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!8079 support GNMT net fix dynamic rnn grad fission pass 

Merge pull request !8079 from liubuyu/op_support
This commit is contained in:
mindspore-ci-bot 2020-11-05 10:01:32 +08:00 committed by Gitee
parent 7123a2c1d1 662976a75d
commit a5b0d13141
9 changed files with 671 additions and 259 deletions
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6
mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc
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@ -19,7 +19,7 @@
#include <memory>
#include <string>
#include "backend/optimizer/common/optimizer.h"
#include "backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission.h"
#include "backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.h"
#include "backend/optimizer/ascend/ir_fission/bn_split.h"
#include "backend/optimizer/ascend/ir_fission/bn_grad_split.h"
#include "backend/optimizer/ascend/ir_fission/batch_norm_grad_split.h"
@ -61,6 +61,7 @@
#include "backend/optimizer/ascend/ir_fusion/confusion_mul_grad_fusion.h"
#include "backend/optimizer/ascend/ir_fusion/softmax_grad_ext_fusion.h"
#include "backend/optimizer/ascend/format_type/insert_trans_op.h"
#include "backend/optimizer/ascend/format_type/dynamic_rnn_grad_reformat.h"
#include "backend/optimizer/ascend/format_type/insert_transpose_for_basiclstm_op.h"
#include "backend/optimizer/ascend/format_type/rectify_do_mask_kernel_info.h"
#include "backend/optimizer/ascend/format_type/chang_axis_of_reduce_kernel.h"
@ -215,6 +216,7 @@ void AscendDataLayout(const std::shared_ptr<session::KernelGraph> &kernel_graph)
auto optimizer = std::make_shared<GraphOptimizer>();
auto data_layout_pm = std::make_shared<PassManager>("transop_pm");
data_layout_pm->AddPass(std::make_shared<RectifyDoMaskKernelInfo>());
data_layout_pm->AddPass(std::make_shared<DynamicRNNGradReformat>());
data_layout_pm->AddPass(std::make_shared<InsertTransOp>());
data_layout_pm->AddPass(std::make_shared<GetitemTuple>());
data_layout_pm->AddPass(std::make_shared<CommonSubexpressionElimination>());
@ -276,7 +278,7 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
ir_fusion_pm->AddPass(std::make_shared<LayerNormGradSplit>());
ir_fusion_pm->AddPass(std::make_shared<InsertPadForNMSWithMask>());
ir_fusion_pm->AddPass(std::make_shared<InsertPlaceholderForDynamicRNN>());
ir_fusion_pm->AddPass(std::make_shared<DynamicRNNGradFission>());
ir_fusion_pm->AddPass(std::make_shared<DynamicRnnGradFissionV2>());
AddAscendIRFusionRulesPass(ir_fusion_pm.get());
AddAscendIRFusionPass(ir_fusion_pm.get());

80
mindspore/ccsrc/backend/optimizer/ascend/format_type/dynamic_rnn_grad_reformat.cc Normal file
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@ -0,0 +1,80 @@
/**
* 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/optimizer/ascend/format_type/dynamic_rnn_grad_reformat.h"
#include <memory>
#include "backend/optimizer/ascend/ascend_helper.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "utils/utils.h"
#include "base/core_ops.h"
namespace mindspore {
namespace opt {
const BaseRef DynamicRNNGradReformat::DefinePattern() const {
VarPtr Xs = std::make_shared<Var>();
VarPtr Xs2 = std::make_shared<Var>();
MS_EXCEPTION_IF_NULL(Xs);
MS_EXCEPTION_IF_NULL(Xs2);
const auto split = std::make_shared<Primitive>(prim::kPrimSplitV->name());
return VectorRef({split, VectorRef({std::make_shared<Primitive>(prim::kPrimMatMul->name()), Xs, Xs2})});
}
const AnfNodePtr DynamicRNNGradReformat::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node);
auto split_v = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(split_v);
auto matmul = CheckAnfNodeIfCNodeAndInputSize(split_v->input(1), 3);
MS_EXCEPTION_IF_NULL(matmul);
auto input_node_with_idx = AnfAlgo::GetPrevNodeOutput(matmul, 0);
auto input_node = input_node_with_idx.first;
MS_EXCEPTION_IF_NULL(input_node);
if (!(input_node->isa<CNode>() &&
AnfAlgo::GetCNodeName(input_node->cast<CNodePtr>()) == kBasicLSTMCellCStateGradV2OpName)) {
return nullptr;
}
// reformat matmul
auto matmul_kernel_build_info = AnfAlgo::GetSelectKernelBuildInfo(matmul);
MS_EXCEPTION_IF_NULL(matmul_kernel_build_info);
auto matmul_new_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
matmul_new_builder->SetInputsFormat({kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ});
matmul_new_builder->SetOutputsFormat({kOpFormat_FRAC_NZ});
matmul_new_builder->SetInputsDeviceType({kNumberTypeFloat16, kNumberTypeFloat16});
matmul_new_builder->SetOutputsDeviceType({kNumberTypeFloat});
matmul_new_builder->SetKernelType(matmul_kernel_build_info->kernel_type());
matmul_new_builder->SetFusionType(matmul_kernel_build_info->fusion_type());
matmul_new_builder->SetProcessor(matmul_kernel_build_info->processor());
AnfAlgo::SetSelectKernelBuildInfo(matmul_new_builder->Build(), matmul.get());
AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), matmul);
// reformat split_v
auto split_kernel_build_info = AnfAlgo::GetSelectKernelBuildInfo(split_v);
MS_EXCEPTION_IF_NULL(split_kernel_build_info);
auto split_new_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
split_new_builder->SetInputsFormat({kOpFormat_FRAC_NZ});
split_new_builder->SetOutputsFormat({kOpFormat_FRAC_NZ, kOpFormat_FRAC_NZ});
split_new_builder->SetInputsDeviceType(split_kernel_build_info->GetAllInputDeviceTypes());
split_new_builder->SetOutputsDeviceType(split_kernel_build_info->GetAllOutputDeviceTypes());
