Merge pull request #13285 from tensor-tang/refine/ut/lac
add analysis unit test of lac and nerfix-develop-build.sh
tensor-tang
7 years ago
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GitHub
commit
ca973139fe
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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 <string>
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#include "paddle/fluid/framework/lod_tensor.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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static void BuildPattern(PDPattern* pattern, const std::string& name_scope,
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bool with_fc_bias) {
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PDNode* x = pattern->NewNode(name_scope, "x")
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->assert_is_op_input("mul")
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->assert_var_not_persistable();
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auto* fc_out = patterns::FC(pattern, name_scope, x, with_fc_bias);
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fc_out->AsIntermediate(); // fc_out is a tmp var, will be removed after fuse.
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patterns::GRU(pattern, name_scope, fc_out);
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VLOG(3) << "fc_gru pattern \n" << pattern->DotString();
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}
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static int BuildFusion(Graph* graph, const std::string& name_scope,
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Scope* scope, bool with_fc_bias) {
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GraphPatternDetector gpd;
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auto* pattern = gpd.mutable_pattern();
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BuildPattern(pattern, name_scope, with_fc_bias);
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// Create New OpDesc
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auto gru_creater = [&](int gru, int x, int weight_x, int weight_h, int bias,
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int hidden, int fc_bias) {
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#define GET_NODE(x) auto* x##_n = graph->RetriveNode(x);
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GET_NODE(x);
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GET_NODE(weight_x);
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GET_NODE(weight_h);
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GET_NODE(bias);
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GET_NODE(hidden);
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GET_NODE(gru);
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OpDesc op_desc;
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op_desc.SetType("fusion_gru");
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#define NEW_NAME(x) name_scope + "/at." #x ".new"
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#define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__##_n->Name()});
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SET_IN(X, x);
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SET_IN(WeightX, weight_x);
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SET_IN(WeightH, weight_h);
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if (with_fc_bias) {
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op_desc.SetInput("Bias", {NEW_NAME(bias) + bias_n->Name()});
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} else {
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SET_IN(Bias, bias);
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}
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#undef SET_IN
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op_desc.SetInput("H0", {});
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op_desc.SetOutput("Hidden", {hidden_n->Name()});
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op_desc.SetAttr("is_reverse", gru_n->Op()->GetAttr("is_reverse"));
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// TODO(TJ): This should be a option for infer
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op_desc.SetAttr("use_seq", true);
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#define SET_IMTERMEDIATE_OUT(key) op_desc.SetOutput(#key, {NEW_NAME(key)})
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SET_IMTERMEDIATE_OUT(ReorderedH0);
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SET_IMTERMEDIATE_OUT(XX);
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SET_IMTERMEDIATE_OUT(BatchedInput);
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SET_IMTERMEDIATE_OUT(BatchedOut);
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#undef SET_IMTERMEDIATE_OUT
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auto* op = graph->CreateOpNode(&op_desc);
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PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
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auto* scope = graph->Get<Scope*>(kParamScopeAttr);
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PADDLE_ENFORCE(scope);
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if (with_fc_bias) {
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// Fusion GRU bias = fcbias + grubias
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auto* fusion_bias_var = scope->Var(NEW_NAME(bias) + bias_n->Name());
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auto* out_bias_tensor =
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fusion_bias_var->GetMutable<framework::LoDTensor>();
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PADDLE_ENFORCE(fusion_bias_var);
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GET_NODE(fc_bias);
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PADDLE_ENFORCE(fc_bias_n);
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auto* gru_bias_var = scope->FindVar(bias_n->Name());
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auto* fc_bias_var = scope->FindVar(fc_bias_n->Name());
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PADDLE_ENFORCE(gru_bias_var);
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PADDLE_ENFORCE(fc_bias_var);
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const auto& gru_bias_tenosr = gru_bias_var->Get<framework::LoDTensor>();
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const auto& fc_bias_tensor = fc_bias_var->Get<framework::LoDTensor>();
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// new bias = fc bias + gru bias
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out_bias_tensor->Resize(gru_bias_tenosr.dims());
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auto* data = out_bias_tensor->mutable_data<float>(platform::CPUPlace());
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for (int i = 0; i < out_bias_tensor->numel(); i++) {
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data[i] =
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fc_bias_tensor.data<float>()[i] + gru_bias_tenosr.data<float>()[i];
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}
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}
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#undef GET_NODE
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#define NEW_IMTERMEDIATE_OUT(key) \
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scope->Var(NEW_NAME(key))->GetMutable<framework::LoDTensor>()
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NEW_IMTERMEDIATE_OUT(ReorderedH0);
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NEW_IMTERMEDIATE_OUT(XX);
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NEW_IMTERMEDIATE_OUT(BatchedInput);
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NEW_IMTERMEDIATE_OUT(BatchedOut);
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#undef NEW_NAME
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#undef NEW_IMTERMEDIATE_OUT
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IR_NODE_LINK_TO(x_n, op);
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IR_NODE_LINK_TO(weight_x_n, op);
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IR_NODE_LINK_TO(weight_h_n, op);
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IR_NODE_LINK_TO(bias_n, op); // actually should link to new bias if have
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IR_NODE_LINK_TO(op, hidden_n);
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// h0?
