DeepFaceLab/core/mplib/MPSharedList.py
2020-12-20 23:04:00 +04:00

112 lines
3.6 KiB
Python

import multiprocessing
import pickle
import struct
from core.joblib import Subprocessor
class MPSharedList():
"""
Provides read-only pickled list of constant objects via shared memory aka 'multiprocessing.Array'
Thus no 4GB limit for subprocesses.
supports list concat via + or sum()
"""
def __init__(self, obj_list):
if obj_list is None:
self.obj_counts = None
self.table_offsets = None
self.data_offsets = None
self.sh_bs = None
else:
obj_count, table_offset, data_offset, sh_b = MPSharedList.bake_data(obj_list)
self.obj_counts = [obj_count]
self.table_offsets = [table_offset]
self.data_offsets = [data_offset]
self.sh_bs = [sh_b]
def __add__(self, o):
if isinstance(o, MPSharedList):
m = MPSharedList(None)
m.obj_counts = self.obj_counts + o.obj_counts
m.table_offsets = self.table_offsets + o.table_offsets
m.data_offsets = self.data_offsets + o.data_offsets
m.sh_bs = self.sh_bs + o.sh_bs
return m
elif isinstance(o, int):
return self
else:
raise ValueError(f"MPSharedList object of class {o.__class__} is not supported for __add__ operator.")
def __radd__(self, o):
return self+o
def __len__(self):
return sum(self.obj_counts)
def __getitem__(self, key):
obj_count = sum(self.obj_counts)
if key < 0:
key = obj_count+key
if key < 0 or key >= obj_count:
raise ValueError("out of range")
for i in range(len(self.obj_counts)):
if key < self.obj_counts[i]:
table_offset = self.table_offsets[i]
data_offset = self.data_offsets[i]
sh_b = self.sh_bs[i]
break
key -= self.obj_counts[i]
sh_b = memoryview(sh_b).cast('B')
offset_start, offset_end = struct.unpack('<QQ', sh_b[ table_offset + key*8 : table_offset + (key+2)*8].tobytes() )
return pickle.loads( sh_b[ data_offset + offset_start : data_offset + offset_end ].tobytes() )
def __iter__(self):
for i in range(self.__len__()):
yield self.__getitem__(i)
@staticmethod
def bake_data(obj_list):
if not isinstance(obj_list, list):
raise ValueError("MPSharedList: obj_list should be list type.")
obj_count = len(obj_list)
if obj_count != 0:
obj_pickled_ar = [pickle.dumps(o, 4) for o in obj_list]
table_offset = 0
table_size = (obj_count+1)*8
data_offset = table_offset + table_size
data_size = sum([len(x) for x in obj_pickled_ar])
sh_b = multiprocessing.RawArray('B', table_size + data_size)
#sh_b[0:8] = struct.pack('<Q', obj_count)
sh_b_view = memoryview(sh_b).cast('B')
offset = 0
sh_b_table = bytes()
offsets = []
offset = 0
for i in range(obj_count):
offsets.append(offset)
offset += len(obj_pickled_ar[i])
offsets.append(offset)
sh_b_view[table_offset:table_offset+table_size] = struct.pack( '<'+'Q'*len(offsets), *offsets )
for i, obj_pickled in enumerate(obj_pickled_ar):
offset = data_offset+offsets[i]
sh_b_view[offset:offset+len(obj_pickled)] = obj_pickled_ar[i]
return obj_count, table_offset, data_offset, sh_b
return 0, 0, 0, None