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PaddleDetection
提交 f7bd1761 编写于 作者: D Dang Qingqing
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into quantize_transpiler_update

上级 d94920ce e9bc5faa
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Showing with 1661 addition and 441 deletion
cmake/flags.cmake
浏览文件 @ f7bd1761
......@@ -27,7 +27,6 @@ endfunction()
CheckCompilerCXX11Flag()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
# safe_set_flag
#
# Set a compile flag only if compiler is support
......@@ -71,6 +70,20 @@ macro(safe_set_nvflag flag_name)
endif()
endmacro()
macro(safe_set_static_flag) # set c_flags and cxx_flags to static or shared
if (BUILD_SHARED_LIBS)
return() # if build shared libs, the flags keep same with '/MD'
endif(BUILD_SHARED_LIBS)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/MD")
string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/MD")
endforeach(flag_var)
endmacro()
CHECK_CXX_SYMBOL_EXISTS(UINT64_MAX "stdint.h" UINT64_MAX_EXISTS)
if(NOT UINT64_MAX_EXISTS)
......@@ -97,9 +110,13 @@ SET(CMAKE_EXTRA_INCLUDE_FILES "")
# Common flags. the compiler flag used for C/C++ sources whenever release or debug
# Do not care if this flag is support for gcc.
# https://github.com/PaddlePaddle/Paddle/issues/12773
if (NOT WIN32)
set(COMMON_FLAGS
-fPIC
-fno-omit-frame-pointer
-Werror
-Wall
-Wextra
-Wnon-virtual-dtor
......@@ -114,11 +131,6 @@ set(COMMON_FLAGS
-Wno-error=terminate # Warning in PADDLE_ENFORCE
)
# https://github.com/PaddlePaddle/Paddle/issues/12773
if (NOT WIN32)
list(APPEND COMMON_FLAGS -Werror)
endif()
set(GPU_COMMON_FLAGS
-fPIC
-fno-omit-frame-pointer
......@@ -133,30 +145,53 @@ set(GPU_COMMON_FLAGS
-Wno-error=array-bounds # Warnings in Eigen::array
)
else(NOT WIN32)
set(COMMON_FLAGS
"/w") #disable all warnings.
set(GPU_COMMON_FLAGS
"/w") #disable all warnings
endif(NOT WIN32)
if (APPLE)
if(NOT CMAKE_CROSSCOMPILING)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
endif()
else()
endif(APPLE)
if(LINUX)
set(GPU_COMMON_FLAGS
-Wall
-Wextra
-Werror
${GPU_COMMON_FLAGS})
endif()
endif(LINUX)
if(UNIX AND NOT APPLE)
# except apple from nix*Os family
set(LINUX TRUE)
endif(UNIX AND NOT APPLE)
foreach(flag ${COMMON_FLAGS})
safe_set_cflag(CMAKE_C_FLAGS ${flag})
safe_set_cxxflag(CMAKE_CXX_FLAGS ${flag})
endforeach()
foreach(flag ${GPU_COMMON_FLAGS})
safe_set_nvflag(${flag})
endforeach()
if(WIN32)
# windows build turn off warnings.
safe_set_static_flag()
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/W3")
string(REGEX REPLACE "/W3" "/w" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/W3")
endforeach(flag_var)
endif(WIN32)
doc/CMakeLists.txt
浏览文件 @ f7bd1761
add_custom_target(paddle_apis ALL
DEPENDS paddle_v2_apis paddle_fluid_apis)
DEPENDS paddle_v2_apis)
add_custom_target(paddle_docs ALL
DEPENDS paddle_v2_docs paddle_v2_docs_cn
paddle_fluid_docs paddle_fluid_docs_cn
paddle_mobile_docs paddle_mobile_docs_cn)
add_subdirectory(v2)
add_subdirectory(fluid)
add_subdirectory(mobile)
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ f7bd1761
......@@ -102,8 +102,8 @@ class Float16Transpiler:
continue
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......@@ -187,7 +187,7 @@ class Float16Transpiler:
shape=var.shape,
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
var.op._rename_output(var_name, tmp_var_name)
self.block._insert_op(
i,
type="cast",
......
paddle/fluid/API.spec
浏览文件 @ f7bd1761
......@@ -6,26 +6,9 @@ paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=
paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.Operator.__init__ ArgSpec(args=['self', 'block', 'desc', 'type', 'inputs', 'outputs', 'attrs'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.Operator.all_attrs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr_type ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr_id ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr_ids ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_kernel ArgSpec(args=['self', 'op_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.input ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.output ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_input ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_output ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.set_attr ArgSpec(args=['self', 'name', 'val'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.to_string ArgSpec(args=['self', 'throw_on_error'], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
......@@ -286,7 +269,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kw
paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.polygon_box_transform ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ f7bd1761
......@@ -77,10 +77,12 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
const std::string BatchedCellPreAct =
patterns::UniqueKey("BatchedCellPreAct");
const std::string BatchedGate = patterns::UniqueKey("BatchedGate");
const std::string CheckedCell = patterns::UniqueKey("CheckedCell");
scope->Var(BatchedInput)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedCellPreAct)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedGate)->GetMutable<framework::LoDTensor>();
scope->Var(CheckedCell)->GetMutable<framework::LoDTensor>();
op_desc.SetInput("H0", {});
op_desc.SetInput("C0", {});
......@@ -90,6 +92,7 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
op_desc.SetOutput("BatchedGate", {BatchedGate});
op_desc.SetOutput("BatchCellPreAct", {BatchedCellPreAct});
op_desc.SetOutput("BatchedInput", {BatchedInput});
op_desc.SetOutput("CheckedCell", {CheckedCell});
op_desc.SetAttr("is_reverse", lstm->Op()->GetAttr("is_reverse"));
op_desc.SetAttr("use_peepholes", lstm->Op()->GetAttr("use_peepholes"));
// TODO(TJ): get from attr
......
paddle/fluid/framework/ir/graph_traits.cc
浏览文件 @ f7bd1761
......@@ -14,6 +14,8 @@
#include "paddle/fluid/framework/ir/graph_traits.h"
#include <vector>
namespace paddle {
namespace framework {
namespace ir {
......
paddle/fluid/framework/op_info.h
浏览文件 @ f7bd1761
......@@ -38,31 +38,27 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
std::string op_type_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
}
const proto::OpProto& Proto() const {
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator %s Proto has not been registered",
op_type_);
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered");
PADDLE_ENFORCE(proto_->IsInitialized(),
"Operator %s Proto must be initialized in op info",
op_type_);
"Operator Proto must be initialized in op info");
return *proto_;
}
const OpCreator& Creator() const {
PADDLE_ENFORCE_NOT_NULL(
creator_, "Operator %s Creator has not been registered", op_type_);
PADDLE_ENFORCE_NOT_NULL(creator_,
"Operator Creator has not been registered");
return creator_;
}
const GradOpMakerFN& GradOpMaker() const {
PADDLE_ENFORCE_NOT_NULL(grad_op_maker_,
"Operator %s GradOpMaker has not been registered.",
op_type_);
"Operator GradOpMaker has not been registered.");
return grad_op_maker_;
}
......@@ -77,9 +73,8 @@ class OpInfoMap {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string& type, OpInfo info) {
void Insert(const std::string& type, const OpInfo& info) {
PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
info.op_type_ = type;
map_.insert({type, info});
}
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ f7bd1761
......@@ -27,8 +27,11 @@ class SelectedRowsTester : public ::testing::Test {
selected_rows_.reset(new SelectedRows(rows, height));
Tensor* value = selected_rows_->mutable_value();
value->mutable_data<float>(
auto* data = value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
for (int64_t i = 0; i < value->numel(); ++i) {
data[i] = static_cast<float>(i);
}
}
protected:
......@@ -60,6 +63,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
auto* dst_data = dst_tensor.value().data<float>();
for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
ASSERT_EQ(dst_data[i], static_cast<float>(i));
}
}
TEST(SelectedRows, SparseTable) {
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ f7bd1761
......@@ -37,12 +37,16 @@ TEST(Analyzer, analysis_without_tensorrt) {
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string &model_path) {
void TestWord2vecPrediction(const std::string& model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
......@@ -73,8 +77,8 @@ void TestWord2vecPrediction(const std::string &model_path) {
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << "data: "
<< static_cast<float *>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
<< static_cast<float*>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
}
}
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
浏览文件 @ f7bd1761
......@@ -97,8 +97,10 @@ void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
}
}
void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
void CreateTrtEngineOp(Node *node, Argument *argument,
framework::proto::BlockDesc *block) {
PADDLE_ENFORCE(argument->main_dfg.get());
const DataFlowGraph &graph = *(argument->main_dfg);
static int counter{0};
PADDLE_ENFORCE(node->IsFunctionBlock());
framework::OpDesc desc;
......@@ -204,7 +206,10 @@ void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
PADDLE_ENFORCE(!block->vars().empty(), "the block has no var-desc");
// Set attrs
SetAttr(desc.Proto(), "subgraph", block->SerializeAsString());
SetAttr(desc.Proto(), "max_batch_size", argument->Get<int>("max_batch_size"));
SetAttr(desc.Proto(), "workspace_size", argument->Get<int>("workspace_size"));
SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
......@@ -248,7 +253,7 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
*block_desc.Proto()->mutable_vars() =
argument_->origin_program_desc->blocks(0).vars();
PADDLE_ENFORCE(!block_desc.Proto()->vars().empty());
CreateTrtEngineOp(node, *argument_->main_dfg, block_desc.Proto());
CreateTrtEngineOp(node, argument_, block_desc.Proto());
auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
auto *op = main_block->add_ops();
PADDLE_ENFORCE(!node->pb_msg().empty(), "failed to set desc for block");
......
paddle/fluid/inference/analysis/subgraph_splitter.cc
浏览文件 @ f7bd1761
......@@ -309,6 +309,8 @@ void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
void SubGraphFuse::ReplaceNodesWithSubGraphs() {
auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
for (auto &subgraph : subgraphs) {
if (subgraph.size() <= argument_->Get<int>("minimum_subgraph_size"))
continue;
std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
// replace this sub-graph with the first node. Two steps: 1. Create a Block
// Node that contains this subgraph 2. Mark the nodes inside the sub-graph
......
paddle/fluid/inference/analysis/subgraph_splitter.h
浏览文件 @ f7bd1761
......@@ -20,6 +20,7 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/analysis/argument.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/node.h"
......@@ -63,8 +64,11 @@ class SubGraphFuse {
public:
using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
: graph_(graph), node_inside_subgraph_teller_(teller) {}
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller,
Argument *argument)
: graph_(graph),
node_inside_subgraph_teller_(teller),
argument_(argument) {}
// The main method which run all the logic.
void operator()();
......@@ -76,6 +80,7 @@ class SubGraphFuse {
private:
DataFlowGraph *graph_;
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument *argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/subgraph_splitter_tester.cc
浏览文件 @ f7bd1761
......@@ -66,10 +66,12 @@ TEST(SubGraphSplitter, Split) {
TEST(SubGraphSplitter, Fuse) {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(3));
size_t count0 = dfg.nodes.size();
SubGraphFuse fuse(&dfg, teller);
SubGraphFuse fuse(&dfg, teller, &argument);
fuse();
int count1 = 0;
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ f7bd1761
......@@ -24,7 +24,7 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
: node_inside_subgraph_teller_(teller) {}
void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_)();
SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
VLOG(4) << "debug info "
<< graph->HumanReadableInfo(false /*show_values*/,
true /*show_functions*/);
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h
浏览文件 @ f7bd1761
......@@ -33,7 +33,10 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
bool Initialize(Argument* argument) override { return true; }
bool Initialize(Argument* argument) override {
argument_ = argument;
return true;
}
// This class get a sub-graph as input and determine whether to transform this
// sub-graph into TensorRT.