split_new_builder->SetKernelType(split_kernel_build_info->kernel_type());
split_new_builder->SetFusionType(split_kernel_build_info->fusion_type());
split_new_builder->SetProcessor(split_kernel_build_info->processor());
AnfAlgo::SetSelectKernelBuildInfo(split_new_builder->Build(), split_v.get());
AnfAlgo::SetNodeAttr("insert_backend", MakeValue(true), split_v);
return split_v;
}
} // namespace opt
} // namespace mindspore

41
mindspore/ccsrc/backend/optimizer/ascend/format_type/dynamic_rnn_grad_reformat.h Normal file
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@ -0,0 +1,41 @@
/**
* 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_OPTIMIZER_ASCEND_FORMAT_TYPE_DYNAMIC_RNN_GRAD_REFORMAT_H_
#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_FORMAT_TYPE_DYNAMIC_RNN_GRAD_REFORMAT_H_
#include <vector>
#include <string>
#include <utility>
#include <memory>
#include "ir/anf.h"
#include "backend/optimizer/common/pattern_engine.h"
#include "backend/optimizer/common/helper.h"
#include "backend/optimizer/common/optimizer.h"
namespace mindspore {
namespace opt {
class DynamicRNNGradReformat : public PatternProcessPass {
public:
explicit DynamicRNNGradReformat(bool multigraph = true)
: PatternProcessPass("dynamic_rnn_grad_reformat", multigraph) {}
~DynamicRNNGradReformat() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_FORMAT_TYPE_DYNAMIC_RNN_GRAD_REFORMAT_H_

250
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission.cc
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@ -1,250 +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/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission.h"
#include <vector>
#include <memory>
#include <algorithm>
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/optimizer/common/helper.h"
namespace mindspore {
namespace opt {
constexpr size_t kDynamicRNNGradInputNum = 16;
constexpr size_t kLSTMInputGradOutputNum = 4;
const BaseRef DynamicRNNGradFission::DefinePattern() const {
VarPtr Xs = std::make_shared<SeqVar>();
return VectorRef({prim::kPrimDynamicRNNGrad, Xs});
}
AnfNodePtr CreateSplitVD(const FuncGraphPtr &graph, const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node);
// SplitV
std::vector<AnfNodePtr> splitvd_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimSplitV->name())), node};
auto split_vd = graph->NewCNode(splitvd_input);
MS_EXCEPTION_IF_NULL(split_vd);
auto dtypes = {AnfAlgo::GetOutputInferDataType(node, 0), AnfAlgo::GetOutputInferDataType(node, 0)};
std::vector<size_t> shape = {AnfAlgo::GetOutputInferShape(node, 0)[0] - 1, AnfAlgo::GetOutputInferShape(node, 0)[1],
AnfAlgo::GetOutputInferShape(node, 0)[2]};
auto shape2 = {IntToSize(1), AnfAlgo::GetOutputInferShape(node, 0)[1], AnfAlgo::GetOutputInferShape(node, 0)[2]};
std::vector<std::vector<size_t>> shapes = {shape, shape2};
AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_vd.get());
AnfAlgo::SetNodeAttr("split_dim", MakeValue(0), split_vd);
AnfAlgo::SetNodeAttr("num_split", MakeValue(2), split_vd);
int tmp = SizeToInt(AnfAlgo::GetOutputInferShape(node, 0)[0]) - 1;
AnfAlgo::SetNodeAttr("size_splits", MakeValue(std::vector<int>{tmp, 1}), split_vd);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_vd);
return split_vd;
}
AnfNodePtr CreateLSTMInputGrad(const FuncGraphPtr &graph, const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node);
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
const auto &dynamic_rnn_grad_inputs = cnode->inputs();
std::vector<AnfNodePtr> lstm_input_grad_inputs = {NewValueNode(std::make_shared<Primitive>(kLSTMInputGradOpName)),
dynamic_rnn_grad_inputs[2],
dynamic_rnn_grad_inputs[6],
dynamic_rnn_grad_inputs[8],
dynamic_rnn_grad_inputs[9],
dynamic_rnn_grad_inputs[10],
dynamic_rnn_grad_inputs[11],
dynamic_rnn_grad_inputs[12],
dynamic_rnn_grad_inputs[13],
dynamic_rnn_grad_inputs[14],
dynamic_rnn_grad_inputs[15],
dynamic_rnn_grad_inputs[16]};
std::vector<AnfNodePtr> ori_outputs;
CreateMultipleOutputsOfAnfNode(graph, node, 5, &ori_outputs);
auto lstm_op = graph->NewCNode(lstm_input_grad_inputs);
MS_EXCEPTION_IF_NULL(lstm_op);
auto ori_type = AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_inputs[8], 0);
auto types = {AnfAlgo::GetOutputInferDataType(ori_outputs[2], 0), AnfAlgo::GetOutputInferDataType(ori_outputs[3], 0),
AnfAlgo::GetOutputInferDataType(ori_outputs[4], 0), ori_type};
std::vector<size_t> ori_shape = {AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_inputs[8], 0)[0],
AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_inputs[8], 0)[1],
4 * AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_inputs[8], 0)[2]};
auto shapes = {AnfAlgo::GetOutputInferShape(ori_outputs[2], 0), AnfAlgo::GetOutputInferShape(ori_outputs[3], 0),
AnfAlgo::GetOutputInferShape(ori_outputs[4], 0), ori_shape};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, lstm_op.get());
return lstm_op;
}
AnfNodePtr CreateBatchMatMul(const FuncGraphPtr &graph, const AnfNodePtr &node1, const AnfNodePtr &node2) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node1);
MS_EXCEPTION_IF_NULL(node2);
// BatchMatMul
std::vector<AnfNodePtr> matmul_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimBatchMatMul->name())),
node2, node1};
auto batch_matmul = graph->NewCNode(matmul_inputs);
MS_EXCEPTION_IF_NULL(batch_matmul);
auto types = {AnfAlgo::GetOutputInferDataType(node1, 0)};
