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提交 bcaa8a3b 编写于 作者: M minqiyang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_production_dockerfile 

test=develop
上级 6274c0f7 6224e61f
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Showing with 1016 addition and 125 deletion
cmake/operators.cmake
浏览文件 @ bcaa8a3b
......@@ -109,7 +109,8 @@ function(op_library TARGET)
# Define operators that don't need pybind here.
foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op"
"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op")
"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op"
"fusion_transpose_flatten_concat_op")
if ("${TARGET}" STREQUAL "${manual_pybind_op}")
set(pybind_flag 1)
endif()
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ bcaa8a3b
......@@ -117,7 +117,7 @@ cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
if (NOT WIN32)
cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto)
cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto device_context)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler transfer_scope_cache)
else()
......
paddle/fluid/framework/executor.cc
浏览文件 @ bcaa8a3b
......@@ -392,8 +392,8 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
int64_t max_memory_size = GetEagerDeletionThreshold();
std::unique_ptr<GarbageCollector<Tensor>> gc;
// WhileOp would set keep_kids to false
// WhileGradOp would need the scopes created in WhileOp
// WhileOp would set keep_kids to true,
// because WhileGradOp needs the scopes created in WhileOp.
// Perhaps, we should not perform eager deletion in WhileOp
// The scopes and variables created by WhileOp would be deleted
// in WhileGradOp.
......
paddle/fluid/framework/transfer_scope_cache.cc
浏览文件 @ bcaa8a3b
......@@ -17,16 +17,28 @@
namespace paddle {
namespace framework {
// Holds all the transfer scope across the process.
std::unordered_map<size_t, Scope*>& global_transfer_data_cache() {
thread_local auto* x = new std::unordered_map<size_t, Scope*>;
typedef std::unordered_map<size_t, Scope*> map_t;
thread_local std::unique_ptr<map_t> x(new map_t);
return *x;
}
// Holds all the transfer scope for this thread.
std::unordered_set<Scope*>& global_transfer_scope_cache() {
thread_local auto* x = new std::unordered_set<Scope*>;
typedef std::unordered_set<Scope*> set_t;
thread_local std::unique_ptr<set_t> x(new set_t);
return *x;
}
// Try to create a transfer scope. If one cached scope has match the
// requirement, just return that one.
// Inputs:
// @type0: the source kernel type.
// @type1: the target kernel type.
// @scope: the execution scope of this op.
// Returns: A scope used to hold the transfer data across the different kernel
// type.
Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
const Scope* scope) {
Scope* new_scope{nullptr};
......@@ -46,27 +58,5 @@ Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
return new_scope;
}
void RemoveKidsFromTransferScopeCache(Scope* scope) {
auto it = global_transfer_scope_cache().find(scope);
if (it != global_transfer_scope_cache().end()) {
global_transfer_scope_cache().erase(it);
}
for (auto* s : scope->kids()) {
auto it = global_transfer_scope_cache().find(s);
if (it != global_transfer_scope_cache().end()) {
global_transfer_scope_cache().erase(it);
}
}
// remove global transfer data cache
auto& cache = global_transfer_data_cache();
for (auto it = cache.begin(); it != cache.end();) {
if (it->second == scope)
it = cache.erase(it);
else
it++;
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ bcaa8a3b
......@@ -4,6 +4,7 @@ endif()
# analysis and tensorrt must be added before creating static library,
# otherwise, there would be undefined reference to them in static library.
add_subdirectory(analysis)
add_subdirectory(utils)
if (TENSORRT_FOUND)
add_subdirectory(tensorrt)
endif()
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ bcaa8a3b
......@@ -30,7 +30,9 @@ cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager)
cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce)
cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS
lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config
analysis_config paddle_pass_builder zero_copy_tensor reset_tensor_array)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ bcaa8a3b
......@@ -31,6 +31,7 @@
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#endif
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -174,7 +175,6 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
inference::Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
......@@ -215,17 +215,29 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<int64_t>(ddim, place_);
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<float>(ddim, place_);
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
if (platform::is_cpu_place(place_)) {
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
}
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ bcaa8a3b
......@@ -24,6 +24,7 @@ limitations under the License. */
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -138,7 +139,6 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ != nullptr ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
......@@ -194,17 +194,30 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<int64_t>(ddim, place_);
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<float>(ddim, place_);
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
if (platform::is_cpu_place(place_)) {
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
}
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ bcaa8a3b
......@@ -46,8 +46,6 @@ if(WITH_GPU)
endif()
endif(NOT WIN32)
endif()
include_directories("D:/Paddle/")
include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ bcaa8a3b
......@@ -74,7 +74,7 @@ inference_analysis_api_test(test_analyzer_seq_conv1 ${SEQ_CONV1_INSTALL_DIR} ana
# ocr
set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr")
if (NOT EXISTS ${OCR_INSTALL_DIR})
inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Focr.tar.gz")
inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Focr.tar.gz")
endif()
inference_analysis_api_test(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
......@@ -88,31 +88,31 @@ inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet
# anakin
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# anakin rnn1
