baihuawei
5 years ago
parent
650a45b233
commit
9bcdf4cbdc
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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/cpu/mkldnn/lstm_cpu_kernel.h"
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#include <string>
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#include "common/utils.h"
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#include "kernel/cpu/mkldnn/mkl_kernel_engine.h"
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#include "device/cpu/cpu_device_address.h"
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namespace mindspore {
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namespace kernel {
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void LstmCPUKernel::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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std::vector<size_t> src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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bidirectional_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "bidirectional");
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input_size_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "input_size");
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hidden_size_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "hidden_size");
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num_layers_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "num_layers");
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batch_size_ = SizeToInt(src_shape[1]);
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seq_len_ = SizeToInt(src_shape[0]);
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num_directions_ = 1;
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if (bidirectional_) {
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num_directions_ = 2;
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}
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int gate_size = 4 * hidden_size_;
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for (int i = 0; i < num_layers_; ++i) {
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weight_size_ += gate_size * (i == 0 ? input_size_ : hidden_size_ * num_directions_);
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weight_h_size_ += gate_size * hidden_size_;
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}
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weight_size_ = weight_size_ * num_directions_;
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weight_h_size_ = weight_h_size_ * num_directions_;
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}
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bool LstmCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> & /*workspace*/,
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const std::vector<kernel::AddressPtr> &outputs) {
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using dt = dnnl::memory::data_type;
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using tag = dnnl::memory::format_tag;
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using dim = dnnl::memory::dims;
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auto eng = MKLKernelEngine::Get().engine();
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dnnl::stream s(eng);
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auto formatted_md = [](dim dimensions, tag layout) { return dnnl::memory::desc{{dimensions}, dt::f32, layout}; };
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dnnl::rnn_direction direction = dnnl::rnn_direction::unidirectional;
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if (bidirectional_) {
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direction = dnnl::rnn_direction::bidirectional_concat;
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}
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dim src_dims = {seq_len_, batch_size_, input_size_};
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dim src_h_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim src_c_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim weights_dims = {num_layers_, num_directions_, input_size_, 4, hidden_size_};
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dim weights_h_dims = {num_layers_, num_directions_, hidden_size_, 4, hidden_size_};
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dim bias_dims = {num_layers_, num_directions_, 4, hidden_size_};
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dim dst_dims = {seq_len_, batch_size_, hidden_size_ * num_directions_};
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dim dst_h_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim dst_c_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dnnl::memory::desc src_desc = formatted_md(src_dims, tag::tnc);
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dnnl::memory::desc src_h_desc = formatted_md(src_h_dims, tag::ldnc);
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dnnl::memory::desc src_c_desc = formatted_md(src_c_dims, tag::ldnc);
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dnnl::memory::desc weights_desc = formatted_md(weights_dims, tag::ldigo);
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dnnl::memory::desc weights_h_desc = formatted_md(weights_h_dims, tag::ldigo);
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dnnl::memory::desc bias_desc = formatted_md(bias_dims, tag::ldgo);
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dnnl::memory::desc dst_desc = formatted_md(dst_dims, tag::tnc);
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dnnl::memory::desc dst_h_desc = formatted_md(dst_h_dims, tag::ldnc);
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dnnl::memory::desc dst_c_desc = formatted_md(dst_c_dims, tag::ldnc);
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dnnl::lstm_forward::desc desc =
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dnnl::lstm_forward::desc(dnnl::prop_kind::forward_training, direction, src_desc, src_h_desc, src_c_desc,
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weights_desc, weights_h_desc, bias_desc, dst_desc, dst_h_desc, dst_c_desc);
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auto prim_desc = dnnl::lstm_forward::primitive_desc(desc, MKLKernelEngine::Get().engine());
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auto workspace_memory = dnnl::memory(prim_desc.workspace_desc(), eng);
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auto src_memory = dnnl::memory(formatted_md(src_dims, tag::tnc), eng);
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write_to_dnnl_memory(inputs[0]->addr, src_memory);
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auto src_h_memory = dnnl::memory(prim_desc.src_iter_desc(), eng);
