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提交 c13efe02 编写于 作者: N nhzlx
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_tensorrt_elementwise_add

上级 b241a47e baff71d5
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Showing with 1010 addition and 365 deletion
paddle/fluid/framework/details/exception_holder.h 0 → 100644
浏览文件 @ c13efe02
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace details {
class ExceptionHolder {
public:
void Catch(const platform::EnforceNotMet& exp) {
std::lock_guard<std::mutex> lock(mu_);
exception_.reset(new platform::EnforceNotMet(exp));
type_ = kEnforceNotMet;
}
void Catch(const platform::EOFException& exp) {
std::lock_guard<std::mutex> lock(mu_);
// EOFException will not cover up existing EnforceNotMet.
if (exception_.get() == nullptr) {
exception_.reset(new platform::EOFException(exp));
type_ = kEOF;
}
}
bool ExceptionCatched() const {
std::lock_guard<std::mutex> lock(mu_);
return exception_.get() != nullptr;
}
void Throw() {
std::lock_guard<std::mutex> lock(mu_);
switch (type_) {
case kNone:
break;
case kEnforceNotMet: {
auto e = *static_cast<platform::EnforceNotMet*>(exception_.get());
throw e;
break;
}
case kEOF: {
auto e = *static_cast<platform::EOFException*>(exception_.get());
throw e;
break;
}
default:
LOG(FATAL) << "Unknown exception.";
}
exception_.reset();
type_ = kNone;
}
void Clear() {
std::lock_guard<std::mutex> lock(mu_);
exception_.reset();
type_ = kNone;
}
private:
enum ExceptionType { kNone, kEnforceNotMet, kEOF };
ExceptionType type_{kNone};
std::unique_ptr<std::exception> exception_;
mutable std::mutex mu_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
浏览文件 @ c13efe02
......@@ -41,7 +41,9 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
std::unique_ptr<SSAGraphExecutor>&& underlying_executor);
const ir::Graph& Graph() const { return underlying_executor_->Graph(); }
const ir::Graph& Graph() const override {
return underlying_executor_->Graph();
}
FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ c13efe02
......@@ -83,7 +83,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
// Clean run context
run_op_futures_.clear();
exception_.reset();
exception_holder_.Clear();
// Step 3. Execution
while (!pending_vars.empty()) {
......@@ -103,23 +103,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
if (timeout) {
std::unique_lock<std::mutex> l(exception_mu_);
if (exception_) {
l.unlock();
if (exception_holder_.ExceptionCatched()) {
for (auto &run_op_future : run_op_futures_) {
run_op_future.wait();
}
l.lock();
std::exception *exp = exception_.get();
if (dynamic_cast<platform::EOFException *>(exp)) {
auto e = *static_cast<platform::EOFException *>(exp);
throw e;
} else if (dynamic_cast<platform::EnforceNotMet *>(exp)) {
auto e = *static_cast<platform::EnforceNotMet *>(exp);
throw e;
} else {
LOG(FATAL) << "Unknown exception.";
}
exception_holder_.Throw();
} else {
continue;
}
......@@ -229,14 +217,9 @@ void ThreadedSSAGraphExecutor::RunOp(
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << "Signal posted";
} catch (platform::EOFException ex) {
std::lock_guard<std::mutex> l(exception_mu_);
// EOFException will not cover up existing EnforceNotMet.
if (exception_.get() == nullptr) {
exception_.reset(new platform::EOFException(ex));
}
exception_holder_.Catch(ex);
} catch (platform::EnforceNotMet ex) {
std::lock_guard<std::mutex> l(exception_mu_);
exception_.reset(new platform::EnforceNotMet(ex));
exception_holder_.Catch(ex);
} catch (...) {
LOG(FATAL) << "Unknown exception catched";
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ c13efe02
......@@ -24,6 +24,7 @@
#include <functional>
#include "ThreadPool.h" // ThreadPool in thrird party
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
......@@ -42,7 +43,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
const ir::Graph &Graph() const { return *graph_; }
const ir::Graph &Graph() const override { return *graph_; }
// Run a SSAGraph by a thread pool
// Use topological sort algorithm
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
......@@ -59,8 +60,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
std::mutex exception_mu_;
std::unique_ptr<std::exception> exception_;
ExceptionHolder exception_holder_;
std::atomic<int> running_ops_;
void InsertPendingOp(std::unordered_map<OpHandleBase *, size_t> *pending_ops,
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ c13efe02
......@@ -6,9 +6,11 @@ cc_library(analysis SRCS pass_manager.cc dot.cc node.cc data_flow_graph.cc graph
tensorrt_subgraph_node_mark_pass.cc
analyzer.cc
helper.cc
model_store_pass.cc
DEPS framework_proto proto_desc)
cc_test(test_node SRCS node_tester.cc DEPS analysis)
cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
......@@ -40,3 +42,4 @@ inference_analysis_test(test_tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass_
inference_analysis_test(test_pass_manager SRCS pass_manager_tester.cc)
inference_analysis_test(test_tensorrt_subgraph_node_mark_pass SRCS tensorrt_subgraph_node_mark_pass_tester.cc)
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc)
inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ c13efe02
......@@ -17,6 +17,7 @@
#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include "paddle/fluid/inference/analysis/model_store_pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
......@@ -29,6 +30,9 @@ DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, false,
DEFINE_string(inference_analysis_graphviz_log_root, "./",
"Graphviz debuger for data flow graphs.");
DEFINE_string(inference_analysis_output_storage_path, "",
"optimized model output path");
namespace inference {
namespace analysis {
......@@ -47,6 +51,9 @@ class DfgPassManagerImpl final : public DfgPassManager {
AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
}
AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
if (!FLAGS_inference_analysis_output_storage_path.empty()) {
AddPass("model-store-pass", new ModelStorePass);
}
}
std::string repr() const override { return "dfg-pass-manager"; }
......
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ c13efe02
......@@ -16,28 +16,23 @@ limitations under the License. */
/*
* This file contains Analyzer, an class that exposed as a library that analyze
* and optimize
* Fluid ProgramDesc for inference. Similar to LLVM, it has multiple flags to
* control whether
* an process is applied on the program.
* and optimize Fluid ProgramDesc for inference. Similar to LLVM, it has
* multiple flags to
* control whether an process is applied on the program.
*
* The processes are called Passes in analysis, the Passes are placed in a
* pipeline, the first
* Pass is the FluidToDataFlowGraphPass which transforms a Fluid ProgramDesc to
* a data flow
* graph, the last Pass is DataFlowGraphToFluidPass which transforms a data flow
* graph to a
* Fluid ProgramDesc. The passes in the middle of the pipeline can be any Passes
* which take a
* node or data flow graph as input.
* pipeline, the first Pass is the FluidToDataFlowGraphPass which transforms a
* Fluid ProgramDesc to
* a data flow graph, the last Pass is DataFlowGraphToFluidPass which transforms
* a data flow graph to a Fluid ProgramDesc. The passes in the middle of the
* pipeline can be any Passes
* which take a node or data flow graph as input.