......@@ -46,6 +49,7 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
private:
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument* argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc
浏览文件 @ f7bd1761
......@@ -36,6 +36,10 @@ TEST(TensorRTSubGraphPass, main) {
};
Argument argument(FLAGS_inference_model_dir);
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ f7bd1761
......@@ -35,8 +35,6 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
VLOG(3) << "Predictor::init()";
FLAGS_tensorrt_max_batch_size = config_.max_batch_size;
FLAGS_tensorrt_workspace_size = config_.workspace_size;
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......@@ -92,6 +90,14 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
void OptimizeInferenceProgram() {
// Analyze inference_program
Argument argument;
argument.Set<int>("minimum_subgraph_size",
new int(config_.minimum_subgraph_size));
argument.Set<int>("max_batch_size", new int(config_.max_batch_size));
argument.Set<int>("workspace_size", new int(config_.workspace_size));
argument.Set<std::string>("precision_mode",
new std::string(config_.precision_mode));
if (!config_.model_dir.empty()) {
argument.fluid_model_dir.reset(new std::string(config_.model_dir));
} else {
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ f7bd1761
......@@ -194,6 +194,14 @@ struct MixedRTConfig : public NativeConfig {
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
int workspace_size{1 << 30};
// We transform the Ops that can be converted into TRT layer in the model,
// and aggregate these Ops into subgraphs for TRT execution.
// We set this variable to control the minimum number of nodes in the
// subgraph, 3 as default value.
int minimum_subgraph_size = 3;
// Reserved configuration
// We just support "FP32" now, "FP16" and "INT8" will be supported.
std::string precision_mode = "FP32";
};
// NOTE WIP, not stable yet.
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ f7bd1761
......@@ -90,3 +90,13 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
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} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
cc_test(test_trt_models SRCS trt_models_tester.cc
ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models
DEPS paddle_inference_tensorrt_subgraph_engine)
endif()
paddle/fluid/inference/tests/api/trt_models_tester.cc 0 → 100644
浏览文件 @ f7bd1761
// 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 <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
using paddle::contrib::MixedRTConfig;
DEFINE_string(dirname, "", "Directory of the inference model.");
NativeConfig GetConfigNative() {
NativeConfig config;
config.model_dir = FLAGS_dirname;
// LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.45;
config.use_gpu = true;
config.device = 0;
return config;
}
MixedRTConfig GetConfigTRT() {
MixedRTConfig config;
config.model_dir = FLAGS_dirname;
config.use_gpu = true;
config.fraction_of_gpu_memory = 0.2;
config.device = 0;
config.max_batch_size = 3;
return config;
}
void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
NativeConfig config0 = GetConfigNative();
config0.model_dir = model_dirname;
MixedRTConfig config1 = GetConfigTRT();
config1.model_dir = model_dirname;
config1.max_batch_size = batch_size;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
// Prepare inputs
int height = 224;
int width = 224;
float *data = new float[batch_size * 3 * height * width];
memset(data, 0, sizeof(float) * (batch_size * 3 * height * width));
data[0] = 1.0f;
// Prepare inputs
PaddleTensor tensor;
tensor.name = "input_0";
tensor.shape = std::vector<int>({batch_size, 3, height, width});
tensor.data = PaddleBuf(static_cast<void *>(data),
sizeof(float) * (batch_size * 3 * height * width));
tensor.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
// Prepare outputs
std::vector<PaddleTensor> outputs0;
std::vector<PaddleTensor> outputs1;
CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
// Get output.
ASSERT_EQ(outputs0.size(), 1UL);
ASSERT_EQ(outputs1.size(), 1UL);
const size_t num_elements = outputs0.front().data.length() / sizeof(float);
const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
EXPECT_EQ(num_elements, num_elements1);
auto *data0 = static_cast<float *>(outputs0.front().data.data());
auto *data1 = static_cast<float *>(outputs1.front().data.data());
ASSERT_GT(num_elements, 0UL);
for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
EXPECT_NEAR(data0[i], data1[i], 1e-3);
}
}
TEST(trt_models_test, main) {
std::vector<std::string> infer_models = {"mobilenet", "resnet50",
"resnext50"};
for (auto &model_dir : infer_models) {
CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + model_dir);
}
}
} // namespace paddle
paddle/fluid/operators/auc_op.cc
浏览文件 @ f7bd1761
......@@ -36,11 +36,16 @@ class AucOp : public framework::OperatorWithKernel {
"Out and Label should have same height.");
int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
int slide_steps = ctx->Attrs().Get<int>("slide_steps");
PADDLE_ENFORCE_GE(num_pred_buckets, 1, "num_thresholds must larger than 1");
PADDLE_ENFORCE_GE(slide_steps, 0, "slide_steps must be natural number");
ctx->SetOutputDim("AUC", {1});
ctx->SetOutputDim("BatchAUC", {1});
ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
slide_steps = slide_steps == 0 ? 1 : slide_steps;
ctx->SetOutputDim("StatPosOut", {slide_steps, num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {slide_steps, num_pred_buckets});
}
protected:
......@@ -62,6 +67,7 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"A 2D int tensor indicating the label of the training data. "
"shape: [batch_size, 1]");
// TODO(typhoonzero): support weight input
AddInput("StatPos", "Statistic value when label = 1");
AddInput("StatNeg", "Statistic value when label = 0");
......@@ -69,18 +75,19 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("AUC",
"A scalar representing the "
"current area-under-the-curve.");
AddOutput("BatchAUC", "The AUC for current batch");
AddOutput("StatPosOut", "Statistic value when label = 1");
AddOutput("StatNegOut", "Statistic value when label = 0");
AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
AddAttr<int>("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
AddAttr<int>(
"num_thresholds",
"The number of thresholds to use when discretizing the roc curve.")
.SetDefault((2 << 12) - 1);
AddAttr<int>("slide_steps", "Use slide steps to calc batch auc.")
.SetDefault(1);
AddComment(R"DOC(
Area Under The Curve (AUC) Operator.
......
paddle/fluid/operators/auc_op.h
浏览文件 @ f7bd1761
......@@ -32,7 +32,9 @@ class AucKernel : public framework::OpKernel<T> {
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
// buckets contain numbers from 0 to num_thresholds
int num_pred_buckets = num_thresholds + 1;
int slide_steps = ctx.Attr<int>("slide_steps");
// Only use output var for now, make sure it's persistable and
// not cleaned up for each batch.
......@@ -40,16 +42,19 @@ class AucKernel : public framework::OpKernel<T> {
auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
auc);
auto *origin_stat_pos = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *origin_stat_neg = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
auto *batch_auc = ctx.Output<Tensor>("BatchAUC");
std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_batch(num_pred_buckets, 0);
calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
num_thresholds, batch_auc);
std::vector<int64_t> stat_pos_data(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_data(num_pred_buckets, 0);
auto stat_pos_calc = stat_pos_data.data();
auto stat_neg_calc = stat_neg_data.data();
statAuc(label, predict, num_pred_buckets, num_thresholds, slide_steps,
origin_stat_pos, origin_stat_neg, &stat_pos_calc, &stat_neg_calc);
calcAuc(ctx, stat_pos_calc, stat_neg_calc, num_thresholds, auc);
}
private:
......@@ -58,29 +63,76 @@ class AucKernel : public framework::OpKernel<T> {
return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
const framework::Tensor *label,
inline static void statAuc(const framework::Tensor *label,
const framework::Tensor *predict,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
const int num_pred_buckets,
const int num_thresholds, const int slide_steps,
int64_t *origin_stat_pos, int64_t *origin_stat_neg,
int64_t **stat_pos, int64_t **stat_neg) {
size_t batch_size = predict->dims()[0];
size_t inference_width = predict->dims()[1];
const T *inference_data = predict->data<T>();
const auto *label_data = label->data<int64_t>();
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
for (size_t i = 0; i < batch_size; i++) {
uint32_t binIdx = static_cast<uint32_t>(
inference_data[i * inference_width + 1] * num_thresholds);
if (label_data[i]) {
stat_pos[binIdx] += 1.0;
(*stat_pos)[binIdx] += 1.0;
} else {
stat_neg[binIdx] += 1.0;
(*stat_neg)[binIdx] += 1.0;
}
}
int bucket_length = num_pred_buckets * sizeof(int64_t);
// will stat auc unlimited.
