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提交 99302a70 编写于 作者: R root
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_import_plot_py3

上级 5a220dc2 177720a7
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Showing with 3507 addition and 971 deletion
.gitignore
浏览文件 @ 99302a70
......@@ -28,3 +28,4 @@ third_party/
build_*
# clion workspace.
cmake-build-*
model_test
CMakeLists.txt
浏览文件 @ 99302a70
......@@ -69,6 +69,7 @@ option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(WITH_INFERENCE "Compile fluid inference library" ON)
option(ON_INFER "Turn on inference optimization." OFF)
option(WITH_INFERENCE_API_TEST "Test fluid inference high-level api interface" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
......@@ -179,6 +180,7 @@ include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
include(external/xxhash) # download xxhash
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
......@@ -301,3 +303,8 @@ if(WITH_DOC)
find_python_module(recommonmark REQUIRED)
add_subdirectory(doc)
endif()
if (ON_INFER)
message(WARNING "On inference mode, will take place some specific optimization.")
add_definitions(-DPADDLE_ON_INFERENCE)
endif()
Dockerfile
浏览文件 @ 99302a70
......@@ -75,14 +75,14 @@ RUN pip3 install -U wheel && \
pip3 install -U docopt PyYAML sphinx==1.5.6 && \
pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
easy_install -U pip && \
pip install -U wheel && \
pip install -U pip setuptools wheel && \
pip install -U docopt PyYAML sphinx==1.5.6 && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip3 install pre-commit 'ipython==5.3.0' && \
RUN pip3 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 install opencv-python && \
pip install pre-commit 'ipython==5.3.0' && \
pip install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
......
benchmark/fluid/args.py
浏览文件 @ 99302a70
......@@ -142,5 +142,10 @@ def parse_args():
choices=['reduce', 'all_reduce'],
default='all_reduce',
help='Specify the reduce strategy, can be reduce, all_reduce')
parser.add_argument(
'--fuse_broadcast_op',
action='store_true',
help='If set, would fuse multiple broadcast operators into one fused_broadcast operator.'
)
args = parser.parse_args()
return args
benchmark/fluid/fluid_benchmark.py
浏览文件 @ 99302a70
......@@ -177,6 +177,7 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
else:
build_strategy.reduce_strategy = fluid.BuildStrategy(
).ReduceStrategy.AllReduce
build_strategy.fuse_broadcast_op = args.fuse_broadcast_op
avg_loss = train_args[0]
......@@ -240,7 +241,6 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
if args.use_fake_data or args.use_reader_op:
try:
fetch_ret = exe.run(fetch_list)
except fluid.core.EOFException as eof:
break
......
cmake/external/xxhash.cmake 0 → 100644
浏览文件 @ 99302a70
INCLUDE(ExternalProject)
set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash)
set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
IF(WITH_STATIC_LIB)
SET(BUILD_CMD make lib)
ELSE()
SET(BUILD_CMD sed -i "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
ENDIF()
ExternalProject_Add(
extern_xxhash
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/Cyan4973/xxHash"
GIT_TAG "v0.6.5"
PREFIX ${XXHASH_SOURCE_DIR}
DOWNLOAD_NAME "xxhash"
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
PATCH_COMMAND
BUILD_COMMAND ${BUILD_CMD}
INSTALL_COMMAND export PREFIX=${XXHASH_INSTALL_DIR}/ && make install
TEST_COMMAND ""
)
set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
INCLUDE_DIRECTORIES(${XXHASH_INCLUDE_DIR})
add_library(xxhash STATIC IMPORTED GLOBAL)
set_property(TARGET xxhash PROPERTY IMPORTED_LOCATION ${XXHASH_LIBRARIES})
include_directories(${XXHASH_INCLUDE_DIR})
add_dependencies(xxhash extern_xxhash)
LIST(APPEND external_project_dependencies xxhash)
IF(WITH_C_API)
INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
IF(ANDROID)
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
ENDIF()
ENDIF()
cmake/inference_lib.cmake
浏览文件 @ 99302a70
......@@ -14,6 +14,9 @@
# make package for paddle fluid shared and static library
function(copy TARGET)
if (NOT ON_INFER)
message(WARNING "Turn on the ON_INFER flag when building inference_lib only.")
endif()
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DSTS DEPS)
......@@ -31,7 +34,7 @@ function(copy TARGET)
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
list(GET copy_lib_DSTS ${index} dst)
add_custom_command(TARGET ${TARGET} PRE_BUILD
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND mkdir -p "${dst}"
COMMAND cp -r "${src}" "${dst}"
COMMENT "copying ${src} -> ${dst}")
......@@ -67,6 +70,13 @@ copy(boost_lib
DEPS boost
)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/xxhash")
copy(xxhash_lib
SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS xxhash
)
if(NOT PROTOBUF_FOUND)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
copy(protobuf_lib
......@@ -186,7 +196,7 @@ copy(cmake_cache
DSTS ${FLUID_INSTALL_DIR})
# This command generates a complete fluid library for both train and inference
add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
# Following commands generate a inference-only fluid library
# third_party, version.txt and CMakeCache.txt are the same position with ${FLUID_INSTALL_DIR}
......
paddle/fluid/API.spec
浏览文件 @ 99302a70
......@@ -86,7 +86,7 @@ paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer'))
paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None))
......@@ -107,7 +107,7 @@ paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label',
paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
......@@ -174,7 +174,9 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -353,6 +355,8 @@ paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_wind
paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
paddle.fluid.regularizer.L2DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -16,12 +16,14 @@ if(WITH_GPU)
dynload_cuda variable_visitor)
nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim dynload_cuda)
nv_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor dynload_cuda)
nv_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
else()
cc_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
variable_visitor)
cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim)
cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
endif()
cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
......@@ -34,7 +36,7 @@ if(WITH_GPU)
endif()
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle)
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
if(WITH_GPU)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass)
......@@ -58,4 +60,4 @@ cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executo
cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass)
fuse_elewise_add_act_pass multi_batch_merge_pass)
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 99302a70
......@@ -48,16 +48,23 @@ void BroadcastOpHandle::RunImpl() {
var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
}
BroadcastOneVar(*in_var_handle, out_var_handles, var_scopes);
}
void BroadcastOpHandle::BroadcastOneVar(
const VarHandle &in_var_handle,
const std::vector<VarHandle *> &out_var_handles,
const std::vector<const Scope *> &var_scopes) {
auto *in_var =
var_scopes.at(in_var_handle->scope_idx_)->FindVar(in_var_handle->name_);
var_scopes.at(in_var_handle.scope_idx_)->FindVar(in_var_handle.name_);
PADDLE_ENFORCE_NOT_NULL(in_var);
Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
InitOutputValue(*in_var_handle, out_var_handles);
InitOutputValue(in_var_handle, out_var_handles);
if (platform::is_cpu_place(in_tensor.place())) {
for (auto *out_var_handle : out_var_handles) {
if (out_var_handle->IsTheSameVar(*in_var_handle)) {
if (out_var_handle->IsTheSameVar(in_var_handle)) {
continue;
}
auto &out_p = out_var_handle->place_;
......@@ -114,12 +121,12 @@ void BroadcastOpHandle::RunImpl() {
}
}
if (!out_handle->IsTheSameVar(*in_var_handle)) {
auto out_var = var_scopes.at(in_var_handle->scope_idx_)
if (!out_handle->IsTheSameVar(in_var_handle)) {
auto out_var = var_scopes.at(in_var_handle.scope_idx_)
->FindVar(out_var_handles[0]->name_);
paddle::framework::TensorCopy(
in_tensor, in_var_handle->place_,
*(dev_ctxes_.at(in_var_handle->place_)),
in_tensor, in_var_handle.place_,
*(dev_ctxes_.at(in_var_handle.place_)),
&VariableVisitor::GetMutableTensor(out_var));
}
});
......
paddle/fluid/framework/details/broadcast_op_handle.h
浏览文件 @ 99302a70
......@@ -61,7 +61,10 @@ struct BroadcastOpHandle : public OpHandleBase {
protected:
void RunImpl() override;
private:
void BroadcastOneVar(const VarHandle &in_var_handle,
const std::vector<VarHandle *> &out_var_handles,
const std::vector<const Scope *> &var_scopes);
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
#ifdef PADDLE_WITH_CUDA
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 99302a70
......@@ -121,6 +121,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_batch_merge_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 99302a70
......@@ -69,6 +69,8 @@ struct BuildStrategy {
bool enable_data_balance_{false};
bool fuse_broadcast_op_{false};
// User normally doesn't need to call this API.
// The PassBuilder allows for more customized insert, remove of passes
// from python side.
......
paddle/fluid/framework/details/fused_broadcast_op_handle.cc 0 → 100644
浏览文件 @ 99302a70
// 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/fused_broadcast_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
namespace details {
void FusedBroadcastOpHandle::RunImpl() {
platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
if (places_.size() == 1UL) return;
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
auto out_var_handles = DynamicCast<VarHandle>(outputs_);
WaitInputVarGenerated();
std::vector<const Scope *> var_scopes;
for (auto *s : local_scopes_) {
var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
}
size_t place_num = places_.size();
PADDLE_ENFORCE_EQ(in_var_handles.size() * place_num, out_var_handles.size());
for (size_t i = 0; i < in_var_handles.size(); ++i) {
BroadcastOneVar(
*in_var_handles[i],
std::vector<VarHandle *>(out_var_handles.begin() + i * place_num,
out_var_handles.begin() + (i + 1) * place_num),
var_scopes);
}
}
std::string FusedBroadcastOpHandle::Name() const { return "fused_broadcast"; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fused_broadcast_op_handle.h 0 → 100644
浏览文件 @ 99302a70
// 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 <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/platform/device_context.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
namespace details {
struct FusedBroadcastOpHandle : public BroadcastOpHandle {
public:
#ifdef PADDLE_WITH_CUDA
FusedBroadcastOpHandle(ir::Node *node,
const std::vector<Scope *> local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *nccl_ctx)
: BroadcastOpHandle(node, local_scopes, places, nccl_ctx) {}
#else
FusedBroadcastOpHandle(ir::Node* node, const std::vector<Scope*> local_scopes,
const std::vector<platform::Place>& places)
: BroadcastOpHandle(node, local_scopes, places) {}
#endif
std::string Name() const override;
protected:
void RunImpl() override;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 99302a70
......@@ -21,6 +21,7 @@
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/rpc_op_handle.h"
......@@ -252,9 +253,9 @@ std::vector<ir::Node *> SortOpsAndDelayOptimizeOp(const ir::Graph &graph) {
std::vector<ir::Node *> sorted_ret;
for (size_t i = 0; i < ret.size(); ++i) {
if (i < last_backward) {
if (boost::get<int>(ret[i]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kOptimize)) {
if (static_cast<bool>(boost::get<int>(ret[i]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kOptimize))) {
optimize_ops.push_back(ret[i]);
} else {
sorted_ret.push_back(ret[i]);
......@@ -347,7 +348,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
BuildStrategy::GradientScaleStrategy::kCustomized) {
// TODO(paddle-dev): Why is there no input for this op_handle?
auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
CreateScaleLossGradOp(&result, loss_grad_name);
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
......@@ -436,10 +437,14 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
if ((use_gpu &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) ||
is_dist_train) {
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(&result, bcast_var_name_set);
} else {
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
}
}
}
}
......@@ -508,6 +513,44 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
}
}
void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
ir::Graph *result,
const std::vector<std::unordered_set<std::string>> &bcast_varnames) const {
#ifdef PADDLE_WITH_CUDA
auto *op_handle = new FusedBroadcastOpHandle(
result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
local_scopes_, places_, nccl_ctxs_);
#else
auto *op_handle = new FusedBroadcastOpHandle(
result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
local_scopes_, places_);
#endif
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
}
for (size_t dev_id = 0; dev_id < bcast_varnames.size(); ++dev_id) {
for (auto &p_name : bcast_varnames[dev_id]) {
auto *in =
result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back().get();
op_handle->AddInput(in);
for (size_t out_dev_id = 0; out_dev_id < places_.size(); ++out_dev_id) {
auto &p = places_[out_dev_id];
auto &vars =
result->Get<GraphVars>(kGraphVars).at(out_dev_id).at(p_name);
auto *out_var = new VarHandle(
result->CreateEmptyNode(p_name, ir::Node::Type::kVariable),
vars.size(), out_dev_id, p_name, p);
vars.emplace_back(out_var);
op_handle->AddOutput(out_var);
}
}
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
ir::Node *node,
int dev_id) const {
......@@ -602,7 +645,8 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(const ir::Graph &graph,
}
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
ir::Graph *result, const std::string &loss_grad_name) const {
ir::Graph *result, const std::string &loss_grad_name,
ir::Node *out_var_node) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
......@@ -617,10 +661,8 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
// loss->pending_ops_.emplace_back(op_handle);
// op_handle->inputs_.emplace_back(loss);
CreateOpOutput(
result, op_handle,
result->CreateEmptyNode(loss_grad_name, ir::Node::Type::kVariable),
places_[i], i);
CreateOpOutput(result, op_handle,
result->CreateVarNode(out_var_node->Var()), places_[i], i);
}
}
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 99302a70
......@@ -61,7 +61,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
size_t num_places) const;
void CreateScaleLossGradOp(ir::Graph *result,
const std::string &loss_grad_name) const;
const std::string &loss_grad_name,
ir::Node *out_var_node) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
......@@ -78,6 +79,10 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
size_t src_dev_id) const;
void CreateFusedBroadcastOp(
ir::Graph *result,
const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
bool IsSparseGradient(const std::string &og) const;
size_t GetAppropriateDeviceID(
......
paddle/fluid/framework/framework.proto
浏览文件 @ 99302a70
......@@ -80,7 +80,6 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool dispensable = 5 [ default = false ];
optional string reuse = 6;
}
// AttrProto describes the C++ type Attribute.