set(ANAKIN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/anakin")
set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn.anakin2.model.bin")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn_data.txt")
cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared SERIAL)
# anakin mobilenet
if(WITH_GPU)
set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet")
inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin")
cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
# anakin rnn1
set(ANAKIN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/anakin")
set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn.anakin2.model.bin")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn_data.txt")
cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared SERIAL)
# anakin mobilenet
if(WITH_GPU)
set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet")
inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin")
cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
endif()
if(WITH_GPU AND TENSORRT_FOUND)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
endif()
paddle/fluid/inference/utils/CMakeLists.txt 0 → 100644
浏览文件 @ bcaa8a3b
cc_library(benchmark SRCS benchmark.cc DEPS enforce)
cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
paddle/fluid/inference/utils/benchmark.cc 0 → 100644
浏览文件 @ bcaa8a3b
// 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/inference/utils/benchmark.h"
#include <sstream>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace inference {
std::string Benchmark::SerializeToString() const {
std::stringstream ss;
ss << "-----------------------------------------------------\n";
ss << "name\t";
ss << "batch_size\t";
ss << "num_threads\t";
ss << "latency\t";
ss << "qps";
ss << '\n';
ss << name_ << "\t";
ss << batch_size_ << "\t";
ss << num_threads_ << "\t";
ss << latency_ << "\t";
ss << 1000 / latency_;
ss << '\n';
return ss.str();
}
void Benchmark::PersistToFile(const std::string &path) const {
std::ofstream file(path, std::ios::app);
PADDLE_ENFORCE(file.is_open(), "Can not open %s to add benchmark", path);
file << SerializeToString();
file.flush();
file.close();
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/utils/benchmark.h 0 → 100644
浏览文件 @ bcaa8a3b
// 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 <fstream>
#include <iostream>
namespace paddle {
namespace inference {
/*
* Helper class to calculate the performance.
*/
struct Benchmark {
int batch_size() const { return batch_size_; }
void SetBatchSize(int x) { batch_size_ = x; }
int num_threads() const { return num_threads_; }
void SetNumThreads(int x) { num_threads_ = x; }
bool use_gpu() const { return use_gpu_; }
void SetUseGpu() { use_gpu_ = true; }
int latency() const { return latency_; }
void SetLatency(int x) { latency_ = x; }
const std::string& name() const { return name_; }
void SetName(const std::string& name) { name_ = name; }
std::string SerializeToString() const;
void PersistToFile(const std::string& path) const;
private:
bool use_gpu_{false};
int batch_size_{0};
int latency_;
int num_threads_{1};
std::string name_;
};
} // namespace inference
} // namespace paddle
paddle/fluid/inference/utils/benchmark_tester.cc 0 → 100644
浏览文件 @ bcaa8a3b
// 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/inference/utils/benchmark.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
using namespace paddle::inference;
TEST(Benchmark, basic) {
Benchmark benchmark;
benchmark.SetName("key0");
benchmark.SetBatchSize(10);
benchmark.SetUseGpu();
benchmark.SetLatency(220);
LOG(INFO) << "benchmark:\n" << benchmark.SerializeToString();
}
TEST(Benchmark, PersistToFile) {
Benchmark benchmark;
benchmark.SetName("key0");
benchmark.SetBatchSize(10);
benchmark.SetUseGpu();
benchmark.SetLatency(220);
benchmark.PersistToFile("1.log");
benchmark.PersistToFile("1.log");
benchmark.PersistToFile("1.log");
}
\ No newline at end of file
paddle/fluid/memory/allocation/retry_allocator_test.cc
浏览文件 @ bcaa8a3b
......@@ -41,7 +41,7 @@ TEST(RetryAllocator, RetryAllocator) {
size_t thread_num = 32;
size_t sleep_time = 40;
size_t extra_time = 2;
size_t extra_time = 10;
// Reserve to perform more tests in the future
std::vector<std::shared_ptr<Allocator>> allocators;
......
paddle/fluid/operators/fused/CMakeLists.txt
浏览文件 @ bcaa8a3b
include(operators)
register_operators()
register_operators(EXCLUDES fusion_transpose_flatten_concat_op)
if (WITH_GPU)
op_library(fusion_transpose_flatten_concat_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(fusion_transpose_flatten_concat);\n")
endif()
paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.cc 0 → 100644
浏览文件 @ bcaa8a3b
/* Copyright (c) 2016 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/operators/fused/fusion_transpose_flatten_concat_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class TransposeFlattenConcatFusionOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL,
"Inputs(X) of ConcatOp should be empty.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ConcatOp should not be null.");
auto ins = ctx->GetInputsDim("X");
const size_t n = ins.size();
PADDLE_ENFORCE_GT(n, 0, "Input tensors count should > 0.");
std::vector<int> trans_axis =
ctx->Attrs().Get<std::vector<int>>("trans_axis");
int flatten_axis = ctx->Attrs().Get<int>("flatten_axis");
int concat_axis = ctx->Attrs().Get<int>("concat_axis");
size_t x_rank = ins[0].size();
size_t trans_axis_size = trans_axis.size();
PADDLE_ENFORCE_EQ(x_rank, trans_axis_size,
"The input tensor's rank(%d) "
"should be equal to the permutation axis's size(%d)",
x_rank, trans_axis_size);
auto dims0 =
GetFlattenShape(flatten_axis, GetPermuteShape(trans_axis, ins[0]));
std::vector<int> out_dims(dims0);
for (size_t i = 1; i < n; i++) {
auto dimsi =
GetFlattenShape(flatten_axis, GetPermuteShape(trans_axis, ins[i]));
for (int j = 0; j < static_cast<int>(dims0.size()); j++) {
if (j == concat_axis) {
out_dims[concat_axis] += dimsi[j];
} else {
PADDLE_ENFORCE_EQ(out_dims[j], dimsi[j],
"After flatting, the %d-th dim should be save "
"except the specify axis.",
j);
}
}
}
if (out_dims[concat_axis] < 0) {
out_dims[concat_axis] = -1;
}
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
}
};
class TransposeFlattenConcatFusionOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"X",
"(Tensor) The input tensor, tensors with rank up to 6 are supported.")