if (slide_steps == 0) {
for (int slide = 0; slide < num_pred_buckets; ++slide) {
origin_stat_pos[slide] += (*stat_pos)[slide];
origin_stat_neg[slide] += (*stat_neg)[slide];
}
*stat_pos = origin_stat_pos;
*stat_neg = origin_stat_neg;
} else {
for (int slide = 1; slide < slide_steps; ++slide) {
int dst_idx = (slide - 1) * num_pred_buckets;
int src_inx = slide * num_pred_buckets;
std::memcpy(origin_stat_pos + dst_idx, origin_stat_pos + src_inx,
bucket_length);
std::memcpy(origin_stat_neg + dst_idx, origin_stat_neg + src_inx,
bucket_length);
}
std::memcpy(origin_stat_pos + (slide_steps - 1) * num_pred_buckets,
*stat_pos, bucket_length);
std::memcpy(origin_stat_neg + (slide_steps - 1) * num_pred_buckets,
*stat_neg, bucket_length);
std::memset(*stat_pos, 0, bucket_length);
std::memset(*stat_neg, 0, bucket_length);
for (int slide = 0; slide < num_pred_buckets; ++slide) {
int stat_pos_steps = 0;
int stat_neg_steps = 0;
for (int step = 0; step < slide_steps; ++step) {
stat_pos_steps += origin_stat_pos[slide + step * num_pred_buckets];
stat_neg_steps += origin_stat_neg[slide + step * num_pred_buckets];
}
(*stat_pos)[slide] += stat_pos_steps;
(*stat_neg)[slide] += stat_neg_steps;
}
}
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
*auc = 0.0f;
double totPos = 0.0;
......@@ -96,7 +148,6 @@ class AucKernel : public framework::OpKernel<T> {
totPos += stat_pos[idx];
totNeg += stat_neg[idx];
*auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
--idx;
}
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ f7bd1761
......@@ -76,12 +76,18 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
PADDLE_ENFORCE_EQ(
b_dims[1], (ctx->Attrs().Get<bool>("use_peepholes") ? 7 : 4) * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection or"
"4 * %d if disable peepholes",
frame_size, frame_size);
if (ctx->Attrs().Get<bool>("use_peepholes")) {
PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection",
frame_size);
ctx->SetOutputDim("CheckedCell", {2, frame_size});
} else {
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes",
frame_size);
}
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
......@@ -173,6 +179,8 @@ void FusionLSTMOpMaker::Make() {
AddOutput("BatchedCell", "(LoDTensor) (T x D).").AsIntermediate();
AddOutput("ReorderedH0", "(LoDTensor) (N x D).").AsIntermediate();
AddOutput("ReorderedC0", "(LoDTensor) (N x D).").AsIntermediate();
AddOutput("CheckedCell", "(Tensor) (2 x D) only for peephole.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
......@@ -250,19 +258,19 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const int D3 = D * 3; \
const int D4 = wh_dims[1];
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/ \
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/ \
Tensor checked_cell; \
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/ \
checked_cell_data = checked_cell.mutable_data<T>({2, D}, place); \
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/ \
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/ \
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/ \
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
}
/// Compute LSTM
......
paddle/fluid/operators/read_op.cc
浏览文件 @ f7bd1761
......@@ -45,12 +45,10 @@ class ReadInferVarType : public framework::VarTypeInference {
framework::VarDesc* reader = block->FindVarRecursive(reader_name);
auto dtypes = reader->GetDataTypes();
PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
auto lod_levels = reader->GetLoDLevels();
for (size_t i = 0; i < dtypes.size(); ++i) {
framework::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
out.SetType(framework::proto::VarType::LOD_TENSOR);
out.SetDataType(dtypes[i]);
out.SetLoDLevel(lod_levels[i]);
}
}
};
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ f7bd1761
......@@ -77,8 +77,10 @@ class ScaleOpVarTypeInference : public framework::VarTypeInference {
auto out_var_name = op_desc.Output("Out").front();
auto *out_var = block->FindVarRecursive(out_var_name);
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
if (in_var_name != out_var_name) {
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
}
}
};
......
paddle/fluid/operators/sgd_op.cu
浏览文件 @ f7bd1761
......@@ -12,7 +12,7 @@ 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 <algorithm>
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/sgd_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -33,21 +33,22 @@ __global__ void SGDKernel(const T* g, const T* p, const T* learning_rate,
}
}
template <typename T>
template <typename T, int block_size>
__global__ void SparseSGDFunctorKernel(const T* selected_rows,
const int64_t* rows,
const T* learning_rate, T* tensor_out,
int64_t row_numel, int64_t limit) {
for (int64_t i = blockIdx.x; i < limit; i += gridDim.x) {
const T* selected_rows_ptr = selected_rows + i * row_numel;
T* tensor_out_ptr = tensor_out + rows[i] * row_numel;
for (int64_t index = threadIdx.x; index < row_numel; index += blockDim.x) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out_ptr + index,
-1.0 * learning_rate[0] * selected_rows_ptr[index]);
}
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
}
}
} // namespace
......@@ -96,15 +97,13 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
auto* in_data = in_value.data<T>();
auto* out_data = param_out->data<T>();
const int kThreadsPerBlock = 256;
int thread_x = kThreadsPerBlock;
int max_threads = ctx.cuda_device_context().GetMaxPhysicalThreadCount();
int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
SparseSGDFunctorKernel<<<max_blocks, thread_x, 0,
ctx.cuda_device_context().stream()>>>(
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in_rows.size());
SparseSGDFunctorKernel<
T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
out_data, in_row_numel, in_rows.size());
out_data, in_row_numel);
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
......
paddle/fluid/operators/shrink_rnn_memory_op.cc
浏览文件 @ f7bd1761
......@@ -52,26 +52,16 @@ class ShrinkRNNMemoryOp : public ArrayOp {
size_t height = dst_num_rows;
// do shrink for the top level LoD
if (x_tensor.lod().size() > 0 &&
x_tensor.lod()[0].size() > static_cast<size_t>(dst_num_rows)) {
if (x_tensor.lod().size() > 1) { // MultiLevel LoD
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(
x_tensor.lod(), 0, dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
} else {
// Shrink LoD
auto lod_item = x_tensor.lod()[0];
lod_item.resize(dst_num_rows + 1);
out_tensor.set_lod({lod_item});
const auto &const_lod_item = lod_item;
height = const_lod_item.back();
}
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(x_tensor.lod(), 0,
dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
}
if (height != 0) {
if (dst_num_rows != 0) {
out_tensor.mutable_data(place, x_tensor.type());
auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
framework::TensorCopy(x_tensor.Slice(0, height), place, *dev_ctx,
......@@ -144,11 +134,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
} else {
auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
auto height = dout_tensor.dims()[0];
if (height != 0) {
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx,
&slice);
}
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx, &slice);
if (dx_tensor.dims()[0] > height) {
auto rest_tensor = dx_tensor.Slice(
static_cast<int>(height), static_cast<int>(dx_tensor.dims()[0]));
......
paddle/fluid/operators/sum_op.h
浏览文件 @ f7bd1761
......@@ -32,7 +32,7 @@ class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto in_vars = context.MultiInputVar("X");
int N = in_vars.size();
size_t in_num = in_vars.size();
auto out_var = context.OutputVar("Out");
bool in_place = out_var == in_vars[0];
......@@ -53,7 +53,7 @@ class SumKernel : public framework::OpKernel<T> {
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
// If in_place, just skip the first tensor
for (int i = in_place ? 1 : 0; i < N; i++) {
for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
if (in_t.numel() == 0) {
......@@ -101,13 +101,13 @@ class SumKernel : public framework::OpKernel<T> {
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
......@@ -116,7 +116,8 @@ class SumKernel : public framework::OpKernel<T> {
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim =
framework::vectorize(get_selected_row(in_num - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
......@@ -133,7 +134,7 @@ class SumKernel : public framework::OpKernel<T> {
math::SelectedRowsAddTo<DeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ f7bd1761
......@@ -22,8 +22,6 @@
namespace paddle {
DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
DEFINE_int32(tensorrt_max_batch_size, 1, "TensorRT maximum batch size");
DEFINE_int32(tensorrt_workspace_size, 16 << 20, "TensorRT workspace size");
namespace operators {
......@@ -34,6 +32,8 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Ys", "A list of outputs").AsDuplicable();
AddAttr<std::string>("subgraph", "the subgraph.");
AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
AddAttr<int>("max_batch_size", "the maximum batch size.");
AddAttr<int>("workspace_size", "the workspace size.");
AddComment("TensorRT engine operator.");
}
};
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ f7bd1761
......@@ -28,8 +28,6 @@
namespace paddle {
DECLARE_int32(tensorrt_engine_batch_size);
DECLARE_int32(tensorrt_max_batch_size);
DECLARE_int32(tensorrt_workspace_size);
namespace operators {
......@@ -92,14 +90,14 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto engine_name = context.Attr<std::string>("engine_uniq_key");
int max_batch_size = context.Attr<int>("max_batch_size");
if (!Singleton<TRT_EngineManager>::Global().HasEngine(engine_name)) {
Prepare(context);
}
auto* engine = Singleton<TRT_EngineManager>::Global().Get(engine_name);
auto input_names = context.op().Inputs("Xs");
PADDLE_ENFORCE(!input_names.empty(), "should pass more than one inputs");
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size,
FLAGS_tensorrt_max_batch_size);
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size, max_batch_size);
std::vector<std::string> output_maps =
context.Attr<std::vector<std::string>>("output_name_mapping");
......@@ -173,8 +171,9 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// Get the ProgramDesc and pass to convert.
framework::proto::BlockDesc block_desc;
block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
int max_batch = FLAGS_tensorrt_max_batch_size;
auto max_workspace = FLAGS_tensorrt_workspace_size;
int max_batch_size = context.Attr<int>("max_batch_size");
int workspace_size = context.Attr<int>("workspace_size");
auto params = context.Attr<std::vector<std::string>>("parameters");
std::unordered_set<std::string> parameters;
for (const auto& param : params) {
......@@ -186,7 +185,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// TODO(Superjomn) replace this with a different stream
auto* engine = Singleton<TRT_EngineManager>::Global().Create(
max_batch, max_workspace, nullptr /*engine hold its own stream*/,
max_batch_size, workspace_size, nullptr /*engine hold its own stream*/,
context.Attr<std::string>("engine_uniq_key"),
boost::get<platform::CUDAPlace>(context.GetPlace()).device);
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ f7bd1761
......@@ -58,8 +58,6 @@ void AddTensorToBlockDesc(framework::proto::BlockDesc* block,
using inference::analysis::SetAttr;
TEST(TensorRTEngineOp, manual) {
FLAGS_tensorrt_engine_batch_size = 2;
FLAGS_tensorrt_max_batch_size = 2;
framework::ProgramDesc program;
auto* block_ = program.Proto()->add_blocks();
block_->set_idx(0);
......@@ -101,6 +99,8 @@ TEST(TensorRTEngineOp, manual) {
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", 2);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "a_engine");
SetAttr<std::vector<std::string>>(engine_op_desc.Proto(), "parameters",
std::vector<std::string>({}));
......@@ -129,8 +129,6 @@ TEST(TensorRTEngineOp, manual) {
}
void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
FLAGS_tensorrt_engine_batch_size = batch_size;
FLAGS_tensorrt_max_batch_size = batch_size;
framework::ProgramDesc program;
framework::Scope scope;
platform::CUDAPlace place;
......@@ -195,8 +193,8 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 2 << 10);
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::vector<std::string>>(
engine_op_desc.Proto(), "parameters",
std::vector<std::string>({"y0", "y1", "y2", "y3"}));
......
paddle/fluid/platform/device_context.cc
浏览文件 @ f7bd1761
......@@ -201,7 +201,6 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
compute_capability = GetCUDAComputeCapability(place_.device);
multi_process = GetCUDAMultiProcessors(place_.device);
max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
grid_max_dims_ = GpuMaxGridDim(place_.device);
PADDLE_ENFORCE(cudaStreamCreate(&stream_));
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
......@@ -240,10 +239,6 @@ int CUDADeviceContext::GetMaxPhysicalThreadCount() const {
return multi_process * max_threads_per_mp;
}
std::tuple<int, int, int> CUDADeviceContext::GetMaxGridDims() const {
return grid_max_dims_;
}
Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
return eigen_device_.get();
}
......