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -36,18 +36,17 @@ pass_library(fc_lstm_fuse_pass inference)
pass_library(embedding_fc_lstm_fuse_pass inference)
pass_library(fc_gru_fuse_pass inference)
pass_library(seq_concat_fc_fuse_pass inference)
pass_library(multi_batch_merge_pass base)
pass_library(conv_bn_fuse_pass inference)
pass_library(seqconv_eltadd_relu_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(conv_bias_mkldnn_fuse_pass inference)
pass_library(conv_relu_mkldnn_fuse_pass inference)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
endif()
cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
if(WITH_MKLDNN)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
endif()
set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ 99302a70
......@@ -24,79 +24,23 @@ namespace paddle {
namespace framework {
namespace ir {
std::vector<std::string> FindDistTrainSendVars(
const std::vector<ir::Node *> &nodes) {
std::vector<std::string> send_vars;
// since parameters are all in block 0,
// it's enough to only scan send ops in block 0
for (auto &node : nodes) {
auto op_vars = node->Op()->InputArgumentNames();
send_vars.reserve(send_vars.size() +
std::distance(op_vars.begin(), op_vars.end()));
send_vars.insert(send_vars.end(), op_vars.begin(), op_vars.end());
}
return send_vars;
}
std::vector<std::string> FindDistTrainRecvVars(
const std::vector<ir::Node *> &nodes) {
std::vector<std::string> recv_vars;
for (auto &node : nodes) {
auto op_vars = node->Op()->OutputArgumentNames();
recv_vars.reserve(recv_vars.size() +
std::distance(op_vars.begin(), op_vars.end()));
recv_vars.insert(recv_vars.end(), op_vars.begin(), op_vars.end());
}
return recv_vars;
}
bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) {
if (send_vars.size() == 0 || recv_vars.size() == 0) {
return false;
}
/**
* Check any of opvars contains `.block` and in sendvars
*/
auto checker = [](const std::vector<std::string> &opvars,
const std::vector<std::string> &rpc_vars) -> bool {
for (auto &var : opvars) {
// a variable name with the suffix `.block` means it's a splited
// variable by (DistributeTranspiler)
// [python/paddle/fluid/transpiler/distribute_transpiler.py]
if (var.find(".block") != std::string::npos &&
std::find(rpc_vars.begin(), rpc_vars.end(), var) != rpc_vars.end()) {
return true;
}
}
return false;
};
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
}
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
}
return checker(output_var_names, send_vars) ||
checker(input_var_names, recv_vars);
}
Graph::Graph(const ProgramDesc &program) : program_(program) {
// Make the nodes id start from 0.
Node::ResetId();
auto var_nodes = InitFromProgram(program_);
ResolveHazard(var_nodes);
}
std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
const ProgramDesc &program) {
VLOG(3) << "block in program:" << program_.Size();
std::unordered_map<std::string, VarDesc *> all_vars;
// var nodes for each var name, will have multiple versions in SSA
std::map<std::string, std::vector<ir::Node *>> var_nodes;
for (auto *var : program.Block(0).AllVars()) {
all_vars.emplace(var->Name(), var);
}
std::map<std::string, std::vector<ir::Node *>> var_nodes;
for (auto *op : program.Block(0).AllOps()) {
ir::Node *node = CreateOpNode(op);
// For input args, reuse the same var name if it was created before.
......@@ -134,7 +78,11 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
var->inputs.push_back(node);
}
}
return std::move(var_nodes);
}
void Graph::ResolveHazard(
const std::map<std::string, std::vector<ir::Node *>> &var_nodes) {
/**
* We should handle write after read(WAR) and write after write(WAW) here.
* Because some of the operators of the program can be executed parallelly.
......@@ -153,6 +101,7 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
auto it_old = versions.rbegin();
++it_old;
for (; it_old != versions.rend(); it_new = it_old, ++it_old) {
VLOG(3) << "deal with var: " << (*it_new)->Name();
ir::Node *write_op =
(*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
const auto &read_ops = (*it_old)->outputs;
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 99302a70
......@@ -160,6 +160,12 @@ class Graph {
return nullptr;
}
std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
const ProgramDesc &program);
void ResolveHazard(
const std::map<std::string, std::vector<ir::Node *>> &var_nodes);
private:
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
......
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ 99302a70
......@@ -120,19 +120,25 @@ size_t GraphNum(const Graph &graph) {
std::deque<ir::Node *> q_nodes;
std::vector<std::unordered_set<ir::Node *>> graph_nodes;
std::unordered_set<ir::Node *> g_nodes;
// q_set used to record records in the queue.
std::unordered_set<ir::Node *> q_set;
size_t graph_count = 0;
auto traverse_nodes = [&visited_nodes,
&q_nodes](const std::vector<ir::Node *> &nodes) {
std::copy_if(
nodes.begin(), nodes.end(), std::back_inserter(q_nodes),
[&visited_nodes](Node *node) { return !visited_nodes.count(node); });
auto traverse_nodes = [&visited_nodes, &q_nodes,
&q_set](const std::vector<ir::Node *> &nodes) {
for (auto n : nodes) {
if (visited_nodes.count(n) == 0 && q_set.count(n) == 0) {
q_nodes.push_back(n);
q_set.insert(n);
}
}
};
while (visited_nodes.size() != nodes.size()) {
if (!q_nodes.empty()) {
auto cur_node = q_nodes.front();
q_nodes.pop_front();
q_set.erase(cur_node);
visited_nodes.insert(cur_node);
g_nodes.insert(cur_node);
traverse_nodes(cur_node->inputs);
......@@ -146,6 +152,7 @@ size_t GraphNum(const Graph &graph) {
for (auto &n : nodes) {
if (visited_nodes.count(n) == 0) {
q_nodes.push_back(n);
q_set.insert(n);
break;
}
}
......
paddle/fluid/framework/ir/graph_helper_test.cc
浏览文件 @ 99302a70
......@@ -200,15 +200,15 @@ TEST(GraphHelperTest, GraphNum) {
Graph g(prog);
BuildZeroGraph(&g);
ASSERT_EQ(GraphNum(g), 0);
ASSERT_EQ(GraphNum(g), 0UL);
Graph g2(prog);
BuildOneGraph(&g2);
ASSERT_EQ(GraphNum(g2), 1);
ASSERT_EQ(GraphNum(g2), 1UL);
Graph g3(prog);
BuildTwoGraphs(&g3);
ASSERT_EQ(GraphNum(g3), 2);
ASSERT_EQ(GraphNum(g3), 2UL);
}
} // namespace ir
......
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 99302a70
......@@ -124,7 +124,7 @@ TEST(GraphTest, Basic) {
ASSERT_EQ(n->outputs.size(), 0UL);
}
}
ASSERT_EQ(nodes.size(), 5);
ASSERT_EQ(nodes.size(), 5UL);
}
TEST(GraphTest, WriteAfterRead) {
......
paddle/fluid/framework/ir/multi_batch_merge_pass.cc 0 → 100644
浏览文件 @ 99302a70
// 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/ir/multi_batch_merge_pass.h"
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
namespace ir {
static const char kNumRepeats[] = "num_repeats";
typedef std::unordered_map<std::string, std::vector<ir::Node*>> SSAVarList;
ir::Node* SameNameVar(std::unordered_set<ir::Node*> all, ir::Node* target) {
for (auto n : all) {
if (target->IsVar() && target->Name() == n->Name()) {
return n;
}
}
return nullptr;
}
VarDesc CopyVarDesc(VarDesc* var_desc) {
VarDesc repeated_var(var_desc->Name());
// copy other variable attributes
if (var_desc->GetType() != proto::VarType::READER) {
repeated_var.SetType(var_desc->GetType());
repeated_var.SetShape(var_desc->GetShape());
repeated_var.SetDataType(var_desc->GetDataType());
repeated_var.SetLoDLevel(var_desc->GetLoDLevel());
repeated_var.SetPersistable(var_desc->Persistable());
} else {
// TODO(typhoonzero): copy reader var
}
return repeated_var;
}
VarDesc UpdateGradVarDesc(
VarDesc* var_desc, int repeat,
const std::unordered_set<std::string>& grad_names,
const std::unordered_set<std::string>& bn_vars_need_rename) {
if (grad_names.find(var_desc->Name()) != grad_names.end() ||
bn_vars_need_rename.find(var_desc->Name()) != bn_vars_need_rename.end()) {
std::string new_gname =
string::Sprintf("%s.repeat.%d", var_desc->Name(), repeat);
VarDesc repeated_var = CopyVarDesc(var_desc);
repeated_var.SetName(new_gname);
VLOG(3) << "update " << var_desc->Name() << " to repeat " << repeat;
return repeated_var;
}
return *var_desc;
}
std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
std::unique_ptr<Graph> graph) const {
int num_repeats = Get<const int>(kNumRepeats);
std::vector<Node*> forward_backward_ops;
std::vector<Node*> optimize_ops;
std::vector<Node*> lr_ops; // ops other than forward/backward/optimize
std::unordered_set<std::string> grad_names;
std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
auto origin_nodes = graph->ReleaseNodes();
VLOG(3) << "origin nodes count: " << origin_nodes.size();
ir::Graph& result = *graph;
// 1. record op nodes of different roles
for (auto node : nodes) {
if (node->IsVar()) continue;
int op_role = boost::get<int>(node->Op()->GetAttr(
framework::OpProtoAndCheckerMaker::OpRoleAttrName()));
if ((op_role == static_cast<int>(framework::OpRole::kForward)) ||
(op_role & static_cast<int>(framework::OpRole::kBackward)) ||
(op_role & static_cast<int>(framework::OpRole::kLoss))) {
forward_backward_ops.push_back(node);
} else if ((op_role & static_cast<int>(framework::OpRole::kOptimize)) ||
(op_role & static_cast<int>(framework::OpRole::kDist)) ||
(op_role & static_cast<int>(framework::OpRole::kRPC))) {
optimize_ops.push_back(node);
auto op_role_var = node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName());
auto op_role_vars = boost::get<std::vector<std::string>>(op_role_var);
for (size_t i = 0; i < op_role_vars.size(); i += 2) {
grad_names.insert(op_role_vars[i + 1]);
}
} else if (op_role & static_cast<int>(framework::OpRole::kLRSched)) {
lr_ops.push_back(node);
} else { // NOLINT
PADDLE_THROW("Invalid op_role: %d", static_cast<int>(op_role));
}
}
// 2. copy forward backward
ir::Node* prev_repeat_last_op_node = nullptr;
// record origin_grad -> repeated grad list map.
std::map<ir::Node*, std::vector<ir::Node*>> grad_repeated_map;
std::map<std::string, std::vector<ir::Node*>> created;
std::unordered_set<std::string> bn_vars_need_rename;
for (int i = 0; i < num_repeats; ++i) {
std::unordered_set<ir::Node*> copied;
for (size_t node_idx = 0; node_idx < forward_backward_ops.size();
++node_idx) {
auto node = forward_backward_ops[node_idx];
OpDesc repeated_op(*(node->Op()), node->Op()->Block());
// 3. rename grad outputs to current repeat.
for (auto outname : repeated_op.OutputArgumentNames()) {
if (grad_names.find(outname) != grad_names.end()) {
std::string new_gname = string::Sprintf("%s.repeat.%d", outname, i);
repeated_op.RenameOutput(outname, new_gname);
}
}
// 3.5 let batch_norm ops use independent vars, note batch_norm_grad do
// not need this update
if (node->Name() == "batch_norm") {
// NOTE: assume bn op created by layers use save var as output mean and
// variance
std::string new_mean_name =
string::Sprintf("%s.repeat.%d", repeated_op.Input("Mean")[0], i);
std::string new_var_name = string::Sprintf(
"%s.repeat.%d", repeated_op.Input("Variance")[0], i);
bn_vars_need_rename.insert(repeated_op.Input("Mean")[0]);
bn_vars_need_rename.insert(repeated_op.Input("Variance")[0]);
VLOG(3) << "renaming " << repeated_op.Input("Mean")[0] << " to "
<< new_mean_name;
repeated_op.RenameInput(repeated_op.Input("Mean")[0], new_mean_name);
repeated_op.RenameInput(repeated_op.Input("Variance")[0], new_var_name);
repeated_op.RenameOutput(repeated_op.Output("MeanOut")[0],
new_mean_name);
repeated_op.RenameOutput(repeated_op.Output("VarianceOut")[0],
new_var_name);
}
// 3.9 do copy
auto repeated_node = result.CreateOpNode(&repeated_op);
copied.insert(node);
// 4. add deps between repeats
if (node_idx == forward_backward_ops.size() - 1) {
prev_repeat_last_op_node = repeated_node;
}
if (node_idx == 0 && prev_repeat_last_op_node) {
auto* depvar = result.CreateControlDepVar();
prev_repeat_last_op_node->outputs.push_back(depvar);
depvar->inputs.push_back(prev_repeat_last_op_node);
repeated_node->inputs.push_back(depvar);
depvar->outputs.push_back(repeated_node);
}
for (auto in_node : node->inputs) {
if (in_node->IsCtrlVar()) {
continue;
}
ir::Node* var = nullptr;
auto updated_var = UpdateGradVarDesc(in_node->Var(), i, grad_names,
bn_vars_need_rename);
// should be initialized by startup, how to initilize tensor in the
// scope?
