// Copyright (c) 2018 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 "paddle/fluid/framework/ir/fc_fuse_pass.h" #include #include "paddle/fluid/framework/ir/pass_tester_helper.h" namespace paddle { namespace framework { namespace ir { void AddVarToScope(Scope* param_scope, const std::string& name, const DDim& dims) { auto* tensor = param_scope->Var(name)->GetMutable(); tensor->Resize(dims); tensor->mutable_data(platform::CPUPlace()); } Scope* CreateParamScope() { auto param_scope = new Scope(); AddVarToScope(param_scope, "conv2d_filters_0", {}); AddVarToScope(param_scope, "conv2d_bias_0", {}); AddVarToScope(param_scope, "weights_0", {}); AddVarToScope(param_scope, "weights_1", {}); AddVarToScope(param_scope, "bias_1", {}); AddVarToScope(param_scope, "bias_2", {}); return param_scope; } TEST(FCFusePass, basic) { // inputs operator output // -------------------------------------------------------- // (a, filters_0 bias_0) conv2d -> conv2d_out // conv2d_out relu -> relu_out_0 // (relu_out_0, weights_0) mul -> mul_out_0 // (mul_out_0, bias_1) elementwise_add -> add_out_0 // add_out_0 relu -> relu_out_1 // (relu_out_1, weights_1) mul -> mul_out_1 // (mul_out_1, bias_2) elementwise_add -> add_out_1 Layers layers; auto* a = layers.data("a"); auto* filters_0 = layers.data("conv2d_filters_0", {}, true); auto* bias_0 = layers.data("conv2d_bias_0", {}, true); auto* conv2d_out = layers.conv2d(a, filters_0, bias_0, false); auto* relu_out_0 = layers.relu(conv2d_out); auto* weights_0 = layers.data("weights_0", {}, true); auto* mul_out_0 = layers.mul(relu_out_0, weights_0); auto* bias_1 = layers.data("bias_1", {}, true); auto* add_out_0 = layers.elementwise_add(mul_out_0, bias_1); auto* relu_out_1 = layers.relu(add_out_0); auto* weights_1 = layers.data("weights_1", {}, true); auto* mul_out_1 = layers.mul(relu_out_1, weights_1); auto* bias_2 = layers.data("bias_2", {}, true); auto* add_out_1 = layers.elementwise_add(mul_out_1, bias_2); VLOG(4) << add_out_1; std::unique_ptr graph(new ir::Graph(layers.main_program())); auto pass = PassRegistry::Instance().Get("fc_fuse_pass"); pass->Set("use_gpu", new bool(true)); graph->Set("__param_scope__", CreateParamScope()); int num_nodes_before = graph->Nodes().size(); int num_mul_nodes_before = GetNumOpNodes(graph, "mul"); VLOG(3) << DebugString(graph); graph.reset(pass->Apply(graph.release())); int num_nodes_after = graph->Nodes().size(); int num_fc_nodes_after = GetNumOpNodes(graph, "fc"); VLOG(3) << DebugString(graph); PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after + 6, platform::errors::InvalidArgument( "num_nodes_before=%d, num_nodes_after=%d.", num_nodes_before, num_nodes_after)); PADDLE_ENFORCE_EQ(num_fc_nodes_after, 2, platform::errors::InvalidArgument("num_fc_nodes_after=%d.", num_fc_nodes_after)); PADDLE_ENFORCE_EQ(num_mul_nodes_before, num_fc_nodes_after, platform::errors::InvalidArgument( "num_mul_nodes_before=%d, num_fc_nodes_after=%d.", num_mul_nodes_before, num_fc_nodes_after)); } } // namespace ir } // namespace framework } // namespace paddle USE_PASS(fc_fuse_pass);