fix fc_lstm_fuse when multi sub-graph use same fc_bias. test=develop (#22551)
当一个模型中有多个fc_lstm子图的时候，且其中fc共用了同一个persistable的bias，此时不应该将bias节点删除，只将非persistable的节点去除即可。revert-22710-feature/integrated_ps_api
Wilber
5 years ago
committed by
GitHub
parent
266106da75
commit
9a8203aa25
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// Copyright (c) 2018 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/fc_gru_fuse_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void AddVarToScope(Scope* param_scope, const std::string& name,
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const DDim& dims) {
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auto* tensor = param_scope->Var(name)->GetMutable<LoDTensor>();
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tensor->Resize(dims);
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tensor->mutable_data<float>(platform::CPUPlace());
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}
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Scope* CreateParamScope() {
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auto param_scope = new Scope();
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AddVarToScope(param_scope, "gru_fc_w", {});
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AddVarToScope(param_scope, "gru_fc_b", {});
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AddVarToScope(param_scope, "gru_w", {});
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AddVarToScope(param_scope, "gru_b", {});
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AddVarToScope(param_scope, "gru_batch_gate_0", {});
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AddVarToScope(param_scope, "gru_batch_reset_hidden_prev_0", {});
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AddVarToScope(param_scope, "gru_batch_hidden_0", {});
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AddVarToScope(param_scope, "gru_hidden_0", {});
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AddVarToScope(param_scope, "gru_batch_gate_1", {});
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AddVarToScope(param_scope, "gru_batch_reset_hidden_prev_1", {});
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AddVarToScope(param_scope, "gru_batch_hidden_1", {});
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AddVarToScope(param_scope, "gru_hidden_1", {});
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return param_scope;
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}
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TEST(FCFusePass, basic) {
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// inputs operator output
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// --------------------------------------------------------
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// (a, gru_fc_w) mul -> fc_0_tmp_0
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// (fc_0_tmp_0, gru_fc_b) elementwise_add -> fc_0_tmp_1
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// (fc_0_tmp_1,gru_w,gru_b gru -> gru_out_0
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// (b, gru_fc_w) mul -> fc_1_tmp_0
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// (fc_1_tmp_0, gru_fc_b) elementwise_add -> fc_1_tmp_1
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// (fc_1_tmp_1,gru_w,gru_b) gru -> gru_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* b = layers.data("b");
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auto* fc_w = layers.data("gru_fc_w", {}, true);
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auto* fc_b = layers.data("gru_fc_b", {}, true);
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auto* gru_w = layers.data("gru_w", {}, true);
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auto* gru_b = layers.data("gru_b", {}, true);
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auto* fc_0_tmp0 = layers.mul(a, fc_w);
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auto* fc_0_tmp1 = layers.elementwise_add(fc_0_tmp0, fc_b);
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auto* gru_batch_gate_0 = layers.data("gru_batch_gate_0", {}, false);
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auto* gru_batch_reset_hidden_prev_0 =
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layers.data("gru_batch_reset_hidden_prev_0", {}, false);
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auto* gru_batch_hidden_0 = layers.data("gru_batch_hidden_0", {}, false);
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auto* gru_hidden_0 = layers.data("gru_hidden_0", {}, false);
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layers.gru(fc_0_tmp1, gru_w, gru_b, gru_batch_gate_0,
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gru_batch_reset_hidden_prev_0, gru_batch_hidden_0, gru_hidden_0);
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auto* fc_1_tmp0 = layers.mul(b, fc_w);
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auto* fc_1_tmp1 = layers.elementwise_add(fc_1_tmp0, fc_b);
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auto* gru_batch_gate_1 = layers.data("gru_batch_gate_1", {}, false);
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auto* gru_batch_reset_hidden_prev_1 =
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layers.data("gru_batch_reset_hidden_prev_1", {}, false);
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auto* gru_batch_hidden_1 = layers.data("gru_batch_hidden_1", {}, false);
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auto* gru_hidden_1 = layers.data("gru_hidden_1", {}, false);
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layers.gru(fc_1_tmp1, gru_w, gru_b, gru_batch_gate_1,
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gru_batch_reset_hidden_prev_1, gru_batch_hidden_1, gru_hidden_1);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass = PassRegistry::Instance().Get("fc_gru_fuse_pass");
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pass->Set("use_gpu", new bool(true));
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graph->Set("__param_scope__", CreateParamScope());
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int num_nodes_before = graph->Nodes().size();
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int num_gru_nodes_before = GetNumOpNodes(graph, "gru");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = graph->Nodes().size();
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int num_fuse_gru_nodes_after = GetNumOpNodes(graph, "fusion_gru");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after + 6,
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platform::errors::PreconditionNotMet(
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"The number of nodes before and after "
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"the fuse does not meet expectations"));
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PADDLE_ENFORCE_EQ(
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num_fuse_gru_nodes_after, 2,
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platform::errors::PreconditionNotMet("The number of gru nodes before the "
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"fuse does not meet expectations"));
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PADDLE_ENFORCE_EQ(num_gru_nodes_before, num_fuse_gru_nodes_after,
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platform::errors::PreconditionNotMet(
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"The number of fusion_gru nodes does not meet "
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"expectations after fuse"));