if (node->Name() == "batch_norm" &&
bn_vars_need_rename.find(in_node->Name()) !=
bn_vars_need_rename.end()) {
// Create bn mean/variance for each repeat
var = result.CreateVarNode(&updated_var);
created[updated_var.Name()].push_back(var);
copied.insert(in_node);
repeated_node->inputs.push_back(var);
var->outputs.push_back(repeated_node);
continue;
}
// for other ops
if (in_node->inputs.empty() && i > 0) {
// do not copy head vars (inputs, params) in repeats > 0
var = created.at(in_node->Name()).back();
} else {
if (copied.find(in_node) == copied.end()) {
var = result.CreateVarNode(&updated_var);
if (grad_names.find(in_node->Var()->Name()) != grad_names.end()) {
grad_repeated_map[in_node].push_back(var);
}
copied.insert(in_node);
created[updated_var.Name()].push_back(var);
} else {
var = created.at(updated_var.Name()).back();
}
}
repeated_node->inputs.push_back(var);
var->outputs.push_back(repeated_node);
}
for (auto out_node : node->outputs) {
if (out_node->IsCtrlVar()) {
continue;
}
ir::Node* var = nullptr;
auto updated_var = UpdateGradVarDesc(out_node->Var(), i, grad_names,
bn_vars_need_rename);
if (copied.find(out_node) == copied.end()) {
var = result.CreateVarNode(&updated_var);
if (grad_names.find(out_node->Var()->Name()) != grad_names.end()) {
grad_repeated_map[out_node].push_back(var);
}
copied.insert(out_node);
created[updated_var.Name()].push_back(var);
} else {
var = created.at(updated_var.Name()).back();
}
repeated_node->outputs.push_back(var);
var->inputs.push_back(repeated_node);
}
}
}
// 5. create GRAD merge op node
for (auto kv : grad_repeated_map) {
OpDesc sum_op;
sum_op.SetType("sum");
std::vector<std::string> repeated_grad_names;
for (auto r : kv.second) {
repeated_grad_names.push_back(r->Var()->Name());
}
sum_op.SetInput("X", repeated_grad_names);
sum_op.SetOutput("Out", {kv.first->Var()->Name()});
sum_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kBackward));
auto sum_op_node = result.CreateOpNode(&sum_op);
for (auto r : kv.second) {
sum_op_node->inputs.push_back(r);
r->outputs.push_back(sum_op_node);
}
auto sum_out_var_node = result.CreateVarNode(kv.first->Var());
sum_op_node->outputs.push_back(sum_out_var_node);
sum_out_var_node->inputs.push_back(sum_op_node);
created[sum_out_var_node->Name()].push_back(sum_out_var_node);
OpDesc scale_op;
scale_op.SetType("scale");
scale_op.SetInput("X", {sum_out_var_node->Var()->Name()});
// NOTE: inplace scale.
scale_op.SetOutput("Out", {sum_out_var_node->Var()->Name()});
scale_op.SetAttr("scale", static_cast<float>(1.0f / num_repeats));
scale_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kBackward));
auto scale_op_node = result.CreateOpNode(&scale_op);
scale_op_node->inputs.push_back(sum_out_var_node);
sum_out_var_node->outputs.push_back(scale_op_node);
auto scale_out_var_node = result.CreateVarNode(sum_out_var_node->Var());
scale_op_node->outputs.push_back(scale_out_var_node);
scale_out_var_node->inputs.push_back(scale_op_node);
created[scale_out_var_node->Name()].push_back(scale_out_var_node);
}
// 6. add optimize ops
{
auto copy_node = [&result, &created](ir::Node* node) {
auto op_node = result.CreateOpNode(node->Op());
// copy op ins/outs
// NOTE: for send/recv ops, the OpDesc uses ctrldepvar to describe
// dependencies, so create those depvars if OpDesc have in/outs.
for (auto in_node : node->inputs) {
if (in_node->IsCtrlVar() && !in_node->Var()) {
continue;
}
ir::Node* var = nullptr;
if (created.find(in_node->Name()) == created.end()) {
var = result.CreateVarNode(in_node->Var());
created[in_node->Name()].push_back(var);
} else {
var = created.at(in_node->Name()).back();
}
op_node->inputs.push_back(var);
var->outputs.push_back(op_node);
}
for (auto out_node : node->outputs) {
if (out_node->IsCtrlVar() && !out_node->Var()) {
continue;
}
auto var = result.CreateVarNode(out_node->Var());
created[out_node->Name()].push_back(var);
op_node->outputs.push_back(var);
var->inputs.push_back(op_node);
}
};
for (auto node : lr_ops) {
copy_node(node);
}
for (auto node : optimize_ops) {
copy_node(node);
}
}
result.ResolveHazard(created);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(multi_batch_merge_pass, paddle::framework::ir::BatchMergePass)
.RequirePassAttr(paddle::framework::ir::kNumRepeats);
paddle/fluid/framework/ir/multi_batch_merge_pass.h 0 → 100644
浏览文件 @ 99302a70
// 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/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
// BatchMergePass is used to copy forward and backward ops for several
// times to run several batches to simulate large batch size training
// as if we have more than 1 GPUs.
// User can define how many batches to run, gradients will be merged
// through those repeats, and then do optimization using merged gradients.
// This pass is extremely useful when doing large batch-size distributed
// sync training, we can simulate even large batch size as if we have more
// GPUs.
class BatchMergePass : public Pass {
public:
virtual ~BatchMergePass() {}
protected:
std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 99302a70
......@@ -44,6 +44,7 @@ class Node {
return op_desc_.get();
}
// Please don't use this API!
int id() const { return id_; }
bool IsOp() const { return type_ == Type::kOperation; }
......@@ -92,6 +93,7 @@ class Node {
Node() = delete;
static int count_;
// Please don't use this API or make this public.
static void ResetId() { count_ = 0; }
DISABLE_COPY_AND_ASSIGN(Node);
};
......
paddle/fluid/framework/lod_tensor_array.h
浏览文件 @ 99302a70
......@@ -18,6 +18,82 @@ limitations under the License. */
namespace paddle {
namespace framework {
// NOTE The vector<LoDTensor> can't be replaced with the class LoDTensorArray
// directly, because there are many vector<LoDTensor> used accross the project,
// and some of them are treated as LoDTensorArray.
#if !defined(PADDLE_ON_INFERENCE)
using LoDTensorArray = std::vector<LoDTensor>;
}
#else // !PADDLE_ON_INFERENCE
#pragma message "LoDTensorArray is replaced with the inference one."
/*
* A LoDTensorArray which will not deallocate buffer when resized, fix the data
* diff in inference, and more performance friendly in the concurrency
* scenerios.
*/
class LoDTensorArray {
public:
LoDTensorArray() = default;
using iterator = std::vector<LoDTensor>::iterator;
using const_iterator = std::vector<LoDTensor>::const_iterator;
const_iterator begin() const { return array_.begin(); }
const_iterator end() const { return array_.begin() + size_; }
iterator begin() { return array_.begin(); }
iterator end() { return array_.begin() + size_; }
void push_back(const LoDTensor& x) {
if (size_ < array_.size()) {
array_[size_++] = x;
} else {
array_.push_back(x);
++size_;
}
}
void resize(size_t size) {
if (array_.size() < size) {
array_.resize(size);
}
size_ = size;
}
void emplace_back() { array_.emplace_back(); }
void emplace_back(LoDTensor&& x) { array_.emplace_back(std::move(x)); }
LoDTensor& back() { return array_.back(); }
size_t space() const { return array_.size(); }
void reserve(size_t size) {
// Naive warning to tell user this array might be to large. The memory and
// buffer used by this TensorArray will not be deleted during the training
// and inference phase, so attention not to make it expand too long.
if (size > 800UL) {
LOG(WARNING) << "TensorArray has more than 800 items";
}
array_.reserve(size);
}
bool empty() const { return size_ == 0UL; }
void clear() { size_ = 0UL; }
LoDTensor& operator[](size_t id) { return array_[id]; }
const LoDTensor& operator[](size_t id) const { return array_[id]; }
LoDTensor& at(size_t id) { return array_.at(id); }
const LoDTensor& at(size_t id) const { return array_.at(id); }
size_t size() const { return size_; }
private:
size_t size_{0};
std::vector<LoDTensor> array_;
};
#endif // !PADDLE_ON_INFERENCE
} // namespace framework
} // namespace paddle
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 99302a70
......@@ -542,6 +542,33 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
this->reserve(this->size() + size_t(end - begin));
this->insert(this->end(), begin, end);
}
const T *CUDAData(platform::Place place) const {
PADDLE_THROW(
"Vector::CUDAData() method is not supported in CPU-only version");
}
T *CUDAMutableData(platform::Place place) {
PADDLE_THROW(
"Vector::CUDAMutableData() method is not supported in CPU-only "
"version");
}
const T *Data(platform::Place place) const {
PADDLE_ENFORCE(
platform::is_cpu_place(place),
"Vector::Data() method is not supported when not in CPUPlace");
return this->data();
}
T *MutableData(platform::Place place) {
PADDLE_ENFORCE(
platform::is_cpu_place(place),
"Vector::MutableData() method is not supported when not in CPUPlace");
return this->data();
}
const void *Handle() const { return static_cast<const void *>(this); }
};
template <typename T>
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ 99302a70
......@@ -146,22 +146,5 @@ void NaiveExecutor::CleanFeedFetchOps() {
ops_.swap(ops);
}
void NaiveExecutor::EnableMKLDNN(const ProgramDesc &program) {
#ifdef PADDLE_WITH_MKLDNN
VLOG(3) << "use_mkldnn=True";
for (size_t block_id = 0; block_id < program.Size(); ++block_id) {
auto *block = const_cast<ProgramDesc &>(program).MutableBlock(block_id);
for (auto *op : block->AllOps()) {
if (op->HasAttr("use_mkldnn")) {
op->SetAttr("use_mkldnn", true);
}
}
}
#else
LOG(WARNING)
<< "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
#endif
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.h
浏览文件 @ 99302a70
......@@ -48,8 +48,6 @@ class NaiveExecutor {
void CleanFeedFetchOps();
void EnableMKLDNN(const ProgramDesc& program);
protected:
void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 99302a70
......@@ -121,10 +121,6 @@ class OpDesc {
BlockDesc *Block() { return this->block_; }
const BlockDesc &BlockRef() const { return *this->block_; }
void SetBlock(BlockDesc *block) { this->block_ = block; }
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 99302a70
......@@ -21,7 +21,6 @@ namespace framework {
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
CheckReuseVars();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
......@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
return OpProtoAndCheckerMaker::VariableBuilder{output};
}
void OpProtoAndCheckerMaker::Reuse(const std::string& name,
const std::string& reused_name) {
bool found = false;
proto::OpProto::Var* var;
for (auto& var : proto_->inputs()) {
if (var.name() == reused_name) {
found = true;
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
found = false;
for (int i = 0; i < proto_->outputs().size(); ++i) {
var = proto_->mutable_outputs()->Mutable(i);
if (var->name() == name) {
PADDLE_ENFORCE(!var->has_reuse(),
"Output(%s) has been set reused var of %s", name,
var->reuse());
found = true;
var->set_reuse(reused_name);
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
}
void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
......@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
}
}
void OpProtoAndCheckerMaker::CheckReuseVars() {
std::unordered_set<std::string> names;
for (auto& input : proto_->inputs()) {
names.insert(input.name());
}
auto checker = [&](const std::string& name, const std::string& reused) {
PADDLE_ENFORCE(
names.count(reused),
"Output [%s] reuse Input [%s], but the input is not registered.", name,
reused);
};
for (auto& output : proto_->outputs()) {
if (output.has_reuse()) {
checker(output.name(), output.reuse());
}
}
}
void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
OpAttrChecker* attr_checker) {
proto_ = proto;
......@@ -124,6 +71,8 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kLoss) |
static_cast<int>(OpRole::kBackward),
static_cast<int>(OpRole::kOptimize) |
static_cast<int>(OpRole::kLRSched),
static_cast<int>(OpRole::kNotSpecified)})
.SetDefault(static_cast<int>(OpRole::kNotSpecified));
AddAttr<std::vector<std::string>>(OpRoleVarAttrName(),
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 99302a70
......@@ -14,25 +14,26 @@ limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "glog/logging.h"
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
namespace paddle {
namespace framework {
//////////////////////////
// Don't add more roles to make this too complicated!
