mindspore-ci-bot
4 years ago
committed by
Gitee
commit
03dde07455
@ -1,31 +0,0 @@
|
|||||||
/**
|
|
||||||
* Copyright 2020 Huawei Technologies Co., Ltd
|
|
||||||
*
|
|
||||||
* 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 "backend/kernel_compiler/gpu/nn/ctclossv2_gpu_kernel.h"
|
|
||||||
|
|
||||||
namespace mindspore {
|
|
||||||
namespace kernel {
|
|
||||||
MS_REG_GPU_KERNEL_ONE(CTCLossV2,
|
|
||||||
KernelAttr()
|
|
||||||
.AddInputAttr(kNumberTypeFloat32)
|
|
||||||
.AddInputAttr(kNumberTypeInt32)
|
|
||||||
.AddInputAttr(kNumberTypeInt32)
|
|
||||||
.AddInputAttr(kNumberTypeInt32)
|
|
||||||
.AddOutputAttr(kNumberTypeFloat32)
|
|
||||||
.AddOutputAttr(kNumberTypeFloat32),
|
|
||||||
CtcLossV2GpuKernel, float)
|
|
||||||
} // namespace kernel
|
|
||||||
} // namespace mindspore
|
|
||||||
@ -1,192 +0,0 @@
|
|||||||
/**
|
|
||||||
* Copyright 2020 Huawei Technologies Co., Ltd
|
|
||||||
*
|
|
||||||
* 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.
|
|
||||||
*/
|
|
||||||
|
|
||||||
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_CTCLOSS_GPU_KERNEL_H_
|
|
||||||
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_CTCLOSS_GPU_KERNEL_H_
|
|
||||||
|
|
||||||
#include <cuda_runtime_api.h>
|
|
||||||
#include <vector>
|
|
||||||
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
|
|
||||||
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
|
|
||||||
#include "runtime/device/gpu/gpu_memory_allocator.h"
|
|
||||||
|
|
||||||
namespace mindspore {
|
|
||||||
namespace kernel {
|
|
||||||
template <typename T>
|
|
||||||
class CtcLossV2GpuKernel : public GpuKernel {
|
|
||||||
public:
|
|
||||||
CtcLossV2GpuKernel()
|
|
||||||
: cudnn_handle_(nullptr),
|
|
||||||
probs_desc_(nullptr),
|
|
||||||
ctcloss_desc_(nullptr),
|
|
||||||
label_size_(0),
|
|
||||||
input_lengths_size_(0),
|
|
||||||
label_lengths_size_(0) {}
|
|
||||||
~CtcLossV2GpuKernel() override { DestroyResource(); }
|
|
||||||
|
|
||||||
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
|
|
||||||
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
|
|
||||||
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
|
|
||||||
|
|
||||||
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
|
|
||||||
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
|
|
||||||
float *probs = GetDeviceAddress<float>(inputs, 0);
|
|
||||||
float *costs = GetDeviceAddress<float>(outputs, 0);
|
|
||||||
float *grads = GetDeviceAddress<float>(outputs, 1);
|
|
||||||
|
|
||||||
// Copy labels/input_lengths/label_length to host as cudnn7.x.x requires
|
|
||||||
int *labels_host = nullptr;
|
|
||||||
int *no_blank_labels_host = nullptr;
|
|
||||||
void *input_lengths_host = nullptr;
|
|
||||||
void *label_lengths_host = nullptr;
|
|
||||||
cudaStream_t stream = reinterpret_cast<cudaStream_t>(stream_ptr);
|
|
||||||
AllocHostMem(&labels_host, &no_blank_labels_host, &input_lengths_host, &label_lengths_host, inputs);
|
|
||||||
CopyToHostSync(labels_host, no_blank_labels_host, input_lengths_host, label_lengths_host, inputs, stream);
|
|
||||||
|
|
||||||
size_t workspace_size = 0;
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(
|
|
||||||
cudnnGetCTCLossWorkspaceSize(
|
|
||||||
cudnn_handle_, probs_desc_, probs_desc_, reinterpret_cast<int *>(no_blank_labels_host),
|
|
||||||
reinterpret_cast<int *>(label_lengths_host), reinterpret_cast<int *>(input_lengths_host),
|
|
||||||
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, ctcloss_desc_, &workspace_size),
|
|
||||||
