Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
提交 77fc42d2 编写于 作者: T tensor-tang
浏览文件
操作

Merge remote-tracking branch 'ups/develop' into fea/jitkernel

上级 2937314d 7a5f3f75
隐藏空白更改
内联 并排
Showing with 6167 addition and 1096 deletion
cmake/external/anakin.cmake
浏览文件 @ 77fc42d2
......@@ -52,6 +52,7 @@ ExternalProject_Add(
PREFIX ${ANAKIN_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS ${CMAKE_ARGS_PREFIX}
-DUSE_LOGGER=YES
-DUSE_X86_PLACE=YES
-DBUILD_WITH_UNIT_TEST=NO
-DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
......
cmake/flags.cmake
浏览文件 @ 77fc42d2
......@@ -27,7 +27,6 @@ endfunction()
CheckCompilerCXX11Flag()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
# safe_set_flag
#
# Set a compile flag only if compiler is support
......@@ -71,6 +70,20 @@ macro(safe_set_nvflag flag_name)
endif()
endmacro()
macro(safe_set_static_flag) # set c_flags and cxx_flags to static or shared
if (BUILD_SHARED_LIBS)
return() # if build shared libs, the flags keep same with '/MD'
endif(BUILD_SHARED_LIBS)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/MD")
string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/MD")
endforeach(flag_var)
endmacro()
CHECK_CXX_SYMBOL_EXISTS(UINT64_MAX "stdint.h" UINT64_MAX_EXISTS)
if(NOT UINT64_MAX_EXISTS)
......@@ -97,9 +110,13 @@ SET(CMAKE_EXTRA_INCLUDE_FILES "")
# Common flags. the compiler flag used for C/C++ sources whenever release or debug
# Do not care if this flag is support for gcc.
# https://github.com/PaddlePaddle/Paddle/issues/12773
if (NOT WIN32)
set(COMMON_FLAGS
-fPIC
-fno-omit-frame-pointer
-Werror
-Wall
-Wextra
-Wnon-virtual-dtor
......@@ -114,11 +131,6 @@ set(COMMON_FLAGS
-Wno-error=terminate # Warning in PADDLE_ENFORCE
)
# https://github.com/PaddlePaddle/Paddle/issues/12773
if (NOT WIN32)
list(APPEND COMMON_FLAGS -Werror)
endif()
set(GPU_COMMON_FLAGS
-fPIC
-fno-omit-frame-pointer
......@@ -133,30 +145,53 @@ set(GPU_COMMON_FLAGS
-Wno-error=array-bounds # Warnings in Eigen::array
)
else(NOT WIN32)
set(COMMON_FLAGS
"/w") #disable all warnings.
set(GPU_COMMON_FLAGS
"/w") #disable all warnings
endif(NOT WIN32)
if (APPLE)
if(NOT CMAKE_CROSSCOMPILING)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
endif()
else()
endif(APPLE)
if(LINUX)
set(GPU_COMMON_FLAGS
-Wall
-Wextra
-Werror
${GPU_COMMON_FLAGS})
endif()
endif(LINUX)
if(UNIX AND NOT APPLE)
# except apple from nix*Os family
set(LINUX TRUE)
endif(UNIX AND NOT APPLE)
foreach(flag ${COMMON_FLAGS})
safe_set_cflag(CMAKE_C_FLAGS ${flag})
safe_set_cxxflag(CMAKE_CXX_FLAGS ${flag})
endforeach()
foreach(flag ${GPU_COMMON_FLAGS})
safe_set_nvflag(${flag})
endforeach()
if(WIN32)
# windows build turn off warnings.
safe_set_static_flag()
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/W3")
string(REGEX REPLACE "/W3" "/w" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/W3")
endforeach(flag_var)
endif(WIN32)
doc/CMakeLists.txt
浏览文件 @ 77fc42d2
add_custom_target(paddle_apis ALL
DEPENDS paddle_v2_apis paddle_fluid_apis)
DEPENDS paddle_v2_apis)
add_custom_target(paddle_docs ALL
DEPENDS paddle_v2_docs paddle_v2_docs_cn
paddle_fluid_docs paddle_fluid_docs_cn
paddle_mobile_docs paddle_mobile_docs_cn)
add_subdirectory(v2)
add_subdirectory(fluid)
add_subdirectory(mobile)
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ 77fc42d2
......@@ -102,8 +102,8 @@ class Float16Transpiler:
continue
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......@@ -187,7 +187,7 @@ class Float16Transpiler:
shape=var.shape,
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
var.op._rename_output(var_name, tmp_var_name)
self.block._insert_op(
i,
type="cast",
......
paddle/fluid/API.spec
浏览文件 @ 77fc42d2
......@@ -6,26 +6,9 @@ paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=
paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.Operator.__init__ ArgSpec(args=['self', 'block', 'desc', 'type', 'inputs', 'outputs', 'attrs'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.Operator.all_attrs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr_type ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr_id ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr_ids ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_kernel ArgSpec(args=['self', 'op_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.input ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.output ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_input ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_output ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.set_attr ArgSpec(args=['self', 'name', 'val'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.to_string ArgSpec(args=['self', 'throw_on_error'], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
......@@ -38,7 +21,7 @@ paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'en
paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174'))
paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False))
paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspilerConfig.__init__
paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None))
......@@ -170,6 +153,13 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=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)
......@@ -241,13 +231,6 @@ paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwarg
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -286,7 +269,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kw
paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.polygon_box_transform ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
......@@ -315,13 +298,18 @@ paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000))
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174'))
paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False))
paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.HashName.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.HashName.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -13,3 +13,5 @@ if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
endif()
add_subdirectory(train)
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -56,9 +56,9 @@ else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
......@@ -141,20 +141,22 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
if(WITH_DISTRIBUTE)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass elementwise_add_op)
endif()
if (NOT WIN32)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fast_threaded_ssa_graph_executor fuse_elewise_add_act_pass)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph build_strategy
fast_threaded_ssa_graph_executor)
endif() # NOT WIN32
cc_library(prune SRCS prune.cc DEPS framework_proto)
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -54,3 +54,8 @@ cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_execu
# device_context reduce_op_handle )
cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
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)
paddle/fluid/framework/details/build_strategy.cc 0 → 100644
浏览文件 @ 77fc42d2
/* 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/build_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
namespace framework {
namespace details {
class ParallelExecutorPassBuilder : public ir::PassBuilder {
public:
explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
: ir::PassBuilder(), strategy_(strategy) {
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
// Add op fusion.
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
// Add a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path",
new std::string(graph_path));
}
}
// Convert graph to run on multi-devices.
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
multi_devices_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy_.debug_graphviz_path_);
multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
}
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
}
private:
BuildStrategy strategy_;
};
std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy()
const {
pass_builder_.reset(new ParallelExecutorPassBuilder(*this));
return pass_builder_;
}
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
#ifdef PADDLE_WITH_CUDA
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
const bool use_cuda) const {
#endif
// Create a default one if not initialized by user.
if (!pass_builder_) {
CreatePassesFromStrategy();
}
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (pass->Type() == "multi_devices_pass") {
pass->Erase("places");
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("params");
pass->SetNotOwned<const std::unordered_set<std::string>>("params",
&param_names);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
}
graph = pass->Apply(std::move(graph));
}
return graph;
}
} // namespace details
} // namespace framework
} // namespace paddle
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_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 @ 77fc42d2
......@@ -15,6 +15,17 @@
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
......@@ -57,6 +68,30 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool enable_data_balance_{false};
// User normally doesn't need to call this API.
// The PassBuilder allows for more customized insert, remove of passes
// from python side.
// A new PassBuilder is created based on configs defined above and
// passes are owned by the PassBuilder.
std::shared_ptr<ir::PassBuilder> CreatePassesFromStrategy() const;
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(
const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
#ifdef PADDLE_WITH_CUDA
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const;
#else
const bool use_cuda) const;
#endif
private:
mutable std::shared_ptr<ir::PassBuilder> pass_builder_;
};
} // namespace details
......
paddle/fluid/framework/details/cow_ptr.h
浏览文件 @ 77fc42d2
......@@ -20,79 +20,37 @@ namespace paddle {
namespace framework {
namespace details {
// Change it to thread safe flags if needed.
class ThreadUnsafeOwnershipFlags {
template <class T>
class COWPtr {
public:
explicit ThreadUnsafeOwnershipFlags(bool flag) : flag_(flag) {}
ThreadUnsafeOwnershipFlags(const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags& operator=(
const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags(ThreadUnsafeOwnershipFlags&& other) = default;
void SetOwnership(bool flag) { flag_ = flag; }
// Invoke the callback if it is not owned.
template <typename Callback>
void AcquireOwnershipOnce(Callback acquire) {
if (!flag_) {
acquire();
flag_ = true;
}
}
typedef std::shared_ptr<T> RefPtr;
private:
bool flag_;
};
RefPtr m_sp;
// Copy-On-Write pointer.
// It will hold a T* pointer, and only copy once when `MutableData` is invoked.
//
// The template parameter OwnershipFlags should have:
// * a constructor takes a bool. True if own.
// * SetOwnership(bool flag).
// * AcquireOwnershipOnce(Callback). It will invoke the callback if it is not
// owned.
//
// https://en.wikipedia.org/wiki/Copy-on-write
template <typename T, typename OwnershipFlags = ThreadUnsafeOwnershipFlags>
class COWPtr {
public:
// Ctor from raw pointer.
explicit COWPtr(T* ptr) : payload_(ptr), ownership_{true} {}
COWPtr() : m_sp(nullptr) {}
explicit COWPtr(T* t) : m_sp(t) {}
// Move methods. Steal ownership from origin
COWPtr(COWPtr&& other)
: payload_(other.payload_), ownership_{std::move(other.ownership_)} {}
COWPtr& operator=(COWPtr&& origin) = default;
const T& Data() const { return *m_sp; }
// Copy methods. Not own payload
COWPtr(const COWPtr& other) : payload_(other.payload_), ownership_{false} {}
COWPtr& operator=(const COWPtr& other) {
payload_ = other.payload_;
ownership_.SetOwnership(false);
return *this;
}
// Access read only data.
const T& Data() const { return *payload_; }
// Access mutable data. If the data is not owned, the data will be copied
// before.
T* MutableData() {
ownership_.AcquireOwnershipOnce(
[this] { payload_.reset(new T(*payload_)); });
return payload_.get();
DetachIfNotUnique();
return m_sp.get();
}
private:
// Actual data pointer.
std::shared_ptr<T> payload_;
void DetachIfNotUnique() {
T* tmp = m_sp.get();
if (!(tmp == nullptr || m_sp.unique())) {
Detach();
}
}
// Ownership flag.
OwnershipFlags ownership_;
void Detach() {
T* tmp = m_sp.get();
m_sp = RefPtr(new T(*tmp));
}
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/cow_ptr_test.cc
浏览文件 @ 77fc42d2
......@@ -30,6 +30,14 @@ TEST(COWPtr, all) {
ASSERT_EQ(ptr2.Data(), 10);
}
TEST(COWPtr, change_old) {
COWPtr<int> ptr(new int{0});
COWPtr<int> ptr2 = ptr;
*ptr.MutableData() = 10;
ASSERT_EQ(ptr2.Data(), 0);
ASSERT_EQ(ptr.Data(), 10);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -28,9 +28,9 @@ cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph grap
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(fc_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif()
if (WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif ()
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
pass_library(fc_lstm_fuse_pass inference)
......@@ -41,12 +41,14 @@ cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass
set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
cc_library(pass_builder SRCS pass_builder.cc DEPS pass)
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
if(WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif()
if (WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif ()
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ 77fc42d2
......@@ -257,6 +257,22 @@ std::unique_ptr<ir::Graph> AttentionLSTMFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PDPattern external_pattern, subblock_pattern;
// Use the following variables to tell whether this model is RNN1.
// This fuse can only works on the RNN1 model.
std::unordered_set<std::string> specified_vars({"data_lod_attention",
"cell_init", "hidden_init",
"data", "week", "minute"});
int count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsVar() && specified_vars.count(node->Name())) {
++count;
}
}
if (count < specified_vars.size()) {
return graph;
}
// Continue to fuse.
FindWhileOp(graph.get());
return graph;
}
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ 77fc42d2
......@@ -77,10 +77,12 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
const std::string BatchedCellPreAct =
patterns::UniqueKey("BatchedCellPreAct");
const std::string BatchedGate = patterns::UniqueKey("BatchedGate");
const std::string CheckedCell = patterns::UniqueKey("CheckedCell");
scope->Var(BatchedInput)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedCellPreAct)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedGate)->GetMutable<framework::LoDTensor>();
scope->Var(CheckedCell)->GetMutable<framework::LoDTensor>();
op_desc.SetInput("H0", {});
op_desc.SetInput("C0", {});
......@@ -90,6 +92,7 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
op_desc.SetOutput("BatchedGate", {BatchedGate});
op_desc.SetOutput("BatchCellPreAct", {BatchedCellPreAct});
op_desc.SetOutput("BatchedInput", {BatchedInput});
op_desc.SetOutput("CheckedCell", {CheckedCell});
op_desc.SetAttr("is_reverse", lstm->Op()->GetAttr("is_reverse"));
op_desc.SetAttr("use_peepholes", lstm->Op()->GetAttr("use_peepholes"));
// TODO(TJ): get from attr
......
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ 77fc42d2
......@@ -12,11 +12,11 @@ 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/graph_helper.h"
#include <algorithm>
#include <deque>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace ir {
......@@ -113,6 +113,74 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
return adj_list;
}
size_t GraphNum(const Graph &graph) {
std::unordered_set<ir::Node *> nodes = graph.Nodes();
std::unordered_set<ir::Node *> visited_nodes;
visited_nodes.reserve(nodes.size());
std::deque<ir::Node *> q_nodes;
std::vector<std::unordered_set<ir::Node *>> graph_nodes;
std::unordered_set<ir::Node *> g_nodes;
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); });
};
while (visited_nodes.size() != nodes.size()) {
if (!q_nodes.empty()) {
auto cur_node = q_nodes.front();
q_nodes.pop_front();
visited_nodes.insert(cur_node);
g_nodes.insert(cur_node);
traverse_nodes(cur_node->inputs);
traverse_nodes(cur_node->outputs);
} else {
++graph_count;
if (g_nodes.size()) {
graph_nodes.emplace_back(g_nodes);
}
g_nodes.clear();
for (auto &n : nodes) {
if (visited_nodes.count(n) == 0) {
q_nodes.push_back(n);
break;
}
}
}
}
if (g_nodes.size()) {
graph_nodes.emplace_back(g_nodes);
}
if (VLOG_IS_ON(10)) {
VLOG(10) << "graph_num: " << graph_nodes.size();
for (auto &g_n : graph_nodes) {
VLOG(10) << "graph_nodes: " << g_n.size();
if (g_n.size() < 10) {
std::stringstream out;
for (auto &node : g_n) {
out << "\nNode: " << node->Name() << " in [";
for (auto &n : node->inputs) {
out << n->Name() << ", ";
}
out << "], out[";
for (auto &n : node->outputs) {
out << n->Name() << ", ";
}
out << "]";
}
VLOG(10) << out.str();
}
}
}
return graph_count;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper.h
浏览文件 @ 77fc42d2
......@@ -27,6 +27,8 @@ namespace ir {
// Test if the graph contains circle.
bool HasCircle(const Graph &graph);
size_t GraphNum(const Graph &graph);
// Topology Sort the operations in the graph from inputs to outputs.
// `graph` cannot contain circle.
std::vector<ir::Node *> TopologySortOperations(const Graph &graph);
......
paddle/fluid/framework/ir/graph_helper_test.cc
浏览文件 @ 77fc42d2
......@@ -120,6 +120,97 @@ TEST(GraphHelperTest, Basic) {
ASSERT_EQ(node_map.at("op2"), 1UL);
ASSERT_TRUE(node_map.at("op3") < node_map.at("op5"));
}
void BuildZeroGraph(Graph* g) {}
void BuildOneGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
void BuildTwoGraphs(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
// o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
// v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
// o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
// v4->outputs.push_back(o5);
}
TEST(GraphHelperTest, GraphNum) {
ProgramDesc prog;
Graph g(prog);
BuildZeroGraph(&g);
ASSERT_EQ(GraphNum(g), 0);
Graph g2(prog);
BuildOneGraph(&g2);
ASSERT_EQ(GraphNum(g2), 1);
Graph g3(prog);
BuildTwoGraphs(&g3);
ASSERT_EQ(GraphNum(g3), 2);
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_traits.cc
浏览文件 @ 77fc42d2
......@@ -14,6 +14,8 @@
#include "paddle/fluid/framework/ir/graph_traits.h"
#include <vector>
namespace paddle {
namespace framework {
namespace ir {
......
paddle/fluid/framework/ir/pass.cc
浏览文件 @ 77fc42d2
......@@ -19,7 +19,6 @@ namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<Graph> Pass::Apply(std::unique_ptr<Graph> graph) const {
PADDLE_ENFORCE(!applied_, "Pass can only Apply() once.");
PADDLE_ENFORCE(graph.get(), "graph passed to Pass::Apply() cannot be empty.");
for (const std::string& attr : required_pass_attrs_) {
PADDLE_ENFORCE(attrs_.find(attr) != attrs_.end(),
......
paddle/fluid/framework/ir/pass.h
浏览文件 @ 77fc42d2
......@@ -42,6 +42,8 @@ class Pass {
attr_dels_.clear();
}
std::string Type() const { return type_; }
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const;
// Get a reference to the attributed previously set.
......@@ -52,6 +54,21 @@ class Pass {
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
}
bool Has(const std::string &attr_name) const {
return attrs_.find(attr_name) != attrs_.end();
}
void Erase(const std::string &attr_name) {
if (!Has(attr_name)) {
return;
}
if (attr_dels_.find(attr_name) != attr_dels_.end()) {
attr_dels_[attr_name]();
attr_dels_.erase(attr_name);
}
attrs_.erase(attr_name);
}
// Set a pointer to the attribute. Pass takes ownership of the attribute.
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr) {
......@@ -68,13 +85,15 @@ class Pass {
// should delete the attribute.
template <typename AttrType>
void SetNotOwned(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0);
PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the pass",
attr_name);
attrs_[attr_name] = attr;
}
protected:
virtual std::unique_ptr<Graph> ApplyImpl(
std::unique_ptr<Graph> graph) const = 0;
virtual std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const {
LOG(FATAL) << "Calling virtual Pass not implemented.";
}
private:
template <typename PassType>
......@@ -89,7 +108,10 @@ class Pass {
required_graph_attrs_.insert(attrs.begin(), attrs.end());
}
void RegisterType(const std::string &type) { type_ = type; }
mutable bool applied_{false};
std::string type_;
std::unordered_set<std::string> required_pass_attrs_;
std::unordered_set<std::string> required_graph_attrs_;
std::map<std::string, boost::any> attrs_;
......@@ -143,10 +165,11 @@ struct PassRegistrar : public Registrar {
PADDLE_ENFORCE(!PassRegistry::Instance().Has(pass_type),
"'%s' is registered more than once.", pass_type);
PassRegistry::Instance().Insert(
pass_type, [this]() -> std::unique_ptr<Pass> {
pass_type, [this, pass_type]() -> std::unique_ptr<Pass> {
std::unique_ptr<Pass> pass(new PassType());
pass->RegisterRequiredPassAttrs(this->required_pass_attrs_);
pass->RegisterRequiredGraphAttrs(this->required_graph_attrs_);
pass->RegisterType(pass_type);
return pass;
});
}
......
paddle/fluid/framework/ir/pass_builder.cc 0 → 100644
浏览文件 @ 77fc42d2
/* 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/pass_builder.h"
namespace paddle {
namespace framework {
namespace ir {
std::shared_ptr<Pass> PassBuilder::AppendPass(const std::string& pass_type) {
auto pass = ir::PassRegistry::Instance().Get(pass_type);
passes_.emplace_back(pass.release());
return passes_.back();
}
void PassBuilder::RemovePass(size_t idx) {
PADDLE_ENFORCE(passes_.size() > idx);
passes_.erase(passes_.begin() + idx);
}
std::shared_ptr<Pass> PassBuilder::InsertPass(size_t idx,
const std::string& pass_type) {
PADDLE_ENFORCE(passes_.size() >= idx);
std::shared_ptr<Pass> pass(
ir::PassRegistry::Instance().Get(pass_type).release());
passes_.insert(passes_.begin() + idx, std::move(pass));
return passes_[idx];
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass_builder.h 0 → 100644
浏览文件 @ 77fc42d2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class PassBuilder {
public:
PassBuilder() {}
virtual ~PassBuilder() {}
// Append a new pass to the end.
std::shared_ptr<Pass> AppendPass(const std::string& pass_type);
// Insert a new pass after `idx`.
std::shared_ptr<Pass> InsertPass(size_t idx, const std::string& pass_type);
// Remove a new pass at `idx`.
void RemovePass(size_t idx);
// Returns a list of all passes.
std::vector<std::shared_ptr<Pass>> AllPasses() const { return passes_; }
protected:
std::vector<std::shared_ptr<Pass>> passes_;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass_test.cc
浏览文件 @ 77fc42d2
......@@ -82,12 +82,10 @@ TEST(PassTest, TestPassAttrCheck) {
ASSERT_EQ(graph->Get<int>("copy_test_pass_attr"), 2);
ASSERT_EQ(graph->Get<int>("copy_test_graph_attr"), 2);
try {
graph = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("Pass can only Apply() once") != exception.npos);
// Allow apply more than once.
graph.reset(new Graph(prog));
graph->Set<int>("test_graph_attr", new int);
graph = pass->Apply(std::move(graph));
pass = PassRegistry::Instance().Get("test_pass");
pass->SetNotOwned<int>("test_pass_attr", &val);
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 77fc42d2
......@@ -17,10 +17,13 @@
#include <algorithm>
#include <initializer_list>
#include <memory>
#include <mutex> // NOLINT
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/memcpy.h"
#include "glog/logging.h"
......@@ -28,206 +31,436 @@ namespace paddle {
namespace framework {
#if defined(PADDLE_WITH_CUDA)
namespace details {
struct CUDABuffer {
void *data_{nullptr};
size_t size_{0};
platform::CUDAPlace place_;
CUDABuffer() {}
CUDABuffer(platform::Place place, size_t size)
: size_(size), place_(boost::get<platform::CUDAPlace>(place)) {
data_ = memory::Alloc(place_, size);
}
~CUDABuffer() { ClearMemory(); }
CUDABuffer(const CUDABuffer &o) = delete;
CUDABuffer &operator=(const CUDABuffer &o) = delete;
void Resize(platform::Place place, size_t size) {
ClearMemory();
place_ = boost::get<platform::CUDAPlace>(place);
data_ = memory::Alloc(place_, size);
PADDLE_ENFORCE_NOT_NULL(data_);
size_ = size;
}
void Swap(CUDABuffer &o) {
std::swap(data_, o.data_);
std::swap(place_, o.place_);
std::swap(size_, o.size_);
}
private:
void ClearMemory() const {
if (data_ != nullptr) {
memory::Free(place_, data_);
}
}
};
} // namespace details
// Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside.
template <typename T>
class Vector {
public:
using value_type = T;
using iterator = typename std::vector<T>::iterator;
using const_iterator = typename std::vector<T>::const_iterator;
// Default ctor. Create empty Vector
Vector() { InitEmpty(); }
private:
// The actual class to implement vector logic
class VectorData {
public:
VectorData() : flag_(kDataInCPU) {}
VectorData(size_t count, const T &value)
: cpu_(count, value), flag_(kDataInCPU) {}
VectorData(std::initializer_list<T> init) : cpu_(init), flag_(kDataInCPU) {}
template <typename U>
explicit VectorData(const std::vector<U> &dat)
: cpu_(dat), flag_(kDataInCPU) {}
~VectorData() {}
VectorData(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
}
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T()) {
InitEmpty();
if (count != 0) {
resize(count);
T *ptr = begin();
for (size_t i = 0; i < count; ++i) {
ptr[i] = value;
VectorData &operator=(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
details::CUDABuffer null;
gpu_.Swap(null);
return *this;
}
T &operator[](size_t i) {
MutableCPU();
return cpu_[i];
}
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_[i];
}
size_t size() const { return cpu_.size(); }
iterator begin() {
MutableCPU();
return cpu_.begin();
}
iterator end() {
MutableCPU();
return cpu_.end();
}
T &front() {
MutableCPU();
return cpu_.front();
}
T &back() {
MutableCPU();
return cpu_.back();
}
const_iterator begin() const {
ImmutableCPU();
return cpu_.begin();
}
const_iterator end() const {
ImmutableCPU();
return cpu_.end();
}
const T &back() const {
ImmutableCPU();
return cpu_.back();
}
T *data() { return &(*this)[0]; }
const T *data() const { return &(*this)[0]; }
const T &front() const {
ImmutableCPU();
return cpu_.front();
}
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
MutableCPU();
cpu_.assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
MutableCPU();
cpu_.push_back(elem);
}
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
MutableCPU();
auto out_it = std::back_inserter<std::vector<T>>(this->cpu_);
std::copy(begin, end, out_it);
}
// resize the vector
void resize(size_t size) {
MutableCPU();
cpu_.resize(size);
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return reinterpret_cast<T *>(gpu_.data_);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
}
// clear
void clear() {
cpu_.clear();
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return cpu_.capacity(); }
// reserve data
void reserve(size_t size) const { cpu_.reserve(size); }
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
ImmutableCPU();
return cpu_;
}
bool operator==(const VectorData &other) const {
ImmutableCPU();
other.ImmutableCPU();
return cpu_ == other.cpu_;
}
std::mutex &Mutex() const { return mtx_; }
std::unique_ptr<platform::CUDAPlace> CUDAPlace() const {
if (gpu_.data_ == nullptr) {
return nullptr;
} else {
return std::unique_ptr<platform::CUDAPlace>(
new platform::CUDAPlace(gpu_.place_));
}
}
}
// Ctor with init_list
Vector(std::initializer_list<T> init) {
if (init.size() == 0) {
InitEmpty();
} else {
InitByIter(init.size(), init.begin(), init.end());
private:
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(
platform::Place(gpu_.place_)));
auto stream = dev_ctx->stream();
void *src = gpu_.data_;
void *dst = cpu_.data();
memory::Copy(platform::CPUPlace(), dst, gpu_.place_, src, gpu_.size_,
stream);
dev_ctx->Wait();
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
CopyCPUDataToCUDA(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() &&
!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
PADDLE_THROW("This situation should not happen");
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
CopyCPUDataToCUDA(place);
SetFlag(kDataInCUDA);
} else if (!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
PADDLE_THROW("This situation should not happen.");
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void CopyCPUDataToCUDA(const platform::Place &place) const {
void *src = cpu_.data();
gpu_.Resize(place, cpu_.size() * sizeof(T));
void *dst = gpu_.data_;
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place));
auto stream = dev_ctx->stream();
memory::Copy(gpu_.place_, dst, platform::CPUPlace(), src, gpu_.size_,
stream);
}
void ImmutableCPU() const {
if (IsDirty() && !IsInCPU()) { // If data has been changed in CUDA, or
// CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
mutable details::CUDABuffer gpu_;
mutable int flag_;
mutable std::mutex mtx_;
};
public:
// Default ctor. Create empty Vector
Vector() : m_(new VectorData()) {}
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T())
: m_(new VectorData(count, value)) {}
// Ctor with init_list
Vector(std::initializer_list<T> init) : m_(new VectorData(init)) {}
// implicit cast from std::vector.
template <typename U>
Vector(const std::vector<U> &dat) { // NOLINT
if (dat.size() == 0) {
InitEmpty();
} else {
InitByIter(dat.size(), dat.begin(), dat.end());
}
Vector(const std::vector<U> &dat) : m_(new VectorData(dat)) { // NOLINT
}
// Copy ctor
Vector(const Vector<T> &other) { this->operator=(other); }
Vector(const Vector<T> &other) { m_ = other.m_; }
// Copy operator
Vector<T> &operator=(const Vector<T> &other) {
if (other.size() != 0) {
this->InitByIter(other.size(), other.begin(), other.end());
} else {
InitEmpty();
}
m_ = other.m_;
return *this;
}
// Move ctor
Vector(Vector<T> &&other) {
this->size_ = other.size_;
this->flag_ = other.flag_;
if (other.cuda_vec_.memory_size()) {
this->cuda_vec_.ShareDataWith(other.cuda_vec_);
}
if (other.cpu_vec_.memory_size()) {
this->cpu_vec_.ShareDataWith(other.cpu_vec_);
}
}
Vector(Vector<T> &&other) { m_ = std::move(other.m_); }
// CPU data access method. Mutable.