//////////////////////////
enum class OpRole {
kForward = 0x0000,
kBackward = 0x0001,
kOptimize = 0x0002,
// RPC role is for send/recv releated op
kRPC = 0x0003,
kRPC = 0x0004,
// Dist role is for split_byref/split_selected_rows/concat
// used for distributed training.
kDist = 0x0004,
kDist = 0x0008,
// Tag all learning rate scheduler operators.
kLRSched = 0x0005,
kLRSched = 0x0010,
kLoss = 0x0100,
// The default value of op's role. This should be only used for unittests and
......@@ -73,11 +74,6 @@ class OpProtoAndCheckerMaker {
var_->set_dispensable(true);
return *this;
}
VariableBuilder &Reuse(const std::string &name) {
var_->set_reuse(name);
return *this;
}
};
VariableBuilder AddInput(const std::string &name, const std::string &comment);
......@@ -85,8 +81,6 @@ class OpProtoAndCheckerMaker {
VariableBuilder AddOutput(const std::string &name,
const std::string &comment);
void Reuse(const std::string &name, const std::string &reused_name);
template <typename T>
TypedAttrChecker<T> &AddAttr(const std::string &name,
const std::string &comment,
......@@ -105,8 +99,6 @@ class OpProtoAndCheckerMaker {
void CheckNoDuplicatedInOutAttrs();
void Validate();
void CheckReuseVars();
proto::OpProto *proto_;
OpAttrChecker *op_checker_;
bool validated_{false};
......
paddle/fluid/framework/op_proto_maker_test.cc
浏览文件 @ 99302a70
......@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
}
class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
}
};
class TestInplaceProtoMaker2
: public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
AddOutput("NoOut", "output of test op").Reuse("NotExists");
}
};
TEST(ProtoMaker, InplaceOutput) {
paddle::framework::proto::OpProto op_proto, op_proto2;
paddle::framework::OpAttrChecker op_checker;
TestInplaceProtoMaker proto_maker;
TestInplaceProtoMaker2 proto_maker2;
proto_maker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
}
// normal reuse
class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddInput("Y", "input of test op");
AddOutput("Out", "output of test op");
AddOutput("XOut", "output of test op");
// avoid destructor exception.
// Validate();
TestReuse();
}
virtual void TestReuse() {}
};
// test duplicate reuse error
class TestReuseProtoMaker2 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "X");
Reuse("Out", "Y");
}
};
// NotExists Input
class TestReuseProtoMaker3 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "NotExists");
Reuse("XOut", "X");
}
};
// NotExists Output
class TestReuseProtoMaker4 : public TestReuseProtoMaker {
public:
void TestReuse() { Reuse("NotExists", "X"); }
};
TEST(ProtoMaker, Reuse) {
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker proto_maker;
proto_maker(&op_proto, &op_checker);
}
// NOTE(dzhwinter):
// There is a Fatal CHECK on base class destructor, which will call abort inside
// instead of
// throw an exception. If we throw an exception in Make(), we will trigger the
// CHECK and terminate the tests.
//
// I had tried to replace the default CHECK with a exception, however, it's
// still not supported by glog.
// the details:
// https://github.com/google/glog/issues/249
// https://github.com/facebookresearch/TensorComprehensions/issues/351
/*
TEST(ProtoMaker, ReuseWithException) {
paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker2 proto_maker2;
TestReuseProtoMaker3 proto_maker3;
TestReuseProtoMaker4 proto_maker4;
EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
paddle::platform::EnforceNotMet);
}
void FailureFunction() {
throw std::runtime_error("Check failed in destructor.");
// return 0;
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
google::InstallFailureFunction(&FailureFunction);
return RUN_ALL_TESTS();
}
*/
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 99302a70
......@@ -109,18 +109,9 @@ ParallelExecutor::ParallelExecutor(
if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
BCastParamsToDevices(bcast_vars);
}
// Startup Program has been run. All local scopes has correct parameters.
// Startup Program has been run. All local scopes has correct parameters.
// Step 2. Create vars in each scope;
std::vector<details::VariableInfo> var_infos;
for (auto *var : main_program.Block(0).AllVars()) {
var_infos.emplace_back();
var_infos.back().name_ = var->Name();
var_infos.back().type_ = var->GetType();
var_infos.back().persistable_ = var->Persistable();
}
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
......@@ -156,13 +147,22 @@ ParallelExecutor::ParallelExecutor(
params, member_->local_scopes_, member_->use_cuda_);
#endif
if (VLOG_IS_ON(5)) {
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
// Step 3. Create vars in each scope. Passes may also create new vars.
// skip control vars and empty vars
std::vector<details::VariableInfo> var_infos;
for (auto &node : graph->Nodes()) {
if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
var_infos.emplace_back();
var_infos.back().name_ = node->Var()->Name();
var_infos.back().type_ = node->Var()->GetType();
var_infos.back().persistable_ = node->Var()->Persistable();
}
}
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ 99302a70
......@@ -103,7 +103,7 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
found_sub_block = true;
ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
ASSERT_EQ(2UL, op->GetBlocksAttrIds("sub_blocks").size());
found_sub_blocks = true;
}
}
......
paddle/fluid/framework/reader_test.cc
浏览文件 @ 99302a70
......@@ -40,7 +40,7 @@ TEST(READER, decorate_chain) {
auto endpoints = root->GetEndPoints();
ASSERT_EQ(endpoints.size(), 2U);
ASSERT_NE(endpoints.count(end_point1.get()), 0UL);
ASSERT_NE(endpoints.count(end_point2.get()), 0);
ASSERT_NE(endpoints.count(end_point2.get()), 0UL);
}
{
......
paddle/fluid/framework/scope.h
浏览文件 @ 99302a70
......@@ -78,6 +78,8 @@ class Scope {
/// Drop all kids scopes belonged to this scope.
void DropKids();
std::list<Scope*>& kids() const { return kids_; }
/// Find if a scope exists in the kid scopes
bool HasKid(const Scope* scope) const;
......
paddle/fluid/framework/threadpool.cc
浏览文件 @ 99302a70
......@@ -25,7 +25,6 @@ DEFINE_int32(dist_threadpool_size, 0,
namespace paddle {
namespace framework {
std::unique_ptr<ThreadPool> ThreadPool::threadpool_(nullptr);
std::once_flag ThreadPool::init_flag_;
......@@ -47,8 +46,7 @@ void ThreadPool::Init() {
}
}
ThreadPool::ThreadPool(int num_threads)
: total_threads_(num_threads), idle_threads_(num_threads), running_(true) {
ThreadPool::ThreadPool(int num_threads) : running_(true) {
threads_.resize(num_threads);
for (auto& thread : threads_) {
// TODO(Yancey1989): binding the thread on the specify CPU number
......@@ -59,6 +57,7 @@ ThreadPool::ThreadPool(int num_threads)
ThreadPool::~ThreadPool() {
{
// notify all threads to stop running
std::lock_guard<std::mutex> l(mutex_);
running_ = false;
scheduled_.notify_all();
}
......@@ -69,36 +68,24 @@ ThreadPool::~ThreadPool() {
}
}
void ThreadPool::Wait() {
std::unique_lock<std::mutex> lock(mutex_);
completed_.wait(lock, [=] { return Done() == true; });
}
void ThreadPool::TaskLoop() {
while (running_) {
while (true) {
std::unique_lock<std::mutex> lock(mutex_);
scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
if (!running_) {
break;
scheduled_.wait(
lock, [this] { return !this->tasks_.empty() || !this->running_; });
if (!running_ || tasks_.empty()) {
return;
}
// pop a task from the task queue
auto task = std::move(tasks_.front());
tasks_.pop();
--idle_threads_;
lock.unlock();
// run the task
task();
{
std::unique_lock<std::mutex> lock(mutex_);
++idle_threads_;
if (Done()) {
completed_.notify_all();
}
}
}
}
......
paddle/fluid/framework/threadpool.h
浏览文件 @ 99302a70
......@@ -57,15 +57,6 @@ class ThreadPool {
~ThreadPool();
// Returns the number of threads created by the constructor.
size_t Threads() const { return total_threads_; }
// Returns the number of currently idle threads.
size_t IdleThreads() {
std::unique_lock<std::mutex> lock(mutex_);
return idle_threads_;
}
// Run pushes a function to the task queue and returns a std::future
// object. To wait for the completion of the task, call
// std::future::wait().
......@@ -94,25 +85,13 @@ class ThreadPool {
});
std::future<std::unique_ptr<platform::EnforceNotMet>> f = task.get_future();
tasks_.push(std::move(task));
lock.unlock();
scheduled_.notify_one();
return f;
}
// Wait until all the tasks are completed.
void Wait();
private:
DISABLE_COPY_AND_ASSIGN(ThreadPool);
// If the task queue is empty and avaialbe is equal to the number of
// threads, means that all tasks are completed. Note: this function
// is not thread-safe. Returns true if all tasks are completed.
// Note: don't delete the data member total_threads_ and use
// threads_.size() instead; because you'd need to lock the mutex
// before accessing threads_.
bool Done() { return tasks_.empty() && idle_threads_ == total_threads_; }
// The constructor starts threads to run TaskLoop, which retrieves
// and runs tasks from the queue.
void TaskLoop();
......@@ -125,14 +104,11 @@ class ThreadPool {
static std::once_flag init_flag_;
std::vector<std::unique_ptr<std::thread>> threads_;
const size_t total_threads_;
size_t idle_threads_;
std::queue<Task> tasks_;
std::mutex mutex_;
bool running_;
std::condition_variable scheduled_;
std::condition_variable completed_;
};
class ThreadPoolIO : ThreadPool {
......
paddle/fluid/framework/threadpool_test.cc
浏览文件 @ 99302a70
......@@ -19,10 +19,11 @@ limitations under the License. */
namespace framework = paddle::framework;
void do_sum(framework::ThreadPool* pool, std::atomic<int>* sum, int cnt) {
std::vector<std::future<void>> fs;
void do_sum(std::vector<std::future<void>>* fs, std::mutex* mu,
std::atomic<int>* sum, int cnt) {
for (int i = 0; i < cnt; ++i) {
fs.push_back(framework::Async([sum]() { sum->fetch_add(1); }));
std::lock_guard<std::mutex> l(*mu);
fs->push_back(framework::Async([sum]() { sum->fetch_add(1); }));
}
}
......@@ -40,18 +41,21 @@ TEST(ThreadPool, ConcurrentInit) {
}
TEST(ThreadPool, ConcurrentRun) {
framework::ThreadPool* pool = framework::ThreadPool::GetInstance();
std::atomic<int> sum(0);
std::vector<std::thread> threads;
std::vector<std::future<void>> fs;
std::mutex fs_mu;
int n = 50;
// sum = (n * (n + 1)) / 2
for (int i = 1; i <= n; ++i) {
std::thread t(do_sum, pool, &sum, i);
std::thread t(do_sum, &fs, &fs_mu, &sum, i);
threads.push_back(std::move(t));
}
for (auto& t : threads) {
t.join();
}
pool->Wait();
for (auto& t : fs) {
t.wait();
}
EXPECT_EQ(sum, ((n + 1) * n) / 2);
}
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -30,7 +30,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
endif()
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor)
cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
......@@ -40,7 +40,7 @@ endif()
# Create shared library
cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
DEPS ${fluid_modules} paddle_fluid_api)
DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
if(NOT APPLE)
......
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ 99302a70
......@@ -107,6 +107,9 @@ void Analyzer::Run(Argument* argument) {
passes.push_back("mkldnn_placement_pass");
}
#endif
// infer_clean_graph_pass should be the first default pass
// after mkldnn_placement_pass.
passes.push_back("infer_clean_graph_pass");
for (auto& pass : ir_passes_) {
if (!disabled_ir_passes_.count(pass)) {
passes.push_back(pass);
......
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ 99302a70
......@@ -67,7 +67,6 @@ class Analyzer : public OrderedRegistry<PassManager> {
// larger fusion.
const std::vector<std::string> all_ir_passes_{{
// Manual update the passes here.
