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Paddle/paddle/fluid/framework/ir/fc_fuse_pass_tester.cc

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// 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 <gtest/gtest.h>
#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<LoDTensor>();
tensor->Resize(dims);
tensor->mutable_data<float>(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<ir::Graph> 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);