T &operator[](size_t i) {
MutableCPU();
return const_cast<T *>(cpu_vec_.data<T>())[i];
}
T &operator[](size_t i) { return (*m_.MutableData())[i]; }
// CPU data access method. Immutable.
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_vec_.data<T>()[i];
}
const T &operator[](size_t i) const { return m_.Data()[i]; }
// std::vector iterator methods. Based on CPU data access method
size_t size() const { return size_; }
size_t size() const { return m_.Data().size(); }
T *begin() { return capacity() == 0 ? &EmptyDummy() : &this->operator[](0); }
iterator begin() { return m_.MutableData()->begin(); }
T *end() {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
iterator end() { return m_.MutableData()->end(); }
T &front() { return *begin(); }
T &front() { return m_.MutableData()->front(); }
T &back() {
auto it = end();
--it;
return *it;
}
T &back() { return m_.MutableData()->back(); }
const T *begin() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](0);
}
const_iterator begin() const { return m_.Data().begin(); }
const T *end() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
const_iterator end() const { return m_.Data().end(); }
const T *cbegin() const { return begin(); }
const_iterator cbegin() const { return begin(); }
const T *cend() const { return end(); }
const_iterator cend() const { return end(); }
const T &back() const {
auto it = end();
--it;
return *it;
}
const T &back() const { return m_.Data().back(); }
T *data() { return begin(); }
T *data() { return m_.MutableData()->data(); }
const T *data() const { return begin(); }
const T *data() const { return m_.Data().data(); }
const T &front() const { return *begin(); }
const T &front() const { return m_.Data().front(); }
// end of std::vector iterator methods
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
InitByIter(end - begin, begin, end);
m_.MutableData()->assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
if (size_ + 1 > capacity()) {
reserve((size_ + 1) << 1);
}
*end() = elem;
++size_;
}
void push_back(T elem) { m_.MutableData()->push_back(elem); }
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
size_t pre_size = size_;
resize(pre_size + (end - begin));
T *ptr = this->begin() + pre_size;
for (; begin < end; ++begin, ++ptr) {
*ptr = *begin;
}
m_.MutableData()->Extend(begin, end);
}
// resize the vector
void resize(size_t size) {
if (size + 1 <= capacity()) {
size_ = size;
} else {
MutableCPU();
Tensor cpu_tensor;
platform::Place cpu = platform::CPUPlace();
T *ptr = cpu_tensor.mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
const T *old_ptr =
cpu_vec_.memory_size() == 0 ? nullptr : cpu_vec_.data<T>();
if (old_ptr != nullptr) {
std::copy(old_ptr, old_ptr + size_, ptr);
}
size_ = size;
cpu_vec_.ShareDataWith(cpu_tensor);
if (m_.Data().size() != size) {
m_.MutableData()->resize(size);
}
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return cuda_vec_.data<T>();
{
auto &mtx = m_.Data().Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
if (cuda_place == nullptr ||
*cuda_place == boost::get<platform::CUDAPlace>(place)) {
return m_.Data().CUDAData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
return CUDAData(place);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
{
auto &mtx = m_.Data().Mutex();
std::lock_guard<std::mutex> guard(mtx);
auto cuda_place = m_.Data().CUDAPlace();
if (cuda_place == nullptr ||
*cuda_place == boost::get<platform::CUDAPlace>(place)) {
return m_.MutableData()->CUDAMutableData(place);
}
}
// If m_ contains CUDAData in a different place. Detach manually.
m_.Detach();
return CUDAMutableData(place);
}
// clear
void clear() {
size_ = 0;
flag_ = kDirty | kDataInCPU;
}
void clear() { m_.MutableData()->clear(); }
size_t capacity() const {
return cpu_vec_.memory_size() / SizeOfType(typeid(T));
}
size_t capacity() const { return m_.Data().capacity(); }
// reserve data
void reserve(size_t size) {
size_t pre_size = size_;
resize(size);
resize(pre_size);
}
void reserve(size_t size) { m_.Data().reserve(size); }
// the unify method to access CPU or CUDA data. immutable.
const T *Data(platform::Place place) const {
......@@ -248,12 +481,7 @@ class Vector {
}
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
std::vector<T> result;
result.resize(size());
std::copy(begin(), end(), result.begin());
return result;
}
operator std::vector<T>() const { return m_.Data(); }
bool operator==(const Vector<T> &other) const {
if (size() != other.size()) return false;
......@@ -267,118 +495,11 @@ class Vector {
return true;
}
private:
void InitEmpty() {
size_ = 0;
flag_ = kDataInCPU;
}
template <typename Iter>
void InitByIter(size_t size, Iter begin, Iter end) {
platform::Place cpu = platform::CPUPlace();
T *ptr = this->cpu_vec_.template mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
for (size_t i = 0; i < size; ++i) {
*ptr++ = *begin++;
}
flag_ = kDataInCPU | kDirty;
size_ = size;
}
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
TensorCopy(cuda_vec_, platform::CPUPlace(), &cpu_vec_);
WaitPlace(cuda_vec_.place());
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() && !(place == cuda_vec_.place())) {
framework::Tensor tmp;
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
cuda_vec_.ShareDataWith(tmp);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
SetFlag(kDataInCUDA);
} else if (!(place == cuda_vec_.place())) {
framework::Tensor tmp;
WaitPlace(cuda_vec_.place());
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
WaitPlace(place);
cuda_vec_.ShareDataWith(tmp);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void ImmutableCPU() const {
if (IsDirty() &&
!IsInCPU()) { // If data has been changed in CUDA, or CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
const void *Handle() const { return &m_.Data(); }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
static void WaitPlace(const platform::Place place) {
if (platform::is_gpu_place(place)) {
platform::DeviceContextPool::Instance()
.Get(boost::get<platform::CUDAPlace>(place))
->Wait();
}
}
static T &EmptyDummy() {
static T dummy = T();
return dummy;
}
mutable int flag_;
mutable Tensor cpu_vec_;
mutable Tensor cuda_vec_;
size_t size_;
private:
// Vector is an COW object.
mutable details::COWPtr<VectorData> m_;
};
#else // PADDLE_WITH_CUDA
......
paddle/fluid/framework/naive_executor.cc 0 → 100644
浏览文件 @ 77fc42d2
// 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/naive_executor.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/string/pretty_log.h"
namespace paddle {
namespace framework {
// These code can be shared with Executor.
static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
if (var_type == proto::VarType::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == proto::VarType::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == proto::VarType::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
} else if (var_type == proto::VarType::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == proto::VarType::LOD_TENSOR_ARRAY) {
var->GetMutable<LoDTensorArray>();
} else if (var_type == proto::VarType::PLACE_LIST) {
var->GetMutable<platform::PlaceList>();
} else if (var_type == proto::VarType::READER) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::CHANNEL) {
var->GetMutable<ChannelHolder>();
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else {
PADDLE_THROW(
"Variable type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, RAW]",
var_type);
}
}
void NaiveExecutor::Prepare(Scope *parent_scope,
const ProgramDesc &program_desc, int block_id,
bool with_feed_fetch_ops) {
if (!parent_scope) {
scope_ = new framework::Scope;
} else {
scope_ = &parent_scope->NewScope();
}
CreateVariables(program_desc, scope_, block_id);
CreateOps(program_desc, block_id, with_feed_fetch_ops);
}
void NaiveExecutor::Run() {
for (auto &op : ops_) {
VLOG(4) << "run " << op->Type();
op->Run(*scope_, place_);
}
}
void NaiveExecutor::CreateVariables(const ProgramDesc &desc, Scope *scope,
int block_id) {
PADDLE_ENFORCE(scope);
auto &global_block = desc.Block(block_id);
const Scope *ancestor_scope = scope;
while (ancestor_scope->parent()) {
ancestor_scope = ancestor_scope->parent();
}
if (ancestor_scope != scope) {
for (auto &var : global_block.AllVars()) {
if (var->Name() == framework::kEmptyVarName) {
continue;
}
// Create persistable vars in ancestor scope.
if (var->Persistable()) {
auto *ptr = const_cast<Scope *>(ancestor_scope)->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else { // Create temporary variables in local scope.
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto &var : global_block.AllVars()) {
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
}
void NaiveExecutor::CreateOps(const ProgramDesc &desc, int block_id,
bool with_feed_fetch_ops) {
for (const auto &op_desc : desc.Block(block_id).AllOps()) {
if (!with_feed_fetch_ops &&
(op_desc->Type() == "feed" || op_desc->Type() == "fetch")) {
string::PrettyLogEndl(string::Style::detail(), "--- skip [%s], %s -> %s",
op_desc->Input("X")[0], op_desc->Type(),
op_desc->Output("Out")[0]);
continue;
}
ops_.emplace_back(OpRegistry::CreateOp(*op_desc));
}
}
LoDTensor *NaiveExecutor::FindTensor(const std::string &name) {
PADDLE_ENFORCE(scope_, "Need to init scope first");
auto *var = scope_->FindVar(name);
PADDLE_ENFORCE(var, "No variable [%s] in the scope");
auto *tensor = const_cast<LoDTensor *>(&var->Get<LoDTensor>());
return tensor;
}
void NaiveExecutor::CleanFeedFetchOps() {
std::vector<std::unique_ptr<OperatorBase>> ops;
for (auto &op : ops_) {
if (op->Type() != "feed" && op->Type() != "fetch") {
ops.emplace_back(std::move(op));
}
}
ops_.swap(ops);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.h 0 → 100644
浏览文件 @ 77fc42d2
// 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/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
/*
* Simple, intuitive and effective. Only single thread is supported, and
* currently designed for inference.
*/
class NaiveExecutor {
public:
explicit NaiveExecutor(const platform::Place& place) : place_(place) {}
// Create child scope.
// Create variables.
// @with_feed_fetch_ops: whether to work with the feed and fetch operators.
void Prepare(Scope* parent_scope, const ProgramDesc& program_desc,
int block_id, bool with_feed_fetch_ops);
// Run all the operators.
void Run();
// Get an tensor to operating directly, without the need for feed_ops.
LoDTensor* FindTensor(const std::string& name);
Scope* scope() { return scope_; }
void CleanFeedFetchOps();
protected:
void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
void CreateOps(const ProgramDesc& desc, int block_id,
bool with_feed_fetch_ops);
private:
const platform::Place place_;
// Catch the required resource to avoid recreate.
std::vector<std::unique_ptr<OperatorBase>> ops_;
Scope* scope_;
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor_test.cc 0 → 100644
浏览文件 @ 77fc42d2
// 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/naive_executor.h"
#include <gtest/gtest.h>
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
TEST(NaiveExecutor, Basic) {
ProgramDesc program;
auto* main_block = program.MutableBlock(0);
auto* a = main_block->Var("a"); // input
auto* b = main_block->Var("b"); // input
auto* c = main_block->Var("c"); // input
a->SetType(proto::VarType::LOD_TENSOR);
b->SetType(proto::VarType::LOD_TENSOR);
c->SetType(proto::VarType::LOD_TENSOR);
auto* add = main_block->AppendOp();
add->SetType("elementwise_add");
add->SetInput("X", {"a"});
add->SetInput("Y", {"b"});
add->SetOutput("Out", {"c"});
auto place = platform::CPUPlace();
NaiveExecutor exe(place);
exe.Prepare(nullptr, program, 0, false /*with feed fetch ops*/);
auto* a_tensor = exe.FindTensor("a");
auto* b_tensor = exe.FindTensor("b");
auto* c_tensor = exe.FindTensor("c");
a_tensor->Resize({1, 4});
b_tensor->Resize({1, 4});
c_tensor->Resize({1, 4});
b_tensor->mutable_data<float>(place);
a_tensor->mutable_data<float>(place);
float a_arr[] = {0, 1, 2, 3};
float b_arr[] = {0.0, .1, .2, .3};
std::copy_n(a_arr, 4, a_tensor->mutable_data<float>(place));
std::copy_n(b_arr, 4, b_tensor->mutable_data<float>(place));
exe.Run();
auto* c_data = c_tensor->mutable_data<float>(place);
for (int i = 0; i < 4; i++) {
EXPECT_NEAR(c_data[i], 1.1 * i, 1e-3);
}
}
} // namespace framework
} // namespace paddle
USE_OP(elementwise_add);
paddle/fluid/framework/operator.cc
浏览文件 @ 77fc42d2
......@@ -154,9 +154,15 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
platform::SetDeviceId(dev_id);
#endif
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
}
RunImpl(scope, place);
if (VLOG_IS_ON(3)) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 77fc42d2
......@@ -13,21 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/parallel_executor.h"
#include <string>
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -35,80 +33,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
std::unique_ptr<ir::Graph> ApplyParallelExecutorPass(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes, const bool use_cuda,
#ifdef PADDLE_WITH_CUDA
const BuildStrategy &strategy, platform::NCCLContextMap *nccl_ctxs) {
#else
const BuildStrategy &strategy) {
#endif
// Convert the program to graph.
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
// Apply a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
graph = viz_pass->Apply(std::move(graph));
}
// Apply op fusion.
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass =
ir::PassRegistry::Instance().Get("fuse_elewise_add_act_pass");
graph = fuse_elewise_add_act_pass->Apply(std::move(graph));
// Apply a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
graph = viz_pass->Apply(std::move(graph));
}
}
// Convert graph to run on multi-devices.
auto multi_devices_pass =
ir::PassRegistry::Instance().Get("multi_devices_pass");
multi_devices_pass->SetNotOwned<const std::vector<platform::Place>>("places",
&places);
multi_devices_pass->SetNotOwned<const std::string>("loss_var_name",
&loss_var_name);
multi_devices_pass->SetNotOwned<const std::unordered_set<std::string>>(
"params", &param_names);
multi_devices_pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy", &strategy);
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
multi_devices_pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
graph = multi_devices_pass->Apply(std::move(graph));
// Apply a graph print pass to record a graph with device info.
if (!strategy.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass =
ir::PassRegistry::Instance().Get("multi_devices_print_pass");
multi_devices_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy.debug_graphviz_path_);
multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
graph = multi_devices_print_pass->Apply(std::move(graph));
}
// Verify that the graph is correct for multi-device executor.
auto multi_devices_check_pass =
ir::PassRegistry::Instance().Get("multi_devices_check_pass");
graph = multi_devices_check_pass->Apply(std::move(graph));
return graph;
}
class ParallelExecutorPrivate {
public:
explicit ParallelExecutorPrivate(const std::vector<platform::Place> &places)
......@@ -199,10 +123,9 @@ ParallelExecutor::ParallelExecutor(
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy,
member_->nccl_ctxs_.get());
member_->local_scopes_, member_->use_cuda_, member_->nccl_ctxs_.get());
auto max_memory_size = GetEagerDeletionThreshold();
if (max_memory_size >= 0) {
......@@ -228,11 +151,17 @@ ParallelExecutor::ParallelExecutor(
}
}
#else
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy);
std::unique_ptr<ir::Graph> graph =
build_strategy.Apply(main_program, member_->places_, loss_var_name,
params, member_->local_scopes_, member_->use_cuda_);
#endif
// 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(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
......@@ -373,12 +302,6 @@ ParallelExecutor::~ParallelExecutor() {
} // namespace framework
} // namespace paddle
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
#ifdef PADDLE_WITH_CUDA
USE_PASS(reference_count_pass);
#endif
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 77fc42d2
......@@ -14,14 +14,14 @@ limitations under the License. */
#pragma once
#include <paddle/fluid/framework/details/build_strategy.h>
#include <atomic>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
......
paddle/fluid/framework/scope.cc
浏览文件 @ 77fc42d2
......@@ -20,6 +20,13 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/string/printf.h"
// The mutex is not needed by training and inference, only for distribution.
#if PADDLE_WITH_DISTRIBUTE
#define WITH_LOCK 1
#else
#define WITH_LOCK 0
#endif
DEFINE_bool(benchmark, false,
"Doing memory benchmark. It will make deleting scope synchronized, "
"and add some memory usage logs."
......@@ -49,18 +56,24 @@ int64_t GetEagerDeletionThreshold() {
Scope::~Scope() { DropKids(); }
Scope& Scope::NewScope() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable* Scope::Var(const std::string& name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = new_name;
......@@ -69,29 +82,39 @@ Variable* Scope::Var(std::string* name) {
}
Variable* Scope::FindVar(const std::string& name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindVarInternal(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindScopeInternal(var);
}
void Scope::DropKids() {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
for (Scope* s : kids_) delete s;
kids_.clear();
}
bool Scope::HasKid(const Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
return it != this->kids_.end();
}
std::vector<std::string> Scope::LocalVarNames() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -101,7 +124,9 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -114,7 +139,9 @@ void Scope::DeleteScope(Scope* scope) const {
}
void Scope::EraseVars(const std::vector<std::string>& var_names) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......@@ -127,12 +154,16 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ 77fc42d2
......@@ -27,8 +27,11 @@ class SelectedRowsTester : public ::testing::Test {
selected_rows_.reset(new SelectedRows(rows, height));
Tensor* value = selected_rows_->mutable_value();
value->mutable_data<float>(
auto* data = value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
for (int64_t i = 0; i < value->numel(); ++i) {
data[i] = static_cast<float>(i);
}
}
protected:
......@@ -60,6 +63,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
auto* dst_data = dst_tensor.value().data<float>();
for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
ASSERT_EQ(dst_data[i], static_cast<float>(i));
}
}
TEST(SelectedRows, SparseTable) {
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -53,7 +53,7 @@ if(NOT APPLE)
endif()
if(WITH_TESTING)
# tests/book depends the models that generated by python/paddle/fluid/tests/book
# tests/book depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
if(WITH_INFERENCE_API_TEST)
add_subdirectory(tests/api)
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ 77fc42d2
cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
set(analysis_deps
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
analyzer.cc
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ 77fc42d2
......@@ -37,12 +37,16 @@ TEST(Analyzer, analysis_without_tensorrt) {
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string &model_path) {
void TestWord2vecPrediction(const std::string& model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
......@@ -73,8 +77,8 @@ void TestWord2vecPrediction(const std::string &model_path) {
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << "data: "
<< static_cast<float *>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
<< static_cast<float*>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
}
}
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
浏览文件 @ 77fc42d2
......@@ -97,8 +97,10 @@ void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
}
}
void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
void CreateTrtEngineOp(Node *node, Argument *argument,
framework::proto::BlockDesc *block) {
PADDLE_ENFORCE(argument->main_dfg.get());
const DataFlowGraph &graph = *(argument->main_dfg);
static int counter{0};
PADDLE_ENFORCE(node->IsFunctionBlock());
framework::OpDesc desc;
......@@ -204,7 +206,10 @@ void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
PADDLE_ENFORCE(!block->vars().empty(), "the block has no var-desc");
// Set attrs
SetAttr(desc.Proto(), "subgraph", block->SerializeAsString());
SetAttr(desc.Proto(), "max_batch_size", argument->Get<int>("max_batch_size"));
SetAttr(desc.Proto(), "workspace_size", argument->Get<int>("workspace_size"));
SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
......@@ -248,7 +253,7 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
*block_desc.Proto()->mutable_vars() =
argument_->origin_program_desc->blocks(0).vars();
PADDLE_ENFORCE(!block_desc.Proto()->vars().empty());
CreateTrtEngineOp(node, *argument_->main_dfg, block_desc.Proto());
CreateTrtEngineOp(node, argument_, block_desc.Proto());
auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
auto *op = main_block->add_ops();
PADDLE_ENFORCE(!node->pb_msg().empty(), "failed to set desc for block");
......
paddle/fluid/inference/analysis/subgraph_splitter.cc
浏览文件 @ 77fc42d2
......@@ -309,6 +309,8 @@ void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
void SubGraphFuse::ReplaceNodesWithSubGraphs() {
auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
for (auto &subgraph : subgraphs) {
if (subgraph.size() <= argument_->Get<int>("minimum_subgraph_size"))
continue;
std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
// replace this sub-graph with the first node. Two steps: 1. Create a Block
// Node that contains this subgraph 2. Mark the nodes inside the sub-graph
......
paddle/fluid/inference/analysis/subgraph_splitter.h
浏览文件 @ 77fc42d2
......@@ -20,6 +20,7 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/analysis/argument.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/node.h"
......@@ -63,8 +64,11 @@ class SubGraphFuse {
public:
using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
: graph_(graph), node_inside_subgraph_teller_(teller) {}
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller,
Argument *argument)
: graph_(graph),
node_inside_subgraph_teller_(teller),
argument_(argument) {}
// The main method which run all the logic.
void operator()();
......@@ -76,6 +80,7 @@ class SubGraphFuse {
private:
DataFlowGraph *graph_;
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument *argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/subgraph_splitter_tester.cc
浏览文件 @ 77fc42d2
......@@ -66,10 +66,12 @@ TEST(SubGraphSplitter, Split) {
TEST(SubGraphSplitter, Fuse) {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(3));
size_t count0 = dfg.nodes.size();
SubGraphFuse fuse(&dfg, teller);
SubGraphFuse fuse(&dfg, teller, &argument);
fuse();
int count1 = 0;
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ 77fc42d2
......@@ -24,7 +24,7 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
: node_inside_subgraph_teller_(teller) {}
void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_)();
SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
VLOG(4) << "debug info "
<< graph->HumanReadableInfo(false /*show_values*/,
true /*show_functions*/);
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h
浏览文件 @ 77fc42d2
......@@ -33,7 +33,10 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
bool Initialize(Argument* argument) override { return true; }
bool Initialize(Argument* argument) override {
argument_ = argument;
return true;
}
// This class get a sub-graph as input and determine whether to transform this
// sub-graph into TensorRT.
......@@ -46,6 +49,7 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
private:
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument* argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc
浏览文件 @ 77fc42d2
......@@ -36,6 +36,10 @@ TEST(TensorRTSubGraphPass, main) {
};
Argument argument(FLAGS_inference_model_dir);
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -18,10 +18,10 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${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)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
endif()
function(inference_api_test TARGET_NAME)
......@@ -43,8 +43,10 @@ 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)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS 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)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
DEPS paddle_inference_api)
......@@ -52,18 +54,22 @@ cc_test(test_paddle_inference_api
inference_api_test(test_api_impl SRC api_impl_tester.cc
ARGS test_word2vec test_image_classification)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book)
if(WITH_GPU AND TENSORRT_FOUND)
cc_library(paddle_inference_tensorrt_subgraph_engine
SRCS api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter)
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
endif()
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml scope zero_copy_tensor_dummy)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber scope)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endfunction()
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 77fc42d2
......@@ -16,11 +16,15 @@
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -28,8 +32,11 @@ DECLARE_bool(profile);
namespace paddle {
using contrib::AnalysisConfig;
bool AnalysisPredictor::Init(
const std::shared_ptr<framework::Scope>& parent_scope) {
const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program) {
VLOG(3) << "Predictor::init()";
#if !defined(_WIN32)
if (FLAGS_profile) {
......@@ -43,7 +50,8 @@ bool AnalysisPredictor::Init(
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
LOG(WARNING) << "ir optimize only supports CPU currently";
LOG(WARNING) << "ir optimize only supports CPU currently, enable_ir_optim "
"is turned false.";
config_.enable_ir_optim = false;
} else {
place_ = paddle::platform::CPUPlace();
......@@ -56,37 +64,134 @@ bool AnalysisPredictor::Init(
scope_.reset(new paddle::framework::Scope());
}
executor_.reset(new paddle::framework::Executor(place_));
executor_.reset(new paddle::framework::NaiveExecutor(place_));
// Initialize the inference program
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
if (!program) {
if (!LoadProgramDesc()) return false;
OptimizeInferenceProgram();
} else {
LOG(ERROR) << "fail to load inference model from " << config_.model_dir;
inference_program_ = program;
}
executor_->Prepare(scope_.get(), *inference_program_, 0,
config_.use_feed_fetch_ops);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
return true;
}
bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size) {
VLOG(3) << "Predictor::predict";
inference::Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
return false;
}
// Run the inference program
// if share variables, we need not create variables
executor_->Run();
OptimizeInferenceProgram();
if (config_._use_mkldnn) {
executor_->EnableMKLDNN(*inference_program_);
// get fetch variable
if (!GetFetch(output_data, scope)) {
LOG(ERROR) << "fail to get fetches";
return false;
}
ctx_ = executor_->Prepare(*inference_program_, 0);
VLOG(3) << "predict cost: " << timer.toc() << "ms";
return true;
}
VLOG(5) << "to create variables";
PADDLE_ENFORCE(scope_.get());
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feeds_.size()) {
LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
<< inputs.size();
return false;
}
// Cache the inputs memory for better concurrency performance.
feed_tensors_.resize(inputs.size());
for (size_t i = 0; i < inputs.size(); ++i) {
auto &input = feed_tensors_[i];
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
lod.emplace_back(level);
}
input.set_lod(lod);
int idx = -1;
if (config_.specify_input_name) {
idx = feed_names_[inputs[i].name];
} else {
idx = boost::get<int>(feeds_[i]->GetAttr("col"));
}
framework::SetFeedVariable(scope, input, "feed", idx);
}
return true;
}
template <typename T>
void AnalysisPredictor::GetFetchOne(const framework::LoDTensor &fetch,
PaddleTensor *output) {
// set shape.
auto shape = framework::vectorize(fetch.dims());
output->shape.assign(shape.begin(), shape.end());
// set data.
const T *data = fetch.data<T>();
int num_elems = inference::VecReduceToInt(shape);
output->data.Resize(num_elems * sizeof(T));
// The fetched tensor output by fetch op, should always in CPU memory, so just
// copy.
memcpy(output->data.data(), data, num_elems * sizeof(T));
// set lod
output->lod.clear();
for (auto &level : fetch.lod()) {
output->lod.emplace_back(level.begin(), level.end());
}
}
bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::get_fetch";
outputs->resize(fetchs_.size());
for (size_t i = 0; i < fetchs_.size(); ++i) {
int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
PADDLE_ENFORCE((size_t)idx == i);
framework::LoDTensor &fetch =
framework::GetFetchVariable(*scope, "fetch", idx);
auto type = fetch.type();
auto output = &(outputs->at(i));
if (type == typeid(float)) {
GetFetchOne<float>(fetch, output);
output->dtype = PaddleDType::FLOAT32;
} else if (type == typeid(int64_t)) {
GetFetchOne<int64_t>(fetch, output);
output->dtype = PaddleDType::INT64;
} else {
LOG(ERROR) << "unknown type, only support float32 and int64 now.";
}
}
return true;
}
......@@ -107,6 +212,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
new std::string(config_.prog_file));
argument_.fluid_model_param_path.reset(new std::string(config_.param_file));
}
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
PADDLE_ENFORCE(
......@@ -127,9 +233,8 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
const contrib::AnalysisConfig& config) {
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
if (config.use_gpu) {
// 1. GPU memeroy
......@@ -150,15 +255,90 @@ CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
}
std::unique_ptr<PaddlePredictor> predictor(new AnalysisPredictor(config));
if (!dynamic_cast<AnalysisPredictor*>(predictor.get())->Init(nullptr)) {
if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
return nullptr;
}
return predictor;
}
void AnalysisPredictor::PrepareFeedFetch() {
for (auto *op : inference_program_->Block(0).AllOps()) {
if (op->Type() == "feed") {
int idx = boost::get<int>(op->GetAttr("col"));
if (feeds_.size() <= static_cast<size_t>(idx)) {
feeds_.resize(idx + 1);
}
feeds_[idx] = op;
feed_names_[op->Output("Out")[0]] = idx;
} else if (op->Type() == "fetch") {
int idx = boost::get<int>(op->GetAttr("col"));
if (fetchs_.size() <= static_cast<size_t>(idx)) {
fetchs_.resize(idx + 1);
}
fetchs_[idx] = op;
}
}
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = true;
res->SetName(name);
return res;
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = false;
res->SetName(name);
return res;
}
bool AnalysisPredictor::ZeroCopyRun() {
executor_->Run();
return true;
}
bool AnalysisPredictor::LoadProgramDesc() {
// Initialize the inference program
std::unique_ptr<framework::Executor> tmp_exe(
new framework::Executor(platform::CPUPlace()));
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.prog_file, config_.param_file);
} else {
LOG(ERROR) << string::Sprintf(
"not valid model path '%s' or program path '%s'.", config_.model_dir,
config_.param_file);
return false;
}
return true;
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
auto *x = new AnalysisPredictor(config_);
x->Init(scope_, inference_program_);
return std::unique_ptr<PaddlePredictor>(x);
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<contrib::AnalysisConfig>(
const contrib::AnalysisConfig& config) {
const contrib::AnalysisConfig &config) {
return CreatePaddlePredictor<contrib::AnalysisConfig,
PaddleEngineKind::kAnalysis>(config);
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 77fc42d2
......@@ -12,42 +12,81 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#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/paddle_inference_api.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
using inference::analysis::Argument;
using inference::analysis::Analyzer;
using framework::proto::ProgramDesc;
using framework::NaiveExecutor;
using contrib::AnalysisConfig;
/* This predictor is based on the original native predictor with IR and Analysis
* support. It will optimize IR and Parameters in the runtime.
