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PaddleDetection
提交 ff052c0e 编写于 作者: N nhzlx
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merge develop

上级 c6a5c4b0 3ae97aab
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Showing with 1340 addition and 239 deletion
CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -204,6 +204,11 @@ include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
if(WITH_GPU)
include(cuda)
include(tensorrt)
......@@ -212,15 +217,11 @@ elseif()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in GPU only now." FORCE)
endif()
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
include(util) # set unittest and link libs
include(rdma) # set rdma libraries
include(flags) # set paddle compile flags
include(version) # set PADDLE_VERSION
include(coveralls) # set code coverage
include(inference_lib) # add paddle fluid inference libraries
......
cmake/configure.cmake
浏览文件 @ ff052c0e
......@@ -50,16 +50,16 @@ if(NOT WITH_PROFILER)
endif(NOT WITH_PROFILER)
if(NOT CMAKE_CROSSCOMPILING)
if(WITH_AVX AND AVX_FOUND)
if(WITH_AVX AND AVX512F_FOUND)
set(SIMD_FLAG ${AVX512F_FLAG})
elseif(WITH_AVX AND AVX2_FOUND)
set(SIMD_FLAG ${AVX2_FLAG})
elseif(WITH_AVX AND AVX_FOUND)
set(SIMD_FLAG ${AVX_FLAG})
elseif(SSE3_FOUND)
set(SIMD_FLAG ${SSE3_FLAG})
endif()
endif()
if(UNIX AND NOT APPLE)
# except apple from nix*Os family
set(LINUX TRUE)
endif(UNIX AND NOT APPLE)
if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
......@@ -103,15 +103,20 @@ if(WITH_GPU)
endif()
if(WITH_ANAKIN)
if(${CUDA_VERSION_MAJOR} VERSION_LESS 8)
message(FATAL_ERROR "Anakin needs CUDA >= 8.0 to compile")
message(WARNING "Anakin needs CUDA >= 8.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDA >= 8.0." FORCE)
endif()
if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
message(FATAL_ERROR "Anakin needs CUDNN >= 7.0 to compile")
message(WARNING "Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDNN >= 7.0." FORCE)
endif()
set(ENV{CUDNN_INCLUDE_DIR} ${CUDNN_INCLUDE_DIR})
set(ENV{CUDNN_LIBRARY} ${CUDNN_LIBRARY})
message(STATUS "cudnn include header is ${CUDNN_INCLUDE_DIR}/cudnn.h")
message(STATUS "cudnn library is ${CUDNN_LIBRARY}")
endif()
if(WITH_ANAKIN)
# NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR
# is a softlink to real cudnn.h directory
set(ENV{CUDNN_INCLUDE_DIR} "${CUDNN_INCLUDE_DIR}/")
get_filename_component(CUDNN_LIBRARY_DIR ${CUDNN_LIBRARY} DIRECTORY)
set(ENV{CUDNN_LIBRARY} ${CUDNN_LIBRARY_DIR})
endif()
elseif(WITH_AMD_GPU)
add_definitions(-DPADDLE_WITH_HIP)
......
cmake/cudnn.cmake
浏览文件 @ ff052c0e
......@@ -25,8 +25,25 @@ list(APPEND CUDNN_CHECK_LIBRARY_DIRS
$ENV{CUDNN_ROOT}
$ENV{CUDNN_ROOT}/lib64
$ENV{CUDNN_ROOT}/lib
/usr/lib)
find_library(CUDNN_LIBRARY NAMES libcudnn.so libcudnn.dylib # libcudnn_static.a
/usr/lib
${CUDA_TOOLKIT_ROOT_DIR}
${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
)
set(CUDNN_LIB_NAME "")
if (LINUX)
set(CUDNN_LIB_NAME "libcudnn.so")
endif(LINUX)
if(WIN32)
# only support cudnn7
set(CUDNN_LIB_NAME "cudnn.lib" "cudnn64_7.dll")
endif(WIN32)
if(Apple)
set(CUDNN_LIB_NAME "libcudnn.dylib" "libcudnn.so")
endif(Apple)
find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME} # libcudnn_static.a
PATHS ${CUDNN_CHECK_LIBRARY_DIRS} ${CUDNN_INCLUDE_DIR} ${__libpath_hist}
NO_DEFAULT_PATH
DOC "Path to cuDNN library.")
......
cmake/external/anakin.cmake
浏览文件 @ ff052c0e
......@@ -19,17 +19,17 @@ execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-cer
include_directories(${ANAKIN_INCLUDE})
include_directories(${ANAKIN_INCLUDE}/saber/)
set(ANAKIN_COMPILE_EXTRA_FLAGS
set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=format-extra-args -Wno-format-extra-args
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=switch -Wno-switch
-Wno-error=return-type -Wno-return-type
-Wno-error=return-type -Wno-return-type
-Wno-error=non-virtual-dtor -Wno-non-virtual-dtor
-Wno-sign-compare
-Wno-reorder
-Wno-reorder
-Wno-error=cpp)
ExternalProject_Add(
......@@ -47,6 +47,7 @@ ExternalProject_Add(
-DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
-DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
-DCUDNN_ROOT=${CUDNN_ROOT}
-DCUDNN_INCLUDE_DIR=${CUDNN_INCLUDE_DIR}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
)
......
cmake/flags.cmake
浏览文件 @ ff052c0e
......@@ -142,6 +142,11 @@ else()
${GPU_COMMON_FLAGS})
endif()
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})
......