std::vector<size_t> shape = {AnfAlgo::GetOutputInferShape(node2, 0)[0], AnfAlgo::GetOutputInferShape(node2, 0)[2],
AnfAlgo::GetOutputInferShape(node1, 0)[2]};
auto shapes = {shape};
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul);
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, batch_matmul.get());
return batch_matmul;
}
AnfNodePtr AddHConcatD(const FuncGraphPtr &graph, const AnfNodePtr &node1, const AnfNodePtr &node2) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node1);
MS_EXCEPTION_IF_NULL(node2);
std::vector<AnfNodePtr> ori_outputs;
CreateMultipleOutputsOfAnfNode(graph, node2, 2, &ori_outputs);
auto ori_shape = AnfAlgo::GetOutputInferShape(node1, 0);
std::vector<std::vector<size_t>> shape_tmp;
if (ori_shape.size() == 3) {
shape_tmp = {ori_shape};
} else {
shape_tmp = {{IntToSize(1), ori_shape[0], ori_shape[1]}};
}
auto ori_dtype = {AnfAlgo::GetOutputInferDataType(node1, 0)};
// reshape
std::vector<AnfNodePtr> reshape_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReshape->name())),
node1};
auto reshape = graph->NewCNode(reshape_input);
AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), reshape);
AnfAlgo::SetOutputInferTypeAndShape(ori_dtype, shape_tmp, reshape.get());
// concatd --> concat
std::vector<AnfNodePtr> concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name())),
reshape, ori_outputs[0]};
auto concat_op = graph->NewCNode(concat_inputs);
MS_EXCEPTION_IF_NULL(concat_op);
std::vector<size_t> input = {AnfAlgo::GetOutputInferShape(node2, 0)[0] + 1, AnfAlgo::GetOutputInferShape(node2, 0)[1],
AnfAlgo::GetOutputInferShape(node2, 0)[2]};
auto types = {AnfAlgo::GetOutputInferDataType(node1, 0)};
auto shapes = {input};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, concat_op.get());
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(2), concat_op);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{2}), concat_op);
AnfAlgo::SetNodeAttr("axis", MakeValue(0), concat_op);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat_op);
return concat_op;
}
AnfNodePtr AddConcatD(const FuncGraphPtr &graph, const AnfNodePtr &node1, const AnfNodePtr &node2) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node1);
MS_EXCEPTION_IF_NULL(node2);
// concatd --> concat
std::vector<AnfNodePtr> concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name())), node1,
node2};
auto concat_op = graph->NewCNode(concat_inputs);
MS_EXCEPTION_IF_NULL(concat_op);
std::vector<size_t> input = {AnfAlgo::GetOutputInferShape(node1, 0)[0], AnfAlgo::GetOutputInferShape(node1, 0)[1],
AnfAlgo::GetOutputInferShape(node1, 0)[2] + AnfAlgo::GetOutputInferShape(node2, 0)[2]};
auto types = {AnfAlgo::GetOutputInferDataType(node1, 0)};
auto shapes = {input};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, concat_op.get());
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(2), concat_op);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{2}), concat_op);
AnfAlgo::SetNodeAttr("axis", MakeValue(2), concat_op);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat_op);
return concat_op;
}
AnfNodePtr AddDwReduceSum(const FuncGraphPtr &graph, const AnfNodePtr &node1, const AnfNodePtr &node2) {
// node1 : dynamic output
// node2 : matmul
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node1);
MS_EXCEPTION_IF_NULL(node2);
std::vector<AnfNodePtr> ori_outputs;
CreateMultipleOutputsOfAnfNode(graph, node1, 5, &ori_outputs);
// ReduceSumd
std::vector<AnfNodePtr> reducesum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())),
node2};
auto reduce_sumd = graph->NewCNode(reducesum_inputs);
MS_EXCEPTION_IF_NULL(reduce_sumd);
auto types = {AnfAlgo::GetOutputInferDataType(ori_outputs[0], 0)};
auto shapes = {AnfAlgo::GetOutputInferShape(ori_outputs[0], 0)};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, reduce_sumd.get());
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int>{0}), reduce_sumd);
AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd);
return reduce_sumd;
}
AnfNodePtr AddDbReduceSum(const FuncGraphPtr &graph, const AnfNodePtr &node1, const AnfNodePtr &node2) {
// node1 lstm output
// node2 // dynamic output
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(node1);
MS_EXCEPTION_IF_NULL(node2);
std::vector<AnfNodePtr> ori_outputs;
CreateMultipleOutputsOfAnfNode(graph, node2, 5, &ori_outputs);
// ReduceSumd --> ReduceSum
std::vector<AnfNodePtr> reducerum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())),
node1};
auto reduce_sumd = graph->NewCNode(reducerum_inputs);
MS_EXCEPTION_IF_NULL(reduce_sumd);
auto types = {AnfAlgo::GetOutputInferDataType(ori_outputs[1], 0)};
auto shapes = {AnfAlgo::GetOutputInferShape(ori_outputs[1], 0)};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, reduce_sumd.get());
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int>{0, 1}), reduce_sumd);
AnfAlgo::SetNodeAttr("keep_dims", MakeValue(false), reduce_sumd);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sumd);
return reduce_sumd;
}
const AnfNodePtr DynamicRNNGradFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node);
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (cnode->size() < kDynamicRNNGradInputNum + 1) {
MS_LOG(INFO) << "The input num of DynamicRNNGrad less than" << kDynamicRNNGradInputNum
<< ". The node should not be changed";
return nullptr;
}
// input_list of dynamic_rnn_grad