"cudnnGetCTCLossWorkspaceSize failed.");
|
|
||||||
void *workspace = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(workspace_size);
|
|
||||||
if (workspace == nullptr) {
|
|
||||||
MS_LOG(EXCEPTION) << "Failed to alloc workspace, size: " << workspace_size;
|
|
||||||
}
|
|
||||||
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(
|
|
||||||
cudnnCTCLoss(cudnn_handle_, probs_desc_, probs, reinterpret_cast<int *>(no_blank_labels_host),
|
|
||||||
reinterpret_cast<int *>(label_lengths_host), reinterpret_cast<int *>(input_lengths_host), costs,
|
|
||||||
probs_desc_, grads, CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, ctcloss_desc_, workspace, workspace_size),
|
|
||||||
"cudnnCtcLoss failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(stream), "cudaStreamSynchronize failed.");
|
|
||||||
|
|
||||||
device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(workspace);
|
|
||||||
FreeHostMem(labels_host, no_blank_labels_host, input_lengths_host, label_lengths_host);
|
|
||||||
return true;
|
|
||||||
}
|
|
||||||
bool Init(const CNodePtr &kernel_node) override {
|
|
||||||
InitResource();
|
|
||||||
auto probs_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
|
|
||||||
if (probs_shape.size() != 3) {
|
|
||||||
MS_LOG(EXCEPTION) << "probs dims: " << probs_shape.size() << " not support.";
|
|
||||||
}
|
|
||||||
probs_dims_[0] = probs_shape[0];
|
|
||||||
probs_dims_[1] = probs_shape[1];
|
|
||||||
probs_dims_[2] = probs_shape[2];
|
|
||||||
|
|
||||||
auto labels_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
|
|
||||||
if (labels_dims.size() != 1 && labels_dims.size() != 2) {
|
|
||||||
MS_LOG(EXCEPTION) << "labels dims: " << labels_dims.size() << " not support.";
|
|
||||||
}
|
|
||||||
label_size_ = sizeof(int);
|
|
||||||
for (auto i : labels_dims) {
|
|
||||||
label_size_ *= i;
|
|
||||||
}
|
|
||||||
|
|
||||||
auto input_length_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
|
|
||||||
input_lengths_size_ = input_length_dims[0] * sizeof(int);
|
|
||||||
auto label_length_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3);
|
|
||||||
label_lengths_size_ = label_length_dims[0] * sizeof(int);
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(
|
|
||||||
cudnnSetTensorNdDescriptorEx(probs_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 3, probs_dims_),
|
|
||||||
"cudnnSetTensorNdDescriptorEx failed.");
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetCTCLossDescriptorEx(ctcloss_desc_, CUDNN_DATA_FLOAT,
|
|
||||||
CUDNN_LOSS_NORMALIZATION_SOFTMAX, CUDNN_PROPAGATE_NAN),
|
|
||||||
"cudnnSetCTCLossDescriptorEx failed.");
|
|
||||||
InitSizeLists();
|
|
||||||
return true;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
void InitResource() override {
|
|
||||||
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&probs_desc_), "cudnnCreateTensorDescriptor failed.");
|
|
||||||
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateCTCLossDescriptor(&ctcloss_desc_), "cudnnCreateCTCLossDescriptor failed.");
|
|
||||||
}
|
|
||||||
|
|
||||||
void InitSizeLists() override {
|
|
||||||
input_size_list_.push_back(probs_dims_[0] * probs_dims_[1] * probs_dims_[2] * sizeof(float));
|
|
||||||
input_size_list_.push_back(label_size_);
|
|
||||||
input_size_list_.push_back(input_lengths_size_);
|
|
||||||
input_size_list_.push_back(label_lengths_size_);
|
|
||||||
|
|
||||||
output_size_list_.push_back(probs_dims_[1] * sizeof(float));
|
|
||||||