*
* The Analyzer can be used in two methods, the first is a executable file which
* can be used to
* pre-process the inference model and can be controlled by passing difference
* command flags;
* can be used to pre-process the inference model and can be controlled by
* passing difference command flags;
* the other way is to compose inside the inference API as a runtime pre-process
* phase in the
* inference service.
* phase in the inference service.
*/
#include <gflags/gflags.h>
......@@ -50,6 +45,7 @@ namespace paddle {
// flag if not available.
DECLARE_bool(inference_analysis_enable_tensorrt_subgraph_engine);
DECLARE_string(inference_analysis_graphviz_log_root);
DECLARE_string(inference_analysis_output_storage_path);
namespace inference {
namespace analysis {
......
paddle/fluid/inference/analysis/analyzer_main.cc 0 → 100644
浏览文件 @ c13efe02
// 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.
/*
* This file implements analysizer -- an executation help to analyze and
* optimize trained model.
*/
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h>
int main(int argc, char** argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
using paddle::inference::analysis::Analyzer;
using paddle::inference::analysis::Argument;
Argument argument;
Analyzer analyzer;
analyzer.Run(&argument);
return 0;
}
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ c13efe02
......@@ -20,14 +20,18 @@ namespace paddle {
namespace inference {
namespace analysis {
TEST_F(DFG_Tester, analysis_without_tensorrt) {
TEST(Analyzer, analysis_without_tensorrt) {
FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = false;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
TEST_F(DFG_Tester, analysis_with_tensorrt) {
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
......
paddle/fluid/inference/analysis/argument.h
浏览文件 @ c13efe02
......@@ -36,6 +36,16 @@ namespace analysis {
* All the fields should be registered here for clearness.
*/
struct Argument {
Argument() = default;
explicit Argument(const std::string& fluid_model_dir)
: fluid_model_dir(new std::string(fluid_model_dir)) {}
// The directory of the trained model.
std::unique_ptr<std::string> fluid_model_dir;
// The path of `__model__` and `param`, this is used when the file name of
// model and param is changed.
std::unique_ptr<std::string> fluid_model_program_path;
std::unique_ptr<std::string> fluid_model_param_path;
// The graph that process by the Passes or PassManagers.
std::unique_ptr<DataFlowGraph> main_dfg;
......@@ -44,6 +54,9 @@ struct Argument {
// The processed program desc.
std::unique_ptr<framework::proto::ProgramDesc> transformed_program_desc;
// The output storage path of ModelStorePass.
std::unique_ptr<std::string> model_output_store_path;
};
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
......
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ c13efe02
......@@ -36,6 +36,8 @@ namespace analysis {
/*
* DataFlowGraph - A container of Value and Function Nodes.
*
* This is the base graph for any other type of graphs, such as SSA or CFG.
*/
struct DataFlowGraph {
NodeMap nodes;
......
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
浏览文件 @ c13efe02
......@@ -20,7 +20,7 @@ namespace inference {
namespace analysis {
TEST(DataFlowGraph, BFS) {
auto desc = LoadProgramDesc();
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
dfg.Build();
......@@ -44,7 +44,7 @@ TEST(DataFlowGraph, BFS) {
}
TEST(DataFlowGraph, DFS) {
auto desc = LoadProgramDesc();
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
dfg.Build();
GraphTraits<DataFlowGraph> trait(&dfg);
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass_tester.cc
浏览文件 @ c13efe02
......@@ -26,21 +26,21 @@ namespace paddle {
namespace inference {
namespace analysis {
TEST_F(DFG_Tester, Test) {
DataFlowGraph graph;
TEST(DataFlowGraph, Test) {
Argument argument(FLAGS_inference_model_dir);
FluidToDataFlowGraphPass pass0;
DataFlowGraphToFluidPass pass1;
ASSERT_TRUE(pass0.Initialize(&argument));
ASSERT_TRUE(pass1.Initialize(&argument));
pass0.Run(&graph);
pass1.Run(&graph);
pass0.Run(argument.main_dfg.get());
pass1.Run(argument.main_dfg.get());
pass0.Finalize();
pass1.Finalize();
LOG(INFO) << graph.nodes.size();
LOG(INFO) << argument.main_dfg->nodes.size();
}
}; // namespace analysis
......
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc
浏览文件 @ c13efe02
......@@ -23,12 +23,18 @@ namespace paddle {
namespace inference {
namespace analysis {
TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
auto dfg = ProgramDescToDFG(*argument.origin_program_desc);
TEST(DFG_GraphvizDrawPass, dfg_graphviz_draw_pass_tester) {
Argument argument(FLAGS_inference_model_dir);
FluidToDataFlowGraphPass pass0;
ASSERT_TRUE(pass0.Initialize(&argument));
pass0.Run(argument.main_dfg.get());
// auto dfg = ProgramDescToDFG(*argument.origin_program_desc);
DFG_GraphvizDrawPass::Config config("./", "test");
DFG_GraphvizDrawPass pass(config);
pass.Initialize(&argument);
pass.Run(&dfg);
pass.Run(argument.main_dfg.get());
// test content
std::ifstream file("./0-graph_test.dot");
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc
浏览文件 @ c13efe02
......@@ -12,6 +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 <glog/logging.h>
#include <string>
#include <vector>
......@@ -25,8 +26,20 @@ namespace analysis {
bool FluidToDataFlowGraphPass::Initialize(Argument *argument) {
ANALYSIS_ARGUMENT_CHECK_FIELD(argument);
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc);
PADDLE_ENFORCE(argument);
if (argument->origin_program_desc) {
LOG(WARNING) << "argument's origin_program_desc is already set, might "
"duplicate called";
}
if (!argument->fluid_model_program_path) {
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_dir);
argument->fluid_model_program_path.reset(
new std::string(*argument->fluid_model_dir + "/__model__"));
}
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_program_path);
auto program = LoadProgramDesc(*argument->fluid_model_program_path);
argument->origin_program_desc.reset(
new framework::proto::ProgramDesc(program));
if (!argument->main_dfg) {
argument->main_dfg.reset(new DataFlowGraph);
}
......@@ -40,6 +53,8 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(desc_);
// insert vars
// The `var2id` keeps a map from a variable's name to its Node-id, the Node-id
// will keep updating to its latest alias during the graph-building.
std::unordered_map<std::string, size_t> var2id;
auto &main_block = desc_->blocks(framework::kRootBlockIndex);
for (int i = 0; i < main_block.vars_size(); i++) {
......@@ -51,6 +66,15 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
var2id[var.name()] = v->id();
}
// The variables in a SSA can only write once, so if a variable is written
// multiple times(quite common in our ProgramDesc design), multiple alias
// Nodes of this variable will be created, and each will just write once.