cmake/simd.cmake
浏览文件 @ ff052c0e
......@@ -10,6 +10,7 @@ if(CMAKE_COMPILER_IS_GNUCC OR CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID
set(SSE3_FLAG "-msse3")
set(AVX_FLAG "-mavx")
set(AVX2_FLAG "-mavx2")
set(AVX512F_FLAG "-mavx512f")
elseif(MSVC)
set(MMX_FLAG "/arch:MMX")
set(SSE2_FLAG "/arch:SSE2")
......@@ -81,5 +82,16 @@ int main()
return 0;
}" AVX2_FOUND)
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m512i a = _mm512_undefined_epi32();
return 0;
}" AVX512F_FOUND)
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND)
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND AVX512F_FOUND)
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -99,8 +99,13 @@ else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method)
endif()
cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_pass multi_devices_graph_print_pass multi_devices_graph_check_pass)
if (NOT WIN32)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fast_threaded_ssa_graph_executor)
endif() # NOT WIN32
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -42,3 +42,5 @@ cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_b
cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
#cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
# device_context reduce_op_handle )
cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ ff052c0e
......@@ -19,10 +19,13 @@ namespace framework {
namespace details {
struct ExecutionStrategy {
enum ExecutorType { kDefault = 0, kExperimental = 1 };
size_t num_threads_{0};
bool use_cuda_{true};
bool allow_op_delay_{false};
size_t num_iteration_per_drop_scope_{100};
ExecutorType type_{kDefault};
};
} // namespace details
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc 0 → 100644
浏览文件 @ ff052c0e
// 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/framework/details/fast_threaded_ssa_graph_executor.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
namespace paddle {
namespace framework {
namespace details {
FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph)
: strategy_(strategy),
local_scopes_(local_scopes),
places_(places),
graph_(std::move(graph)),
pool_(strategy.num_threads_ +
1), // add one more thread for generate op_deps
fetch_ctxs_(places) {
auto &ops = graph_->Get<details::GraphOps>("ops");
for (auto &op : ops) {
int dep = static_cast<int>(op->NotReadyInputSize());
op_deps_.emplace(op.get(), dep);
if (dep == 0) {
bootstrap_ops_.emplace_back(op.get());
}
}
PrepareAtomicOpDeps();
}
FeedFetchList FastThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>
op_deps = atomic_op_deps_.get();
PrepareAtomicOpDeps();
paddle::framework::FeedFetchList fetches;
fetches.resize(fetch_tensors.size());
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
std::vector<std::unique_ptr<ir::Node>> fetch_nodes;
std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
auto it = var_map.find(fetch_var_name);
if (it != var_map.end()) {
fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
}
}
}
for (size_t i = 0; i < fetch_tensors.size(); ++i) {
auto &var_name = fetch_tensors[i];
auto fetched_var_it = fetched_vars.find(var_name);
PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)");
auto &vars = fetched_var_it->second;
fetch_nodes.emplace_back(new ir::Node("fetch", ir::Node::Type::kOperation));
auto *op = new FetchOpHandle(fetch_nodes.back().get(), &fetches, i,
&local_scopes_);
fetch_ops.emplace_back(op);
for (auto &p : places_) {
op->SetDeviceContext(p, fetch_ctxs_.Get(p));
}
for (auto *var : vars) {
op->AddInput(var);
}
(*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
}
size_t num_complete = 0;
remaining_ = 0;
BlockingQueue<size_t> complete_q;
for (auto op : bootstrap_ops_) {
RunOpAsync(op_deps.get(), op, &complete_q);
}
while (num_complete != op_deps->size()) {
size_t num_comp = complete_q.Pop();
if (num_comp == -1UL) {
int remaining = 0;
while (true) {
remaining = remaining_;
if (remaining == 0) {
break;
}
for (int i = 0; i < remaining; ++i) {
complete_q.Pop();
}
}
exception_.ReThrow();
}
num_complete += num_comp;
}
// Wait FetchOps.
if (!fetch_ops.empty()) {
fetch_ops.clear();
}
return fetches;
}
void FastThreadedSSAGraphExecutor::RunOpAsync(
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
++remaining_;
this->pool_.enqueue([=] {
OpHandleBase *op_to_run = op;
size_t complete = 0;
while (op_to_run != nullptr) {
try {
op_to_run->Run(strategy_.use_cuda_);
++complete;
} catch (...) {
exception_.Catch(std::current_exception());
--remaining_;
complete_q->Push(-1UL);
return;
}
auto &outputs = op_to_run->Outputs();
op_to_run = nullptr;
for (auto &output : outputs) {
for (auto &pending_op : output->PendingOps()) {
std::atomic<int> &deps = op_deps->at(pending_op);
if (deps.fetch_sub(1) == 1) { // pending_op ready
if (op_to_run == nullptr) {
op_to_run = pending_op;
} else {
this->RunOpAsync(op_deps, pending_op, complete_q);
}
}
}
}
}
--remaining_;
complete_q->Push(complete);
});
}
void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
atomic_op_deps_ = pool_.enqueue([&] {
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
new std::unordered_map<OpHandleBase *, std::atomic<int>>;
for (auto &pair : op_deps_) {
(*op_deps)[pair.first] = pair.second;
}
return std::unique_ptr<
std::unordered_map<OpHandleBase *, std::atomic<int>>>(op_deps);
});
}
const ir::Graph &FastThreadedSSAGraphExecutor::Graph() const { return *graph_; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h 0 → 100644
浏览文件 @ ff052c0e
// 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 <string>
#include <vector>
#include "ThreadPool.h"
#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/ssa_graph_executor.h"
namespace paddle {
namespace framework {
class Scope;
namespace details {
class OpHandleBase;
class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
public:
FastThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
const ir::Graph &Graph() const override;
private:
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
std::unique_ptr<ir::Graph> graph_;
std::unordered_map<OpHandleBase *, int> op_deps_;
std::vector<OpHandleBase *> bootstrap_ops_;
::ThreadPool pool_;
platform::DeviceContextPool fetch_ctxs_;
std::atomic<int> remaining_;
void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q);
void PrepareAtomicOpDeps();
std::future<
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
atomic_op_deps_;
ExceptionHolder exception_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ ff052c0e
......@@ -158,6 +158,16 @@ void OpHandleBase::RunAndRecordEvent(platform::Place p,
#endif
}
size_t OpHandleBase::NotReadyInputSize() const {
std::unordered_set<VarHandleBase *> res;
for (auto *var : inputs_) {
if (var->GeneratedOp() != nullptr) {
res.emplace(var);
}
}
return res.size();
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ ff052c0e
......@@ -81,6 +81,8 @@ class OpHandleBase {
return res.size();
}
size_t NotReadyInputSize() const;
const std::vector<VarHandleBase *> &Outputs() const { return outputs_; }
size_t NoDummyInputSize() const;
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ ff052c0e
......@@ -117,7 +117,15 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
}
// For output args, always create a new var.
for (auto &each_var_name : op->OutputArgumentNames()) {
ir::Node *var = CreateVarNode(all_vars.at(each_var_name));
ir::Node *var = nullptr;
if (all_vars.count(each_var_name) != 0) {
var = CreateVarNode(all_vars.at(each_var_name));
} else {
// Operation output vars can be @EMPTY@. For example, while_grad
// can have multi @EMPTY@ outputs with no VarDesc.
// TODO(panyx0718): Add a test.
var = CreateEmptyNode(each_var_name, ir::Node::Type::kVariable);
}
var_nodes[each_var_name].push_back(var);
node->outputs.push_back(var);
var->inputs.push_back(node);
......@@ -208,7 +216,8 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
// Add write after write dependence
ir::Node *upstream_op =
(*it_old)->inputs.empty() ? nullptr : (*it_old)->inputs[0];
if (upstream_op) {
// TODO(zcd): Add a test.
if (upstream_op && upstream_op != write_op) {
ir::Node *dep_var = CreateControlDepVar();
write_op->inputs.push_back(dep_var);
upstream_op->outputs.push_back(dep_var);
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ ff052c0e
......@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
......@@ -193,8 +194,14 @@ ParallelExecutor::ParallelExecutor(
member_->local_scopes_, member_->use_cuda_, build_strategy);
#endif
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
} else {
member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
}
member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, std::move(var_infos),
member_->places_, std::move(member_->executor_)));
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ ff052c0e
......@@ -55,11 +55,20 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
auto all_ops = blocks_[block_id]->AllOps();
for (size_t op_id = 0; op_id < all_ops.size(); ++op_id) {
auto &op = all_ops[op_id];
for (const std::string &attr_name : op->AttrNames()) {
if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
int sub_block_id =
o.Block(block_id).Op(op_id)->GetBlockAttrId(attr_name);
op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
} else if (op->GetAttrType(attr_name) == proto::AttrType::BLOCKS) {
std::vector<int> sub_block_ids =
o.Block(block_id).Op(op_id)->GetBlocksAttrIds(attr_name);
std::vector<BlockDesc *> block_descs;
for (int block_id : sub_block_ids) {
block_descs.push_back(MutableBlock(block_id));
}
op->SetBlocksAttr(attr_name, block_descs);
}
}
}
......@@ -68,24 +77,16 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
desc_ = desc;
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
for (auto &block : blocks_) {
for (auto *op : block->AllOps()) {
for (const auto &attr : op->Proto()->attrs()) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
}
}
}
}
InitFromProto();
}
ProgramDesc::ProgramDesc(const std::string &binary_str) {
PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
"Fail to parse program_desc from binary string.");
InitFromProto();
}
void ProgramDesc::InitFromProto() {
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
......@@ -95,6 +96,13 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
} else if (attr.type() == proto::AttrType::BLOCKS) {
auto blks_idx = attr.blocks_idx();
std::vector<BlockDesc *> block_descs;
for (int blk_idx : blks_idx) {
block_descs.push_back(this->MutableBlock(blk_idx));
}
op->SetBlocksAttr(attr.name(), block_descs);
}
}
}
......