output_size_list_.push_back(probs_dims_[0] * probs_dims_[1] * probs_dims_[2] * sizeof(float));
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
void DestroyResource() noexcept {
|
|
||||||
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyCTCLossDescriptor(ctcloss_desc_), "cudnnDestroyCTCLossDescriptor failed.");
|
|
||||||
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(probs_desc_), "cudnnDestroyTensorDescriptor failed.");
|
|
||||||
}
|
|
||||||
|
|
||||||
void AllocHostMem(int **labels_host, int **no_blank_labels_host, void **input_lengths_host, void **label_lengths_host,
|
|
||||||
const std::vector<AddressPtr> &inputs) {
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(labels_host, inputs[1]->size), "cudaMallocHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(no_blank_labels_host, inputs[1]->size), "cudaMallocHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(input_lengths_host, inputs[2]->size), "cudaMallocHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(label_lengths_host, inputs[3]->size), "cudaMallocHost failed.");
|
|
||||||
}
|
|
||||||
|
|
||||||
void FreeHostMem(int *labels_host, int *no_blank_labels_host, void *input_lengths_host, void *label_lengths_host) {
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(label_lengths_host), "cudaFreeHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(input_lengths_host), "cudaFreeHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(labels_host), "cudaFreeHost failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(no_blank_labels_host), "cudaFreeHost failed.");
|
|
||||||
}
|
|
||||||
|
|
||||||
void CopyToHostSync(int *labels_host, int *no_blank_labels_host, void *input_lengths_host, void *label_lengths_host,
|
|
||||||
const std::vector<AddressPtr> &inputs, cudaStream_t stream) {
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(stream), "cudaStreamSynchronize failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(
|
|
||||||
cudaMemcpyAsync(labels_host, inputs[1]->addr, inputs[1]->size, cudaMemcpyDeviceToHost, stream),
|
|
||||||
"cudaMemcpyAsync failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(
|
|
||||||
cudaMemcpyAsync(input_lengths_host, inputs[2]->addr, inputs[2]->size, cudaMemcpyDeviceToHost, stream),
|
|
||||||
"cudaMemcpyAsync failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(
|
|
||||||
cudaMemcpyAsync(label_lengths_host, inputs[3]->addr, inputs[3]->size, cudaMemcpyDeviceToHost, stream),
|
|
||||||
"cudaMemcpyAsync failed.");
|
|
||||||
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(stream), "cudaStreamSynchronize failed.");
|
|
||||||
|
|
||||||
// remove blank element
|
|
||||||
size_t j = 0;
|
|
||||||
for (size_t i = 0; i < inputs[1]->size / sizeof(int); i++) {
|
|
||||||
if (labels_host[i] != 0) {
|
|
||||||
no_blank_labels_host[j] = labels_host[i];
|
|
||||||
j++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
std::vector<size_t> input_size_list_;
|
|
||||||
std::vector<size_t> output_size_list_;
|
|
||||||
std::vector<size_t> workspace_size_list_;
|
|
||||||
|
|
||||||
cudnnHandle_t cudnn_handle_;
|
|
||||||
cudnnTensorDescriptor_t probs_desc_;
|
|
||||||
cudnnCTCLossDescriptor_t ctcloss_desc_;
|
|
||||||
int probs_dims_[3] = {0};
|
|
||||||
int label_size_;
|
|
||||||
int input_lengths_size_;
|
|
||||||
int label_lengths_size_;
|
|
||||||
};
|
|
||||||
} // namespace kernel
|
|
||||||
} // namespace mindspore
|
|
||||||
|
|
||||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_CTCLOSS_GPU_KERNEL_H_
|
|
||||||
Loading…
Reference in new issue