wilfChen
5 years ago
parent
622b97f3b6
commit
d54154a1f9
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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*/
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#include "kernel/gpu/nn/ctcloss_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(CTCLossV2,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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CtcLossGpuKernel, float)
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_CTCLOSS_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_NN_CTCLOSS_GPU_KERNEL_H_
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#include <cuda_runtime_api.h>
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#include <vector>
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#include "kernel/gpu/gpu_kernel.h"
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#include "kernel/gpu/gpu_kernel_factory.h"
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#include "device/gpu/gpu_memory_allocator.h"
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namespace mindspore {
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namespace kernel {
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template <typename T>
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class CtcLossGpuKernel : public GpuKernel {
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public:
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CtcLossGpuKernel()
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: cudnn_handle_(nullptr),
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probs_desc_(nullptr),
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ctcloss_desc_(nullptr),
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label_size_(0),
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input_lengths_size_(0),
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label_lengths_size_(0) {}
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~CtcLossGpuKernel() override { DestroyResource(); }
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
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float *probs = GetDeviceAddress<float>(inputs, 0);
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int *labels = GetDeviceAddress<int>(inputs, 1);
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int *input_lengths = GetDeviceAddress<int>(inputs, 2);
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int *label_lengths = GetDeviceAddress<int>(inputs, 3);
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float *costs = GetDeviceAddress<float>(outputs, 0);
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float *grads = GetDeviceAddress<float>(outputs, 1);
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// Copy labels/input_lengths/label_length to host as cudnn7.x.x requires
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void *labels_host = nullptr;
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void *input_lengths_host = nullptr;
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void *label_lengths_host = nullptr;
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CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(&labels_host, inputs[1]->size), "cudaMallocHost failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(&input_lengths_host, inputs[2]->size), "cudaMallocHost failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(&label_lengths_host, inputs[3]->size), "cudaMallocHost failed.");
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(stream_ptr);
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CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(labels_host, labels, inputs[1]->size, cudaMemcpyDeviceToHost, stream),
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"cudaMemcpyAsync failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(
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cudaMemcpyAsync(input_lengths_host, input_lengths, inputs[2]->size, cudaMemcpyDeviceToHost, stream),
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"cudaMemcpyAsync failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(
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cudaMemcpyAsync(label_lengths_host, label_lengths, inputs[3]->size, cudaMemcpyDeviceToHost, stream),
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"cudaMemcpyAsync failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(stream), "cudaStreamSynchronize failed.");
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size_t workspace_size = 0;
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CHECK_CUDNN_RET_WITH_EXCEPT(
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cudnnGetCTCLossWorkspaceSize(cudnn_handle_, probs_desc_, probs_desc_, reinterpret_cast<int *>(labels_host),
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reinterpret_cast<int *>(label_lengths_host),
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reinterpret_cast<int *>(input_lengths_host), CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
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ctcloss_desc_, &workspace_size),
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"cudnnGetCTCLossWorkspaceSize failed.");
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void *workspace = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(workspace_size);
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if (workspace == nullptr) {
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MS_LOG(EXCEPTION) << "Failed to alloc workspace, size: " << workspace_size;
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}
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CHECK_CUDNN_RET_WITH_EXCEPT(
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cudnnCTCLoss(cudnn_handle_, probs_desc_, probs, reinterpret_cast<int *>(labels_host),
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reinterpret_cast<int *>(label_lengths_host), reinterpret_cast<int *>(input_lengths_host), costs,
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probs_desc_, grads, CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, ctcloss_desc_, workspace, workspace_size),
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"cudnnCtcLoss failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(stream), "cudaStreamSynchronize failed.");
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device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(workspace);
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CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(label_lengths_host), "cudaFreeHost failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(input_lengths_host), "cudaFreeHost failed.");
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CHECK_CUDA_RET_WITH_EXCEPT(cudaFreeHost(labels_host), "cudaFreeHost failed.");
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return true;
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}
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bool Init(const CNodePtr &kernel_node) override {