// An set that keep all the names of the variables(the original, not alias)
// that have been written(as outputs). Once an Op's output variable hit the
// set, it should create a new alias and update the global alias for this
// variable. And that make a Data Flow Graph a SSA.
std::unordered_set<Node *> unique_written_vars;
for (int i = 0; i < main_block.ops_size(); i++) {
const auto &op = main_block.ops(i);
auto *o = graph->nodes.Create(Node::Type::kFunction);
......@@ -62,33 +86,33 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
o->SetPbMsg(op.SerializeAsString());
// set inputs and outputs
std::unordered_set<Node *> inlinks;
for (int j = 0; j < op.inputs_size(); j++) {
auto &in_var = op.inputs(j);
for (int k = 0; k < in_var.arguments_size(); k++) {
auto *in = graph->nodes.GetMutable(var2id.at(in_var.arguments(k)));
in->outlinks.push_back(o);
o->inlinks.push_back(in);
inlinks.insert(in);
}
}
for (int j = 0; j < op.outputs_size(); j++) {
auto &out_var = op.outputs(j);
for (int k = 0; k < out_var.arguments_size(); k++) {
auto *out = graph->nodes.GetMutable(var2id[out_var.arguments(k)]);
if (inlinks.count(out)) {
if (unique_written_vars.count(out)) {
// Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
auto *out_alias = graph->nodes.Create(Node::Type::kValue);
out_alias->SetName(out->name());
out_alias->SetPbDesc(out->pb_desc());
out_alias->SetPbMsg(out->pb_msg());
var2id[out_alias->name()] = out_alias->id(); // update a -> a0
var2id[out_alias->name()] =
out_alias->id(); // update variable's alias Node
LOG(INFO) << "loop found in graph, create SSA alias node ["
<< out_alias->repr() << "] for [" << out->repr() << "]";
out = out_alias;
}
out->inlinks.push_back(o);
o->outlinks.push_back(out);
unique_written_vars.insert(out);
}
}
}
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h
浏览文件 @ c13efe02
......@@ -30,7 +30,7 @@ namespace inference {
namespace analysis {
/*
* Transform a FluidDesc to a data flow graph.
* Transform a FluidDesc to a SSA.
*/
class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
public:
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc
浏览文件 @ c13efe02
......@@ -21,8 +21,9 @@ namespace paddle {
namespace inference {
namespace analysis {
TEST_F(DFG_Tester, Init) {
TEST(FluidToDataFlowGraphPass, Test) {
FluidToDataFlowGraphPass pass;
Argument argument(FLAGS_inference_model_dir);
pass.Initialize(&argument);
pass.Run(argument.main_dfg.get());
// Analysis is sensitive to ProgramDesc, careful to change the original model.
......
paddle/fluid/inference/analysis/helper.h
浏览文件 @ c13efe02
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <cstdio>
#include <fstream>
#include <string>
#include <typeindex>
#include <unordered_map>
......@@ -136,6 +137,20 @@ static void ExecShellCommand(const std::string &cmd, std::string *message) {
}
}
static framework::proto::ProgramDesc LoadProgramDesc(
const std::string &model_path) {
std::ifstream fin(model_path, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(fin.is_open(), "Cannot open file %s", model_path);
fin.seekg(0, std::ios::end);
std::string buffer(fin.tellg(), ' ');
fin.seekg(0, std::ios::beg);
fin.read(&buffer[0], buffer.size());
fin.close();
framework::proto::ProgramDesc program_desc;
program_desc.ParseFromString(buffer);
return program_desc;
}
} // namespace analysis
} // namespace inference
} // namespace paddle
......
paddle/fluid/inference/analysis/model_store_pass.cc 0 → 100644
浏览文件 @ c13efe02
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/model_store_pass.h"
#include <stdio.h>
#include <stdlib.h>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/argument.h"
namespace paddle {
namespace inference {
namespace analysis {
void ModelStorePass::Run(DataFlowGraph *x) {
if (!argument_->fluid_model_param_path) {
PADDLE_ENFORCE_NOT_NULL(argument_->fluid_model_dir);
argument_->fluid_model_param_path.reset(
new std::string(*argument_->fluid_model_dir + "param"));
}
PADDLE_ENFORCE_NOT_NULL(argument_->model_output_store_path);
// Directly copy param file to destination.
std::stringstream ss;
// NOTE these commands only works on linux.
ss << "mkdir -p " << *argument_->model_output_store_path;
LOG(INFO) << "run command: " << ss.str();
PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
ss.str("");
ss << "cp " << *argument_->fluid_model_dir << "/*"
<< " " << *argument_->model_output_store_path;
LOG(INFO) << "run command: " << ss.str();
PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
// Store program
PADDLE_ENFORCE_NOT_NULL(argument_->transformed_program_desc,
"program desc is not transformed, should call "
"DataFlowGraphToFluidPass first.");
const std::string program_output_path =
*argument_->model_output_store_path + "/__model__";
std::ofstream file(program_output_path, std::ios::binary);
PADDLE_ENFORCE(file.is_open(), "failed to open %s to write.",
program_output_path);
const std::string serialized_message =
argument_->transformed_program_desc->SerializeAsString();
file.write(serialized_message.c_str(), serialized_message.size());
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/model_store_pass.h 0 → 100644
浏览文件 @ c13efe02
// 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.
/*
* This file defines ModelStorePass, which store the runtime DFG to a Paddle
* model in the disk, and that model can be reloaded for prediction.
*/
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
namespace analysis {
class ModelStorePass : public DataFlowGraphPass {
public:
bool Initialize(Argument* argument) override {
if (!argument) {
LOG(ERROR) << "invalid argument";
return false;
}
argument_ = argument;
return true;
}
void Run(DataFlowGraph* x) override;
std::string repr() const override { return "DFG-store-pass"; }
std::string description() const override {
return R"DD(This file defines ModelStorePass, which store the runtime DFG to a Paddle
model in the disk, and that model can be reloaded for prediction again.)DD";
}
private:
Argument* argument_{nullptr};
};
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/model_store_pass_tester.cc 0 → 100644
浏览文件 @ c13efe02
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/model_store_pass.h"
#include <gflags/gflags.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/analyzer.h"
namespace paddle {
namespace inference {
namespace analysis {
DEFINE_string(inference_model_dir, "", "Model path");
TEST(DFG_StorePass, test) {
Analyzer analyzer;
Argument argument(FLAGS_inference_model_dir);
argument.model_output_store_path.reset(
new std::string("./_dfg_store_pass_tmp"));
// disable storage in alalyzer
FLAGS_inference_analysis_output_storage_path = "";
analyzer.Run(&argument);
ModelStorePass pass;
pass.Initialize(&argument);
pass.Run(argument.main_dfg.get());
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/pass.h
浏览文件 @ c13efe02
......@@ -50,6 +50,7 @@ class Pass {
// Create a debugger Pass that draw the DFG by graphviz toolkit.
virtual Pass *CreateGraphvizDebugerPass() const { return nullptr; }
virtual void Run() { LOG(FATAL) << "not valid"; }
// Run on a single Node.
virtual void Run(Node *x) { LOG(FATAL) << "not valid"; }
// Run on a single Function.
