# 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 sys import atexit # The legacy core need to be removed before "import core", # in case of users installing paddlepadde without -U option core_suffix = 'so' if os.name == 'nt': core_suffix = 'pyd' legacy_core = os.path.abspath(os.path.dirname( __file__)) + os.sep + 'core.' + core_suffix if os.path.exists(legacy_core): sys.stderr.write('Deleting legacy file ' + legacy_core + '\n') try: os.remove(legacy_core) except Exception as e: raise e # import all class inside framework into fluid module from . import framework from .framework import * # import all class inside executor into fluid module from . import executor from .executor import * from . import data_feed_desc from .data_feed_desc import * from . import dataset from .dataset import * from .data import * from . import trainer_desc from . import io from . import evaluator from . import initializer from .initializer import set_global_initializer from . import layers from . import dygraph from . import eager from . import contrib from . import nets from . import optimizer from . import backward from .backward import gradients from . import regularizer from . import average from . import metrics from . import transpiler from . import incubate from .input import embedding, one_hot from . import distribute_lookup_table from .param_attr import ParamAttr, WeightNormParamAttr from .data_feeder import DataFeeder from .core import LoDTensor, LoDTensorArray, Scope, _Scope from .core import CPUPlace, XPUPlace, CUDAPlace, CUDAPinnedPlace, NPUPlace, IPUPlace from .incubate import fleet from .transpiler import DistributeTranspiler, \ memory_optimize, release_memory, DistributeTranspilerConfig from .lod_tensor import create_lod_tensor, create_random_int_lodtensor from . import clip from . import profiler from . import unique_name from . import parallel_executor from .parallel_executor import * from . import compiler from .compiler import * from paddle.fluid.layers.math_op_patch import monkey_patch_variable from . import install_check from .dygraph.nn import * from .dygraph.layers import * from .dygraph.base import enable_dygraph, disable_dygraph from .io import save, load, load_program_state, set_program_state from .dygraph.checkpoint import save_dygraph, load_dygraph from .dygraph.varbase_patch_methods import monkey_patch_varbase from .eager.eager_tensor_patch_methods import monkey_patch_eagertensor from . import generator from .core import _cuda_synchronize from .generator import Generator from .trainer_desc import TrainerDesc, DistMultiTrainer, PipelineTrainer, HeterPipelineTrainer, MultiTrainer, HeterXpuTrainer from .transpiler import HashName, RoundRobin from .backward import append_backward Tensor = LoDTensor enable_imperative = enable_dygraph disable_imperative = disable_dygraph __all__ = framework.__all__ + executor.__all__ + \ trainer_desc.__all__ + transpiler.__all__ + \ parallel_executor.__all__ + lod_tensor.__all__ + \ data_feed_desc.__all__ + compiler.__all__ + backward.__all__ + generator.__all__ + [ 'io', 'initializer', 'embedding', 'one_hot', 'layers', 'contrib', 'data', 'dygraph', 'eager', 'enable_dygraph', 'disable_dygraph', 'enable_imperative', 'disable_imperative', 'transpiler', 'nets', 'optimizer', 'backward', 'regularizer', 'LoDTensor', 'LoDTensorArray', 'CPUPlace', 'XPUPlace', 'CUDAPlace', 'CUDAPinnedPlace', 'NPUPlace', 'IPUPlace', 'Tensor', 'ParamAttr', 'WeightNormParamAttr', 'DataFeeder', 'clip', 'profiler', 'unique_name', 'Scope', 'install_check', 'save', 'load', '_cuda_synchronize' ] def __bootstrap__(): """ Enable reading gflags from environment variables. Returns: None """ import sys import os import platform from . import core # NOTE(zhiqiu): When (1)numpy < 1.19; (2) python < 3.7, # unittest is always imported in numpy (maybe some versions not). # so is_test is True and p2p is not inited. in_test = 'unittest' in sys.modules try: num_threads = int(os.getenv('OMP_NUM_THREADS', '1')) except ValueError: num_threads = 1 if num_threads > 1: print( 'WARNING: OMP_NUM_THREADS set to {0}, not 1. The computation ' 'speed will not be optimized if you use data parallel. It will ' 'fail if this PaddlePaddle binary is compiled with OpenBlas since' ' OpenBlas does not support multi-threads.'.format(num_threads), file=sys.stderr) print('PLEASE USE OMP_NUM_THREADS WISELY.', file=sys.stderr) os.environ['OMP_NUM_THREADS'] = str(num_threads) flag_prefix = "FLAGS_" read_env_flags = [ key[len(flag_prefix):] for key in core.globals().keys() if key.startswith(flag_prefix) ] def remove_flag_if_exists(name): if name in read_env_flags: read_env_flags.remove(name) sysstr = platform.system() if 'Darwin' in sysstr: remove_flag_if_exists('use_pinned_memory') if os.name == 'nt': remove_flag_if_exists('cpu_deterministic') if core.is_compiled_with_ipu(): # Currently we request all ipu available for training and testing # finer control of pod of IPUs will be added later read_env_flags += [] core.init_gflags(["--tryfromenv=" + ",".join(read_env_flags)]) # Note(zhouwei25): sys may not have argv in some cases, # Such as: use Python/C API to call Python from C++ try: core.init_glog(sys.argv[0]) except Exception: sys.argv = [""] core.init_glog(sys.argv[0]) # don't init_p2p when in unittest to save time. core.init_devices() # TODO(panyx0718): Avoid doing complex initialization logic in __init__.py. # Consider paddle.init(args) or paddle.main(args) monkey_patch_variable() __bootstrap__() monkey_patch_varbase() monkey_patch_eagertensor() # NOTE(zhiqiu): register npu_finalize on the exit of Python, # do some clean up manually. if core.is_compiled_with_npu(): atexit.register(core.npu_finalize) # NOTE(Aurelius84): clean up ExecutorCacheInfo in advance manually. atexit.register(core.clear_executor_cache)