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InitResource();
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auto probs_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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if (probs_shape.size() != 3) {
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MS_LOG(EXCEPTION) << "probs dims: " << probs_shape.size() << " not support.";
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}
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probs_dims_[0] = probs_shape[0];
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probs_dims_[1] = probs_shape[1];
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probs_dims_[2] = probs_shape[2];
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auto labels_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
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if (labels_dims.size() != 1 && labels_dims.size() != 2) {
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MS_LOG(EXCEPTION) << "labels dims: " << labels_dims.size() << " not support.";
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}
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label_size_ = sizeof(int);
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for (auto i : labels_dims) {
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label_size_ *= i;
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}
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auto input_length_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
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input_lengths_size_ = input_length_dims[0] * sizeof(int);
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auto label_length_dims = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3);
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label_lengths_size_ = label_length_dims[0] * sizeof(int);
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CHECK_CUDNN_RET_WITH_EXCEPT(
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cudnnSetTensorNdDescriptorEx(probs_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 3, probs_dims_),
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"cudnnSetTensorNdDescriptorEx failed.");
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CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetCTCLossDescriptorEx(ctcloss_desc_, CUDNN_DATA_FLOAT,
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CUDNN_LOSS_NORMALIZATION_SOFTMAX, CUDNN_PROPAGATE_NAN),
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"cudnnSetCTCLossDescriptorEx failed.");
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InitSizeLists();
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return true;
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}
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protected:
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void InitResource() override {
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cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
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CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&probs_desc_), "cudnnCreateTensorDescriptor failed.");
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CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateCTCLossDescriptor(&ctcloss_desc_), "cudnnCreateCTCLossDescriptor failed.");
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}
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void InitSizeLists() override {
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input_size_list_.push_back(probs_dims_[0] * probs_dims_[1] * probs_dims_[2] * sizeof(float));
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input_size_list_.push_back(label_size_);
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input_size_list_.push_back(input_lengths_size_);
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input_size_list_.push_back(label_lengths_size_);
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output_size_list_.push_back(probs_dims_[1] * sizeof(float));
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output_size_list_.push_back(probs_dims_[0] * probs_dims_[1] * probs_dims_[2] * sizeof(float));
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}
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private:
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void DestroyResource() noexcept {
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CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyCTCLossDescriptor(ctcloss_desc_), "cudnnDestroyCTCLossDescriptor failed.");
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CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(probs_desc_), "cudnnDestroyTensorDescriptor failed.");
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}
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std::vector<size_t> input_size_list_;
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std::vector<size_t> output_size_list_;
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std::vector<size_t> workspace_size_list_;
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cudnnHandle_t cudnn_handle_;
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cudnnTensorDescriptor_t probs_desc_;
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cudnnCTCLossDescriptor_t ctcloss_desc_;
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int probs_dims_[3] = {0};
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int label_size_;
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int input_lengths_size_;
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int label_lengths_size_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_NN_CTCLOSS_GPU_KERNEL_H_
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@ -0,0 +1,119 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.common import dtype as mstype
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from mindspore.ops.composite import GradOperation
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.loss = P.CTCLossV2()
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self.div = P.RealDiv()
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self.cast = P.Cast()
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self.mean = P.ReduceMean()
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def construct(self, probs, label, input_length, label_length):
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x, _ = self.loss(probs, label, input_length, label_length)
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x = self.div(x, self.cast(label_length, mstype.float32))
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x = self.mean(x)
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return x
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class GradData(nn.Cell):
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def __init__(self, network):
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super(GradData, self).__init__()
