Add multi_gru_fuse_pass and tests (#28601)
* Add multi_gru_fuse_pass and tests * fix date * cleaned up headersmusl/disable_test_yolov3_temporarily
Wojciech Uss
5 years ago
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/fluid/framework/ir/mkldnn/multi_gru_fuse_pass.h"
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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#include "paddle/fluid/platform/errors.h"
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#include "paddle/fluid/string/pretty_log.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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using EigenVectorArrayMap = Eigen::Map<Eigen::Array<double, Eigen::Dynamic, 1>>;
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using string::PrettyLogDetail;
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namespace {
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std::vector<std::string> JoinInputs(Node* op1, Node* op2,
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std::string input_name) {
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auto in1 = op1->Op()->Input(input_name);
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auto& in2 = op2->Op()->Input(input_name);
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in1.insert(in1.end(), in2.begin(), in2.end());
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return in1;
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}
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} // namespace
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void MultiGRUFusePass::ApplyImpl(ir::Graph* graph) const {
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VLOG(3) << "Fusing two concatenated multi_gru ops.";
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PADDLE_ENFORCE_NOT_NULL(graph,
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platform::errors::InvalidArgument(
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"Pointer to graph argument cannot be NULL."));
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FusePassBase::Init(name_scope_, graph);
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PADDLE_ENFORCE_NOT_NULL(param_scope(), platform::errors::InvalidArgument(
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"Scope cannot be nullptr."));
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GraphPatternDetector gpd;
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patterns::TwoFusionGruConcat pattern{gpd.mutable_pattern(), name_scope_};
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pattern();
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int fused_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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GET_IR_NODE_FROM_SUBGRAPH(x, x, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(gru1, gru1, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(gru2, gru2, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(wh1, wh1, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(wh2, wh2, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(wx1, wx1, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(wx2, wx2, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(b1, b1, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(b2, b2, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(h1, h1, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(h2, h2, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(concat, concat, pattern);
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GET_IR_NODE_FROM_SUBGRAPH(out, out, pattern);
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if (gru1->Op()->GetAttrIfExists<bool>("origin_mode") !=
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gru2->Op()->GetAttrIfExists<bool>("origin_mode")) {
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LOG(INFO) << "The two fusion_gru ops have different values of the "
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"origin_mode attribute. Skipping fuse.";
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return;
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}
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auto wx = JoinInputs(gru1, gru2, "WeightX");
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auto wh = JoinInputs(gru1, gru2, "WeightH");
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auto b = JoinInputs(gru1, gru2, "Bias");
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OpDesc multi_gru_desc;
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multi_gru_desc.SetType("multi_gru");
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multi_gru_desc.SetInput("X", std::vector<std::string>({x->Name()}));
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multi_gru_desc.SetInput("WeightX", wx);
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multi_gru_desc.SetInput("WeightH", wh);
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multi_gru_desc.SetInput("Bias", b);
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multi_gru_desc.SetOutput("Hidden", std::vector<std::string>({out->Name()}));
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auto attrs_to_skip = {"is_reverse", "use_seq"};
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for (auto& attr : gru1->Op()->GetAttrMap()) {
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if (std::find(attrs_to_skip.begin(), attrs_to_skip.end(), attr.first) ==
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attrs_to_skip.end())
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multi_gru_desc.SetAttr(attr.first, attr.second);
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}
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multi_gru_desc.SetAttr("layers", 1);
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auto multi_gru =
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g->CreateOpNode(&multi_gru_desc); // OpDesc will be copied.
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IR_NODE_LINK_TO(x, multi_gru);
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IR_NODE_LINK_TO(b1, multi_gru);
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IR_NODE_LINK_TO(b2, multi_gru);
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IR_NODE_LINK_TO(wh1, multi_gru);
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IR_NODE_LINK_TO(wh2, multi_gru);
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IR_NODE_LINK_TO(wx1, multi_gru);
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IR_NODE_LINK_TO(wx2, multi_gru);
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IR_NODE_LINK_TO(multi_gru, out);
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GraphSafeRemoveNodes(graph, {gru1, gru2, h1, h2, concat});
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++fused_count;
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};
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gpd(graph, handler);
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AddStatis(fused_count);
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PrettyLogDetail("--- fused %d pairs of concatenated multi_gru ops",
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fused_count);
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(multi_gru_fuse_pass, paddle::framework::ir::MultiGRUFusePass);
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@ -0,0 +1,42 @@
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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// This pass fuses two concatenated fusion_gru ops into a single multi_gru op.