const auto &ori_inputs = cnode->inputs();
// create split_vd
auto split_vd = CreateSplitVD(func_graph, ori_inputs[7]);
// create concat_1
auto h_concat = AddHConcatD(func_graph, ori_inputs[5], split_vd);
// create concat_2
auto concat = AddConcatD(func_graph, ori_inputs[1], h_concat);
// create lsym_input_grad
auto lstm_input_grad = CreateLSTMInputGrad(func_graph, cnode);
std::vector<AnfNodePtr> lstm_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_input_grad, kLSTMInputGradOutputNum, &lstm_outputs);
// create matmul
auto batch_matmul = CreateBatchMatMul(func_graph, lstm_outputs[3], concat);
// create reduce_sum_1
auto dw_reduce_sum = AddDwReduceSum(func_graph, node, batch_matmul);
// create reduce_sum_2
auto db_reduce_sum = AddDbReduceSum(func_graph, lstm_outputs[3], node);
std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple),
dw_reduce_sum,
db_reduce_sum,
lstm_outputs[0],
lstm_outputs[1],
lstm_outputs[2]};
auto make_tuple = func_graph->NewCNode(make_tuple_inputs);
MS_EXCEPTION_IF_NULL(make_tuple);
return make_tuple;
}
} // namespace opt
} // namespace mindspore

483
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.cc Normal file
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@ -0,0 +1,483 @@
/**
* 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/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.h"
#include <vector>
#include <memory>
#include "backend/session/anf_runtime_algorithm.h"
namespace mindspore {
namespace opt {
namespace {
constexpr size_t kDynamicRNNGradInputNum = 16;
constexpr size_t kSplitVOutputNum = 2;
constexpr size_t kLSTMInputGradOutputNum = 4;
void CreateTLoopNode(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
std::vector<std::vector<AnfNodePtr>> *result_nodes) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
MS_EXCEPTION_IF_NULL(result_nodes);
std::vector<AnfNodePtr> basic_lstm_cell_c_state_grad_nodes;
std::vector<AnfNodePtr> matmul_nodes;
std::vector<AnfNodePtr> split_nodes;
// Get the size of t
auto origin_input9_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(10), 0);
size_t t_size = origin_input9_shape[0];
auto input_i_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(12), 0);
for (size_t i = 0; i < t_size; ++i) {
// Create basic_lstm_cell_c_state_grad
std::vector<AnfNodePtr> basic_lstm_cell_c_state_grad_inputs = {
NewValueNode(std::make_shared<Primitive>(kBasicLSTMCellCStateGradV2OpName))};
auto basic_lstm_cell_c_state_grad = func_graph->NewCNode(basic_lstm_cell_c_state_grad_inputs);
std::vector<size_t> output0_dims{origin_input9_shape[0], 4 * (((origin_input9_shape[1] + 15) / 16) * 16)};
std::vector<size_t> output1_dims{input_i_shape[1], input_i_shape[2]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, {output0_dims, output1_dims},
basic_lstm_cell_c_state_grad.get());
AnfAlgo::SetNodeAttr("forget_bias", MakeValue(1.0f), basic_lstm_cell_c_state_grad);
AnfAlgo::SetNodeAttr("activation", MakeValue("Tanh"), basic_lstm_cell_c_state_grad);
// Create matmul
auto origin_input1_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(2), 0);
std::vector<AnfNodePtr> matmul_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimMatMul->name()))};
auto matmul = func_graph->NewCNode(matmul_inputs);
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {{output0_dims[0], origin_input1_shape[0]}},
matmul.get());
AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), matmul);
AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(true), matmul);
// Create split
std::vector<AnfNodePtr> splitv_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimSplitV->name()))};
auto split_v = func_graph->NewCNode(splitv_input);
auto origin_output2_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 2);
auto origin_output3_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 3);
std::vector<size_t> split_v_output0_shape{origin_output2_shape[1], origin_output2_shape[2]};
std::vector<size_t> split_v_output1_shape{origin_output3_shape[0], origin_output3_shape[1]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32},
{split_v_output0_shape, split_v_output1_shape}, split_v.get());
AnfAlgo::SetNodeAttr(kAttrSizeSplits,
MakeValue(std::vector<int>{SizeToInt((origin_output2_shape[2] + 15) / 16),
SizeToInt((origin_output3_shape[1] + 15) / 16)}),
split_v);
AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(0), split_v);
AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(2), split_v);
basic_lstm_cell_c_state_grad_nodes.emplace_back(basic_lstm_cell_c_state_grad);
matmul_nodes.emplace_back(matmul);
split_nodes.emplace_back(split_v);
}
result_nodes->emplace_back(basic_lstm_cell_c_state_grad_nodes);
result_nodes->emplace_back(matmul_nodes);
result_nodes->emplace_back(split_nodes);
}
AnfNodePtr CreateLSTMSPlitV(const FuncGraphPtr &func_graph, const AnfNodePtr &input,
const std::vector<std::vector<size_t>> &split_shapes,
const std::vector<TypeId> &split_types, const std::vector<int> &size_split,
size_t num_split_x) {
std::vector<AnfNodePtr> lstm_split_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimSplitV->name())),
input};
auto lstm_split = func_graph->NewCNode(lstm_split_input);
AnfAlgo::SetOutputInferTypeAndShape(split_types, split_shapes, lstm_split.get());
AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(size_split), lstm_split);
AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(0), lstm_split);
AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(SizeToInt(num_split_x)), lstm_split);
return lstm_split;
}
AnfNodePtr AddLSTMInputGradNode(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
std::vector<AnfNodePtr> *outputs) {
std::vector<std::vector<AnfNodePtr>> result_nodes;
CreateTLoopNode(func_graph, dynamic_rnn_grad_cnode, &result_nodes);
auto origin_input5_shape = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(6), 0);
std::vector<size_t> split_c_dims{1, origin_input5_shape[0], origin_input5_shape[1]};
auto origin_input7 = dynamic_rnn_grad_cnode->input(8);
size_t num_split_x = AnfAlgo::GetOutputInferShape(origin_input7, 0)[0];
std::vector<std::vector<size_t>> split_shapes;
std::vector<TypeId> split_types;
std::vector<int> size_split;
for (size_t i = 0; i < num_split_x; ++i) {
split_shapes.emplace_back(split_c_dims);
split_types.emplace_back(kNumberTypeFloat32);
size_split.emplace_back(1);
}
// Create lstm_split_c
auto lstm_split_c = CreateLSTMSPlitV(func_graph, origin_input7, split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_c_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_c, num_split_x, &lstm_split_c_outputs);
// Create lstm_split_dy
auto lstm_split_dy =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(9), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_dy_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_dy, num_split_x, &lstm_split_dy_outputs);
// Create lstm_split_i
auto lstm_split_i =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(12), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_i_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_i, num_split_x, &lstm_split_i_outputs);
// Create lstm_split_j
auto lstm_split_j =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(13), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_j_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_j, num_split_x, &lstm_split_j_outputs);
// Create lstm_split_f
auto lstm_split_f =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(14), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_f_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_f, num_split_x, &lstm_split_f_outputs);
// Create lstm_split_o
auto lstm_split_o =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(15), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_o_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_o, num_split_x, &lstm_split_o_outputs);
// Create lstm_split_tanh
auto lstm_split_tanh =
CreateLSTMSPlitV(func_graph, dynamic_rnn_grad_cnode->input(16), split_shapes, split_types, size_split, num_split_x);
std::vector<AnfNodePtr> lstm_split_tanh_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, lstm_split_tanh, num_split_x, &lstm_split_tanh_outputs);
// Add edges
std::vector<AnfNodePtr> pre_basic_lstm_cell_c_state_grad_outputs;
std::vector<AnfNodePtr> pre_split_outputs;
auto basic_lstm_cell_c_state_grad_nodes = result_nodes[0];
auto matmul_nodes = result_nodes[1];
auto split_nodes = result_nodes[2];
std::vector<AnfNodePtr> lstm_x_concat_input(num_split_x + 1);
lstm_x_concat_input[0] = NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()));
std::vector<AnfNodePtr> lstm_gage_concat_input(num_split_x + 1);
lstm_gage_concat_input[0] = NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()));
for (size_t i = 0; i < num_split_x; ++i) {
size_t idx = num_split_x - i - 1;
// Create basic_lstm_cell_c_state_grad
std::vector<AnfNodePtr> basic_lstm_cell_c_state_grad_inputs = {
NewValueNode(std::make_shared<Primitive>(kBasicLSTMCellCStateGradV2OpName))};
if (i == num_split_x - 1) {
std::vector<AnfNodePtr> reshape_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReshape->name())),
dynamic_rnn_grad_cnode->input(6)};
auto reshape = func_graph->NewCNode(reshape_inputs);
auto reshape_out_shape = {IntToSize(1), AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(6), 0)[0],
AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(6), 0)[1]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {reshape_out_shape}, reshape.get());
basic_lstm_cell_c_state_grad_inputs.emplace_back(reshape);
} else {
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_c_outputs[idx - 1]);
}
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_dy_outputs[idx]);
if (i == 0) {
basic_lstm_cell_c_state_grad_inputs.emplace_back(dynamic_rnn_grad_cnode->input(10));
basic_lstm_cell_c_state_grad_inputs.emplace_back(dynamic_rnn_grad_cnode->input(11));
} else {
basic_lstm_cell_c_state_grad_inputs.emplace_back(pre_split_outputs[1]);
basic_lstm_cell_c_state_grad_inputs.emplace_back(pre_basic_lstm_cell_c_state_grad_outputs[1]);
}
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_i_outputs[idx]);
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_j_outputs[idx]);
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_f_outputs[idx]);
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_o_outputs[idx]);
basic_lstm_cell_c_state_grad_inputs.emplace_back(lstm_split_tanh_outputs[idx]);
auto basic_lstm_cell_c_state_grad = func_graph->NewCNode(basic_lstm_cell_c_state_grad_inputs);
MS_EXCEPTION_IF_NULL(basic_lstm_cell_c_state_grad);
basic_lstm_cell_c_state_grad->set_abstract(basic_lstm_cell_c_state_grad_nodes[i]->abstract());
AnfAlgo::CopyNodeAttrs(basic_lstm_cell_c_state_grad_nodes[i], basic_lstm_cell_c_state_grad);
// Create outputs for current basic_lstm_cell_c_state_grad node
