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100 lines
4.1 KiB
100 lines
4.1 KiB
// 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_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, "conv2d_filters_0", {});
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AddVarToScope(param_scope, "conv2d_bias_0", {});
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AddVarToScope(param_scope, "weights_0", {});
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AddVarToScope(param_scope, "weights_1", {});
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AddVarToScope(param_scope, "bias_1", {});
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AddVarToScope(param_scope, "bias_2", {});
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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, filters_0 bias_0) conv2d -> conv2d_out
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// conv2d_out relu -> relu_out_0
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// (relu_out_0, weights_0) mul -> mul_out_0
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// (mul_out_0, bias_1) elementwise_add -> add_out_0
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// add_out_0 relu -> relu_out_1
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// (relu_out_1, weights_1) mul -> mul_out_1
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// (mul_out_1, bias_2) elementwise_add -> add_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* filters_0 = layers.data("conv2d_filters_0", {}, true);
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auto* bias_0 = layers.data("conv2d_bias_0", {}, true);
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auto* conv2d_out = layers.conv2d(a, filters_0, bias_0, false);
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auto* relu_out_0 = layers.relu(conv2d_out);
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auto* weights_0 = layers.data("weights_0", {}, true);
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auto* mul_out_0 = layers.mul(relu_out_0, weights_0);
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auto* bias_1 = layers.data("bias_1", {}, true);
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auto* add_out_0 = layers.elementwise_add(mul_out_0, bias_1);
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auto* relu_out_1 = layers.relu(add_out_0);
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auto* weights_1 = layers.data("weights_1", {}, true);
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auto* mul_out_1 = layers.mul(relu_out_1, weights_1);
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auto* bias_2 = layers.data("bias_2", {}, true);
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auto* add_out_1 = layers.elementwise_add(mul_out_1, bias_2);
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VLOG(4) << add_out_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_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_mul_nodes_before = GetNumOpNodes(graph, "mul");
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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_fc_nodes_after = GetNumOpNodes(graph, "fc");
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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::InvalidArgument(
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"num_nodes_before=%d, num_nodes_after=%d.",
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num_nodes_before, num_nodes_after));
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PADDLE_ENFORCE_EQ(num_fc_nodes_after, 2,
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platform::errors::InvalidArgument("num_fc_nodes_after=%d.",
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num_fc_nodes_after));
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PADDLE_ENFORCE_EQ(num_mul_nodes_before, num_fc_nodes_after,
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platform::errors::InvalidArgument(
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"num_mul_nodes_before=%d, num_fc_nodes_after=%d.",
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num_mul_nodes_before, num_fc_nodes_after));
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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_fuse_pass);
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