Merge remote-tracking branch 'ups/develop' into feature/op/fusion_gru

createGenDocLib
tensor-tang 7 years ago
commit d9bf73f3ab

@ -2,6 +2,11 @@ if (NOT WITH_ANAKIN)
return()
endif()
option(ANAKIN_ENABLE_OP_TIMER "Get more detailed information with Anakin op time" OFF)
if(ANAKIN_ENABLE_OP_TIMER)
add_definitions(-DPADDLE_ANAKIN_ENABLE_OP_TIMER)
endif()
INCLUDE(ExternalProject)
set(ANAKIN_SOURCE_DIR ${THIRD_PARTY_PATH}/anakin)
# the anakin install dir is only default one now
@ -11,13 +16,21 @@ set(ANAKIN_LIBRARY ${ANAKIN_INSTALL_DIR})
set(ANAKIN_SHARED_LIB ${ANAKIN_LIBRARY}/libanakin.so)
set(ANAKIN_SABER_LIB ${ANAKIN_LIBRARY}/libanakin_saber_common.so)
# TODO(luotao): ANAKIN_MODLE_URL will move to demo ci later.
set(ANAKIN_MODLE_URL "http://paddle-inference-dist.bj.bcebos.com/mobilenet_v2.anakin.bin")
# TODO(luotao): ANAKIN_MODLE_URL etc will move to demo ci later.
set(INFERENCE_URL "http://paddle-inference-dist.bj.bcebos.com")
set(ANAKIN_MODLE_URL "${INFERENCE_URL}/mobilenet_v2.anakin.bin")
set(ANAKIN_RNN_MODLE_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn.anakin2.model.bin")
set(ANAKIN_RNN_DATA_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn_data.txt")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_SOURCE_DIR}")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL}")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL} -N")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_MODLE_URL} -N")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_DATA_URL} -N")
include_directories(${ANAKIN_INCLUDE})
include_directories(${ANAKIN_INCLUDE}/saber/)
include_directories(${ANAKIN_INCLUDE}/saber/core/)
include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/x86/)
include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/cuda/base/cuda_c/)
set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
@ -25,9 +38,12 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=format-extra-args -Wno-format-extra-args
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=maybe-uninitialized -Wno-maybe-uninitialized
-Wno-error=switch -Wno-switch
-Wno-error=return-type -Wno-return-type
-Wno-error=non-virtual-dtor -Wno-non-virtual-dtor
-Wno-error=ignored-qualifiers
-Wno-ignored-qualifiers
-Wno-sign-compare
-Wno-reorder
-Wno-error=cpp)
@ -38,7 +54,7 @@ ExternalProject_Add(
DEPENDS ${MKLML_PROJECT}
# Anakin codes error on Intel(R) Xeon(R) Gold 5117 CPU, temporary do not compile avx512 related code.
GIT_REPOSITORY "https://github.com/luotao1/Anakin"
GIT_TAG "bcf17aabe7921ceb7bce591244b4f9dce7dba5c8"
GIT_TAG "211d1fc5d813d70c0c14072f9083cf25f40940ea"
PREFIX ${ANAKIN_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DUSE_GPU_PLACE=YES
@ -48,6 +64,7 @@ ExternalProject_Add(
-DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
-DCUDNN_ROOT=${CUDNN_ROOT}
-DCUDNN_INCLUDE_DIR=${CUDNN_INCLUDE_DIR}
-DENABLE_OP_TIMER=${ANAKIN_ENABLE_OP_TIMER}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
)

@ -119,10 +119,29 @@ $$Out = scale*X$$
这个例子有`AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数作为参数属性并且设置默认值为1.0。
### 定义GradProtoMaker类
每个Op的必须有一个对应的GraProtoMaker若未定制对应前向Op的GradProtoMakerfluid提供了DefaultGradProtoMaker默认注册会使用全部输入输出包括Input, Output, Output@Grad等使用不需要的变量的会造成显存浪费。
下面示例定义了ScaleOp的GradProtoMaker。
```cpp
class ScaleGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", GetAttr("scale"));
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
```
### 定义Operator类
下面的点实现了MulOp的定义
下面实现了MulOp的定义
```cpp
class MulOp : public framework::OperatorWithKernel {
@ -334,3 +353,83 @@ ctest -R test_mul_op
- 注册Op时的类型名需要和该Op的名字一样。即不允许在`A_op.cc`里面,注册`REGISTER_OPERATOR(B, ...)`等,这将会导致单元测试出错。
- 如果Op没有实现CUDA Kernel请不要创建空的`*_op.cu`,这将会导致单元测试出错。
- 如果多个Op依赖一些共用的函数可以创建非`*_op.*`格式的文件来存放,如`gather.h`文件。
### PADDLE_ENFORCE使用注意
实现Op时检查数据的合法性需要使用PADDLE_ENFORCE以及PADDLE_ENFORCE_EQ等宏定义基本格式如下
```
PADDLE_ENFORCE(表达式, 错误提示信息)
PADDLE_ENFORCE_EQ(比较对象A, 比较对象B, 错误提示信息)
```
如果表达式为真或者比较对象A=B则检查通过否则会终止程序运行向用户反馈相应的错误提示信息。
为了确保提示友好易懂,开发者需要注意其使用方法。
#### 总体原则
任何使用了PADDLE_ENFORCE与PADDLE_ENFORCE_**检查的地方,必须有详略得当的备注解释!**错误提示信息**不能为空!
