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trainer_desc.py
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trainer_desc.py
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# Copyright (c) 2019 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.
"""Defination of trainers."""
import sys
import os
__all__ = [
'TrainerDesc', 'MultiTrainer', 'DistMultiTrainer', 'PipelineTrainer',
'HeterXpuTrainer', 'HeterPipelineTrainer'
]
class TrainerDesc(object):
'''
Set proto from python to c++.
Can be initialized from train_desc.
'''
def __init__(self):
'''
self.proto_desc = data_feed_pb2.DataFeedDesc()
with open(proto_file, 'r') as f:
text_format.Parse(f.read(), self.proto_desc)
'''
# Workaround for relative import in protobuf under python3
# TODO: should be fixed
cur_path = os.path.dirname(__file__)
if cur_path not in sys.path:
sys.path.append(cur_path)
if cur_path + "/proto" not in sys.path:
sys.path.append(cur_path + "/proto")
from proto import trainer_desc_pb2
self.proto_desc = trainer_desc_pb2.TrainerDesc()
import multiprocessing as mp
# set default thread num == cpu count
self.proto_desc.thread_num = mp.cpu_count()
self._fleet_desc = None
self._device_worker = None
self._program = None
self._infer = False
def _set_heter_info(self, ret):
#ret = = fu.split_program_by_device(program)
#start_list, end_list, send_list, recv_list, program_list = fu.split_program_by_device(program)
#if len(start_list) != 3:
# print("start_list len=", len(start_list), " will not set heter info")
# return
#for i in start_list[0]:
# self.proto_desc.op_run_start_idx.append(i)
#for i in end_list[0]:
# self.proto_desc.op_run_end_idx.append(i)
#for i in send_list[0]:
# self.proto_desc.op_run_send_list.append(i)
#for i in recv_list[0]:
# self.proto_desc.op_run_recv_list.append(i)
if ret is None:
return
#for i in ret[0]: # start_list[1]:
# self.proto_desc.xpu_start_idx.append(i)
self.proto_desc.xpu_start_idx = ret[0]
#for i in ret[1]: #end_list[1]:
# self.proto_desc.o_end_idx.append(i)
self.proto_desc.xpu_end_idx = ret[1]
for i in ret[2]: # send_list[1]:
self.proto_desc.xpu_send_list.append(i)
for i in ret[3]: # recv_list[1]:
self.proto_desc.xpu_recv_list.append(i)
#for i in start_list[2]:
# self.proto_desc.op_run_end_start_idx.append(i)
#for i in end_list[2]:
# self.proto_desc.op_run_end_idx.append(i)
#for i in send_list[2]:
# self.proto_desc.op_run_end_send_list.append(i)
#for i in recv_list[2]:
# self.proto_desc.op_run_end_recv_list.append(i)
def _set_fetch_var_and_info(self, fetch_vars, fetch_info, print_period):
# convert fetch_info to list
fetch_info = list(fetch_info)
for i, v in enumerate(fetch_vars):
self.proto_desc.fetch_config.fetch_var_names.extend([v.name])
self.proto_desc.fetch_config.fetch_var_str_format.extend(
[fetch_info[i]])
self.proto_desc.fetch_config.print_period = print_period
def _set_debug(self, debug):
self.proto_desc.debug = debug
def _set_thread(self, thread_num):
self.proto_desc.thread_num = thread_num
def _set_device_worker(self, device_worker):
self._device_worker = device_worker
def _set_infer(self, infer):
self._infer = infer
def _set_fleet_desc(self, fleet_desc):
self._fleet_desc = fleet_desc
## serialize fleet_desc
from google.protobuf import text_format
fleet_desc_str = text_format.MessageToString(fleet_desc)
self.proto_desc.fleet_desc = fleet_desc_str
def _gen_trainer_desc(self):
pass
def _set_program(self, program):
self._program = program
def _set_trainer_id(self, trainer_id):
self.proto_desc.trainer_id = trainer_id
def _set_trainers(self, trainers):
for trainer_num in trainers:
self.proto_desc.trainers.append(trainer_num)
def _set_use_cvm(self, use_cvm=False):
self.proto_desc.use_cvm = use_cvm
def _set_no_cvm(self, no_cvm=False):
self.proto_desc.no_cvm = no_cvm
def _set_scale_sparse_grad_with_batch_size(
self, scale_sparse_gradient_with_batch_size=True):
self.proto_desc.scale_sparse_gradient_with_batch_size = scale_sparse_gradient_with_batch_size
def _set_scale_datanorm(self, scale_datanorm=-1):