paddle/fluid/framework/program_desc.h
浏览文件 @ ff052c0e
......@@ -76,6 +76,8 @@ class ProgramDesc {
void SetFetchHolderName(const std::string &fetch_holder_name);
private:
void InitFromProto();
proto::ProgramDesc desc_;
std::vector<std::unique_ptr<BlockDesc>> blocks_;
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ ff052c0e
......@@ -42,6 +42,19 @@ TEST(ProgramDesc, copy_ctor) {
out->SetType(proto::VarType::LOD_TENSOR);
op->SetOutput("Y", {out->Name()});
BlockDesc* new_block = program.AppendBlock(*global_block);
op = new_block->AppendOp();
op->SetType("mul");
op = global_block->AppendOp();
op->SetType("op_with_subblock");
op->SetAttr("sub_block", new_block);
std::vector<BlockDesc*> sub_blocks;
sub_blocks.push_back(program.AppendBlock(*global_block));
sub_blocks.push_back(program.AppendBlock(*global_block));
op->SetAttr("sub_blocks", sub_blocks);
ProgramDesc program_copy(program);
auto* global_block_copy = program_copy.MutableBlock(0);
......@@ -64,6 +77,8 @@ TEST(ProgramDesc, copy_ctor) {
assert_same_var("Y", y);
assert_same_var("Out", out);
bool found_sub_block = false;
bool found_sub_blocks = false;
for (size_t i = 0; i < global_block->OpSize(); ++i) {
auto op_origin = global_block->Op(i);
auto op_copy = global_block_copy->Op(i);
......@@ -74,8 +89,17 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ(op_copy->Proto()->SerializeAsString(),
op_origin->Proto()->SerializeAsString());
}
if (op->Type() == "op_with_subblock") {
ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
found_sub_block = true;
ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
found_sub_blocks = true;
}
}
ASSERT_TRUE(found_sub_block);
ASSERT_TRUE(found_sub_blocks);
// Not check block's protostr are same it because the order of vars could be
// different and it is correct.
}
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -62,13 +62,13 @@ endif()
if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
#nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin)
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endfunction()
anakin_target(inference_anakin_api)
#anakin_target(inference_anakin_api_shared)
anakin_target(inference_anakin_api_shared)
if (WITH_TESTING)
cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
......
paddle/fluid/inference/api/high_level_api_cn.md
浏览文件 @ ff052c0e
......@@ -65,13 +65,13 @@ config.model_dir = "xxx";
config.use_gpu = false;
// 创建一个原生的 PaddlePredictor
auto predictor =
paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
paddle::CreatePaddlePredictor<paddle::NativeConfig, paddle::PaddleEngineKind::kNative>(config);
// 创建输入 tensor
int64_t data[4] = {1, 2, 3, 4};
paddle::PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
.data = paddle::PaddleBuf(data, sizeof(data)),
.dtype = paddle::PaddleDType::INT64};
// 创建输出 tensor，输出 tensor 的内存可以复用
std::vector<paddle::PaddleTensor> outputs;
// 执行预测
......
paddle/fluid/inference/tensorrt/CMakeLists.txt
浏览文件 @ ff052c0e
nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto)
nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto device_context)
nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
add_subdirectory(convert)
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -84,6 +84,15 @@ function(op_library TARGET)
message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file")
endif()
#remove windows unsupported op
if (WIN32)
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
endforeach()
endif(WIN32)
list(LENGTH op_library_DEPS op_library_DEPS_len)
if (${op_library_DEPS_len} GREATER 0)
set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE)
......@@ -181,19 +190,19 @@ function(op_library TARGET)
endfunction()
add_subdirectory(math)
if (NOT WIN32)
add_subdirectory(nccl)
if(WITH_GPU)
op_library(nccl_op DEPS nccl_common)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(ncclAllReduce);\n")
else()
set(DEPS_OPS ${DEPS_OPS} nccl_op)
endif()
endif() # NOT WIN32
set(DISTRIBUTE_DEPS "")
if(WITH_DISTRIBUTE)
add_subdirectory(distributed)
set(DISTRIBUTE_DEPS "")
if(WITH_GRPC)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
......@@ -222,7 +231,7 @@ if(WITH_DISTRIBUTE)
#set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
#cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
# listen_and_serv_op sum_op executor SERIAL)
if(WITH_GPU)
if(WITH_GPU AND NOT WIN32)
set_source_files_properties(test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS listen_and_serv_op ${DISTRIBUTE_DEPS} executor SERIAL)
if(WITH_GRPC)
......@@ -233,7 +242,7 @@ if(WITH_DISTRIBUTE)
set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
endif()
endif() # WITH_GPU AND NOT WIN32
else()
set(DEPS_OPS ${DEPS_OPS} checkpoint_notify_op prefetch_op recv_op listen_and_serv_op send_op send_barrier_op fetch_barrier_op gen_nccl_id_op)
endif()
......@@ -331,5 +340,7 @@ cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_sea
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
if(NOT WIN32)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
endif()
nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)
paddle/fluid/operators/conditional_block_op.cc
浏览文件 @ ff052c0e
......@@ -29,9 +29,9 @@ class ConditionalOp : public framework::OperatorBase {
protected:
std::vector<const framework::LoDTensor *> InputTensors(
const framework::Scope &scope) const {
const framework::Scope &scope, const std::string &in_name) const {
std::vector<const framework::LoDTensor *> retv;
auto xs = Inputs("X");
auto xs = Inputs(in_name);
retv.resize(xs.size(), nullptr);
std::transform(
xs.begin(), xs.end(), retv.begin(),
......@@ -81,12 +81,18 @@ class ConditionalBlockOp : public ConditionalOp {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = InputTensors(scope);
bool need_run;
if (Attr<bool>("is_scalar_condition")) {
// When is_scalar_condition is True, the conditional variable is a scalar,
// whether need to execute the operators in sub-block depends on the
// conditional variable (Cond).
auto xs = InputTensors(scope, "Cond");
need_run = ScalarCondition(xs);
} else {
// When is_scalar_condition is False, the conditional variable maybe a
// vector or tensor, whether need to execute the operators in sub-block
// depends on the input variables (Input).
auto xs = InputTensors(scope, "Input");
need_run = std::all_of(
xs.begin(), xs.end(),
[](const framework::LoDTensor *t) { return t->numel() != 0; });
......@@ -110,11 +116,11 @@ class ConditionalBlockOp : public ConditionalOp {
class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The conditional variable of this operator. If X is empty, the "
AddInput("Cond",
"The conditional variable of this operator. If Cond is empty, the "
"whole sub-block will not be executed.")