......
paddle/fluid/inference/analysis/pass_manager_tester.cc
浏览文件 @ c13efe02
......@@ -56,7 +56,7 @@ class TestNodePass final : public NodePass {
std::string description() const override { return "some doc"; }
};
TEST_F(DFG_Tester, DFG_pass_manager) {
TEST(PassManager, DFG_pass_manager) {
TestDfgPassManager manager;
DFG_GraphvizDrawPass::Config config("./", "dfg.dot");
......@@ -64,12 +64,15 @@ TEST_F(DFG_Tester, DFG_pass_manager) {
manager.Register("graphviz", new DFG_GraphvizDrawPass(config));
manager.Register("dfg-to-fluid", new DataFlowGraphToFluidPass);
Argument argument(FLAGS_inference_model_dir);
ASSERT_TRUE(&argument);
ASSERT_TRUE(manager.Initialize(&argument));
manager.RunAll();
}
TEST_F(DFG_Tester, Node_pass_manager) {
TEST(PassManager, Node_pass_manager) {
Argument argument(FLAGS_inference_model_dir);
// Pre-process: initialize the DFG with the ProgramDesc first.
FluidToDataFlowGraphPass pass0;
pass0.Initialize(&argument);
......
paddle/fluid/inference/analysis/subgraph_splitter_tester.cc
浏览文件 @ c13efe02
......@@ -31,8 +31,8 @@ SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
return false;
};
TEST_F(DFG_Tester, Split) {
auto desc = LoadProgramDesc();
TEST(SubGraphSplitter, Split) {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
LOG(INFO) << "spliter\n" << dfg.DotString();
......@@ -63,8 +63,8 @@ TEST_F(DFG_Tester, Split) {
ASSERT_EQ(subgraphs.back().size(), 6UL);
}
TEST_F(DFG_Tester, Fuse) {
auto desc = LoadProgramDesc();
TEST(SubGraphSplitter, Fuse) {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
size_t count0 = dfg.nodes.size();
......
paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass_tester.cc
浏览文件 @ c13efe02
......@@ -22,11 +22,11 @@ namespace paddle {
namespace inference {
namespace analysis {
TEST_F(DFG_Tester, tensorrt_subgraph_node_mark_pass) {
TEST(TensorRTSubgraphNodeMarkPass, test) {
// init
FluidToDataFlowGraphPass pass;
Argument argument(FLAGS_inference_model_dir);
ASSERT_TRUE(pass.Initialize(&argument));
argument.main_dfg.reset(new DataFlowGraph);
pass.Run(argument.main_dfg.get());
TensorRTSubgraphNodeMarkPass::teller_t teller = [](const Node* node) {
......@@ -41,7 +41,7 @@ TEST_F(DFG_Tester, tensorrt_subgraph_node_mark_pass) {
for (auto& node : argument.main_dfg->nodes.nodes()) {
counter += node->attr(ATTR_supported_by_tensorrt).Bool();
}
ASSERT_EQ(counter, 2);
LOG(INFO) << counter << " nodes marked";
}
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc
浏览文件 @ c13efe02
......@@ -25,7 +25,7 @@ namespace analysis {
DEFINE_string(dot_dir, "./", "");
TEST_F(DFG_Tester, tensorrt_single_pass) {
TEST(TensorRTSubGraphPass, main) {
std::unordered_set<std::string> teller_set(
{"elementwise_add", "mul", "sigmoid"});
SubGraphSplitter::NodeInsideSubgraphTeller teller = [&](const Node* node) {
......@@ -35,7 +35,8 @@ TEST_F(DFG_Tester, tensorrt_single_pass) {
return false;
};
LOG(INFO) << "init";
Argument argument(FLAGS_inference_model_dir);
DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};
......@@ -44,13 +45,11 @@ TEST_F(DFG_Tester, tensorrt_single_pass) {
FluidToDataFlowGraphPass pass0;
TensorRTSubGraphPass trt_pass(std::move(teller));
LOG(INFO) << "Initialize";
dfg_pass.Initialize(&argument);
dfg_pass1.Initialize(&argument);
pass0.Initialize(&argument);
trt_pass.Initialize(&argument);
LOG(INFO) << "Run";
argument.main_dfg.reset(new DataFlowGraph);
pass0.Run(argument.main_dfg.get());
dfg_pass.Run(argument.main_dfg.get());
......
paddle/fluid/inference/analysis/ut_helper.h
浏览文件 @ c13efe02
......@@ -20,7 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/analysis/helper.h"
namespace paddle {
namespace inference {
......@@ -32,27 +32,12 @@ namespace analysis {
DEFINE_string(inference_model_dir, "", "inference test model dir");
static framework::proto::ProgramDesc LoadProgramDesc(
const std::string& model_dir = FLAGS_inference_model_dir) {
std::string msg;
std::string net_file = FLAGS_inference_model_dir + "/__model__";
std::ifstream fin(net_file, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", net_file);
fin.seekg(0, std::ios::end);
msg.resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(msg.at(0)), msg.size());
fin.close();
framework::proto::ProgramDesc program_desc;
program_desc.ParseFromString(msg);
return program_desc;
}
static DataFlowGraph ProgramDescToDFG(
const framework::proto::ProgramDesc& desc) {
DataFlowGraph graph;
FluidToDataFlowGraphPass pass;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
pass.Initialize(&argument);
pass.Run(&graph);
......@@ -63,7 +48,7 @@ static DataFlowGraph ProgramDescToDFG(
class DFG_Tester : public ::testing::Test {
protected:
void SetUp() override {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir);
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
}
......
paddle/fluid/inference/api/api_anakin_engine_tester.cc
浏览文件 @ c13efe02
......@@ -37,19 +37,21 @@ TEST(inference, anakin) {
float data[1 * 3 * 224 * 224] = {1.0f};
PaddleTensor tensor{.name = "input_0",
.shape = std::vector<int>({1, 3, 224, 224}),
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::FLOAT32};
PaddleTensor tensor;
tensor.name = "input_0";
tensor.shape = std::vector<int>({1, 3, 224, 224});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.dtype = PaddleDType::FLOAT32;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.emplace_back(std::move(tensor));
PaddleTensor tensor_out{.name = "prob_out",
.shape = std::vector<int>({1000, 1}),
.data = PaddleBuf(),
.dtype = PaddleDType::FLOAT32};
PaddleTensor tensor_out;
tensor_out.name = "prob_out";
tensor_out.shape = std::vector<int>({1000, 1});
tensor_out.data = PaddleBuf();
tensor_out.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> outputs;
outputs.emplace_back(std::move(tensor_out));
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ c13efe02
......@@ -183,6 +183,13 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
lod.emplace_back(level);
}
input.set_lod(lod);
feeds->push_back(input);
}
return true;
......@@ -248,6 +255,10 @@ bool NativePaddlePredictor::GetFetch(
buffer.Resize(sizeof(float) * data.size());
}
std::memcpy(buffer.data(), data.data(), buffer.length());
// copy LoD
for (const auto &level : fetchs[i].lod()) {
outputs->at(i).lod.emplace_back(level);
}
outputs->at(i).dtype = PaddleDType::FLOAT32;
// TODO(panyx0718): support other types? fill tensor name? avoid a copy.