#### 提示信息书写标准
1. [required] 哪里错了?为什么错了?
- 例如:`ValueError: Mismatched label shape`
2. [optional] 期望的输入是什么样的?实际的输入是怎样的?
- 例如:`Expected labels dimension=1. Received 4.`
3. [optional] 能否给出修改意见?
- 例如:`Suggested Fix:If your classifier expects one-hot encoding label,check your n_classes argument to the estimatorand/or the shape of your label.Otherwise, check the shape of your label.`
如果并非必要或者简洁的描述即可表达清楚以上要点,根据情况书写亦可。
##### FAQ 典型问题
1. 无报错信息或报错信息过于简单,不能给用户提供有效的提示!
问题示例1 :未写提示信息
```
PADDLE_ENFORCE(ctx->HasInput("X"), "");
```
问题示例2 :提示信息过于简单
```
PADDLE_ENFORCE(i != nullptr, "I must be set"); // I是什么
```
2. 在报错信息中使用开发人员定义的变量缩写,不易理解!
问题示例:
```
PADDLE_ENFORCE(forward_pd != nullptr,
"Fail to find eltwise_fwd_pd in device context"); //eltwise_fwd_pd用户可能看不懂
```
3. OP内部调用非法接口Op内部如果出现Output = ShareDataWith(Input)
问题示例:
```cpp
auto *out = ctx.Output<framework::LoDTensor>("Out");
auto *in = ctx.Input<framework::LoDTensor>("X");
out->ShareDataWith(*in);
```
Op内部如果出现Output = ShareDataWith(Input)相当于operator图的中有一条隐藏边连接了Input和Output这条边无法在图分析中表达引发基于图优化的错误。
4. OP实现的性能实践
调用了eigen的broadcast, chop等操作性能会比手写cuda kernel差几倍以上。此时cpu的实现可以复用eigengpu实现可以实现cuda kernel.
#### OP InferShape检查提示信息特别说明
- 检查输入输出变量,请统一遵循以下格式
`Input(变量名) of OP名 operator should not be null.`
正确示例:
```
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
```
- 反向Op的输入输出检查要写明反向Op的名字
正确示例:
```
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of LoDResetGrad opreator should not be null.");
```

@ -104,7 +104,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
for (auto &adj_n : var->inputs) {
PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
adj_list[n].insert(adj_n);
VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
}

@ -22,7 +22,7 @@ function (inference_analysis_test TARGET)
if(WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS)
set(multiValueArgs SRCS EXTRA_DEPS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(mem_opt "")
@ -31,22 +31,43 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
endfunction(inference_analysis_test)
cc_test(test_analyzer SRCS analyzer_tester.cc DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
# ir
fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detecter
pass
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
#set_tests_properties(test_analyzer PROPERTIES DEPENDS test_word2vec)
#inference_api_test(test_analyzer SRC analyzer_tester.cc ARGS test_word2vec)
set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
set(DITU_RNN_MODEL ${DITU_INSTALL_DIR}/model)
set(DITU_RNN_DATA ${DITU_INSTALL_DIR}/data.txt)
function (inference_download_and_uncompress target url gz_filename)
message(STATUS "Download inference test stuff ${gz_filename} from ${url}")
execute_process(COMMAND bash -c "mkdir -p ${DITU_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && wget -q ${url}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && tar xzf ${gz_filename}")
message(STATUS "finish downloading ${gz_filename}")
endfunction(inference_download_and_uncompress)
if (NOT EXISTS ${DITU_INSTALL_DIR})
inference_download_and_uncompress(ditu_rnn_model ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
inference_download_and_uncompress(ditu_rnn_data ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
endif()
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
# ir
fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detecter
infer_clean_graph_pass
pass
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model
--infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
--infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)

@ -23,8 +23,6 @@
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
namespace paddle {
DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
"Enable subgraph to TensorRT engine for acceleration");
@ -35,6 +33,7 @@ DEFINE_string(IA_graphviz_log_root, "./",
DEFINE_string(IA_output_storage_path, "", "optimized model output path");
namespace paddle {
namespace inference {
namespace analysis {

@ -39,8 +39,6 @@ limitations under the License. */
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
namespace paddle {
// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
// flag if not available.
DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
@ -48,6 +46,7 @@ DECLARE_string(IA_graphviz_log_root);
DECLARE_string(IA_output_storage_path);
DECLARE_bool(IA_enable_ir);
namespace paddle {
namespace inference {
namespace analysis {

File diff suppressed because it is too large Load Diff

@ -18,7 +18,10 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
graph_viz_pass fc_fuse_pass
infer_clean_graph_pass
)
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
@ -62,7 +65,7 @@ endif()
if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
@ -70,9 +73,12 @@ if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
anakin_target(inference_anakin_api)
anakin_target(inference_anakin_api_shared)
if (WITH_TESTING)
cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
cc_test(api_anakin_engine_tester SRCS api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api dynload_cuda SERIAL)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
cc_test(api_anakin_engine_rnn_tester SRCS api_anakin_engine_rnn_tester.cc
ARGS --model=${ANAKIN_SOURCE_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=${ANAKIN_SOURCE_DIR}/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif(WITH_TESTING)
endif()

@ -1,11 +1,8 @@
/* 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.

@ -13,9 +13,22 @@
// limitations under the License.
#include "paddle/fluid/inference/api/api_anakin_engine.h"
#ifdef PADDLE_WITH_CUDA
#include <cuda.h>
#endif
#include <mkl_service.h>
#include <omp.h>
#include <map>
#include <string>
#include <utility>
#include <vector>
#include "framework/core/net/net.h"
#include "framework/operators/ops.h"
#include "saber/funcs/timer.h"
namespace paddle {
template <typename Target>
@ -23,16 +36,24 @@ PaddleInferenceAnakinPredictor<Target>::PaddleInferenceAnakinPredictor(
const AnakinConfig &config) {
CHECK(Init(config));
}
template <>
PaddleInferenceAnakinPredictor<anakin::X86>::PaddleInferenceAnakinPredictor(
const AnakinConfig &config) {
omp_set_dynamic(0);
omp_set_num_threads(1);
mkl_set_num_threads(1);
CHECK(Init(config));
}
template <typename Target>
bool PaddleInferenceAnakinPredictor<Target>::Init(const AnakinConfig &config) {
if (!(graph_.load(config.model_file))) {
LOG(FATAL) << "fail to load graph from " << config.model_file;
VLOG(3) << "fail to load graph from " << config.model_file;
return false;
}
auto inputs = graph_.get_ins();
for (auto &input_str : inputs) {
graph_.ResetBatchSize(input_str, config.max_batch_size);
max_batch_size_ = config.max_batch_size;
}
// optimization for graph
if (!(graph_.Optimize())) {
@ -52,15 +73,15 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
std::vector<PaddleTensor> *output_data, int batch_size) {
for (const auto &input : inputs) {
if (input.dtype != PaddleDType::FLOAT32) {
LOG(ERROR) << "Only support float type inputs. " << input.name
<< "'s type is not float";
VLOG(3) << "Only support float type inputs. " << input.name
<< "'s type is not float";
return false;
}
auto d_tensor_in_p = executor_p_->get_in(input.name);
auto net_shape = d_tensor_in_p->valid_shape();
auto net_shape = d_tensor_in_p->shape();
if (net_shape.size() != input.shape.size()) {
LOG(ERROR) << " input " << input.name
<< "'s shape size should be equal to that of net";
VLOG(3) << " input " << input.name
<< "'s shape size should be equal to that of net";
return false;
}
int sum = 1;
@ -79,21 +100,45 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
}
d_tensor_in_p->reshape(tmp_shape);
if (input.lod.size() > 0) {
if (input.lod.size() > 1) {
VLOG(3) << " input lod first dim should <=1, but you set "
<< input.lod.size();
return false;
}
std::vector<int> offset(input.lod[0].begin(), input.lod[0].end());
d_tensor_in_p->set_seq_offset(offset);
VLOG(3) << "offset.size(): " << offset.size();
for (int i = 0; i < offset.size(); i++) {
VLOG(3) << offset[i];
}
}
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
LOG(ERROR) << "copy data from CPU to GPU error";
return false;
#ifdef PADDLE_WITH_CUDA
if (std::is_same<anakin::NV, Target>::value) {
if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