self.proto_desc.scale_datanorm = scale_datanorm
def _set_dump_slot(self, dump_slot):
self.proto_desc.dump_slot = dump_slot
def _set_mpi_rank(self, mpi_rank):
self.proto_desc.mpi_rank = mpi_rank
def _set_mpi_size(self, mpi_size):
self.proto_desc.mpi_size = mpi_size
def _set_dump_fields(self, dump_fields):
for field in dump_fields:
self.proto_desc.dump_fields.append(field)
def _set_is_dump_in_simple_mode(self, is_dump_in_simple_mode):
self.proto_desc.is_dump_in_simple_mode = is_dump_in_simple_mode
def _set_dump_fields_path(self, path):
self.proto_desc.dump_fields_path = path
def _set_dump_file_num(self, dump_file_num):
self.proto_desc.dump_file_num = dump_file_num
def _set_user_define_dump_filename(self, user_define_dump_filename):
self.proto_desc.user_define_dump_filename = user_define_dump_filename
def _set_dump_converter(self, converter):
self.proto_desc.dump_converter = converter
def _set_enable_random_dump(self, enable_random_dump):
self.proto_desc.enable_random_dump = enable_random_dump
def _set_dump_interval(self, dump_interval):
self.proto_desc.dump_interval = dump_interval
def _set_random_with_lineid(self, random_with_lineid):
self.proto_desc.random_with_lineid = random_with_lineid
def _set_dump_param(self, dump_param):
for param in dump_param:
self.proto_desc.dump_param.append(param)
def _set_worker_places(self, worker_places):
for place in worker_places:
self.proto_desc.worker_places.append(place)
def _set_use_ps_gpu(self, use_ps_gpu=False):
self.proto_desc.use_ps_gpu = use_ps_gpu
def _set_thread_barrier(self, thread_barrier):
self.proto_desc.thread_barrier = thread_barrier
def _set_check_nan_var_names(self, check_nan_var_names):
for var in check_nan_var_names:
self.proto_desc.check_nan_var_names.append(var)
def _set_loss_names(self, loss_names):
for loss in loss_names:
self.proto_desc.loss_names.append(loss)
def _set_adjust_ins_weight(self, config_dict):
self.proto_desc.adjust_ins_weight_config.need_adjust = \
config_dict.get("need_adjust", False)
self.proto_desc.adjust_ins_weight_config.nid_slot = \
config_dict.get("nid_slot", "")
self.proto_desc.adjust_ins_weight_config.nid_adjw_threshold = \
config_dict.get("nid_adjw_threshold", 0.0)
self.proto_desc.adjust_ins_weight_config.nid_adjw_ratio = \
config_dict.get("nid_adjw_ratio", 0.0)
self.proto_desc.adjust_ins_weight_config.ins_weight_slot = \
config_dict.get("ins_weight_slot", "")
def _set_copy_table_config(self, config_dict):
config = self.proto_desc.copy_table_config
config.need_copy = config_dict.get("need_copy", False)
config.batch_num = config_dict.get("batch_num", 100)
src_sparse_tables = config_dict.get("src_sparse_tables", [])
if not isinstance(src_sparse_tables, list):
src_sparse_tables = [src_sparse_tables]
dest_sparse_tables = config_dict.get("dest_sparse_tables", [])
if not isinstance(dest_sparse_tables, list):
dest_sparse_tables = [dest_sparse_tables]
if len(src_sparse_tables) != len(dest_sparse_tables):
raise ValueError(
"len(src_sparse_tables) != len(dest_sparse_tables)," \
" %s vs %s" % (len(src_sparse_tables), \
len(dest_sparse_tables)))
for i in src_sparse_tables:
config.src_sparse_tables.append(i)
for i in dest_sparse_tables:
config.dest_sparse_tables.append(i)
src_dense_tables = config_dict.get("src_dense_tables", [])
if not isinstance(src_dense_tables, list):
src_dense_tables = [src_dense_tables]
dest_dense_tables = config_dict.get("dest_dense_tables", [])
if not isinstance(dest_dense_tables, list):
dest_dense_tables = [dest_dense_tables]
if len(src_dense_tables) != len(dest_dense_tables):
raise ValueError(
"len(src_dense_tables) != len(dest_dense_tables)," \
" %s vs %s" % (len(src_dense_tables), \
len(dest_dense_tables)))
for i in src_dense_tables:
config.src_dense_tables.append(i)
for i in dest_dense_tables:
config.dest_dense_tables.append(i)
# user can also specify dense variables to copy,
# instead of copy dense table
src_var_list = config_dict.get("src_var_list", [])
if not isinstance(src_var_list, list):