* TODO(Superjomn) Replace the Navive predictor?
*/
class AnalysisPredictor : public NativePaddlePredictor {
class AnalysisPredictor : public PaddlePredictor {
public:
explicit AnalysisPredictor(const contrib::AnalysisConfig& config)
: NativePaddlePredictor(config), config_(config) {}
explicit AnalysisPredictor(const AnalysisConfig &config) : config_(config) {}
bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
bool Init(const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program = nullptr);
bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data,
int batch_size = -1) override {
return NativePaddlePredictor::Run(inputs, output_data, batch_size);
}
bool Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size = -1) override;
std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string &name) override;
std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string &name) override;
bool ZeroCopyRun() override;
void PrepareFeedFetch();
void OptimizeInferenceProgram();
Argument& analysis_argument() { return argument_; }
Argument &analysis_argument() { return argument_; }
std::unique_ptr<PaddlePredictor> Clone() override;
framework::Scope *scope() { return executor_->scope(); }
framework::ProgramDesc &program() { return *inference_program_; }
protected:
bool LoadProgramDesc();
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
bool GetFetch(std::vector<PaddleTensor> *output_data,
framework::Scope *scope);
template <typename T>
void GetFetchOne(const framework::LoDTensor &fetchs,
PaddleTensor *output_data);
private:
contrib::AnalysisConfig config_;
Argument argument_;
std::unique_ptr<NaiveExecutor> executor_;
platform::Place place_;
std::shared_ptr<framework::Scope> scope_;
framework::Scope *sub_scope_{nullptr};
std::shared_ptr<framework::ProgramDesc> inference_program_;
std::vector<framework::OpDesc *> feeds_;
std::map<std::string, size_t> feed_names_;
std::vector<framework::OpDesc *> fetchs_;
// Memory buffer for feed inputs. The temporary LoDTensor will cause serious
// concurrency problems, so cache them.
std::vector<framework::LoDTensor> feed_tensors_;
};
} // namespace paddle
paddle/fluid/inference/api/analysis_predictor_tester.cc 0 → 100644
浏览文件 @ 77fc42d2
// 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 <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(dirname, "", "dirname to tests.");
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
TEST(AnalysisPredictor, ZeroCopy) {
AnalysisConfig config;
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.use_feed_fetch_ops = false;
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto w0 = predictor->GetInputTensor("firstw");
auto w1 = predictor->GetInputTensor("secondw");
auto w2 = predictor->GetInputTensor("thirdw");
auto w3 = predictor->GetInputTensor("forthw");
w0->Reshape({4, 1});
w1->Reshape({4, 1});
w2->Reshape({4, 1});
w3->Reshape({4, 1});
auto* w0_data = w0->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w1_data = w1->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w2_data = w2->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w3_data = w3->mutable_data<int64_t>(PaddlePlace::kCPU);
for (int i = 0; i < 4; i++) {
w0_data[i] = i;
w1_data[i] = i;
w2_data[i] = i;
w3_data[i] = i;
}
predictor->ZeroCopyRun();
auto out = predictor->GetOutputTensor("fc_1.tmp_2");
PaddlePlace place;
int size = 0;
auto* out_data = out->data<float>(&place, &size);
LOG(INFO) << "output size: " << size / sizeof(float);
LOG(INFO) << "output_data: " << out_data;
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/api/api.cc
浏览文件 @ 77fc42d2
/* 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. */
// 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/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle_inference_api.h"
namespace paddle {
......@@ -26,7 +32,7 @@ int PaddleDtypeSize(PaddleDType dtype) {
}
}
PaddleBuf::PaddleBuf(PaddleBuf&& other)
PaddleBuf::PaddleBuf(PaddleBuf &&other)
: data_(other.data_),
length_(other.length_),
memory_owned_(other.memory_owned_) {
......@@ -35,9 +41,9 @@ PaddleBuf::PaddleBuf(PaddleBuf&& other)
other.length_ = 0;
}
PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
PaddleBuf::PaddleBuf(const PaddleBuf &other) { *this = other; }
PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
if (!other.memory_owned_) {
data_ = other.data_;
length_ = other.length_;
......@@ -51,7 +57,7 @@ PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
return *this;
}
PaddleBuf& PaddleBuf::operator=(PaddleBuf&& other) {
PaddleBuf &PaddleBuf::operator=(PaddleBuf &&other) {
// only the buffer with external memory can be copied
data_ = other.data_;
length_ = other.length_;
......@@ -75,7 +81,7 @@ void PaddleBuf::Resize(size_t length) {
}
}
void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Reset(void *data, size_t length) {
Free();
memory_owned_ = false;
data_ = data;
......@@ -85,7 +91,7 @@ void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Free() {
if (memory_owned_ && data_) {
PADDLE_ENFORCE_GT(length_, 0);
free(static_cast<char*>(data_));
free(static_cast<char *>(data_));
data_ = nullptr;
length_ = 0;
}
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 77fc42d2
......@@ -145,7 +145,7 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
VLOG(4) << "Run prepared context";
executor_->RunPreparedContext(ctx_.get(), scope,
false, /* don't create local scope each time*/
false /* don't create variable eatch time */);
false /* don't create variable each time */);
VLOG(4) << "Finish prepared context";
// get fetch variable
if (!GetFetch(output_data, scope)) {
......
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 77fc42d2
/* 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
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
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. */
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
......@@ -30,6 +30,8 @@
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -52,6 +54,8 @@ class NativePaddlePredictor : public PaddlePredictor {
~NativePaddlePredictor() override;
framework::Scope *scope() { return sub_scope_ ? sub_scope_ : scope_.get(); }
protected:
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ 77fc42d2
......@@ -43,7 +43,7 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
......@@ -110,7 +110,7 @@ void MainImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
const bool is_combined = false;
std::vector<std::vector<int64_t>> feed_target_shapes =
......@@ -214,7 +214,7 @@ void MainThreadsImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<framework::LoDTensor> jobs(num_jobs);
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ 77fc42d2
......@@ -35,8 +35,6 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
VLOG(3) << "Predictor::init()";
FLAGS_tensorrt_max_batch_size = config_.max_batch_size;
FLAGS_tensorrt_workspace_size = config_.workspace_size;
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......@@ -92,6 +90,14 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
void OptimizeInferenceProgram() {
// Analyze inference_program
Argument argument;
argument.Set<int>("minimum_subgraph_size",
new int(config_.minimum_subgraph_size));
argument.Set<int>("max_batch_size", new int(config_.max_batch_size));
argument.Set<int>("workspace_size", new int(config_.workspace_size));
argument.Set<std::string>("precision_mode",
new std::string(config_.precision_mode));
if (!config_.model_dir.empty()) {
argument.fluid_model_dir.reset(new std::string(config_.model_dir));
} else {
......
paddle/fluid/inference/api/details/zero_copy_tensor.cc 0 → 100644
浏览文件 @ 77fc42d2
// 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/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(input_or_output_,
"Can't reshape the output tensor, it is readonly");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(shape));
}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
switch (static_cast<int>(place)) {
case static_cast<int>(PaddlePlace::kCPU): {
return tensor->mutable_data<T>(platform::CPUPlace());
}
case static_cast<int>(PaddlePlace::kGPU): {
return tensor->mutable_data<T>(platform::CUDAPlace());
}
default:
PADDLE_THROW("Unsupported place: %d", static_cast<int>(place));
break;
}
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
auto *res = tensor->data<T>();
if (platform::is_cpu_place(tensor->place())) {
*place = PaddlePlace::kCPU;
} else if (platform::is_gpu_place(tensor->place())) {
*place = PaddlePlace::kGPU;
} else {
*place = PaddlePlace::kUNK;
}
*size = tensor->numel();
return res;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data<float>(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data<int64_t>(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
return tensor;
}
std::vector<int64_t> ZeroCopyTensor::shape() {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
PADDLE_ENFORCE(tensor, "not found tensor called %s in the scope", name_);
return framework::vectorize(tensor->dims());
}
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
framework::LoD lod;
for (auto &level : x) {
lod.emplace_back(level);
}
tensor->set_lod(lod);
}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
std::vector<std::vector<size_t>> res;
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
for (auto &level : tensor->lod()) {
res.emplace_back(level);
}
return res;
}
} // namespace paddle
paddle/fluid/inference/api/details/zero_copy_tensor_dummy.cc 0 → 100644
浏览文件 @ 77fc42d2
// 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/paddle_inference_api.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
return nullptr;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const { return nullptr; }
std::vector<int64_t> ZeroCopyTensor::shape() { return {}; }
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
return std::vector<std::vector<size_t>>();
}
} // namespace paddle
paddle/fluid/inference/api/helper.h
浏览文件 @ 77fc42d2
......@@ -21,8 +21,10 @@
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace inference {
......@@ -93,6 +95,20 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
template <typename T>
static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
int size{0};
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
int c = 0;
for (const auto &f : data) {
for (T v : f) {
ptr[c++] = v;
}
}
return size;
}
static std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
......@@ -138,5 +154,127 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
}
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
ss << "\n---- tensor ---" << '\n';
ss << "lod: [";
for (const auto &level : tensor.lod()) {
ss << "[ ";
for (auto i : level) {
ss << i << ", ";
}
ss << "]";
}
ss << "]\n";
ss << "shape: [";
int size = 1;
for (int i = 0; i < tensor.dims().size(); i++) {
int dim = tensor.dims()[i];
ss << dim << ", ";
size *= dim;
}
ss << "]\n";
ss << "data: ";
for (int i = 0; i < std::min(20, size); i++) {
ss << tensor.data<T>()[i] << " ";
}
ss << "\n";
return ss.str();
}
static bool CompareLoD(const framework::LoD &a, const framework::LoD &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("lod size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
auto &al = a[i];
auto &bl = b[i];
if (al.size() != bl.size()) {
LOG(ERROR) << string::Sprintf("level size %d != %d", al.size(),
bl.size());
return false;
}
}
return true;
}
static bool CompareShape(const std::vector<int64_t> &a,
const std::vector<int64_t> &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("shape size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) {
LOG(ERROR) << string::Sprintf("shape %d-th element not match %d != %d", i,
a[i], b[i]);
return false;
}
}
return true;
}
static bool CompareTensorData(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
auto a_shape = framework::vectorize(a.dims());
auto b_shape = framework::vectorize(b.dims());
size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
[](int a, int b) { return a * b; });
size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
[](int a, int b) { return a * b; });
if (a_size != b_size) {
LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",
a_size, b_size);
}
for (size_t i = 0; i < a_size; i++) {
if (a.type() == typeid(float)) {
const auto *a_data = a.data<float>();
const auto *b_data = b.data<float>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
} else if (a.type() == typeid(int64_t)) {
const auto *a_data = a.data<int64_t>();
const auto *b_data = b.data<int64_t>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
}
}
return true;
}
static bool CompareTensor(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
if (!CompareLoD(a.lod(), b.lod())) {
return false;
}
if (!CompareShape(framework::vectorize(a.dims()),
framework::vectorize(b.dims()))) {
return false;
}
if (!CompareTensorData(a, b)) {
return false;
}
return true;
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 77fc42d2
......@@ -101,6 +101,40 @@ struct PaddleTensor {
std::vector<std::vector<size_t>> lod; // Tensor+LoD equals LoDTensor
};
enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
// Tensor without copy, currently only supports AnalysisPredictor.
class ZeroCopyTensor {
public:
void Reshape(const std::vector<int>& shape);
// Get the memory in CPU or GPU with specific data type, should Reshape first
// to tell the data size.
// Once can directly call this data to feed the data.
// This is for write the input tensor.
template <typename T>
T* mutable_data(PaddlePlace place);
// Get the memory directly, will return the place and memory size by pointer.
// This is for reading the output tensor.
template <typename T>
T* data(PaddlePlace* place, int* size);
std::vector<int64_t> shape();
void SetLoD(const std::vector<std::vector<size_t>>& x);
std::vector<std::vector<size_t>> lod() const;
protected:
ZeroCopyTensor(void* scope) : scope_{scope} {}
void SetName(const std::string& name) { name_ = name; }
void* FindTensor() const;
private:
std::string name_;
bool input_or_output_;
friend class AnalysisPredictor;
void* scope_{nullptr};
};
/*
* A simple Inference API for Paddle.
*/
......@@ -120,6 +154,19 @@ class PaddlePredictor {
std::vector<PaddleTensor>* output_data,
int batch_size = -1) = 0;
// Zero copy input and output optimization.
// Get the input or output tensors, and operate on their memory directly,
// without copy.
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string& name) {
return nullptr;
}
virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string& name) {
return nullptr;
}
virtual bool ZeroCopyRun() { return false; }
// Clone a predictor that share the model weights, the Cloned predictor should
// be thread-safe.
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
......@@ -194,6 +241,14 @@ struct MixedRTConfig : public NativeConfig {
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
int workspace_size{1 << 30};
// We transform the Ops that can be converted into TRT layer in the model,
// and aggregate these Ops into subgraphs for TRT execution.
// We set this variable to control the minimum number of nodes in the
// subgraph, 3 as default value.
int minimum_subgraph_size = 3;
// Reserved configuration
// We just support "FP32" now, "FP16" and "INT8" will be supported.
std::string precision_mode = "FP32";
};
// NOTE WIP, not stable yet.
......@@ -204,12 +259,18 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
IrPassMode ir_mode{IrPassMode::kExclude};
// attention lstm fuse works only on some specific models, disable as default.
std::vector<std::string> ir_passes{"attention_lstm_fuse_pass"};
std::vector<std::string> ir_passes;
// NOT stable yet.
bool use_feed_fetch_ops{true};
// NOTE this is just for internal development, please not use it.
// NOTE this is just for internal development, please not use it. NOT
// stable
// yet.
bool _use_mkldnn{false};
};
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -90,3 +90,13 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
endif()
if(WITH_GPU AND TENSORRT_FOUND)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
cc_test(test_trt_models SRCS trt_models_tester.cc
ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models
DEPS paddle_inference_tensorrt_subgraph_engine)
endif()
paddle/fluid/inference/tests/api/analyzer_lac_tester.cc
浏览文件 @ 77fc42d2
......@@ -18,6 +18,8 @@ namespace paddle {
namespace inference {
namespace analysis {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<int64_t> data;
std::vector<size_t> lod;
......@@ -78,6 +80,7 @@ struct DataRecord {
}
}
}
DataRecord NextBatch() {
DataRecord data;
data.data = batched_datas[batch_iter];
......@@ -155,7 +158,9 @@ TEST(Analyzer_LAC, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_TRUE(fuse_statis.count("fc_gru_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
......
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
浏览文件 @ 77fc42d2
......@@ -16,6 +16,7 @@
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> word_data_all, mention_data_all;
......@@ -145,7 +146,9 @@ TEST(Analyzer_Chinese_ner, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_TRUE(fuse_statis.count("fc_gru_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
......
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @ 77fc42d2
......@@ -12,12 +12,16 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/tests/api/tester_helper.h"
DEFINE_bool(with_precision_check, true, "turn on test");
namespace paddle {
namespace inference {
using namespace framework; // NOLINT
using namespace contrib; // NOLINT
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
......@@ -29,10 +33,12 @@ struct DataRecord {
size_t batch_iter{0};
size_t batch_size{1};
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
......@@ -101,6 +107,7 @@ struct DataRecord {
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
......@@ -149,7 +156,55 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
void PrepareZeroCopyInputs(ZeroCopyTensor *lod_attention_tensor,
ZeroCopyTensor *cell_init_tensor,
ZeroCopyTensor *data_tensor,
ZeroCopyTensor *hidden_init_tensor,
ZeroCopyTensor *week_tensor,
ZeroCopyTensor *minute_tensor,
DataRecord *data_record, int batch_size) {
auto one_batch = data_record->NextBatch();
std::vector<int> rnn_link_data_shape(
{static_cast<int>(one_batch.rnn_link_data.size()),
static_cast<int>(one_batch.rnn_link_data.front().size())});
lod_attention_tensor->Reshape({1, 2});
lod_attention_tensor->SetLoD({one_batch.lod1, one_batch.lod2});
cell_init_tensor->Reshape({batch_size, 15});
cell_init_tensor->SetLoD({one_batch.lod3});
hidden_init_tensor->Reshape({batch_size, 15});
hidden_init_tensor->SetLoD({one_batch.lod3});
data_tensor->Reshape(rnn_link_data_shape);
data_tensor->SetLoD({one_batch.lod1});
week_tensor->Reshape(
{static_cast<int>(one_batch.rnn_week_datas.size()),
static_cast<int>(one_batch.rnn_week_datas.front().size())});
week_tensor->SetLoD({one_batch.lod3});
minute_tensor->Reshape(
{static_cast<int>(one_batch.rnn_minute_datas.size()),
static_cast<int>(one_batch.rnn_minute_datas.front().size())});
minute_tensor->SetLoD({one_batch.lod3});
// assign data
float arr0[] = {0, 0};
std::vector<float> zeros(batch_size * 15, 0);
std::copy_n(arr0, 2,
lod_attention_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(arr0, 2, data_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(zeros.begin(), zeros.size(),
cell_init_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(zeros.begin(), zeros.size(),
hidden_init_tensor->mutable_data<float>(PaddlePlace::kCPU));
ZeroCopyTensorAssignData(data_tensor, one_batch.rnn_link_data);
ZeroCopyTensorAssignData(week_tensor, one_batch.rnn_week_datas);
ZeroCopyTensorAssignData(minute_tensor, one_batch.rnn_minute_datas);
}
void SetConfig(AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
......@@ -187,7 +242,9 @@ TEST(Analyzer_rnn1, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
......@@ -214,7 +271,229 @@ TEST(Analyzer_rnn1, multi_thread) {
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(cfg, input_slots_all, &outputs, 4 /* num_threads */);
TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
}
bool CompareTensors(framework::Scope &a_scope, framework::Scope &b_scope,
const std::vector<std::string> &tensors) {
for (auto &x : tensors) {
auto *a_var = a_scope.FindVar(x);
auto *b_var = b_scope.FindVar(x);
if (a_var && b_var) {
if (a_var->Type() == typeid(framework::LoDTensor) ||
a_var->Type() == typeid(framework::Tensor)) {
LOG(INFO) << "comparing tensor " << x;
auto &a_t = a_var->Get<framework::LoDTensor>();
auto &b_t = b_var->Get<framework::LoDTensor>();
if (!inference::CompareTensor(a_t, b_t)) {
LOG(ERROR) << string::Sprintf("tensor %s not match in two scopes", x);
}
} else {
LOG(INFO) << "skip no tensor " << x;
}
} else {
LOG(INFO) << "skip tensor " << x;
}
}
return true;
}
// Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
// on the complex RNN1 model.
TEST(Analyzer_rnn1, ZeroCopy) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
PaddlePlace place;
int output_size{0};
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
config.use_feed_fetch_ops = true;
auto native_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
config.use_feed_fetch_ops = true; // the analysis predictor needs feed/fetch.
auto analysis_predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
NEW_TENSOR(data_lod_attention);
NEW_TENSOR(cell_init);
NEW_TENSOR(data);
NEW_TENSOR(week);
NEW_TENSOR(minute);
NEW_TENSOR(hidden_init);
// Prepare data for AnalysisPredictor
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
PrepareZeroCopyInputs(data_lod_attention_tensor.get(), cell_init_tensor.get(),
data_tensor.get(), hidden_init_tensor.get(),
week_tensor.get(), minute_tensor.get(), &data,
FLAGS_batch_size);
// Prepare data for NativePredictor
std::vector<std::vector<PaddleTensor>> native_inputs;
SetInput(&native_inputs);
std::vector<PaddleTensor> native_outputs;
std::vector<PaddleTensor> analysis_outputs;
auto output_tensor = predictor->GetOutputTensor("final_output.tmp_1");
// Run analysis predictor
int num_ops;
auto fuse_statis = GetFuseStatis(predictor.get(), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_EQ(fuse_statis.at("fc_fuse"), 1);
ASSERT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
ASSERT_EQ(fuse_statis.at("seq_concat_fc_fuse"), 1);
ASSERT_EQ(num_ops,
13); // After graph optimization, only 13 operators exists.
Timer timer;
double total_time{0};
double native_total_time{0};
double analysis_total_time{0.};
for (int i = 0; i < FLAGS_repeat; i++) {
timer.tic();
predictor->ZeroCopyRun();
total_time += timer.toc();
}
auto *output_data = output_tensor->data<float>(&place, &output_size);
ASSERT_GT(output_size, 0); // more than one output!
for (int i = 0; i < FLAGS_repeat; i++) {
// Run native predictor.
timer.tic();
ASSERT_TRUE(native_predictor->Run(native_inputs.front(), &native_outputs));
native_total_time += timer.toc();
}
for (int i = 0; i < FLAGS_repeat; i++) {
timer.tic();
ASSERT_TRUE(
analysis_predictor->Run(native_inputs.front(), &analysis_outputs));
analysis_total_time += timer.toc();
}
if (!FLAGS_with_precision_check) {
return;
}
int native_output_size = VecReduceToInt(native_outputs.front().shape);
EXPECT_EQ(native_output_size, output_size);
// Compare tensors between analysis and zerocopy
auto *p0 = static_cast<AnalysisPredictor *>(predictor.get());
auto *p1 = static_cast<AnalysisPredictor *>(analysis_predictor.get());
auto *p2 = static_cast<NativePaddlePredictor *>(native_predictor.get());
std::vector<std::string> tensor_names;
for (auto &var_desc : p0->program().Block(0).AllVars()) {
tensor_names.push_back(var_desc->Name());
}
LOG(INFO) << "Comparing tensors";
ASSERT_TRUE(
CompareTensors(*p0->scope(), *p1->scope(), {"final_output.tmp_1"}));
ASSERT_TRUE(
CompareTensors(*p0->scope(), *p2->scope(), {"final_output.tmp_1"}));
LOG(INFO) << "output1 " << inference::LoDTensorSummary<float>(
p0->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
LOG(INFO) << "output2 " << inference::LoDTensorSummary<float>(
p1->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
LOG(INFO) << "output3 " << inference::LoDTensorSummary<float>(
p2->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
for (int i = 0; i < output_size; i++) {
LOG(INFO) << output_data[i] << " "
<< static_cast<float *>(native_outputs.front().data.data())[i]
<< " "
<< static_cast<float *>(analysis_outputs.front().data.data())[i];
EXPECT_NEAR(output_data[i],
static_cast<float *>(native_outputs.front().data.data())[i],
1e-3);
}
LOG(INFO) << "batch_size: " << FLAGS_batch_size;
LOG(INFO) << "zero average time: "
<< total_time / (FLAGS_repeat * FLAGS_batch_size);
LOG(INFO) << "analysis average time: "
<< analysis_total_time / (FLAGS_repeat * FLAGS_batch_size);
LOG(INFO) << "native average time: "
<< native_total_time / (FLAGS_repeat * FLAGS_batch_size);
}
TEST(Analyzer_rnn1, ZeroCopyMultiThread) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
auto base_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
double total_time_of_threads{0};
std::vector<std::thread> threads;
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
for (int tid = 0; tid < FLAGS_num_threads; tid++) {
predictors.emplace_back(CreatePaddlePredictor<AnalysisConfig>(config));
}
for (int tid = 0; tid < FLAGS_num_threads; tid++) {
threads.emplace_back([config, &total_time_of_threads, &predictors, tid] {
// auto predictor = base_predictor->Clone();
auto &predictor = predictors[tid];
NEW_TENSOR(data_lod_attention);
NEW_TENSOR(cell_init);
NEW_TENSOR(data);
NEW_TENSOR(week);
NEW_TENSOR(minute);
NEW_TENSOR(hidden_init);
// Prepare data for AnalysisPredictor
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
Timer timer;
double total_time{0};
for (int i = 0; i < FLAGS_repeat; i++) {
PrepareZeroCopyInputs(data_lod_attention_tensor.get(),
cell_init_tensor.get(), data_tensor.get(),
hidden_init_tensor.get(), week_tensor.get(),
minute_tensor.get(), &data, FLAGS_batch_size);
timer.tic();
predictor->ZeroCopyRun();
total_time += timer.toc();
}
total_time_of_threads += total_time;
LOG(INFO) << "thread time: " << total_time / FLAGS_repeat;
});
}
for (auto &t : threads) {
t.join();
}
LOG(INFO) << "average time: "
<< total_time_of_threads / FLAGS_num_threads / FLAGS_repeat;
}
} // namespace inference
......
paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
浏览文件 @ 77fc42d2
......@@ -182,7 +182,8 @@ TEST(Analyzer_seq_conv1, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
GetFuseStatis(predictor.get(), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
......