.AsDuplicable();
AddOutput("Out", "(Tensor)The output tensor.");
AddAttr<std::vector<int>>(
"trans_axis",
"(vector<int>) A list of values, and the size of the list should be "
"the same with the input tensor rank. This operator permutes the input "
"tensor's axes according to the values given.");
AddAttr<int>("flatten_axis",
"(int)"
"Indicate up to which input dimensions (exclusive) should be"
"flattened to the outer dimension of the output. The value"
"for axis must be in the range [0, R], where R is the rank of"
"the input tensor. When axis = 0, the shape of the output"
"tensor is (1, (d_0 X d_1 ... d_n), where the shape of the"
"input tensor is (d_0, d_1, ... d_n).");
AddAttr<int>("concat_axis",
"The axis along which the input tensors will be concatenated. "
"It should be 0 or 1, since the tensor is 2D after flatting.");
AddComment(R"DOC(
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_transpose_flatten_concat,
ops::TransposeFlattenConcatFusionOp,
ops::TransposeFlattenConcatFusionOpMaker,
paddle::framework::EmptyGradOpMaker);
paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.cu.cc 0 → 100644
浏览文件 @ bcaa8a3b
/* Copyright (c) 2016 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/operators/fused/fusion_transpose_flatten_concat_op.h"
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
template <typename T>
class TransposeFlattenConcatFusionKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto odims = out->dims();
std::vector<int> trans_axis = ctx.Attr<std::vector<int>>("trans_axis");
int flatten_axis = ctx.Attr<int>("flatten_axis");
int concat_axis = ctx.Attr<int>("concat_axis");
int rank = ins[0]->dims().size();
// use at least 4D in cudnnTransformTensor
int max_dim = rank < 4 ? 4 : rank;
std::vector<int> stride_x(max_dim, 0);
std::vector<int> stride_y(max_dim, 0);
std::vector<int> dims_y(max_dim, 0);
cudnnTensorDescriptor_t in_desc;
cudnnTensorDescriptor_t out_desc;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&in_desc));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&out_desc));
cudnnDataType_t cudnn_dtype = CudnnDataType<T>::type;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
T* odata = out->data<T>();
for (size_t k = 0; k < ins.size(); ++k) {
auto perm_shape = GetPermuteShape(trans_axis, ins[k]->dims());
int osize = 1;
auto idims = ins[k]->dims();
for (int i = 0; i < rank; i++) {
stride_x[i] = 1;
for (int j = trans_axis[i] + 1; j < rank; j++) {
stride_x[i] *= idims[j];
}
dims_y[i] = perm_shape[i];
osize *= perm_shape[i];
}
stride_y[rank - 1] = 1;
for (int i = rank - 2; i >= 0; i--) {
if (((i + 1) == flatten_axis) && (concat_axis == 1)) {
stride_y[i] = odims[1];
} else {
stride_y[i] = stride_y[i + 1] * perm_shape[i + 1];
}
}
// Since concat is aftern flatten, the output is 2D tensor.
// If concat_axis is 0, each input's permutated tensor is continuous.
// If concat_axis is 1, the stride of 0-th dim of each input's
// permutated tensor is odims()[1].
for (int i = rank; i < max_dim; i++) {
stride_x[i] = 1;
stride_y[i] = 1;
dims_y[i] = 1;
}
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
in_desc, cudnn_dtype, max_dim, dims_y.data(), stride_x.data()));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
out_desc, cudnn_dtype, max_dim, dims_y.data(), stride_y.data()));
CUDNN_ENFORCE(platform::dynload::cudnnTransformTensor(
handle, CudnnDataType<T>::kOne(), in_desc,
static_cast<const void*>(ins[k]->data<T>()),
CudnnDataType<T>::kZero(), out_desc, static_cast<void*>(odata)));
if (concat_axis == 0) {
odata += osize;
} else {
auto flat_shape = GetFlattenShape(flatten_axis, perm_shape);
odata += flat_shape[1];
}
}
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(in_desc));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(out_desc));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(fusion_transpose_flatten_concat,
ops::TransposeFlattenConcatFusionKernel<float>,
ops::TransposeFlattenConcatFusionKernel<double>);
paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.h 0 → 100644
浏览文件 @ bcaa8a3b
/* Copyright (c) 2016 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. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
namespace paddle {
namespace operators {
inline std::vector<int32_t> GetPermuteShape(const std::vector<int>& axis,
const framework::DDim& in_dims) {
std::vector<int32_t> out_dims(in_dims.size());
for (size_t i = 0; i < axis.size(); i++) {
out_dims[i] = in_dims[axis[i]];
}
return out_dims;
}
inline std::vector<int32_t> GetFlattenShape(const int axis,
const std::vector<int>& in_dims) {
int64_t outer = 1, inner = 1;
for (int i = 0; i < static_cast<int>(in_dims.size()); ++i) {
if (i < axis) {
outer *= in_dims[i];
} else {
inner *= in_dims[i];
}
}
std::vector<int32_t> out_shape(2);
out_shape[0] = outer;
out_shape[1] = inner;
return out_shape;
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/lookup_sparse_table_op.cc
浏览文件 @ bcaa8a3b
......@@ -67,6 +67,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
framework::proto::VarType::FP32,
"The sparse table only support FP32");
w_t->Get(ids_t, out_t, true, is_test);
out_t->set_lod(ids_t.lod());
}
};
......