src_var_list = [src_var_list]
dest_var_list = config_dict.get("dest_var_list", [])
if not isinstance(dest_var_list, list):
dest_var_list = [dest_var_list]
if len(src_var_list) != len(dest_var_list):
raise ValueError(
"len(src_var_list) != len(dest_var_list), %s vs" \
" %s" % (len(src_var_list), len(dest_var_list)))
for i in src_var_list:
config.src_var_list.append(i)
for i in dest_var_list:
config.dest_var_list.append(i)
dependency_map = config_dict.get("dependency_map", {})
for key in dependency_map:
m = config.table_denpendency_map.add()
m.key = key
values = dependency_map[key]
if not isinstance(values, list):
values = [values]
if len(values) != 1:
raise ValueError("dependency len %s != 1" % len(values))
for value in values:
m.values.append(value)
config.dense_pull_after_copy = \
config_dict.get("dense_pull_after_copy", True)
config.enable_dependency = \
config_dict.get("enable_dependency", False)
config.sparse_copy_by_feasign = \
config_dict.get("sparse_copy_by_feasign", True)
def _desc(self):
from google.protobuf import text_format
return self.proto_desc.SerializeToString()
def __str__(self):
from google.protobuf import text_format
return text_format.MessageToString(self.proto_desc)
class MultiTrainer(TrainerDesc):
'''
Implement of MultiTrainer.
Can be init from TrainerDesc.
'''
def __init__(self):
super(MultiTrainer, self).__init__()
pass
def _set_program(self, program):
super(MultiTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(MultiTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "MultiTrainer"
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
class DistMultiTrainer(TrainerDesc):
"""
Implement of DistMultiTrainer.
It's for Distributed training.
"""
def __init__(self):
super(DistMultiTrainer, self).__init__()
pass
def _set_program(self, program):
super(DistMultiTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(DistMultiTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "DistMultiTrainer"
if self._program == None:
raise RuntimeError("None Program")
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
class HeterXpuTrainer(TrainerDesc):
"""
Implement of HeterXpuTrainer.
It's for Distributed training.
"""
def __init__(self):
super(HeterXpuTrainer, self).__init__()
pass
def _set_program(self, program):
super(HeterXpuTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(HeterXpuTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "HeterXpuTrainer"
if self._program == None:
raise RuntimeError("None Program")
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
class PSGPUTrainer(TrainerDesc):
"""
Implement of PSGPUTrainer.
It's for Distributed training.
"""
def __init__(self):
super(PSGPUTrainer, self).__init__()
pass
def _set_program(self, program):
super(PSGPUTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(PSGPUTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "PSGPUTrainer"
if self._program == None:
raise RuntimeError("None Program")
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
class HeterPipelineTrainer(TrainerDesc):
"""
Implement of HeterPipelineTrainer.
It's for HeterPS Pipeline training.
"""
def __init__(self):
super(HeterPipelineTrainer, self).__init__()
pass
def _set_program(self, program):
super(HeterPipelineTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(HeterPipelineTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "HeterPipelineTrainer"
if self._program == None:
raise RuntimeError("None Program")
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
class PipelineTrainer(TrainerDesc):
"""
Implement of PipelineTrainer.
It's for Pipeline.
"""
def __init__(self):
super(PipelineTrainer, self).__init__()
pass
def _set_program(self, program):
super(PipelineTrainer, self)._set_program(program)
self._program = program
def _gen_trainer_desc(self):
super(PipelineTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "PipelineTrainer"
if self._program == None:
raise RuntimeError("None Program")
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)