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提交 b10cd435 编写于 作者: Q Qiao Longfei 提交者： Yu Yang
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rm cpp executor_test, rewrite in python later (#4849) 

* rm cpp executor_test, rewrite in python later

* remove executor_test code in CMakeList.txt
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paddle/framework/CMakeLists.txt
浏览文件 @ b10cd435
......@@ -43,13 +43,6 @@ cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
set(EXECUTOR_TEST_OP elementwise_add_op gaussian_random_op feed_op fetch_op
mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op mean_op)
if(WITH_GPU)
nv_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
else()
cc_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
endif()
cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
......
paddle/framework/executor_test.cc 已删除 100644 → 0
浏览文件 @ cdc236cb
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/executor.h"
#include <memory>
#include <vector>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/backward.h"
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(elementwise_add);
USE_OP(gaussian_random);
USE_NO_KERNEL_OP(feed);
USE_NO_KERNEL_OP(fetch);
USE_OP(mul);
USE_OP(sum);
USE_OP(squared_l2_distance);
USE_OP(fill_constant);
USE_OP(mean);
USE_OP(sgd);
constexpr auto kFeedValueName = "feed_value";
constexpr auto kFetchValueName = "fetch_value";
using namespace paddle::platform;
using namespace paddle::framework;
void AddOp(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, AttributeMap attrs,
paddle::framework::BlockDescBind* block) {
// insert output
for (auto kv : outputs) {
for (auto v : kv.second) {
// <<<<<<< HEAD
// auto var = block->Var(v);
// var->SetType(VarDesc::LOD_TENSOR);
// var->SetDataType(paddle::framework::DataType::FP32);
// =======
if (!block->HasVar(v)) {
auto var = block->Var(v);
var->SetDataType(paddle::framework::DataType::FP32);
}
// >>>>>>> origin/develop
}
}
// insert op
auto op = block->AppendOp();
op->SetType(type);
for (auto& kv : inputs) {
op->SetInput(kv.first, kv.second);
}
for (auto& kv : outputs) {
op->SetOutput(kv.first, kv.second);
}
op->SetAttrMap(attrs);
op->CheckAttrs();
}
// Tensors in feed value variable will only be in CPUPlace
// So we can memcpy the data from vector<T> to feed_value
template <typename T>
void SetFeedVariable(const std::vector<std::vector<T>>& inputs,
const std::vector<std::vector<int64_t>>& dims) {
Variable* g_feed_value = GetGlobalScope().FindVar(kFeedValueName);
auto& feed_inputs =
*(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
size_t size = inputs.size();
feed_inputs.resize(size);
for (size_t i = 0; i < size; i++) {
T* dst = feed_inputs[i].mutable_data<T>(make_ddim(dims[i]), CPUPlace());
memcpy(dst, inputs[i].data(), inputs[i].size() * sizeof(T));
}
}
// Tensors in fetch value variable will only be in CPUPlace
// So we can memcpy the data from fetch_value to vector<T>
template <typename T>
std::vector<std::vector<T>> GetFetchVariable() {
Variable* g_fetch_value = GetGlobalScope().FindVar(kFetchValueName);
auto& fetch_outputs =
*(g_fetch_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
size_t size = fetch_outputs.size();
std::vector<std::vector<T>> result;
result.reserve(size);
for (size_t i = 0; i < size; i++) {
std::vector<T> tmp;
tmp.resize(fetch_outputs[i].numel());
memcpy(tmp.data(), fetch_outputs[i].data<T>(),
fetch_outputs[i].numel() * sizeof(T));
result.push_back(tmp);
}
return result;
}
class ExecutorTesterRandom : public ::testing::Test {
public:
virtual void SetUp() override {
int input_dim = 3, batch_size = 2, embed_dim = 5;
auto temp_init_root_block = init_pdesc_.add_blocks();
temp_init_root_block->set_idx(0);
temp_init_root_block->set_parent_idx(-1);
paddle::framework::ProgramDescBind& init_program =
paddle::framework::ProgramDescBind::Instance(&init_pdesc_);
paddle::framework::BlockDescBind* init_root_block = init_program.Block(0);
AddOp("gaussian_random", {}, {{"Out", {"w1"}}},
{{"dims", std::vector<int>{input_dim, embed_dim}}}, init_root_block);
AddOp("gaussian_random", {}, {{"Out", {"w2"}}},
{{"dims", std::vector<int>{embed_dim, input_dim}}}, init_root_block);
AddOp("fetch", {{"Input", {"w1"}}}, {{"Out", {kFetchValueName}}},
{{"col", 0}}, init_root_block);
AddOp("fetch", {{"Input", {"w2"}}}, {{"Out", {kFetchValueName}}},
{{"col", 1}}, init_root_block);
// flush
init_program.Proto();
// run block
auto temp_root_block = pdesc_.add_blocks();
temp_root_block->set_idx(0);
temp_root_block->set_parent_idx(-1);
paddle::framework::ProgramDescBind& program =
paddle::framework::ProgramDescBind::Instance(&pdesc_);
paddle::framework::BlockDescBind* root_block = program.Block(0);
// feed data
inputs_.push_back({1.0, 1.0, 1.0, 1.0, 1.0, 1.0});
dims_.push_back({batch_size, input_dim});
AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"a"}}},
{{"dims", std::vector<int>{batch_size, input_dim}}, {"col", 0}},
