lianliguang
5 years ago
parent
c0c0b0985e
commit
00e4306518
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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 "pre_activate/pass/convert_tuple_output_to_maketuple.h"
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#include <algorithm>
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#include <memory>
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#include "session/anf_runtime_algorithm.h"
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#include "pre_activate/common/helper.h"
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#include "session/kernel_graph.h"
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namespace mindspore {
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namespace opt {
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namespace {
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CNodePtr ConvertTupleInputToMakeTuple(const FuncGraphPtr &graph, const CNodePtr &cnode_ptr) {
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MS_EXCEPTION_IF_NULL(cnode_ptr);
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MS_EXCEPTION_IF_NULL(graph);
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std::vector<AnfNodePtr> convert_inputs = {cnode_ptr->input(0)};
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for (size_t index = 0; index < AnfAlgo::GetInputTensorNum(cnode_ptr); ++index) {
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auto input_node = AnfAlgo::GetInputNode(cnode_ptr, index);
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if (AnfAlgo::IsTupleOutput(input_node)) {
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std::vector<TypeId> types;
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std::vector<std::vector<size_t>> shapes;
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std::vector<AnfNodePtr> make_tuple_inputs_list = {NewValueNode(prim::kPrimMakeTuple)};
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for (size_t tuple_out_index = 0; tuple_out_index < AnfAlgo::GetOutputTensorNum(input_node); ++tuple_out_index) {
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make_tuple_inputs_list.emplace_back(CreatTupleGetItemNode(graph, input_node, tuple_out_index));
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types.push_back(AnfAlgo::GetOutputInferDataType(input_node, tuple_out_index));
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shapes.emplace_back(AnfAlgo::GetOutputInferShape(input_node, tuple_out_index));
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}
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auto make_tuple = graph->NewCNode(make_tuple_inputs_list);
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AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get());
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convert_inputs.emplace_back(make_tuple);
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} else {
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convert_inputs.push_back(input_node);
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}
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}
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cnode_ptr->set_inputs(convert_inputs);
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return cnode_ptr;
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}
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} // namespace
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const BaseRef ConvertTupleOutputToMaketuple::DefinePattern() const {
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VarPtr V = std::make_shared<Var>();
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VarPtr Xs = std::make_shared<SeqVar>();
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return VectorRef({V, Xs});
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}
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const AnfNodePtr ConvertTupleOutputToMaketuple::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
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const EquivPtr &) const {
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if (node == nullptr || !node->isa<CNode>()) {
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return nullptr;
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}
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auto cnode = node->cast<CNodePtr>();
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MS_EXCEPTION_IF_NULL(cnode);
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if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimTupleGetItem->name()) {
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return nullptr;
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}
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if (std::any_of(cnode->inputs().begin() + 1, cnode->inputs().end(), [](const AnfNodePtr &node) {
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return AnfAlgo::IsTupleOutput(node) && AnfAlgo::GetCNodeName(node) != prim::kPrimMakeTuple->name();
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})) {
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return ConvertTupleInputToMakeTuple(func_graph, cnode);
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}
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return nullptr;
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}
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} // namespace opt
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} // namespace mindspore
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@ -0,0 +1,40 @@
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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_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
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#define MINDSPORE_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
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#include <string>
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#include <vector>
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#include "ir/anf.h"
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#include "pre_activate/common/optimizer.h"
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namespace mindspore {
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namespace opt {
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class ConvertTupleOutputToMaketuple : public PatternProcessPass {
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public:
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explicit ConvertTupleOutputToMaketuple(bool multigraph = true)
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: PatternProcessPass("convert_tuple_output_to_maketuple", multigraph) {}
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~ConvertTupleOutputToMaketuple() override = default;
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const BaseRef DefinePattern() const override;
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const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
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};
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} // namespace opt
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} // namespace mindspore
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#endif // MINDSPORE_CONVERT_TUPLE_OUTPUT_TO_MAKETUPLE_H
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@ -0,0 +1,65 @@
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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 "common/backend_common_test.h"
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#include "ir/anf.h"
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#include "ir/meta_tensor.h"
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#include "debug/anf_ir_dump.h"
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#include "common/py_func_graph_fetcher.h"
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#include "session/anf_runtime_algorithm.h"
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#include "pre_activate/common/optimizer.h"
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#include "pre_activate/common/pass_manager.h"
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#include "pre_activate/pass/convert_tuple_output_to_maketuple.h"
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#include "utils/utils.h"
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namespace mindspore {
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namespace opt {
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class TestHWTupleOutputToMakeTuple : public BackendCommon {
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public:
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TestHWTupleOutputToMakeTuple()
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: getPyFun_("gtest_input.pre_activate.convert_tuple_output_to_maketuple_test", true) {}
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~TestHWTupleOutputToMakeTuple() override = default;
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public:
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UT::PyFuncGraphFetcher getPyFun_;
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};
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TEST_F(TestHWTupleOutputToMakeTuple, test_convert_tuple_output_to_maketuple) {
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FuncGraphPtr g = getPyFun_.CallAndParseRet("test_convert_tuple_output_to_maketuple", "before");
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ASSERT_TRUE(g != nullptr);
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std::vector<int> shp_x{5, 2, 10};
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std::vector<int> shp_h{1, 2, 2};
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std::vector<int> shp_c{1, 2, 2};
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std::vector<int> shp_w{112, 1, 1};
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auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
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auto h_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_h);
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auto c_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_c);
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auto w_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_w);
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AbstractBasePtrList args_spec_list{x_abstract, h_abstract, c_abstract, w_abstract};
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auto func_graph = GetKernelGraph(g, args_spec_list);
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ASSERT_TRUE(func_graph != nullptr);
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auto optimizer = std::make_shared<opt::GraphOptimizer>();
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auto pm = std::make_shared<opt::PassManager>();
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pm->AddPass(std::make_shared<opt::ConvertTupleOutputToMaketuple>());
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optimizer->AddPassManager(pm);
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optimizer->Optimize(func_graph);
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FuncGraphPtr g_after = getPyFun_.CallAndParseRet("test_convert_tuple_output_to_maketuple", "after");
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ASSERT_TRUE(g_after != nullptr);
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EXPECT_TRUE(CheckEqualGraph(func_graph, g_after));
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}
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} // namespace opt
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} // namespace mindspore
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@ -0,0 +1,54 @@
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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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from mindspore.ops import operations as P
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from mindspore.ops import Primitive
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import mindspore as ms
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import mindspore.common.dtype as mstype
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from mindspore.common.tensor import Tensor
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import numpy as np
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make_tuple = Primitive('make_tuple')
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tuple_get_item = Primitive("tuple_getitem");
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LSTM = P.LSTM(input_size=10,hidden_size=2,num_layers=1,has_bias=True,bidirectional=False,dropout=0.0)
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add = P.TensorAdd()
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class FnDict:
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def __init__(self):
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self.fnDict = {}
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def __call__(self, fn):
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self.fnDict[fn.__name__] = fn
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def __getitem__(self, name):
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return self.fnDict[name]
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def test_convert_tuple_output_to_maketuple(tag):
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fns = FnDict()
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@fns
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def before(x, h, c, w):
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res = LSTM(x, h, c, w)
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return res
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@fns
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def after(x, h, c, w):
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res = LSTM(x, h, c, w)
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res = make_tuple(
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make_tuple(tuple_get_item(res, 0), tuple_get_item(res, 1), tuple_get_item(res, 2), tuple_get_item(res, 3),
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tuple_get_item(res, 4)));
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return res
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return fns[tag]
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