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
浏览文件 @ 77fc42d2
......@@ -19,6 +19,7 @@ limitations under the License. */
namespace paddle {
namespace inference {
namespace analysis {
using contrib::AnalysisConfig;
struct Record {
std::vector<float> data;
......@@ -114,7 +115,8 @@ TEST(Analyzer_vis, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
GetFuseStatis(predictor.get(), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 77fc42d2
......@@ -86,11 +86,9 @@ std::unique_ptr<PaddlePredictor> CreateTestPredictor(
size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); }
std::unordered_map<std::string, int> GetFuseStatis(AnalysisConfig config,
std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
int *num_ops) {
auto predictor = CreateTestPredictor(config);
AnalysisPredictor *analysis_predictor =
dynamic_cast<AnalysisPredictor *>(predictor.get());
auto *analysis_predictor = static_cast<AnalysisPredictor *>(predictor);
auto &fuse_statis = analysis_predictor->analysis_argument()
.Get<std::unordered_map<std::string, int>>(
framework::ir::kFuseStatisAttr);
......
paddle/fluid/inference/tests/api/trt_models_tester.cc 0 → 100644
浏览文件 @ 77fc42d2
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
using paddle::contrib::MixedRTConfig;
DEFINE_string(dirname, "", "Directory of the inference model.");
NativeConfig GetConfigNative() {
NativeConfig config;
config.model_dir = FLAGS_dirname;
// LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.45;
config.use_gpu = true;
config.device = 0;
return config;
}
MixedRTConfig GetConfigTRT() {
MixedRTConfig config;
config.model_dir = FLAGS_dirname;
config.use_gpu = true;
config.fraction_of_gpu_memory = 0.2;
config.device = 0;
config.max_batch_size = 3;
return config;
}
void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
NativeConfig config0 = GetConfigNative();
config0.model_dir = model_dirname;
MixedRTConfig config1 = GetConfigTRT();
config1.model_dir = model_dirname;
config1.max_batch_size = batch_size;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
// Prepare inputs
int height = 224;
int width = 224;
float *data = new float[batch_size * 3 * height * width];
memset(data, 0, sizeof(float) * (batch_size * 3 * height * width));
data[0] = 1.0f;
// Prepare inputs
PaddleTensor tensor;
tensor.name = "input_0";
tensor.shape = std::vector<int>({batch_size, 3, height, width});
tensor.data = PaddleBuf(static_cast<void *>(data),
sizeof(float) * (batch_size * 3 * height * width));
tensor.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
// Prepare outputs
std::vector<PaddleTensor> outputs0;
std::vector<PaddleTensor> outputs1;
CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
// Get output.
ASSERT_EQ(outputs0.size(), 1UL);
ASSERT_EQ(outputs1.size(), 1UL);
const size_t num_elements = outputs0.front().data.length() / sizeof(float);
const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
EXPECT_EQ(num_elements, num_elements1);
auto *data0 = static_cast<float *>(outputs0.front().data.data());
auto *data1 = static_cast<float *>(outputs1.front().data.data());
ASSERT_GT(num_elements, 0UL);
for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
EXPECT_NEAR(data0[i], data1[i], 1e-3);
}
}
TEST(trt_models_test, main) {
std::vector<std::string> infer_models = {"mobilenet", "resnet50",
"resnext50"};
for (auto &model_dir : infer_models) {
CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + model_dir);
}
}
} // namespace paddle
paddle/fluid/memory/malloc.cc
浏览文件 @ 77fc42d2
......@@ -36,6 +36,8 @@ namespace memory {
using BuddyAllocator = detail::BuddyAllocator;
BuddyAllocator* GetCPUBuddyAllocator() {
// We tried thread_local for inference::RNN1 model, but that not works much
// for multi-thread test.
static std::once_flag init_flag;
static detail::BuddyAllocator* a = nullptr;
......@@ -48,6 +50,25 @@ BuddyAllocator* GetCPUBuddyAllocator() {
return a;
}
// We compared the NaiveAllocator with BuddyAllocator in CPU memory allocation,
// seems they are almost the same overhead.
struct NaiveAllocator {
void* Alloc(size_t size) { return malloc(size); }
void Free(void* p) {
PADDLE_ENFORCE(p);
free(p);
}
static NaiveAllocator* Instance() {
static NaiveAllocator x;
return &x;
}
private:
std::mutex lock_;
};
template <>
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
......
paddle/fluid/operators/auc_op.cc
浏览文件 @ 77fc42d2
......@@ -36,11 +36,16 @@ class AucOp : public framework::OperatorWithKernel {
"Out and Label should have same height.");
int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
int slide_steps = ctx->Attrs().Get<int>("slide_steps");
PADDLE_ENFORCE_GE(num_pred_buckets, 1, "num_thresholds must larger than 1");
PADDLE_ENFORCE_GE(slide_steps, 0, "slide_steps must be natural number");
ctx->SetOutputDim("AUC", {1});
ctx->SetOutputDim("BatchAUC", {1});
ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
slide_steps = slide_steps == 0 ? 1 : slide_steps;
ctx->SetOutputDim("StatPosOut", {slide_steps, num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {slide_steps, num_pred_buckets});
}
protected:
......@@ -62,6 +67,7 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"A 2D int tensor indicating the label of the training data. "
"shape: [batch_size, 1]");
// TODO(typhoonzero): support weight input
AddInput("StatPos", "Statistic value when label = 1");
AddInput("StatNeg", "Statistic value when label = 0");
......@@ -69,18 +75,19 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("AUC",
"A scalar representing the "
"current area-under-the-curve.");
AddOutput("BatchAUC", "The AUC for current batch");
AddOutput("StatPosOut", "Statistic value when label = 1");
AddOutput("StatNegOut", "Statistic value when label = 0");
AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
AddAttr<int>("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
AddAttr<int>(
"num_thresholds",
"The number of thresholds to use when discretizing the roc curve.")
.SetDefault((2 << 12) - 1);
AddAttr<int>("slide_steps", "Use slide steps to calc batch auc.")
.SetDefault(1);
AddComment(R"DOC(
Area Under The Curve (AUC) Operator.
......
paddle/fluid/operators/auc_op.h
浏览文件 @ 77fc42d2
......@@ -32,7 +32,9 @@ class AucKernel : public framework::OpKernel<T> {
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
// buckets contain numbers from 0 to num_thresholds
int num_pred_buckets = num_thresholds + 1;
int slide_steps = ctx.Attr<int>("slide_steps");
// Only use output var for now, make sure it's persistable and
// not cleaned up for each batch.
......@@ -40,16 +42,19 @@ class AucKernel : public framework::OpKernel<T> {
auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
auc);
auto *origin_stat_pos = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *origin_stat_neg = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
auto *batch_auc = ctx.Output<Tensor>("BatchAUC");
std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_batch(num_pred_buckets, 0);
calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
num_thresholds, batch_auc);
std::vector<int64_t> stat_pos_data(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_data(num_pred_buckets, 0);
auto stat_pos_calc = stat_pos_data.data();
auto stat_neg_calc = stat_neg_data.data();
statAuc(label, predict, num_pred_buckets, num_thresholds, slide_steps,
origin_stat_pos, origin_stat_neg, &stat_pos_calc, &stat_neg_calc);
calcAuc(ctx, stat_pos_calc, stat_neg_calc, num_thresholds, auc);
}
private:
......@@ -58,29 +63,76 @@ class AucKernel : public framework::OpKernel<T> {
return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
const framework::Tensor *label,
inline static void statAuc(const framework::Tensor *label,
const framework::Tensor *predict,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
const int num_pred_buckets,
const int num_thresholds, const int slide_steps,
int64_t *origin_stat_pos, int64_t *origin_stat_neg,
int64_t **stat_pos, int64_t **stat_neg) {
size_t batch_size = predict->dims()[0];
size_t inference_width = predict->dims()[1];
const T *inference_data = predict->data<T>();
const auto *label_data = label->data<int64_t>();
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
for (size_t i = 0; i < batch_size; i++) {
uint32_t binIdx = static_cast<uint32_t>(
inference_data[i * inference_width + 1] * num_thresholds);
if (label_data[i]) {
stat_pos[binIdx] += 1.0;
(*stat_pos)[binIdx] += 1.0;
} else {
stat_neg[binIdx] += 1.0;
(*stat_neg)[binIdx] += 1.0;
}
}
int bucket_length = num_pred_buckets * sizeof(int64_t);
// will stat auc unlimited.
if (slide_steps == 0) {
for (int slide = 0; slide < num_pred_buckets; ++slide) {
origin_stat_pos[slide] += (*stat_pos)[slide];
origin_stat_neg[slide] += (*stat_neg)[slide];
}
*stat_pos = origin_stat_pos;
*stat_neg = origin_stat_neg;
} else {
for (int slide = 1; slide < slide_steps; ++slide) {
int dst_idx = (slide - 1) * num_pred_buckets;
int src_inx = slide * num_pred_buckets;
std::memcpy(origin_stat_pos + dst_idx, origin_stat_pos + src_inx,
bucket_length);
std::memcpy(origin_stat_neg + dst_idx, origin_stat_neg + src_inx,
bucket_length);
}
std::memcpy(origin_stat_pos + (slide_steps - 1) * num_pred_buckets,
*stat_pos, bucket_length);
std::memcpy(origin_stat_neg + (slide_steps - 1) * num_pred_buckets,
*stat_neg, bucket_length);
std::memset(*stat_pos, 0, bucket_length);
std::memset(*stat_neg, 0, bucket_length);
for (int slide = 0; slide < num_pred_buckets; ++slide) {
int stat_pos_steps = 0;
int stat_neg_steps = 0;
for (int step = 0; step < slide_steps; ++step) {
stat_pos_steps += origin_stat_pos[slide + step * num_pred_buckets];
stat_neg_steps += origin_stat_neg[slide + step * num_pred_buckets];
}
(*stat_pos)[slide] += stat_pos_steps;
(*stat_neg)[slide] += stat_neg_steps;
}
}
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
*auc = 0.0f;
double totPos = 0.0;
......@@ -96,7 +148,6 @@ class AucKernel : public framework::OpKernel<T> {
totPos += stat_pos[idx];
totNeg += stat_neg[idx];
*auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
--idx;
}
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -30,7 +30,13 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
polygon_box_transform_op.cu)
detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
if(WITH_GPU)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub)
else()
detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
endif()
detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
#Export local libraries to parent
set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 77fc42d2
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -69,7 +70,7 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Anchors")->type()),
platform::CPUPlace());
ctx.device_context());
}
};
......@@ -162,7 +163,7 @@ void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
const T *im_info_data = im_info.data<T>();
T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
T im_scale = im_info_data[2];
keep->Resize({boxes->dims()[0], 1});
keep->Resize({boxes->dims()[0]});
min_size = std::max(min_size, 1.0f);
int *keep_data = keep->mutable_data<int>(ctx.GetPlace());
......@@ -463,7 +464,7 @@ class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("post_nms_topN", "post_nms_topN");
AddAttr<float>("nms_thresh", "nms_thres");
AddAttr<float>("min_size", "min size");
AddAttr<float>("eta", "eta");
AddAttr<float>("eta", "The parameter for adaptive NMS.");
AddComment(R"DOC(
Generate Proposals OP
......
paddle/fluid/operators/detection/generate_proposals_op.cu 0 → 100644
浏览文件 @ 77fc42d2
/* 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 <stdio.h>
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/gather.cu.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
namespace {
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
int const kThreadsPerBlock = sizeof(uint64_t) * 8;
template <typename T>
__global__ void RangeInitKernel(const T start, const T delta, const int size,
T *out) {
CUDA_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; }
}
template <typename T>
void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
Tensor *value_out, Tensor *index_out) {
int num = value.numel();
Tensor index_in_t;
int *idx_in = index_in_t.mutable_data<int>({num}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
RangeInitKernel<<<DIVUP(num, block), block, 0, stream>>>(0, 1, num, idx_in);
int *idx_out = index_out->mutable_data<int>({num}, ctx.GetPlace());
const T *keys_in = value.data<T>();
T *keys_out = value_out->mutable_data<T>({num}, ctx.GetPlace());
// Determine temporary device storage requirements
void *d_temp_storage = NULL;
size_t temp_storage_bytes = 0;
cub::DeviceRadixSort::SortPairsDescending<T, int>(
d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
num);
// Allocate temporary storage
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
d_temp_storage = memory::Alloc(place, temp_storage_bytes);
// Run sorting operation
cub::DeviceRadixSort::SortPairsDescending<T, int>(
d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
num);
memory::Free(place, d_temp_storage);
}
template <typename T>
__device__ __forceinline__ T Min(T x, T y) {
return x < y ? x : y;
}
template <typename T>
__device__ __forceinline__ T Max(T x, T y) {
return x > y ? x : y;
}
template <typename T>
__global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
const T *var, const int *index,
const T *im_info, const int num,
T *proposals) {
T kBBoxClipDefault = log(1000.0 / 16.0);
CUDA_1D_KERNEL_LOOP(i, num) {
int k = index[i] * 4;
T axmin = anchor[k];
T aymin = anchor[k + 1];
T axmax = anchor[k + 2];
T aymax = anchor[k + 3];
T w = axmax - axmin + 1.0;
T h = aymax - aymin + 1.0;
T cx = axmin + 0.5 * w;
T cy = aymin + 0.5 * h;
T dxmin = deltas[k];
T dymin = deltas[k + 1];
T dxmax = deltas[k + 2];
T dymax = deltas[k + 3];
T d_cx = 0., d_cy = 0., d_w = 0., d_h = 0.;
if (var) {
d_cx = cx + dxmin * w * var[k];
d_cy = cy + dymin * h * var[k + 1];
d_w = exp(Min<T>(dxmax * var[k + 2], kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax * var[k + 3], kBBoxClipDefault)) * h;
} else {
d_cx = cx + dxmin * w;
d_cy = cy + dymin * h;
d_w = exp(Min<T>(dxmax, kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax, kBBoxClipDefault)) * h;
}
T oxmin = d_cx - d_w * 0.5;
T oymin = d_cy - d_h * 0.5;
T oxmax = d_cx + d_w * 0.5 - 1.;
T oymax = d_cy + d_h * 0.5 - 1.;
proposals[i * 4] = Max<T>(Min<T>(oxmin, im_info[1] - 1.), 0.);
proposals[i * 4 + 1] = Max<T>(Min<T>(oymin, im_info[0] - 1.), 0.);
proposals[i * 4 + 2] = Max<T>(Min<T>(oxmax, im_info[1] - 1.), 0.);
proposals[i * 4 + 3] = Max<T>(Min<T>(oymax, im_info[0] - 1.), 0.);
}
}
template <typename T, int BlockSize>
__global__ void FilterBBoxes(const T *bboxes, const T *im_info,
const T min_size, const int num, int *keep_num,
int *keep) {
T im_h = im_info[0];
T im_w = im_info[1];
T im_scale = im_info[2];
int cnt = 0;
__shared__ int keep_index[BlockSize];
CUDA_1D_KERNEL_LOOP(i, num) {
keep_index[threadIdx.x] = -1;
__syncthreads();
int k = i * 4;
T xmin = bboxes[k];
T ymin = bboxes[k + 1];
T xmax = bboxes[k + 2];
T ymax = bboxes[k + 3];
T w = xmax - xmin + 1.0;
T h = ymax - ymin + 1.0;
T cx = xmin + w / 2.;
T cy = ymin + h / 2.;
T w_s = (xmax - xmin) / im_scale + 1.;
T h_s = (ymax - ymin) / im_scale + 1.;
if (w_s >= min_size && h_s >= min_size && cx <= im_w && cy <= im_h) {
keep_index[threadIdx.x] = i;
}
__syncthreads();
if (threadIdx.x == 0) {
int size = (num - i) < BlockSize ? num - i : BlockSize;
for (int j = 0; j < size; ++j) {
if (keep_index[j] > -1) {
keep[cnt++] = keep_index[j];
}
}
}
__syncthreads();
}
if (threadIdx.x == 0) {
keep_num[0] = cnt;
}
}
__device__ inline float IoU(const float *a, const float *b) {
float left = max(a[0], b[0]), right = min(a[2], b[2]);
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
float inter_s = width * height;
float s_a = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
float s_b = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
return inter_s / (s_a + s_b - inter_s);
}
__global__ void NMSKernel(const int n_boxes, const float nms_overlap_thresh,
const float *dev_boxes, uint64_t *dev_mask) {
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
const int row_size =
min(n_boxes - row_start * kThreadsPerBlock, kThreadsPerBlock);
const int col_size =
min(n_boxes - col_start * kThreadsPerBlock, kThreadsPerBlock);
__shared__ float block_boxes[kThreadsPerBlock * 4];
if (threadIdx.x < col_size) {
block_boxes[threadIdx.x * 4 + 0] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 0];
block_boxes[threadIdx.x * 4 + 1] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 1];
block_boxes[threadIdx.x * 4 + 2] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 2];
block_boxes[threadIdx.x * 4 + 3] =
dev_boxes[(kThreadsPerBlock * col_start + threadIdx.x) * 4 + 3];
}
__syncthreads();
if (threadIdx.x < row_size) {
const int cur_box_idx = kThreadsPerBlock * row_start + threadIdx.x;
const float *cur_box = dev_boxes + cur_box_idx * 4;
int i = 0;
uint64_t t = 0;
int start = 0;
if (row_start == col_start) {
start = threadIdx.x + 1;
}
for (i = start; i < col_size; i++) {
if (IoU(cur_box, block_boxes + i * 4) > nms_overlap_thresh) {
t |= 1ULL << i;
}
}
const int col_blocks = DIVUP(n_boxes, kThreadsPerBlock);
dev_mask[cur_box_idx * col_blocks + col_start] = t;
}
}
template <typename T>
void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const Tensor &sorted_indices, const T nms_threshold,
Tensor *keep_out) {
int boxes_num = proposals.dims()[0];
PADDLE_ENFORCE_EQ(boxes_num, sorted_indices.dims()[0]);
const int col_blocks = DIVUP(boxes_num, kThreadsPerBlock);
dim3 blocks(DIVUP(boxes_num, kThreadsPerBlock),
DIVUP(boxes_num, kThreadsPerBlock));
dim3 threads(kThreadsPerBlock);
const T *boxes = proposals.data<T>();
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
int size_bytes = boxes_num * col_blocks * sizeof(uint64_t);
uint64_t *d_mask =
reinterpret_cast<uint64_t *>(memory::Alloc(place, size_bytes));
NMSKernel<<<blocks, threads>>>(boxes_num, nms_threshold, boxes, d_mask);
uint64_t *h_mask = reinterpret_cast<uint64_t *>(
memory::Alloc(platform::CPUPlace(), size_bytes));
memory::Copy(platform::CPUPlace(), h_mask, place, d_mask, size_bytes, 0);
std::vector<uint64_t> remv(col_blocks);
memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
std::vector<int> keep_vec;
int num_to_keep = 0;
for (int i = 0; i < boxes_num; i++) {
int nblock = i / kThreadsPerBlock;
int inblock = i % kThreadsPerBlock;
if (!(remv[nblock] & (1ULL << inblock))) {
++num_to_keep;
keep_vec.push_back(i);
uint64_t *p = &h_mask[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv[j] |= p[j];
}
}
}
int *keep = keep_out->mutable_data<int>({num_to_keep}, ctx.GetPlace());
memory::Copy(place, keep, platform::CPUPlace(), keep_vec.data(),
sizeof(int) * num_to_keep, 0);
memory::Free(place, d_mask);
memory::Free(platform::CPUPlace(), h_mask);
}
template <typename T>
std::pair<Tensor, Tensor> ProposalForOneImage(
const platform::CUDADeviceContext &ctx, const Tensor &im_info,
const Tensor &anchors, const Tensor &variances,
const Tensor &bbox_deltas, // [M, 4]
const Tensor &scores, // [N, 1]
int pre_nms_top_n, int post_nms_top_n, float nms_thresh, float min_size,
float eta) {
// 1. pre nms
Tensor scores_sort, index_sort;
SortDescending<T>(ctx, scores, &scores_sort, &index_sort);
int num = scores.numel();
int pre_nms_num = (pre_nms_top_n <= 0 || pre_nms_top_n > num) ? scores.numel()
: pre_nms_top_n;
scores_sort.Resize({pre_nms_num, 1});
index_sort.Resize({pre_nms_num, 1});
// 2. box decode and clipping
Tensor proposals;
proposals.mutable_data<T>({pre_nms_num, 4}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
BoxDecodeAndClipKernel<T><<<DIVUP(pre_nms_num, block), block, 0, stream>>>(
anchors.data<T>(), bbox_deltas.data<T>(), variances.data<T>(),
index_sort.data<int>(), im_info.data<T>(), pre_nms_num,
proposals.data<T>());
// 3. filter
Tensor keep_index, keep_num_t;
keep_index.mutable_data<int>({pre_nms_num}, ctx.GetPlace());
keep_num_t.mutable_data<int>({1}, ctx.GetPlace());
min_size = std::max(min_size, 1.0f);
FilterBBoxes<T, 512><<<1, 512, 0, stream>>>(
proposals.data<T>(), im_info.data<T>(), min_size, pre_nms_num,
keep_num_t.data<int>(), keep_index.data<int>());
int keep_num;
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(platform::CPUPlace(), &keep_num, gpu_place,
keep_num_t.data<int>(), sizeof(int), 0);
keep_index.Resize({keep_num});
Tensor scores_filter, proposals_filter;
proposals_filter.mutable_data<T>({keep_num, 4}, ctx.GetPlace());
scores_filter.mutable_data<T>({keep_num, 1}, ctx.GetPlace());
GPUGather<T>(ctx, proposals, keep_index, &proposals_filter);
GPUGather<T>(ctx, scores_sort, keep_index, &scores_filter);
if (nms_thresh <= 0) {
return std::make_pair(proposals_filter, scores_filter);
}
// 4. nms
Tensor keep_nms;
NMS<T>(ctx, proposals_filter, keep_index, nms_thresh, &keep_nms);
if (post_nms_top_n > 0 && post_nms_top_n < keep_nms.numel()) {
keep_nms.Resize({post_nms_top_n});
}
Tensor scores_nms, proposals_nms;
proposals_nms.mutable_data<T>({keep_nms.numel(), 4}, ctx.GetPlace());
scores_nms.mutable_data<T>({keep_nms.numel(), 1}, ctx.GetPlace());
GPUGather<T>(ctx, proposals_filter, keep_nms, &proposals_nms);
GPUGather<T>(ctx, scores_filter, keep_nms, &scores_nms);
return std::make_pair(proposals_nms, scores_nms);
}
} // namespace
template <typename DeviceContext, typename T>
class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto *anchors = context.Input<Tensor>("Anchors");
auto *variances = context.Input<Tensor>("Variances");
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
int pre_nms_top_n = context.Attr<int>("pre_nms_topN");
int post_nms_top_n = context.Attr<int>("post_nms_topN");
float nms_thresh = context.Attr<float>("nms_thresh");
float min_size = context.Attr<float>("min_size");
float eta = context.Attr<float>("eta");
PADDLE_ENFORCE_GE(eta, 1., "Not support adaptive NMS.");
auto &dev_ctx = context.template device_context<DeviceContext>();
auto scores_dim = scores->dims();
int64_t num = scores_dim[0];
int64_t c_score = scores_dim[1];
int64_t h_score = scores_dim[2];
int64_t w_score = scores_dim[3];
auto bbox_dim = bbox_deltas->dims();
int64_t c_bbox = bbox_dim[1];
int64_t h_bbox = bbox_dim[2];
int64_t w_bbox = bbox_dim[3];
Tensor bbox_deltas_swap, scores_swap;
bbox_deltas_swap.mutable_data<T>({num, h_bbox, w_bbox, c_bbox},
dev_ctx.GetPlace());
scores_swap.mutable_data<T>({num, h_score, w_score, c_score},
dev_ctx.GetPlace());
math::Transpose<DeviceContext, T, 4> trans;
std::vector<int> axis = {0, 2, 3, 1};
trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
trans(dev_ctx, *scores, &scores_swap, axis);
Tensor *anchor = const_cast<framework::Tensor *>(anchors);
anchor->Resize({anchors->numel() / 4, 4});
Tensor *var = const_cast<framework::Tensor *>(variances);
var->Resize({var->numel() / 4, 4});
rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
context.GetPlace());
rpn_roi_probs->mutable_data<T>({scores->numel(), 1}, context.GetPlace());
T *rpn_rois_data = rpn_rois->data<T>();
T *rpn_roi_probs_data = rpn_roi_probs->data<T>();
auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
int64_t num_proposals = 0;
std::vector<size_t> offset(1, 0);
for (int64_t i = 0; i < num; ++i) {
Tensor im_info_slice = im_info->Slice(i, i + 1);
Tensor bbox_deltas_slice = bbox_deltas_swap.Slice(i, i + 1);
Tensor scores_slice = scores_swap.Slice(i, i + 1);
bbox_deltas_slice.Resize({h_bbox * w_bbox * c_bbox / 4, 4});
scores_slice.Resize({h_score * w_score * c_score, 1});
std::pair<Tensor, Tensor> box_score_pair =
ProposalForOneImage<T>(dev_ctx, im_info_slice, *anchor, *var,
bbox_deltas_slice, scores_slice, pre_nms_top_n,
post_nms_top_n, nms_thresh, min_size, eta);
Tensor proposals = box_score_pair.first;
Tensor scores = box_score_pair.second;
memory::Copy(place, rpn_rois_data + num_proposals * 4, place,
proposals.data<T>(), sizeof(T) * proposals.numel(), 0);
memory::Copy(place, rpn_roi_probs_data + num_proposals, place,
scores.data<T>(), sizeof(T) * scores.numel(), 0);
num_proposals += proposals.dims()[0];
offset.emplace_back(num_proposals);
}
framework::LoD lod;
lod.emplace_back(offset);
rpn_rois->set_lod(lod);
rpn_roi_probs->set_lod(lod);
rpn_rois->Resize({num_proposals, 4});
rpn_roi_probs->Resize({num_proposals, 1});
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(generate_proposals,
ops::CUDAGenerateProposalsKernel<
paddle::platform::CUDADeviceContext, float>);
paddle/fluid/operators/detection_map_op.h
浏览文件 @ 77fc42d2
......@@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num");
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
auto& label_lod = in_label->lod();
auto& detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
......@@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto label_lod = input_label.lod();
auto detect_lod = input_detect.lod();
auto& label_lod = input_label.lod();
auto& detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto label_index = label_lod[0];
auto& label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
......@@ -274,7 +274,6 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
output_true_pos->set_lod(true_pos_lod);
output_false_pos->set_lod(false_pos_lod);
return;
}
void GetInputPos(const framework::Tensor& input_pos_count,
......@@ -292,7 +291,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>();
auto pos_data_lod = pos_tensor.lod()[0];
auto& pos_data_lod = pos_tensor.lod()[0];
for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) {
for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) {
T score = pos_data[j * 2];
......@@ -317,20 +316,23 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
std::map<int, std::vector<std::pair<T, int>>>* false_pos) const {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
auto& image_gt_boxes = gt_boxes[n];
for (auto& image_gt_box : image_gt_boxes) {
size_t count = 0;
auto labeled_bboxes = it->second;
auto& labeled_bboxes = image_gt_box.second;
if (evaluate_difficult) {
count = labeled_bboxes.size();
} else {
for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!(labeled_bboxes[i].is_difficult)) ++count;
for (auto& box : labeled_bboxes) {
if (!box.is_difficult) {
++count;
}
}
}
if (count == 0) {
continue;
}
int label = it->first;
int label = image_gt_box.first;
if (label_pos_count->find(label) == label_pos_count->end()) {
(*label_pos_count)[label] = count;
} else {
......
paddle/fluid/operators/extract_rows_op.cc
浏览文件 @ 77fc42d2
......@@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto in_rows = in.rows();
auto &in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ 77fc42d2
......@@ -76,12 +76,18 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
PADDLE_ENFORCE_EQ(
b_dims[1], (ctx->Attrs().Get<bool>("use_peepholes") ? 7 : 4) * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection or"
"4 * %d if disable peepholes",
frame_size, frame_size);
if (ctx->Attrs().Get<bool>("use_peepholes")) {
PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection",
frame_size);
ctx->SetOutputDim("CheckedCell", {2, frame_size});
} else {
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes",
frame_size);
}
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
......@@ -173,6 +179,8 @@ void FusionLSTMOpMaker::Make() {
AddOutput("BatchedCell", "(LoDTensor) (T x D).").AsIntermediate();
AddOutput("ReorderedH0", "(LoDTensor) (N x D).").AsIntermediate();
AddOutput("ReorderedC0", "(LoDTensor) (N x D).").AsIntermediate();
AddOutput("CheckedCell", "(Tensor) (2 x D) only for peephole.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
......@@ -250,19 +258,19 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const int D3 = D * 3; \
const int D4 = wh_dims[1];
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/ \
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/ \
Tensor checked_cell; \
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/ \
checked_cell_data = checked_cell.mutable_data<T>({2, D}, place); \
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/ \
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/ \
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/ \
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
}
/// Compute LSTM
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 77fc42d2
......@@ -127,10 +127,8 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
// TODO(yuyang18): Strange code here.
memory::Copy(platform::CPUPlace(),
new_rows.CUDAMutableData(context.GetPlace()), gpu_place,
ids_data, ids_num * sizeof(int64_t), stream);
memory::Copy(gpu_place, new_rows.CUDAMutableData(context.GetPlace()),
gpu_place, ids_data, ids_num * sizeof(int64_t), stream);
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 77fc42d2
......@@ -60,11 +60,9 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
auto out_place = context.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(out_place));
memory::Copy(
boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
memory::Copy(boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T), context.stream());
auto* in2_data = in2_value.data<T>();
memory::Copy(boost::get<platform::CUDAPlace>(out_place),
......@@ -148,7 +146,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
......
paddle/fluid/operators/sampling_id_op.cc
浏览文件 @ 77fc42d2
......@@ -53,15 +53,16 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
SamplingId Operator.