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self.grad = GradOperation(name="get_all", get_all=True, sens_param=False)
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self.network = network
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def construct(self, probs, labels, input_lengths, label_lengths):
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return self.grad(self.network)(probs, labels, input_lengths, label_lengths)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ctcloss():
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probs = Tensor([[[-4.4131, -4.6093, -3.4333, -3.9268, -2.8917, -3.4093, -4.2243, -1.1379, -7.1046, -0.6902],
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[-2.5109, -3.3397, -4.9384, -1.2723, -1.1443, -2.4683, -2.6768, -4.1282, -2.7062, -3.1906],
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[-2.5092, -1.6392, -2.0864, -4.0059, -1.5610, -2.3223, -2.4816, -2.9922, -3.1412, -2.3311]],
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[[-2.1243, -3.5773, -3.1108, -4.4253, -2.7080, -1.9653, -2.0499, -2.4418, -1.8620, -1.5229],
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[-2.2479, -3.5128, -1.4189, -2.8701, -1.8562, -2.2752, -2.7019, -2.1865, -2.5634, -2.9869],
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[-3.2144, -1.3986, -3.1083, -3.9634, -3.5131, -3.2317, -2.6200, -1.7938, -1.8159, -1.7255]],
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[[-3.1301, -2.1649, -0.9286, -2.9452, -2.5992, -2.0263, -2.9201, -3.2155, -2.8302, -3.3636],
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[-1.4661, -3.6311, -2.4781, -4.6180, -2.7308, -1.7019, -1.5570, -2.6012, -4.0788, -2.3073],
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[-2.6833, -1.5033, -3.6922, -2.6360, -2.6974, -2.6847, -2.7579, -2.1396, -1.4093, -2.9630]],
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[[-2.0094, -2.3024, -3.3673, -1.0220, -2.8326, -2.2613, -3.0535, -2.9879, -3.7015, -2.4510],
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[-1.9071, -3.2603, -2.3229, -2.0572, -4.3450, -2.1284, -2.6306, -1.3824, -2.9815, -2.5061],
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[-2.7931, -3.7631, -3.2440, -4.3887, -1.0271, -3.8851, -1.2418, -4.5123, -2.2993, -2.4607]],
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[[-1.5763, -2.7539, -3.6941, -3.8166, -1.2599, -2.6903, -2.5826, -4.8208, -2.9562, -1.6321],
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[-3.3031, -3.0087, -1.9982, -1.9081, -3.8731, -2.8764, -2.2485, -2.3808, -1.4283, -2.1625],
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[-2.4516, -3.2394, -4.2053, -4.3541, -2.5229, -4.0717, -1.4894, -2.3151, -1.1098, -2.3465]]],
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dtype=mstype.float32)
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labels = Tensor([9, 4, 6, 4, 7, 1, 4, 6, 6, 8], dtype=mstype.int32)
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input_lengths = Tensor([5, 5, 5], dtype=mstype.int32)
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label_lengths = Tensor([3, 3, 4], dtype=mstype.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = Net()
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ctc_loss = net(probs, labels, input_lengths, label_lengths)
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expect_loss = [2.4099]
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assert np.allclose(ctc_loss.asnumpy(), expect_loss)
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grad = GradData(net)(probs, labels, input_lengths, label_lengths)
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expect_grad = [[[8.8442e-05, 1.1065e-03, 3.5867e-03, 2.1896e-03, 6.1646e-03,
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3.6738e-03, 1.6262e-03, 3.5610e-02, 9.1258e-05, -5.4134e-02],
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[-3.7523e-03, 3.9386e-03, 7.9623e-04, 3.1132e-02, -6.2954e-02,
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9.4143e-03, 7.6425e-03, 1.7902e-03, 7.4211e-03, 4.5719e-03],
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[6.7778e-03, 1.6178e-02, 1.0344e-02, 1.5173e-03, -6.5840e-02,
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8.1707e-03, 6.9674e-03, 4.1814e-03, 3.6026e-03, 8.0991e-03]],
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[[-1.2581e-02, 3.1057e-03, 4.9517e-03, 1.3301e-03, -2.6320e-02,
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1.5568e-02, 1.4305e-02, 9.6671e-03, 1.7262e-02, -2.7292e-02],
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[-1.5566e-02, 3.3126e-03, 2.6887e-02, 6.2993e-03, -3.9716e-02,
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1.1420e-02, 7.4531e-03, -1.4252e-02, 8.5603e-03, 5.6048e-03],
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[3.3483e-03, 2.0579e-02, 3.7231e-03, 1.5832e-03, 2.4837e-03,
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3.2909e-03, -7.7267e-02, 1.3861e-02, 1.3558e-02, 1.4840e-02]],
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[[-8.0007e-03, 1.2751e-02, 4.3901e-02, 5.8435e-03, -7.2627e-02,
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1.4647e-02, -8.0584e-03, 4.4595e-03, 6.5557e-03, 5.2891e-04],
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[-3.6006e-02, 1.5308e-03, 9.3225e-03, 1.0969e-03, -2.5098e-03,
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2.0260e-02, 2.3419e-02, -3.0053e-02, 1.8809e-03, 1.1059e-02],
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[-7.7639e-02, 1.8533e-02, 2.0764e-03, 5.9706e-03, 5.6150e-03,
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5.6868e-03, 5.2854e-03, 9.8085e-03, 2.0360e-02, 4.3053e-03]],
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[[-2.6776e-02, 1.1113e-02, 3.8314e-03, 3.9986e-02, -1.6020e-02,
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1.1579e-02, -4.1635e-02, 5.5992e-03, 2.7429e-03, 9.5786e-03],
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[-6.8619e-03, -6.4066e-03, 1.0888e-02, 1.4201e-02, 1.4413e-03,
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1.3225e-02, 8.0039e-03, -4.9191e-02, 5.6352e-03, 9.0651e-03],
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[5.1026e-03, 1.9343e-03, 3.2506e-03, 1.0347e-03, 2.9837e-02,
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1.7121e-03, -5.9261e-02, 9.1443e-04, 8.3608e-03, 7.1146e-03]],
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[[-2.0848e-02, 7.0754e-03, 2.7633e-03, 2.4447e-03, 3.1520e-02,
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7.5401e-03, -5.8895e-02, 8.9559e-04, 5.7796e-03, 2.1724e-02],
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[-1.3499e-03, -1.0019e-01, 1.5064e-02, 1.6485e-02, 2.3104e-03,
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6.2597e-03, 1.1729e-02, 1.0275e-02, 2.6635e-02, 1.2782e-02],
|
||||
[7.1796e-03, 3.2656e-03, 1.2430e-03, 1.0712e-03, 6.6856e-03,
|
||||
1.4207e-03, 1.8792e-02, 8.2297e-03, -5.5865e-02, 7.9753e-03]]]
|
||||
assert np.allclose(grad[0].asnumpy(), expect_grad, atol=1e-5)
|
||||
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Reference in new issue