root_block);
// forward
AddOp("mul", {{"X", {"a"}}, {"Y", {"w1"}}}, {{"Out", {"b"}}}, {},
root_block);
AddOp("mul", {{"X", {"b"}}, {"Y", {"w2"}}}, {{"Out", {"a_out"}}}, {},
root_block);
AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}},
{{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {},
root_block);
AddOp("mean", {{"X", {"l2_distance"}}}, {{"Out", {"mean_out"}}}, {},
root_block);
// backward
auto target = VarDescBind("mean_out");
AppendBackward(program, target, {});
// update
AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}},
{{"shape", std::vector<int>{1}}, {"value", float(0.001)}},
root_block);
AddOp("sgd", {{"Param", {"w1"}},
{"LearningRate", {"learning_rate"}},
{"Grad", {"w1@GRAD"}}},
{{"ParamOut", {"w1"}}}, {}, root_block);
AddOp("sgd", {{"Param", {"w2"}},
{"LearningRate", {"learning_rate"}},
{"Grad", {"w2@GRAD"}}},
{{"ParamOut", {"w2"}}}, {}, root_block);
AddOp("fetch", {{"Input", {"w1"}}}, {{"Out", {kFetchValueName}}},
{{"col", 0}}, root_block);
AddOp("fetch", {{"Input", {"w2"}}}, {{"Out", {kFetchValueName}}},
{{"col", 1}}, root_block);
AddOp("fetch", {{"Input", {"l2_distance"}}}, {{"Out", {kFetchValueName}}},
{{"col", 0}}, root_block);
// flush
program.Proto();
}
protected:
ProgramDesc init_pdesc_;
ProgramDesc pdesc_;
std::vector<std::vector<float>> inputs_;
std::vector<std::vector<int64_t>> dims_;
};
class ExecutorTesterFeedAndFetch : public ::testing::Test {
public:
virtual void SetUp() override {
auto temp_root_block = pdesc_.add_blocks();
temp_root_block->set_idx(0);
temp_root_block->set_parent_idx(-1);
// wrap to BlockDescBind
paddle::framework::ProgramDescBind& program =
paddle::framework::ProgramDescBind::Instance(&pdesc_);
paddle::framework::BlockDescBind* root_block = program.Block(0);
std::vector<int> dim{6};
AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"a"}}},
{{"dims", dim}, {"col", 0}}, root_block);
AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"b"}}},
{{"dims", dim}, {"col", 1}}, root_block);
AddOp("fetch", {{"Input", {"a"}}}, {{"Out", {kFetchValueName}}},
{{"col", 0}}, root_block);
AddOp("fetch", {{"Input", {"b"}}}, {{"Out", {kFetchValueName}}},
{{"col", 1}}, root_block);
// flush
program.Proto();
std::vector<float> vec1 = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
std::vector<float> vec2 = {4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
inputs_.push_back(vec1);
inputs_.push_back(vec2);
dims_.push_back({static_cast<int64_t>(vec1.size())});
dims_.push_back({static_cast<int64_t>(vec2.size())});
}
protected:
ProgramDesc pdesc_;
std::vector<std::vector<float>> inputs_;
std::vector<std::vector<int64_t>> dims_;
};
#ifndef PADDLE_WITH_CUDA
TEST_F(ExecutorTesterRandom, CPU) {
std::vector<Place> places;
CPUPlace cpu_place;
places.push_back(cpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
paddle::memory::Used(cpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
executor->Run(init_pdesc_, &GetGlobalScope(), 0);
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
}
TEST_F(ExecutorTesterFeedAndFetch, CPU) {
std::vector<Place> places;
CPUPlace cpu_place;
places.emplace_back(cpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
paddle::memory::Used(cpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
ASSERT_EQ(result.size(), inputs_.size());
for (size_t i = 0; i < result.size(); ++i) {
ASSERT_EQ(result[i].size(), inputs_[i].size());
for (size_t j = 0; j < result[i].size(); ++j) {
ASSERT_EQ(result[i][j], inputs_[i][j]);
}
}
}
}
#else
TEST_F(ExecutorTesterRandom, GPU) {
std::vector<Place> places;
GPUPlace gpu_place(0);
places.push_back(gpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
// If paddle is compiled with GPU, both CPU and GPU BuddyAllocator
// need to be used at first.
paddle::memory::Used(CPUPlace());
paddle::memory::Used(gpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
executor->Run(init_pdesc_, &GetGlobalScope(), 0);
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
}
}
TEST_F(ExecutorTesterFeedAndFetch, GPU) {
std::vector<Place> places;
GPUPlace gpu_place(0);
places.push_back(gpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
// If paddle is compiled with GPU, both CPU and GPU BuddyAllocator
// need to be used at first.
paddle::memory::Used(CPUPlace());
paddle::memory::Used(gpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
PADDLE_ENFORCE_EQ(result.size(), inputs_.size());
for (size_t i = 0; i < result.size(); ++i) {
PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size());
for (size_t j = 0; j < result[i].size(); ++j) {
PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]);
}
}
}
}
DECLARE_double(fraction_of_gpu_memory_to_use);
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
// Use less GPU memory for unittest.
FLAGS_fraction_of_gpu_memory_to_use = 0.25;
return RUN_ALL_TESTS();
}
#endif
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