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Add inferface to change the feed/fetch_holder_name.

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fea/docker_cudnn7
Liu Yiqun 7 years ago
parent e359b24a85
commit 9cba062252
12 changed files with 123 additions and 55 deletions
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3
paddle/fluid/framework/block_desc.h
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@ -17,6 +17,7 @@ limitations under the License. */
#include <deque>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <vector>
@ -96,6 +97,8 @@ class BlockDesc {
*/
void RemoveOp(size_t s, size_t e);
void RemoveVar(const std::string &name) { vars_.erase(name); }
std::vector<OpDesc *> AllOps() const;
size_t OpSize() const { return ops_.size(); }

46
paddle/fluid/framework/program_desc.cc
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@ -85,9 +85,9 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
}
const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
BlockDesc *global_block = blocks_[0].get();
auto &global_block = Block(0);
std::vector<std::string> feed_target_names;
for (auto *op : global_block->AllOps()) {
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFeedOpType) {
feed_target_names.insert(feed_target_names.begin(), op->Output("Out")[0]);
}
@ -96,9 +96,9 @@ const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
}
const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
BlockDesc *global_block = blocks_[0].get();
auto &global_block = Block(0);
std::vector<std::string> fetch_target_names;
for (auto *op : global_block->AllOps()) {
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFetchOpType) {
fetch_target_names.push_back(op->Input("X")[0]);
}
@ -106,5 +106,43 @@ const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
return fetch_target_names;
}
void ProgramDesc::SetFeedHolderName(const std::string &feed_holder_name) {
auto *global_block = MutableBlock(0);
int index = 0;
for (auto *op : global_block->AllOps()) {
if (op->Type() == kFeedOpType) {
// Unify the input's name of all feed_ops to feed_holder_name
global_block->RemoveVar(op->Input("X")[0]);
op->SetInput("X", {feed_holder_name});
op->SetAttr("col", {index});
op->CheckAttrs();
index++;
}
}
auto *feed_holder = global_block->Var(feed_holder_name);
feed_holder->SetType(proto::VarType::FEED_MINIBATCH);
feed_holder->SetPersistable(true);
}
void ProgramDesc::SetFetchHolderName(const std::string &fetch_holder_name) {
auto *global_block = MutableBlock(0);
int index = 0;
for (auto *op : global_block->AllOps()) {
if (op->Type() == kFetchOpType) {
// Unify the output's name of all fetch_ops to fetch_holder_name
global_block->RemoveVar(op->Output("Out")[0]);
op->SetOutput("Out", {fetch_holder_name});
op->SetAttr("col", {index});
op->CheckAttrs();
index++;
}
}
auto *fetch_holder = global_block->Var(fetch_holder_name);
fetch_holder->SetType(proto::VarType::FETCH_LIST);
fetch_holder->SetPersistable(true);
}
} // namespace framework
} // namespace paddle

4
paddle/fluid/framework/program_desc.h
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@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/framework.pb.h"
@ -55,6 +56,9 @@ class ProgramDesc {
const std::vector<std::string> GetFeedTargetNames();
const std::vector<std::string> GetFetchTargetNames();
void SetFeedHolderName(const std::string &feed_holder_name);
void SetFetchHolderName(const std::string &fetch_holder_name);
private:
proto::ProgramDesc desc_;

2
paddle/fluid/inference/tests/book/test_inference_image_classification.cc
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@ -35,7 +35,7 @@ TEST(inference, image_classification) {
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [0.0, 1.0].
SetupTensor<float>(input,
SetupTensor<float>(&input,
{FLAGS_batch_size, 3, 32, 32},
static_cast<float>(0),
static_cast<float>(1));