std::vector<AnfNodePtr> basic_lstm_cell_c_state_grad_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, basic_lstm_cell_c_state_grad, 2, &basic_lstm_cell_c_state_grad_outputs);
pre_basic_lstm_cell_c_state_grad_outputs = basic_lstm_cell_c_state_grad_outputs;
// Create MatMul
std::vector<AnfNodePtr> matmul_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimMatMul->name()))};
matmul_inputs.emplace_back(basic_lstm_cell_c_state_grad_outputs[0]);
matmul_inputs.emplace_back(dynamic_rnn_grad_cnode->input(2));
auto matmul = func_graph->NewCNode(matmul_inputs);
MS_EXCEPTION_IF_NULL(matmul);
matmul->set_abstract(matmul_nodes[i]->abstract());
AnfAlgo::CopyNodeAttrs(matmul_nodes[i], matmul);
// Create splitv
std::vector<AnfNodePtr> splitv_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimSplitV->name())),
matmul};
auto split_v = func_graph->NewCNode(splitv_input);
MS_EXCEPTION_IF_NULL(split_v);
split_v->set_abstract(split_nodes[i]->abstract());
AnfAlgo::CopyNodeAttrs(split_nodes[i], split_v);
// Create outputs for current split node
std::vector<AnfNodePtr> split_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, split_v, 2, &split_outputs);
pre_split_outputs = split_outputs;
lstm_x_concat_input[idx + 1] = split_outputs[0];
lstm_gage_concat_input[idx + 1] = basic_lstm_cell_c_state_grad_outputs[0];
}
// Create lstm_x_concat
auto lstm_x_concat = func_graph->NewCNode(lstm_x_concat_input);
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 2)},
lstm_x_concat.get());
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToInt(num_split_x)), lstm_x_concat);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{SizeToInt(num_split_x)}), lstm_x_concat);
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(0), lstm_x_concat);
// Create lstm_gage_concat
auto lstm_gage_concat = func_graph->NewCNode(lstm_gage_concat_input);
auto origin_input7_shape = AnfAlgo::GetOutputInferShape(origin_input7, 0);
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32},
{{origin_input7_shape[0], origin_input7_shape[1], 4 * origin_input7_shape[2]}},
lstm_gage_concat.get());
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToInt(num_split_x)), lstm_gage_concat);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{SizeToInt(num_split_x)}), lstm_gage_concat);
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(0), lstm_gage_concat);
outputs->emplace_back(lstm_x_concat);
outputs->emplace_back(pre_split_outputs[1]);
outputs->emplace_back(pre_basic_lstm_cell_c_state_grad_outputs[1]);
return lstm_gage_concat;
}
AnfNodePtr CreateSplitV(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
// Create node
auto origin_input6 = dynamic_rnn_grad_cnode->input(7);
std::vector<AnfNodePtr> splitv_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimSplitV->name())),
origin_input6};
auto split_v = func_graph->NewCNode(splitv_input);
// Set infer data type and shape
auto dtypes = {AnfAlgo::GetOutputInferDataType(origin_input6, 0), AnfAlgo::GetOutputInferDataType(origin_input6, 0)};
auto origin_input6_shape = AnfAlgo::GetOutputInferShape(origin_input6, 0);
std::vector<size_t> shape1 = {origin_input6_shape[0] - 1, origin_input6_shape[1], origin_input6_shape[2]};
std::vector<size_t> shape2 = {1, origin_input6_shape[1], origin_input6_shape[2]};
std::vector<std::vector<size_t>> shapes = {shape1, shape2};
AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, split_v.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrSplitDim, MakeValue(0), split_v);
AnfAlgo::SetNodeAttr(kAttrNumSplit, MakeValue(2), split_v);
AnfAlgo::SetNodeAttr(kAttrSizeSplits, MakeValue(std::vector<int>{SizeToInt(origin_input6_shape[0] - 1), 1}), split_v);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), split_v);
return split_v;
}
AnfNodePtr CreateHConcat(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &splitv) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
MS_EXCEPTION_IF_NULL(splitv);
// Create node
std::vector<AnfNodePtr> splitv_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, splitv, kSplitVOutputNum, &splitv_outputs);
if (splitv_outputs.size() != kSplitVOutputNum) {
MS_LOG(EXCEPTION) << "Create outputs of node " << splitv->DebugString() << " failed";
}
auto origin_input4 = dynamic_rnn_grad_cnode->input(5);
auto origin_input4_shape = AnfAlgo::GetOutputInferShape(origin_input4, 0);
// Create reshape to change shape
std::vector<size_t> shape_tmp;
if (origin_input4_shape.size() == 3) {
shape_tmp = origin_input4_shape;
} else {
shape_tmp = {1, origin_input4_shape[0], origin_input4_shape[1]};
}
std::vector<AnfNodePtr> reshape_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReshape->name())),
origin_input4};
auto reshape = func_graph->NewCNode(reshape_input);
AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), reshape);
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, reshape.get());
std::vector<AnfNodePtr> concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name())),
reshape, splitv_outputs[0]};
auto concat = func_graph->NewCNode(concat_inputs);
// Set infer data type and shape
auto splitv_output0_shape = AnfAlgo::GetOutputInferShape(splitv, 0);
std::vector<size_t> shape = {splitv_output0_shape[0] + 1, origin_input4_shape[0], origin_input4_shape[1]};
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape}, concat.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(2), concat);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{2}), concat);