paddle/fluid/platform/device_context.h
浏览文件 @ f7bd1761
......@@ -13,7 +13,6 @@ limitations under the License. */
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <tuple>
#include <unordered_map>
#include <vector>
......@@ -92,8 +91,6 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return the max physical thread count in the device context */
int GetMaxPhysicalThreadCount() const;
std::tuple<int, int, int> GetMaxGridDims() const;
/*! \brief Return eigen device in the device context. */
Eigen::GpuDevice* eigen_device() const;
......@@ -138,8 +135,6 @@ class CUDADeviceContext : public DeviceContext {
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::tuple<int, int, int> grid_max_dims_;
int compute_capability;
int multi_process;
int max_threads_per_mp;
......
paddle/fluid/platform/for_range.h
浏览文件 @ f7bd1761
......@@ -48,54 +48,35 @@ __global__ static void ForRangeElemwiseOpGridIsOne(Function func) {
}
template <typename Function>
__global__ static void ForRangeElemwiseOp(Function func, size_t limit) {
__global__ static void ForRangeElemwiseOp(Function func, int limit) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
if (idx < limit) {
func(idx);
}
}
template <typename Function>
__global__ static void ForRangeElemwiseOpGridLarge(Function func, size_t limit,
int grid_dim) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
while (idx < limit) {
func(idx);
idx += grid_dim;
}
}
template <>
struct ForRange<CUDADeviceContext> {
ForRange(const CUDADeviceContext& dev_ctx, size_t limit)
: dev_ctx_(dev_ctx), limit_(limit) {}
: dev_ctx_(dev_ctx), limit_(static_cast<int>(limit)) {}
template <typename Function>
inline void operator()(Function func) const {
constexpr int num_threads = 1024;
int block_size = limit_ <= num_threads ? limit_ : num_threads;
size_t grid_size = (limit_ + num_threads - 1) / num_threads;
int max_grid_dim = std::get<0>(dev_ctx_.GetMaxGridDims());
if (grid_size < max_grid_dim) {
int grid_size_int = static_cast<int>(grid_size);
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOp<<<grid_size_int, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
int grid_size = (limit_ + num_threads - 1) / num_threads;
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOpGridLarge<<<max_grid_dim, block_size, 0,
dev_ctx_.stream()>>>(func, limit_,
max_grid_dim);
ForRangeElemwiseOp<<<grid_size, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
}
const CUDADeviceContext& dev_ctx_;
size_t limit_;
int limit_;
};
#endif
......
paddle/fluid/platform/gpu_info.cc
浏览文件 @ f7bd1761
......@@ -152,22 +152,5 @@ void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {
PADDLE_ENFORCE(cudaMemsetAsync(dst, value, count, stream),
"cudaMemsetAsync failed in paddle::platform::GpuMemsetAsync");
}
std::tuple<int, int, int> GpuMaxGridDim(int id) {
std::tuple<int, int, int> result;
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<0>(result), cudaDevAttrMaxBlockDimX, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<1>(result), cudaDevAttrMaxBlockDimY, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<2>(result), cudaDevAttrMaxBlockDimZ, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
return result;
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/gpu_info.h
浏览文件 @ f7bd1761
......@@ -19,7 +19,6 @@ limitations under the License. */
#include <cuda_runtime.h>
#include <stddef.h>
#include <string>
#include <tuple>
namespace paddle {
namespace platform {
......@@ -73,8 +72,6 @@ void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
//! Set memory dst with value count size asynchronously
void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream);
std::tuple<int, int, int> GpuMaxGridDim(int id);
} // namespace platform
} // namespace paddle
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ f7bd1761
......@@ -285,12 +285,12 @@ void BindOpDesc(pybind11::module *m) {
.def("set_output", &pd::OpDesc::SetOutput)
.def("input_arg_names", &pd::OpDesc::InputArgumentNames)
.def("output_arg_names", &pd::OpDesc::OutputArgumentNames)
.def("rename_input", &pd::OpDesc::RenameInput)
.def("rename_output", &pd::OpDesc::RenameOutput)
.def("_rename_input", &pd::OpDesc::RenameInput)
.def("_rename_output", &pd::OpDesc::RenameOutput)
.def("has_attr", &pd::OpDesc::HasAttr)
.def("attr_type", &pd::OpDesc::GetAttrType)
.def("attr_names", &pd::OpDesc::AttrNames)
.def("set_attr", &pd::OpDesc::SetAttr)
.def("_set_attr", &pd::OpDesc::SetAttr)
.def("attr", &pd::OpDesc::GetAttr)
.def("set_block_attr", &pd::OpDesc::SetBlockAttr)
.def("set_blocks_attr", &pd::OpDesc::SetBlocksAttr)
......@@ -300,8 +300,8 @@ void BindOpDesc(pybind11::module *m) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("_block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("_blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
......
paddle/scripts/paddle_build.sh
浏览文件 @ f7bd1761
......@@ -381,7 +381,7 @@ function run_mac_test() {
EOF
# TODO: jiabin need to refine this part when these tests fixed on mac
ctest --output-on-failure -j8
ctest --output-on-failure -j $1
# make install should also be test when unittest
make install -j 8
pip install /usr/local/opt/paddle/share/wheels/*.whl
......@@ -629,10 +629,10 @@ EOF
function gen_capi_package() {
if [[ ${WITH_C_API} == "ON" ]]; then
install_prefix="${PADDLE_ROOT}/build/capi_output"
rm -rf $install_prefix
make DESTDIR="$install_prefix" install
cd $install_prefix/usr/local
capi_install_prefix=${INSTALL_PREFIX:-/paddle/build}/capi_output
rm -rf $capi_install_prefix
make DESTDIR="$capi_install_prefix" install
cd $capi_install_prefix/
ls | egrep -v "^Found.*item$" | xargs tar -czf ${PADDLE_ROOT}/build/paddle.tgz
fi
}
......@@ -729,7 +729,7 @@ function main() {
maccheck)
cmake_gen ${PYTHON_ABI:-""}
build_mac
run_mac_test
run_mac_test ${PROC_RUN:-1}
;;
cicheck_py35)
cmake_gen ${PYTHON_ABI:-""}
......
python/paddle/dataset/common.py
浏览文件 @ f7bd1761
......@@ -77,13 +77,14 @@ def download(url, module_name, md5sum, save_name=None):
retry_limit = 3
while not (os.path.exists(filename) and md5file(filename) == md5sum):
if os.path.exists(filename):
print("file md5", md5file(filename), md5sum)
sys.stderr.write("file %s md5 %s" % (md5file(filename), md5sum))
if retry < retry_limit:
retry += 1
else:
raise RuntimeError("Cannot download {0} within retry limit {1}".
format(url, retry_limit))
print("Cache file %s not found, downloading %s" % (filename, url))
sys.stderr.write("Cache file %s not found, downloading %s" %
(filename, url))
r = requests.get(url, stream=True)
total_length = r.headers.get('content-length')
......@@ -100,10 +101,11 @@ def download(url, module_name, md5sum, save_name=None):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done,
sys.stderr.write("\r[%s%s]" % ('=' * done,
' ' * (50 - done)))
sys.stdout.flush()
sys.stderr.write("\n")
sys.stdout.flush()
return filename
......
python/paddle/fluid/backward.py
浏览文件 @ f7bd1761
......@@ -38,8 +38,8 @@ def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
op_desc = op_descs[i]
if isinstance(op_desc, tuple):
op_desc = op_desc[0]
op_desc.rename_input(old_name, new_name)
op_desc.rename_output(old_name, new_name)
op_desc._rename_input(old_name, new_name)
op_desc._rename_output(old_name, new_name)
def _create_op_desc_(op_type, inputs, outputs, attrs):
......@@ -70,7 +70,7 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
if isinstance(val, framework.Block):
op_desc.set_block_attr(name, val.desc)
else:
op_desc.set_attr(name, val)
op_desc._set_attr(name, val)
return op_desc
......@@ -346,7 +346,7 @@ def _append_backward_ops_(block,
grad_sub_block_list = []
# If the op has its own sub-block, deal with the sub-block first
if op.has_attr("sub_block"):
sub_block = program.block(op.block_attr_id("sub_block"))
sub_block = program.block(op._block_attr_id("sub_block"))
grad_sub_block = program._create_block()
grad_sub_block._set_forward_block_idx(sub_block.idx)
cb = _callback_lookup_(op)
......@@ -382,7 +382,7 @@ def _append_backward_ops_(block,
for op_desc in grad_op_descs:
new_op_desc = target_block.desc.append_op()
new_op_desc.copy_from(op_desc)
new_op_desc.set_attr(op_role_attr_name, backward)
new_op_desc._set_attr(op_role_attr_name, backward)
grad_to_var["__current_op_desc__"] = new_op_desc
if callbacks is not None:
assert (isinstance(callbacks, list))
......@@ -408,7 +408,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
for op_idx in range(start_op_idx, block.desc.op_size()):
op_desc = block.desc.op(op_idx)
if op_desc.has_attr("sub_block"):
sub_block = block.program.block(op_desc.block_attr_id("sub_block"))
sub_block = block.program.block(op_desc._block_attr_id("sub_block"))
_append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map)
new_vars = set()
# create new gradient variables
......@@ -438,12 +438,12 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
op_desc = block.desc.op(op_idx)
for name in op_desc.input_arg_names():
if name in var_map:
op_desc.rename_input(name, var_map[name])
op_desc._rename_input(name, var_map[name])
for name in op_desc.output_arg_names():
if block.desc.find_var(name.encode("ascii")):
new_name = unique_name.generate(name)
op_desc.rename_output(name, new_name)
op_desc._rename_output(name, new_name)
var_map[name] = new_name
for g, ng in six.iteritems(var_map):
......@@ -542,9 +542,9 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
if loss.op is None:
raise ValueError("loss.op is None. Should not happend")
loss.op.set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
loss.op._set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
if callbacks is not None:
isinstance(callbacks, list)
......@@ -631,7 +631,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
attr_val = [p.name, g.name]
if g.op.has_attr(op_role_var_attr_name):
attr_val.extend(g.op.attr(op_role_var_attr_name))
g.op.set_attr(op_role_var_attr_name, attr_val)
g.op._set_attr(op_role_var_attr_name, attr_val)
return params_and_grads
......
python/paddle/fluid/clip.py
浏览文件 @ f7bd1761
......@@ -75,8 +75,8 @@ class ErrorClipByValue(BaseErrorClipAttr):
clip_op_desc.set_type("clip")
clip_op_desc.set_input("X", [grad_name])
clip_op_desc.set_output("Out", [grad_name])
clip_op_desc.set_attr("min", self.min)
clip_op_desc.set_attr("max", self.max)
clip_op_desc._set_attr("min", self.min)
clip_op_desc._set_attr("max", self.max)
def error_clip_callback(block, context):
......
python/paddle/fluid/framework.py
浏览文件 @ f7bd1761
......@@ -40,11 +40,9 @@ PADDLE_ON_MODEL_CE = os.environ.get('PADDLE_ON_MODEL_CE', None) is not None
__all__ = [
'Program',
'Operator',
'default_startup_program',
'default_main_program',
'program_guard',
'get_var',
'name_scope',
]
......@@ -663,11 +661,11 @@ class Operator(object):
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
if self.has_kernel(type):
if self._has_kernel(type):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
def has_kernel(self, op_type):
def _has_kernel(self, op_type):
return op_type not in self.OP_WITHOUT_KERNEL_SET
def to_string(self, throw_on_error):
......@@ -708,7 +706,7 @@ class Operator(object):
"""
return self.desc.input(name)
def rename_input(self, old_name, new_name):
def _rename_input(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -719,9 +717,9 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_input(old_name, new_name)
self.desc._rename_input(old_name, new_name)
def rename_output(self, old_name, new_name):
def _rename_output(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -732,7 +730,7 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_output(old_name, new_name)
self.desc._rename_output(old_name, new_name)
@property
def input_names(self):
......@@ -796,7 +794,7 @@ class Operator(object):
"""
return self.desc.attr_type(name)
def set_attr(self, name, val):
def _set_attr(self, name, val):
"""
Set the value of attribute by attribute's name.