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
"embedding_fc_lstm_fuse_pass", //
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -18,7 +18,8 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB}
)
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
......@@ -31,10 +32,17 @@ function(inference_api_test TARGET_NAME)
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_test(${TARGET_NAME}
SRCS ${inference_test_SRC}
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
if (WITH_GPU)
cc_test(${TARGET_NAME}
SRCS ${inference_test_SRC}
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/ --fraction_of_gpu_memory_to_use=0.15)
else()
cc_test(${TARGET_NAME}
SRCS ${inference_test_SRC}
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
endif()
if(inference_test_ARGS)
set_tests_properties(${TARGET_NAME}
PROPERTIES DEPENDS "${inference_test_ARGS}")
......@@ -42,7 +50,8 @@ function(inference_api_test TARGET_NAME)
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope)
cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 99302a70
......@@ -82,6 +82,7 @@ bool AnalysisPredictor::Init(
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
return true;
}
......@@ -109,6 +110,10 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
return false;
}
VLOG(3) << "predict cost: " << timer.toc() << "ms";
// Fix TensorArray reuse not cleaned bug.
tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
tensor_array_batch_cleaner_.ResetTensorArray();
return true;
}
......@@ -322,6 +327,9 @@ std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
bool AnalysisPredictor::ZeroCopyRun() {
executor_->Run();
// Fix TensorArray reuse not cleaned bug.
tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
tensor_array_batch_cleaner_.ResetTensorArray();
return true;
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 99302a70
......@@ -18,6 +18,7 @@
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/string/printf.h"
......@@ -88,6 +89,7 @@ class AnalysisPredictor : public PaddlePredictor {
// Memory buffer for feed inputs. The temporary LoDTensor will cause serious
// concurrency problems, so cache them.
std::vector<framework::LoDTensor> feed_tensors_;
details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
};
} // namespace paddle
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 99302a70
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -157,6 +158,10 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
return false;
}
VLOG(3) << "predict cost: " << timer.toc() << "ms";
// Fix TensorArray reuse not cleaned bug.
tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
tensor_array_batch_cleaner_.ResetTensorArray();
return true;
}
......
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 99302a70
......@@ -26,11 +26,11 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/init.h"
......@@ -77,6 +77,7 @@ class NativePaddlePredictor : public PaddlePredictor {
std::vector<framework::OpDesc *> fetchs_;
// Do not use unique_ptr, use parent scope to delete
framework::Scope *sub_scope_{nullptr};
details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
};
} // namespace paddle
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -52,6 +52,7 @@ include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
if (NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
......@@ -61,8 +62,8 @@ endif(NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
if (NOT WIN32)
if (USE_TENSORRT AND WITH_GPU)
if (NOT WIN32)
if (USE_TENSORRT AND WITH_GPU)
include_directories("${TENSORRT_INCLUDE_DIR}")
link_directories("${TENSORRT_LIB_DIR}")
endif()
......@@ -77,13 +78,14 @@ endif(NOT WIN32)
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/paddle/lib")
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
if(WITH_MKL)
include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
if(EXISTS ${MKLDNN_PATH})
......@@ -107,7 +109,7 @@ if (NOT WIN32)
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
glog gflags protobuf snappystream snappy z xxhash
${EXTERNAL_LIB})
else()
set(DEPS ${DEPS}
......@@ -120,7 +122,7 @@ endif(NOT WIN32)
if(WITH_GPU)
if(NOT WIN32)
if (USE_TENSORRT)
if (USE_TENSORRT)
set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
......
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ 99302a70
......@@ -16,12 +16,12 @@ if [ $2 == ON ]; then
fi
if [ $3 == ON ]; then
use_gpu_list='true false'
else
else
use_gpu_list='false'
fi
USE_TENSORRT=OFF
if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
USE_TENSORRT=ON
fi
......@@ -60,7 +60,8 @@ for WITH_STATIC_LIB in ON OFF; do
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=simple_on_word2vec \
-DWITH_GPU=$TEST_GPU_CPU \
-DWITH_STATIC_LIB=$WITH_STATIC_LIB
-DWITH_STATIC_LIB=$WITH_STATIC_LIB \
-DON_INFER=ON
make -j
word2vec_model=${PADDLE_ROOT}'/build/python/paddle/fluid/tests/book/word2vec.inference.model'
if [ -d $word2vec_model ]; then
......@@ -80,10 +81,11 @@ for WITH_STATIC_LIB in ON OFF; do
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=vis_demo \
-DWITH_GPU=$TEST_GPU_CPU \
-DWITH_STATIC_LIB=$WITH_STATIC_LIB
-DWITH_STATIC_LIB=$WITH_STATIC_LIB \
-DON_INFER=ON
make -j
for use_gpu in $use_gpu_list; do
for vis_demo_name in $vis_demo_list; do
for vis_demo_name in $vis_demo_list; do
./vis_demo \
--modeldir=$DATA_DIR/$vis_demo_name/model \
--data=$DATA_DIR/$vis_demo_name/data.txt \
......@@ -95,7 +97,7 @@ for WITH_STATIC_LIB in ON OFF; do
fi
done
done
# --------tensorrt mobilenet------
if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
rm -rf *
......@@ -106,8 +108,9 @@ for WITH_STATIC_LIB in ON OFF; do
-DWITH_STATIC_LIB=$WITH_STATIC_LIB \
-DUSE_TENSORRT=$USE_TENSORRT \
-DTENSORRT_INCLUDE_DIR=$TENSORRT_INCLUDE_DIR \
-DTENSORRT_LIB_DIR=$TENSORRT_LIB_DIR
make -j
-DTENSORRT_LIB_DIR=$TENSORRT_LIB_DIR \
-DON_INFER=ON
make -j
./trt_mobilenet_demo \
--modeldir=$DATA_DIR/mobilenet/model \
--data=$DATA_DIR/mobilenet/data.txt \
......
paddle/fluid/inference/api/details/reset_tensor_array.cc 0 → 100644
浏览文件 @ 99302a70
// 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/api/details/reset_tensor_array.h"
namespace paddle {
namespace details {
// Should be called after the parameters are loaded.
void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
if (flag_) {
for (auto &var_name : scope->LocalVarNames()) {
auto *var = scope->FindVar(var_name);
// TODO(Superjomn) should avoid the case when a TensorArray is a
// parameter.
if (var_name == "feed" || var_name == "fetch") continue;
if (var->Type() == typeid(framework::LoDTensorArray)) {
VLOG(4) << "collect " << var_name;
arrays_.push_back(var->GetMutable<framework::LoDTensorArray>());
}
}
for (auto *kid : scope->kids()) {
CollectTensorArrays(kid);
}
VLOG(3) << "Collect " << arrays_.size() << " arrays";
flag_ = false;
}
}
// Should be called when `Run` finished.
void TensorArrayBatchCleaner::ResetTensorArray() {
for (auto *arr : arrays_) {
arr->clear();
}
}
} // namespace details
} // namespace paddle
paddle/fluid/inference/api/details/reset_tensor_array.h 0 → 100644
浏览文件 @ 99302a70
// 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 <vector>
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/scope.h"
namespace paddle {
namespace details {
// Clean the TensorArray each batch to make the behavior the same with the
// training phase.
struct TensorArrayBatchCleaner {
// Fix the tensor array not clear in the inference scenarios.
void CollectTensorArrays(framework::Scope *scope);
void ResetTensorArray();
private:
bool flag_{true};
std::vector<framework::LoDTensorArray *> arrays_;
};
} // namespace details
} // namespace paddle
paddle/fluid/inference/api/helper.h
浏览文件 @ 99302a70
......@@ -160,7 +160,8 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
double latency, int epoch = 1) {
LOG(INFO) << "====== batch_size: " << batch_size << ", repeat: " << repeat
<< ", threads: " << num_threads << ", thread id: " << tid
<< ", latency: " << latency << "ms ======";
<< ", latency: " << latency << "ms, fps: " << 1 / (latency / 1000.f)
<< " ======";
if (epoch > 1) {
int samples = batch_size * epoch;
LOG(INFO) << "====== sample number: " << samples
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 99302a70
......@@ -124,7 +124,7 @@ class ZeroCopyTensor {
std::vector<std::vector<size_t>> lod() const;
protected:
ZeroCopyTensor(void* scope) : scope_{scope} {}
explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
void SetName(const std::string& name) { name_ = name; }
void* FindTensor() const;
......@@ -259,12 +259,6 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
void SetIncludeMode() {
ir_mode = IrPassMode::kInclude;
// this pass has to be run at the beginning of all fuse passes
ir_passes = {"infer_clean_graph_pass"};
}
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ 99302a70
......@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
if (global_pooling == true) {
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nv_ksize.d[0] = input_shape.d[nbDims - 2];
nv_ksize.d[1] = input_shape.d[nbDims - 1];
nv_strides.h() = 1;
nv_strides.w() = 1;
nv_paddings.h() = 0;
nv_paddings.w() = 0;
}
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
......@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
if (ceil_mode) {
nvinfer1::DimsHW pre_pad(0, 0);
nvinfer1::DimsHW post_pad(0, 0);
int input_height = input_shape.d[nbDims - 2];
int input_width = input_shape.d[nbDims - 1];
int floor_h_output_size =
(input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
int ceil_h_output_size =
(input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
strides[0] +
1;
int floor_w_output_size =
(input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
int ceil_w_output_size =
(input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
strides[1] +
1;
if (floor_h_output_size != ceil_h_output_size) {
post_pad.h() = strides[0] - 1;
}
if (floor_w_output_size != ceil_w_output_size) {
post_pad.w() = strides[1] - 1;
}
auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
post_pad);
input1 = layer->getOutput(0);
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @ 99302a70
......@@ -20,18 +20,20 @@ namespace paddle {
namespace inference {
namespace tensorrt {
void test_pool2d(bool global_pooling) {
void test_pool2d(bool global_pooling, bool ceil_mode) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
if (global_pooling)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
else if (ceil_mode)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
else
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
// Prepare Op description
framework::OpDesc desc;
......@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> ksize({3, 3});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
......@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
desc.SetAttr("global_pooling", global_pooling);
desc.SetAttr("ceil_mode", ceil_mode);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
......@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
validator.Execute(3);
}
TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 99302a70
......@@ -71,7 +71,7 @@ void profile(bool use_mkldnn = false) {
}
TEST(Analyzer_resnet50, profile) { profile(); }
#ifndef PADDLE_WITH_MKLDNN
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_resnet50, profile_mkldnn) { profile(true /* use_mkldnn */); }
#endif
......
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @ 99302a70
......@@ -228,6 +228,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
TEST(Analyzer_rnn1, profile) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
cfg.use_gpu = false;
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 99302a70
......@@ -50,7 +50,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
auto &ref_out = ref_outputs[i];
size_t size = VecReduceToInt(out.shape);
size_t ref_size = VecReduceToInt(ref_out.shape);
EXPECT_GT(size, 0);
EXPECT_GT(size, 0UL);
EXPECT_EQ(size, ref_size);
EXPECT_EQ(out.dtype, ref_out.dtype);
switch (out.dtype) {
......@@ -139,6 +139,9 @@ void TestMultiThreadPrediction(
}
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
#ifdef PADDLE_WITH_MKLDNN
platform::set_cur_thread_id(static_cast<int>(tid) + 1);
#endif
// Each thread should have local inputs and outputs.
// The inputs of each thread are all the same.
std::vector<std::vector<PaddleTensor>> inputs_tid = inputs;
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -268,6 +268,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
else()
set(DEPS_OPS ${DEPS_OPS} tensorrt_engine_op)
endif()
op_library(hash_op DEPS xxhash)
op_library(clip_by_norm_op DEPS selected_rows_functor selected_rows)
op_library(sum_op DEPS selected_rows_functor)
op_library(sgd_op DEPS selected_rows_functor)
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 99302a70
......@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddOutput("Out", "Output of " #OP_NAME " operator"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ 99302a70
......@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddAttr<float>("beta1",
"(float, default 0.9) "
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 99302a70
......@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("Y", "result after normalization");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")
.Reuse("Mean");
"Store the global mean when training");
AddOutput("VarianceOut",
"Share memory with Variance. "
"Store the global Variance when training")
.Reuse("Variance");
"Store the global Variance when training");
AddOutput("SavedMean",
"Mean of the current mini batch, "
"will apply to output when training")
......
paddle/fluid/operators/beam_search_decode_op.cc
浏览文件 @ 99302a70
......@@ -79,6 +79,9 @@ struct BeamSearchDecodeFunctor {
bool tensor_on_gpu_;
size_t beam_size_;
int end_id_;
// TODO(Superjomn) Here might result serious performance issue in the
// concurrency
// scenarios.
const LoDTensorArray& step_ids_origin_;
const LoDTensorArray& step_scores_origin_;
LoDTensorArray step_ids_ = LoDTensorArray();
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 99302a70
......@@ -130,8 +130,7 @@ void Conv2DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.")
.Reuse("Input");
"The format of output tensor is also NCHW.");
AddInput("ResidualData",
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
......@@ -238,8 +237,7 @@ void Conv3DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.")
.Reuse("Input");
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
......