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(0), concat);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat);
return concat;
}
AnfNodePtr CreateConcat(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &h_concat) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
// Create node
auto origin_input0 = dynamic_rnn_grad_cnode->input(1);
std::vector<AnfNodePtr> concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name())),
origin_input0, h_concat};
auto concat = func_graph->NewCNode(concat_inputs);
// Set infer data type and shape
auto origin_output0_shape = AnfAlgo::GetOutputInferShape(origin_input0, 0);
auto h_concat_output_shape = AnfAlgo::GetOutputInferShape(h_concat, 0);
std::vector<size_t> shape = {origin_output0_shape[0], origin_output0_shape[1],
origin_output0_shape[2] + h_concat_output_shape[2]};
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input0, 0)}, {shape}, concat.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(2), concat);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{2}), concat);
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(2), concat);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat);
return concat;
}
AnfNodePtr CreateConcatNodeT1(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
// Create node
auto origin_input0 = dynamic_rnn_grad_cnode->input(1);
auto origin_input4 = dynamic_rnn_grad_cnode->input(5);
auto origin_input4_shape = AnfAlgo::GetOutputInferShape(origin_input4, 0);
// Create reshape to change shape
std::vector<size_t> shape_tmp;
if (origin_input4_shape.size() == 3) {
shape_tmp = origin_input4_shape;
} else {
shape_tmp = {1, origin_input4_shape[0], origin_input4_shape[1]};
}
std::vector<AnfNodePtr> reshape_input = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReshape->name())),
origin_input4};
auto reshape = func_graph->NewCNode(reshape_input);
AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), reshape);
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input4, 0)}, {shape_tmp}, reshape.get());
std::vector<AnfNodePtr> concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name())),
origin_input0, reshape};
auto concat = func_graph->NewCNode(concat_inputs);
// Set infer data type and shape
auto origin_input0_shape = AnfAlgo::GetOutputInferShape(origin_input0, 0);
std::vector<size_t> shape = {origin_input0_shape[0], origin_input0_shape[1], origin_input0_shape[2] + shape_tmp[2]};
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_input0, 0)}, {shape}, concat.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(2), concat);
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(std::vector<int>{2}), concat);
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(2), concat);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), concat);
return concat;
}
AnfNodePtr CreateBatchMatMul(const FuncGraphPtr &func_graph, const AnfNodePtr &lstm_input_grad,
const AnfNodePtr &concat) {
MS_EXCEPTION_IF_NULL(func_graph);
// Create node
std::vector<AnfNodePtr> matmul_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimBatchMatMul->name())),
concat, lstm_input_grad};
auto batch_matmul = func_graph->NewCNode(matmul_inputs);
// Set infer data type and shape
auto concat_shape = AnfAlgo::GetOutputInferShape(concat, 0);
auto lstm_input_grad_shape = AnfAlgo::GetOutputInferShape(lstm_input_grad, 0);
std::vector<size_t> shape = {concat_shape[0], concat_shape[2], lstm_input_grad_shape[2]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {shape}, batch_matmul.get());
// Set attr
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(true), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul);
return batch_matmul;
}
AnfNodePtr CreateDwReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &batch_matmul) {
MS_EXCEPTION_IF_NULL(func_graph);
// Create node
std::vector<AnfNodePtr> reduce_sum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())),
batch_matmul};
auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs);
// Set infer data type and shape
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 0)},
{AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 0)}, reduce_sum.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int>{0}), reduce_sum);
AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum);
return reduce_sum;
}
AnfNodePtr CreateDbReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &lstm_input_grad) {
MS_EXCEPTION_IF_NULL(func_graph);
// Create node
std::vector<AnfNodePtr> reduce_sum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())),
lstm_input_grad};
auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs);
// Set infer data type and shape
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 1)},
{AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 1)}, reduce_sum.get());
// Set attr
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int>{0, 1}), reduce_sum);
AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum);
return reduce_sum;
}
} // namespace
const BaseRef DynamicRnnGradFissionV2::DefinePattern() const {
VarPtr Xs = std::make_shared<SeqVar>();
return VectorRef({prim::kPrimDynamicRNNGrad, Xs});
}
const AnfNodePtr DynamicRnnGradFissionV2::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node);
auto dynamic_rnn_grad_cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(dynamic_rnn_grad_cnode);