......@@ -829,7 +827,7 @@ class Operator(object):
isinstance(val, core.ProgramDesc):
self.desc.set_serialized_attr(name, val.serialize_to_string())
else:
self.desc.set_attr(name, val)
self.desc._set_attr(name, val)
@property
def attr_names(self):
......@@ -848,7 +846,7 @@ class Operator(object):
"""
return self.desc.attr(name)
def block_attr_id(self, name):
def _block_attr_id(self, name):
"""
Get the block attribute's id by name.
......@@ -858,9 +856,9 @@ class Operator(object):
Returns:
int: the block index.
"""
return self.desc.block_attr_id(name)
return self.desc._block_attr_id(name)
def block_attr(self, name):
def _block_attr(self, name):
"""
Get the block attribute by name.
......@@ -871,11 +869,11 @@ class Operator(object):
block: the block attribute.
"""
id = self.block_attr_id(name)
id = self._block_attr_id(name)
assert (id >= 0 and id < len(self.block.program.blocks))
return self.block.program.blocks[id]
def blocks_attr(self, name):
def _blocks_attr(self, name):
"""
Get the blocks attribute by name.
......@@ -886,13 +884,13 @@ class Operator(object):
list: list of the blocks attribute.
"""
attrs = []
for i in self.blocks_attr_ids(name):
for i in self._blocks_attr_ids(name):
assert (i >= 0 and i < len(self.block.program.blocks))
attrs.append(self.block.program.blocks[i])
return attrs
def blocks_attr_ids(self, name):
def _blocks_attr_ids(self, name):
"""
Get the blocks attribute's ids by name.
......@@ -903,7 +901,7 @@ class Operator(object):
list: list of the blocks ids.
"""
return self.desc.blocks_attr_ids(name)
return self.desc._blocks_attr_ids(name)
def all_attrs(self):
"""
......@@ -917,11 +915,11 @@ class Operator(object):
for n in attr_names:
attr_type = self.desc.attr_type(n)
if attr_type == core.AttrType.BLOCK:
attr_map[n] = self.block_attr(n)
attr_map[n] = self._block_attr(n)
continue
if attr_type == core.AttrType.BLOCKS:
attr_map[n] = self.blocks_attr(n)
attr_map[n] = self._blocks_attr(n)
continue
attr_map[n] = self.attr(n)
......@@ -1795,7 +1793,7 @@ class Program(object):
for j in six.moves.range(block.op_size()):
op = block.op(j)
if op.has_attr('is_test'):
op.set_attr('is_test', True)
op._set_attr('is_test', True)
res.blocks = [
Block(res, i) for i in six.moves.range(res.desc.num_blocks())
]
......@@ -2169,7 +2167,7 @@ def program_guard(main_program, startup_program=None):
switch_startup_program(startup_program)
def get_var(name, program=None):
def _get_var(name, program=None):
"""
Get a variable by name from the global block of a program.
......
python/paddle/fluid/layers/io.py
浏览文件 @ f7bd1761
......@@ -311,7 +311,6 @@ def _copy_reader_var_(block, var):
new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.desc.set_lod_levels(var.desc.lod_levels())
new_var.persistable = True
return new_var
......@@ -633,7 +632,6 @@ def py_reader(capacity,
})
startup_var.desc.set_dtypes(dtypes)
startup_var.desc.set_lod_levels(lod_levels)
startup_var.persistable = True
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ f7bd1761
......@@ -78,7 +78,12 @@ def accuracy(input, label, k=1, correct=None, total=None):
return acc_out
def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
def auc(input,
label,
curve='ROC',
num_thresholds=2**12 - 1,
topk=1,
slide_steps=1):
"""
**Area Under the Curve (AUC) Layer**
......@@ -105,6 +110,8 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
topk(int): only topk number of prediction output will be used for auc.
slide_steps: when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
Returns:
Variable: A scalar representing the current AUC.
......@@ -120,16 +127,48 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
batch_stat_pos = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
batch_stat_neg = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
# for global auc
stat_pos = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
stat_neg = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
for var in [stat_pos, stat_neg]:
for var in [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg]:
helper.set_variable_initializer(
var, Constant(
value=0.0, force_cpu=True))
# Batch AUC
helper.append_op(
type="auc",
inputs={
"Predict": [input],
"Label": [label],
"StatPos": [batch_stat_pos],
"StatNeg": [batch_stat_neg]
},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": slide_steps
},
outputs={
"AUC": [batch_auc_out],
"StatPosOut": [batch_stat_pos],
"StatNegOut": [batch_stat_neg]
})
# Global AUC
helper.append_op(
type="auc",
inputs={
......@@ -138,12 +177,16 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
"StatPos": [stat_pos],
"StatNeg": [stat_neg]
},
attrs={"curve": curve,
"num_thresholds": num_thresholds},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": 0
},
outputs={
"AUC": [auc_out],
"BatchAUC": [batch_auc_out],
"StatPosOut": [stat_pos],
"StatNegOut": [stat_neg]
})
return auc_out, batch_auc_out, [stat_pos, stat_neg]
return auc_out, batch_auc_out, [
batch_stat_pos, batch_stat_neg, stat_pos, stat_neg
]
python/paddle/fluid/layers/ops.py
浏览文件 @ f7bd1761
......@@ -63,6 +63,8 @@ for _OP in set(__all__):
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')
globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
__all__ += __activations_noattr__
for _OP in set(__activations_noattr__):
......
python/paddle/fluid/optimizer.py
浏览文件 @ f7bd1761
......@@ -26,6 +26,7 @@ from .layer_helper import LayerHelper
from .regularizer import append_regularization_ops
from .clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
from .layers import ops
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
......@@ -1301,7 +1302,7 @@ class ModelAverage(Optimizer):
x=tmp, dtype='float32' if self._dtype == None else self._dtype)
sum = layers.cast(
x=sum, dtype='float32' if self._dtype == None else self._dtype)
layers.elementwise_div(x=sum, y=tmp, out=param)
ops._elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
......
python/paddle/fluid/tests/unittests/dist_ctr.py 0 → 100644
浏览文件 @ f7bd1761
# 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 paddle
import paddle.fluid as fluid
import dist_ctr_reader
from test_dist_base import TestDistRunnerBase, runtime_main
IS_SPARSE = True
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
class TestDistCTR2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
""" network definition """
dnn_data = fluid.layers.data(
name="dnn_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
lr_data = fluid.layers.data(
name="lr_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
label = fluid.layers.data(
name="click",
shape=[-1, 1],
dtype="int64",
lod_level=0,
append_batch_size=False)
# build dnn model
dnn_layer_dims = [128, 64, 32, 1]
dnn_embedding = fluid.layers.embedding(
is_distributed=False,
input=dnn_data,
size=[dnn_input_dim, dnn_layer_dims[0]],
param_attr=fluid.ParamAttr(
name="deep_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
dnn_pool = fluid.layers.sequence_pool(
input=dnn_embedding, pool_type="sum")
dnn_out = dnn_pool
for i, dim in enumerate(dnn_layer_dims[1:]):
fc = fluid.layers.fc(
input=dnn_out,
size=dim,
act="relu",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)),
name='dnn-fc-%d' % i)
dnn_out = fc
# build lr model
lr_embbding = fluid.layers.embedding(
is_distributed=False,
input=lr_data,
size=[lr_input_dim, 1],
param_attr=fluid.ParamAttr(
name="wide_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")
merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
acc = fluid.layers.accuracy(input=predict, label=label)
auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
label=label)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
inference_program = paddle.fluid.default_main_program().clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
sgd_optimizer.minimize(avg_cost)
dataset = dist_ctr_reader.Dataset()
train_reader = paddle.batch(dataset.train(), batch_size=batch_size)
test_reader = paddle.batch(dataset.test(), batch_size=batch_size)
return inference_program, avg_cost, train_reader, test_reader, None, predict
if __name__ == "__main__":
runtime_main(TestDistCTR2x2)
python/paddle/fluid/tests/unittests/dist_ctr_reader.py 0 → 100644
浏览文件 @ f7bd1761
# 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.
import logging
import paddle
import tarfile
logging.basicConfig()
logger = logging.getLogger("paddle")
logger.setLevel(logging.INFO)
DATA_URL = "http://paddle-ctr-data.cdn.bcebos.com/avazu_ctr_data.tgz"
DATA_MD5 = "c11df99fbd14e53cd4bfa6567344b26e"
"""
avazu_ctr_data/train.txt
avazu_ctr_data/infer.txt
avazu_ctr_data/test.txt
avazu_ctr_data/data.meta.txt
"""
def read_data(file_name):
path = paddle.dataset.common.download(DATA_URL, "avazu_ctr_data", DATA_MD5)
tar = tarfile.open(path, "r:gz")
tar_info = None
for member in tar.getmembers():
if member.name.endswith(file_name):
tar_info = member
f = tar.extractfile(tar_info)
ret_lines = [_.decode('utf-8') for _ in f.readlines()]
return ret_lines
class TaskMode:
TRAIN_MODE = 0
TEST_MODE = 1
INFER_MODE = 2
def __init__(self, mode):
self.mode = mode
def is_train(self):
return self.mode == self.TRAIN_MODE
def is_test(self):
return self.mode == self.TEST_MODE
def is_infer(self):
return self.mode == self.INFER_MODE
@staticmethod
def create_train():
return TaskMode(TaskMode.TRAIN_MODE)
@staticmethod
def create_test():
return TaskMode(TaskMode.TEST_MODE)
@staticmethod
def create_infer():
return TaskMode(TaskMode.INFER_MODE)
class ModelType:
CLASSIFICATION = 0
REGRESSION = 1
def __init__(self, mode):
self.mode = mode
def is_classification(self):
return self.mode == self.CLASSIFICATION
def is_regression(self):
return self.mode == self.REGRESSION
@staticmethod
def create_classification():
return ModelType(ModelType.CLASSIFICATION)
@staticmethod
def create_regression():
return ModelType(ModelType.REGRESSION)
def load_dnn_input_record(sent):
return list(map(int, sent.split()))
def load_lr_input_record(sent):
res = []
for _ in [x.split(':') for x in sent.split()]:
res.append(int(_[0]))
return res
feeding_index = {'dnn_input': 0, 'lr_input': 1, 'click': 2}
class Dataset(object):
def train(self):
'''
Load trainset.