}
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ c13efe02
......@@ -90,6 +90,18 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
void OptimizeInferenceProgram() {
// Analyze inference_program
Argument argument;
if (!config_.model_dir.empty()) {
argument.fluid_model_dir.reset(new std::string(config_.model_dir));
} else {
PADDLE_ENFORCE(
!config_.param_file.empty(),
"Either model_dir or (param_file, prog_file) should be set.");
PADDLE_ENFORCE(!config_.prog_file.empty());
argument.fluid_model_program_path.reset(
new std::string(config_.prog_file));
argument.fluid_model_param_path.reset(
new std::string(config_.param_file));
}
argument.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
Singleton<Analyzer>::Global().Run(&argument);
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc
浏览文件 @ c13efe02
......@@ -49,11 +49,10 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
std::vector<int64_t> data(20);
for (int i = 0; i < 20; i++) data[i] = i;
PaddleTensor tensor{
.name = "",
.shape = std::vector<int>({10, 1}),
.data = PaddleBuf(data.data(), data.size() * sizeof(int64_t)),
.dtype = PaddleDType::INT64};
PaddleTensor tensor;
tensor.shape = std::vector<int>({10, 1});
tensor.data = PaddleBuf(data.data(), data.size() * sizeof(int64_t));
tensor.dtype = PaddleDType::INT64;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> slots(4, tensor);
......
paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc
浏览文件 @ c13efe02
......@@ -47,10 +47,10 @@ void Main(bool use_gpu) {
//# 2. Prepare input.
int64_t data[4] = {1, 2, 3, 4};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
PaddleTensor tensor;
tensor.shape = std::vector<int>({4, 1});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.dtype = PaddleDType::INT64;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> slots(4, tensor);
......@@ -94,10 +94,11 @@ void MainThreads(int num_threads, bool use_gpu) {
for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
// 2. Dummy Input Data
int64_t data[4] = {1, 2, 3, 4};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
PaddleTensor tensor;
tensor.shape = std::vector<int>({4, 1});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.dtype = PaddleDType::INT64;
std::vector<PaddleTensor> inputs(4, tensor);
std::vector<PaddleTensor> outputs;
// 3. Run
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ c13efe02
......@@ -123,11 +123,11 @@ void Main(bool use_gpu) {
file.close();
// Inference.
PaddleTensor input{
.name = "xx",
.shape = record.shape,
.data = PaddleBuf(record.data.data(), record.data.size() * sizeof(float)),
.dtype = PaddleDType::FLOAT32};
PaddleTensor input;
input.shape = record.shape;
input.data =
PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
input.dtype = PaddleDType::FLOAT32;
VLOG(3) << "run executor";
std::vector<PaddleTensor> output;
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ c13efe02
......@@ -67,9 +67,9 @@ struct PaddleTensor {
PaddleTensor() = default;
std::string name; // variable name.
std::vector<int> shape;
// TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
PaddleBuf data; // blob of data.
PaddleDType dtype;
std::vector<std::vector<uint64_t>> lod; // lod data
};
enum class PaddleEngineKind {
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ c13efe02
......@@ -19,12 +19,17 @@ limitations under the License. */
#include <thread> // NOLINT
#include <vector>
#include "gflags/gflags.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_int32(listen_and_serv_profile_period, 0,
"the period of listen_and_serv to do profile");
namespace paddle {
namespace operators {
......@@ -122,7 +127,18 @@ void ListenAndServOp::RunSyncLoop(
std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
rpc_service_->ResetBarrierCounter();
int32_t profile_step = 0;
while (true) {
PADDLE_ENFORCE_LE(profile_step, FLAGS_listen_and_serv_profile_period,
"profile_step should not be larger then "
"FLAGS_listen_and_serv_profile_period");
if (FLAGS_listen_and_serv_profile_period > 0) {
if (profile_step == 0) {
auto pf_state = paddle::platform::ProfilerState::kCPU;
paddle::platform::EnableProfiler(pf_state);
}
}
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(distributed::kRequestSend);
......@@ -164,6 +180,15 @@ void ListenAndServOp::RunSyncLoop(
// reset received sparse vars to avoid reuse it in the next mini-batch
dynamic_cast<distributed::RequestSendHandler *>(request_send_handler_.get())
->ResetSparseVarRecorder();
if (FLAGS_listen_and_serv_profile_period > 0) {
if (profile_step == FLAGS_listen_and_serv_profile_period) {
paddle::platform::DisableProfiler(
paddle::platform::EventSortingKey::kTotal, "/dev/null");
profile_step = 0;
} else {
profile_step++;
}
}
} // while(true)
}
......
paddle/fluid/operators/math/im2col.cc
浏览文件 @ c13efe02
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
namespace paddle {
namespace operators {
......@@ -35,61 +36,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
// TODO(TJ): change me to template
// further optimaze:
// 1. padding != 1
// 2. could also support stride_h != 1
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 &&
dilation[1] == 1 && padding[0] == 0 && padding[1] == 0) {
int col_matrix_width = output_width * output_height;
size_t copy_size = sizeof(T) * output_width;
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + oh * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_height * im_width;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
return;
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
dilation[1] == 1) {
if (padding[0] == 0 && padding[1] == 0) {
im2col_sh1sw1dh1dw1ph0pw0<T>(im, col);
return;
} else if (padding[0] == 1 && padding[1] == 1) {
im2col_sh1sw1dh1dw1ph1pw1<T>(im, col);
return;
}
// TODO(TJ): complete padding >=2
}
im2col_common<T>(im, dilation, stride, padding, col);
}
};
......
paddle/fluid/operators/math/im2col_cfo_cpu.h 0 → 100644
浏览文件 @ c13efe02
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
/**
* The most common im2col algorithm.
* Support dilation, stride and padding.
*/
template <typename T>
inline void im2col_common(const framework::Tensor& im,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 0
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int col_matrix_width = output_width * output_height;
int im_size = im_height * im_width;
size_t copy_size = sizeof(T) * output_width;
const T* im_data_oh = im_data;
T* dst_data_oh = col_data;
for (int oh = 0; oh < output_height; ++oh) {
const T* src_data_ic = im_data_oh;
T* dst_data = dst_data_oh;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = src_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
src_data_ic = src_data_ic + im_size;
}
im_data_oh = im_data_oh + im_width;
dst_data_oh = dst_data_oh + output_width;
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 1
* and filter_width == 1 have a special implementation
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
constexpr int plh = 1;
constexpr int prh = 1;
constexpr int plw = 1;
constexpr int prw = 1;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int im_size = im_height * im_width;
int col_matrix_width = output_width * output_height;
int col_block_fh = filter_width * col_matrix_width; // fw*oh*ow
int col_block_ic = filter_height * col_block_fh; // fh*fw*oh*ow
// fill height padding
{
size_t copy_size = sizeof(T) * output_width;
T* col_start_l = col_data;
T* col_start_r = col_data + (filter_height - 1) * col_block_fh +
col_matrix_width - output_width;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_l = col_start_l;
T* dst_data_r = col_start_r;
for (int kw = 0; kw < filter_width; ++kw) {
std::memset(dst_data_l, 0, copy_size);
std::memset(dst_data_r, 0, copy_size);
dst_data_l = dst_data_l + col_matrix_width;
dst_data_r = dst_data_r + col_matrix_width;
}
col_start_l = col_start_l + col_block_ic;
col_start_r = col_start_r + col_block_ic;
}
}
auto pad = static_cast<T>(0);
if (filter_width == 1) {
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
T* dst_data = dst_data_kh;
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width - 1;
*dst_data = pad;
++dst_data;
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// fill core
size_t copy_size = sizeof(T) * (output_width - plw - prw);
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start =
im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + col_matrix_width;
continue;
}
std::memcpy(dst_data + plw, src_data, copy_size);
dst_data = dst_data + col_matrix_width;
src_data = src_data + im_width;
}
}
}
return;
}
// filter_width != 1
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (T* dst_data :
{dst_data_kh, dst_data_kh + (filter_width - prw) * col_matrix_width +
output_width - 1}) {
// TODO(TJ): from plh, saving repeated assignment
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width;
}
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// TODO(TJ): use array like: size_t copy_size[kw]={sizeof(T) *
// (output_width-1)}
// length of copy_size is equal kw.
