未验证 提交 1ec18e53 编写于 作者: T tensor-tang 提交者: GitHub

enable shuffle channel fuse (#1834)

test=develop
上级 6fc8574a
......@@ -29,6 +29,7 @@ USE_MIR_PASS(graph_visualze);
USE_MIR_PASS(lite_conv_bn_fuse_pass);
USE_MIR_PASS(lite_fc_fuse_pass);
USE_MIR_PASS(lite_shuffle_channel_fuse_pass);
USE_MIR_PASS(identity_scale_eliminate_pass);
USE_MIR_PASS(lite_conv_elementwise_fuse_pass);
USE_MIR_PASS(lite_conv_activation_fuse_pass);
......
......@@ -11,6 +11,7 @@ add_subdirectory(subgraph)
lite_cc_library(mir_passes
SRCS
fusion/fc_fuse_pass.cc
fusion/shuffle_channel_fuse_pass.cc
fusion/conv_elementwise_fuse_pass.cc
fusion/conv_activation_fuse_pass.cc
fusion/conv_bn_fuse_pass.cc
......
lite_cc_library(fuse_fc
SRCS fc_fuser.cc
DEPS pattern_matcher_high_api)
lite_cc_library(fuse_shuffle_channel
SRCS shuffle_channel_fuser.cc
DEPS pattern_matcher_high_api)
lite_cc_library(fuse_conv_elementwise
SRCS conv_elementwise_fuser.cc
DEPS pattern_matcher_high_api)
......@@ -19,6 +22,7 @@ lite_cc_library(fuse_quant_dequant
set(mir_fusers
fuse_fc
fuse_shuffle_channel
fuse_conv_elementwise
fuse_conv_activation
fuse_conv_bn
......
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 "lite/core/mir/fusion/shuffle_channel_fuse_pass.h"
#include <memory>
#include <vector>
#include "lite/core/mir/fusion/shuffle_channel_fuser.h"
#include "lite/core/mir/pass_registry.h"
namespace paddle {
namespace lite {
namespace mir {
void ShuffleChannelFusePass::Apply(const std::unique_ptr<SSAGraph>& graph) {
fusion::ShuffleChannelFuser fuser("reshape", "transpose");
fuser(graph.get());
fusion::ShuffleChannelFuser fuser2("reshape2", "transpose2");
fuser2(graph.get());
}
} // namespace mir
} // namespace lite
} // namespace paddle
REGISTER_MIR_PASS(lite_shuffle_channel_fuse_pass,
paddle::lite::mir::ShuffleChannelFusePass);
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <memory>
#include <string>
#include "lite/core/mir/pass.h"
namespace paddle {
namespace lite {
namespace mir {
class ShuffleChannelFusePass : public ProgramPass {
public:
void Apply(const std::unique_ptr<SSAGraph>& graph) override;
};
} // namespace mir
} // namespace lite
} // namespace paddle
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 "lite/core/mir/fusion/shuffle_channel_fuser.h"
#include <memory>
#include <vector>
namespace paddle {
namespace lite {
namespace mir {
namespace fusion {
void ShuffleChannelFuser::BuildPattern() {
// create nodes.
auto* x1 = VarNode("x1")->assert_is_op_input(reshape_type_, "X");
auto* y1 = VarNode("y1")->assert_is_op_output(reshape_type_, "Out");
auto* y2 = VarNode("y2")->assert_is_op_output(transpose_type_, "Out");
auto* out = VarNode("out")->assert_is_op_output(reshape_type_, "Out");
auto* xshape1 =
VarNode("xshape1")->assert_is_op_output(reshape_type_, "XShape");
auto* xshape2 =
VarNode("xshape2")->assert_is_op_output(transpose_type_, "XShape");
auto* xshape3 =
VarNode("xshape3")->assert_is_op_output(reshape_type_, "XShape");
auto* reshape1 = OpNode("reshape1", reshape_type_)
->assert_op_attr_satisfied<std::vector<int>>(
"shape", [](const std::vector<int>& attr) {
return attr.size() >= 5 && attr[1] > 0;
});
auto* transpose =
OpNode("transpose_op", transpose_type_)
->assert_op_attr_satisfied<std::vector<int>>(
"axis", [](const std::vector<int>& attr) {
return attr.size() >= 5 && attr[1] == 2 && attr[2] == 1;
});
auto* reshape2 = OpNode("reshape2", reshape_type_)
->assert_op_attr_satisfied<std::vector<int>>(
"shape", [](const std::vector<int>& attr) {
return attr.size() >= 4;
});
// create topology.