16
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
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@ -36,35 +36,35 @@ TEST(inference, label_semantic_roles) {
int64_t predicate_dict_len = 3162;
int64_t mark_dict_len = 2;
SetupLoDTensor(word,
SetupLoDTensor(&word,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(predicate,
SetupLoDTensor(&predicate,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(predicate_dict_len - 1));
SetupLoDTensor(ctx_n2,
SetupLoDTensor(&ctx_n2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_n1,
SetupLoDTensor(&ctx_n1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_0,
SetupLoDTensor(&ctx_0,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p1,
SetupLoDTensor(&ctx_p1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p2,
SetupLoDTensor(&ctx_p2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(mark,
SetupLoDTensor(&mark,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(mark_dict_len - 1));

2
paddle/fluid/inference/tests/book/test_inference_recognize_digits.cc
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@ -35,7 +35,7 @@ TEST(inference, recognize_digits) {
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [-1.0, 1.0].
SetupTensor<float>(input,
SetupTensor<float>(&input,
{FLAGS_batch_size, 1, 28, 28},
static_cast<float>(-1),
static_cast<float>(1));

14
paddle/fluid/inference/tests/book/test_inference_recommender_system.cc
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@ -36,25 +36,25 @@ TEST(inference, recommender_system) {
// Use the first data from paddle.dataset.movielens.test() as input
std::vector<int64_t> user_id_data = {1};
SetupTensor<int64_t>(user_id, {batch_size, 1}, user_id_data);
SetupTensor<int64_t>(&user_id, {batch_size, 1}, user_id_data);
std::vector<int64_t> gender_id_data = {1};
SetupTensor<int64_t>(gender_id, {batch_size, 1}, gender_id_data);
SetupTensor<int64_t>(&gender_id, {batch_size, 1}, gender_id_data);
std::vector<int64_t> age_id_data = {0};
SetupTensor<int64_t>(age_id, {batch_size, 1}, age_id_data);
SetupTensor<int64_t>(&age_id, {batch_size, 1}, age_id_data);
std::vector<int64_t> job_id_data = {10};
SetupTensor<int64_t>(job_id, {batch_size, 1}, job_id_data);
SetupTensor<int64_t>(&job_id, {batch_size, 1}, job_id_data);
std::vector<int64_t> movie_id_data = {783};
SetupTensor<int64_t>(movie_id, {batch_size, 1}, movie_id_data);
SetupTensor<int64_t>(&movie_id, {batch_size, 1}, movie_id_data);
std::vector<int64_t> category_id_data = {10, 8, 9};
SetupLoDTensor<int64_t>(category_id, {3, 1}, {{0, 3}}, category_id_data);
SetupLoDTensor<int64_t>(&category_id, {3, 1}, {{0, 3}}, category_id_data);
std::vector<int64_t> movie_title_data = {1069, 4140, 2923, 710, 988};
SetupLoDTensor<int64_t>(movie_title, {5, 1}, {{0, 5}}, movie_title_data);
SetupLoDTensor<int64_t>(&movie_title, {5, 1}, {{0, 5}}, movie_title_data);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&user_id);

4
paddle/fluid/inference/tests/book/test_inference_rnn_encoder_decoder.cc
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@ -33,9 +33,9 @@ TEST(inference, rnn_encoder_decoder) {
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(
word_data, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
&word_data, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(
trg_word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
&trg_word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word_data);

2
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
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@ -33,7 +33,7 @@ TEST(inference, understand_sentiment) {
paddle::framework::LoD lod{{0, 4, 10}};
int64_t word_dict_len = 5147;
SetupLoDTensor(words,
SetupLoDTensor(&words,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));

8
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
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@ -33,10 +33,10 @@ TEST(inference, word2vec) {
paddle::framework::LoD lod{{0, 1}};
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&first_word);

