1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
|
from dataclasses import dataclass, field
from enum import Enum
from typing import List
import torch
from torch.distributed._shard.metadata import ShardMetadata
class MEM_FORMAT_ENCODING(Enum):
TORCH_CONTIGUOUS_FORMAT = 0
TORCH_CHANNELS_LAST = 1
TORCH_PRESERVE_FORMAT = 2
@dataclass
class TensorProperties(object):
""" Properties used to create :class:`Tensor` """
# Regular tensor fields
dtype: torch.dtype = field(default=torch.get_default_dtype())
layout: torch.layout = field(default=torch.strided)
requires_grad: bool = False
memory_format: torch.memory_format = field(default=torch.contiguous_format)
pin_memory: bool = False
def __getstate__(self):
# Since torch.memory_format cannot be pickled!
memory_format = self.memory_format
if memory_format == torch.contiguous_format:
mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT
elif memory_format == torch.channels_last:
mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST
elif memory_format == torch.preserve_format:
mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT
else:
raise RuntimeError(f'Invalid torch.memory_format: {memory_format}')
return (
self.dtype,
self.layout,
self.requires_grad,
mem_format_encoding,
self.pin_memory,
)
def __setstate__(
self,
state,
):
(self.dtype, self.layout, self.requires_grad, mem_format_encoding, self.pin_memory) = state
if mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT:
memory_format = torch.contiguous_format
elif mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST:
memory_format = torch.channels_last
elif mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT:
memory_format = torch.preserve_format
else:
raise RuntimeError(f'Invalid torch.memory_format encoding: {mem_format_encoding}')
self.memory_format = memory_format
@staticmethod
def create_from_tensor(tensor: torch.Tensor) -> "TensorProperties":
return TensorProperties(
dtype=tensor.dtype,
layout=tensor.layout,
requires_grad=tensor.requires_grad,
memory_format=torch.contiguous_format,
pin_memory=tensor.is_pinned()
)
@dataclass
class ShardedTensorMetadata(object):
"""
Represents metadata for :class:`ShardedTensor`
"""
# Metadata about each shard of the Tensor
shards_metadata: List[ShardMetadata] = field(default_factory=list)
# Size of each dim of the overall Tensor.
size: torch.Size = field(default=torch.Size([]))
tensor_properties: TensorProperties = field(default=TensorProperties())
|