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return op;
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};
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int fusion_count{0};
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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#define GET_NODE(name__) \
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std::string name__##key = name_scope + "/" + #name__; \
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auto* name__##n = pattern->RetrieveNode(name__##key); \
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PADDLE_ENFORCE(name__##n); \
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PADDLE_ENFORCE(subgraph.count(name__##n)); \
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Node* name__##_n = subgraph.at(name__##n); \
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int name__ __attribute__((unused)) = name__##_n->id();
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GET_NODE(x);
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GET_NODE(w); // fc weight
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GET_NODE(mul);
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GET_NODE(fc_out);
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GET_NODE(Weight);
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GET_NODE(gru);
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GET_NODE(Bias);
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GET_NODE(Hidden);
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// nodes need be removed
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GET_NODE(BatchGate);
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GET_NODE(BatchResetHiddenPrev);
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GET_NODE(BatchHidden);
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if (with_fc_bias) {
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GET_NODE(mul_out);
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GET_NODE(fc_bias);
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GET_NODE(elementwise_add);
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gru_creater(gru, x, w, Weight, Bias, Hidden, fc_bias);
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// Remove unneeded nodes.
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std::unordered_set<const Node*> marked_nodes(
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{mul_n, gru_n, elementwise_add_n, fc_bias_n, fc_out_n, mul_out_n,
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BatchGate_n, BatchResetHiddenPrev_n, BatchHidden_n});
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GraphSafeRemoveNodes(graph, marked_nodes);
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} else {
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gru_creater(gru, x, w, Weight, Bias, Hidden, -1);
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// Remove unneeded nodes.
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std::unordered_set<const Node*> marked_nodes(
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{mul_n, gru_n, BatchGate_n, BatchResetHiddenPrev_n, BatchHidden_n});
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GraphSafeRemoveNodes(graph, marked_nodes);
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}
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#undef GET_NODE
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++fusion_count;
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};
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gpd(graph, handler);
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return fusion_count;
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}
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std::unique_ptr<ir::Graph> MulGRUFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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FusePassBase::Init(name_scope_, graph.get());
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int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope(),
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false /*with_fc_bias*/);
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AddStatis(fusion_count);
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return graph;
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}
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std::unique_ptr<ir::Graph> FCGRUFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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FusePassBase::Init(name_scope_, graph.get());
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int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope(),
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true /*with_fc_bias*/);
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AddStatis(fusion_count);
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return graph;
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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(mul_gru_fuse_pass, paddle::framework::ir::MulGRUFusePass);
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REGISTER_PASS(fc_gru_fuse_pass, paddle::framework::ir::FCGRUFusePass);
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@ -0,0 +1,50 @@
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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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#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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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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// The MulGRUFusePass and MulGRUFusePass will fuse to the same FusionGRU op.
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class FCGRUFusePass : public FusePassBase {
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public:
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virtual ~FCGRUFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"fc_gru_fuse"};
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};
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// Just FC without bias
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class MulGRUFusePass : public FusePassBase {
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public:
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virtual ~MulGRUFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"fc_nobias_gru_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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