VLOG(3) << "copy data from CPU to GPU error";
return false;
}
}
#endif
if (std::is_same<anakin::X86, Target>::value) {
memcpy(d_data_p, static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float));
}
cudaStreamSynchronize(NULL);
}
#ifdef PADDLE_WITH_CUDA
cudaDeviceSynchronize();
executor_p_->prediction();
cudaDeviceSynchronize();
#endif
if (output_data->empty()) {
LOG(ERROR) << "At least one output should be set with tensors' names.";
VLOG(3) << "At least one output should be set with tensors' names.";
return false;
}
for (auto &output : *output_data) {
@ -102,14 +147,22 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
if (output.data.length() < tensor->valid_size() * sizeof(float)) {
output.data.Resize(tensor->valid_size() * sizeof(float));
}
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
LOG(ERROR) << "copy data from GPU to CPU error";
return false;
#if PADDLE_WITH_CUDA
if (std::is_same<anakin::NV, Target>::value) {
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
VLOG(3) << "copy data from GPU to CPU error";
return false;
}
}
#endif
if (std::is_same<anakin::X86, Target>::value) {
memcpy(output.data.data(), tensor->mutable_data(),
tensor->valid_size() * sizeof(float));
}
cudaStreamSynchronize(NULL);
}
return true;
}
@ -132,7 +185,7 @@ PaddleInferenceAnakinPredictor<Target>::Clone() {
auto anakin_predictor_p =
dynamic_cast<PaddleInferenceAnakinPredictor<Target> *>(cls.get());
if (!anakin_predictor_p) {
LOG(ERROR) << "fail to call Init";
VLOG(3) << "fail to call Init";
return nullptr;
}
anakin_predictor_p->get_executer().init(graph_);
@ -162,6 +215,44 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
VLOG(3) << "Anakin Predictor create on unknown platform.";
return nullptr;
}
};
}
#ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER
template <typename Target>
using executor_t =
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>;
template <typename Target>
void DisplayOpTimer(executor_t<Target> *net_executor, int epoch) {
std::vector<float> op_time = net_executor->get_op_time();
auto exec_funcs = net_executor->get_exec_funcs();
auto op_param = net_executor->get_op_param();
for (int i = 0; i < op_time.size(); i++) {
LOG(INFO) << "name: " << exec_funcs[i].name
<< " op_type: " << exec_funcs[i].op_name
<< " op_param: " << op_param[i] << " time " << op_time[i] / epoch;
}
std::map<std::string, float> op_map;
for (int i = 0; i < op_time.size(); i++) {
auto it = op_map.find(op_param[i]);
if (it != op_map.end())
op_map[op_param[i]] += op_time[i];
else
op_map.insert(std::pair<std::string, float>(op_param[i], op_time[i]));
}
for (auto it = op_map.begin(); it != op_map.end(); ++it) {
LOG(INFO) << it->first << " " << (it->second) / epoch << " ms";
}
}
#endif
template <typename Target>
PaddleInferenceAnakinPredictor<Target>::~PaddleInferenceAnakinPredictor() {
#ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER
DisplayOpTimer<Target>(executor_p_, max_batch_size_);
#endif
delete executor_p_;
executor_p_ = nullptr;
}
} // namespace paddle

@ -47,10 +47,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>&
get_executer();
~PaddleInferenceAnakinPredictor() override {
delete executor_p_;
executor_p_ = nullptr;
};
~PaddleInferenceAnakinPredictor() override;
private:
bool Init(const AnakinConfig& config);
@ -60,6 +57,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>*
executor_p_{nullptr};
AnakinConfig config_;
int max_batch_size_{0};
};
} // namespace paddle

File diff suppressed because it is too large Load Diff

@ -137,8 +137,11 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
return false;
}
for (size_t i = 0; i < feed_target_names_.size(); ++i) {
VLOG(4) << "setting " << i << "-th target";
feed_targets[feed_target_names_[i]] = &feeds[i];
if (config_.specify_input_name) {
feed_targets[inputs[i].name] = &feeds[i];
} else {
feed_targets[feed_target_names_[i]] = &feeds[i];
}
}
// get fetch variable
std::map<std::string, framework::LoDTensor *> fetch_targets;

@ -0,0 +1,110 @@
// 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.