'''
file_name = "train.txt"
logger.info("load trainset from %s" % file_name)
mode = TaskMode.create_train()
return self._parse_creator(file_name, mode)
def test(self):
'''
Load testset.
'''
file_name = "test.txt"
logger.info("load testset from %s" % file_name)
mode = TaskMode.create_test()
return self._parse_creator(file_name, mode)
def infer(self):
'''
Load infer set.
'''
file_name = "infer.txt"
logger.info("load inferset from %s" % file_name)
mode = TaskMode.create_infer()
return self._parse_creator(file_name, mode)
def _parse_creator(self, file_name, mode):
'''
Parse dataset.
'''
def _parse():
data = read_data(file_name)
for line_id, line in enumerate(data):
fs = line.strip().split('\t')
dnn_input = load_dnn_input_record(fs[0])
lr_input = load_lr_input_record(fs[1])
if not mode.is_infer():
click = int(fs[2])
yield [dnn_input, lr_input, click]
else:
yield [dnn_input, lr_input]
return _parse
def load_data_meta():
'''
load data meta info from path, return (dnn_input_dim, lr_input_dim)
'''
lines = read_data('data.meta.txt')
err_info = "wrong meta format"
assert len(lines) == 2, err_info
assert 'dnn_input_dim:' in lines[0] and 'lr_input_dim:' in lines[
1], err_info
res = map(int, [_.split(':')[1] for _ in lines])
res = list(res)
logger.info('dnn input dim: %d' % res[0])
logger.info('lr input dim: %d' % res[1])
return res
python/paddle/fluid/tests/unittests/dist_mnist.py
浏览文件 @ f7bd1761
......@@ -47,7 +47,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.3)))
value=0.01)))
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......@@ -56,7 +56,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.2)))
value=0.01)))
SIZE = 10
input_shape = conv_pool_2.shape
......@@ -68,7 +68,7 @@ def cnn_model(data):
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
initializer=fluid.initializer.Constant(value=0.01)))
return predict
......
python/paddle/fluid/tests/unittests/dist_simnet_bow.py 0 → 100644
浏览文件 @ f7bd1761
# 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 numpy as np
import argparse
import time
import math
import random
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "int64"
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000'
DATA_MD5 = '24e49366eb0611c552667989de2f57d5'
# For Net
base_lr = 0.2
emb_lr = base_lr * 3
dict_dim = 1500
emb_dim = 128
hid_dim = 128
margin = 0.1
sample_rate = 1
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
def get_acc(cos_q_nt, cos_q_pt, batch_size):
cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
cond = fluid.layers.cast(cond, dtype='float64')
cond_3 = fluid.layers.reduce_sum(cond)
acc = fluid.layers.elementwise_div(
cond_3,
fluid.layers.fill_constant(
shape=[1], value=batch_size * 1.0, dtype='float64'),
name="simnet_acc")
return acc
def get_loss(cos_q_pt, cos_q_nt):
loss_op1 = fluid.layers.elementwise_sub(
fluid.layers.fill_constant_batch_size_like(
input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'),
cos_q_pt)
loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
loss_op3 = fluid.layers.elementwise_max(
fluid.layers.fill_constant_batch_size_like(
input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_op2)
avg_cost = fluid.layers.mean(loss_op3)
return avg_cost
def get_optimizer():
# SGD optimizer
optimizer = fluid.optimizer.SGD(learning_rate=base_lr)
return optimizer
def train_network(batch_size, is_distributed=False, is_sparse=False):
# query
q = fluid.layers.data(
name="query_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
q_emb = fluid.layers.embedding(
input=q,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
## fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__q_fc__",
learning_rate=base_lr))
# label data
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
# pt
pt = fluid.layers.data(
name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
pt_emb = fluid.layers.embedding(
input=pt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
## fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
# nt
nt = fluid.layers.data(
name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
nt_emb = fluid.layers.embedding(
input=nt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
## fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
# loss
avg_cost = get_loss(cos_q_pt, cos_q_nt)
# acc
acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
return [avg_cost, acc, cos_q_pt]
def combination(x, y):
res = [[[xi, yi] for yi in y] for xi in x]
return res[0]
def get_one_data(file_list):
for file in file_list:
contents = []
with open(file, "r") as fin:
for i in fin:
contents.append(i.strip())
for index, q in enumerate(contents):
try:
one_data = [[int(j) for j in i.split(" ")]
for i in q.split(";")[:-1]]
if one_data[1][0] + one_data[1][1] != len(one_data) - 3:
q = fin.readline()
continue
tmp = combination(one_data[3:3 + one_data[1][0]],
one_data[3 + one_data[1][0]:])
except Exception as e:
continue
for each in tmp:
yield [one_data[2], 0, each[0], each[1]]
def get_batch_reader(file_list, batch_size):
def batch_reader():
res = []
for i in get_one_data(file_list):
if random.random() <= sample_rate:
res.append(i)
if len(res) >= batch_size:
yield res
res = []
return batch_reader
def get_train_reader(batch_size):
# The training data set.
train_file = os.path.join(paddle.dataset.common.DATA_HOME, "simnet",
"train")
train_reader = get_batch_reader([train_file], batch_size)
train_feed = ["query_ids", "pos_title_ids", "neg_title_ids", "label"]
return train_reader, train_feed
class TestDistSimnetBow2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
# Train program
avg_cost, acc, predict = \
train_network(batch_size, bool(int(os.environ["IS_DISTRIBUTED"])), bool(int(os.environ["IS_SPARSE"])))
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer()
opt.minimize(avg_cost)
# Reader
train_reader, _ = get_train_reader(batch_size)
return inference_program, avg_cost, train_reader, train_reader, acc, predict
if __name__ == "__main__":
paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
runtime_main(TestDistSimnetBow2x2)
python/paddle/fluid/tests/unittests/dist_text_classification.py 0 → 100644
浏览文件 @ f7bd1761
# 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 numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
import six
import tarfile
import string
import re
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "float32"
VOCAB_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/imdb.vocab'
VOCAB_MD5 = '23c86a0533c0151b6f12fa52b106dcc2'
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/text_classification.tar.gz'
DATA_MD5 = '29ebfc94f11aea9362bbb7f5e9d86b8a'
# Load dictionary.
def load_vocab(filename):
vocab = {}
if six.PY2:
with open(filename, 'r') as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
else:
with open(filename, 'r', encoding="utf-8") as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
return vocab
def get_worddict(dict_path):
word_dict = load_vocab(dict_path)
word_dict["<unk>"] = len(word_dict)
dict_dim = len(word_dict)
return word_dict, dict_dim
def conv_net(input,
dict_dim,
emb_dim=128,
window_size=3,
num_filters=128,
fc0_dim=96,
class_dim=2):
emb = fluid.layers.embedding(
input=input,
size=[dict_dim, emb_dim],
is_sparse=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.01)))
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=num_filters,
filter_size=window_size,
act="tanh",
pool_type="max",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
fc_0 = fluid.layers.fc(
input=[conv_3],
size=fc0_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
prediction = fluid.layers.fc(
input=[fc_0],
size=class_dim,
act="softmax",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
return prediction
def inference_network(dict_dim):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
out = conv_net(data, dict_dim)
return out
def get_reader(word_dict, batch_size):
# The training data set.
train_reader = paddle.batch(train(word_dict), batch_size=batch_size)
# The testing data set.
test_reader = paddle.batch(test(word_dict), batch_size=batch_size)
return train_reader, test_reader
def get_optimizer(learning_rate):
optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
return optimizer
class TestDistTextClassification2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
vocab = os.path.join(paddle.dataset.common.DATA_HOME,
"text_classification", "imdb.vocab")
word_dict, dict_dim = get_worddict(vocab)
# Input data
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = conv_net(data, dict_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=predict, label=label)
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer(learning_rate=0.001)
opt.minimize(avg_cost)
# Reader
train_reader, test_reader = get_reader(word_dict, batch_size)
return inference_program, avg_cost, train_reader, test_reader, acc, predict
def tokenize(pattern):
"""
Read files that match the given pattern. Tokenize and yield each file.
"""
with tarfile.open(
paddle.dataset.common.download(DATA_URL, 'text_classification',
DATA_MD5)) as tarf:
# Note that we should use tarfile.next(), which does
# sequential access of member files, other than
# tarfile.extractfile, which does random access and might
# destroy hard disks.
tf = tarf.next()
while tf != None:
if bool(pattern.match(tf.name)):
# newline and punctuations removal and ad-hoc tokenization.
yield tarf.extractfile(tf).read().rstrip(six.b(
"\n\r")).translate(
None, six.b(string.punctuation)).lower().split()
tf = tarf.next()
def reader_creator(pos_pattern, neg_pattern, word_idx):
UNK = word_idx['<unk>']
INS = []
def load(pattern, out, label):
for doc in tokenize(pattern):
out.append(([word_idx.get(w, UNK) for w in doc], label))
load(pos_pattern, INS, 0)
load(neg_pattern, INS, 1)
def reader():
for doc, label in INS:
yield doc, label
return reader
def train(word_idx):
"""
IMDB training set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
re.compile("train/pos/.*\.txt$"),
re.compile("train/neg/.*\.txt$"), word_idx)
def test(word_idx):
"""
IMDB test set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Test reader creator
:rtype: callable
"""
return reader_creator(
re.compile("test/pos/.*\.txt$"),
re.compile("test/neg/.*\.txt$"), word_idx)
if __name__ == "__main__":
paddle.dataset.common.download(VOCAB_URL, 'text_classification', VOCAB_MD5)
paddle.dataset.common.download(DATA_URL, 'text_classification', DATA_MD5)
runtime_main(TestDistTextClassification2x2)
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ f7bd1761
......@@ -1488,7 +1488,7 @@ def wrap_decoder(trg_vocab_size,
if weight_sharing:
predict = layers.matmul(
x=dec_output,
y=fluid.get_var(word_emb_param_names[0]),
y=fluid.framework._get_var(word_emb_param_names[0]),
transpose_y=True)
else:
predict = layers.fc(input=dec_output,
......@@ -1699,10 +1699,9 @@ class DistTransformer2x2(TestDistRunnerBase):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
TrainTaskConfig.use_gpu = use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......@@ -1718,6 +1717,11 @@ class DistTransformer2x2(TestDistRunnerBase):
TrainTaskConfig.batch_size = 20
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
TrainTaskConfig.local = not args.is_dist
......