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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(fc_gru_fuse_pass);
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@ -0,0 +1,116 @@
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// Copyright (c) 2018 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/fc_lstm_fuse_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void AddVarToScope(Scope* param_scope, const std::string& name,
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const DDim& dims) {
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auto* tensor = param_scope->Var(name)->GetMutable<LoDTensor>();
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tensor->Resize(dims);
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tensor->mutable_data<float>(platform::CPUPlace());
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}
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Scope* CreateParamScope() {
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auto param_scope = new Scope();
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AddVarToScope(param_scope, "lstm_fc_w", {});
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AddVarToScope(param_scope, "lstm_fc_b", {});
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AddVarToScope(param_scope, "lstm_w", {});
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AddVarToScope(param_scope, "lstm_b", {});
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AddVarToScope(param_scope, "lstm_cell_0", {});
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AddVarToScope(param_scope, "lstm_batch_gate_0", {});
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AddVarToScope(param_scope, "lstm_batch_cell_pre_gate_0", {});
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AddVarToScope(param_scope, "lstm_hidden_0", {});
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AddVarToScope(param_scope, "lstm_cell_1", {});
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AddVarToScope(param_scope, "lstm_batch_gate_1", {});
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AddVarToScope(param_scope, "lstm_batch_cell_pre_gate_1", {});
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AddVarToScope(param_scope, "lstm_hidden_1", {});
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return param_scope;
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}
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TEST(FCLSTMFusePass, basic) {
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// inputs operator output
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// --------------------------------------------------------
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// (a, lstm_fc_w) mul -> fc_0_tmp_0
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// (fc_0_tmp_0, lstm_fc_b) elementwise_add -> fc_0_tmp_1
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// fc_0_tmp_1,lstm_w,lstm_b lstm -> lstm_out_0
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// (b, lstm_fc_w) mul -> fc_1_tmp_0
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// (fc_1_tmp_0, lstm_fc_b) elementwise_add -> fc_1_tmp_1
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// (fc_1_tmp_1,lstm_w,lstm_b) lstm -> lstm_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* b = layers.data("b");
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auto* fc_w = layers.data("lstm_fc_w", {}, true);
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auto* fc_b = layers.data("lstm_fc_b", {}, true);
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auto* lstm_w = layers.data("lstm_w", {}, true);
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auto* lstm_b = layers.data("lstm_b", {}, true);
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auto* fc_0_tmp0 = layers.mul(a, fc_w);
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auto* fc_0_tmp1 = layers.elementwise_add(fc_0_tmp0, fc_b);
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auto* lstm_cell_0 = layers.data("lstm_cell_0", {}, false);
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auto* lstm_batch_gate_0 = layers.data("lstm_batch_gate_0", {}, false);
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auto* lstm_batch_cell_pre_gate_0 =
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layers.data("lstm_batch_cell_pre_gate_0", {}, false);
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auto* lstm_hidden_0 = layers.data("lstm_hidden_0", {}, false);
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layers.lstm(fc_0_tmp1, lstm_w, lstm_b, lstm_cell_0, lstm_batch_gate_0,
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lstm_hidden_0, lstm_batch_cell_pre_gate_0);
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auto* fc_1_tmp0 = layers.mul(b, fc_w);
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auto* fc_1_tmp1 = layers.elementwise_add(fc_1_tmp0, fc_b);
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auto* lstm_cell_1 = layers.data("lstm_cell_1", {}, false);
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auto* lstm_batch_gate_1 = layers.data("lstm_batch_gate_1", {}, false);
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auto* lstm_batch_cell_pre_gate_1 =
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layers.data("lstm_batch_cell_pre_gate_1", {}, false);
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auto* lstm_hidden_1 = layers.data("lstm_hidden_1", {}, false);
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layers.lstm(fc_1_tmp1, lstm_w, lstm_b, lstm_cell_1, lstm_batch_gate_1,
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lstm_hidden_1, lstm_batch_cell_pre_gate_1);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass = PassRegistry::Instance().Get("fc_lstm_fuse_pass");
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pass->Set("use_gpu", new bool(false));
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graph->Set("__param_scope__", CreateParamScope());
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int num_nodes_before = graph->Nodes().size();
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int num_lstm_nodes_before = GetNumOpNodes(graph, "lstm");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = graph->Nodes().size();
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int num_fusion_lstm_nodes_after = GetNumOpNodes(graph, "fusion_lstm");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after - 6,
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platform::errors::PreconditionNotMet(
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"The number of nodes before and after "
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"the fuse does not meet expectations"));
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PADDLE_ENFORCE_EQ(num_fusion_lstm_nodes_after, 2,
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platform::errors::PreconditionNotMet(
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"The number of lstm nodes before the "
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"fuse does not meet expectations"));
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PADDLE_ENFORCE_EQ(num_lstm_nodes_before, num_fusion_lstm_nodes_after,
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platform::errors::PreconditionNotMet(
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"The number of fusion_gru nodes does "
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"not meet expectations after fuse"));
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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(fc_lstm_fuse_pass);
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