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__init__.py
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/
__init__.py
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# 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
# 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 . import trainer_desc
from . import inferencer
from . import io
from . import evaluator
from . import initializer
from . import layers
from . import dygraph
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 . import distribute_lookup_table
from .param_attr import ParamAttr, WeightNormParamAttr
from .data_feeder import DataFeeder
from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope, _Scope
from .incubate import fleet
from .incubate import data_generator
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 dygraph_grad_clip
from . import profiler
from . import unique_name
from . import recordio_writer
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 *
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + \
trainer_desc.__all__ + inferencer.__all__ + transpiler.__all__ + \
parallel_executor.__all__ + lod_tensor.__all__ + \
data_feed_desc.__all__ + compiler.__all__ + backward.__all__ + [
'io',
'initializer',
'layers',
'contrib',
'dygraph',
'transpiler',
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'LoDTensorArray',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'dygraph_grad_clip',
'profiler',
'unique_name',
'recordio_writer',
'Scope',
'install_check',
]
def __bootstrap__():
"""
Enable reading gflags from environment variables.
Returns:
None
"""
import sys
import os
import platform
from . import core
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)
sysstr = platform.system()
read_env_flags = [
'check_nan_inf', 'benchmark', 'eager_delete_scope',
'initial_cpu_memory_in_mb', 'init_allocated_mem', 'free_idle_memory',
'paddle_num_threads', "dist_threadpool_size", 'eager_delete_tensor_gb',
'fast_eager_deletion_mode', 'memory_fraction_of_eager_deletion',
'allocator_strategy', 'reader_queue_speed_test_mode',
'print_sub_graph_dir', 'pe_profile_fname', 'inner_op_parallelism',
'enable_parallel_graph', 'fuse_parameter_groups_size',
'multiple_of_cupti_buffer_size', 'fuse_parameter_memory_size',
'tracer_profile_fname', 'dygraph_debug'
]
if 'Darwin' not in sysstr:
read_env_flags.append('use_pinned_memory')
if os.name != 'nt':
read_env_flags.append('cpu_deterministic')
if core.is_compiled_with_mkldnn():
read_env_flags.append('use_mkldnn')
if core.is_compiled_with_ngraph():
read_env_flags.append('use_ngraph')
if core.is_compiled_with_dist():
#env for rpc
read_env_flags.append('rpc_deadline')
read_env_flags.append('rpc_server_profile_path')
read_env_flags.append('enable_rpc_profiler')
read_env_flags.append('rpc_send_thread_num')
read_env_flags.append('rpc_get_thread_num')
read_env_flags.append('rpc_prefetch_thread_num')
read_env_flags.append('rpc_disable_reuse_port')
# env for communicator
read_env_flags.append('communicator_independent_recv_thread')
read_env_flags.append('communicator_send_queue_size')
read_env_flags.append('communicator_min_send_grad_num_before_recv')
read_env_flags.append('communicator_thread_pool_size')
read_env_flags.append('communicator_max_merge_var_num')
read_env_flags.append('communicator_fake_rpc')
read_env_flags.append('communicator_send_wait_times')
if core.is_compiled_with_brpc():
read_env_flags.append('max_body_size')
#set brpc max body size
os.environ['FLAGS_max_body_size'] = "2147483647"
if core.is_compiled_with_cuda():
read_env_flags += [
'fraction_of_gpu_memory_to_use', 'initial_gpu_memory_in_mb',
'reallocate_gpu_memory_in_mb', 'cudnn_deterministic',
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'selected_gpus', 'sync_nccl_allreduce',
'limit_of_tmp_allocation',
'times_excess_than_required_tmp_allocation',
'enable_inplace_whitelist', 'cudnn_batchnorm_spatial_persistent'
]
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
core.init_glog(sys.argv[0])
# don't init_p2p when in unittest to save time.
core.init_devices(not in_test)
# TODO(panyx0718): Avoid doing complex initialization logic in __init__.py.
# Consider paddle.init(args) or paddle.main(args)
monkey_patch_variable()
__bootstrap__()