.AsDuplicable();
AddInput("Params", "The input variables of the sub-block.").AsDuplicable();
AddInput("Input", "The input variables of the sub-block.").AsDuplicable();
AddOutput("Out", "The output variables of the sub-block.").AsDuplicable();
AddOutput("Scope",
"(std::vector<Scope*>) The step scope of conditional block. To "
......@@ -123,13 +129,18 @@ class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<framework::BlockDesc *>(
"sub_block", "The step block of conditional block operator");
AddAttr<bool>("is_scalar_condition",
"the input X is used as scalar "
"condition")
"The conditional variable (Cond) is used as scalar "
"condition.")
.SetDefault(false);
AddComment(R"DOC(Conditional block operator
Run the sub-block if X is not empty. Params is the other inputs and Out is the
outputs of the sub-block.
If `is_scalar_condition` is True, the conditional variable (Cond) is a scalar,
run the operators in sub-block if Cond is True.
If `is_scalar_condition` is False, the conditional variable (Cond) is a vector or
tensor, run the operators in sub-block if all of input variables are not empty.
)DOC");
}
};
......@@ -145,12 +156,12 @@ class ConditionalBlockGradOp : public ConditionalOp {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = this->InputTensors(scope);
bool need_run;
if (Attr<bool>("is_scalar_condition")) {
auto xs = this->InputTensors(scope, "Cond");
need_run = ScalarCondition(xs);
} else {
auto xs = this->InputTensors(scope, "Input");
need_run = std::all_of(
xs.begin(), xs.end(),
[](const framework::LoDTensor *t) { return t->numel() != 0; });
......@@ -166,11 +177,11 @@ class ConditionalBlockGradOp : public ConditionalOp {
auto *block = Attr<framework::BlockDesc *>("sub_block");
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Params"),
Outputs(framework::GradVarName("Params")));
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Input"),
Outputs(framework::GradVarName("Input")));
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("X"),
Outputs(framework::GradVarName("X")));
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Cond"),
Outputs(framework::GradVarName("Cond")));
}
}
......@@ -199,15 +210,15 @@ class ConditionalBlockGradOp : public ConditionalOp {
class ConditionalBlockGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInputs("X"));
if (context->HasInputs("Params")) {
PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Params")));
context->SetOutputsDim(framework::GradVarName("Params"),
context->GetInputsDim("Params"));
PADDLE_ENFORCE(context->HasInputs("Cond"));
if (context->HasInputs("Input")) {
PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Input")));
context->SetOutputsDim(framework::GradVarName("Input"),
context->GetInputsDim("Input"));
}
if (context->HasOutputs(framework::GradVarName("X"))) {
context->SetOutputsDim(framework::GradVarName("X"),
context->GetInputsDim("X"));
if (context->HasOutputs(framework::GradVarName("Cond"))) {
context->SetOutputsDim(framework::GradVarName("Cond"),
context->GetInputsDim("Cond"));
}
}
};
......@@ -220,14 +231,15 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDesc> Apply() const override {
auto grad_op = new framework::OpDesc();
grad_op->SetType("conditional_block_grad");
grad_op->SetInput("X", Input("X"));
grad_op->SetInput("Params", Input("Params"));
grad_op->SetInput("Cond", Input("Cond"));
grad_op->SetInput("Input", Input("Input"));
grad_op->SetInput("Out", Output("Out"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetInput("Scope", Output("Scope"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
grad_op->SetOutput(framework::GradVarName("Params"),
InputGrad("Params", false));
grad_op->SetOutput(framework::GradVarName("Cond"),
InputGrad("Cond", false));
grad_op->SetOutput(framework::GradVarName("Input"),
InputGrad("Input", false));
grad_op->SetBlockAttr("sub_block", this->grad_block_[0]);
grad_op->SetAttr("is_scalar_condition", GetAttr("is_scalar_condition"));
return std::unique_ptr<framework::OpDesc>(grad_op);
......
paddle/fluid/operators/crf_decoding_op.h
浏览文件 @ ff052c0e
......@@ -85,6 +85,199 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
int* track_value =
track.mutable_data<int>(emission_dims, platform::CPUPlace());
#ifdef __AVX__
// It use the AVX or AVX512 instruction to deal the data as the vector of 8 or
// 16 elements per iteration. Then it can implement the parallel processing.
// Only optimize for float type.
#ifdef __AVX512F__
size_t step_size = 16;
#else
size_t step_size = 8;
#endif
if (std::is_same<T, float>::value && (tag_num >= step_size)) {
size_t steps = tag_num / step_size;
size_t remain = tag_num % step_size;
int last_offset = static_cast<int>(remain) - static_cast<int>(step_size);
// Setup the alpha initial value.
size_t i_offset = 0;
for (size_t i = 0; i <= steps; ++i) {
#ifdef __AVX512F__
// Declare the variable for the content of weights, input and alpha
// values.
__m512 w_content, x_content, alpha_content;
// Load the relevant data into the variables from un-aligned address.
w_content = _mm512_loadu_ps((const float*)(w + i_offset));
x_content = _mm512_loadu_ps((const float*)(x + i_offset));
alpha_content = _mm512_add_ps(w_content, x_content);
// Save the alpha value.
_mm512_storeu_ps(reinterpret_cast<float*>(alpha_value + i_offset),
alpha_content);
#else
// Declare the variable for the content of weights, input and alpha
// values.
__m256 w_content, x_content, alpha_content;
// Load the relevant data into the variables from un-aligned address.
w_content = _mm256_loadu_ps((const float*)(w + i_offset));
x_content = _mm256_loadu_ps((const float*)(x + i_offset));
alpha_content = _mm256_add_ps(w_content, x_content);
// Save the alpha value.
_mm256_storeu_ps(reinterpret_cast<float*>(alpha_value + i_offset),
alpha_content);
#endif
i_offset += step_size;
if (i == steps - 1) {
if (remain > 0) {
i_offset += last_offset;
} else {
break;
}
}
}
// Use the column-major strategy to get the location of maximum score.
size_t seq_offset = 0;
for (size_t k = 1; k < seq_len; ++k) {
size_t j_offset = 0;
for (size_t j = 0; j <= steps; ++j) {
#ifdef __AVX512F__
// Initialize the variables of maximum score and location.
__m512 max_score = _mm512_set1_ps(-std::numeric_limits<T>::max());
__m512i max_j = _mm512_setzero_si512();
#else
// Initialize the variables of maximum score and location.
__m256 max_score = _mm256_set1_ps(-std::numeric_limits<T>::max());
__m256i max_j = _mm256_set1_epi32(0);
#endif
// Calculate the offset of transition_weights.
size_t trans_offset = state_trans_base_idx * tag_num + j_offset;
for (size_t i = 0; i < tag_num; ++i) {
#ifdef __AVX512F__
// Initalize the content of alpha variable with related offset.
__m512 alpha_content =
_mm512_set1_ps(*(const float*)(alpha_value + seq_offset + i));
// Obtain the content of weights from un-aligned address.
__m512 w_content =
_mm512_loadu_ps((const float*)(w + trans_offset));
__m512 score_v = _mm512_add_ps(alpha_content, w_content);
__mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS);
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm512_mask_set1_epi32(max_j, mask, i);
// Update the max_score value.
max_score = _mm512_max_ps(max_score, score_v);
#else
// Initalize the content of alpha variable with related offset.
__m256 alpha_content = _mm256_broadcast_ss(
(const float*)(alpha_value + seq_offset + i));
// Obtain the content of weights from un-aligned address.