paddle/fluid/operators/sum_op.h
浏览文件 @ bcaa8a3b
......@@ -127,6 +127,9 @@ class SumKernel : public framework::OpKernel<T> {
math::scatter::MergeAdd<DeviceContext, T> merge_add;
merge_add(context.template device_context<DeviceContext>(), inputs,
out);
out->SyncIndex();
} else {
// no data, just set a empty out tensor.
out->mutable_value()->mutable_data<T>(framework::make_ddim({0}),
......
paddle/fluid/operators/tensor_array_to_tensor_op.cc
浏览文件 @ bcaa8a3b
......@@ -106,9 +106,9 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
out_inx_dim[0] = inx.size();
out_inx.Resize(out_inx_dim);
auto &local_scope = scope.NewScope();
std::string var_name = "out_index";
framework::Variable *tmp_index_var =
const_cast<framework::Scope &>(scope).Var(var_name);
framework::Variable *tmp_index_var = local_scope.Var(var_name);
auto &tmp_index_tensor =
*(tmp_index_var->GetMutable<paddle::framework::LoDTensor>());
tmp_index_tensor.Resize(out_inx_dim);
......@@ -128,12 +128,12 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
out_dims[axis] = out_dim_sum;
out.Resize(out_dims);
LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
// Invoke Reshape Op
LodTensorArray2LodTensorVector(local_scope, base_name, Input("X"), &names);
// Invoke concat Op
auto concat_op = framework::OpRegistry::CreateOp(
"concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
concat_op->Run(scope, place);
concat_op->Run(local_scope, place);
}
};
......
paddle/fluid/platform/dynload/cublas.cc
浏览文件 @ bcaa8a3b
......@@ -32,6 +32,9 @@ CUBLAS_BLAS_ROUTINE_EACH_R2(DEFINE_WRAP);
CUBLAS_BLAS_ROUTINE_EACH_R3(DEFINE_WRAP);
#endif
#ifdef CUBLAS_BLAS_ROUTINE_EACH_R4
CUBLAS_BLAS_ROUTINE_EACH_R4(DEFINE_WRAP);
#endif
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/cublas.h
浏览文件 @ bcaa8a3b
......@@ -90,23 +90,33 @@ CUBLAS_BLAS_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
// APIs available after CUDA 8.0
#if CUDA_VERSION >= 8000
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmEx);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgemmStridedBatched);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgemmStridedBatched);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasCgemmStridedBatched);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasZgemmStridedBatched);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasHgemmStridedBatched);
#define CUBLAS_BLAS_ROUTINE_EACH_R2(__macro) \
__macro(cublasGemmEx); \
__macro(cublasSgemmStridedBatched); \
__macro(cublasDgemmStridedBatched); \
__macro(cublasCgemmStridedBatched); \
__macro(cublasZgemmStridedBatched); \
__macro(cublasHgemmStridedBatched);
CUBLAS_BLAS_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
// APIs available after CUDA 9.0
#if CUDA_VERSION >= 9000
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSetMathMode);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGetMathMode);
#define CUBLAS_BLAS_ROUTINE_EACH_R3(__macro) \
__macro(cublasSetMathMode); \
__macro(cublasGetMathMode);
CUBLAS_BLAS_ROUTINE_EACH_R3(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
// APIs available after CUDA 9.1
#if CUDA_VERSION >= 9010
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmBatchedEx);
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmStridedBatchedEx);
#define CUBLAS_BLAS_ROUTINE_EACH_R4(__macro) \
__macro(cublasGemmBatchedEx); \
__macro(cublasGemmStridedBatchedEx);
CUBLAS_BLAS_ROUTINE_EACH_R4(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
#undef DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP
......
paddle/testing/paddle_gtest_main.cc
浏览文件 @ bcaa8a3b
......@@ -31,6 +31,11 @@ int main(int argc, char** argv) {
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
new_argv.push_back(
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,allocator_strategy"));
#elif __clang__
new_argv.push_back(
strdup("--tryfromenv=use_mkldnn,initial_cpu_memory_in_"
"mb,allocator_strategy"));
new_argv.push_back(strdup("--undefok=use_mkldnn,initial_cpu_memory_in_mb"));
#else
new_argv.push_back(
strdup("--tryfromenv=use_pinned_memory,use_mkldnn,initial_cpu_memory_in_"
......