#pragma once
#include <sys/time.h>
#include <algorithm>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
namespace inference {
// Timer for timer
class Timer {
public:
double start;
double startu;
void tic() {
struct timeval tp;
gettimeofday(&tp, NULL);
start = tp.tv_sec;
startu = tp.tv_usec;
}
double toc() {
struct timeval tp;
gettimeofday(&tp, NULL);
double used_time_ms =
(tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
return used_time_ms;
}
};
void split(const std::string &str, char sep, std::vector<std::string> *pieces) {
pieces->clear();
if (str.empty()) {
return;
}
size_t pos = 0;
size_t next = str.find(sep, pos);
while (next != std::string::npos) {
pieces->push_back(str.substr(pos, next - pos));
pos = next + 1;
next = str.find(sep, pos);
}
if (!str.substr(pos).empty()) {
pieces->push_back(str.substr(pos));
}
}
void split_to_float(const std::string &str, char sep, std::vector<float> *fs) {
std::vector<std::string> pieces;
split(str, sep, &pieces);
std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs),
[](const std::string &v) { return std::stof(v); });
}
template <typename T>
std::string to_string(const std::vector<T> &vec) {
std::stringstream ss;
for (const auto &c : vec) {
ss << c << " ";
}
return ss.str();
}
template <>
std::string to_string<std::vector<float>>(
const std::vector<std::vector<float>> &vec) {
std::stringstream ss;
for (const auto &piece : vec) {
ss << to_string(piece) << "\n";
}
return ss.str();
}
template <>
std::string to_string<std::vector<std::vector<float>>>(
const std::vector<std::vector<std::vector<float>>> &vec) {
std::stringstream ss;
for (const auto &line : vec) {
for (const auto &rcd : line) {
ss << to_string(rcd) << ";\t";
}
ss << '\n';
}
return ss.str();
}
// clang-format off
void TensorAssignData(PaddleTensor *tensor, const std::vector<std::vector<float>> &data) {
// Assign buffer
int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; });
tensor->data.Resize(sizeof(float) * dim);
int c = 0;
for (const auto &f : data) {
for (float v : f) { static_cast<float *>(tensor->data.data())[c++] = v; }
}
}
} // namespace inference
} // namespace paddle

@ -45,7 +45,7 @@ class PaddleBuf {
PaddleBuf(void* data, size_t length)
: data_(data), length_(length), memory_owned_{false} {}
// Own memory.
explicit PaddleBuf(size_t length)
PaddleBuf(size_t length)
: data_(new char[length]), length_(length), memory_owned_(true) {}
// Resize to `length` bytes.
void Resize(size_t length);
@ -70,7 +70,7 @@ struct PaddleTensor {
std::vector<int> shape;
PaddleBuf data; // blob of data.
PaddleDType dtype;
std::vector<std::vector<uint64_t>> lod; // lod data
std::vector<std::vector<size_t>> lod; // Tensor+LoD equals LoDTensor
};
enum class PaddleEngineKind {
@ -120,6 +120,8 @@ struct NativeConfig : public PaddlePredictor::Config {
bool use_gpu{false};
int device{0};
float fraction_of_gpu_memory{-1.f}; // Negative to notify initialization.