__m256 w_content =
_mm256_loadu_ps((const float*)(w + trans_offset));
__m256 score_v = _mm256_add_ps(alpha_content, w_content);
__m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);
#ifdef __AVX2__
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm256_or_si256(
_mm256_andnot_si256((__m256i)mask, max_j),
_mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i)));
#else
__m128i lo_max_j = _mm256_extractf128_si256(max_j, 0);
__m128i hi_max_j = _mm256_extractf128_si256(max_j, 1);
__m128i lo_mask = _mm256_extractf128_si256((__m256i)mask, 0);
__m128i hi_mask = _mm256_extractf128_si256((__m256i)mask, 1);
lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j);
hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j);
lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i));
hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i));
lo_max_j = _mm_or_si128(lo_mask, lo_max_j);
hi_max_j = _mm_or_si128(hi_mask, hi_max_j);
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0);
max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1);
#endif
// Update the max_score value.
max_score = _mm256_max_ps(max_score, score_v);
#endif
trans_offset += tag_num;
}
#ifdef __AVX512F__
// Update the alpha and track values.
__m512 x_content = _mm512_loadu_ps(
(const float*)(x + seq_offset + tag_num + j_offset));
max_score = _mm512_add_ps(max_score, x_content);
_mm512_storeu_ps(reinterpret_cast<float*>(alpha_value + seq_offset +
tag_num + j_offset),
max_score);
_mm512_storeu_si512(
reinterpret_cast<__m512i*>(track_value + seq_offset + tag_num +
j_offset),
max_j);
#else
// Update the alpha and track values.
__m256 x_content = _mm256_loadu_ps(
(const float*)(x + seq_offset + tag_num + j_offset));
max_score = _mm256_add_ps(max_score, x_content);
_mm256_storeu_ps(reinterpret_cast<float*>(alpha_value + seq_offset +
tag_num + j_offset),
max_score);
_mm256_storeu_si256(
reinterpret_cast<__m256i*>(track_value + seq_offset + tag_num +
j_offset),
max_j);
#endif
// Calculate the offset of next step
j_offset += step_size;
if (j == steps - 1) {
if (remain > 0) {
j_offset += last_offset;
} else {
break;
}
}
}
seq_offset += tag_num;
}
} else {
for (size_t i = 0; i < tag_num; ++i) alpha_value[i] = w[i] + x[i];
for (size_t k = 1; k < seq_len; ++k) {
for (size_t i = 0; i < tag_num; ++i) {
T max_score = -std::numeric_limits<T>::max();
int max_j = 0;
for (size_t j = 0; j < tag_num; ++j) {
T score = alpha_value[(k - 1) * tag_num + j] +
w[(j + state_trans_base_idx) * tag_num + i];
if (score > max_score) {
max_score = score;
max_j = j;
}
}
alpha_value[k * tag_num + i] = max_score + x[k * tag_num + i];
track_value[k * tag_num + i] = max_j;
}
}
}
#else
for (size_t i = 0; i < tag_num; ++i) alpha_value[i] = w[i] + x[i];
for (size_t k = 1; k < seq_len; ++k) {
......@@ -105,6 +298,7 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
}
}
#endif
T max_score = -std::numeric_limits<T>::max();
int max_i = 0;
for (size_t i = 0; i < tag_num; ++i) {
......
paddle/fluid/operators/elementwise_op_function.h
浏览文件 @ ff052c0e
......@@ -80,6 +80,9 @@ inline framework::DDim trim_trailing_singular_dims(
for (int i = 0; i < actual_dims_size; ++i) {
trim_dims[i] = dims[i];
}
if (trim_dims.size() == 0) {
return framework::DDim(framework::make_dim());
}
framework::DDim actual_dims = framework::make_ddim(trim_dims);
return actual_dims;
}
......
paddle/fluid/operators/fc_op.cc
浏览文件 @ ff052c0e
......@@ -15,8 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fc_op.h"
#include <vector>
#include "paddle/fluid/operators/math/blas.h"
DECLARE_int32(paddle_num_threads);
#include "paddle/fluid/operators/math/fc_compute.h"
namespace paddle {
namespace operators {
......@@ -110,13 +109,8 @@ void FCOpMaker::Make() {
AddComment(R"DOC(
Fully Connected Operator.
The fully connected operation calculates the output based on the input, weights and bias attribute.
The fully connected operation calculates the output based on the input, weights and bias.
The size of each dimension of the parameters checked in the infer-shape.
The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
Additional parametrs are use_mkldnn and bias_attr.
The input(X) size and output(Out) size may be diffrent.
The fully connected layer only supports MKLDNN version
)DOC");
}
......@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> {
auto in_dims = input->dims();
auto w_dims = w->dims();
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(dev_ctx);
const T* input_data = input->data<T>();
const T* w_data = w->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
math::FCCompute<platform::CPUDeviceContext, T>(
blas, in_dims[0], w_dims[1], w_dims[0], input_data, w_data, output_data,
bias ? bias->data<T>() : NULL);
blas.GEMM(CblasNoTrans, CblasNoTrans, in_dims[0], w_dims[1], w_dims[0],
static_cast<T>(1), input_data, w_data, static_cast<T>(0),
output_data);
if (bias) {
const T* bias_data = bias->data<T>();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int bs = 0; bs < in_dims[0]; bs++) {
blas.AXPY(w_dims[1], static_cast<T>(1), bias_data,
output_data + bs * w_dims[1]);
}
}
// TODO(TJ): fuse act
}
};
......
paddle/fluid/operators/fusion_lstm_op.cc 0 → 100644
浏览文件 @ ff052c0e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
namespace paddle {
namespace operators {
void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightX"),
"Input(WeightX) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightH"),
"Input(WeightH) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XX"),
"Output(XX) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedGate"),
"Output(BatchedGate) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
"Output(BatchedGate) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
if (ctx->HasInput("H0")) {
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(Cell) and Input(Hidden) of LSTM should not "
"be null at the same time.");
auto h_dims = ctx->GetInputDim("H0");
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
}
auto wx_dims = ctx->GetInputDim("WeightX");
PADDLE_ENFORCE_EQ(wx_dims.size(), 2,
"The rank of Input(WeightX) should be 2.");
PADDLE_ENFORCE_EQ(wx_dims[0], x_dims[1],
"The first dimension of Input(WeightX) "
"should be %d.",
x_dims[1]);
int frame_size = wx_dims[1] / 4;
auto wh_dims = ctx->GetInputDim("WeightH");
PADDLE_ENFORCE_EQ(wh_dims.size(), 2,
"The rank of Input(WeightH) should be 2.");
PADDLE_ENFORCE_EQ(wh_dims[0], frame_size,
"The first dimension of Input(WeightH) "
"should be %d.",
frame_size);
PADDLE_ENFORCE_EQ(wh_dims[1], 4 * frame_size,
"The second dimension of Input(WeightH) "
"should be 4 * %d.",
frame_size);
auto b_dims = ctx->GetInputDim("Bias");
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(!ctx->Attrs().Get<bool>("use_peepholes"),
"Do not support peephole yet.");
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection",
frame_size);
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("BatchedGate", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchCellPreAct", out_dims);
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
int xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
}
framework::OpKernelType FusionLSTMOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
void FusionLSTMOpMaker::Make() {
AddInput("X",
"(LoDTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x.");
AddInput("WeightX",
"(Tensor) the learnable weights of X."