53
paddle/fluid/inference/tests/test_helper.h
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@ -12,58 +12,63 @@ 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. */
#pragma once
#include <time.h>
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/profiler.h"
template <typename T>
void SetupTensor(paddle::framework::LoDTensor& input,
void SetupTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims,
T lower,
T upper) {
srand(time(0));
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] =
(static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
lower;
const T lower,
const T upper) {
T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
unsigned int seed = time(NULL);
for (int i = 0; i < input->numel(); ++i) {
input_ptr[i] = (static_cast<T>(rand_r(&seed)) / static_cast<T>(RAND_MAX)) *
(upper - lower) +
lower;
}
}
template <typename T>
void SetupTensor(paddle::framework::LoDTensor& input,
void SetupTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims,
std::vector<T>& data) {
const std::vector<T>& data) {
CHECK_EQ(paddle::framework::product(dims), static_cast<int64_t>(data.size()));
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
memcpy(input_ptr, data.data(), input.numel() * sizeof(T));
T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
memcpy(input_ptr, data.data(), input->numel() * sizeof(T));
}
template <typename T>
void SetupLoDTensor(paddle::framework::LoDTensor& input,
paddle::framework::LoD& lod,
T lower,
T upper) {
input.set_lod(lod);
void SetupLoDTensor(paddle::framework::LoDTensor* input,
const paddle::framework::LoD& lod,
const T lower,
const T upper) {
input->set_lod(lod);
int dim = lod[0][lod[0].size() - 1];
SetupTensor<T>(input, {dim, 1}, lower, upper);
}
template <typename T>
void SetupLoDTensor(paddle::framework::LoDTensor& input,
void SetupLoDTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims,
paddle::framework::LoD lod,
std::vector<T>& data) {
const std::vector<T>& data) {
const size_t level = lod.size() - 1;
CHECK_EQ(dims[0], static_cast<int64_t>((lod[level]).back()));
input.set_lod(lod);
input->set_lod(lod);
SetupTensor<T>(input, dims, data);
}
template <typename T>
void CheckError(paddle::framework::LoDTensor& output1,
paddle::framework::LoDTensor& output2) {
void CheckError(const paddle::framework::LoDTensor& output1,
const paddle::framework::LoDTensor& output2) {
// Check lod information
EXPECT_EQ(output1.lod(), output2.lod());
@ -91,7 +96,7 @@ void CheckError(paddle::framework::LoDTensor& output1,
template <typename Place>
void TestInference(const std::string& dirname,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const int repeat = 1,
const bool is_combined = false) {
// 1. Define place, executor, scope

24
paddle/fluid/inference/tests/test_multi_thread_helper.h
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@ -12,6 +12,8 @@ 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. */
#pragma once
#include <thread>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/io.h"
@ -20,13 +22,23 @@ void ThreadedRunInference(
std::unique_ptr<paddle::framework::ProgramDesc>& inference_program,
paddle::framework::Executor& executor,
paddle::framework::Scope* scope,
const int thread_id,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
auto copy_program = std::unique_ptr<paddle::framework::ProgramDesc>(
new paddle::framework::ProgramDesc(*inference_program));
std::string feed_holder_name = "feed_" + paddle::string::to_string(thread_id);
std::string fetch_holder_name =
"fetch_" + paddle::string::to_string(thread_id);
copy_program->SetFeedHolderName(feed_holder_name);
copy_program->SetFetchHolderName(fetch_holder_name);
// 3. Get the feed_target_names and fetch_target_names
const std::vector<std::string>& feed_target_names =
inference_program->GetFeedTargetNames();
copy_program->GetFeedTargetNames();
const std::vector<std::string>& fetch_target_names =
inference_program->GetFetchTargetNames();
copy_program->GetFetchTargetNames();
// 4. Prepare inputs: set up maps for feed targets
std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
@ -42,7 +54,12 @@ void ThreadedRunInference(
}
// 6. Run the inference program
executor.Run(*inference_program, scope, feed_targets, fetch_targets);
executor.Run(*copy_program,
scope,
feed_targets,
fetch_targets,
feed_holder_name,
fetch_holder_name);
}
template <typename Place>
@ -66,6 +83,7 @@ void TestMultiThreadInference(
std::ref(inference_program),
std::ref(executor),
scope,
i,
std::ref(cpu_feeds[i]),
std::ref(cpu_fetchs[i])));
}

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