A layer for sampling id from multinomial distribution from the
input. Sampling one id for one sample.)DOC");
AddAttr<float>("min", "Minimum value of random. [default 0.0].")
AddAttr<float>("min", "Minimum value of random. (float, default 0.0).")
.SetDefault(0.0f);
AddAttr<float>("max", "Maximun value of random. [default 1.0].")
AddAttr<float>("max", "Maximun value of random. (float, default 1.0).")
.SetDefault(1.0f);
AddAttr<int>("seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. [default 0].")
AddAttr<int>(
"seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. (int, default 0).")
.SetDefault(0);
}
};
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ 77fc42d2
......@@ -77,8 +77,10 @@ class ScaleOpVarTypeInference : public framework::VarTypeInference {
auto out_var_name = op_desc.Output("Out").front();
auto *out_var = block->FindVarRecursive(out_var_name);
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
if (in_var_name != out_var_name) {
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
}
}
};
......
paddle/fluid/operators/sequence_slice_op.h
浏览文件 @ 77fc42d2
......@@ -75,11 +75,11 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
}
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
PADDLE_ENFORCE_LE(0, offset_data[i],
"The offset[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(0, length_data[i],
"The length[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(lod[0][i] + offset_data[i] + length_data[i],
PADDLE_ENFORCE_LE(lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1], "The target tensor's length overflow.");
}
......
paddle/fluid/operators/sgd_op.cu
浏览文件 @ 77fc42d2
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include <algorithm>
#include "paddle/fluid/operators/sgd_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -33,22 +33,21 @@ __global__ void SGDKernel(const T* g, const T* p, const T* learning_rate,
}
}
template <typename T, int block_size>
template <typename T>
__global__ void SparseSGDFunctorKernel(const T* selected_rows,
const int64_t* rows,
const T* learning_rate, T* tensor_out,
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
int64_t row_numel, int64_t limit) {
for (int64_t i = blockIdx.x; i < limit; i += gridDim.x) {
const T* selected_rows_ptr = selected_rows + i * row_numel;
T* tensor_out_ptr = tensor_out + rows[i] * row_numel;
for (int64_t index = threadIdx.x; index < row_numel; index += blockDim.x) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out_ptr + index,
-1.0 * learning_rate[0] * selected_rows_ptr[index]);
}
}
}
} // namespace
......@@ -89,7 +88,7 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
auto& in_value = grad->value();
framework::Vector<int64_t> in_rows(grad->rows());
auto& in_rows = grad->rows();
int64_t in_row_numel = in_value.numel() / in_rows.size();
PADDLE_ENFORCE_EQ(in_row_numel, param_out->numel() / in_height);
......@@ -97,13 +96,15 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
auto* in_data = in_value.data<T>();
auto* out_data = param_out->data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in_rows.size());
SparseSGDFunctorKernel<
T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
const int kThreadsPerBlock = 256;
int thread_x = kThreadsPerBlock;
int max_threads = ctx.cuda_device_context().GetMaxPhysicalThreadCount();
int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
SparseSGDFunctorKernel<<<max_blocks, thread_x, 0,
ctx.cuda_device_context().stream()>>>(
in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
out_data, in_row_numel);
out_data, in_row_numel, in_rows.size());
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
......
paddle/fluid/operators/sum_op.h
浏览文件 @ 77fc42d2
......@@ -32,7 +32,7 @@ class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto in_vars = context.MultiInputVar("X");
int N = in_vars.size();
size_t in_num = in_vars.size();
auto out_var = context.OutputVar("Out");
bool in_place = out_var == in_vars[0];
......@@ -53,7 +53,7 @@ class SumKernel : public framework::OpKernel<T> {
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
// If in_place, just skip the first tensor
for (int i = in_place ? 1 : 0; i < N; i++) {
for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
if (in_t.numel() == 0) {
......@@ -101,13 +101,13 @@ class SumKernel : public framework::OpKernel<T> {
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
......@@ -116,14 +116,14 @@ class SumKernel : public framework::OpKernel<T> {
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim =
framework::vectorize(get_selected_row(in_num - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
......@@ -133,7 +133,7 @@ class SumKernel : public framework::OpKernel<T> {
math::SelectedRowsAddTo<DeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 77fc42d2
......@@ -22,8 +22,6 @@
namespace paddle {
DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
DEFINE_int32(tensorrt_max_batch_size, 1, "TensorRT maximum batch size");
DEFINE_int32(tensorrt_workspace_size, 16 << 20, "TensorRT workspace size");
namespace operators {
......@@ -34,6 +32,8 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Ys", "A list of outputs").AsDuplicable();
AddAttr<std::string>("subgraph", "the subgraph.");
AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
AddAttr<int>("max_batch_size", "the maximum batch size.");
AddAttr<int>("workspace_size", "the workspace size.");
AddComment("TensorRT engine operator.");
}
};
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ 77fc42d2
......@@ -28,8 +28,6 @@
namespace paddle {
DECLARE_int32(tensorrt_engine_batch_size);
DECLARE_int32(tensorrt_max_batch_size);
DECLARE_int32(tensorrt_workspace_size);
namespace operators {
......@@ -92,14 +90,14 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto engine_name = context.Attr<std::string>("engine_uniq_key");
int max_batch_size = context.Attr<int>("max_batch_size");
if (!Singleton<TRT_EngineManager>::Global().HasEngine(engine_name)) {
Prepare(context);
}
auto* engine = Singleton<TRT_EngineManager>::Global().Get(engine_name);
auto input_names = context.op().Inputs("Xs");
PADDLE_ENFORCE(!input_names.empty(), "should pass more than one inputs");
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size,
FLAGS_tensorrt_max_batch_size);
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size, max_batch_size);
std::vector<std::string> output_maps =
context.Attr<std::vector<std::string>>("output_name_mapping");
......@@ -173,8 +171,9 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// Get the ProgramDesc and pass to convert.
framework::proto::BlockDesc block_desc;
block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
int max_batch = FLAGS_tensorrt_max_batch_size;
auto max_workspace = FLAGS_tensorrt_workspace_size;
int max_batch_size = context.Attr<int>("max_batch_size");
int workspace_size = context.Attr<int>("workspace_size");
auto params = context.Attr<std::vector<std::string>>("parameters");
std::unordered_set<std::string> parameters;
for (const auto& param : params) {
......@@ -186,7 +185,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// TODO(Superjomn) replace this with a different stream
auto* engine = Singleton<TRT_EngineManager>::Global().Create(
max_batch, max_workspace, nullptr /*engine hold its own stream*/,
max_batch_size, workspace_size, nullptr /*engine hold its own stream*/,
context.Attr<std::string>("engine_uniq_key"),
boost::get<platform::CUDAPlace>(context.GetPlace()).device);
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ 77fc42d2
......@@ -58,8 +58,6 @@ void AddTensorToBlockDesc(framework::proto::BlockDesc* block,
using inference::analysis::SetAttr;
TEST(TensorRTEngineOp, manual) {
FLAGS_tensorrt_engine_batch_size = 2;
FLAGS_tensorrt_max_batch_size = 2;
framework::ProgramDesc program;
auto* block_ = program.Proto()->add_blocks();
block_->set_idx(0);
......@@ -101,6 +99,8 @@ TEST(TensorRTEngineOp, manual) {
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", 2);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "a_engine");
SetAttr<std::vector<std::string>>(engine_op_desc.Proto(), "parameters",
std::vector<std::string>({}));
......@@ -129,8 +129,6 @@ TEST(TensorRTEngineOp, manual) {
}
void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
FLAGS_tensorrt_engine_batch_size = batch_size;
FLAGS_tensorrt_max_batch_size = batch_size;
framework::ProgramDesc program;
framework::Scope scope;
platform::CUDAPlace place;
......@@ -195,8 +193,8 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 2 << 10);
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::vector<std::string>>(
engine_op_desc.Proto(), "parameters",
std::vector<std::string>({"y0", "y1", "y2", "y3"}));
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 77fc42d2
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method)
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method pass_builder)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc)
if(NOT WIN32)
list(APPEND PYBIND_DEPS parallel_executor profiler)
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 77fc42d2
......@@ -285,12 +285,12 @@ void BindOpDesc(pybind11::module *m) {
.def("set_output", &pd::OpDesc::SetOutput)
.def("input_arg_names", &pd::OpDesc::InputArgumentNames)
.def("output_arg_names", &pd::OpDesc::OutputArgumentNames)
.def("rename_input", &pd::OpDesc::RenameInput)
.def("rename_output", &pd::OpDesc::RenameOutput)
.def("_rename_input", &pd::OpDesc::RenameInput)
.def("_rename_output", &pd::OpDesc::RenameOutput)
.def("has_attr", &pd::OpDesc::HasAttr)
.def("attr_type", &pd::OpDesc::GetAttrType)
.def("attr_names", &pd::OpDesc::AttrNames)
.def("set_attr", &pd::OpDesc::SetAttr)
.def("_set_attr", &pd::OpDesc::SetAttr)
.def("attr", &pd::OpDesc::GetAttr)
.def("set_block_attr", &pd::OpDesc::SetBlockAttr)
.def("set_blocks_attr", &pd::OpDesc::SetBlocksAttr)
......@@ -300,8 +300,8 @@ void BindOpDesc(pybind11::module *m) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("_block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("_blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 77fc42d2
......@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
......@@ -595,6 +596,29 @@ All parameter, weight, gradient are variables in Paddle.
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("set_str", [](ir::Pass &self, const std::string &name,
const std::string &attr) {
self.Set<std::string>(name, new std::string(attr));
});
py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(
m, "PassBuilder");
pb.def(py::init())
.def("append_pass",
[](ir::PassBuilder &self,
const std::string &pass_type) -> std::shared_ptr<ir::Pass> {
return self.AppendPass(pass_type);
})
.def("all_passes", [](ir::PassBuilder &self) { return self.AllPasses(); })
.def("insert_pass",
[](ir::PassBuilder &self, size_t idx, const std::string &pass_type) {
return self.InsertPass(idx, pass_type);
})
.def("remove_pass",
[](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy");
......@@ -677,7 +701,11 @@ All parameter, weight, gradient are variables in Paddle.
},
[](BuildStrategy &self, bool b) {
self.fuse_elewise_add_act_ops_ = b;
});
})
.def("_create_passes_from_strategy",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy();
});
pe.def(py::init<const std::vector<platform::Place> &,
const std::unordered_set<std::string> &,
......
paddle/fluid/string/pretty_log.h
浏览文件 @ 77fc42d2
......@@ -56,13 +56,13 @@ struct Style {
};
template <typename... Args>
static void PrettyLogEndl(const std::string& style, const char* fmt,
const Args&... args) {
static void PrettyLogEndl(const std::string &style, const char *fmt,
const Args &... args) {
std::cerr << style << Sprintf(fmt, args...) << reset() << std::endl;
}
template <typename... Args>
static void PrettyLog(const std::string& style, const char* fmt,
const Args&... args) {
static void PrettyLog(const std::string &style, const char *fmt,
const Args &... args) {
std::cerr << style << Sprintf(fmt, args...) << reset();
}
......
paddle/fluid/train/CMakeLists.txt 0 → 100644
浏览文件 @ 77fc42d2
function(train_test TARGET_NAME)
set(options "")
set(oneValueArgs "")
set(multiValueArgs ARGS)
cmake_parse_arguments(train_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
set(arg_list "")
if(train_test_ARGS)
foreach(arg ${train_test_ARGS})
list(APPEND arg_list "_${arg}")
endforeach()
else()
list(APPEND arg_list "_")
endif()
foreach(arg ${arg_list})
string(REGEX REPLACE "^_$" "" arg "${arg}")
cc_test(test_train_${TARGET_NAME}${arg}
SRCS test_train_${TARGET_NAME}.cc
DEPS paddle_fluid_origin
ARGS --dirname=${PYTHON_TESTS_DIR}/book/${TARGET_NAME}${arg}.train.model/)
set_tests_properties(test_train_${TARGET_NAME}${arg}
PROPERTIES DEPENDS test_${TARGET_NAME})
endforeach()
endfunction(train_test)
if(WITH_TESTING)
train_test(recognize_digits ARGS mlp conv)
endif()
paddle/fluid/train/test_train_recognize_digits.cc 0 → 100644
浏览文件 @ 77fc42d2
/* 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 <time.h>
#include <fstream>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
DEFINE_string(dirname, "", "Directory of the train model.");
namespace paddle {
void Train() {
CHECK(!FLAGS_dirname.empty());
framework::InitDevices(false);
const auto cpu_place = platform::CPUPlace();
framework::Executor executor(cpu_place);
framework::Scope scope;
auto train_program = inference::Load(
&executor, &scope, FLAGS_dirname + "__model_combined__.main_program",
FLAGS_dirname + "__params_combined__");
std::string loss_name = "";
for (auto op_desc : train_program->Block(0).AllOps()) {
if (op_desc->Type() == "mean") {
loss_name = op_desc->Output("Out")[0];
break;
}
}
PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
// prepare data
auto x_var = scope.Var("img");
auto x_tensor = x_var->GetMutable<framework::LoDTensor>();
x_tensor->Resize({64, 1, 28, 28});
auto x_data = x_tensor->mutable_data<float>(cpu_place);
for (int i = 0; i < 64 * 28 * 28; ++i) {
x_data[i] = 1.0;
}
auto y_var = scope.Var("label");
auto y_tensor = y_var->GetMutable<framework::LoDTensor>();
y_tensor->Resize({64, 1});
auto y_data = y_tensor->mutable_data<int64_t>(cpu_place);
for (int i = 0; i < 64 * 1; ++i) {
y_data[i] = static_cast<int64_t>(1);
}
auto loss_var = scope.Var(loss_name);
float first_loss = 0.0;
float last_loss = 0.0;
for (int i = 0; i < 100; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
if (i == 0) {
first_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
} else if (i == 99) {
last_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
}
}
EXPECT_LT(last_loss, first_loss);
}
TEST(train, recognize_digits) { Train(); }
} // namespace paddle
paddle/scripts/paddle_build.sh
浏览文件 @ 77fc42d2
......@@ -70,8 +70,8 @@ function cmake_gen() {
PYTHON_FLAGS=""
SYSTEM=`uname -s`
if [ "$SYSTEM" == "Darwin" ]; then
echo "Using python abi: $1"
if [[ "$1" == "cp27-cp27m" ]] || [[ "$1" == "" ]]; then
echo "using python abi: $1"
if [ -d "/Library/Frameworks/Python.framework/Versions/2.7" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
......@@ -82,7 +82,18 @@ function cmake_gen() {
else
exit 1
fi
# TODO: qiyang add python3 part here
elif [ "$1" == "cp35-cp35m" ]; then
if [ -d "/Library/Frameworks/Python.framework/Versions/3.5" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export PATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/:${PATH}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
else
exit 1
fi
fi
else
if [ "$1" != "" ]; then
......@@ -381,7 +392,7 @@ function run_mac_test() {
EOF
# TODO: jiabin need to refine this part when these tests fixed on mac
ctest --output-on-failure -j8
ctest --output-on-failure -j $1
# make install should also be test when unittest
make install -j 8
pip install /usr/local/opt/paddle/share/wheels/*.whl
......@@ -629,10 +640,10 @@ EOF
function gen_capi_package() {
if [[ ${WITH_C_API} == "ON" ]]; then
install_prefix="${PADDLE_ROOT}/build/capi_output"
rm -rf $install_prefix
make DESTDIR="$install_prefix" install
cd $install_prefix/usr/local
capi_install_prefix=${INSTALL_PREFIX:-/paddle/build}/capi_output
rm -rf $capi_install_prefix
make DESTDIR="$capi_install_prefix" install
cd $capi_install_prefix/
ls | egrep -v "^Found.*item$" | xargs tar -czf ${PADDLE_ROOT}/build/paddle.tgz
fi
}
......@@ -729,7 +740,11 @@ function main() {
maccheck)
cmake_gen ${PYTHON_ABI:-""}
build_mac
run_mac_test
run_mac_test ${PROC_RUN:-1}
;;
macbuild)
cmake_gen ${PYTHON_ABI:-""}
build_mac
;;
cicheck_py35)
cmake_gen ${PYTHON_ABI:-""}
......
python/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -87,6 +87,7 @@ if (WITH_TESTING)
endif()
endif()
add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/fluid/contrib/tests)
endif()
install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
DESTINATION opt/paddle/share/wheels
......
python/paddle/dataset/common.py
浏览文件 @ 77fc42d2
......@@ -77,13 +77,14 @@ def download(url, module_name, md5sum, save_name=None):
retry_limit = 3
while not (os.path.exists(filename) and md5file(filename) == md5sum):
if os.path.exists(filename):
print("file md5", md5file(filename), md5sum)
sys.stderr.write("file %s md5 %s" % (md5file(filename), md5sum))
if retry < retry_limit:
retry += 1
else:
raise RuntimeError("Cannot download {0} within retry limit {1}".
format(url, retry_limit))
print("Cache file %s not found, downloading %s" % (filename, url))
sys.stderr.write("Cache file %s not found, downloading %s" %
(filename, url))
r = requests.get(url, stream=True)
total_length = r.headers.get('content-length')
......@@ -100,10 +101,11 @@ def download(url, module_name, md5sum, save_name=None):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done,
sys.stderr.write("\r[%s%s]" % ('=' * done,
' ' * (50 - done)))
sys.stdout.flush()
sys.stderr.write("\n")
sys.stdout.flush()
return filename
......
python/paddle/dataset/wmt14.py
浏览文件 @ 77fc42d2
......@@ -89,7 +89,8 @@ def reader_creator(tar_file, file_name, dict_size):
]
for name in names:
for line in f.extractfile(name):
line_split = line.strip().split(six.b('\t'))
line = cpt.to_text(line)
line_split = line.strip().split('\t')
if len(line_split) != 2:
continue
src_seq = line_split[0] # one source sequence
......
python/paddle/dataset/wmt16.py
浏览文件 @ 77fc42d2
......@@ -64,7 +64,8 @@ def __build_dict(tar_file, dict_size, save_path, lang):
word_dict = defaultdict(int)
with tarfile.open(tar_file, mode="r") as f:
for line in f.extractfile("wmt16/train"):
line_split = line.strip().split(six.b("\t"))
line = cpt.to_text(line)
line_split = line.strip().split("\t")
if len(line_split) != 2: continue
sen = line_split[0] if lang == "en" else line_split[1]
for w in sen.split():
......@@ -123,7 +124,8 @@ def reader_creator(tar_file, file_name, src_dict_size, trg_dict_size, src_lang):
with tarfile.open(tar_file, mode="r") as f:
for line in f.extractfile(file_name):
line_split = line.strip().split(six.b("\t"))
line = cpt.to_text(line)
line_split = line.strip().split("\t")
if len(line_split) != 2:
continue
src_words = line_split[src_col].split()
......
python/paddle/fluid/backward.py
浏览文件 @ 77fc42d2
......@@ -38,8 +38,8 @@ def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
op_desc = op_descs[i]
if isinstance(op_desc, tuple):
op_desc = op_desc[0]
op_desc.rename_input(old_name, new_name)
op_desc.rename_output(old_name, new_name)
op_desc._rename_input(old_name, new_name)
op_desc._rename_output(old_name, new_name)
def _create_op_desc_(op_type, inputs, outputs, attrs):
......@@ -70,7 +70,7 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
if isinstance(val, framework.Block):
op_desc.set_block_attr(name, val.desc)
else:
op_desc.set_attr(name, val)
op_desc._set_attr(name, val)
return op_desc
......@@ -346,7 +346,7 @@ def _append_backward_ops_(block,
grad_sub_block_list = []
# If the op has its own sub-block, deal with the sub-block first
if op.has_attr("sub_block"):
sub_block = program.block(op.block_attr_id("sub_block"))
sub_block = program.block(op._block_attr_id("sub_block"))
grad_sub_block = program._create_block()
grad_sub_block._set_forward_block_idx(sub_block.idx)
cb = _callback_lookup_(op)
......@@ -382,7 +382,7 @@ def _append_backward_ops_(block,
for op_desc in grad_op_descs:
new_op_desc = target_block.desc.append_op()
new_op_desc.copy_from(op_desc)
new_op_desc.set_attr(op_role_attr_name, backward)
new_op_desc._set_attr(op_role_attr_name, backward)
grad_to_var["__current_op_desc__"] = new_op_desc
if callbacks is not None:
assert (isinstance(callbacks, list))
......@@ -408,7 +408,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
for op_idx in range(start_op_idx, block.desc.op_size()):
op_desc = block.desc.op(op_idx)
if op_desc.has_attr("sub_block"):
sub_block = block.program.block(op_desc.block_attr_id("sub_block"))
sub_block = block.program.block(op_desc._block_attr_id("sub_block"))
_append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map)
new_vars = set()
# create new gradient variables
......@@ -438,12 +438,12 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
op_desc = block.desc.op(op_idx)
for name in op_desc.input_arg_names():
if name in var_map:
op_desc.rename_input(name, var_map[name])
op_desc._rename_input(name, var_map[name])
for name in op_desc.output_arg_names():
if block.desc.find_var(name.encode("ascii")):
new_name = unique_name.generate(name)
op_desc.rename_output(name, new_name)
op_desc._rename_output(name, new_name)
var_map[name] = new_name
for g, ng in six.iteritems(var_map):
......@@ -542,9 +542,9 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
if loss.op is None:
raise ValueError("loss.op is None. Should not happend")
loss.op.set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
loss.op._set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
if callbacks is not None:
isinstance(callbacks, list)
......@@ -631,7 +631,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
attr_val = [p.name, g.name]
if g.op.has_attr(op_role_var_attr_name):
attr_val.extend(g.op.attr(op_role_var_attr_name))
g.op.set_attr(op_role_var_attr_name, attr_val)
g.op._set_attr(op_role_var_attr_name, attr_val)
return params_and_grads
......
python/paddle/fluid/clip.py
浏览文件 @ 77fc42d2
......@@ -75,8 +75,8 @@ class ErrorClipByValue(BaseErrorClipAttr):
clip_op_desc.set_type("clip")
clip_op_desc.set_input("X", [grad_name])
clip_op_desc.set_output("Out", [grad_name])
clip_op_desc.set_attr("min", self.min)
clip_op_desc.set_attr("max", self.max)
clip_op_desc._set_attr("min", self.min)
clip_op_desc._set_attr("max", self.max)
def error_clip_callback(block, context):
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 77fc42d2
......@@ -18,5 +18,13 @@ from . import decoder
from .decoder import *
from . import memory_usage_calc
from .memory_usage_calc import *
from . import op_frequence
from .op_frequence import *
from . import quantize
from .quantize import *
__all__ = decoder.__all__ + memory_usage_calc.__all__
__all__ = []
__all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += quantize.__all__
python/paddle/fluid/contrib/op_frequence.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from collections import OrderedDict
from ..framework import Program
__all__ = ['op_freq_statistic']
def op_freq_statistic(program):
"""
Statistics of Op frequency.
Args:
program(Program): The current Program.
Returns:
uni_op_freq(dict): the single op frequency.
adj_2_op_freq(dict): the two adjacent ops frequency.
Examples:
>>> import paddle.fluid as fluid
>>> uni_op_freq, adj_2_op_freq = fluid.contrib.op_freq_statistic(
>>> fluid.default_main_program())
>>> for op_type, op_num in uni_op_freq:
>>> print("%s \t %d" % (op_type, op_num))
>>> for op_type, op_num in adj_2_op_freq:
>>> print("%s \t %d" % (op_type, op_num))
"""
if not isinstance(program, Program):
raise TypeError("The input type should be Porgram."
"But you passed in %s" % (type(program)))
uni_op_freq = OrderedDict()
adj_2_op_freq = OrderedDict()
op_in_ops = OrderedDict()
parameters = [p.name for p in program.blocks[0].all_parameters()]
# get uni_op_freq
for op in program.global_block().ops:
had_recorded = False
for var_name in op.output_arg_names:
if var_name in parameters:
continue
if not had_recorded and uni_op_freq.has_key(op.type):
uni_op_freq[op.type] += 1
had_recorded = True
elif not had_recorded:
uni_op_freq[op.type] = 1
had_recorded = True
# get adj_2_op_freq
var_gen_op = {}
for op in program.global_block().ops:
for var_name in op.input_arg_names:
if var_name in parameters:
continue
if var_gen_op.has_key(var_name):
assert len(var_gen_op[var_name]) > 0
if op_in_ops.has_key(op.type):
op_in_ops[op.type].append(var_gen_op[var_name][-1])
else:
op_in_ops[op.type] = [var_gen_op[var_name][-1]]
else:
print("Var's generate op is not found,%s, %s" %
(var_name, op.type))
for var_name in op.output_arg_names:
if var_gen_op.has_key(var_name):
var_gen_op[var_name].append(op.type)
else:
var_gen_op[var_name] = [op.type]
for op, in_ops in op_in_ops.iteritems():
for in_op in in_ops:
op_op = in_op + "->" + op
if adj_2_op_freq.has_key(op_op):
adj_2_op_freq[op_op] += 1
else:
adj_2_op_freq[op_op] = 1
uni_op_freq = sorted(
uni_op_freq.items(), key=lambda item: item[1], reverse=True)
adj_2_op_freq = sorted(
adj_2_op_freq.items(), key=lambda item: item[1], reverse=True)
return uni_op_freq, adj_2_op_freq
python/paddle/fluid/contrib/quantize/__init__.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from . import quantize_transpiler
from .quantize_transpiler import *
__all__ = quantize_transpiler.__all__
python/paddle/fluid/contrib/quantize/quantize_transpiler.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import numpy as np
from paddle.fluid.framework import default_main_program, default_startup_program, program_guard
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid import unique_name
from paddle.fluid import core
from paddle.fluid.initializer import Constant
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.nn import autoincreased_step_counter
from paddle.fluid.framework import Variable
from paddle.fluid.executor import global_scope
from paddle.fluid.transpiler.inference_transpiler import InferenceTranspiler
__all__ = ['QuantizeTranspiler']
_QUANTIZABLE_OP_TYPES = ['conv2d', 'depthwise_conv2d', 'mul']
def _quantized_var_name(var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.quantized" % (var_name)
def _dequantized_var_name(var_name):
"""
Return dequantized variable name for the input `var_name`.
"""
return "%s.dequantized" % (var_name)
def _quantized_scale_name(var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.scale" % (var_name)
def _original_var_name(var_name):
"""
Return the original variable name.