*x1 >> *reshape1 >> *y1 >> *transpose >> *y2 >> *reshape2 >> *out;
*reshape1 >> *xshape1;
*transpose >> *xshape2;
*reshape2 >> *xshape3;
// Some op specialities.
y1->AsIntermediate();
y2->AsIntermediate();
xshape1->AsIntermediate();
xshape2->AsIntermediate();
xshape3->AsIntermediate();
reshape1->AsIntermediate();
transpose->AsIntermediate();
reshape2->AsIntermediate();
}
void ShuffleChannelFuser::InsertNewNode(SSAGraph* graph,
const key2nodes_t& matched) {
auto op_desc = GenOpDesc(matched);
auto shuffle_channel_op = LiteOpRegistry::Global().Create("shuffle_channel");
auto transpose = matched.at("transpose_op")->stmt()->op();
auto* scope = transpose->scope();
auto& valid_places = transpose->valid_places();
shuffle_channel_op->Attach(op_desc, scope);
auto* new_op_node =
graph->GraphCreateInstructNode(shuffle_channel_op, valid_places);
IR_NODE_LINK_TO(matched.at("x1"), new_op_node);
IR_NODE_LINK_TO(new_op_node, matched.at("out"));
}
cpp::OpDesc ShuffleChannelFuser::GenOpDesc(const key2nodes_t& matched) {
cpp::OpDesc op_desc;
op_desc.SetType("shuffle_channel");
op_desc.SetInput("X", {matched.at("x1")->arg()->name});
op_desc.SetOutput("Out", {matched.at("out")->arg()->name});
op_desc.SetAttr("group",
matched.at("reshape1")
->stmt()
->op_info()
->GetAttr<std::vector<int>>("shape")[1]);
return op_desc;
}
} // namespace fusion
} // namespace mir
} // namespace lite
} // namespace paddle
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <memory>
#include <string>
#include "lite/core/mir/pattern_matcher_high_api.h"
namespace paddle {
namespace lite {
namespace mir {
namespace fusion {
class ShuffleChannelFuser : public FuseBase {
public:
explicit ShuffleChannelFuser(const std::string& reshape_type,
const std::string& transpose_type)
: reshape_type_(reshape_type), transpose_type_(transpose_type) {}
void BuildPattern() override;
void InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) override;
private:
cpp::OpDesc GenOpDesc(const key2nodes_t& matched) override;
std::string reshape_type_;
std::string transpose_type_;
};
} // namespace fusion
} // namespace mir
} // namespace lite
} // namespace paddle
......@@ -141,18 +141,26 @@ struct PMNode {
int nth);
template <typename T>
PMNode* assert_op_attr(const std::string& attr_name, const T& attr) {
PMNode* assert_op_attr_satisfied(
const std::string& attr_name,
const std::function<bool(const T&)>& condition) {
asserts_.push_back([=](const Node* x) {
if (x && x->IsStmt()) {
auto* op_info = x->stmt()->op_info();
return op_info->HasAttr(attr_name) &&
op_info->GetAttr<T>(attr_name) == attr;
condition(op_info->GetAttr<T>(attr_name));
}
return false;
});
return this;
}
template <typename T>
PMNode* assert_op_attr(const std::string& attr_name, const T& attr) {
return assert_op_attr_satisfied<T>(
attr_name, [&](const T& src) { return src == attr; });
}
private:
PMNode(PMPattern* pattern,
const std::string& name = "",
......
......@@ -64,6 +64,7 @@ class Optimizer {
"lite_conv_elementwise_fuse_pass", //
"lite_conv_activation_fuse_pass", //
"lite_fc_fuse_pass", //
"lite_shuffle_channel_fuse_pass", //
"identity_scale_eliminate_pass", //
#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
"lite_elementwise_add_activation_fuse_pass", //
......
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