python/paddle/fluid/__init__.py
浏览文件 @ bcaa8a3b
......@@ -91,6 +91,7 @@ def __bootstrap__():
"""
import sys
import os
import platform
from . import core
in_test = 'unittest' in sys.modules
......@@ -110,14 +111,17 @@ def __bootstrap__():
print('PLEASE USE OMP_NUM_THREADS WISELY.', file=sys.stderr)
os.environ['OMP_NUM_THREADS'] = str(num_threads)
sysstr = platform.system()
read_env_flags = [
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
'use_mkldnn', 'use_ngraph', 'initial_cpu_memory_in_mb',
'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
"dist_threadpool_size", 'eager_delete_tensor_gb', 'allocator_strategy',
'check_nan_inf', 'benchmark', 'eager_delete_scope', 'use_mkldnn',
'use_ngraph', 'initial_cpu_memory_in_mb', 'init_allocated_mem',
'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
'eager_delete_tensor_gb', 'allocator_strategy',
'reader_queue_speed_test_mode', 'print_sub_graph_dir'
]
if 'Darwin' not in sysstr:
read_env_flags.append('use_pinned_memory')
if os.name != 'nt':
read_env_flags.append('warpctc_dir')
read_env_flags.append('cpu_deterministic')
......
python/paddle/fluid/contrib/utils/__init__.py
浏览文件 @ bcaa8a3b
......@@ -13,8 +13,10 @@
# limitations under the License.
from __future__ import print_function
from . import lookup_table_utils
from .lookup_table_utils import *
from . import hdfs_utils
from .hdfs_utils import *
__all__ = lookup_table_utils.__all__
__all__ = hdfs_utils.__all__
python/paddle/fluid/contrib/utils/lookup_table_utils.py 0 → 100644
浏览文件 @ bcaa8a3b
# 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.
from __future__ import print_function
import os
import time
import logging
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import io
from paddle.fluid import Program
__all__ = [
"load_inference_model", "load_persistable_vars",
"convert_dist_to_sparse_program"
]
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
_logger = logging.getLogger("lookup_table_utils")
_logger.setLevel(logging.INFO)
model_filename = "__model__"
lookup_table_dir = "__lookup_table__"
def __insert_lookup_sparse_table_op(main_program, idx, ids, w, out):
main_program.global_block()._insert_op(
index=idx,
type="lookup_sparse_table",
inputs={"Ids": [ids],
"W": [w]},
outputs={"Out": [out]},
attrs={
"is_distributed": False,
"is_sparse": True,
"grad_inplace": False
})
def __get_prefetch_op_tuples(main_program):
# current lookup tables op is split_ids->prefetch->merge_ids
prefetch_op_tuples = None
op_types = [op.type for op in main_program.global_block().ops]
for i in range(len(op_types)):
if op_types[i] == "prefetch":
if op_types[i - 1] == "split_ids" and op_types[i +
1] == "merge_ids":
split_ids_op_id = i - 1
split_ids_inputs = main_program.global_block().ops[i - 1].input(
"Ids")
prefetch_op_inputs = main_program.global_block().ops[i].input(
"X")
prefetch_op_outputs = main_program.global_block().ops[i].output(
"Out")
merge_ids_outputs = main_program.global_block().ops[
i + 1].output("Out")
need_delete_vars = []
need_delete_vars.extend(prefetch_op_inputs)
need_delete_vars.extend(prefetch_op_outputs)
prefetch_op_tuples = (split_ids_op_id, split_ids_inputs,
merge_ids_outputs, need_delete_vars)
break
return prefetch_op_tuples
def convert_dist_to_sparse_program(main_program):
if not main_program._distributed_lookup_table:
_logger.warn(
"There are no distributed lookup tables need to be converted")
return
# create table param and grad var in pserver program
origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
emb_var = main_program._distributed_lookup_table
main_program.global_block()._rename_var(emb_var, origin_emb_var)
origin_param_var = main_program.global_block().vars[origin_emb_var]
param_var = main_program.global_block().create_var(
name=emb_var,
shape=origin_param_var.shape,
dtype=origin_param_var.dtype,
type=core.VarDesc.VarType.SELECTED_ROWS,
persistable=True)
# parameter must be selected rows
param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
main_program._sync_with_cpp()
prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
split_ids_id = prefetch_op_tuples[0]
for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
main_program.global_block()._remove_op(idx)
main_program.desc.flush()
in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
for in_out_pair in in_out_pairs:
idx = split_ids_id
ids = main_program.global_block().vars[in_out_pair[0]]
out = main_program.global_block().vars[in_out_pair[1]]
__insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
main_program.desc.flush()
return main_program
def load_persistable_vars(executor, dirname, program, lookup_table_var):
def _is_checkpoint_var(exclude_fluid_vars=None):
"""
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var(Variable)
"""
if exclude_fluid_vars is None:
exclude_fluid_vars = []
def is_valid(var):
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.RAW:
return False
# @GRAD are named for gradient variables, checkpoint will not save it.
if "@GRAD" in var.name:
return False
# .trainer_ are named for distribute train variables, checkpoint will not save it.
if ".trainer_" in var.name:
return False
# .block is named for distribute train variables, checkpoint will not save it.
if ".block" in var.name:
return False
if "tmp_" in var.name:
return False
if var.name in exclude_fluid_vars:
return False
return var.persistable
return is_valid
def _load_lookup_table_vars(executor, dirname, main_program,
lookup_table_vars):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
lookup_table_dirname = os.path.join(dirname, lookup_table_dir)
emb_var_name = lookup_table_vars[0]
emb_var = main_program.global_block().var(emb_var_name)
emb_files = []
for emb_name in os.listdir(lookup_table_dirname):
if emb_var_name in emb_name:
emb_files.append(emb_name)
convert_program = Program()
global_block = convert_program.global_block()
emb_var = global_block.create_var(
name=emb_var.name,
shape=emb_var.shape,
dtype=emb_var.dtype,
type=core.VarDesc.VarType.SELECTED_ROWS,
persistable=True)
emb_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
sums = []
for i, emb_file in enumerate(emb_files):
var_name = "{}_{}".format(emb_var.name, i)
param_var = global_block.create_var(
name=var_name,
shape=emb_var.shape,
dtype=emb_var.dtype,
type=core.VarDesc.VarType.SELECTED_ROWS,
persistable=True)
param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
global_block.append_op(
type='load',
inputs={},
outputs={'Out': [param_var]},
attrs={
'file_path': os.path.join(lookup_table_dirname, var_name)
})
sums.append(param_var)
global_block.append_op(
type='sum', inputs={"X": sums}, outputs={'Out': emb_var}, attrs={})
global_block.append_op(type='delete_var', inputs={'X': sums})
executor.run(convert_program)
_logger.info("Start Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
lookup_table_vars = [lookup_table_var]
io.load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var(lookup_table_vars),
filename=None)
_load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
_logger.info("Finish Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
def load_inference_model(dirname, executor, lookup_table_var_name):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
local_model = os.path.join(dirname, model_filename)
with open(local_model, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
if not core._is_program_version_supported(program._version()):
raise ValueError("Unsupported program version: %d\n" %
program._version())
# Binary data also need version.
load_persistable_vars(executor, dirname, program, lookup_table_var_name)
feed_target_names = program.desc.get_feed_target_names()
fetch_target_names = program.desc.get_fetch_target_names()
fetch_targets = [
program.global_block().var(name) for name in fetch_target_names
]
return [program, feed_target_names, fetch_targets]
python/paddle/fluid/framework.py
浏览文件 @ bcaa8a3b
......@@ -1698,6 +1698,7 @@ class Program(object):
p._copy_param_info_from(self)
p._copy_data_info_from(self)
p._copy_dist_param_info_from(self)
return p
def _prune(self, targets):
......@@ -1938,6 +1939,25 @@ class Program(object):
"program, with represent the same topology")
self.global_block()._copy_param_info_from(other.global_block())
def _copy_dist_param_info_from(self, other):
"""
Copy the information of distributed information from other program.
Args:
other(Program): Other program
Returns:
None
"""
if not isinstance(other, Program):
raise TypeError("_copy_dist_param_info_from should be invoked with "
"Program")
self._is_distributed = other._is_distributed
self._is_chief = other._is_chief
self._slice_vars_and_attrs = other._slice_vars_and_attrs
self._endpoints = other._endpoints
self._distributed_lookup_table = other._distributed_lookup_table
def _copy_data_info_from(self, other):
"""
Copy the information of data variables from other program.
......
python/paddle/fluid/io.py
浏览文件 @ bcaa8a3b
......@@ -165,6 +165,7 @@ def save_vars(executor,
save_vars(
executor,
main_program=main_program,
dirname=dirname,
vars=list(filter(predicate, main_program.list_vars())),
filename=filename)
......@@ -172,11 +173,18 @@ def save_vars(executor,
save_program = Program()
save_block = save_program.global_block()
if main_program is None:
main_program = default_main_program()
if not isinstance(main_program, Program):
raise TypeError("program should be as Program type or None")
save_var_map = {}
for each_var in vars:
# NOTE: don't save the variable which type is RAW
if each_var.type == core.VarDesc.VarType.RAW:
continue
if each_var.name == main_program._distributed_lookup_table:
continue
new_var = _clone_var_in_block_(save_block, each_var)
if filename is None:
save_block.append_op(
......@@ -198,6 +206,16 @@ def save_vars(executor,
outputs={},
attrs={'file_path': os.path.join(dirname, filename)})
# if there is lookup table, the trainer 0 will notify all pserver to save.
if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
lookup_table_filename = os.path.join(dirname, "__lookup_table__")
attrs = {}
attrs['epmap'] = main_program._endpoints
attrs['dir'] = lookup_table_filename
attrs['lookup_table'] = main_program._distributed_lookup_table
save_block.append_op(
type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
executor.run(save_program)
......@@ -379,11 +397,22 @@ def load_vars(executor,
load_prog = Program()
load_block = load_prog.global_block()
if main_program is None:
main_program = default_main_program()
if not isinstance(main_program, Program):
raise TypeError("program should be as Program type or None")
load_slice_vars = []
for each_var in main_program._slice_vars_and_attrs:
load_slice_vars.append(each_var[2].name)
load_var_map = {}
for each_var in vars:
assert isinstance(each_var, Variable)
if each_var.type == core.VarDesc.VarType.RAW:
continue
if each_var.name in load_slice_vars:
continue
new_var = _clone_var_in_block_(load_block, each_var)
if filename is None:
load_block.append_op(
......@@ -406,9 +435,6 @@ def load_vars(executor,
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(load_prog)
if main_program is None:
main_program = default_main_program()