" - The shape is (M x 4D), where M is the dim size of x, D is the "
"hidden size. "
" - Weight = {W_cx, W_ix, W_fx, W_ox}");
AddInput("WeightH",
"(Tensor) same as LSTMOp, the learnable hidden-hidden weights."
" - The shape is (D x 4D), where D is the hidden size. "
" - Weight = {W_ch, W_ih, W_fh, W_oh}");
AddInput("Bias",
"(Tensor) the learnable weights. Almost same as LSTMOp"
"Note: we should add the fc bias into this (1x4D) in bias."
"input-hidden bias weight and peephole connections weight if "
"setting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddInput("H0",
"(Tensor, optional) (same as LSTMOp) the initial hidden state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size and D is the hidden size.")
.AsDispensable();
AddInput("C0",
"(Tensor, optional) (same as LSTMOp) (the initial cell state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time.")
.AsDispensable();
AddOutput("Hidden",
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("XX",
"(LoDTensor) the result after X * WeightX (size is T x 4D)"
" or batched_X (size is T x M), this will be automatically chosen,"
" where T is the total time steps in this mini-batch,"
" D is the hidden size, M is the dim size of x input.")
.AsIntermediate();
AddOutput("BatchedGate", "(LoDTensor) (same as LSTMOp).").AsIntermediate();
AddOutput("BatchCellPreAct", "(LoDTensor) (same as LSTMOp).")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<std::string>("gate_activation",
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Fusion Long-Short Term Memory (LSTM) Operator.
This operator fuse the X into LSTM, more details can refer to LSTM op.
)DOC");
}
template <typename DeviceContext, typename T>
inline void ReorderInitState(const DeviceContext& ctx,
const framework::Tensor& src,
framework::Vector<size_t> index_lod,
framework::Tensor* dst, bool indexed_src) {
math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
dst->mutable_data<T>(src.dims(), ctx.GetPlace());
// TODO(TJ): check mem copy perf
row_shuffle(ctx, src, index_lod, dst, indexed_src);
}
template <typename DeviceContext, typename T>
class FuisonLSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_t0 = ctx.Input<Tensor>("H0");
auto* cell_t0 = ctx.Input<Tensor>("C0");
auto* xx = ctx.Output<LoDTensor>("XX");
auto* batched_gate = ctx.Output<LoDTensor>("BatchedGate");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
bool is_reverse = ctx.Attr<bool>("is_reverse");
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_gate_data = batched_gate->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
cell_out->mutable_data<T>(ctx.GetPlace());
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
auto x_dims = x->dims();
auto wx_dims = wx->dims();
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
if (x_dims[1] > wx_dims[1]) {
math::FCCompute<DeviceContext, T>(blas, x_dims[0], wx_dims[1], x_dims[1],
x_data, wx_data, xx_data,
bias->data<T>());
to_batch(dev_ctx, *xx, batched_gate, true, is_reverse);
} else {
to_batch(dev_ctx, *x, xx, true, is_reverse);
batched_gate->set_lod(xx->lod());
math::FCCompute<DeviceContext, T>(blas, x_dims[0], wx_dims[1], x_dims[1],
xx_data, wx_data, batched_gate_data,
bias->data<T>());
}
int frame_size = static_cast<int>(wx_dims[1] / 4);
framework::DDim out_dims({x_dims[0], frame_size});
math::LstmMetaValue<T> lstm_value;
// no peephole
lstm_value.check_ig = nullptr;
lstm_value.check_fg = nullptr;
lstm_value.check_og = nullptr;
lstm_value.prev_state_value = nullptr;
Tensor ordered_c0;
framework::Vector<size_t> order(batched_gate->lod()[2]);
if (cell_t0) {
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState<DeviceContext, T>(dev_ctx, *cell_t0, order, &ordered_c0,
true);
lstm_value.prev_state_value = ordered_c0.data<T>();
}
// Use the local variable as here.
LoDTensor batch_hidden, batch_cell;
auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
batch_hidden.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell_pre_act->mutable_data<T>(out_dims, ctx.GetPlace());
auto batch_starts = batched_gate->lod()[0];
size_t max_seq_len = batch_starts.size() - 1;
auto gate_act = math::detail::GetActivationType(
ctx.Attr<std::string>("gate_activation"));
auto cell_act = math::detail::GetActivationType(
ctx.Attr<std::string>("cell_activation"));
auto cand_act = math::detail::GetActivationType(
ctx.Attr<std::string>("candidate_activation"));
for (size_t n = 0; n < max_seq_len; n++) {
int bstart = static_cast<int>(batch_starts[n]);
int bend = static_cast<int>(batch_starts[n + 1]);
Tensor gate_t = batched_gate->Slice(bstart, bend);
Tensor out_t = batch_hidden.Slice(bstart, bend);
Tensor cell_t = batch_cell.Slice(bstart, bend);
Tensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);
int cur_batch_size = bend - bstart;
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
// TODO(TJ): use gemm directly
blas.MatMul(pre_hidden_t, false, *wh, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
} else if (hidden_t0) {
// TODO(TJ): move h0 outside for
// If n == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros, the calculation W_h * H0 will be skiped.
// If n == 0 and there is initialized hidden state, calculate W_h * H0.
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor ordered_h0;
ReorderInitState<DeviceContext, T>(dev_ctx, *hidden_t0, order,
&ordered_h0, true);
// TODO(TJ): use gemm directly
blas.MatMul(ordered_h0, false, *wh, false, static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
}
lstm_value.gate_value = gate_t.data<T>();
lstm_value.output_value = out_t.data<T>();
lstm_value.state_value = cell_t.data<T>();
lstm_value.state_active_value = cell_pre_act_t.data<T>();
math::LstmUnitFunctor<DeviceContext, T>::compute(
dev_ctx, lstm_value, frame_size, cur_batch_size, gate_act, cell_act,
cand_act);
lstm_value.prev_state_value = lstm_value.state_value;
}
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
batch_hidden.set_lod(batched_gate->lod());
// restore the output hidden in LoDTensor from the batch hidden
to_seq(dev_ctx, batch_hidden, hidden_out);
batch_cell.set_lod(batched_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
to_seq(dev_ctx, batch_cell, cell_out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_lstm, ops::FusionLSTMOp, ops::FusionLSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(
fusion_lstm,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, float>,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/fusion_lstm_op.h 0 → 100644
浏览文件 @ ff052c0e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
// #include <string>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class FusionLSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class FusionLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/fc_compute.h 0 → 100644
浏览文件 @ ff052c0e
/* 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 "paddle/fluid/operators/math/blas.h"
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace operators {
namespace math {
template <typename DeviceContext, typename T>
inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
const int N, const int K, const T* X, const T* W, T* Y,
const T* B = NULL) {
blas.GEMM(CblasNoTrans, CblasNoTrans, M, N, K, static_cast<T>(1), X, W,
static_cast<T>(0), Y);
if (B) {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int i = 0; i < M; i++) {
blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
}
}
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/nccl/CMakeLists.txt
浏览文件 @ ff052c0e
if(WITH_GPU)
if(WITH_GPU AND NOT WIN32)
nv_library(nccl_common SRCS nccl_gpu_common.cc DEPS device_context operator )
endif()
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ ff052c0e
......@@ -23,9 +23,9 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SqueezeOp should not be null.");
"Input(X) of Squeeze operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SqueezeOp should not be null.");
"Output(Out) of Squeeze operator should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
// Check input tensor dims (<6) Eigen limit.