"""
if var_name.endswith('.quantized.dequantized'):
return var_name[:-len('.quantized.dequantized')]
if var_name.endswith('.quantized'):
return var_name[:-len('.quantized')]
if var_name.endswith('.dequantized'):
return var_name[:-len('.dequantized')]
if var_name.endswith('.scale'):
return var_name[:-len('.scale')]
else:
return var_name
def _is_float(v):
return isinstance(v, float) or isinstance(v, np.float32)
def quant(x, scale, num_bits):
y = np.round(x / scale * ((1 << (num_bits - 1)) - 1))
return y
class QuantizeTranspiler(object):
def __init__(self,
weight_bits=8,
activation_bits=8,
activation_quantize_type='abs_max',
weight_quantize_type='abs_max',
window_size=10000):
"""
Convert and rewrite the fluid Program according to weight and
activation quantization type.
Args:
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
.. code-block:: python
# the original program will be rewrite, if you don't want to
# change it, please clone at first.
# quantize_program = program.clone()
t = fluid.QuantizeTranspiler()
t.transpile(quantize_program)
"""
self.weight_bits = weight_bits
self.activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max']
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(weight_quantize_type))
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(activation_quantize_type))
self.weight_quantize_type = weight_quantize_type
self.activation_quantize_type = activation_quantize_type
self.window_size = window_size
self.helper = LayerHelper(self.__class__.__name__)
self.fake_quant_op_types = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self.fake_dequant_op_types = ['fake_dequantize_max_abs']
self.is_test = None
self.global_step = None
def training_transpile(self, program=None, startup_program=None):
"""Rewrites a training input program in place for simulated
quantization. Insert fake quantization and de-quantization ops into
program to simulate the error introduced by quantization. And change
the graident ops' input by using the faked quantization weights and
activation. Since the program is transformed in place, the graph
connection will change.
Args:
program (Program): the input program to be transpile.
"""
self.is_test = False
program = default_main_program() if program is None else program
startup_program = default_startup_program() if startup_program is \
None else startup_program
# marked the variable which has been quantized and dequantized.
dequanted_vars = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
grad_op_types = ['%s_grad' % (type) for type in _QUANTIZABLE_OP_TYPES]
params = [p.name for p in program.global_block().iter_parameters()]
def _transpile_forward(block, op):
idx = block.ops.index(op)
block_id = block.idx
# insert quant op and dequant op
for name in op.input_arg_names:
if name in dequanted_vars[block_id]:
dequant_var = dequanted_vars[block_id][name]
else:
var = block.var(name)
quant_bits = self.weight_bits if var.name in params \
else self.activation_bits
quant_type = self.weight_quantize_type if var.name \
in params else self.activation_quantize_type
quant_var, scale_var = self._insert_quant_op(
block, idx, var, quant_bits, quant_type)
dequant_var = self._insert_dequant_op(
block, idx + 1, quant_var, scale_var, quant_bits)
dequanted_vars[block_id][name] = dequant_var
# rename the forward op inputs
op._rename_input(name, dequant_var.name)
def _transpile_backward(block, op):
block_id = block.idx
no_dequanted_input_vars = True
for name in op.input_arg_names:
if name in dequanted_vars[block_id]:
dequant_var = dequanted_vars[block_id][name]
op._rename_input(name, dequant_var.name)
no_dequanted_input_vars = False
if no_dequanted_input_vars:
raise ValueError("There is no dequanted inputs for op %s." %
(op.type))
with program_guard(program, startup_program):
self._create_global_step()
for block in program.blocks:
ops = list(block.ops)
block_id = block.idx
for op in ops:
# rewrite the forward ProgramDes
if op.type in _QUANTIZABLE_OP_TYPES:
_transpile_forward(block, op)
# rename the backward op inputs
if op.type in grad_op_types:
_transpile_backward(block, op)
def _create_global_step(self):
if self.weight_quantize_type == 'range_abs_max' or \
self.activation_quantize_type == 'range_abs_max':
self.global_step = autoincreased_step_counter()
def freeze_program(self, program, place, fuse_bn=False, scope=None):
"""Freeze input training program for inference.
Args:
program (Program): the input program to be transpile.
"""
self.is_test = True
scope = global_scope() if scope is None else scope
program = default_main_program() if program is None else program
if fuse_bn:
bn_fuse_transpiler = BNFuseTranspiler()
bn_fuse_transpiler.transpile(program, place)
persistable_vars = [
v.name
for v in filter(lambda var: var.persistable, program.list_vars())
]
op_in_rename_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
op_out_rename_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
var_scale_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
def _remove_fake_quant_and_dequant_op(block, op):
idx = block.ops.index(op)
block_id = block.idx
k = op.output('Out')[0]
v = op.input('X')[0]
if v not in op_in_rename_map[block_id]:
op_in_rename_map[block_id][k] = v
else:
op_in_rename_map[block_id][k] = op_in_rename_map[block_id][v]
block._remove_op(idx)
def _insert_post_dequant_op(block, op):
idx = block.ops.index(op)
block_id = block.idx
max_range = None
scale_var = None
for name in op.input_arg_names:
if name in op_in_rename_map[block_id]:
op._rename_input(name, op_in_rename_map[block_id][name])
scale_v = var_scale_map[block_id][_original_var_name(name)]
if _original_var_name(name) in persistable_vars:
param_range = (1 << (self.weight_bits - 1)) - 1
act_range = (1 << (self.activation_bits - 1)) - 1
assert _is_float(scale_v)
max_range = param_range * act_range / scale_v
else:
assert isinstance(scale_v, Variable)
scale_var = var_scale_map[block_id][_original_var_name(
name)]
if len(op.output_arg_names) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op.type))
out_var = block.var(op.output_arg_names[0])
dequant_var = block.create_var(
name=_dequantized_var_name(out_var.name),
type=out_var.type,
shape=out_var.shape,
dtype=out_var.dtype)
# insert fake_dequantize_op
dequant_op = block._insert_op(
idx + 1,
type="fake_dequantize_max_abs",
attrs={'max_range': float(max_range)},
inputs={"X": out_var,
'Scale': scale_var},
outputs={"Out": dequant_var})
op_out_rename_map[block_id][out_var.name] = dequant_var.name
return dequant_var
def _load_var(name):
return np.array(scope.find_var(name).get_tensor())
def _restore_var(name, arr):
t = scope.find_var(name).get_tensor()
t.set(arr, place)
for block in program.blocks:
ops = list(block.ops)
block_id = block.idx
for op in ops:
op_type = op.type
# insert dequant_op after fc/conv, need to rename
# input of the followed ops
for name in op.input_arg_names:
if name in op_out_rename_map[block_id]:
op._rename_input(name,
op_out_rename_map[block_id][name])
if op_type in self.fake_quant_op_types:
in_arg_name = op.input('X')[0]
if in_arg_name in persistable_vars:
if self.weight_quantize_type == 'abs_max':
param = _load_var(in_arg_name)
scale_v = np.max(np.abs(param))
else:
scale_v = _load_var(op.output('OutScale')[0])
var_scale_map[block_id][in_arg_name] = scale_v
else:
scale_v = block.var(op.output('OutScale')[0])
var_scale_map[block_id][in_arg_name] = scale_v
if in_arg_name in persistable_vars:
_remove_fake_quant_and_dequant_op(block, op)
# quantize weight and restore
param_t = _load_var(in_arg_name)
param_q_t = quant(param_t, scale_v, self.weight_bits)
_restore_var(in_arg_name, param_q_t)
if op_type in self.fake_dequant_op_types:
_remove_fake_quant_and_dequant_op(block, op)
if op_type in _QUANTIZABLE_OP_TYPES:
dequant_var = _insert_post_dequant_op(block, op)
# remove the unused var in ProgramDesc
self._remove_unused_var(program)
#program = program.clone()
def convert_to_int8(self, program, place, scope=None):
scope = global_scope() if scope is None else scope
program = default_main_program() if program is None else program
def _load_var(name):
return np.array(scope.find_var(name).get_tensor())
global_block = program.global_block()
def convert_to_int8(var):
int8_var_name = var.name + ".int8"
int8_var = global_block.create_parameter(
name=int8_var_name.encode('ascii'),
type=var.type,
dtype=core.VarDesc.VarType.INT8,
shape=var.shape)
tensor = _load_var(var.name)
scope.var(int8_var_name)
int8_tensor = scope.find_var(int8_var_name).get_tensor()
int8_tensor.set(tensor.astype(np.int8), place)
return int8_var
input_map = {}
for block in program.blocks:
for op in list(block.ops):
if op.type in _QUANTIZABLE_OP_TYPES:
for name in op.input_arg_names:
var = block.var(name)
if var.persistable:
if name not in input_map:
int8_var = convert_to_int8(var)
input_map[name] = int8_var.name
op._rename_input(name, input_map[name])
self._remove_unused_var(program)
def _remove_unused_var(self, program):
all_remove_vars = []
for block in program.blocks:
args = []
for op in block.ops:
args += op.input_arg_names
args += op.output_arg_names
args = list(set(args))
var_names = block.vars.keys()
sub_block_remove_vars = []
for var in var_names:
if var not in args:
sub_block_remove_vars.append(var)
all_remove_vars.append(sub_block_remove_vars)
remove_vars = [list(set(v)) for v in all_remove_vars]
for i, block in enumerate(program.blocks):
for v in remove_vars[i]:
block._remove_var(v)
def _insert_quant_abs_max_op(self, block, idx, var, quant_bits):
"""Insert fake_quantize_abs_max op.
"""
quant_var = block.create_var(
name=_quantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
scale = block.create_var(
name=_quantized_scale_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
quant_op = block._insert_op(
idx,
type='fake_quantize_abs_max',
attrs={'bit_length': quant_bits},
inputs={'X': var},
outputs={'Out': quant_var,
'OutScale': scale})
return quant_var, scale
def _insert_quant_range_abs_max_op(self, block, idx, var, quant_bits):
"""Insert fake_quantize_range_abs_max
"""
quant_var = block.create_var(
name=_quantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
scale = self.helper.create_parameter(
attr=ParamAttr(
name=_quantized_scale_name(var.name),
initializer=Constant(0.001),
trainable=False),
shape=[1],
dtype=var.dtype)
scale.stop_gradient = True
ins = {'X': var, 'InScale': scale}
outs = {'Out': quant_var, 'OutScale': scale}
if not self.is_test:
# A global step counter variable with type int64
scales = self.helper.create_global_variable(
name=unique_name.generate('scales'),
persistable=True,
dtype=var.dtype,
shape=[self.window_size])
self.helper.set_variable_initializer(
scales, initializer=Constant(value=0))
ins['Iter'] = self.global_step
outs['OutScales'] = scales
attrs = {
'window_size': self.window_size,
'bit_length': quant_bits,
'is_test': self.is_test
}
quant_op = block._insert_op(
idx,
type='fake_quantize_range_abs_max',
attrs=attrs,
inputs=ins,
outputs=outs)
return quant_var, scale
def _insert_quant_op(self, block, idx, var, quant_bits, quant_type):
"""
Insert fake_quantize_op
"""
if quant_type == 'abs_max':
return self._insert_quant_abs_max_op(block, idx, var, quant_bits)
elif quant_type == 'range_abs_max':
return self._insert_quant_range_abs_max_op(block, idx, var,
quant_bits)
def _insert_dequant_op(self, block, idx, var, scale, quant_bits):
"""
Insert fake_quantize_op
"""
dequant_var = block.create_var(
name=_dequantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
# insert fake_dequantize_op
max_range = (1 << (quant_bits - 1)) - 1
dequant_op = block._insert_op(
idx,
type="fake_dequantize_max_abs",
attrs={'max_range': float(max_range)},
inputs={"X": var,
'Scale': scale},
outputs={"Out": dequant_var})
return dequant_var
class BNFuseTranspiler(InferenceTranspiler):
def _fuse_param(self, current_op, bn_op, bias_op, with_bias):
def _update_param(op, param_name, new_param):
var = self.block.vars[param_name]
tensor = self.scope.find_var(param_name).get_tensor()
tensor.set(np.array(new_param), self.place)
def _load_param(param_name):
return np.array(self.scope.find_var(param_name).get_tensor())
bias_bn = _load_param(bn_op.input("Bias")[0]) #Bias
scale_bn = _load_param(bn_op.input("Scale")[0]) #Scale
mean_bn = _load_param(bn_op.input("Mean")[0]) #Mean
var_bn = _load_param(bn_op.input("Variance")[0]) #Variance
if current_op.type in ['conv2d', 'depthwise_conv2d']:
current_param = _load_param(
_original_var_name(current_op.input("Filter")[0]))
elif current_op.type == 'mul':
current_param = _load_param(
_original_var_name(current_op.input("Y")[0]))
std_bn = np.float32(np.sqrt(np.add(var_bn, 1e-5)))
tmp = np.float32(np.divide(scale_bn, std_bn))
# add bias of batch_norm_op to conv2d
if with_bias:
bias = _load_param(bias_op.input("Y"))
else:
bias = np.zeros(bias_bn.shape)
bias = np.float32(
np.add(np.multiply(np.subtract(bias, mean_bn), tmp), bias_bn))
# re-compute weight of conv2d/fc
tmp = tmp.reshape(tmp.shape[0], -1)
dst_param = current_param.reshape((tmp.shape[0], -1))
dst_param = np.float32(np.multiply(dst_param, tmp))
dst_param = dst_param.reshape(current_param.shape)
# update parameters
if current_op.type in ['conv2d', 'depthwise_conv2d']:
_update_param(current_op,
_original_var_name(current_op.input("Filter")[0]),
dst_param)
elif current_op.type == 'mul':
_update_param(current_op,
_original_var_name(current_op.input("Y")[0]),
dst_param)
_update_param(bias_op, bias_op.input("Y")[0], bias)
# collect the renamed input
self.input_map[bn_op.output("Y")[0]] = bias_op.output("Out")[0]
python/paddle/fluid/contrib/tests/CMakeLists.txt 0 → 100644
浏览文件 @ 77fc42d2
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py 0 → 100644
浏览文件 @ 77fc42d2
# copyright (c) 2018 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
import numpy as np
import six
import unittest
import paddle
import paddle.fluid as fluid
from paddle.fluid.contrib.quantize.quantize_transpiler import _original_var_name
from paddle.fluid.contrib.quantize.quantize_transpiler import QuantizeTranspiler
def linear_fc(num):
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
hidden = fluid.layers.fc(hidden, size=128, act='relu')
loss = fluid.layers.cross_entropy(input=hidden, label=label)
loss = fluid.layers.mean(loss)
return loss
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
class TestQuantizeTranspiler(unittest.TestCase):
def setUp(self):
# since quant_op and dequant_op is not ready, use cos and sin for test
self.weight_quant_op_type = 'fake_quantize_abs_max'
self.dequant_op_type = 'fake_dequantize_max_abs'
self.quantizable_op_and_inputs = {
'conv2d': ['Input', 'Filter'],
'depthwise_conv2d': ['Input', 'Filter'],
'mul': ['X', 'Y']
}
self.quantizable_op_grad_and_inputs = {
'conv2d_grad': ['Input', 'Filter'],
'depthwise_conv2d_grad': ['Input', 'Filter'],
'mul_grad': ['X', 'Y']
}
def check_program(self, program):
quantized_ops = {}
persistable_vars = [
v.name
for v in filter(lambda var: var.persistable, program.list_vars())
]
for block in program.blocks:
for idx, op in enumerate(block.ops):
# check forward
if op.type in self.quantizable_op_and_inputs:
for i, arg_name in enumerate(op.input_arg_names):
quant_op_type = self.weight_quant_op_type if \
_original_var_name(arg_name) \
in persistable_vars else self.act_quant_op_type
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
if arg_name not in quantized_ops:
self.assertEqual(block.ops[idx - 2 * i - 1].type,
self.dequant_op_type)
self.assertEqual(block.ops[idx - 2 * i - 2].type,
quant_op_type)
quantized_ops[arg_name] = block.ops[idx - 2 * i - 2]
else:
op_idx = block.ops.index(quantized_ops[arg_name])
self.assertLess(op_idx, idx)
# check backward
if op.type in self.quantizable_op_grad_and_inputs:
for pname in self.quantizable_op_grad_and_inputs[op.type]:
arg_name = op.input(pname)[0]
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = linear_fc(3)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
t = QuantizeTranspiler(activation_quantize_type=quant_type)
t.training_transpile(main)
self.check_program(main)
def test_linear_fc_quant_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.linear_fc_quant('abs_max')
def test_linear_fc_quant_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.linear_fc_quant('range_abs_max')
def residual_block_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
t = QuantizeTranspiler(activation_quantize_type=quant_type)
t.training_transpile(main)
self.check_program(main)
def test_residual_block_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.residual_block_quant('abs_max')
def test_residual_block_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.residual_block_quant('range_abs_max')
def freeze_program(self, use_cuda):
def build_program(main, startup, is_test):
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
quant_transpiler = QuantizeTranspiler()
quant_transpiler.training_transpile(main)
quant_transpiler.training_transpile(test_program)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
iter = 5
batch_size = 8
class_num = 10
exe.run(startup)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.program_guard(main):
for _ in range(iter):
data = next(train_reader())
loss_v = exe.run(program=main,
feed=feeder.feed(data),
fetch_list=[loss])
with fluid.program_guard(test_program):
test_data = next(test_reader())
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
test_program)
# Testing during training
test_loss1, w_quant = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze program for inference, but the weight of fc/conv is still float type.
quant_transpiler.freeze_program(test_program, place)
test_loss2, = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=1e-3)
w_freeze = np.array(fluid.global_scope().find_var('conv2d_1.w_0')
.get_tensor())
self.assertEqual(np.sum(w_freeze), np.sum(w_quant))
# Convert parameter to 8-bit.
quant_transpiler.convert_to_int8(test_program, place)
# Save the 8-bit parameter and model file.
fluid.io.save_inference_model('model_8bit', ['image', 'label'],
[loss], exe, test_program)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model('model_8bit',
exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(fluid.global_scope().find_var('conv2d_1.w_0.int8')
.get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
def test_freeze_program_cuda(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_program(True)
def test_freeze_program_cpu(self):
with fluid.unique_name.guard():
self.freeze_program(False)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/framework.py
浏览文件 @ 77fc42d2
......@@ -40,11 +40,9 @@ PADDLE_ON_MODEL_CE = os.environ.get('PADDLE_ON_MODEL_CE', None) is not None
__all__ = [
'Program',
'Operator',
'default_startup_program',
'default_main_program',
'program_guard',
'get_var',
'name_scope',
]
......@@ -663,11 +661,11 @@ class Operator(object):
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
if self.has_kernel(type):
if self._has_kernel(type):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
def has_kernel(self, op_type):
def _has_kernel(self, op_type):
return op_type not in self.OP_WITHOUT_KERNEL_SET
def to_string(self, throw_on_error):
......@@ -708,7 +706,7 @@ class Operator(object):
"""
return self.desc.input(name)
def rename_input(self, old_name, new_name):
def _rename_input(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -719,9 +717,9 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_input(old_name, new_name)
self.desc._rename_input(old_name, new_name)
def rename_output(self, old_name, new_name):
def _rename_output(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -732,7 +730,7 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_output(old_name, new_name)
self.desc._rename_output(old_name, new_name)
@property
def input_names(self):
......@@ -796,7 +794,7 @@ class Operator(object):
"""
return self.desc.attr_type(name)
def set_attr(self, name, val):
def _set_attr(self, name, val):
"""
Set the value of attribute by attribute's name.
......@@ -829,7 +827,7 @@ class Operator(object):
isinstance(val, core.ProgramDesc):
self.desc.set_serialized_attr(name, val.serialize_to_string())
else:
self.desc.set_attr(name, val)
self.desc._set_attr(name, val)
@property
def attr_names(self):
......@@ -848,7 +846,7 @@ class Operator(object):
"""
return self.desc.attr(name)
def block_attr_id(self, name):
def _block_attr_id(self, name):
"""
Get the block attribute's id by name.
......@@ -858,9 +856,9 @@ class Operator(object):
Returns:
int: the block index.
"""
return self.desc.block_attr_id(name)
return self.desc._block_attr_id(name)
def block_attr(self, name):
def _block_attr(self, name):
"""
Get the block attribute by name.
......@@ -871,11 +869,11 @@ class Operator(object):
block: the block attribute.
"""
id = self.block_attr_id(name)
id = self._block_attr_id(name)
assert (id >= 0 and id < len(self.block.program.blocks))
return self.block.program.blocks[id]
def blocks_attr(self, name):
def _blocks_attr(self, name):
"""
Get the blocks attribute by name.
......@@ -886,13 +884,13 @@ class Operator(object):
list: list of the blocks attribute.
"""
attrs = []
for i in self.blocks_attr_ids(name):
for i in self._blocks_attr_ids(name):
assert (i >= 0 and i < len(self.block.program.blocks))
attrs.append(self.block.program.blocks[i])
return attrs
def blocks_attr_ids(self, name):
def _blocks_attr_ids(self, name):
"""
Get the blocks attribute's ids by name.
......@@ -903,7 +901,7 @@ class Operator(object):
list: list of the blocks ids.
"""
return self.desc.blocks_attr_ids(name)
return self.desc._blocks_attr_ids(name)
def all_attrs(self):
"""
......@@ -917,11 +915,11 @@ class Operator(object):
for n in attr_names:
attr_type = self.desc.attr_type(n)
if attr_type == core.AttrType.BLOCK:
attr_map[n] = self.block_attr(n)
attr_map[n] = self._block_attr(n)
continue
if attr_type == core.AttrType.BLOCKS:
attr_map[n] = self.blocks_attr(n)
attr_map[n] = self._blocks_attr(n)
continue
attr_map[n] = self.attr(n)
......@@ -1795,7 +1793,7 @@ class Program(object):
for j in six.moves.range(block.op_size()):
op = block.op(j)
if op.has_attr('is_test'):
op.set_attr('is_test', True)
op._set_attr('is_test', True)
res.blocks = [
Block(res, i) for i in six.moves.range(res.desc.num_blocks())
]
......@@ -2169,7 +2167,7 @@ def program_guard(main_program, startup_program=None):
switch_startup_program(startup_program)
def get_var(name, program=None):
def _get_var(name, program=None):
"""
Get a variable by name from the global block of a program.
......
python/paddle/fluid/io.py
浏览文件 @ 77fc42d2
......@@ -600,7 +600,7 @@ def save_inference_model(dirname,
"""
if isinstance(feeded_var_names, six.string_types):
feeded_var_names = [feeded_var_names]
else:
elif export_for_deployment:
if len(feeded_var_names) > 0:
# TODO(paddle-dev): polish these code blocks
if not (bool(feeded_var_names) and all(
......@@ -610,61 +610,60 @@ def save_inference_model(dirname,
if isinstance(target_vars, Variable):
target_vars = [target_vars]
else:
elif export_for_deployment:
if not (bool(target_vars) and all(
isinstance(var, Variable) for var in target_vars)):
raise ValueError("'target_vars' should be a list of Variable.")
if main_program is None:
main_program = default_main_program()
copy_program = main_program.clone()
# if there is lookup table, the trainer 0 will notify all pserver to save.
if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
lookup_table_filename = os.path.join(dirname, "__lookup_table__")
_save_lookup_tables_by_notify(executor, lookup_table_filename,
main_program._distributed_lookup_table,
main_program._endpoints)
if not os.path.isdir(dirname):
os.makedirs(dirname)
if model_filename is not None:
model_basename = os.path.basename(model_filename)
else:
model_basename = "__model__"
model_basename = os.path.join(dirname, model_basename)
# When export_for_deployment is true, we modify the program online so that
# it can only be loaded for inference directly. If it's false, the whole
# original program and related meta are saved so that future usage can be
# more flexible.
if export_for_deployment:
global_block = copy_program.global_block()
main_program = main_program.clone()
global_block = main_program.global_block()
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
global_block._remove_op(i)
copy_program.desc.flush()
main_program.desc.flush()
pruned_program = copy_program._prune(targets=target_vars)
saved_program = pruned_program._inference_optimize(prune_read_op=True)
main_program = main_program._prune(targets=target_vars)
main_program = main_program._inference_optimize(prune_read_op=True)
fetch_var_names = [v.name for v in target_vars]
prepend_feed_ops(saved_program, feeded_var_names)
append_fetch_ops(saved_program, fetch_var_names)
prepend_feed_ops(main_program, feeded_var_names)
append_fetch_ops(main_program, fetch_var_names)
with open(model_basename, "wb") as f:
f.write(main_program.desc.serialize_to_string())
else:
# TODO(panyx0718): Save more information so that it can also be used
# for training and more flexible post-processing.
saved_program = copy_program
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_filename = "__model__"
model_filename = os.path.join(dirname, model_filename)
with open(model_basename + ".main_program", "wb") as f:
f.write(main_program.desc.serialize_to_string())
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_filename, "wb") as f:
f.write(saved_program.desc.serialize_to_string())
save_persistables(executor, dirname, saved_program, params_filename)
# if there is lookup table, the trainer 0 will notify all pserver to save.
if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
lookup_table_filename = os.path.join(dirname, "__lookup_table__")
_save_lookup_tables_by_notify(executor, lookup_table_filename,
main_program._distributed_lookup_table,
main_program._endpoints)
save_persistables(executor, dirname, main_program, params_filename)
def load_inference_model(dirname,
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 77fc42d2
......@@ -284,7 +284,7 @@ def detection_output(loc,
target_box=loc,
code_type='decode_center_size')
compile_shape = scores.shape
run_shape = ops.shape(scores)
run_shape = nn.shape(scores)
scores = nn.flatten(x=scores, axis=2)
scores = nn.softmax(input=scores)
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
......@@ -697,7 +697,7 @@ def ssd_loss(location,
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
conf_shape = ops.shape(confidence)
conf_shape = nn.shape(confidence)
def __reshape_to_2d(var):
return nn.flatten(x=var, axis=2)
......@@ -724,7 +724,7 @@ def ssd_loss(location,
target_label.stop_gradient = True
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
actual_shape = ops.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape.stop_gradient = True
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
......
python/paddle/fluid/layers/io.py
浏览文件 @ 77fc42d2
......@@ -507,7 +507,6 @@ def py_reader(capacity,
1. The basic usage of :code:`py_reader` is as follows:
>>> import paddle.v2
>>> import paddle.fluid as fluid
>>> import paddle.dataset.mnist as mnist
>>>
......@@ -515,7 +514,7 @@ def py_reader(capacity,
>>> shapes=[(-1,3,224,224), (-1,1)],
>>> dtypes=['float32', 'int64'])
>>> reader.decorate_paddle_reader(
>>> paddle.v2.reader.shuffle(paddle.batch(mnist.train())
>>> paddle.reader.shuffle(paddle.batch(mnist.train())
>>>
>>> img, label = fluid.layers.read_file(reader)
>>> loss = network(img, label) # some network definition
......@@ -534,7 +533,6 @@ def py_reader(capacity,
2. When training and testing are both performed, two different
:code:`py_reader` should be created with different names, e.g.:
>>> import paddle.v2
>>> import paddle.fluid as fluid
>>> import paddle.dataset.mnist as mnist
>>>
......@@ -548,7 +546,7 @@ def py_reader(capacity,
>>> dtypes=['float32', 'int64'],
>>> name='train_reader')
>>> train_reader.decorate_paddle_reader(
>>> paddle.v2.reader.shuffle(paddle.batch(mnist.train())
>>> paddle.reader.shuffle(paddle.batch(mnist.train())
>>>
>>> test_reader = fluid.layers.py_reader(capacity=32,
>>> shapes=[(-1,3,224,224), (-1,1)],
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 77fc42d2
......@@ -78,7 +78,12 @@ def accuracy(input, label, k=1, correct=None, total=None):
return acc_out
def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
def auc(input,
label,
curve='ROC',
num_thresholds=2**12 - 1,
topk=1,
slide_steps=1):
"""
**Area Under the Curve (AUC) Layer**
......@@ -105,6 +110,8 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
topk(int): only topk number of prediction output will be used for auc.
slide_steps: when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
Returns:
Variable: A scalar representing the current AUC.