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + filter_width * col_matrix_width;
continue;
}
// TODO(TJ): reuse plw-kw outside this for
// try to unify
for (int kw = 0; kw < plw; ++kw) {
std::memcpy(dst_data + (plw - kw), src_data,
sizeof(T) * (output_width - (plw - kw)));
dst_data = dst_data + col_matrix_width;
}
for (int kw = plw; kw < filter_width - prw; ++kw) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * output_width);
dst_data = dst_data + col_matrix_width;
}
int i = 1;
for (int kw = filter_width - prw; kw < filter_width; ++kw, ++i) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * (output_width - i));
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/im2col_test.cc
浏览文件 @ c13efe02
......@@ -14,7 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <gtest/gtest.h>
#include <sys/time.h>
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
template <typename DeviceContext, typename Place>
void testIm2col() {
......@@ -160,82 +162,111 @@ void testIm2col() {
delete context;
}
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
paddle::framework::Tensor input;
paddle::framework::Tensor output;
paddle::framework::Tensor ref_output;
std::vector<int> padding({ph, pw});
std::vector<int> stride({1, 1}); // stride_y, stride_x
std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1;
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1;
float* input_ptr =
input.mutable_data<float>({ic, ih, iw}, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] = static_cast<float>(i + 1);
}
paddle::platform::CPUPlace place;
paddle::platform::CPUDeviceContext context(place);
output.mutable_data<float>({ic, fh, fw, output_height, output_width}, place);
ref_output.mutable_data<float>({ic, fh, fw, output_height, output_width},
place);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO,
paddle::platform::CPUDeviceContext, float>
im2col;
im2col(context, input, dilation, stride, padding, &output);
auto ref_im2col = [&](
const paddle::framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& padding,
paddle::framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const float* im_data = im.data<float>();
float* col_data = col->data<float>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? 0.f
: im_data[im_idx];
}
}
}
};
ref_im2col(input, dilation, stride, padding, &ref_output);
float* out_cfo_ptr = output.data<float>();
float* out_ref_ptr = ref_output.data<float>();
for (int i = 0; i < output.numel(); ++i) {
EXPECT_EQ(out_cfo_ptr[i], out_ref_ptr[i]);
}
}
TEST(math, im2col) {
testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
testIm2colCPU(/*ic*/ 3, /*ih*/ 5, /*iw*/ 5, /*fh*/ 3, /*fw*/ 2, /*ph*/ 0,
/*pw*/ 0);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 1);
#ifdef PADDLE_WITH_CUDA
testIm2col<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace>();
#endif
}
#define PREPARE_IM2COL_CPU \
paddle::platform::CPUPlace place; \
paddle::platform::CPUDeviceContext context(place); \
paddle::framework::Tensor input; \
paddle::framework::Tensor out; \
paddle::framework::Tensor ref; \
std::vector<int> padding({ph, pw}); \
std::vector<int> stride({1, 1}); \
std::vector<int> dilation({1, 1}); \
float* input_ptr = input.mutable_data<float>({ic, ih, iw}, place); \
for (int i = 0; i < input.numel(); ++i) { \
input_ptr[i] = static_cast<float>(i + 1); \
} \
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1; \
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1; \
out.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
ref.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
paddle::operators::math::Im2ColFunctor< \
paddle::operators::math::ColFormat::kCFO, \
paddle::platform::CPUDeviceContext, float> \
im2col
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
im2col(context, input, dilation, stride, padding, &out);
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
float* ref_data = ref.data<float>();
float* out_data = out.data<float>();
for (int i = 0; i < out.numel(); ++i) {
EXPECT_EQ(out_data[i], ref_data[i]);
}
}
void benchIm2col(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
constexpr int repeat = 100;
auto GetCurrentMs = []() -> double {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
};
auto t1 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
im2col(context, input, dilation, stride, padding, &out);
}
auto t2 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
}
auto t3 = GetCurrentMs();
LOG(INFO) << "before: " << (t3 - t2) / repeat
<< ",after: " << (t2 - t1) / repeat
<< ",boost: " << ((t3 - t2) / (t2 - t1) - 1) * 100 << "%";
}
TEST(math, im2col_cputest) {
// padding_h == padding_w
for (int p = 0; p < 4; ++p) {
// width == height
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 5, /*fh*/ 4, /*fw*/ 4, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 2, /*ph*/ p,
/*pw*/ p);
// height != width
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 1, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 5, /*fh*/ 3, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
// filter == 1
testIm2colCPU(/*ic*/ 3, /*ih*/ 4, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 3, /*ih*/ 3, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
}
// padding_h != padding_w
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 2);
// benchmark
for (int p : {0, 1}) {
for (int k : {1, 3, 5}) {
LOG(INFO) << "padding == " << p << ", filter == " << k;
benchIm2col(/*ic*/ 3, /*ih*/ 224, /*iw*/ 224, /*fh*/ k, /*fw*/ k,
/*ph*/ p, /*pw*/ p);
}
}
}
paddle/fluid/operators/reshape_op.cc
浏览文件 @ c13efe02
......@@ -127,12 +127,6 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
AddAttr<std::vector<int>>(
"shape", "(std::vector<int>) Target shape of reshape operator.");
AddAttr<bool>("inplace",
"(default: false) Change the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x).")
.SetDefault(false);
AddComment(R"DOC(
Reshape Operator.
......@@ -233,16 +227,9 @@ class ReshapeKernel {
"sequence_reshape op.");
}
bool inplace = ctx.Attr<bool>("inplace");
out->mutable_data(ctx.GetPlace(), in->type());
framework::TensorCopySync(*in, ctx.GetPlace(), out);
out->Resize(out_dims);
if (!inplace) {
out->mutable_data(ctx.GetPlace(), in->type());
framework::TensorCopySync(*in, ctx.GetPlace(), out);
out->Resize(out_dims);
} else {
out->ShareDataWith(*in);
out->Resize(out_dims);
}
}
};
......@@ -251,19 +238,11 @@ class ReshapeGradKernel {
void operator()(const framework::ExecutionContext &ctx) const {
auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto in_dims = d_x->dims();
d_x->mutable_data(ctx.GetPlace(), d_out->type());
bool inplace = ctx.Attr<bool>("inplace");
auto in_dims = d_x->dims();
if (!inplace) {
framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
ctx.device_context().Wait();
d_x->Resize(in_dims);
} else {
d_x->ShareDataWith(*d_out);
d_x->Resize(in_dims);
}
framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
d_x->Resize(in_dims);
}
};
......
paddle/fluid/platform/cuda_helper_test.cu
浏览文件 @ c13efe02
......@@ -13,7 +13,6 @@
// limitations under the License.