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// It turns
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// a -> fusion_gru -> c -> concat -> e
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// \> fusion_gru -> d /
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// into
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// a -> multi_gru -> e
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class MultiGRUFusePass : public FusePassBase {
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public:
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virtual ~MultiGRUFusePass() {}
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protected:
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void ApplyImpl(ir::Graph* graph) const override;
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const std::string name_scope_{"multi_gru"};
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,156 @@
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/fluid/framework/ir/mkldnn/multi_gru_fuse_pass.h"
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#include <gtest/gtest.h>
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namespace paddle {
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namespace framework {
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namespace ir {
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void SetOp(ProgramDesc* prog, const std::string& type,
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const std::vector<std::string>& inputs,
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const std::vector<std::string>& outputs, bool is_reverse = false,
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bool origin_mode = false) {
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auto* op = prog->MutableBlock(0)->AppendOp();
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op->SetType(type);
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if (type == "fusion_gru") {
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op->SetInput("X", {inputs[0]});
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op->SetInput("WeightX", {inputs[1]});
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op->SetInput("WeightH", {inputs[2]});
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op->SetInput("Bias", {inputs[3]});
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op->SetOutput("Hidden", {outputs[0]});
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op->SetAttr("is_reverse", is_reverse);
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op->SetAttr("origin_mode", origin_mode);
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} else if (type == "concat") {
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op->SetInput("X", {inputs[0], inputs[1]});
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op->SetOutput("Out", {outputs[0]});
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} else {
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FAIL() << "Unexpected operator type.";
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}
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}
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static const std::initializer_list<std::string> variable_names = {
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"x", "wx1", "wx2", "wh1", "wh2", "b1", "b2", "h1", "h2", "out"};
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// (x, wx1, wh1, b1) -> fusion_gru1 -> h1
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// (x, wx2, wh2, b2) -> fusion_gru2 -> h2
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// (h1, h2) -> concat -> out
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ProgramDesc BuildProgramDesc(bool origin_mode1, bool origin_mode2) {
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ProgramDesc prog;
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for (auto& v : variable_names) {
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prog.MutableBlock(0)->Var(v);
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}
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SetOp(&prog, "fusion_gru", {"x", "wx1", "wh1", "b1"}, {"h1"}, false,
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origin_mode1);
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SetOp(&prog, "fusion_gru", {"x", "wx2", "wh2", "b2"}, {"h2"}, true,
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origin_mode2);
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SetOp(&prog, "concat", {"h1", "h2"}, {"out"});
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return prog;
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}
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void MainTest(const ProgramDesc& prog, int removed_nodes_count,
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int added_nodes_count,
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const std::vector<std::string> multi_gru_inputs,
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const std::string multi_gru_output, bool origin_mode) {
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// Apply pass
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std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
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Scope scope;
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graph->SetNotOwned(kParamScopeAttr, &scope);
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int original_nodes_num = graph->Nodes().size();
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auto pass = PassRegistry::Instance().Get("multi_gru_fuse_pass");
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graph.reset(pass->Apply(graph.release()));
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int current_nodes_num = graph->Nodes().size();
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// Verify graph after fuse
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int count_multi_gru = 0;
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for (auto* node : graph->Nodes()) {
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if (node->IsOp()) {
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auto* op = node->Op();
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if (op->Type() == "multi_gru") {
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EXPECT_EQ(op->Input("X")[0], multi_gru_inputs[0]);
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EXPECT_EQ(op->Input("WeightX").size(), 2u);
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EXPECT_EQ(op->Input("WeightX")[0], multi_gru_inputs[1]);
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EXPECT_EQ(op->Input("WeightX")[1], multi_gru_inputs[2]);
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EXPECT_EQ(op->Input("WeightH").size(), 2u);
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EXPECT_EQ(op->Input("WeightH")[0], multi_gru_inputs[3]);
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EXPECT_EQ(op->Input("WeightH")[1], multi_gru_inputs[4]);
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EXPECT_EQ(op->Input("Bias").size(), 2u);
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EXPECT_EQ(op->Input("Bias")[0], multi_gru_inputs[5]);
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EXPECT_EQ(op->Input("Bias")[1], multi_gru_inputs[6]);
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EXPECT_EQ(op->Output("Hidden")[0], multi_gru_output);
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EXPECT_EQ(op->GetAttrIfExists<int>("layers"), 1);
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EXPECT_EQ(op->GetAttrIfExists<bool>("origin_mode"), origin_mode);
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++count_multi_gru;
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}
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}
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}
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EXPECT_EQ(original_nodes_num - removed_nodes_count + added_nodes_count,
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current_nodes_num);
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EXPECT_EQ(count_multi_gru, added_nodes_count);
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}
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TEST(MultiGruFusePass, same_origin_modes_1) {
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bool origin_mode1 = false;
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bool origin_mode2 = false;
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// nodes to be removed: 2x fusion_gru + 2x hidden(output) + concat
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const int removed_nodes_count = 5;
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// nodes to be added: multi_gru
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const int added_nodes_count = 1;
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const std::initializer_list<std::string> multi_gru_inputs = {
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"x", "wx1", "wx2", "wh1", "wh2", "b1", "b2"};
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MainTest(BuildProgramDesc(origin_mode1, origin_mode2), removed_nodes_count,
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added_nodes_count, multi_gru_inputs, "out", origin_mode1);
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}
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TEST(MultiGruFusePass, same_origin_modes_2) {
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bool origin_mode1 = true;
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bool origin_mode2 = true;
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// nodes to be removed: 2x fusion_gru + 2x hidden(output) + concat
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const int removed_nodes_count = 5;
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// nodes to be added: multi_gru
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const int added_nodes_count = 1;
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const std::initializer_list<std::string> multi_gru_inputs = {
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"x", "wx1", "wx2", "wh1", "wh2", "b1", "b2"};
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MainTest(BuildProgramDesc(origin_mode1, origin_mode2), removed_nodes_count,
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added_nodes_count, multi_gru_inputs, "out", origin_mode1);
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}
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TEST(MultiGruFusePass, different_origin_modes) {
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bool origin_mode1 = true;
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bool origin_mode2 = false;
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// the fuse should not be applied, so
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// nodes to be removed: none
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const int removed_nodes_count = 0;
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// nodes to be added: none
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const int added_nodes_count = 0;
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const std::initializer_list<std::string> multi_gru_inputs = {
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"x", "wx1", "wx2", "wh1", "wh2", "b1", "b2"};
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MainTest(BuildProgramDesc(origin_mode1, origin_mode2), removed_nodes_count,
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added_nodes_count, multi_gru_inputs, "out", origin_mode1);
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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USE_PASS(multi_gru_fuse_pass);
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