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 99302a70
......@@ -284,7 +284,7 @@ static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
selected_indices.push_back(idx);
++selected_num;
}
sorted_indices.erase(sorted_indices.end());
sorted_indices.erase(sorted_indices.end() - 1);
if (flag && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 99302a70
......@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
......@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ScoreIndex", {-1});
ctx->SetOutputDim("TargetLabel", {-1, 1});
ctx->SetOutputDim("TargetBBox", {-1, 4});
ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
}
protected:
......@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
const float rpn_positive_overlap,
const float rpn_negative_overlap, std::vector<int>* fg_inds,
std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
std::minstd_rand engine, bool use_random) {
float epsilon = 0.00001;
int anchor_num = anchor_to_gt_max.dims()[0];
......@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
// Reservoir Sampling
int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
fg_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_num; ++i) {
int fg_fake_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_fake_num; ++i) {
target_label[fg_inds_fake[i]] = 1;
}
int bg_num = rpn_batch_size_per_im - fg_num;
int bg_num = rpn_batch_size_per_im - fg_fake_num;
for (int64_t i = 0; i < anchor_num; ++i) {
if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
bg_inds_fake.push_back(i);
......@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
}
ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
bg_num = static_cast<int>(bg_inds_fake.size());
int fake_num = 0;
for (int64_t i = 0; i < bg_num; ++i) {
// fg fake found
if (target_label[bg_inds_fake[i]] == 1) {
fake_num++;
fg_fake->emplace_back(fg_inds_fake[0]);
for (int j = 0; j < 4; ++j) {
bbox_inside_weight->emplace_back(T(0.));
}
}
target_label[bg_inds_fake[i]] = 0;
}
for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
bbox_inside_weight->emplace_back(T(1.));
}
for (int64_t i = 0; i < anchor_num; ++i) {
if (target_label[i] == 1) fg_inds->emplace_back(i);
if (target_label[i] == 1) {
fg_inds->emplace_back(i);
fg_fake->emplace_back(i);
}
if (target_label[i] == 0) bg_inds->emplace_back(i);
}
fg_num = fg_inds->size();
......@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
std::vector<int> bg_inds;
std::vector<int> gt_inds;
std::vector<int> tgt_lbl;
std::vector<int> fg_fake;
std::vector<T> bbox_inside_weight;
// Calculate the max IoU between anchors and gt boxes
// Map from anchor to gt box that has highest overlap
auto place = ctx.GetPlace();
......@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
// Follow the Faster RCNN's implementation
ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
use_random);
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
&bbox_inside_weight, engine, use_random);
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
gt_inds.reserve(fg_num);
for (int i = 0; i < fg_num; ++i) {
gt_inds.emplace_back(argmax[fg_inds[i]]);
int fg_fake_num = fg_fake.size();
gt_inds.reserve(fg_fake_num);
for (int i = 0; i < fg_fake_num; ++i) {
gt_inds.emplace_back(argmax[fg_fake[i]]);
}
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
int* score_index_data =
score_index_t.mutable_data<int>({fg_num + bg_num}, place);
int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
T* bbox_inside_weight_data =
bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
bbox_inside_weight_data);
std::vector<Tensor> loc_score_tgtlbl_gt;
loc_score_tgtlbl_gt.emplace_back(loc_index_t);
loc_score_tgtlbl_gt.emplace_back(score_index_t);
loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
return loc_score_tgtlbl_gt;
}
......@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* score_index = context.Output<LoDTensor>("ScoreIndex");
auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD");
......@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->mutable_data<int>({max_num}, place);
tgt_bbox->mutable_data<T>({max_num, 4}, place);
tgt_lbl->mutable_data<int>({max_num, 1}, place);
bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
std::random_device rnd;
......@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
int loc_num = sampled_loc_index.dims()[0];
int score_num = sampled_score_index.dims()[0];
......@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
&sampled_bbox_inside_weight);
total_loc_num += loc_num;
total_score_num += score_num;
......@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->set_lod(loc_score);
tgt_bbox->set_lod(lod_loc);
tgt_lbl->set_lod(loc_score);
bbox_inside_weight->set_lod(lod_loc);
loc_index->Resize({total_loc_num});
score_index->Resize({total_score_num});
tgt_bbox->Resize({total_loc_num, 4});
tgt_lbl->Resize({total_score_num, 1});
bbox_inside_weight->Resize({total_loc_num, 4});
}
};
......@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"TargetLabel",
"(Tensor<int>), The target labels of each anchor with shape "
"[F + B, 1], F and B are sampled foreground and backgroud number.");
AddOutput("BBoxInsideWeight",
"(Tensor), The bbox inside weight with shape "
"[F, 4], F is the sampled foreground number.");
AddComment(R"DOC(
This operator can be, for a given set of ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
......
paddle/fluid/operators/dropout_op.cc
浏览文件 @ 99302a70
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/dropout_op.h"
#include <string>
namespace paddle {
namespace operators {
......@@ -57,6 +58,29 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
"will be dropped.")
.SetDefault(false);
AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
AddAttr<std::string>(
"dropout_implementation",
"[\"downgrade_in_infer\"|\"upscale_in_train\"]"
"There are two kinds of ways to implement dropout"
"(the mask below is a tensor have the same shape with input"
"the value of mask is 0 or 1, the ratio of 0 is dropout_prob)"
"1. downgrade_in_infer(default), downgrade the outcome at inference "
"time"
" train: out = input * mask"
" inference: out = input * dropout_prob"
"2. upscale_in_train, upscale the outcome at training time, do nothing "
"in inference"
" train: out = input * mask / ( 1.0 - dropout_prob )"
" inference: out = input"
" dropout op can be removed from the program. the program will be "
"efficient")
.SetDefault("downgrade_in_infer")
.AddCustomChecker([](const std::string& type) {
PADDLE_ENFORCE(
type == "downgrade_in_infer" || type == "upscale_in_train",
"dropout_implementation can only be downgrade_in_infer or "
"upscale_in_train");
});
AddComment(R"DOC(
Dropout Operator.
......@@ -104,7 +128,9 @@ REGISTER_OPERATOR(dropout, ops::DropoutOp, ops::DropoutOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);
REGISTER_OP_CPU_KERNEL(
dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>);
dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
dropout_grad,
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/dropout_op.cu
浏览文件 @ 99302a70
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <thrust/iterator/counting_iterator.h>
#include <thrust/random.h>
#include <thrust/transform.h>
#include <string>
#include "paddle/fluid/operators/dropout_op.h"
#include "paddle/fluid/platform/float16.h"
......@@ -26,7 +27,8 @@ namespace operators {
template <typename T>
__global__ void RandomGenerator(const size_t n, const int seed,
const float dropout_prob, const T* src,
T* mask_data, T* dst) {
T* mask_data, T* dst,
bool is_upscale_in_train) {
thrust::minstd_rand rng;
rng.seed(seed);
thrust::uniform_real_distribution<float> dist(0, 1);
......@@ -47,7 +49,11 @@ __global__ void RandomGenerator(const size_t n, const int seed,
if (dist(rng) < dropout_prob) {
mask = static_cast<T>(0);
} else {
mask = static_cast<T>(1);
if (is_upscale_in_train) {
mask = static_cast<T>(1.0f / (1.0f - dropout_prob));
} else {
mask = static_cast<T>(1);
}
}
dest = s * mask;
mask_data[idx] = mask;
......@@ -67,6 +73,8 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto dropout_implementation =
context.Attr<std::string>("dropout_implementation");
auto& place = *context.template device_context<Place>().eigen_device();
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
......@@ -83,11 +91,16 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
int grid = (x->numel() + threads - 1) / threads;
RandomGenerator<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, seed, dropout_prob, x_data, mask_data, y_data);
size, seed, dropout_prob, x_data, mask_data, y_data,
(dropout_implementation == "upscale_in_train"));
} else {
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
if (dropout_implementation == "upscale_in_train") {
Y.device(place) = X;
} else {
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
}
}
}
};
......@@ -99,6 +112,8 @@ namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(dropout_grad,
ops::DropoutGradKernel<plat::CUDADeviceContext, float>);
ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>,
ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
ops::DropoutGradKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/dropout_op.h
浏览文件 @ 99302a70
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <random>
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -36,6 +37,8 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto* y_data = y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto dropout_implementation =
context.Attr<std::string>("dropout_implementation");
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
......@@ -49,14 +52,20 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
engine.seed(seed);
std::uniform_real_distribution<float> dist(0, 1);
size_t size = framework::product(mask->dims());
for (size_t i = 0; i < size; ++i) {
if (dist(engine) < dropout_prob) {
mask_data[i] = 0;
y_data[i] = 0;
} else {
mask_data[i] = 1;
y_data[i] = x_data[i];
if (dropout_implementation == "upscale_in_train") {
mask_data[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
} else {
mask_data[i] = 1;
y_data[i] = x_data[i];
}
}
}
} else {
......@@ -64,7 +73,11 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
Y.device(place) = X * (1.0f - dropout_prob);
if (dropout_implementation == "upscale_in_train") {
Y.device(place) = X;
} else {
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
}
}
}
};
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ 99302a70
......@@ -80,8 +80,6 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() final {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
// AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
// memory.").AsIntermediate();
AddOutput("Out", "The output of elementwise op.");
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
......@@ -129,13 +127,11 @@ But the output only shares the LoD information with the input $X$.
)DOC",
GetName(), GetEquation()));
SetReuse();
}
protected:
virtual std::string GetName() const = 0;
virtual std::string GetEquation() const = 0;
virtual void SetReuse() {}
};
class ElementwiseOpGrad : public framework::OperatorWithKernel {
......@@ -269,7 +265,6 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
protected: \
virtual std::string GetName() const { return op_name; } \
virtual std::string GetEquation() const { return equation; } \
virtual void SetReuse() { Reuse(__VA_ARGS__); } \
}; \
REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
__ElemwiseOp##op_type##Maker__, \
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ 99302a70
......@@ -16,10 +16,9 @@ limitations under the License. */
#include <cstring> // for memcpy
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
......@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
}
}
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_state; \
std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_state_str = ctx.Attr<std::string>("activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::avx>; \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::isa_any>; \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D3 = wh_dims[1]; \
const int D2 = D * 2;
#define INIT_BASE_DEFINES \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto x_lod = x->lod(); \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const auto& ker = math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::GRUKernel<T>, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("activation"), D); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto x_lod = x->lod();
INIT_BASE_DEFINES;
INIT_OTHER_DEFINES;
const int N = x_lod[0].size() - 1;
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* wh_state_data = wh_data + D * D2;
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(place);
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
xx_data,
......@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
} else {
// W: {W_update, W_reset; W_state}
// update gate
act_gate(D, xx_data, xx_data);
// state gate
act_state(D, xx_data + D2, xx_data + D2);
// out = a*b
blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
// save prev
ker->ComputeH1(xx_data, hidden_out_data);
prev_hidden_data = hidden_out_data;
tstart = 1;
move_step();
......@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
D3);
act_gate(D2, xx_data, xx_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
// gemm rt * Ws
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
xx_data + D2, D3);
act_state(D, xx_data + D2, xx_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
// save prev
prev_hidden_data = hidden_out_data;
move_step();
......@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
INIT_BASE_DEFINES;
if (x_lod[0].size() == 2) {
xx->Resize({total_T, D3});
SeqCompute(ctx);
return;
}
INIT_VEC_FUNC
INIT_OTHER_DEFINES;
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(place);
T* batched_out_data = batched_out->mutable_data<T>(place);
hidden_out->mutable_data<T>(place);
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
......@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* prev_hidden_data = nullptr;
if (h0) {
// reorder h0
T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
prev_hidden_data = reordered_h0_data;
size_t sz = sizeof(T) * D;
......@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
// W: {W_update, W_reset; W_state}
for (int i = 0; i < max_bs; ++i) {
// update gate
act_gate(D, cur_in_data, cur_in_data);
// state gate
act_state(D, cur_in_data + D2, cur_in_data + D2);
// out = a*b
blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
ker->ComputeH1(cur_in_data, cur_out_data);
// add offset
cur_in_data += D3;
cur_out_data += D;
......@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
T* cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
act_gate(D2, cur_batched_data, cur_batched_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
// ht~ = act_state(...)
act_state(D, cur_batched_data + D2, cur_batched_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
cur_out_data);
ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
batched_out->set_lod(batched_lod);
to_seq(dev_ctx, *batched_out, hidden_out);
}
#undef INIT_VEC_FUNC
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
} // namespace operators
......
paddle/fluid/operators/hash_op.cc 0 → 100644
浏览文件 @ 99302a70
/* 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/hash_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
class HashOp : public framework::OperatorWithKernel {
public:
HashOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of HashOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of HashOp should not be null.");
auto dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(dims.size(), 2UL,
"The input of hash_op's dimensions must be 2");
std::vector<int64_t> out_dims;
out_dims.reserve(dims.size() + 1);
// copy all dims except the last one
for (size_t i = 0u; i != dims.size() - 1; ++i) {
out_dims.emplace_back(dims[i]);
}
int num_hash = ctx->Attrs().Get<int>("num_hash");
out_dims.emplace_back(num_hash);
// keep the last dim to 1
out_dims.emplace_back(1);
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class HashOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) Input tensor of scale operator.");
AddOutput("Out", "(Tensor) Output tensor of scale operator.");
AddComment(R"DOC(
**Hash Operator**
$$Out = scale * X$$
)DOC");
AddAttr<int>("num_hash", "").SetDefault(1);
AddAttr<int>("mod_by", "").SetDefault(100000);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);
paddle/fluid/operators/hash_op.h 0 → 100644
浏览文件 @ 99302a70
/* 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
extern "C" {
#include <xxhash.h>
}
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
// template <typename DeviceContext, typename T>
template <typename T>
class HashKerel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& context) const {
auto* out_t = context.Output<framework::LoDTensor>("Out");
auto* in_t = context.Input<framework::LoDTensor>("X");
int mod_by = context.Attr<int>("mod_by");
int num_hash = context.Attr<int>("num_hash");
auto* output = out_t->mutable_data<T>(context.GetPlace());
auto in_dims = in_t->dims();
auto in_lod = in_t->lod();
PADDLE_ENFORCE_EQ(
static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
"The actual input data's size mismatched with LoD information.");
auto seq_length = in_dims[0];
auto last_dim = in_dims[in_dims.size() - 1];
auto* input = in_t->data<T>();
for (int idx = 0; idx < seq_length; ++idx) {
for (int ihash = 0; ihash != num_hash; ++ihash) {
output[idx * num_hash + ihash] =
XXH64(input, sizeof(int) * last_dim, ihash) % mod_by;
}
input += last_dim;
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/lars_momentum_op.cc 0 → 100644
浏览文件 @ 99302a70
/* 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/operators/lars_momentum_op.h"
#include "paddle/fluid/operators/momentum_op.h"
namespace paddle {
namespace operators {
class LarsMomentumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Param",
"(LoDTensor, default LoDTensor<float>) "
"Input parameter that has to be updated");
AddInput("Grad",
"(LoDTensor, default LoDTensor<float>) "
"Input gradient of the parameter");
AddInput("Velocity",
"(LoDTensor, default LoDTensor<float>) "
"Input velocity (corresponding to the parameter) "
"that has to be updated");
AddInput("LearningRate",
"(LoDTensor, default LoDTensor<float>) "
"Input learning rate");
AddOutput("ParamOut",
"(LoDTensor) This output is updated parameter. "
"It shared memory with Input(Param).");
AddOutput("VelocityOut",
"(LoDTensor) This output is updated velocity. "
"It shared memory with Input(Velocity).");
AddAttr<float>("mu", "(float) Momentum coefficient");
AddAttr<float>("lars_coeff", "(float, default 0.001) LARS coefficient.")