#include <gtest/gtest.h>
#include <bitset>
#include <iostream>
#include <random>
......@@ -25,13 +24,13 @@
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
using paddle::platform::float16;
#define CUDA_ATOMIC_KERNEL(op, T) \
__global__ void op##Kernel(const T* data_a, T* data_b, size_t num) { \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num; \
i += blockDim.x * gridDim.x) { \
paddle::platform::CudaAtomic##op(&data_b[i], data_a[i]); \
} \
template <typename T>
__global__ void AddKernel(const T* data_a, T* data_b, size_t num) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
paddle::platform::CudaAtomicAdd(&data_b[i], data_a[i]);
}
}
template <typename T>
struct AddFunctor {
......@@ -39,80 +38,116 @@ struct AddFunctor {
};
template <typename T>
struct SubFunctor {
T operator()(const T& a, const T& b) { return a - b; }
};
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
#define ARITHMETIC_KERNEL_LAUNCH(op, T) \
void Test##T##op(size_t num) { \
T *in1, *in2, *out; \
T *d_in1, *d_in2; \
size_t size = sizeof(T) * num; \
cudaMalloc(reinterpret_cast<void**>(&d_in1), size); \
cudaMalloc(reinterpret_cast<void**>(&d_in2), size); \
in1 = reinterpret_cast<T*>(malloc(size)); \
in2 = reinterpret_cast<T*>(malloc(size)); \
out = reinterpret_cast<T*>(malloc(size)); \
std::minstd_rand engine; \
std::uniform_real_distribution<double> dist(0.0, 1.0); \
for (size_t i = 0; i < num; ++i) { \
in1[i] = static_cast<T>(dist(engine)); \
in2[i] = static_cast<T>(dist(engine)); \
} \
cudaMemcpy(d_in1, in1, size, cudaMemcpyHostToDevice); \
cudaMemcpy(d_in2, in2, size, cudaMemcpyHostToDevice); \
op##Kernel<<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num); \
cudaDeviceSynchronize(); \
cudaMemcpy(out, d_in2, size, cudaMemcpyDeviceToHost); \
cudaDeviceSynchronize(); \
for (size_t i = 0; i < num; ++i) { \
EXPECT_NEAR(static_cast<float>(out[i]), \
static_cast<float>(op##Functor<T>()(in1[i], in2[i])), \
0.001); \
} \
free(in1); \
free(in2); \
free(out); \
cudaFree(d_in1); \
cudaFree(d_in2); \
void TestCase(size_t num) {
T *in1, *in2, *out;
T *d_in1, *d_in2;
size_t size = sizeof(T) * num;
cudaMalloc(reinterpret_cast<void**>(&d_in1), size);
cudaMalloc(reinterpret_cast<void**>(&d_in2), size);
in1 = reinterpret_cast<T*>(malloc(size));
in2 = reinterpret_cast<T*>(malloc(size));
out = reinterpret_cast<T*>(malloc(size));
std::minstd_rand engine;
std::uniform_real_distribution<double> dist(0.0, 1.0);
for (size_t i = 0; i < num; ++i) {
in1[i] = static_cast<T>(dist(engine));
in2[i] = static_cast<T>(dist(engine));
}
CUDA_ATOMIC_KERNEL(Add, float);
CUDA_ATOMIC_KERNEL(Add, double);
CUDA_ATOMIC_KERNEL(Add, float16);
ARITHMETIC_KERNEL_LAUNCH(Add, float);
ARITHMETIC_KERNEL_LAUNCH(Add, double);
ARITHMETIC_KERNEL_LAUNCH(Add, float16);
namespace paddle {
namespace platform {
USE_CUDA_ATOMIC(Sub, int);
};
};
CUDA_ATOMIC_KERNEL(Sub, int);
ARITHMETIC_KERNEL_LAUNCH(Sub, int);
cudaMemcpy(d_in1, in1, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2, size, cudaMemcpyHostToDevice);
AddKernel<T><<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num);
cudaDeviceSynchronize();
cudaMemcpy(out, d_in2, size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (size_t i = 0; i < num; ++i) {
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
EXPECT_NEAR(static_cast<float>(out[i]),
static_cast<float>(AddFunctor<T>()(in1[i], in2[i])), 0.001);
}
free(in1);
free(in2);
free(out);
cudaFree(d_in1);
cudaFree(d_in2);
}
// cuda primitives
TEST(CudaAtomic, Add) {
TestfloatAdd(static_cast<size_t>(10));
TestfloatAdd(static_cast<size_t>(1024 * 1024));
TestdoubleAdd(static_cast<size_t>(10));
TestdoubleAdd(static_cast<size_t>(1024 * 1024));
}
TestCase<float>(static_cast<size_t>(10));
TestCase<float>(static_cast<size_t>(1024 * 1024));
TEST(CudaAtomic, Sub) {
TestintSub(static_cast<size_t>(10));
TestintSub(static_cast<size_t>(1024 * 1024));
TestCase<double>(static_cast<size_t>(10));
TestCase<double>(static_cast<size_t>(1024 * 1024));
}
TEST(CudaAtomic, float16) {
using paddle::platform::float16;
Testfloat16Add(static_cast<size_t>(1));
Testfloat16Add(static_cast<size_t>(2));
Testfloat16Add(static_cast<size_t>(3));
TestCase<float16>(static_cast<size_t>(1));
TestCase<float16>(static_cast<size_t>(2));
TestCase<float16>(static_cast<size_t>(3));
TestCase<float16>(static_cast<size_t>(10));
TestCase<float16>(static_cast<size_t>(1024 * 1024));
}
// unalignment of uint8
void TestUnalign(size_t num, const int shift_bit) {
PADDLE_ENFORCE(num % 2 == 0, "must be a multiple of 2");
float16 *in1, *in2, *out;
float16 *d_in1, *d_in2;
size_t size = sizeof(uint8_t) * (num + shift_bit);
size_t array_size = sizeof(float16) * (num / 2);
cudaMalloc(reinterpret_cast<void**>(&d_in1), size);
cudaMalloc(reinterpret_cast<void**>(&d_in2), size);
in1 = reinterpret_cast<float16*>(malloc(size));
in2 = reinterpret_cast<float16*>(malloc(size));
out = reinterpret_cast<float16*>(malloc(size));
// right shift 1, mimic the unalignment of address
float16* r_in1 =
reinterpret_cast<float16*>(reinterpret_cast<uint8_t*>(in1) + shift_bit);
float16* r_in2 =
reinterpret_cast<float16*>(reinterpret_cast<uint8_t*>(in2) + shift_bit);
std::minstd_rand engine;
std::uniform_real_distribution<double> dist(0.0, 1.0);
for (size_t i = 0; i < num / 2; ++i) {
r_in1[i] = static_cast<float16>(dist(engine));
r_in2[i] = static_cast<float16>(dist(engine));
}
cudaMemcpy(d_in1, r_in1, array_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, r_in2, array_size, cudaMemcpyHostToDevice);
AddKernel<float16><<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num / 2);
cudaDeviceSynchronize();
cudaMemcpy(out, d_in2, array_size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (size_t i = 0; i < num / 2; ++i) {
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
EXPECT_NEAR(static_cast<float>(out[i]),
static_cast<float>(AddFunctor<float16>()(r_in1[i], r_in2[i])),
0.001);
}
free(in1);
free(in2);
free(out);
cudaFree(d_in1);
cudaFree(d_in2);
}
TEST(CudaAtomic, float16Unalign) {
// same with float16 testcase
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 2);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 2);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 2);
// shift the address.