......@@ -120,16 +127,48 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
batch_stat_pos = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
batch_stat_neg = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
# for global auc
stat_pos = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
stat_neg = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
for var in [stat_pos, stat_neg]:
for var in [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg]:
helper.set_variable_initializer(
var, Constant(
value=0.0, force_cpu=True))
# Batch AUC
helper.append_op(
type="auc",
inputs={
"Predict": [input],
"Label": [label],
"StatPos": [batch_stat_pos],
"StatNeg": [batch_stat_neg]
},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": slide_steps
},
outputs={
"AUC": [batch_auc_out],
"StatPosOut": [batch_stat_pos],
"StatNegOut": [batch_stat_neg]
})
# Global AUC
helper.append_op(
type="auc",
inputs={
......@@ -138,12 +177,16 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
"StatPos": [stat_pos],
"StatNeg": [stat_neg]
},
attrs={"curve": curve,
"num_thresholds": num_thresholds},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": 0
},
outputs={
"AUC": [auc_out],
"BatchAUC": [batch_auc_out],
"StatPosOut": [stat_pos],
"StatNegOut": [stat_neg]
})
return auc_out, batch_auc_out, [stat_pos, stat_neg]
return auc_out, batch_auc_out, [
batch_stat_pos, batch_stat_neg, stat_pos, stat_neg
]
python/paddle/fluid/layers/nn.py
浏览文件 @ 77fc42d2
......@@ -29,110 +29,29 @@ from .. import unique_name
from functools import reduce
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'sequence_expand_as',
'sequence_pad',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'scatter',
'sequence_scatter',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'brelu',
'leaky_relu',
'soft_relu',
'flatten',
'sequence_mask',
'stack',
'pad2d',
'unstack',
'sequence_enumerate',
'expand',
'sequence_concat',
'scale',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'sequence_expand', 'sequence_expand_as', 'sequence_pad', 'lstm_unit',
'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'squeeze', 'unsqueeze',
'lod_reset', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool',
'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear',
'gather', 'scatter', 'sequence_scatter', 'random_crop', 'mean_iou', 'relu',
'log', 'crop', 'rank_loss', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid',
'swish', 'prelu', 'brelu', 'leaky_relu', 'soft_relu', 'flatten',
'sequence_mask', 'stack', 'pad2d', 'unstack', 'sequence_enumerate',
'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
]
......@@ -6463,6 +6382,246 @@ def expand(x, expand_times, name=None):
return out
from paddle.fluid.framework import convert_np_dtype_to_dtype_
@templatedoc()
def uniform_random_batch_size_like(input,
shape,
dtype='float32',
input_dim_idx=0,
output_dim_idx=0,
min=-1.0,
max=1.0,
seed=0):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='uniform_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'min': min,
'max': max,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def gaussian_random(shape,
mean=0.0,
std=1.0,
seed=0,
dtype='float32',
use_mkldnn=False):
"""
${comment}
Args:
shape (tuple|list): ${shape_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
use_mkldnn (Bool): Only used in mkldnn kernel.
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random',
outputs={'Out': out},
attrs={
'shape': shape,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'use_mkldnn': use_mkldnn
})
return out
@templatedoc()
def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
"""
${comment}
Args:
x (Variable): ${x_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Float): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sampling_id', **locals())
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='sampling_id',
inputs={'X': x},
outputs={'Out': out},
attrs={'min': min,
'max': max,
'seed': seed})
return out
@templatedoc()
def gaussian_random_batch_size_like(input,
shape,
input_dim_idx=0,
output_dim_idx=0,
mean=0.0,
std=1.0,
seed=0,
dtype='float32'):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def sum(x, use_mkldnn=False):
"""
${comment}
Args:
x (Variable): ${x_comment}
use_mkldnn (Bool): ${use_mkldnn_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
helper.append_op(
type='sum',
inputs={'X': x},
outputs={'Out': out},
attrs={'use_mkldnn': use_mkldnn})
return out
@templatedoc()
def slice(input, axes, starts, ends):
"""
${comment}
Args:
input (Variable): ${input_comment}.
axes (List): ${axes_comment}
starts (List): ${starts_comment}
ends (List): ${ends_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('slice', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='slice',
inputs={'Input': input},
outputs={'Out': out},
attrs={'axes': axes,
'starts': starts,
'ends': ends})
return out
@templatedoc()
def shape(input):
"""
${comment}
Args:
input (Variable): ${input_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('shape', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='shape', inputs={'Input': input}, outputs={'Out': out})
return out
def _elementwise_op(helper):
op_type = helper.layer_type
x = helper.kwargs.get('x', None)
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 77fc42d2
......@@ -45,13 +45,6 @@ __all__ = [
'logical_or',
'logical_xor',
'logical_not',
'uniform_random_batch_size_like',
'gaussian_random',
'sampling_id',
'gaussian_random_batch_size_like',
'sum',
'slice',
'shape',
'maxout',
]
......@@ -63,6 +56,8 @@ for _OP in set(__all__):
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')
globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
__all__ += __activations_noattr__
for _OP in set(__activations_noattr__):
......
python/paddle/fluid/optimizer.py
浏览文件 @ 77fc42d2
......@@ -26,6 +26,7 @@ from .layer_helper import LayerHelper
from .regularizer import append_regularization_ops
from .clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
from .layers import ops
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
......@@ -1301,7 +1302,7 @@ class ModelAverage(Optimizer):
x=tmp, dtype='float32' if self._dtype == None else self._dtype)
sum = layers.cast(
x=sum, dtype='float32' if self._dtype == None else self._dtype)
layers.elementwise_div(x=sum, y=tmp, out=param)
ops._elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 77fc42d2
......@@ -67,6 +67,7 @@ def train(nn_type,
use_cuda,
parallel,
save_dirname=None,
save_full_dirname=None,
model_filename=None,
params_filename=None,
is_local=True):
......@@ -143,6 +144,13 @@ def train(nn_type,
exe,
model_filename=model_filename,
params_filename=params_filename)
if save_full_dirname is not None:
fluid.io.save_inference_model(
save_full_dirname, [], [],
exe,
model_filename=model_filename,
params_filename=params_filename,
export_for_deployment=False)
return
else:
print(
......@@ -214,10 +222,12 @@ def infer(use_cuda,
def main(use_cuda, parallel, nn_type, combine):
save_dirname = None
save_full_dirname = None
model_filename = None
params_filename = None
if not use_cuda and not parallel:
save_dirname = "recognize_digits_" + nn_type + ".inference.model"
save_full_dirname = "recognize_digits_" + nn_type + ".train.model"
if combine == True:
model_filename = "__model_combined__"
params_filename = "__params_combined__"
......@@ -228,6 +238,7 @@ def main(use_cuda, parallel, nn_type, combine):
use_cuda=use_cuda,
parallel=parallel,
save_dirname=save_dirname,
save_full_dirname=save_full_dirname,
model_filename=model_filename,
params_filename=params_filename)
infer(
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 77fc42d2
......@@ -28,7 +28,6 @@ list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/Paddl
list(REMOVE_ITEM TEST_OPS op_test) # op_test is a helper python file, not a test
list(REMOVE_ITEM TEST_OPS decorators) # decorators is a helper python file, not a test
if(APPLE)
if(NOT WITH_DISTRIBUTE)
list(REMOVE_ITEM TEST_OPS test_desc_clone)
......
python/paddle/fluid/tests/unittests/dist_ctr.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import dist_ctr_reader
from test_dist_base import TestDistRunnerBase, runtime_main
IS_SPARSE = True
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
class TestDistCTR2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
""" network definition """
dnn_data = fluid.layers.data(
name="dnn_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
lr_data = fluid.layers.data(
name="lr_data",
shape=[-1, 1],
dtype="int64",
lod_level=1,
append_batch_size=False)
label = fluid.layers.data(
name="click",
shape=[-1, 1],
dtype="int64",
lod_level=0,
append_batch_size=False)
# build dnn model
dnn_layer_dims = [128, 64, 32, 1]
dnn_embedding = fluid.layers.embedding(
is_distributed=False,
input=dnn_data,
size=[dnn_input_dim, dnn_layer_dims[0]],
param_attr=fluid.ParamAttr(
name="deep_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
dnn_pool = fluid.layers.sequence_pool(
input=dnn_embedding, pool_type="sum")
dnn_out = dnn_pool
for i, dim in enumerate(dnn_layer_dims[1:]):
fc = fluid.layers.fc(
input=dnn_out,
size=dim,
act="relu",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)),
name='dnn-fc-%d' % i)
dnn_out = fc
# build lr model
lr_embbding = fluid.layers.embedding(
is_distributed=False,
input=lr_data,
size=[lr_input_dim, 1],
param_attr=fluid.ParamAttr(
name="wide_embedding",
initializer=fluid.initializer.Constant(value=0.01)),
is_sparse=IS_SPARSE)
lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")
merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
acc = fluid.layers.accuracy(input=predict, label=label)
auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
label=label)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
inference_program = paddle.fluid.default_main_program().clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
sgd_optimizer.minimize(avg_cost)
dataset = dist_ctr_reader.Dataset()
train_reader = paddle.batch(dataset.train(), batch_size=batch_size)
test_reader = paddle.batch(dataset.test(), batch_size=batch_size)
return inference_program, avg_cost, train_reader, test_reader, None, predict
if __name__ == "__main__":
runtime_main(TestDistCTR2x2)
python/paddle/fluid/tests/unittests/dist_ctr_reader.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import paddle
import tarfile
logging.basicConfig()
logger = logging.getLogger("paddle")
logger.setLevel(logging.INFO)
DATA_URL = "http://paddle-ctr-data.cdn.bcebos.com/avazu_ctr_data.tgz"
DATA_MD5 = "c11df99fbd14e53cd4bfa6567344b26e"
"""
avazu_ctr_data/train.txt
avazu_ctr_data/infer.txt
avazu_ctr_data/test.txt
avazu_ctr_data/data.meta.txt
"""
def read_data(file_name):
path = paddle.dataset.common.download(DATA_URL, "avazu_ctr_data", DATA_MD5)
tar = tarfile.open(path, "r:gz")
tar_info = None
for member in tar.getmembers():
if member.name.endswith(file_name):
tar_info = member
f = tar.extractfile(tar_info)
ret_lines = [_.decode('utf-8') for _ in f.readlines()]
return ret_lines
class TaskMode:
TRAIN_MODE = 0
TEST_MODE = 1
INFER_MODE = 2
def __init__(self, mode):
self.mode = mode
def is_train(self):
return self.mode == self.TRAIN_MODE
def is_test(self):
return self.mode == self.TEST_MODE
def is_infer(self):
return self.mode == self.INFER_MODE
@staticmethod
def create_train():
return TaskMode(TaskMode.TRAIN_MODE)
@staticmethod
def create_test():
return TaskMode(TaskMode.TEST_MODE)
@staticmethod
def create_infer():
return TaskMode(TaskMode.INFER_MODE)
class ModelType:
CLASSIFICATION = 0
REGRESSION = 1
def __init__(self, mode):
self.mode = mode
def is_classification(self):
return self.mode == self.CLASSIFICATION
def is_regression(self):
return self.mode == self.REGRESSION
@staticmethod
def create_classification():
return ModelType(ModelType.CLASSIFICATION)
@staticmethod
def create_regression():
return ModelType(ModelType.REGRESSION)
def load_dnn_input_record(sent):
return list(map(int, sent.split()))
def load_lr_input_record(sent):
res = []
for _ in [x.split(':') for x in sent.split()]:
res.append(int(_[0]))
return res
feeding_index = {'dnn_input': 0, 'lr_input': 1, 'click': 2}
class Dataset(object):
def train(self):
'''
Load trainset.
'''
file_name = "train.txt"
logger.info("load trainset from %s" % file_name)
mode = TaskMode.create_train()
return self._parse_creator(file_name, mode)
def test(self):
'''
Load testset.
'''
file_name = "test.txt"
logger.info("load testset from %s" % file_name)
mode = TaskMode.create_test()
return self._parse_creator(file_name, mode)
def infer(self):
'''
Load infer set.
'''
file_name = "infer.txt"
logger.info("load inferset from %s" % file_name)
mode = TaskMode.create_infer()
return self._parse_creator(file_name, mode)
def _parse_creator(self, file_name, mode):
'''
Parse dataset.
'''
def _parse():
data = read_data(file_name)
for line_id, line in enumerate(data):
fs = line.strip().split('\t')
dnn_input = load_dnn_input_record(fs[0])
lr_input = load_lr_input_record(fs[1])
if not mode.is_infer():
click = int(fs[2])
yield [dnn_input, lr_input, click]
else:
yield [dnn_input, lr_input]
return _parse
def load_data_meta():
'''
load data meta info from path, return (dnn_input_dim, lr_input_dim)
'''
lines = read_data('data.meta.txt')
err_info = "wrong meta format"
assert len(lines) == 2, err_info
assert 'dnn_input_dim:' in lines[0] and 'lr_input_dim:' in lines[
1], err_info
res = map(int, [_.split(':')[1] for _ in lines])
res = list(res)
logger.info('dnn input dim: %d' % res[0])
logger.info('lr input dim: %d' % res[1])
return res
python/paddle/fluid/tests/unittests/dist_mnist.py
浏览文件 @ 77fc42d2
......@@ -47,7 +47,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.3)))
value=0.01)))
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......@@ -56,7 +56,7 @@ def cnn_model(data):
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.2)))
value=0.01)))
SIZE = 10
input_shape = conv_pool_2.shape
......@@ -68,7 +68,7 @@ def cnn_model(data):
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
initializer=fluid.initializer.Constant(value=0.01)))
return predict
......
python/paddle/fluid/tests/unittests/dist_simnet_bow.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import argparse
import time
import math
import random
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "int64"
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000'
DATA_MD5 = '24e49366eb0611c552667989de2f57d5'
# For Net
base_lr = 0.2
emb_lr = base_lr * 3
dict_dim = 1500
emb_dim = 128
hid_dim = 128
margin = 0.1
sample_rate = 1
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
def get_acc(cos_q_nt, cos_q_pt, batch_size):
cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
cond = fluid.layers.cast(cond, dtype='float64')
cond_3 = fluid.layers.reduce_sum(cond)
acc = fluid.layers.elementwise_div(
cond_3,
fluid.layers.fill_constant(
shape=[1], value=batch_size * 1.0, dtype='float64'),
name="simnet_acc")
return acc
def get_loss(cos_q_pt, cos_q_nt):
loss_op1 = fluid.layers.elementwise_sub(
fluid.layers.fill_constant_batch_size_like(
input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'),
cos_q_pt)
loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
loss_op3 = fluid.layers.elementwise_max(
fluid.layers.fill_constant_batch_size_like(
input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_op2)
avg_cost = fluid.layers.mean(loss_op3)
return avg_cost
def get_optimizer():
# SGD optimizer
optimizer = fluid.optimizer.SGD(learning_rate=base_lr)
return optimizer
def train_network(batch_size, is_distributed=False, is_sparse=False):
# query
q = fluid.layers.data(
name="query_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
q_emb = fluid.layers.embedding(
input=q,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
## fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__q_fc__",
learning_rate=base_lr))
# label data
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
# pt
pt = fluid.layers.data(
name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
pt_emb = fluid.layers.embedding(
input=pt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
## fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
# nt
nt = fluid.layers.data(
name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
## embedding
nt_emb = fluid.layers.embedding(
input=nt,
is_distributed=is_distributed,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__",
learning_rate=emb_lr),
is_sparse=is_sparse)
## vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
## fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
size=hid_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__fc__",
learning_rate=base_lr),
bias_attr=fluid.ParamAttr(name="__fc_b__"))
cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
# loss
avg_cost = get_loss(cos_q_pt, cos_q_nt)
# acc
acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
return [avg_cost, acc, cos_q_pt]
def combination(x, y):
res = [[[xi, yi] for yi in y] for xi in x]
return res[0]
def get_one_data(file_list):
for file in file_list:
contents = []
with open(file, "r") as fin:
for i in fin:
contents.append(i.strip())
for index, q in enumerate(contents):
try:
one_data = [[int(j) for j in i.split(" ")]
for i in q.split(";")[:-1]]
if one_data[1][0] + one_data[1][1] != len(one_data) - 3:
q = fin.readline()
continue
tmp = combination(one_data[3:3 + one_data[1][0]],
one_data[3 + one_data[1][0]:])
except Exception as e:
continue
for each in tmp:
yield [one_data[2], 0, each[0], each[1]]
def get_batch_reader(file_list, batch_size):
def batch_reader():
res = []
for i in get_one_data(file_list):
if random.random() <= sample_rate:
res.append(i)
if len(res) >= batch_size:
yield res
res = []
return batch_reader
def get_train_reader(batch_size):
# The training data set.
train_file = os.path.join(paddle.dataset.common.DATA_HOME, "simnet",
"train")
train_reader = get_batch_reader([train_file], batch_size)
train_feed = ["query_ids", "pos_title_ids", "neg_title_ids", "label"]
return train_reader, train_feed
class TestDistSimnetBow2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
# Train program
avg_cost, acc, predict = \
train_network(batch_size, bool(int(os.environ["IS_DISTRIBUTED"])), bool(int(os.environ["IS_SPARSE"])))
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer()
opt.minimize(avg_cost)
# Reader
train_reader, _ = get_train_reader(batch_size)
return inference_program, avg_cost, train_reader, train_reader, acc, predict
if __name__ == "__main__":
paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
runtime_main(TestDistSimnetBow2x2)
python/paddle/fluid/tests/unittests/dist_text_classification.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
import six
import tarfile
import string
import re
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "float32"
VOCAB_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/imdb.vocab'
VOCAB_MD5 = '23c86a0533c0151b6f12fa52b106dcc2'
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/text_classification.tar.gz'
DATA_MD5 = '29ebfc94f11aea9362bbb7f5e9d86b8a'
# Load dictionary.
def load_vocab(filename):
vocab = {}
if six.PY2:
with open(filename, 'r') as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
else:
with open(filename, 'r', encoding="utf-8") as f:
for idx, line in enumerate(f):
vocab[line.strip()] = idx
return vocab
def get_worddict(dict_path):
word_dict = load_vocab(dict_path)
word_dict["<unk>"] = len(word_dict)
dict_dim = len(word_dict)
return word_dict, dict_dim
def conv_net(input,
dict_dim,
emb_dim=128,
window_size=3,
num_filters=128,
fc0_dim=96,
class_dim=2):
emb = fluid.layers.embedding(
input=input,
size=[dict_dim, emb_dim],
is_sparse=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.01)))
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=num_filters,
filter_size=window_size,
act="tanh",
pool_type="max",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
fc_0 = fluid.layers.fc(
input=[conv_3],
size=fc0_dim,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
prediction = fluid.layers.fc(
input=[fc_0],
size=class_dim,
act="softmax",
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
return prediction
def inference_network(dict_dim):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
out = conv_net(data, dict_dim)
return out
def get_reader(word_dict, batch_size):
# The training data set.
train_reader = paddle.batch(train(word_dict), batch_size=batch_size)
# The testing data set.
test_reader = paddle.batch(test(word_dict), batch_size=batch_size)
return train_reader, test_reader
def get_optimizer(learning_rate):
optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
return optimizer
class TestDistTextClassification2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
vocab = os.path.join(paddle.dataset.common.DATA_HOME,
"text_classification", "imdb.vocab")
word_dict, dict_dim = get_worddict(vocab)
# Input data
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = conv_net(data, dict_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=predict, label=label)
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer(learning_rate=0.001)
opt.minimize(avg_cost)
# Reader
train_reader, test_reader = get_reader(word_dict, batch_size)
return inference_program, avg_cost, train_reader, test_reader, acc, predict
def tokenize(pattern):
"""
Read files that match the given pattern. Tokenize and yield each file.
"""
with tarfile.open(
paddle.dataset.common.download(DATA_URL, 'text_classification',
DATA_MD5)) as tarf:
# Note that we should use tarfile.next(), which does
# sequential access of member files, other than
# tarfile.extractfile, which does random access and might
# destroy hard disks.
tf = tarf.next()
while tf != None:
if bool(pattern.match(tf.name)):
# newline and punctuations removal and ad-hoc tokenization.
yield tarf.extractfile(tf).read().rstrip(six.b(
"\n\r")).translate(
None, six.b(string.punctuation)).lower().split()
tf = tarf.next()
def reader_creator(pos_pattern, neg_pattern, word_idx):
UNK = word_idx['<unk>']
INS = []
def load(pattern, out, label):
for doc in tokenize(pattern):
out.append(([word_idx.get(w, UNK) for w in doc], label))
load(pos_pattern, INS, 0)
load(neg_pattern, INS, 1)
def reader():
for doc, label in INS:
yield doc, label
return reader
def train(word_idx):
"""
IMDB training set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
re.compile("train/pos/.*\.txt$"),
re.compile("train/neg/.*\.txt$"), word_idx)
def test(word_idx):
"""
IMDB test set creator.
It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].
:param word_idx: word dictionary
:type word_idx: dict
:return: Test reader creator
:rtype: callable
"""
return reader_creator(
re.compile("test/pos/.*\.txt$"),
re.compile("test/neg/.*\.txt$"), word_idx)
if __name__ == "__main__":
paddle.dataset.common.download(VOCAB_URL, 'text_classification', VOCAB_MD5)
paddle.dataset.common.download(DATA_URL, 'text_classification', DATA_MD5)
runtime_main(TestDistTextClassification2x2)
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ 77fc42d2
......@@ -1488,7 +1488,7 @@ def wrap_decoder(trg_vocab_size,
if weight_sharing:
predict = layers.matmul(
x=dec_output,
y=fluid.get_var(word_emb_param_names[0]),
y=fluid.framework._get_var(word_emb_param_names[0]),
transpose_y=True)
else:
predict = layers.fc(input=dec_output,
......@@ -1699,10 +1699,9 @@ class DistTransformer2x2(TestDistRunnerBase):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
TrainTaskConfig.use_gpu = use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......@@ -1718,6 +1717,11 @@ class DistTransformer2x2(TestDistRunnerBase):
TrainTaskConfig.batch_size = 20
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
TrainTaskConfig.local = not args.is_dist
......
python/paddle/fluid/tests/unittests/dist_word2vec.py
浏览文件 @ 77fc42d2
......@@ -122,4 +122,7 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
if __name__ == "__main__":
import os
os.environ['CPU_NUM'] = '1'
os.environ['USE_CUDA'] = "FALSE"
runtime_main(TestDistWord2vec2x2)
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 77fc42d2
......@@ -345,7 +345,7 @@ class OpTest(unittest.TestCase):
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t))
str(actual_t) + " in class " + self.__class__.__name__)
if isinstance(expect, tuple):
self.assertListEqual(actual.recursive_sequence_lengths(),
expect[1], "Output (" + out_name +
......
python/paddle/fluid/tests/unittests/test_auc_op.py
浏览文件 @ 77fc42d2
......@@ -36,7 +36,11 @@ class TestAucOp(OpTest):
"StatPos": stat_pos,
"StatNeg": stat_neg
}
self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
self.attrs = {
'curve': 'ROC',
'num_thresholds': num_thresholds,
"slide_steps": 1
}
python_auc = metrics.Auc(name="auc",
curve='ROC',
......@@ -45,7 +49,6 @@ class TestAucOp(OpTest):
self.outputs = {
'AUC': np.array(python_auc.eval()),
'BatchAUC': np.array(python_auc.eval()),
'StatPosOut': np.array(python_auc._stat_pos),
'StatNegOut': np.array(python_auc._stat_neg)
}
......
python/paddle/fluid/tests/unittests/test_detection_map_op.py
浏览文件 @ 77fc42d2
......@@ -20,6 +20,7 @@ import six
import sys
import collections
import math
import paddle.fluid as fluid
from op_test import OpTest
......@@ -32,7 +33,7 @@ class TestDetectionMAPOp(OpTest):
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
if (len(self.class_pos_count) > 0):
if len(self.class_pos_count) > 0:
self.class_pos_count = np.array(self.class_pos_count).astype(
'int32')
self.true_pos = np.array(self.true_pos).astype('float32')
......@@ -273,7 +274,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp):
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1]
self.class_pos_count = [0, 2, 1, 0]
self.true_pos_lod = [[0, 3, 2]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 3, 2]]
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 77fc42d2
......@@ -18,23 +18,27 @@ import time
import unittest
import os
import sys
import six
import signal
import subprocess
import six
import argparse
import paddle.fluid as fluid
RUN_STEP = 10
class TestDistRunnerBase(object):
def get_model(self, batch_size=2):
raise NotImplementedError(
"get_model should be implemented by child classes.")
def get_transpiler(self, trainer_id, main_program, pserver_endpoints,
trainers, sync_mode):
@staticmethod
def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers,
sync_mode):
# NOTE: import fluid until runtime, or else forking processes will cause error.
import paddle
import paddle.fluid as fluid
t = fluid.DistributeTranspiler()
config = fluid.DistributeTranspilerConfig()
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id=trainer_id,
program=main_program,
......@@ -44,11 +48,9 @@ class TestDistRunnerBase(object):
return t
def run_pserver(self, args):
import paddle
import paddle.fluid as fluid
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
# NOTE: pserver should not call memory optimize
t = self.get_transpiler(args.trainer_id,
fluid.default_main_program(), args.endpoints,
args.trainers, args.sync_mode)
......@@ -61,29 +63,34 @@ class TestDistRunnerBase(object):
exe.run(startup_prog)
exe.run(pserver_prog)
def run_trainer(self, use_cuda, args):
import paddle
import paddle.fluid as fluid
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
def run_trainer(self, args):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program(), skip_grads=True)
if args.is_dist:
t = self.get_transpiler(args.trainer_id,
fluid.default_main_program(),
args.endpoints, args.trainers,
args.sync_mode)
trainer_prog = t.get_trainer_program()
else:
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
build_stra = fluid.BuildStrategy()
if args.use_reduce:
......@@ -92,7 +99,7 @@ class TestDistRunnerBase(object):
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
exe = fluid.ParallelExecutor(
use_cuda,
args.use_cuda,
loss_name=avg_cost.name,
exec_strategy=strategy,
build_strategy=build_stra)
......@@ -103,27 +110,26 @@ class TestDistRunnerBase(object):
]
feeder = fluid.DataFeeder(feed_var_list, place)
reader_generator = test_reader()
data = next(reader_generator)
first_loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(data))
print(first_loss)
reader_generator = train_reader()
for i in six.moves.xrange(5):
data = next(reader_generator)
loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
def get_data():
origin_batch = next(reader_generator)
if args.is_dist and args.use_reader_alloc:
new_batch = []
for offset, item in enumerate(origin_batch):
if offset % 2 == args.trainer_id:
new_batch.append(item)
return new_batch
else:
return origin_batch
data = next(reader_generator)
last_loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
print(last_loss)
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
print(loss)
def runtime_main(test_class):
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
parser = argparse.ArgumentParser(description='Run dist test.')