python/paddle/fluid/tests/unittests/dist_word2vec.py
浏览文件 @ f7bd1761
......@@ -122,4 +122,7 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
if __name__ == "__main__":
import os
os.environ['CPU_NUM'] = '1'
os.environ['USE_CUDA'] = "FALSE"
runtime_main(TestDistWord2vec2x2)
python/paddle/fluid/tests/unittests/test_auc_op.py
浏览文件 @ f7bd1761
......@@ -36,7 +36,11 @@ class TestAucOp(OpTest):
"StatPos": stat_pos,
"StatNeg": stat_neg
}
self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
self.attrs = {
'curve': 'ROC',
'num_thresholds': num_thresholds,
"slide_steps": 1
}
python_auc = metrics.Auc(name="auc",
curve='ROC',
......@@ -45,7 +49,6 @@ class TestAucOp(OpTest):
self.outputs = {
'AUC': np.array(python_auc.eval()),
'BatchAUC': np.array(python_auc.eval()),
'StatPosOut': np.array(python_auc._stat_pos),
'StatNegOut': np.array(python_auc._stat_neg)
}
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ f7bd1761
......@@ -18,23 +18,27 @@ import time
import unittest
import os
import sys
import six
import signal
import subprocess
import six
import argparse
import paddle.fluid as fluid
RUN_STEP = 10
class TestDistRunnerBase(object):
def get_model(self, batch_size=2):
raise NotImplementedError(
"get_model should be implemented by child classes.")
def get_transpiler(self, trainer_id, main_program, pserver_endpoints,
trainers, sync_mode):
@staticmethod
def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers,
sync_mode):
# NOTE: import fluid until runtime, or else forking processes will cause error.
import paddle
import paddle.fluid as fluid
t = fluid.DistributeTranspiler()
config = fluid.DistributeTranspilerConfig()
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id=trainer_id,
program=main_program,
......@@ -44,9 +48,9 @@ class TestDistRunnerBase(object):
return t
def run_pserver(self, args):
import paddle
import paddle.fluid as fluid
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
t = self.get_transpiler(args.trainer_id,
......@@ -61,12 +65,10 @@ class TestDistRunnerBase(object):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
import paddle
import paddle.fluid as fluid
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
def run_trainer(self, args):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
if args.is_dist:
......@@ -74,16 +76,23 @@ class TestDistRunnerBase(object):
fluid.default_main_program(),
args.endpoints, args.trainers,
args.sync_mode)
trainer_prog = t.get_trainer_program()
else:
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
build_stra = fluid.BuildStrategy()
if args.use_reduce:
......@@ -92,7 +101,7 @@ class TestDistRunnerBase(object):
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
exe = fluid.ParallelExecutor(
use_cuda,
args.use_cuda,
loss_name=avg_cost.name,
exec_strategy=strategy,
build_strategy=build_stra)
......@@ -103,27 +112,26 @@ class TestDistRunnerBase(object):
]
feeder = fluid.DataFeeder(feed_var_list, place)
reader_generator = test_reader()
data = next(reader_generator)
first_loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(data))
print(first_loss)
reader_generator = train_reader()
for i in six.moves.xrange(5):
data = next(reader_generator)
loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
def get_data():
origin_batch = next(reader_generator)
if args.is_dist and args.use_reader_alloc:
new_batch = []
for offset, item in enumerate(origin_batch):
if offset % 2 == args.trainer_id:
new_batch.append(item)
return new_batch
else:
return origin_batch
data = next(reader_generator)
last_loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
print(last_loss)
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
print(loss)
def runtime_main(test_class):
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
parser = argparse.ArgumentParser(description='Run dist test.')
parser.add_argument(
'--role', type=str, required=True, choices=['pserver', 'trainer'])
......@@ -135,7 +143,10 @@ def runtime_main(test_class):
'--current_endpoint', type=str, required=False, default="")
parser.add_argument('--sync_mode', action='store_true')
parser.add_argument('--mem_opt', action='store_true')
parser.add_argument('--use_cuda', action='store_true')
parser.add_argument('--use_reduce', action='store_true')
parser.add_argument(
'--use_reader_alloc', action='store_true', required=False, default=True)
args = parser.parse_args()
......@@ -143,8 +154,7 @@ def runtime_main(test_class):
if args.role == "pserver" and args.is_dist:
model.run_pserver(args)
else:
use_cuda = True if core.is_compiled_with_cuda() else False
model.run_trainer(use_cuda, args)
model.run_trainer(args)
import paddle.compat as cpt
......@@ -163,8 +173,10 @@ class TestDistBase(unittest.TestCase):
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._use_cuda = True
self._mem_opt = False
self._use_reduce = False
self._use_reader_alloc = True
self._setup_config()
def _find_free_port(self):
......@@ -172,15 +184,15 @@ class TestDistBase(unittest.TestCase):
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
def start_pserver(self, model_file, check_error_log, required_envs):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
ps0_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
ps1_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
if self._sync_mode:
ps0_cmd += " --sync_mode"
......@@ -198,9 +210,15 @@ class TestDistBase(unittest.TestCase):
ps1_pipe = open("/tmp/ps1_err.log", "wb")
ps0_proc = subprocess.Popen(
ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe)
ps0_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps0_pipe,
env=required_envs)
ps1_proc = subprocess.Popen(
ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe)
ps1_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps1_pipe,
env=required_envs)
if not check_error_log:
return ps0_proc, ps1_proc, None, None
......@@ -222,59 +240,60 @@ class TestDistBase(unittest.TestCase):
(e, retry_times))
retry_times -= 1
def check_with_place(self, model_file, delta=1e-3, check_error_log=False):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
"CPU_NUM": "1"
}
def _run_local(self, model, envs, check_error_log):
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
cmd = "%s %s --role trainer" % (self._python_interp, model)
if self._use_cuda:
cmd += " --use_cuda"
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
else:
env_local = {'CPU_NUM': '1'}
envs.update(env_local)
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s %s --role trainer" % (self._python_interp, model_file)
if not check_error_log:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
stderr=err_log,
env=envs)
else:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=err_log,
env=env_local)
stderr=subprocess.PIPE,
env=envs)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = cpt.to_text(out)
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
local_out, local_err = local_proc.communicate()
local_ret = cpt.to_text(local_out)
if check_error_log:
err_log.close()
sys.stderr.write('local_stdout: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % local_err)
local_losses = local_ret.split("\n")
return local_losses
def _run_cluster(self, model, envs, check_error_log):
# Run dist train to compare with local results
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model_file,
check_error_log)
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model,
check_error_log, envs)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
tr0_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
0, ps0_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
0, ps0_ep, self._trainers)
tr1_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
1, ps1_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
1, ps1_ep, self._trainers)
if self._sync_mode:
tr0_cmd += " --sync_mode"
......@@ -285,18 +304,28 @@ class TestDistBase(unittest.TestCase):
if self._use_reduce:
tr0_cmd += " --use_reduce"
tr1_cmd += " --use_reduce"
if self._use_reader_alloc:
tr0_cmd += " --use_reader_alloc"
tr1_cmd += " --use_reader_alloc"
if self._use_cuda:
tr0_cmd += " --use_cuda"
tr1_cmd += " --use_cuda"
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
else:
env0 = {'CPU_NUM': '1'}
env1 = {'CPU_NUM': '1'}
env0.update(envs)
env1.update(envs)
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0.update(required_envs)
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_pipe = subprocess.PIPE
tr1_pipe = subprocess.PIPE
if check_error_log:
print("tr0_cmd:", tr0_cmd)
print("tr1_cmd:", tr1_cmd)
print("tr0_cmd:{}, env0: {}".format(tr0_cmd, env0))
print("tr1_cmd:{}, env1: {}".format(tr1_cmd, env1))
tr0_pipe = open("/tmp/tr0_err.log", "wb")
tr1_pipe = open("/tmp/tr1_err.log", "wb")
......@@ -313,17 +342,11 @@ class TestDistBase(unittest.TestCase):
tr0_proc.wait()
tr1_proc.wait()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = cpt.to_text(out)
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
local_lines = local_ret.split("\n")
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
tr0_out, tr0_err = tr0_proc.communicate()
tr0_loss_text = cpt.to_text(tr0_out)
tr1_out, tr1_err = tr1_proc.communicate()
tr1_loss_text = cpt.to_text(tr1_out)
# close trainer file
if check_error_log:
......@@ -341,5 +364,47 @@ class TestDistBase(unittest.TestCase):
ps1.wait()
FNULL.close()
self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
# print log
sys.stderr.write('trainer 0 stdout:\n %s\n' % tr0_loss_text)
sys.stderr.write('trainer 0 stderr:\n %s\n' % tr0_err)
sys.stderr.write('trainer 1 stdout: %s\n' % tr1_loss_text)
sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
tr0_losses = tr0_loss_text.split("\n")
tr1_losses = tr1_loss_text.split("\n")
return tr0_losses, tr1_losses
def check_with_place(self,
model_file,
delta=1e-3,
check_error_log=False,
need_envs={}):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
}
required_envs.update(need_envs)
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
local_losses\
= self._run_local(model_file, required_envs,
check_error_log)
tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs,
check_error_log)
for step_id in range(RUN_STEP):
local_loss = eval(local_losses[step_id])[0]
tr0_loss = eval(tr0_losses[step_id])[0]
tr1_loss = eval(tr1_losses[step_id])[0]
dist_loss = (tr0_loss + tr1_loss) / 2
print(str(local_loss) + ":" + str(dist_loss))
self.assertAlmostEqual(local_loss, dist_loss, delta=delta)
python/paddle/fluid/tests/unittests/test_dist_ctr.py 0 → 100644
浏览文件 @ f7bd1761
# 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 unittest
from test_dist_base import TestDistBase
class TestDistCTR2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_dist_ctr(self):
self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ f7bd1761
......@@ -23,7 +23,7 @@ class TestDistMnist2x2(TestDistBase):
self._use_reduce = False
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnist2x2WithMemopt(TestDistBase):
......@@ -32,7 +32,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnistAsync(TestDistBase):
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ f7bd1761
......@@ -20,9 +20,10 @@ from test_dist_base import TestDistBase
class TestDistSeResneXt2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
self.check_with_place("dist_se_resnext.py", delta=100)
# TODO(typhoonzero): fix this test
......@@ -38,6 +39,7 @@ class TestDistSeResneXt2x2(TestDistBase):
class TestDistSeResneXt2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py 0 → 100644
浏览文件 @ f7bd1761
# 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 unittest
from test_dist_base import TestDistBase
class TestDistSimnetBowDense2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2DenseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBowSparse2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2SparseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_text_classification.py 0 → 100644
浏览文件 @ f7bd1761
# 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 unittest
from test_dist_base import TestDistBase
class TestDistTextClassification2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_text_classification(self):
self.check_with_place("dist_text_classification.py", delta=1e-6)
class TestDistTextClassification2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_se_resnext(self):
self.check_with_place("dist_text_classification.py", delta=100)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ f7bd1761
......@@ -264,6 +264,25 @@ class TestLRDecay(TranspilerTest):
])
class TestDecayedAdagrad(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
opt = fluid.optimizer.DecayedAdagrad(learning_rate=0.1)
opt.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
class TestLRDecayConditional(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
......