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 1);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 1);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 1);
Testfloat16Add(static_cast<size_t>(10));
Testfloat16Add(static_cast<size_t>(1024 * 1024));
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 3);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 3);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 3);
}
paddle/fluid/platform/cuda_primitives.h
浏览文件 @ c13efe02
......@@ -79,41 +79,41 @@ CUDA_ATOMIC_WRAPPER(Add, double) {
// convert the value into float and do the add arithmetic.
// then store the result into a uint32.
inline __device__ uint32_t add_to_low_half(uint32_t val, float x) {
inline static __device__ uint32_t add_to_low_half(uint32_t val, float x) {
float16 low_half;
// the float16 in lower 16bits
low_half.x = static_cast<uint16_t>(val & 0xffffu);
low_half.x = static_cast<uint16_t>(val & 0xFFFFu);
low_half = static_cast<float16>(static_cast<float>(low_half) + x);
return (val & 0xffff0000u) | low_half.x;
return (val & 0xFFFF0000u) | low_half.x;
}
inline __device__ uint32_t add_to_high_half(uint32_t val, float x) {
inline static __device__ uint32_t add_to_high_half(uint32_t val, float x) {
float16 high_half;
// the float16 in higher 16bits
high_half.x = static_cast<uint16_t>(val >> 16);
high_half = static_cast<float16>(static_cast<float>(high_half) + x);
return (val & 0xffffu) | (static_cast<uint32_t>(high_half.x) << 16);
return (val & 0xFFFFu) | (static_cast<uint32_t>(high_half.x) << 16);
}
CUDA_ATOMIC_WRAPPER(Add, float16) {
// concrete packed float16 value may exsits in lower or higher 16bits
// of the 32bits address.
uint32_t *address_as_ui =
reinterpret_cast<uint32_t *>(reinterpret_cast<char *>(address) -
(reinterpret_cast<size_t>(address) & 2));
uint32_t *address_as_ui = reinterpret_cast<uint32_t *>(
reinterpret_cast<char *>(address) -
(reinterpret_cast<uintptr_t>(address) & 0x02));
float val_f = static_cast<float>(val);
uint32_t old = *address_as_ui;
uint32_t sum;
uint32_t newval;
uint32_t assumed;
if (((size_t)address & 2) == 0) {
if (((uintptr_t)address & 0x02) == 0) {
// the float16 value stay at lower 16 bits of the address.
do {
assumed = old;
old = atomicCAS(address_as_ui, assumed, add_to_low_half(assumed, val_f));
} while (old != assumed);
float16 ret;
ret.x = old & 0xffffu;
ret.x = old & 0xFFFFu;
return ret;
} else {
// the float16 value stay at higher 16 bits of the address.
......
paddle/scripts/paddle_build.sh
浏览文件 @ c13efe02
......@@ -534,7 +534,7 @@ EOF
make -j `nproc` inference_lib_dist
cd ${PADDLE_ROOT}/build
cp -r fluid_install_dir fluid
tar -cf fluid.tgz fluid
tar -czf fluid.tgz fluid
fi
}
......
python/paddle/fluid/__init__.py
浏览文件 @ c13efe02
......@@ -127,6 +127,7 @@ def __bootstrap__():
]
if core.is_compiled_with_dist():
read_env_flags.append('rpc_deadline')
read_env_flags.append('listen_and_serv_profile_period')
if core.is_compiled_with_cuda():
read_env_flags += [
......
python/paddle/fluid/layers/nn.py
浏览文件 @ c13efe02
......@@ -4473,15 +4473,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"except one unknown dimension.")
helper = LayerHelper("reshape", **locals())
reshaped = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape",
inputs=inputs,
attrs={"shape": shape,
"inplace": inplace},
outputs={"Out": reshaped})
attrs={"shape": shape},
outputs={"Out": out})
return helper.append_activation(reshaped)
return helper.append_activation(out)
def lod_reset(x, y=None, target_lod=None):
......
python/paddle/fluid/tests/unittests/test_memory_optimization_transpiler.py
浏览文件 @ c13efe02
......@@ -43,5 +43,29 @@ class TestControlFlowGraph(unittest.TestCase):
print(str(result_program))
class TestMemoryTranspiler2(unittest.TestCase):
def setUp(self):
program = Program()
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
fc = layers.fc(input=x, size=10, act=None)
reshape = layers.reshape(x=fc, shape=[-1, 2, 5])
fc = layers.reshape(x=reshape, shape=[-1, 5, 2])
y_predict = layers.fc(input=fc, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
opt = optimizer.SGD(learning_rate=0.001)
opt.minimize(avg_cost)
self.program = program
def test_inplace_ops(self):
print("before optimization")
print(str(self.program))
result_program = memory_optimize(self.program)
print("after optimization")
print(str(result_program))
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_reshape_op.py
浏览文件 @ c13efe02
......@@ -25,7 +25,7 @@ class TestReshapeOp(OpTest):
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
......@@ -42,7 +42,7 @@ class TestReshapeOpDimInfer1(OpTest):
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])}
def test_check_output(self):
......@@ -60,7 +60,7 @@ class TestReshapeOpDimInfer2(OpTest):
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
def test_check_output(self):
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ c13efe02
......@@ -495,6 +495,7 @@ class DistributeTranspiler(object):
pserver_index = self.pserver_endpoints.index(endpoint)
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(
pserver_index, pserver_program, table_opt_block)
checkpoint_block_id = self._create_checkpoint_save_block(
......
tools/manylinux1/Dockerfile.x64
浏览文件 @ c13efe02
......@@ -13,7 +13,7 @@ ENV PATH /opt/rh/devtoolset-2/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH /opt/rh/devtoolset-2/root/usr/lib64:/opt/rh/devtoolset-2/root/usr/lib:/usr/local/lib64:/usr/local/lib:${LD_LIBRARY_PATH}
ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz
RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz
COPY build_scripts /build_scripts
RUN bash build_scripts/build.sh && \
bash build_scripts/install_nccl2.sh && rm -r build_scripts
......
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