......@@ -107,7 +107,6 @@ class SqueezeOp : public framework::OperatorBase {
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
attrs["inplace"] = Attr<bool>("inplace");
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {Input("X")}}, {"Shape", {}}},
......@@ -125,12 +124,6 @@ class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
"(std::vector<int>). List of integers,"
" indicating the dimensions to squeeze.")
.SetDefault({});
AddAttr<bool>("inplace",
"(default: false) Squeeze 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(
Squeeze Operator.
......@@ -180,7 +173,6 @@ class SqueezeGradOp : public framework::OperatorBase {
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
attrs["inplace"] = Attr<bool>("inplace");
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
......
paddle/fluid/operators/unsqueeze_op.cc
浏览文件 @ ff052c0e
......@@ -23,9 +23,9 @@ class UnsqueezeOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of UnsqueezeOp should not be null.");
"Input(X) of Unsqueeze operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of UnsqueezeOp should not be null.");
"Output(Out) of Unsqueeze operator should not be null.");
const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
const auto &x_dims = ctx->GetInputDim("X");
......@@ -95,7 +95,6 @@ class UnsqueezeOp : public framework::OperatorBase {
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
attrs["inplace"] = Attr<bool>("inplace");
// Invoke Reshape op.
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {Input("X")}}, {"Shape", {}}},
......@@ -126,13 +125,6 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
" within [1, 6] dimensions (Eigen limit).");
}
});
AddAttr<bool>(
"inplace",
"(default: false) Unsqueeze 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(
Unsqueeze Operator.
......@@ -168,7 +160,6 @@ class UnsqueezeGradOp : public framework::OperatorBase {
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
attrs["inplace"] = Attr<bool>("inplace");
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
......
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @ ff052c0e
......@@ -3,7 +3,7 @@ cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags enforce)
list(APPEND CUDA_SRCS cublas.cc cudnn.cc curand.cc)
# There is no macOS version of NCCL.
if (NOT APPLE)
if (NOT APPLE AND NOT WIN32)
list(APPEND CUDA_SRCS nccl.cc)
endif()
......
paddle/fluid/platform/enforce.h
浏览文件 @ ff052c0e
......@@ -44,7 +44,7 @@ limitations under the License. */
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/dynload/curand.h"
#ifndef __APPLE__
#if !defined(__APPLE__) and !defined(_WIN32)
#include "paddle/fluid/platform/dynload/nccl.h"
#endif // __APPLE__
#endif // PADDLE_WITH_CUDA
......@@ -205,7 +205,7 @@ inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
#endif
}
#ifndef __APPLE__
#if !defined(__APPLE__) and !defined(_WIN32)
template <typename... Args>
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
ncclResult_t stat, const Args&... args) {
......@@ -221,7 +221,7 @@ inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
#endif
}
}
#endif // __APPLE__
#endif // __APPLE__ and windows
#endif // PADDLE_WITH_CUDA
template <typename T>
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ ff052c0e
......@@ -596,8 +596,8 @@ All parameter, weight, gradient are variables in Paddle.
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy>(pe, "ExecutionStrategy")
.def(py::init())
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy");
exec_strategy.def(py::init())
.def_property(
"num_threads",
[](const ExecutionStrategy &self) { return self.num_threads_; },
......@@ -624,6 +624,16 @@ All parameter, weight, gradient are variables in Paddle.
[](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
});
exec_strategy.def_property(
"use_experimental_executor",
[](const ExecutionStrategy &self) {
return self.type_ == ExecutionStrategy::kExperimental;
},
[](ExecutionStrategy &self, bool experimental) {
self.type_ = experimental ? ExecutionStrategy::kExperimental
: ExecutionStrategy::kDefault;
});
py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ ff052c0e
......@@ -1272,8 +1272,8 @@ class ConditionalBlock(object):
parent_block.append_op(
type='conditional_block',
inputs={
'X': self.inputs,
'Params': param_list,
'Cond': self.inputs,
'Input': param_list,
},
outputs={'Out': out_list,
'Scope': [step_scope]},
......
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @ ff052c0e
......@@ -30,7 +30,8 @@ import numpy as np
class TestMNISTIfElseOp(unittest.TestCase):
def test_raw_api(self):
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def not_test_raw_api(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
......@@ -91,7 +92,8 @@ class TestMNISTIfElseOp(unittest.TestCase):
return
self.assertFalse(True)
def test_ifelse(self):
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def not_test_ifelse(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
......@@ -153,6 +155,13 @@ class TestIfElse(unittest.TestCase):
self.cond_value = 0.5
self.data = np.random.rand(25, 1).astype(np.float32)
def numpy_cal(self):
s1 = self.data[np.where(self.data < self.cond_value)]
res = np.sum(np.exp(s1))
s2 = self.data[np.where(self.data >= self.cond_value)]
res += np.sum(np.tanh(s2))
return res
def compare_ifelse_op_and_numpy(self, place):
self.set_test_case()
......@@ -166,10 +175,12 @@ class TestIfElse(unittest.TestCase):
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
ie.output(true_target)
with ie.false_block():
false_target = ie.input(src)
false_target = fluid.layers.tanh(false_target)
ie.output(false_target)
if_out = ie()
out = layers.reduce_sum(if_out)
......@@ -180,7 +191,8 @@ class TestIfElse(unittest.TestCase):
o1, = exe.run(fluid.default_main_program(),
feed={'data': self.data},
fetch_list=[out])
o2 = np.sum(self.data)
o2 = self.numpy_cal()
self.assertTrue(
np.allclose(
o1, o2, atol=1e-8),
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ ff052c0e
......@@ -38,7 +38,8 @@ class TestParallelExecutorBase(unittest.TestCase):
seed=None,
use_parallel_executor=True,
use_reduce=False,
optimizer=fluid.optimizer.Adam):
optimizer=fluid.optimizer.Adam,
use_fast_executor=False):
def run_executor(exe, feed, fetch_list, program=None):
if isinstance(exe, fluid.ParallelExecutor):
res = exe.run(fetch_list=fetch_list, feed=feed)
......@@ -71,6 +72,8 @@ class TestParallelExecutorBase(unittest.TestCase):
startup_exe.run(startup)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.allow_op_delay = allow_op_delay
if use_fast_executor:
exec_strategy.use_experimental_executor = True
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
......