// Specify the variable's name of each input.
bool specify_input_name{false};
std::string prog_file;
std::string param_file;

@ -135,7 +135,7 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("Y", "result after normalization");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")

@ -54,9 +54,9 @@ class MulOp : public framework::OperatorWithKernel {
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
PADDLE_ENFORCE_EQ(
x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's "
"height. %s, %s");
std::vector<int64_t> output_dims;
output_dims.reserve(
static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));

@ -57,12 +57,12 @@ class WhileOp : public framework::OperatorBase {
PADDLE_ENFORCE(platform::is_cpu_place(cond.place()),
"Condition of while op must in CPU memory.");
auto ctx = executor.Prepare(*program, block->ID());
while (cond.data<bool>()[0]) {
auto &current_scope = scope.NewScope();
step_scopes->push_back(&current_scope);
executor.Run(*program, &current_scope, block->ID(),
false /*create_local_scope*/);
executor.RunPreparedContext(ctx.get(), &current_scope, false);
}
}
};
@ -109,6 +109,7 @@ class WhileGradOp : public framework::OperatorBase {
framework::Executor executor(dev_place);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
auto ctx = executor.Prepare(*program, block->ID());
auto *step_scopes =
scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
@ -161,8 +162,7 @@ class WhileGradOp : public framework::OperatorBase {
}
}
}
executor.Run(*program, *cur_scope_iter, block->ID(), false);
executor.RunPreparedContext(ctx.get(), *cur_scope_iter, false);
auto &pg_names = Outputs(kXGRAD);
auto &p_names = Inputs(kX);

@ -406,6 +406,9 @@ 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)

@ -27,6 +27,7 @@ from . import utils
import random
from .. import unique_name
from functools import reduce
import warnings
__all__ = [
'fc',
@ -2046,7 +2047,7 @@ def batch_norm(input,
param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
data_layout(string, default NCHW): NCHW|NHWC
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
in_place(bool, Default False): This argument is deprecated since 0.15.0.
use_mkldnn(bool, Default false): ${use_mkldnn_comment}
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
@ -2068,6 +2069,10 @@ def batch_norm(input,
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
if in_place:
raise warnings.warn("The argument in_place is deprecated since 0.15.0, "
"please do not set it True.")
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
@ -2117,7 +2122,7 @@ def batch_norm(input,
saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
batch_norm_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="batch_norm",

@ -229,7 +229,7 @@ def img_conv_group(input,
use_mkldnn=use_mkldnn)
if conv_with_batchnorm[i]:
tmp = layers.batch_norm(input=tmp, act=conv_act, in_place=True)
tmp = layers.batch_norm(input=tmp, act=conv_act)
drop_rate = conv_batchnorm_drop_rate[i]
if abs(drop_rate) > 1e-5:
tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)

@ -256,7 +256,10 @@ def main(net_type, use_cuda, is_local=True):
save_dirname = "image_classification_" + net_type + ".inference.model"
train(net_type, use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
# There is bug in fluid.InferenceTranspiler for VGG.
if net_type == "resnet":
infer(use_cuda, save_dirname)
class TestImageClassification(unittest.TestCase):

@ -56,8 +56,8 @@ def get_numeric_gradient(place,
def get_output():
sum = []
op.run(scope, place)
for output_name in output_names:
op.run(scope, place)
sum.append(
np.array(scope.find_var(output_name).get_tensor()).mean())
return np.array(sum).mean()

@ -25,9 +25,6 @@ import paddle.fluid.core as core
class TestProfiler(unittest.TestCase):
def net_profiler(self, state, profile_path='/tmp/profile'):
enable_if_gpu = state == 'GPU' or state == "All"
if enable_if_gpu and not core.is_compiled_with_cuda():
return
startup_program = fluid.Program()
main_program = fluid.Program()
@ -81,8 +78,6 @@ class TestProfiler(unittest.TestCase):
pass_acc_calculator.add(value=acc, weight=b_size)
pass_acc = pass_acc_calculator.eval()
@unittest.skipIf(not core.is_compiled_with_cuda(),
"profiler is enabled only with GPU")
def test_cpu_profiler(self):
self.net_profiler('CPU')

@ -153,9 +153,6 @@ def append_input_output(block, op_proto, np_list, is_input, dtype):
def append_loss_ops(block, output_names):
mean_inputs = list(map(block.var, output_names))
# for item in mean_inputs:
# print(item)
# print("Item", item.dtype)
if len(mean_inputs) == 1:
loss = block.create_var(dtype=mean_inputs[0].dtype, shape=[1])

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