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auto src_c_memory = dnnl::memory(prim_desc.src_iter_c_desc(), eng);
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write_to_dnnl_memory(inputs[1]->addr, src_h_memory);
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write_to_dnnl_memory(inputs[2]->addr, src_c_memory);
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auto weights_memory = dnnl::memory(formatted_md(weights_dims, tag::ldigo), eng);
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auto weights_h_memory = dnnl::memory(formatted_md(weights_h_dims, tag::ldigo), eng);
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auto bias_memory = dnnl::memory(formatted_md(bias_dims, tag::ldgo), eng);
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write_to_dnnl_memory(inputs[3]->addr, weights_memory);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[3]->addr) + weight_size_, weights_h_memory);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[3]->addr) + weight_size_ + weight_h_size_, bias_memory);
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auto dst_memory = dnnl::memory(formatted_md(dst_dims, tag::tnc), eng);
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auto dst_h_memory = dnnl::memory(prim_desc.dst_iter_desc(), eng);
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auto dst_c_memory = dnnl::memory(prim_desc.dst_iter_c_desc(), eng);
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dnnl::lstm_forward fw_layer(prim_desc);
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workspace_memory.set_data_handle(outputs[3]->addr);
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dst_memory.set_data_handle(outputs[0]->addr);
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dst_h_memory.set_data_handle(outputs[1]->addr);
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dst_c_memory.set_data_handle(outputs[2]->addr);
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fw_layer.execute(s, {{DNNL_ARG_SRC_LAYER, src_memory},
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{DNNL_ARG_SRC_ITER, src_h_memory},
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{DNNL_ARG_SRC_ITER_C, src_c_memory},
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{DNNL_ARG_WEIGHTS_LAYER, weights_memory},
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{DNNL_ARG_WEIGHTS_ITER, weights_h_memory},
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{DNNL_ARG_BIAS, bias_memory},
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{DNNL_ARG_DST_LAYER, dst_memory},
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{DNNL_ARG_DST_ITER, dst_h_memory},
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{DNNL_ARG_DST_ITER_C, dst_c_memory},
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{DNNL_ARG_WORKSPACE, workspace_memory}});
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return true;
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}
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,59 @@
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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_CPU_LSTM_CPU_KERNEL_H
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#define MINDSPORE_CCSRC_KERNEL_CPU_LSTM_CPU_KERNEL_H
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#include <vector>
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#include <memory>
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#include "kernel/cpu/mkldnn/mkl_cpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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class LstmCPUKernel : public MKLCPUKernel {
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public:
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LstmCPUKernel() = default;
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~LstmCPUKernel() override = default;
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void InitKernel(const CNodePtr &kernel_node) override;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) override;
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private:
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int weight_size_ = 0;
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int weight_h_size_ = 0;
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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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int batch_size_;
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int seq_len_;
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int num_directions_;
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bool bidirectional_;
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};
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MS_REG_CPU_KERNEL(LSTM,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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LstmCPUKernel);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_KERNEL_CPU_LSTM_CPU_KERNEL_H
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@ -0,0 +1,169 @@
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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/cpu/mkldnn/lstm_grad_cpu_kernel.h"
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#include <cstring>
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#include <cmath>
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#include <numeric>
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#include <string>
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#include "common/utils.h"
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#include "kernel/cpu/mkldnn/mkl_kernel_engine.h"
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#include "device/cpu/cpu_device_address.h"
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namespace mindspore {
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namespace kernel {
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void LSTMGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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std::vector<size_t> src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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bidirectional_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "bidirectional");
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input_size_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "input_size");
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hidden_size_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "hidden_size");
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num_layers_ = AnfAlgo::GetNodeAttr<int>(kernel_node, "num_layers");
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batch_size_ = SizeToInt(src_shape[1]);