python/paddle/fluid/tests/unittests/test_fc_op.py
浏览文件 @ ff052c0e
......@@ -64,27 +64,47 @@ class TestFCOp(OpTest):
self.check_output()
class TestFCOpBiasBoth(TestFCOp):
class TestFCOpNoBias(TestFCOp):
def init_shapes(self, mb, ic, oc, h, w):
for with_bias in {True, False}:
self.with_bias = with_bias
self.matrix = MatrixGenerate(mb, ic, oc, h, w)
self.with_bias = False
self.matrix = MatrixGenerate(mb, ic, oc, h, w)
class TestFCOp1(TestFCOpBiasBoth):
class TestFCOpWithBias(TestFCOp):
def init_shapes(self, mb, ic, oc, h, w):
self.with_bias = True
self.matrix = MatrixGenerate(mb, ic, oc, h, w)
class TestFCOp1(TestFCOpNoBias):
def init_op_type(self):
self.init_shapes(2, 8, 10, 1, 1)
class TestFCOp2(TestFCOpBiasBoth):
class TestFCOp2(TestFCOpNoBias):
def init_op_type(self):
self.init_shapes(4, 5, 6, 2, 2)
class TestFCOp4(TestFCOpBiasBoth):
class TestFCOp4(TestFCOpNoBias):
def init_op_type(self):
self.init_shapes(1, 32, 64, 3, 3)
class TestFCOpWithBias1(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(3, 8, 10, 2, 1)
class TestFCOpWithBias2(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(4, 5, 6, 2, 2)
class TestFCOpWithBias3(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(1, 64, 32, 3, 3)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py 0 → 100644
浏览文件 @ ff052c0e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from test_lstm_op import lstm, ACTIVATION
def fc(x, w, b):
return np.dot(x, w) + b
def fusion_lstm(
x, # T x M
lod, # 1 x N
wx=None, # M x 4D
bx=None, # 1 x 4D
h0=None, # N x D
c0=None, # N x D
w_h=None, # D x 4D
w_b=None, # 1 x 4D
w_c=None, # 1 x 3D
is_reverse=False,
act_gate=None,
act_cell=None,
act_cand=None):
return lstm(
fc(x, wx, bx), lod, h0, c0, w_h, w_b, w_c, is_reverse, act_gate,
act_cell, act_cand)
class TestLstmOp(OpTest):
def set_argument(self):
self.lod = [[2, 3, 2]]
def setUp(self):
self.op_type = 'fusion_lstm'
self.lod = [[2, 3, 2]]
self.M = 8
self.D = 16
self.has_initial_state = False
self.is_reverse = False
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.set_argument()
T = sum(self.lod[0])
bs = len(self.lod[0])
x = np.random.normal(size=(T, self.M)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(bs, self.D)).astype('float64')
c0 = np.random.normal(size=(bs, self.D)).astype('float64')
else:
h0 = np.zeros((bs, self.D)).astype('float64')
c0 = np.zeros((bs, self.D)).astype('float64')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
w_b = np.copy(b[:, 0:4 * self.D])
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
# this is the weight of fc
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float64')
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx = np.random.normal(size=(1, 4 * self.D)).astype('float64')
b[0, 0:4 * self.D] += bx[0, :]
h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
self.is_reverse, ACTIVATION[self.act_gate],
ACTIVATION[self.act_cell], ACTIVATION[self.act_cand])
self.inputs = {
'X': (x, self.lod),
'WeightX': wx,
'WeightH': wh,
'Bias': b
}
if self.has_initial_state:
self.inputs['H0'] = h0
self.inputs['C0'] = c0
self.outputs = {
'Hidden': (h, self.lod),
'Cell': (c, self.lod),
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand
}
def test_check_output(self):
self.check_output(atol=1e-8)
class TestLstmOpInitReverse(TestLstmOp):
def set_argument(self):
self.has_initial_state = True
self.is_reverse = True
class TestLstmOpMD1(TestLstmOp):
def set_argument(self):
self.M = 36
self.D = 8
class TestLstmOpMD2(TestLstmOp):
def set_argument(self):
self.M = 8
self.D = 8
class TestLstmOpMD3(TestLstmOp):
def set_argument(self):
self.M = 15
self.D = 3
class TestLstmOpBS1(TestLstmOp):
def set_argument(self):
self.lod = [[3]]
self.D = 16
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @ ff052c0e
......@@ -183,7 +183,9 @@ class TestMNIST(TestParallelExecutorBase):
use_parallel_executor=True)
self.assertAlmostEquals(
np.mean(parallel_first_loss), single_first_loss, delta=1e-6)
np.mean(parallel_first_loss),
single_first_loss,
delta=1e-6, )
self.assertAlmostEquals(
np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
......@@ -191,7 +193,7 @@ class TestMNIST(TestParallelExecutorBase):
self.check_simple_fc_parallel_accuracy(True)
self.check_simple_fc_parallel_accuracy(False)
def check_batchnorm_fc_convergence(self, use_cuda):
def check_batchnorm_fc_convergence(self, use_cuda, use_fast_executor):
if use_cuda and not core.is_compiled_with_cuda():
return
......@@ -203,11 +205,13 @@ class TestMNIST(TestParallelExecutorBase):
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda)
use_cuda=use_cuda,
use_fast_executor=use_fast_executor)
def test_batchnorm_fc(self):
self.check_batchnorm_fc_convergence(True)
self.check_batchnorm_fc_convergence(False)
for use_cuda in (False, True):
for use_fast_executor in (False, True):
self.check_batchnorm_fc_convergence(use_cuda, use_fast_executor)
def test_batchnorm_fc_with_new_strategy(self):
# FIXME(zcd): close this test temporally.
......
python/paddle/fluid/tests/unittests/test_squeeze_op.py
浏览文件 @ ff052c0e
......@@ -41,7 +41,7 @@ class TestSqueezeOp(OpTest):
self.new_shape = (3, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": False}
self.attrs = {"axes": self.axes}
# Correct: There is mins axis.
......@@ -68,49 +68,5 @@ class TestSqueezeOp3(TestSqueezeOp):
self.new_shape = (3, 5, 1, 4)
# Correct: Inplace.
class TestSqueezeOpInplace1(TestSqueezeOp):
def init_test_case(self):
self.ori_shape = (1, 3, 1, 5)
self.axes = (0, 2)
self.new_shape = (3, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. There is mins axis.
class TestSqueezeOpInplace2(TestSqueezeOp):
def inti_test_case(self):
self.ori_shape = (1, 3, 1, 5)
self.axes = (0, -2)
self.new_shape = (3, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. No axes input.
class TestSqueezeOpInplace3(TestSqueezeOp):
def init_test_case(self):
self.ori_shape = (1, 3, 1, 5)
self.axes = ()
self.new_shape = (3, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inpalce. Just part of axes be squeezed.
class TestSqueezeOpInplace4(TestSqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 1, 5, 1, 4, 1)
self.axes = (1, -1)
self.new_shape = (3, 5, 1, 4)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
浏览文件 @ ff052c0e
......@@ -41,7 +41,7 @@ class TestUnsqueezeOp(OpTest):
self.new_shape = (3, 1, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": False}
self.attrs = {"axes": self.axes}
# Correct: Single input index.
......@@ -76,38 +76,5 @@ class TestUnsqueezeOp4(TestUnsqueezeOp):
self.new_shape = (3, 1, 1, 2, 5, 1)
# Correct: Inplace.
class TestUnsqueezeOpInplace1(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (0, 2)
self.new_shape = (1, 3, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. There is mins index.
class TestUnsqueezeOpInplace2(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (0, -2)
self.new_shape = (1, 3, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. There is duplicated axis.
class TestUnsqueezeOpInplace3(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 2, 5)
self.axes = (0, 3, 3)
self.new_shape = (1, 3, 2, 1, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/trainer.py
浏览文件 @ ff052c0e
......@@ -285,11 +285,12 @@ class Trainer(object):
self._load_checkpoint()
if param_path and os.path.isdir(param_path):
# load params from param_path into scope
io.load_persistables(
executor=exe,
dirname=param_path,
main_program=self.startup_program)
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_persistables(
executor=exe,
dirname=param_path,
main_program=self.startup_program)
def _transpile_nccl2_dist(self):
# PADDLE_TRAINER_IPS
......
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