parser.add_argument(
'--role', type=str, required=True, choices=['pserver', 'trainer'])
......@@ -135,7 +141,10 @@ def runtime_main(test_class):
'--current_endpoint', type=str, required=False, default="")
parser.add_argument('--sync_mode', action='store_true')
parser.add_argument('--mem_opt', action='store_true')
parser.add_argument('--use_cuda', action='store_true')
parser.add_argument('--use_reduce', action='store_true')
parser.add_argument(
'--use_reader_alloc', action='store_true', required=False, default=True)
args = parser.parse_args()
......@@ -143,8 +152,7 @@ def runtime_main(test_class):
if args.role == "pserver" and args.is_dist:
model.run_pserver(args)
else:
use_cuda = True if core.is_compiled_with_cuda() else False
model.run_trainer(use_cuda, args)
model.run_trainer(args)
import paddle.compat as cpt
......@@ -163,8 +171,10 @@ class TestDistBase(unittest.TestCase):
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._use_cuda = True
self._mem_opt = False
self._use_reduce = False
self._use_reader_alloc = True
self._setup_config()
def _find_free_port(self):
......@@ -172,15 +182,15 @@ class TestDistBase(unittest.TestCase):
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
def start_pserver(self, model_file, check_error_log, required_envs):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
ps0_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
ps1_cmd = ps_cmd % \
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
if self._sync_mode:
ps0_cmd += " --sync_mode"
......@@ -198,9 +208,15 @@ class TestDistBase(unittest.TestCase):
ps1_pipe = open("/tmp/ps1_err.log", "wb")
ps0_proc = subprocess.Popen(
ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe)
ps0_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps0_pipe,
env=required_envs)
ps1_proc = subprocess.Popen(
ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe)
ps1_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps1_pipe,
env=required_envs)
if not check_error_log:
return ps0_proc, ps1_proc, None, None
......@@ -222,59 +238,60 @@ class TestDistBase(unittest.TestCase):
(e, retry_times))
retry_times -= 1
def check_with_place(self, model_file, delta=1e-3, check_error_log=False):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
"CPU_NUM": "1"
}
def _run_local(self, model, envs, check_error_log):
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
cmd = "%s %s --role trainer" % (self._python_interp, model)
if self._use_cuda:
cmd += " --use_cuda"
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
else:
env_local = {'CPU_NUM': '1'}
envs.update(env_local)
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s %s --role trainer" % (self._python_interp, model_file)
if not check_error_log:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
stderr=err_log,
env=envs)
else:
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
cmd.split(" "),
stdout=subprocess.PIPE,
stderr=err_log,
env=env_local)
stderr=subprocess.PIPE,
env=envs)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = cpt.to_text(out)
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
local_out, local_err = local_proc.communicate()
local_ret = cpt.to_text(local_out)
if check_error_log:
err_log.close()
sys.stderr.write('local_stdout: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % local_err)
local_losses = local_ret.split("\n")
return local_losses
def _run_cluster(self, model, envs, check_error_log):
# Run dist train to compare with local results
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model_file,
check_error_log)
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model,
check_error_log, envs)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
tr0_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
0, ps0_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
0, ps0_ep, self._trainers)
tr1_cmd = tr_cmd % \
(self._python_interp, model_file, self._ps_endpoints,
1, ps1_ep, self._trainers)
(self._python_interp, model, self._ps_endpoints,
1, ps1_ep, self._trainers)
if self._sync_mode:
tr0_cmd += " --sync_mode"
......@@ -285,18 +302,28 @@ class TestDistBase(unittest.TestCase):
if self._use_reduce:
tr0_cmd += " --use_reduce"
tr1_cmd += " --use_reduce"
if self._use_reader_alloc:
tr0_cmd += " --use_reader_alloc"
tr1_cmd += " --use_reader_alloc"
if self._use_cuda:
tr0_cmd += " --use_cuda"
tr1_cmd += " --use_cuda"
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
else:
env0 = {'CPU_NUM': '1'}
env1 = {'CPU_NUM': '1'}
env0.update(envs)
env1.update(envs)
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0.update(required_envs)
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_pipe = subprocess.PIPE
tr1_pipe = subprocess.PIPE
if check_error_log:
print("tr0_cmd:", tr0_cmd)
print("tr1_cmd:", tr1_cmd)
print("tr0_cmd:{}, env0: {}".format(tr0_cmd, env0))
print("tr1_cmd:{}, env1: {}".format(tr1_cmd, env1))
tr0_pipe = open("/tmp/tr0_err.log", "wb")
tr1_pipe = open("/tmp/tr1_err.log", "wb")
......@@ -313,17 +340,11 @@ class TestDistBase(unittest.TestCase):
tr0_proc.wait()
tr1_proc.wait()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = cpt.to_text(out)
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
local_lines = local_ret.split("\n")
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
tr0_out, tr0_err = tr0_proc.communicate()
tr0_loss_text = cpt.to_text(tr0_out)
tr1_out, tr1_err = tr1_proc.communicate()
tr1_loss_text = cpt.to_text(tr1_out)
# close trainer file
if check_error_log:
......@@ -341,5 +362,47 @@ class TestDistBase(unittest.TestCase):
ps1.wait()
FNULL.close()
self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
# print log
sys.stderr.write('trainer 0 stdout:\n %s\n' % tr0_loss_text)
sys.stderr.write('trainer 0 stderr:\n %s\n' % tr0_err)
sys.stderr.write('trainer 1 stdout: %s\n' % tr1_loss_text)
sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
tr0_losses = tr0_loss_text.split("\n")
tr1_losses = tr1_loss_text.split("\n")
return tr0_losses, tr1_losses
def check_with_place(self,
model_file,
delta=1e-3,
check_error_log=False,
need_envs={}):
# TODO(typhoonzero): should auto adapt GPU count on the machine.
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1",
}
required_envs.update(need_envs)
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
local_losses\
= self._run_local(model_file, required_envs,
check_error_log)
tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs,
check_error_log)
for step_id in range(RUN_STEP):
local_loss = eval(local_losses[step_id])[0]
tr0_loss = eval(tr0_losses[step_id])[0]
tr1_loss = eval(tr1_losses[step_id])[0]
dist_loss = (tr0_loss + tr1_loss) / 2
print(str(local_loss) + ":" + str(dist_loss))
self.assertAlmostEqual(local_loss, dist_loss, delta=delta)
python/paddle/fluid/tests/unittests/test_dist_ctr.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistCTR2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_dist_ctr(self):
self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ 77fc42d2
......@@ -23,7 +23,7 @@ class TestDistMnist2x2(TestDistBase):
self._use_reduce = False
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnist2x2WithMemopt(TestDistBase):
......@@ -32,7 +32,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnistAsync(TestDistBase):
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 77fc42d2
......@@ -20,24 +20,25 @@ from test_dist_base import TestDistBase
class TestDistSeResneXt2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
self.check_with_place("dist_se_resnext.py", delta=100)
# TODO(typhoonzero): fix this test
# class TestDistseResnXt2x2WithMemopt(TestDistBase):
# def _setup_config(self):
# self._sync_mode = True
# self._mem_opt = True
class TestDistseResnXt2x2WithMemopt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
# def test_dist_train(self):
# self.check_with_place("dist_se_resnext.py", delta=1e-7)
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
class TestDistSeResneXt2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistSimnetBowDense2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2DenseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '0'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBowSparse2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
class TestDistSimnetBow2x2SparseAsync(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_simnet_bow(self):
need_envs = {"IS_DISTRIBUTED": '0', "IS_SPARSE": '1'}
self.check_with_place(
"dist_simnet_bow.py",
delta=100,
check_error_log=False,
need_envs=need_envs)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_text_classification.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import unittest
from test_dist_base import TestDistBase
class TestDistTextClassification2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_cuda = False
def test_text_classification(self):
self.check_with_place("dist_text_classification.py", delta=1e-6)
class TestDistTextClassification2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
self._use_cuda = False
def test_se_resnext(self):
self.check_with_place("dist_text_classification.py", delta=100)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 77fc42d2
......@@ -264,6 +264,25 @@ class TestLRDecay(TranspilerTest):
])
class TestDecayedAdagrad(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
opt = fluid.optimizer.DecayedAdagrad(learning_rate=0.1)
opt.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
class TestLRDecayConditional(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
......
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @ 77fc42d2
......@@ -39,7 +39,7 @@ class TestDistW2V2x2Async(TestDistBase):
self._sync_mode = False
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1)
self.check_with_place("dist_word2vec.py", delta=100)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_generate_proposals_op.py
浏览文件 @ 77fc42d2
......@@ -277,7 +277,6 @@ class TestGenerateProposalsOp(OpTest):
'eta': self.eta
}
print("lod = ", self.lod)
self.outputs = {
'RpnRois': (self.rpn_rois[0], [self.lod]),
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.lod])
......@@ -295,7 +294,7 @@ class TestGenerateProposalsOp(OpTest):
self.post_nms_topN = 5000 # train 6000, test 1000
self.nms_thresh = 0.7
self.min_size = 3.0
self.eta = 0.8
self.eta = 1.
def init_test_input(self):
batch_size = 1
......
python/paddle/fluid/tests/unittests/test_infer_shape.py
浏览文件 @ 77fc42d2
......@@ -76,8 +76,8 @@ class TestInferShape(unittest.TestCase):
mul_op_desc.set_input("X", ["x"])
mul_op_desc.set_input("Y", ["y"])
mul_op_desc.set_output("Out", ["out"])
mul_op_desc.set_attr("x_num_col_dims", 1)
mul_op_desc.set_attr("y_num_col_dims", 1)
mul_op_desc._set_attr("x_num_col_dims", 1)
mul_op_desc._set_attr("y_num_col_dims", 1)
mul_op_desc.check_attrs()
mul_op_desc.infer_shape(block)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 77fc42d2
......@@ -541,7 +541,7 @@ class TestBook(unittest.TestCase):
with program_guard(program):
input = layers.data(
name="input", shape=[3, 100, 100], dtype="float32")
out = layers.shape(input, name="shape")
out = layers.shape(input)
self.assertIsNotNone(out)
print(str(program))
......@@ -758,6 +758,65 @@ class TestBook(unittest.TestCase):
out = layers.expand(x, [1, 2])
print(str(program))
def test_uniform_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.uniform_random_batch_size_like(input, [-1, 11])
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random(self):
program = Program()
with program_guard(program):
out = layers.gaussian_random(shape=[20, 30])
self.assertIsNotNone(out)
print(str(program))
def test_sampling_id(self):
program = Program()
with program_guard(program):
x = layers.data(
name="X",
shape=[13, 11],
dtype='float32',
append_batch_size=False)
out = layers.sampling_id(x)
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.gaussian_random_batch_size_like(
input, shape=[-1, 11], mean=1.0, std=2.0)
self.assertIsNotNone(out)
print(str(program))
def test_sum(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.sum(input)
self.assertIsNotNone(out)
print(str(program))
def test_slice(self):
starts = [1, 0, 2]
ends = [3, 3, 4]
axes = [0, 1, 2]
program = Program()
with program_guard(program):
input = layers.data(
name="input", shape=[3, 4, 5, 6], dtype='float32')
out = layers.slice(input, axes=axes, starts=starts, ends=ends)
def test_softshrink(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_pass_builder.py 0 → 100644
浏览文件 @ 77fc42d2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
import paddle.fluid.core as core
import numpy as np
import unittest
import os
import sys
import math
def simple_fc_net():
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = img
for _ in range(4):
hidden = fluid.layers.fc(
hidden,
size=200,
act='tanh',
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=1.0)))
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestPassBuilder(unittest.TestCase):
def check_network_convergence(self, use_cuda, build_strategy=None):
os.environ['CPU_NUM'] = str(4)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = simple_fc_net()
test_program = main.clone(for_test=True)
opt = fluid.optimizer.SGD(learning_rate=0.001)
opt.minimize(loss)
batch_size = 32
image = np.random.normal(size=(batch_size, 784)).astype('float32')
label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
feed_dict = {'image': image, 'label': label}
train_exe = fluid.ParallelExecutor(
use_cuda=use_cuda,
loss_name=loss.name,
main_program=main,
build_strategy=build_strategy)
test_exe = fluid.ParallelExecutor(
use_cuda=use_cuda,
main_program=test_program,
share_vars_from=train_exe,
build_strategy=build_strategy)
for i in range(5):
test_loss, = test_exe.run([loss.name], feed=feed_dict)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
avg_test_loss_val = np.array(test_loss).mean()
if math.isnan(float(avg_test_loss_val)):
sys.exit("got NaN loss, testing failed.")
avg_train_loss_val = np.array(train_loss).mean()
if math.isnan(float(avg_train_loss_val)):
sys.exit("got NaN loss, training failed.")
self.assertTrue(
np.allclose(
train_loss, test_loss, atol=1e-8),
"Train loss: " + str(train_loss) + "\n Test loss:" +
str(test_loss))
def test_parallel_testing_with_new_strategy(self):
build_strategy = fluid.BuildStrategy()
pass_builder = build_strategy._create_passes_from_strategy()
origin_len = len(pass_builder.all_passes())
viz_pass = pass_builder.append_pass("graph_viz_pass")
self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
pass_builder.insert_pass(
len(pass_builder.all_passes()), "graph_viz_pass")
self.assertEqual(origin_len + 2, len(pass_builder.all_passes()))
pass_builder.remove_pass(len(pass_builder.all_passes()) - 1)
self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
viz_pass.set_str("graph_viz_path", "/tmp/test_viz_pass")
self.check_network_convergence(
use_cuda=core.is_compiled_with_cuda(),
build_strategy=build_strategy)
try:
os.stat("/tmp/test_viz_pass")
except os.error:
self.assertFalse(True)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ 77fc42d2
......@@ -38,40 +38,40 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(['z'], op.output("Out"))
self.assertEqual(["Out"], op.output_names())
op.set_attr("int_attr", 1)
op._set_attr("int_attr", 1)
self.assertEqual(1, op.attr("int_attr"))
self.assertTrue(op.has_attr("int_attr"))
self.assertEqual(core.AttrType.INT, op.attr_type("int_attr"))
op.set_attr("float_attr", -1.32)
op._set_attr("float_attr", -1.32)
self.assertAlmostEqual(-1.32, op.attr("float_attr"), delta=1e-4)
self.assertTrue(op.has_attr("float_attr"))
op.set_attr("bool_attr", False)
op._set_attr("bool_attr", False)
self.assertFalse(op.attr("bool_attr"))
op.set_attr("string_attr", "abc")
op._set_attr("string_attr", "abc")
self.assertEqual("abc", op.attr("string_attr"))
self.assertTrue(op.has_attr("string_attr"))
op.set_attr("ints_attr", [1, 2, 3])
op._set_attr("ints_attr", [1, 2, 3])
self.assertEqual([1, 2, 3], op.attr("ints_attr"))
expected = [1.2, 2.3, 3.4]
op.set_attr("floats_attr", expected)
op._set_attr("floats_attr", expected)
for e, a in zip(expected, op.attr("floats_attr")):
self.assertAlmostEqual(e, a, delta=1e-4)
op.set_attr("strings_attr", ["a", "b", "c"])
op._set_attr("strings_attr", ["a", "b", "c"])
self.assertEqual(["a", "b", "c"], op.attr("strings_attr"))
op.set_attr("bools_attr", [True, False, True])
op._set_attr("bools_attr", [True, False, True])
self.assertEqual([True, False, True], op.attr("bools_attr"))
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr_id("block_attr"))
op.set_block_attr("_block_attr", program_desc.block(0))
self.assertEqual(0, op._block_attr_id("_block_attr"))
mul_op = block.append_op()
mul_op.set_type("mul")
......
python/paddle/fluid/tests/unittests/transformer_model.py
浏览文件 @ 77fc42d2
......@@ -246,6 +246,7 @@ def prepare_encoder(src_word,
padding_idx=pos_pad_idx,
param_attr=fluid.ParamAttr(
name=pos_enc_param_name, trainable=False))
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
# FIXME(guosheng): Decouple the program desc with batch_size.
......
python/paddle/fluid/transpiler/__init__.py
浏览文件 @ 77fc42d2
......@@ -20,6 +20,10 @@ from .memory_optimization_transpiler import memory_optimize, release_memory
from .ps_dispatcher import HashName, RoundRobin
__all__ = [
"DistributeTranspiler", "memory_optimize", "release_memory", "HashName",
"RoundRobin", "DistributeTranspilerConfig"
"DistributeTranspiler",
"memory_optimize",
"release_memory",
"HashName",
"RoundRobin",
"DistributeTranspilerConfig",
]
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 77fc42d2
......@@ -128,7 +128,7 @@ def op_to_code(op):
attr_type = op.desc.attr_type(name)
if attr_type == core.AttrType.BLOCK:
a = "{name} = block[{value}]".format(
name=name, type=attr_type, value=op.block_attr_id(name))
name=name, type=attr_type, value=op._block_attr_id(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......@@ -136,7 +136,7 @@ def op_to_code(op):
if attr_type == core.AttrType.BLOCKS:
a = "{name} = blocks{value}".format(
name=name, type=attr_type, value=op.blocks_attr_ids(name))
name=name, type=attr_type, value=op._blocks_attr_ids(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 77fc42d2
......@@ -39,8 +39,8 @@ import six
from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, Block, \
Parameter, grad_var_name
default_startup_program, Block, \
Parameter, grad_var_name
from .details import *
from functools import reduce
......@@ -178,7 +178,7 @@ class DistributeTranspiler(object):
pserver_program)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
......@@ -470,7 +470,10 @@ class DistributeTranspiler(object):
"""
# remove optimize ops and add a send op to main_program
# FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
lr_ops = self._get_lr_ops()
delete_ops(self.origin_program.global_block(), self.optimize_ops)
delete_ops(self.origin_program.global_block(), lr_ops)
self.origin_program.__str__()
if wait_port:
......@@ -534,7 +537,7 @@ class DistributeTranspiler(object):
})
for varname, splited_var in six.iteritems(self.param_var_mapping):
#add concat ops to merge splited parameters received from parameter servers.
# add concat ops to merge splited parameters received from parameter servers.
if len(splited_var) <= 1:
continue
# NOTE: if enable memory optimization, origin vars maybe removed.
......@@ -668,7 +671,7 @@ in a single call.")
__clone_lr_op_sub_block__(cloned_op, program, new_sub_block)
# reset the block of op
op.set_attr('sub_block', new_sub_block)
op._set_attr('sub_block', new_sub_block)
# append lr decay ops to the child block if exists
lr_ops = self._get_lr_ops()
......@@ -734,19 +737,14 @@ in a single call.")
table_opt_block = self._create_table_optimize_block(
pserver_index, pserver_program, pre_block_idx, grad_to_block_id)
optimize_blocks.append(table_opt_block)
prefetch_var_name_to_block_id = self._create_prefetch_block(
lookup_table_var_name_to_block_id = self._create_prefetch_block(
pserver_index, pserver_program, table_opt_block)
checkpoint_block_id = self._create_checkpoint_save_block(
pserver_program, table_opt_block.idx)
pserver_program._distributed_lookup_table = self.table_name
# NOTE: if has_distributed_lookup_table is False, then prefetch_block will
# not be executed, so it's safe to use optimize_block to hold the place
if self.has_distributed_lookup_table:
assert len(prefetch_var_name_to_block_id) > 0
else:
assert len(prefetch_var_name_to_block_id) == 0
prefetch_var_name_to_block_id.extend(
lookup_table_var_name_to_block_id)
attrs = {
"optimize_blocks": optimize_blocks,
......@@ -755,11 +753,14 @@ in a single call.")
"sync_mode": self.sync_mode,
"grad_to_block_id": grad_to_block_id,
}
if len(prefetch_var_name_to_block_id) > 0:
attrs['prefetch_var_name_to_block_id'] \
= prefetch_var_name_to_block_id
if self.has_distributed_lookup_table:
attrs['checkpint_block_id'] = checkpoint_block_id
if len(prefetch_var_name_to_block_id) > 0:
attrs[
'prefetch_var_name_to_block_id'] = prefetch_var_name_to_block_id
# step5 append the listen_and_serv op
pserver_program.global_block().append_op(
type="listen_and_serv",
......@@ -864,7 +865,7 @@ to transpile() call.")
if op.type in [
"gaussian_random", "fill_constant", "uniform_random"
]:
op.set_attr("shape", list(new_outputs["Out"].shape))
op._set_attr("shape", list(new_outputs["Out"].shape))
s_prog.global_block().append_op(
type=op.type,
inputs=new_inputs,
......@@ -1013,7 +1014,7 @@ to transpile() call.")
for g, p in zip(grad_blocks, param_blocks):
g_name, g_bid, _ = g.split(":")
p_name, p_bid, _ = p.split(":")
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.param_var_mapping[p_name][int(p_bid)]
# create mapping of endpoint -> split var to create pserver side program
......@@ -1320,7 +1321,7 @@ to transpile() call.")
if len(splited) == 1:
if self.sync_mode and add_trainer_suffix:
new_var_name = "%s.trainer_%d" % \
(orig_var.name, self.trainer_id)
(orig_var.name, self.trainer_id)
program.global_block()._rename_var(varname, new_var_name)
var_mapping[varname] = \
[program.global_block().var(new_var_name)]
......@@ -1343,10 +1344,10 @@ to transpile() call.")
new_var_name = ""
if self.sync_mode and add_trainer_suffix:
new_var_name = "%s.block%d.trainer_%d" % \
(varname, i, self.trainer_id)
(varname, i, self.trainer_id)
else:
new_var_name = "%s.block%d" % \
(varname, i)
(varname, i)
var = program.global_block().create_var(
name=new_var_name,
persistable=False,
......@@ -1430,6 +1431,9 @@ to transpile() call.")
elif op_type == "rmsprop":
if varkey in ["Moment", "MeanSquare"]:
return param_shape
elif op_type == "decayed_adagrad":
if varkey == "Moment":
return param_shape
elif op_type == "sgd":
pass
return orig_shape
......@@ -1484,9 +1488,8 @@ to transpile() call.")
vars2merge = []
for i in range(self.trainer_num):
per_trainer_name = "%s.trainer_%d" % \
(merged_var_name, i)
(merged_var_name, i)
vars2merge.append(pserver_block.vars[per_trainer_name])
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
......@@ -1645,7 +1648,7 @@ to transpile() call.")
# one op's output is another op's input, we say
# the two operator is connected.
if set(op1.desc.output_arg_names()) & set(op2.desc.input_arg_names()) or \
set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()):
set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()):
return True
return False
......@@ -1662,7 +1665,7 @@ to transpile() call.")
def _is_optimizer_op(self, op):
if "Param" in op.input_names and \
"LearningRate" in op.input_names:
"LearningRate" in op.input_names:
return True
return False
......@@ -1737,7 +1740,7 @@ to transpile() call.")
# NOTE: we need to skip all optimize ops, since it is connected
# with forward/backward ops and lr ops, we only need the lr ops.
if op1 != op2 and self._is_op_connected(op1, op2) and \
not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
ufind.union(op1, op2)
# find all ops which is related with lr var
for op1 in block.ops:
......
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ 77fc42d2
......@@ -163,7 +163,7 @@ class InferenceTranspiler(object):
next_op = self.block.ops[i + 1]
if next_op.type == 'relu':
# modify bnorm OP to include relu
current_op.set_attr("fuse_with_relu", True)
current_op._set_attr("fuse_with_relu", True)
# remove relu OP
self.block._remove_op(i + 1)
i = i + 1
......@@ -377,7 +377,7 @@ class InferenceTranspiler(object):
type=old_var.type,
dtype=old_var.dtype,
shape=old_var.shape)
op.rename_input(old_param_name, new_param_name)
op._rename_input(old_param_name, new_param_name)
self.scope.var(new_param_name)
tensor = self.scope.find_var(new_param_name).get_tensor()
......@@ -463,8 +463,8 @@ class InferenceTranspiler(object):
current_op = self.block.ops[i]
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 77fc42d2
......@@ -14,10 +14,10 @@
from __future__ import print_function
from collections import defaultdict, OrderedDict, Callable
from collections import defaultdict, MutableSet
from .. import core
from ... import compat as cpt
from ..framework import Program, default_main_program, Parameter, Variable
from ..framework import Program, default_main_program, Parameter, Variable, core
from ..backward import _rename_arg_
from functools import reduce
from six.moves import range
......@@ -44,17 +44,82 @@ SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
PRINT_LOG = False
class OrderedSet(MutableSet):
def __init__(self, iterable=None):
self.end = end = []
end += [None, end, end] # sentinel node for doubly linked list
self.map = {} # key --> [key, prev, next]
if iterable is not None:
self |= iterable
def __len__(self):
return len(self.map)
def __contains__(self, key):
return key in self.map
def add(self, key):
if key not in self.map:
end = self.end
curr = end[1]
curr[2] = end[1] = self.map[key] = [key, curr, end]
def update(self, other):
for e in other:
self.add(e)
def discard(self, key):
if key in self.map:
key, prev, next = self.map.pop(key)
prev[2] = next
next[1] = prev
def remove(self, key):
self.discard(key)
def __iter__(self):
end = self.end
curr = end[2]
while curr is not end:
yield curr[0]
curr = curr[2]
def __reversed__(self):
end = self.end
curr = end[1]
while curr is not end:
yield curr[0]
curr = curr[1]
def pop(self, last=True):
if not self:
raise KeyError('set is empty')
key = self.end[1][0] if last else self.end[2][0]
self.discard(key)
return key
def __repr__(self):
if not self:
return '%s()' % (self.__class__.__name__, )
return '%s(%r)' % (self.__class__.__name__, list(self))
def __eq__(self, other):
if isinstance(other, OrderedSet):
return len(self) == len(other) and list(self) == list(other)
return set(self) == set(other)
class ControlFlowGraph(object):
def __init__(self, program, ops, forward_num, skip_opt):
self._program = program
self._ops = ops
self._forward_num = forward_num
self._successors = defaultdict(set)
self._presuccessors = defaultdict(set)
self._uses = defaultdict(set)
self._defs = defaultdict(set)
self._live_in = defaultdict(set)
self._live_out = defaultdict(set)
self._successors = defaultdict(OrderedSet)
self._presuccessors = defaultdict(OrderedSet)
self._uses = defaultdict(OrderedSet)
self._defs = defaultdict(OrderedSet)
self._live_in = defaultdict(OrderedSet)
self._live_out = defaultdict(OrderedSet)
self._skip_opt = skip_opt
self.pool = []
......@@ -116,7 +181,7 @@ class ControlFlowGraph(object):
# NOTE: must sort the in_diff set for cases that get different cache var.
# FIXME(typhoonzero): maybe use a "sorted set" is better than this.
can_optimize = [
x for x in sorted(list(in_diff))
x for x in in_diff
if self._check_var_validity(block_desc, x, is_forward)
]
if can_optimize:
......@@ -224,7 +289,7 @@ class ControlFlowGraph(object):
if self.pool:
# NOTE: must sort the in_diff set for cases that get different cache var.
defs_can_optimize = [
x for x in sorted(list(self._defs[i]))
x for x in self._defs[i]
if self._check_var_validity(block_desc, x, is_forward)
]
out_pair = [
......@@ -381,7 +446,19 @@ def _get_cfgs(input_program):
return cfgs
def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
def _is_opt_role_op(op):
op_maker = core.op_proto_and_checker_maker
optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
if op_maker.kOpRoleAttrName() in op.attr_names and \
int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role):
return True
def memory_optimize(input_program,
skip_opt_set=None,
print_log=False,
level=0,
skip_grads=False):
"""Optimize memory by reusing var memory.
Note: it doesn't not support subblock nested in subblock.
......@@ -398,6 +475,19 @@ def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
raise ValueError("only support opt_level 0 or 1.")
global PRINT_LOG
PRINT_LOG = print_log
if skip_grads:
grad_set = set()
OP_ROLE_VAR = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
for op in input_program.global_block().ops:
if _is_opt_role_op(op):
if op.attr(OP_ROLE_VAR):
grad_name = op.attr(OP_ROLE_VAR)[1]
grad_set.add(grad_name)
if not skip_opt_set:
skip_opt_set = grad_set
else:
skip_opt_set.update(grad_set)
cfgs = _get_cfgs(input_program)
for cfg in cfgs:
cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)
......
python/setup.py.in
浏览文件 @ 77fc42d2
......@@ -106,6 +106,7 @@ packages=['paddle',
'paddle.fluid.layers',
'paddle.fluid.contrib',
'paddle.fluid.contrib.decoder',
'paddle.fluid.contrib.quantize',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或