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seq_len_ = SizeToInt(src_shape[0]);
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num_directions_ = 1;
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if (bidirectional_) {
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num_directions_ = 2;
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}
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int gate_size = 4 * hidden_size_;
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for (int i = 0; i < num_layers_; ++i) {
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weight_size_ += gate_size * (i == 0 ? input_size_ : hidden_size_ * num_directions_);
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weight_h_size_ += gate_size * hidden_size_;
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}
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weight_size_ = weight_size_ * num_directions_;
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weight_h_size_ = weight_h_size_ * num_directions_;
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}
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bool LSTMGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> &workspace /*workspace*/,
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const std::vector<kernel::AddressPtr> &outputs) {
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using tag = dnnl::memory::format_tag;
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using dt = dnnl::memory::data_type;
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using dim = dnnl::memory::dims;
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auto eng = MKLKernelEngine::Get().engine();
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dnnl::stream s(eng);
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auto formatted_md = [](dim dimensions, tag layout) { return dnnl::memory::desc{{dimensions}, dt::f32, layout}; };
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auto generic_md = [](dim dimensions) { return dnnl::memory::desc{{dimensions}, dt::f32, tag::any}; };
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dnnl::rnn_direction direction = dnnl::rnn_direction::unidirectional;
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if (bidirectional_) {
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direction = dnnl::rnn_direction::bidirectional_concat;
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}
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dim src_dims = {seq_len_, batch_size_, input_size_};
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dim src_h_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim src_c_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim weights_dims = {num_layers_, num_directions_, input_size_, 4, hidden_size_};
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dim weights_h_dims = {num_layers_, num_directions_, hidden_size_, 4, hidden_size_};
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dim bias_dims = {num_layers_, num_directions_, 4, hidden_size_};
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dim dst_dims = {seq_len_, batch_size_, hidden_size_ * num_directions_};
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dim dst_h_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dim dst_c_dims = {num_layers_, num_directions_, batch_size_, hidden_size_};
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dnnl::memory::desc src_desc = formatted_md(src_dims, tag::tnc);
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dnnl::memory::desc src_h_desc = formatted_md(src_h_dims, tag::ldnc);
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dnnl::memory::desc src_c_desc = formatted_md(src_c_dims, tag::ldnc);
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dnnl::memory::desc weights_desc = formatted_md(weights_dims, tag::ldigo);
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dnnl::memory::desc weights_h_desc = formatted_md(weights_h_dims, tag::ldigo);
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dnnl::memory::desc bias_desc = formatted_md(bias_dims, tag::ldgo);
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dnnl::memory::desc dst_desc = formatted_md(dst_dims, tag::tnc);
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dnnl::memory::desc dst_h_desc = formatted_md(dst_h_dims, tag::ldnc);
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dnnl::memory::desc dst_c_desc = formatted_md(dst_c_dims, tag::ldnc);
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dnnl::lstm_forward::desc forward_desc =
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dnnl::lstm_forward::desc(dnnl::prop_kind::forward_training, direction, src_desc, src_h_desc, src_c_desc,
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weights_desc, weights_h_desc, bias_desc, dst_desc, dst_h_desc, dst_c_desc);
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auto prim_forward_desc = dnnl::lstm_forward::primitive_desc(forward_desc, eng);
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dnnl::lstm_backward::desc backward_desc = dnnl::lstm_backward::desc(
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dnnl::prop_kind::backward, direction, src_desc, src_h_desc, src_c_desc, generic_md(weights_dims),
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generic_md(weights_h_dims), generic_md(bias_dims), dst_desc, dst_h_desc, dst_c_desc, src_desc, src_h_desc,
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src_c_desc, weights_desc, weights_h_desc, bias_desc, dst_desc, dst_h_desc, dst_c_desc);
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auto prim_backward_desc = dnnl::lstm_backward::primitive_desc(backward_desc, eng, prim_forward_desc);
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// construct fw memory
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auto src_memory = dnnl::memory(formatted_md(src_dims, tag::tnc), eng);
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write_to_dnnl_memory(inputs[0]->addr, src_memory);
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auto src_h_memory = dnnl::memory(prim_forward_desc.src_iter_desc(), eng);
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auto src_c_memory = dnnl::memory(prim_forward_desc.src_iter_c_desc(), eng);
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write_to_dnnl_memory(inputs[1]->addr, src_h_memory);
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write_to_dnnl_memory(inputs[2]->addr, src_c_memory);
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auto user_weights_memory = dnnl::memory(formatted_md(weights_dims, tag::ldigo), eng);
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auto user_weights_h_memory = dnnl::memory(formatted_md(weights_h_dims, tag::ldigo), eng);