.SetDefault(0.001);
AddAttr<float>("lars_weight_decay",
"(float, default 0.0005) LARS weight decay")
.SetDefault(0.0005);
AddComment(R"DOC(
Lars Momentum Optimizer.
This optimizer use LARS (https://arxiv.org/abs/1708.03888) to optimize each
weight using a local learning rate:
$$
local\_lr = \eta *
\frac{\left \| param \right \|}{\left \| grad \right \| + \beta *\left \| param \right \|} \\
velocity = mu * velocity +
local\_lr * (grad + \beta * param) \\
param = param - velocity. \\
$$
Note that we use lars_weight_decay here to decay weights, you may need not to
use L2 regularizers in case of using LARS.
)DOC");
}
};
class LarsMomentumOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lars_momentum, ops::MomentumOp, ops::LarsMomentumOpMaker,
paddle::framework::EmptyGradOpMaker,
ops::LarsMomentumOpVarTypeInference);
REGISTER_OP_CPU_KERNEL(lars_momentum, ops::LarsMomentumOpKernel<float>,
ops::LarsMomentumOpKernel<double>);
paddle/fluid/operators/lars_momentum_op.cu 0 → 100644
浏览文件 @ 99302a70
/* 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/framework/op_registry.h"
#include "paddle/fluid/operators/lars_momentum_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void MomentumLarsKernel(const T* p, const T* g, const T* v,
const T* learning_rate, const T mu,
const int64_t num, const T lars_coeff,
const T lars_weight_decay, const T* p_norm,
const T* g_norm, T* p_out, T* v_out) {
T lr = learning_rate[0];
T local_lr = learning_rate[0];
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
if (p_norm[0] > 0 && g_norm[0] > 0) {
local_lr = lr * lars_coeff * p_norm[0] /
(g_norm[0] + lars_weight_decay * p_norm[0]);
}
T v_new = v[i] * mu + local_lr * (g[i] + lars_weight_decay * p[i]);
v_out[i] = v_new;
p_out[i] = p[i] - v_new;
}
}
template <typename DeviceContext, typename T>
class LarsMomentumOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
auto param = ctx.Input<framework::LoDTensor>("Param");
auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
auto grad = ctx.Input<framework::LoDTensor>("Grad");
auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
T* p_out = param_out->mutable_data<T>(ctx.GetPlace());
T* v_out = velocity_out->mutable_data<T>(ctx.GetPlace());
T mu = static_cast<T>(ctx.Attr<float>("mu"));
T lars_coeff = ctx.Attr<float>("lars_coeff");
T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
auto* p = param->data<T>();
auto* v = velocity->data<T>();
auto* g = grad->data<T>();
auto* lr = learning_rate->data<T>();
int block = 512;
int grid = (param->numel() + block - 1) / block;
auto eigen_p = framework::EigenVector<T>::Flatten(*param);
auto eigen_g = framework::EigenVector<T>::Flatten(*grad);
// calculate norms using eigein and launch the kernel.
framework::Tensor p_norm_t, g_norm_t;
p_norm_t.Resize({1});
g_norm_t.Resize({1});
auto* p_norm_data = p_norm_t.mutable_data<T>(ctx.GetPlace());
auto* g_norm_data = g_norm_t.mutable_data<T>(ctx.GetPlace());
auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
ep_norm.device(*place) = eigen_p.square().sum().sqrt();
eg_norm.device(*place) = eigen_g.square().sum().sqrt();
MomentumLarsKernel<<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
p, g, v, lr, mu, param->numel(), lars_coeff, lars_weight_decay,
p_norm_data, g_norm_data, p_out, v_out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
lars_momentum,
ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, float>,
ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/lars_momentum_op.h 0 → 100644
浏览文件 @ 99302a70
/* 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/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename T>
class LarsMomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
auto param = ctx.Input<framework::LoDTensor>("Param");
auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
auto* grad_var = ctx.InputVar("Grad");
// only support dense for now.
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>());
auto grad = ctx.Input<framework::LoDTensor>("Grad");
param_out->mutable_data<T>(ctx.GetPlace());
velocity_out->mutable_data<T>(ctx.GetPlace());
T mu = static_cast<T>(ctx.Attr<float>("mu"));
T lars_coeff = ctx.Attr<float>("lars_coeff");
T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
auto p_out = framework::EigenVector<T>::Flatten(*param_out);
auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
auto p = framework::EigenVector<T>::Flatten(*param);
auto v = framework::EigenVector<T>::Flatten(*velocity);
auto g = framework::EigenVector<T>::Flatten(*grad);
auto* lr = learning_rate->data<T>();
framework::Tensor p_norm_t, g_norm_t;
p_norm_t.Resize({1});
g_norm_t.Resize({1});
p_norm_t.mutable_data<T>(ctx.GetPlace());
g_norm_t.mutable_data<T>(ctx.GetPlace());
auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
ep_norm = p.square().sum().sqrt();
eg_norm = g.square().sum().sqrt();
T local_lr = lr[0];
if (ep_norm(0) > 0 && eg_norm(0) > 0) {
local_lr = lr[0] * lars_coeff * ep_norm(0) /
(eg_norm(0) + lars_weight_decay * ep_norm(0));
}
v_out = v * mu + local_lr * (g + lars_weight_decay * p);
p_out = p - v_out;
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 99302a70
......@@ -81,6 +81,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
"Otherwise the given value indicates padding the output "
"with zeros whenever lookup encounters it in Ids.")
.SetDefault(kNoPadding);
// NOTE(minqiyang): grad_inplace is an temporal attribute,
// please do NOT set this attribute in python layer.
AddAttr<bool>("grad_inplace",
"(boolean, default false) "
"If the grad op reuse the input's variable.")
.SetDefault(false);
AddComment(R"DOC(
Lookup Table Operator.
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 99302a70
......@@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
......@@ -68,6 +69,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
const auto *table = table_t.value().data<T>();
auto *output = output_t->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int64_t i = 0; i < ids_numel; ++i) {
if (padding_idx != kNoPadding && ids[i] == padding_idx) {
memset(output + i * row_width, 0, row_width * sizeof(T));
......@@ -75,8 +77,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_GE(ids[i], 0);
auto id_index = table_t.Index(ids[i]);
PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists.");
memcpy(output + i * row_width, table + id_index * row_width,
row_width * sizeof(T));
blas.VCOPY(row_width, table + id_index * row_width,
output + i * row_width);
}
}
}
......@@ -111,27 +113,37 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *ids_data = ids->data<int64_t>();
int64_t ids_num = ids->numel();
framework::Vector<int64_t> new_rows;
new_rows.reserve(ids_num);
for (int64_t i = 0; i < ids_num; i++) {
new_rows.push_back(ids_data[i]);
}
std::vector<int64_t> new_rows;
new_rows.resize(ids_num);
std::memcpy(&new_rows[0], ids_data, ids_num * sizeof(int64_t));
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_num, table_dim[1]});
d_table_value->mutable_data<T>(context.GetPlace());
d_table->set_height(table_dim[0]);
auto *d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
auto d_output_dims = d_output->dims();
PADDLE_ENFORCE_EQ(
d_table_value->dims(),
framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
// FIXME(minqiyang):
// memory optimization will NOT reuse Tensor with SelectedRows
// so we could just share the tensor here directly.
// However, the InferVarType method will infer the output SelectedRows
// to Tensor sometimes, which is a bug, so we will add an attribute
// here to indicate the inplace and remove this attribute after
// the InferVarType's bug was fixed
bool grad_inplace = context.Attr<bool>("grad_inplace");
if (grad_inplace) {
d_table_value->ShareDataWith(*d_output);
} else {
d_table_value->mutable_data<T>(context.GetPlace());
d_table->set_height(table_dim[0]);
auto *d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
auto d_output_dims = d_output->dims();
PADDLE_ENFORCE_EQ(
d_table_value->dims(),
framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
}
} else {
auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 99302a70
......@@ -68,6 +68,7 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
if(WITH_GPU)
nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
......@@ -75,6 +76,6 @@ endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/algorithm.h
浏览文件 @ 99302a70
......@@ -39,6 +39,52 @@ HOSTDEVICE inline int64_t BinarySearch(const T *x, int64_t num, const T &val) {
return -1;
}
template <typename T>
HOSTDEVICE inline size_t LowerBound(const T *x, size_t num, const T &val) {
#ifdef __CUDA_ARCH__
// The following code is from
// https://en.cppreference.com/w/cpp/algorithm/lower_bound
auto *first = x;
int64_t count = static_cast<int64_t>(num);
while (count > 0) {
int64_t step = (count >> 1);
auto *it = first + step;
if (*it < val) {
first = ++it;
count -= (step + 1);
} else {
count = step;
}
}
return static_cast<size_t>(first - x);
#else
return static_cast<size_t>(std::lower_bound(x, x + num, val) - x);
#endif
}
template <typename T>
HOSTDEVICE inline size_t UpperBound(const T *x, size_t num, const T &val) {
#ifdef __CUDA_ARCH__
// The following code is from
// https://en.cppreference.com/w/cpp/algorithm/upper_bound
auto *first = x;
int64_t count = static_cast<int64_t>(num);
while (count > 0) {
auto step = (count >> 1);
auto *it = first + step;
if (val < *it) {
count = step;
} else {
first = ++it;
count -= (step + 1);
}
}
return static_cast<size_t>(first - x);
#else
return static_cast<size_t>(std::upper_bound(x, x + num, val) - x);
#endif
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @ 99302a70
......@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
const T *wp_data = nullptr) const = 0;
};
template <typename T>
class GRUKernel : public Kernel {
public:
// compute h1 without h0
virtual void ComputeH1(T *gates, T *ht) const = 0;
virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
};
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc
浏览文件 @ 99302a70
......@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return nullptr;
}
#ifdef __AVX__
template <jit::cpu_isa_t isa>
static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
if (type == "sigmoid") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
} else if (type == "relu") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
} else if (type == "tanh") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
} else if (type == "identity" || type == "") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
}
PADDLE_THROW("Not support type: %s", type);
return nullptr;
}
#endif
/* LSTM JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class LSTMKernelImpl : public LSTMKernel<T> {
......@@ -192,61 +209,49 @@ class LSTMKernelImpl : public LSTMKernel<T> {
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_cand_ = GetAVXAct<isa>(act_cand); \
avx_act_cell_ = GetAVXAct<isa>(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
// TODO(TJ): optimize keq16
......@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
/* GRU JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class GRUKernelImpl : public GRUKernel<T> {
public:
explicit GRUKernelImpl(const std::string& act_gate,
const std::string& act_state, int d)
: GRUKernel<T>() {
d_ = d;
d2_ = d * 2;
act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
act_gate_d_ = GetActKernel<T>(act_gate, d);
act_state_d_ = GetActKernel<T>(act_state, d);
vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
}
void ComputeH1(T* gates, T* ht) const override {
act_gate_d_->Compute(gates, gates);
act_state_d_->Compute(gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d2_, ht);
}
void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
// W: {W_update, W_reset; W_state}
act_gate_d2_->Compute(gates, gates);
vmul_d_->Compute(ht_1, gates + d_, ht);
}
void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
T* y = gates + d2_;
act_state_d_->Compute(y, y);
// out = zt*ht~ + (1-zt)*ht_1
for (int i = 0; i < d_; ++i) {
ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
}
}
private:
int d_, d2_;
std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
std::shared_ptr<const VMulKernel<T>> vmul_d_;
#ifdef __AVX__
std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl( \
const std::string& act_gate, const std::string& act_state, int d) \
: GRUKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_state_ = GetAVXAct<isa>(act_state); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht) \
const { \
__m256 u, s; \
/* W: {W_update, W_reset; W_state} */ \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
_mm256_storeu_ps(ht, s); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 r, ht0; \
r = _mm256_loadu_ps(gates + 8); \
ht0 = _mm256_loadu_ps(ht_1); \
r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0); \
_mm256_storeu_ps(ht, r); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 u, s, ht0; \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
ht0 = _mm256_loadu_ps(ht_1); \
u = avx_act_gate_->Compute(u); \
s = _mm256_mul_ps(u, avx_act_state_->Compute(s)); \
u = _mm256_sub_ps(_mm256_set1_ps(1.f), u); \
u = _mm256_mul_ps(u, ht0); \
u = _mm256_add_ps(s, u); \
_mm256_storeu_ps(ht, u); \
}
#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
#endif
#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get< \
GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
const std::string& act_gate, const std::string& act_state, int d)
#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_state
#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
#undef JITKERNEL_KEY_GRU
#undef JITKERNEL_DECLARE_GRU
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ 99302a70
......@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
}
};
template <typename T>
class SumSeqPoolGradFunctor {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& out_grad,
framework::LoDTensor* in_grad) {
auto lod = in_grad->lod()[0];
int64_t out_w = out_grad.numel() / out_grad.dims()[0];
int64_t in_w = in_grad->numel() / in_grad->dims()[0];
PADDLE_ENFORCE(in_w == out_w);
const T* out_g_data = out_grad.data<T>();
T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t in_offset = lod[i] * in_w;
const T* out_pos = out_g_data + i * out_w;
T* in_pos = in_g_data + in_offset;
for (int r = 0; r != h; ++r) {
blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
}
}
}
};
template <typename T>
class SequencePoolFunctor<platform::CPUDeviceContext, T> {
public:
......@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
math::SetConstant<platform::CPUDeviceContext, T> functor;
functor(context, in_grad, 0);
}
if (pooltype == "SUM") {
math::SumSeqPoolGradFunctor<T> sum_pool_grad;
sum_pool_grad(context, out_grad, in_grad);
return;
}
auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
......@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
const T* out_g_data = out_g_t.data<T>();
T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
for (int r = 0; r != h; ++r) {
blas.VCOPY(w, out_g_data, in_g_data + r * w);
}
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
......
