_h_desc_;
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backends::gpu::ScopedTensorDescriptor init_c_desc_;
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backends::gpu::ScopedTensorDescriptor last_h_desc_;
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backends::gpu::ScopedTensorDescriptor last_c_desc_;
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backends::gpu::ScopedDropoutDescriptor dropout_desc_;
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backends::gpu::ScopedFilterDescriptor weight_desc_;
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backends::gpu::ScopedRNNDescriptor rnn_desc_;
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};
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} // namespace phi
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