File size: 4,857 Bytes
1cb032f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel.distributed import (DistributedDataParallel,
                                           _find_tensors)

from annotator.uniformer.mmcv import print_log
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .scatter_gather import scatter_kwargs


class MMDistributedDataParallel(DistributedDataParallel):
    """The DDP module that supports DataContainer.

    MMDDP has two main differences with PyTorch DDP:

    - It supports a custom type :class:`DataContainer` which allows more
      flexible control of input data.
    - It implement two APIs ``train_step()`` and ``val_step()``.
    """

    def to_kwargs(self, inputs, kwargs, device_id):
        # Use `self.to_kwargs` instead of `self.scatter` in pytorch1.8
        # to move all tensors to device_id
        return scatter_kwargs(inputs, kwargs, [device_id], dim=self.dim)

    def scatter(self, inputs, kwargs, device_ids):
        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)

    def train_step(self, *inputs, **kwargs):
        """train_step() API for module wrapped by DistributedDataParallel.

        This method is basically the same as
        ``DistributedDataParallel.forward()``, while replacing
        ``self.module.forward()`` with ``self.module.train_step()``.
        It is compatible with PyTorch 1.1 - 1.5.
        """

        # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
        # end of backward to the beginning of forward.
        if ('parrots' not in TORCH_VERSION
                and digit_version(TORCH_VERSION) >= digit_version('1.7')
                and self.reducer._rebuild_buckets()):
            print_log(
                'Reducer buckets have been rebuilt in this iteration.',
                logger='mmcv')

        if getattr(self, 'require_forward_param_sync', True):
            self._sync_params()
        if self.device_ids:
            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
            if len(self.device_ids) == 1:
                output = self.module.train_step(*inputs[0], **kwargs[0])
            else:
                outputs = self.parallel_apply(
                    self._module_copies[:len(inputs)], inputs, kwargs)
                output = self.gather(outputs, self.output_device)
        else:
            output = self.module.train_step(*inputs, **kwargs)

        if torch.is_grad_enabled() and getattr(
                self, 'require_backward_grad_sync', True):
            if self.find_unused_parameters:
                self.reducer.prepare_for_backward(list(_find_tensors(output)))
            else:
                self.reducer.prepare_for_backward([])
        else:
            if ('parrots' not in TORCH_VERSION
                    and digit_version(TORCH_VERSION) > digit_version('1.2')):
                self.require_forward_param_sync = False
        return output

    def val_step(self, *inputs, **kwargs):
        """val_step() API for module wrapped by DistributedDataParallel.

        This method is basically the same as
        ``DistributedDataParallel.forward()``, while replacing
        ``self.module.forward()`` with ``self.module.val_step()``.
        It is compatible with PyTorch 1.1 - 1.5.
        """
        # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
        # end of backward to the beginning of forward.
        if ('parrots' not in TORCH_VERSION
                and digit_version(TORCH_VERSION) >= digit_version('1.7')
                and self.reducer._rebuild_buckets()):
            print_log(
                'Reducer buckets have been rebuilt in this iteration.',
                logger='mmcv')

        if getattr(self, 'require_forward_param_sync', True):
            self._sync_params()
        if self.device_ids:
            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
            if len(self.device_ids) == 1:
                output = self.module.val_step(*inputs[0], **kwargs[0])
            else:
                outputs = self.parallel_apply(
                    self._module_copies[:len(inputs)], inputs, kwargs)
                output = self.gather(outputs, self.output_device)
        else:
            output = self.module.val_step(*inputs, **kwargs)

        if torch.is_grad_enabled() and getattr(
                self, 'require_backward_grad_sync', True):
            if self.find_unused_parameters:
                self.reducer.prepare_for_backward(list(_find_tensors(output)))
            else:
                self.reducer.prepare_for_backward([])
        else:
            if ('parrots' not in TORCH_VERSION
                    and digit_version(TORCH_VERSION) > digit_version('1.2')):
                self.require_forward_param_sync = False
        return output