if (dynamic_rnn_grad_cnode->inputs().size() < kDynamicRNNGradInputNum + 1) {
MS_LOG(INFO) << "The node " << dynamic_rnn_grad_cnode->DebugString() << " has less than "
<< kDynamicRNNGradInputNum + 1 << " inputs";
return nullptr;
}
std::vector<AnfNodePtr> new_outputs;
auto lstm_input_grad = AddLSTMInputGradNode(func_graph, dynamic_rnn_grad_cnode, &new_outputs);
size_t t_size = AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode->input(7), 0)[0];
AnfNodePtr concat = nullptr;
if (t_size != 1) {
auto splitv = CreateSplitV(func_graph, dynamic_rnn_grad_cnode);
auto h_concat = CreateHConcat(func_graph, dynamic_rnn_grad_cnode, splitv);
concat = CreateConcat(func_graph, dynamic_rnn_grad_cnode, h_concat);
} else {
concat = CreateConcatNodeT1(func_graph, dynamic_rnn_grad_cnode);
}
auto batch_matmul = CreateBatchMatMul(func_graph, lstm_input_grad, concat);
std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)};
if (t_size != 1) {
auto dw_reduce_sum = CreateDwReduceSum(func_graph, dynamic_rnn_grad_cnode, batch_matmul);
make_tuple_inputs.emplace_back(dw_reduce_sum);
} else {
make_tuple_inputs.emplace_back(batch_matmul);
}
// create reduce_sum_2
auto db_reduce_sum = CreateDbReduceSum(func_graph, dynamic_rnn_grad_cnode, lstm_input_grad);
make_tuple_inputs.emplace_back(db_reduce_sum);
make_tuple_inputs.insert(make_tuple_inputs.end(), new_outputs.begin(), new_outputs.end());
auto make_tuple = func_graph->NewCNode(make_tuple_inputs);
return make_tuple;
}
} // namespace opt
} // namespace mindspore

15
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission.h → mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.h
View File

@ -13,21 +13,22 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_H_
#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_H_
#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_V2_H_
#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_V2_H_
#include "backend/optimizer/common/optimizer.h"
namespace mindspore {
namespace opt {
class DynamicRNNGradFission : public PatternProcessPass {
class DynamicRnnGradFissionV2 : public PatternProcessPass {
public:
explicit DynamicRNNGradFission(bool multigraph = true) : PatternProcessPass("dynamic_rnn_grad_fission", multigraph) {}
~DynamicRNNGradFission() override = default;
explicit DynamicRnnGradFissionV2(bool multigraph = true)
: PatternProcessPass("dynamic_rnn_grad_fission_v2", multigraph) {}
~DynamicRnnGradFissionV2() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_H_
#endif // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_DYNAMIC_RNN_GRAD_FISSION_V2_H_

3
mindspore/ccsrc/utils/utils.h
View File

@ -234,6 +234,9 @@ constexpr auto kSparseApplyFtrlName = "SparseApplyFtrl";
constexpr auto kSparseApplyFtrlV2Name = "SparseApplyFtrlV2";
constexpr auto kSGDName = "SGD";
constexpr auto kLARSUpdateName = "LARSUpdate";
constexpr auto kBasicLSTMCellCStateGradOpName = "BasicLSTMCellCStateGrad";
constexpr auto kBasicLSTMCellCStateGradV2OpName = "BasicLSTMCellCStateGradV2";
constexpr auto kMatMulV2OpName = "MatMulV2";
// Hcom Op Type
constexpr auto kHcomOpTypeAllReduce = "HcomAllReduce";

1
mindspore/ops/_op_impl/tbe/__init__.py
View File

@ -282,6 +282,7 @@ from .inv import _inv_tbe
from .inv_grad import _inv_grad_tbe
from .invert import _invert_tbe
from .basic_lstm_cell import _basic_lstm_cell_tbe
from .basic_lstm_cell_c_state_grad_v2 import _basic_lstm_cell_c_state_grad_tbe_v2
from .basic_lstm_cell_c_state_grad import _basic_lstm_cell_c_state_grad_tbe
from .basic_lstm_cell_weight_grad import _basic_lstm_cell_weight_grad_tbe
from .basic_lstm_cell_input_grad import _basic_lstm_cell_input_grad_tbe

51
mindspore/ops/_op_impl/tbe/basic_lstm_cell_c_state_grad_v2.py Normal file
View File

@ -0,0 +1,51 @@
# 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.
# ============================================================================
"""BasicLSTMCellCStateGradV2 op"""
from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
basic_lstm_cell_c_state_grad_op_info_v2 = TBERegOp("BasicLSTMCellCStateGradV2") \
.fusion_type("OPAQUE") \
.async_flag(False) \
.binfile_name("basic_lstm_cell_c_state_grad.so") \
.compute_cost(10) \
.kernel_name("basic_lstm_cell_c_state_grad_v2") \
.attr("forget_bias", "optional", "float", "all") \
.attr("activation", "optional", "str", "all") \
.partial_flag(True) \
.input(0, "c", False, "required", "all") \
.input(1, "dy", False, "required", "all") \
.input(2, "dht", False, "required", "all") \
.input(3, "dct", False, "required", "all") \
.input(4, "it", False, "required", "all") \
.input(5, "jt", False, "required", "all") \
.input(6, "ft", False, "required", "all") \
.input(7, "ot", False, "required", "all") \
.input(8, "tanhct", False, "required", "all") \
.output(0, "dgate", False, "required", "all") \
.output(1, "dct_1", False, "required", "all") \
.dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ,
DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ,
DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
.dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ,
DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ,
DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
.get_op_info()
@op_info_register(basic_lstm_cell_c_state_grad_op_info_v2)
def _basic_lstm_cell_c_state_grad_tbe_v2():
"""BasicLSTMCellCStateGradV2 TBE register"""
return