paddle/fluid/operators/math/sequence_pooling_test.cc 0 → 100644
浏览文件 @ 99302a70
/* 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/operators/math/sequence_pooling.h"
#include <gtest/gtest.h>
#include <vector>
template <typename DeviceContext, typename Place, typename T>
void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
paddle::framework::LoDTensor cpu_out_grad;
paddle::framework::LoDTensor cpu_in_grad;
paddle::framework::LoDTensor out_grad;
paddle::framework::LoDTensor in_grad;
const size_t second_dim = 128u;
// construct out_grad's tensor in cpu
const size_t out_first_dim = lod[0].size() - 1;
auto out_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
cpu_out_grad.data<T>()[i] = static_cast<T>(i);
}
// copy to dst out_grad
auto* place = new Place();
DeviceContext* context = new DeviceContext(*place);
if (paddle::platform::is_cpu_place(*place)) {
out_grad = cpu_out_grad;
} else {
TensorCopySync(cpu_out_grad, *place, &out_grad);
}
// construct in_grad
in_grad.set_lod(lod);
auto in_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
in_grad.mutable_data<T>(in_dims, context->GetPlace());
// check tensor contruction result
PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
}
// call functor
paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
*context, "SUM", out_grad, &in_grad);
if (paddle::platform::is_cpu_place(*place)) {
cpu_in_grad = in_grad;
} else {
TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
cpu_in_grad.set_lod(in_grad.lod());
}
EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
EXPECT_EQ(in_grad.lod(), lod);
if (paddle::platform::is_cpu_place(*place)) {
for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = in_grad.lod()[0][i];
int64_t end = in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
out_grad.data<T>()[m + i * second_dim]);
}
}
}
} else {
for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = cpu_in_grad.lod()[0][i];
int64_t end = cpu_in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
cpu_out_grad.data<T>()[m + i * second_dim]);
}
}
}
}
delete place;
delete context;
}
TEST(SequencePoolingGrad, CPU_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod2);
}
#ifdef PADDLE_WITH_CUDA
TEST(SequencePoolingGrad, CUDA_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod2);
}
#endif
paddle/fluid/operators/mean_op.cc
浏览文件 @ 99302a70
......@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of mean op");
AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
AddOutput("Out", "(Tensor) The output of mean op");
AddComment(R"DOC(
Mean Operator calculates the mean of all elements in X.
......
paddle/fluid/operators/momentum_op.cc
浏览文件 @ 99302a70
......@@ -19,54 +19,6 @@ namespace operators {
using Tensor = framework::Tensor;
class MomentumOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(param) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
"Input(grad) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Velocity"),
"Input(velocity) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
"Input(LearningRate) of Momentum should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
"Output(VelocityOut) of Momentum should not be null.");
auto param_dim = ctx->GetInputDim("Param");
if (ctx->GetInputsVarType("Grad")[0] ==
framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Grad"),
"Param and Grad input of MomentumOp should have the same dimension.");
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Velocity"),
"Param and Velocity of MomentumOp should have the same dimension.");
}
PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
"Learning_rate should be a scalar");
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("VelocityOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class MomentumOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
......
paddle/fluid/operators/momentum_op.h
浏览文件 @ 99302a70
......@@ -28,6 +28,54 @@ using framework::SelectedRows;
struct NoNesterov;
struct UseNesterov;
class MomentumOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(param) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
"Input(grad) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Velocity"),
"Input(velocity) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
"Input(LearningRate) of Momentum should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
"Output(VelocityOut) of Momentum should not be null.");
auto param_dim = ctx->GetInputDim("Param");
if (ctx->GetInputsVarType("Grad")[0] ==
framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Grad"),
"Param and Grad input of MomentumOp should have the same dimension.");
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Velocity"),
"Param and Velocity of MomentumOp should have the same dimension.");
}
PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
"Learning_rate should be a scalar");
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("VelocityOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
template <typename T>
class CPUDenseMomentumFunctor {
private:
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ 99302a70
......@@ -151,8 +151,7 @@ void Pool2dOpMaker::Make() {
"The format of output tensor is also NCHW, "
"where N is batch size, C is the number of channels, "
"H is the height of the feature, "
"and W is the width of the feature.")
.Reuse("X");
"and W is the width of the feature.");
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
......@@ -252,8 +251,7 @@ void Pool3dOpMaker::Make() {
"The format of output tensor is also NCDHW, "
"where N is batch size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.")
.Reuse("X");
"width of the feature, respectively.");
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
......
paddle/fluid/operators/reader/reader_blocking_queue_test.cc
浏览文件 @ 99302a70
......@@ -237,7 +237,7 @@ TEST(BlockingQueue, speed_test_mode) {
}
for (size_t i = 0; i < queue_size; ++i) {
q2.Receive(&b);
EXPECT_EQ(b, 0);
EXPECT_EQ(b, 0UL);
}
EXPECT_EQ(q2.Size(), queue_size);
}
paddle/fluid/operators/sequence_reverse_op.cc 0 → 100644
浏览文件 @ 99302a70
// 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/operators/sequence_reverse_op.h"
namespace ops = paddle::operators;
REGISTER_OPERATOR(sequence_reverse, ops::SequenceReverseOp,
ops::SequenceReverseOpMaker,
ops::SequenceReverseGradOpDescMaker);
REGISTER_OP_CPU_KERNEL(
sequence_reverse,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, uint8_t>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/sequence_reverse_op.cu 0 → 100644
浏览文件 @ 99302a70
// 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/operators/sequence_reverse_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_reverse,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, uint8_t>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/sequence_reverse_op.h 0 → 100644
浏览文件 @ 99302a70
// 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/framework/op_registry.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
class SequenceReverseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto x_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_GE(x_dim.size(), 2,
"Rank of Input(X) must be not less than 2.");
ctx->SetOutputDim("Y", x_dim);
ctx->ShareLoD("X", "Y");
}
};
class SequenceReverseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input LoDTensor of sequence_reverse op.");
AddOutput("Y", "The output LoDTensor of sequence_reverse op.");
AddComment(R"DOC(
SequenceReverse Operator.
Reverse each sequence in input X along dim 0.
Assuming X is a LoDTensor with dims [5, 4] and lod [[0, 2, 5]], where:
X.data() = [
[1, 2, 3, 4],
[5, 6, 7, 8], # the 0-th sequence with length 2
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20] # the 1-st sequence with length 3
]
The output Y would be a LoDTensor sharing the same dims and lod with input X,
and:
Y.data() = [
[5, 6, 7, 8],
[1, 2, 3, 4], # the reversed 0-th sequence with length 2
[17, 18, 19, 20],
[13, 14, 15, 16],
[9, 10, 11, 12] # the reversed 1-st sequence with length 3
]
This Operator is useful to build a reverse dynamic RNN network.
This Operator only supports one-level lod currently.
)DOC");
}
};
template <typename T>
struct SequenceReverseFunctor {
SequenceReverseFunctor(const T *x, T *y, const size_t *lod, size_t lod_count,
size_t row_numel)
: x_(x), y_(y), lod_(lod), lod_count_(lod_count), row_numel_(row_numel) {}
HOSTDEVICE void operator()(size_t idx_x) const {
auto row_idx_x = idx_x / row_numel_;
auto lod_idx = math::UpperBound(lod_, lod_count_, row_idx_x);
auto row_idx_y = lod_[lod_idx - 1] + (lod_[lod_idx] - 1 - row_idx_x);
auto idx_y = row_idx_y * row_numel_ + idx_x % row_numel_;
y_[idx_y] = x_[idx_x];
}
const T *x_;
T *y_;
const size_t *lod_;
size_t lod_count_;
size_t row_numel_;
};
template <typename DeviceContext, typename T>
class SequenceReverseOpKernel : public framework::OpKernel<T> {
using LoDTensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto &x = *ctx.Input<LoDTensor>("X");
auto *y = ctx.Output<LoDTensor>("Y");
PADDLE_ENFORCE_EQ(x.lod().size(), 1,
"SequenceReverse Op only support one level lod.");
auto &dev_ctx = ctx.template device_context<DeviceContext>();
const size_t *lod;
size_t lod_count = x.lod()[0].size();
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
lod = x.lod()[0].CUDAData(ctx.GetPlace());
} else {
#endif
lod = x.lod()[0].data();
#ifdef PADDLE_WITH_CUDA
}
#endif
size_t limit = static_cast<size_t>(x.numel());
size_t row_numel = static_cast<size_t>(limit / x.dims()[0]);
auto *x_data = x.data<T>();
auto *y_data = y->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE_NE(x_data, y_data,
"SequenceReverse Op does not support in-place operation");
SequenceReverseFunctor<T> functor(x_data, y_data, lod, lod_count,
row_numel);
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
for_range(functor);
}
};
class SequenceReverseGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("sequence_reverse");
op->SetInput("X", OutputGrad("Y"));
op->SetOutput("Y", InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sgd_op.cc
浏览文件 @ 99302a70
......@@ -77,8 +77,7 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
AddOutput("ParamOut",
"(Tensor or SelectedRows, same with Param) "
"Output parameter, should share the same memory with Param")
.Reuse("Param");
"Output parameter, should share the same memory with Param");
AddComment(R"DOC(
SGD operator
......
paddle/fluid/operators/softmax_cudnn_op.cu.cc
浏览文件 @ 99302a70
......@@ -76,6 +76,8 @@ namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
ops::SoftmaxCUDNNKernel<float>,
ops::SoftmaxCUDNNKernel<double>,
ops::SoftmaxCUDNNKernel<plat::float16>);
REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
ops::SoftmaxGradCUDNNKernel<float>);
ops::SoftmaxGradCUDNNKernel<float>,
ops::SoftmaxGradCUDNNKernel<double>);
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 99302a70
......@@ -80,8 +80,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"The input tensor of softmax, "
"whose last dimension is the input_feature_dimensions.");
AddOutput("Out", "The normalized values with the same shape as X.")
.Reuse("X");
AddOutput("Out", "The normalized values with the same shape as X.");
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......
paddle/fluid/operators/sum_op.cc
浏览文件 @ 99302a70
......@@ -132,7 +132,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of sum operator.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 99302a70
......@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of Topk op");
AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
AddComment(R"DOC(
Top K operator
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ 99302a70
此差异已折叠。
paddle/fluid/operators/top_k_op.h
浏览文件 @ 99302a70
此差异已折叠。
paddle/fluid/pybind/pybind.cc
浏览文件 @ 99302a70
此差异已折叠。
paddle/scripts/paddle_build.sh
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/dataset/wmt16.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/clip.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/evaluator.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/metrics.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/optimizer.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/regularizer.py
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/tests/unittests/dist_mnist_lars.py 0 → 100644
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/tests/unittests/test_hash_op.py 0 → 100644
浏览文件 @ 99302a70
此差异已折叠。
python/paddle/fluid/tests/unittests/test_metrics.py 0 → 100644
浏览文件 @ 99302a70
此差异已折叠。
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