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auto user_bias_memory = dnnl::memory(formatted_md(bias_dims, tag::ldgo), eng);
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write_to_dnnl_memory(inputs[3]->addr, user_weights_memory);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[3]->addr) + weight_size_, user_weights_h_memory);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[3]->addr) + weight_size_ + weight_h_size_, user_bias_memory);
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auto weights_memory = dnnl::memory(prim_backward_desc.weights_layer_desc(), eng);
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auto weights_h_memory = dnnl::memory(prim_backward_desc.weights_iter_desc(), eng);
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auto bias_memory = dnnl::memory(prim_forward_desc.bias_desc(), eng);
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dnnl::reorder(user_weights_memory, weights_memory).execute(s, user_weights_memory, weights_memory);
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dnnl::reorder(user_weights_h_memory, weights_h_memory).execute(s, user_weights_h_memory, weights_h_memory);
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dnnl::reorder(user_bias_memory, bias_memory).execute(s, user_bias_memory, bias_memory);
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auto dst_memory = dnnl::memory(formatted_md(dst_dims, tag::tnc), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[4]->addr), dst_memory);
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auto dst_h_memory = dnnl::memory(prim_backward_desc.dst_iter_desc(), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[5]->addr), dst_h_memory);
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auto dst_c_memory = dnnl::memory(prim_backward_desc.dst_iter_c_desc(), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[6]->addr), dst_c_memory);
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auto workspace_memory = dnnl::memory(prim_forward_desc.workspace_desc(), eng);
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write_to_dnnl_memory(inputs[10]->addr, workspace_memory);
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// construct diff memory
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auto diff_src_memory = dnnl::memory(formatted_md(src_dims, tag::tnc), eng);
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auto diff_src_h_memory = dnnl::memory(prim_backward_desc.diff_src_iter_desc(), eng);
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auto diff_src_c_memory = dnnl::memory(prim_backward_desc.diff_src_iter_c_desc(), eng);
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auto diff_weights_memory = dnnl::memory(prim_backward_desc.diff_weights_layer_desc(), eng);
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auto diff_weights_h_memory = dnnl::memory(prim_backward_desc.diff_weights_iter_desc(), eng);
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auto diff_bias_memory = dnnl::memory(prim_backward_desc.diff_bias_desc(), eng);
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auto diff_dst_memory = dnnl::memory(formatted_md(dst_dims, tag::tnc), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[7]->addr), diff_dst_memory);
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auto diff_dst_h_memory = dnnl::memory(prim_backward_desc.diff_dst_iter_desc(), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[8]->addr), diff_dst_h_memory);
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auto diff_dst_c_memory = dnnl::memory(prim_backward_desc.diff_dst_iter_c_desc(), eng);
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write_to_dnnl_memory(reinterpret_cast<float *>(inputs[9]->addr), diff_dst_c_memory);
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diff_src_memory.set_data_handle(outputs[0]->addr);
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diff_src_h_memory.set_data_handle(outputs[1]->addr);
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diff_src_c_memory.set_data_handle(outputs[2]->addr);
|
||||
diff_weights_memory.set_data_handle(outputs[3]->addr);
|
||||
diff_weights_h_memory.set_data_handle(reinterpret_cast<float *>(outputs[3]->addr) + weight_size_);
|
||||
diff_bias_memory.set_data_handle(reinterpret_cast<float *>(outputs[3]->addr) + weight_size_ + weight_h_size_);
|
||||
dnnl::lstm_backward bwd_layer(prim_backward_desc);
|
||||
bwd_layer.execute(s, {{DNNL_ARG_SRC_LAYER, src_memory},
|
||||
{DNNL_ARG_SRC_ITER, src_h_memory},
|
||||
{DNNL_ARG_SRC_ITER_C, src_c_memory},
|
||||
{DNNL_ARG_WEIGHTS_LAYER, weights_memory},
|
||||
{DNNL_ARG_WEIGHTS_ITER, weights_h_memory},
|
||||
{DNNL_ARG_BIAS, bias_memory},
|
||||
{DNNL_ARG_DST_LAYER, dst_memory},
|
||||
{DNNL_ARG_DST_ITER, dst_h_memory},
|
||||
{DNNL_ARG_DST_ITER_C, dst_c_memory},
|
||||
{DNNL_ARG_DIFF_SRC_LAYER, diff_src_memory},
|
||||
{DNNL_ARG_DIFF_SRC_ITER, diff_src_h_memory},
|
||||
{DNNL_ARG_DIFF_SRC_ITER_C, diff_src_c_memory},
|
||||
{DNNL_ARG_DIFF_WEIGHTS_LAYER, diff_weights_memory},
|
||||
{DNNL_ARG_DIFF_WEIGHTS_ITER, diff_weights_h_memory},
|
||||
{DNNL_ARG_DIFF_BIAS, diff_bias_memory},
|
||||
{DNNL_ARG_DIFF_DST_LAYER, diff_dst_memory},
|
||||
{DNNL_ARG_DIFF_DST_ITER, diff_dst_h_memory},
|
||||
{DNNL_ARG_DIFF_DST_ITER_C, diff_dst_c_memory},
|
||||
{DNNL_ARG_WORKSPACE, workspace_memory}});
|
||||
return true;
|
||||
}
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
@ -0,0 +1,67 @@
|
||||
/**
|
||||
* 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_KERNEL_CPU_LSTM_GRAD_CPU_KERNEL_H_
|
||||
#define MINDSPORE_CCSRC_KERNEL_CPU_LSTM_GRAD_CPU_KERNEL_H_
|
||||
|
||||
#include <vector>
|
||||
#include <memory>
|
||||
#include "kernel/cpu/mkldnn/mkl_cpu_kernel.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
class LSTMGradCPUKernel : public MKLCPUKernel {
|
||||
public:
|
||||
LSTMGradCPUKernel() = default;
|
||||
~LSTMGradCPUKernel() override = default;
|
||||
|
||||
void InitKernel(const CNodePtr &kernel_node) override;
|
||||
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
|
||||
const std::vector<AddressPtr> &outputs) override;
|
||||
|
||||
private:
|
||||
int weight_size_ = 0;
|
||||
int weight_h_size_ = 0;
|
||||
int input_size_;
|
||||
int hidden_size_;
|
||||
int num_layers_;
|
||||
int batch_size_;
|
||||
int seq_len_;
|
||||
int num_directions_;
|
||||
bool bidirectional_;
|
||||
};
|
||||
|
||||
MS_REG_CPU_KERNEL(LSTMGrad,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32),
|
||||
LSTMGradCPUKernel);
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
|
||||
#endif // MINDSPORE_CCSRC_KERNEL_CPU_LSTM_GRAD_CPU_KERNEL_H_
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in new issue