# load slice vars on pserver, if have it.
_load_slice_up_vars(executor, dirname,
main_program._slice_vars_and_attrs)
......@@ -618,13 +644,6 @@ def save_inference_model(dirname,
if main_program is None:
main_program = default_main_program()
# if there is lookup table, the trainer 0 will notify all pserver to save.
if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
lookup_table_filename = os.path.join(dirname, "__lookup_table__")
_save_lookup_tables_by_notify(executor, lookup_table_filename,
main_program._distributed_lookup_table,
main_program._endpoints)
# when a pserver and a trainer running on the same machine, mkdir may conflict
try:
os.makedirs(dirname)
......@@ -642,6 +661,9 @@ def save_inference_model(dirname,
# it can only be loaded for inference directly. If it's false, the whole
# original program and related meta are saved so that future usage can be
# more flexible.
origin_program = main_program.clone()
if export_for_deployment:
main_program = main_program.clone()
global_block = main_program.global_block()
......@@ -666,8 +688,11 @@ def save_inference_model(dirname,
with open(model_basename + ".main_program", "wb") as f:
f.write(main_program.desc.serialize_to_string())
main_program._copy_dist_param_info_from(origin_program)
if params_filename is not None:
params_filename = os.path.basename(params_filename)
save_persistables(executor, dirname, main_program, params_filename)
......@@ -897,6 +922,9 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
slice_var = var_tuple[2]
end = start + slice_var.shape[0]
orig_var_name = orig_var.name
orig_var.name = "{}.origin".format(orig_var_name)
clone_orig_var = load_block.create_var(
name=orig_var.name,
type=orig_var.type,
......@@ -915,7 +943,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
type='load',
inputs={},
outputs={'Out': [clone_orig_var]},
attrs={'file_path': os.path.join(dirname, clone_orig_var.name)})
attrs={'file_path': os.path.join(dirname, orig_var_name)})
load_block.append_op(
type="slice",
inputs={'Input': clone_orig_var},
......@@ -924,6 +952,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
'starts': [start],
'ends': [end]})
need_delete_vars.append(clone_orig_var)
load_block.append_op(
type='delete_var',
inputs={'X': need_delete_vars}, )
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ bcaa8a3b
......@@ -896,9 +896,10 @@ def array_to_lod_tensor(x, table):
def increment(x, value=1.0, in_place=True):
"""
This function performs an operation that increments each value in the
This function performs an operation that increments the value in the
input :math:`x` by an amount: :math:`value` as mentioned in the input
parameter. This operation is performed in-place by default.
parameter. This operation is performed in-place by default. Notice that
the number of elements in :math:`x` must be equal to 1.
Args:
x (Variable|list): The tensor that has the input values.
......@@ -911,7 +912,8 @@ def increment(x, value=1.0, in_place=True):
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
data = fluid.layers.data(name='data', shape=[1], dtype='float32',
append_batch_size=False)
data = fluid.layers.increment(x=data, value=3.0, in_place=True)
"""
helper = LayerHelper("increment", **locals())
......
python/paddle/fluid/layers/nn.py
浏览文件 @ bcaa8a3b
......@@ -6972,18 +6972,18 @@ def prelu(x, mode, param_attr=None, name=None):
"""
Equation:
y = \max(0, x) + alpha \min(0, x)
y = \max(0, x) + alpha * \min(0, x)
Args:
x (Variable): The input tensor.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
mode (string): The mode for weight sharing
all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
mode (string): The mode for weight sharing. It supports all, channel
and element. all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
......@@ -6992,7 +6992,7 @@ def prelu(x, mode, param_attr=None, name=None):
.. code-block:: python
x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
mode = 'channel'
output = fluid.layers.prelu(x,mode)
"""
......