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @ f7bd1761
......@@ -39,7 +39,7 @@ class TestDistW2V2x2Async(TestDistBase):
self._sync_mode = False
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1)
self.check_with_place("dist_word2vec.py", delta=100)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_infer_shape.py
浏览文件 @ f7bd1761
......@@ -76,8 +76,8 @@ class TestInferShape(unittest.TestCase):
mul_op_desc.set_input("X", ["x"])
mul_op_desc.set_input("Y", ["y"])
mul_op_desc.set_output("Out", ["out"])
mul_op_desc.set_attr("x_num_col_dims", 1)
mul_op_desc.set_attr("y_num_col_dims", 1)
mul_op_desc._set_attr("x_num_col_dims", 1)
mul_op_desc._set_attr("y_num_col_dims", 1)
mul_op_desc.check_attrs()
mul_op_desc.infer_shape(block)
......
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ f7bd1761
......@@ -38,40 +38,40 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(['z'], op.output("Out"))
self.assertEqual(["Out"], op.output_names())
op.set_attr("int_attr", 1)
op._set_attr("int_attr", 1)
self.assertEqual(1, op.attr("int_attr"))
self.assertTrue(op.has_attr("int_attr"))
self.assertEqual(core.AttrType.INT, op.attr_type("int_attr"))
op.set_attr("float_attr", -1.32)
op._set_attr("float_attr", -1.32)
self.assertAlmostEqual(-1.32, op.attr("float_attr"), delta=1e-4)
self.assertTrue(op.has_attr("float_attr"))
op.set_attr("bool_attr", False)
op._set_attr("bool_attr", False)
self.assertFalse(op.attr("bool_attr"))
op.set_attr("string_attr", "abc")
op._set_attr("string_attr", "abc")
self.assertEqual("abc", op.attr("string_attr"))
self.assertTrue(op.has_attr("string_attr"))
op.set_attr("ints_attr", [1, 2, 3])
op._set_attr("ints_attr", [1, 2, 3])
self.assertEqual([1, 2, 3], op.attr("ints_attr"))
expected = [1.2, 2.3, 3.4]
op.set_attr("floats_attr", expected)
op._set_attr("floats_attr", expected)
for e, a in zip(expected, op.attr("floats_attr")):
self.assertAlmostEqual(e, a, delta=1e-4)
op.set_attr("strings_attr", ["a", "b", "c"])
op._set_attr("strings_attr", ["a", "b", "c"])
self.assertEqual(["a", "b", "c"], op.attr("strings_attr"))
op.set_attr("bools_attr", [True, False, True])
op._set_attr("bools_attr", [True, False, True])
self.assertEqual([True, False, True], op.attr("bools_attr"))
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr_id("block_attr"))
op.set_block_attr("_block_attr", program_desc.block(0))
self.assertEqual(0, op._block_attr_id("_block_attr"))
mul_op = block.append_op()
mul_op.set_type("mul")
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ f7bd1761
......@@ -128,7 +128,7 @@ def op_to_code(op):
attr_type = op.desc.attr_type(name)
if attr_type == core.AttrType.BLOCK:
a = "{name} = block[{value}]".format(
name=name, type=attr_type, value=op.block_attr_id(name))
name=name, type=attr_type, value=op._block_attr_id(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......@@ -136,7 +136,7 @@ def op_to_code(op):
if attr_type == core.AttrType.BLOCKS:
a = "{name} = blocks{value}".format(
name=name, type=attr_type, value=op.blocks_attr_ids(name))
name=name, type=attr_type, value=op._blocks_attr_ids(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ f7bd1761
......@@ -39,8 +39,8 @@ import six
from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, Block, \
Parameter, grad_var_name
default_startup_program, Block, \
Parameter, grad_var_name
from .details import *
from functools import reduce
......@@ -178,7 +178,7 @@ class DistributeTranspiler(object):
pserver_program)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
......@@ -534,7 +534,7 @@ class DistributeTranspiler(object):
})
for varname, splited_var in six.iteritems(self.param_var_mapping):
#add concat ops to merge splited parameters received from parameter servers.
# add concat ops to merge splited parameters received from parameter servers.
if len(splited_var) <= 1:
continue
# NOTE: if enable memory optimization, origin vars maybe removed.
......@@ -668,7 +668,7 @@ in a single call.")
__clone_lr_op_sub_block__(cloned_op, program, new_sub_block)
# reset the block of op
op.set_attr('sub_block', new_sub_block)
op._set_attr('sub_block', new_sub_block)
# append lr decay ops to the child block if exists
lr_ops = self._get_lr_ops()
......@@ -734,19 +734,14 @@ in a single call.")
table_opt_block = self._create_table_optimize_block(
pserver_index, pserver_program, pre_block_idx, grad_to_block_id)
optimize_blocks.append(table_opt_block)
prefetch_var_name_to_block_id = self._create_prefetch_block(
lookup_table_var_name_to_block_id = self._create_prefetch_block(
pserver_index, pserver_program, table_opt_block)
checkpoint_block_id = self._create_checkpoint_save_block(
pserver_program, table_opt_block.idx)
pserver_program._distributed_lookup_table = self.table_name
# NOTE: if has_distributed_lookup_table is False, then prefetch_block will
# not be executed, so it's safe to use optimize_block to hold the place
if self.has_distributed_lookup_table:
assert len(prefetch_var_name_to_block_id) > 0
else:
assert len(prefetch_var_name_to_block_id) == 0
prefetch_var_name_to_block_id.extend(
lookup_table_var_name_to_block_id)
attrs = {
"optimize_blocks": optimize_blocks,
......@@ -755,11 +750,14 @@ in a single call.")
"sync_mode": self.sync_mode,
"grad_to_block_id": grad_to_block_id,
}
if len(prefetch_var_name_to_block_id) > 0:
attrs['prefetch_var_name_to_block_id'] \
= prefetch_var_name_to_block_id
if self.has_distributed_lookup_table:
attrs['checkpint_block_id'] = checkpoint_block_id
if len(prefetch_var_name_to_block_id) > 0:
attrs[
'prefetch_var_name_to_block_id'] = prefetch_var_name_to_block_id
# step5 append the listen_and_serv op
pserver_program.global_block().append_op(
type="listen_and_serv",
......@@ -864,7 +862,7 @@ to transpile() call.")
if op.type in [
"gaussian_random", "fill_constant", "uniform_random"
]:
op.set_attr("shape", list(new_outputs["Out"].shape))
op._set_attr("shape", list(new_outputs["Out"].shape))
s_prog.global_block().append_op(
type=op.type,
inputs=new_inputs,
......@@ -1013,7 +1011,7 @@ to transpile() call.")
for g, p in zip(grad_blocks, param_blocks):
g_name, g_bid, _ = g.split(":")
p_name, p_bid, _ = p.split(":")
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.param_var_mapping[p_name][int(p_bid)]
# create mapping of endpoint -> split var to create pserver side program
......@@ -1320,7 +1318,7 @@ to transpile() call.")
if len(splited) == 1:
if self.sync_mode and add_trainer_suffix:
new_var_name = "%s.trainer_%d" % \
(orig_var.name, self.trainer_id)
(orig_var.name, self.trainer_id)
program.global_block()._rename_var(varname, new_var_name)
var_mapping[varname] = \
[program.global_block().var(new_var_name)]
......@@ -1343,10 +1341,10 @@ to transpile() call.")
new_var_name = ""
if self.sync_mode and add_trainer_suffix:
new_var_name = "%s.block%d.trainer_%d" % \
(varname, i, self.trainer_id)
(varname, i, self.trainer_id)
else:
new_var_name = "%s.block%d" % \
(varname, i)
(varname, i)
var = program.global_block().create_var(
name=new_var_name,
persistable=False,
......@@ -1430,6 +1428,9 @@ to transpile() call.")
elif op_type == "rmsprop":
if varkey in ["Moment", "MeanSquare"]:
return param_shape
elif op_type == "decayed_adagrad":
if varkey == "Moment":
return param_shape
elif op_type == "sgd":
pass
return orig_shape
......@@ -1484,9 +1485,8 @@ to transpile() call.")
vars2merge = []
for i in range(self.trainer_num):
per_trainer_name = "%s.trainer_%d" % \
(merged_var_name, i)
(merged_var_name, i)
vars2merge.append(pserver_block.vars[per_trainer_name])
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
......@@ -1645,7 +1645,7 @@ to transpile() call.")
# one op's output is another op's input, we say
# the two operator is connected.
if set(op1.desc.output_arg_names()) & set(op2.desc.input_arg_names()) or \
set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()):
set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()):
return True
return False
......@@ -1662,7 +1662,7 @@ to transpile() call.")
def _is_optimizer_op(self, op):
if "Param" in op.input_names and \
"LearningRate" in op.input_names:
"LearningRate" in op.input_names:
return True
return False
......@@ -1737,7 +1737,7 @@ to transpile() call.")
# NOTE: we need to skip all optimize ops, since it is connected
# with forward/backward ops and lr ops, we only need the lr ops.
if op1 != op2 and self._is_op_connected(op1, op2) and \
not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
ufind.union(op1, op2)
# find all ops which is related with lr var
for op1 in block.ops:
......
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ f7bd1761
......@@ -163,7 +163,7 @@ class InferenceTranspiler(object):
next_op = self.block.ops[i + 1]
if next_op.type == 'relu':
# modify bnorm OP to include relu
current_op.set_attr("fuse_with_relu", True)
current_op._set_attr("fuse_with_relu", True)
# remove relu OP
self.block._remove_op(i + 1)
i = i + 1
......@@ -377,7 +377,7 @@ class InferenceTranspiler(object):
type=old_var.type,
dtype=old_var.dtype,
shape=old_var.shape)
op.rename_input(old_param_name, new_param_name)
op._rename_input(old_param_name, new_param_name)
self.scope.var(new_param_name)
tensor = self.scope.find_var(new_param_name).get_tensor()
......@@ -463,8 +463,8 @@ class InferenceTranspiler(object):
current_op = self.block.ops[i]
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......
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