python/paddle/fluid/tests/unittests/test_fusion_transpose_flatten_concat_op.py 0 → 100644
浏览文件 @ bcaa8a3b
# 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.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
class TestFusionTransposeFlattenConcationOp(OpTest):
def setUp(self):
self.init_test_case()
self.op_type = "fusion_transpose_flatten_concat"
ins = []
flats = []
for i in range(len(self.shapes)):
in_shape = self.shapes[i]
a = np.random.random(in_shape).astype("float32")
ins.append(("x%d" % i, a))
b = a.transpose(self.trans_axis)
flat_shape = (np.prod(b.shape[:self.flatten_axis]),
np.prod(b.shape[self.flatten_axis:]))
c = b.reshape(flat_shape)
flats.append(c)
out = np.concatenate(flats, axis=self.concat_axis)
self.inputs = {'X': ins}
self.attrs = {
'trans_axis': list(self.trans_axis),
'flatten_axis': self.flatten_axis,
'concat_axis': self.concat_axis
}
self.outputs = {'Out': out}
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, 1e-6)
else:
pass
def init_test_case(self):
self.shapes = [(3, 4, 17, 17), (3, 8, 7, 7), (3, 12, 5, 5)]
self.trans_axis = (0, 2, 3, 1)
self.flatten_axis = 1
self.concat_axis = 1
class TestCase1(TestFusionTransposeFlattenConcationOp):
def init_test_case(self):
self.shapes = [(3, 4, 18, 17), (3, 8, 18, 7), (6, 12, 9, 5)]
self.trans_axis = (0, 2, 3, 1)
self.flatten_axis = 2
self.concat_axis = 1
class TestCase2(TestFusionTransposeFlattenConcationOp):
def init_test_case(self):
self.shapes = [(3, 8, 20, 17), (3, 8, 19, 17), (3, 8, 40, 17)]
self.trans_axis = (0, 2, 3, 1)
self.flatten_axis = 2
self.concat_axis = 0
class TestCase3(TestFusionTransposeFlattenConcationOp):
def init_test_case(self):
self.shapes = [(3, 8, 20, 17), (3, 8, 19, 17), (3, 8, 40, 17)]
self.trans_axis = (0, 3, 2, 1)
self.flatten_axis = 1
self.concat_axis = 1
class TestCase4(TestFusionTransposeFlattenConcationOp):
def init_test_case(self):
self.shapes = [(3, 8, 9, 17), (8, 3, 9, 17), (4, 6, 9, 17)]
self.trans_axis = (0, 2, 1, 3)
self.flatten_axis = 3
self.concat_axis = 1
class TestCase5(TestFusionTransposeFlattenConcationOp):
def init_test_case(self):
self.shapes = [(3, 8, 9, 17, 2), (3, 8, 2, 17, 9), (3, 17, 9, 8, 2)]
self.trans_axis = (0, 2, 1, 4, 3)
self.flatten_axis = 1
self.concat_axis = 1
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ bcaa8a3b
......@@ -644,6 +644,9 @@ in a single call.")
else:
recv_inputs.append(single_trainer_var)
self._slice_params_and_optimizes = self._get_slice_vars_and_attrs(
endpoint)
# step 3
# Create a union-find data structure from optimize ops,
# If two ops are connected, we could add these two ops
......@@ -766,7 +769,7 @@ in a single call.")
grad_to_block_id, merged_var,
lr_ops)
# dedup grad to ids list
# dedup grad to ids list
grad_to_block_id = list(set(grad_to_block_id))
# append global ops
if global_ops:
......@@ -827,8 +830,8 @@ in a single call.")
attrs=attrs)
# add distributed attrs
pserver_program._slice_vars_and_attrs = self._get_slice_vars_and_attrs(
endpoint)
pserver_program._slice_vars_and_attrs = list(
self._slice_params_and_optimizes.values())
pserver_program._sync_with_cpp()
# save pserver program to generate pserver side startup relatively.
......@@ -941,12 +944,12 @@ to transpile() call.")
outputs={"Out": startup_tmpvar})
# add slice vars
s_prog._slice_vars_and_attrs = self._get_slice_vars_and_attrs(endpoint)
s_prog._slice_vars_and_attrs = pserver_program._slice_vars_and_attrs
return s_prog
def _get_slice_vars_and_attrs(self, endpoint):
slice_vars_and_attrs = []
slice_vars_and_attrs = {}
block_suffix = "block"
for param in self.param_grad_ep_mapping[endpoint]["params"]:
orig_var_name, block_name, _ = self._get_varname_parts(param.name)
......@@ -960,8 +963,7 @@ to transpile() call.")
slice_vars = self.param_var_mapping[orig_var_name]
for slice_var in slice_vars[:block_idx]:
skip_dim0 += slice_var.shape[0]
slice_vars_and_attrs.append([orig_var, skip_dim0, param])
slice_vars_and_attrs[param.name] = [orig_var, skip_dim0, param]
return slice_vars_and_attrs
# ====================== private transpiler functions =====================
......@@ -1662,10 +1664,10 @@ to transpile() call.")
if key in ["Param", "Grad", "LearningRate"]:
continue
var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
param_var = new_inputs["Param"]
# update accumulator variable shape
param_shape = new_inputs["Param"].shape
new_shape = self._get_optimizer_input_shape(opt_op.type, key,
var.shape, param_shape)
new_shape = self._get_optimizer_input_shape(
opt_op.type, key, var.shape, param_var.shape)
tmpvar = pserver_block.create_var(
name=var.name,
persistable=var.persistable,
......@@ -1673,6 +1675,13 @@ to transpile() call.")
shape=new_shape)
new_inputs[key] = tmpvar
# var shape been changed
if new_shape != var.shape:
slice_var_args = self._slice_params_and_optimizes[
param_var.name]
self._slice_params_and_optimizes[
var.name] = [var, slice_var_args[1], tmpvar]
# change output's ParamOut variable
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, opt_op)
......
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