File size: 5,268 Bytes
18dd6ad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
import bisect
import functools
import logging
import numbers
import os
import signal
import sys
import traceback
import warnings

import torch
from pytorch_lightning import seed_everything

LOGGER = logging.getLogger(__name__)


def check_and_warn_input_range(tensor, min_value, max_value, name):
    actual_min = tensor.min()
    actual_max = tensor.max()
    if actual_min < min_value or actual_max > max_value:
        warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}")


def sum_dict_with_prefix(target, cur_dict, prefix, default=0):
    for k, v in cur_dict.items():
        target_key = prefix + k
        target[target_key] = target.get(target_key, default) + v


def average_dicts(dict_list):
    result = {}
    norm = 1e-3
    for dct in dict_list:
        sum_dict_with_prefix(result, dct, '')
        norm += 1
    for k in list(result):
        result[k] /= norm
    return result


def add_prefix_to_keys(dct, prefix):
    return {prefix + k: v for k, v in dct.items()}


def set_requires_grad(module, value):
    for param in module.parameters():
        param.requires_grad = value


def flatten_dict(dct):
    result = {}
    for k, v in dct.items():
        if isinstance(k, tuple):
            k = '_'.join(k)
        if isinstance(v, dict):
            for sub_k, sub_v in flatten_dict(v).items():
                result[f'{k}_{sub_k}'] = sub_v
        else:
            result[k] = v
    return result


class LinearRamp:
    def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0):
        self.start_value = start_value
        self.end_value = end_value
        self.start_iter = start_iter
        self.end_iter = end_iter

    def __call__(self, i):
        if i < self.start_iter:
            return self.start_value
        if i >= self.end_iter:
            return self.end_value
        part = (i - self.start_iter) / (self.end_iter - self.start_iter)
        return self.start_value * (1 - part) + self.end_value * part


class LadderRamp:
    def __init__(self, start_iters, values):
        self.start_iters = start_iters
        self.values = values
        assert len(values) == len(start_iters) + 1, (len(values), len(start_iters))

    def __call__(self, i):
        segment_i = bisect.bisect_right(self.start_iters, i)
        return self.values[segment_i]


def get_ramp(kind='ladder', **kwargs):
    if kind == 'linear':
        return LinearRamp(**kwargs)
    if kind == 'ladder':
        return LadderRamp(**kwargs)
    raise ValueError(f'Unexpected ramp kind: {kind}')


def print_traceback_handler(sig, frame):
    LOGGER.warning(f'Received signal {sig}')
    bt = ''.join(traceback.format_stack())
    LOGGER.warning(f'Requested stack trace:\n{bt}')


def register_debug_signal_handlers(sig=None, handler=print_traceback_handler):
    LOGGER.warning(f'Setting signal {sig} handler {handler}')
    signal.signal(sig, handler)


def handle_deterministic_config(config):
    seed = dict(config).get('seed', None)
    if seed is None:
        return False

    seed_everything(seed)
    return True


def get_shape(t):
    if torch.is_tensor(t):
        return tuple(t.shape)
    elif isinstance(t, dict):
        return {n: get_shape(q) for n, q in t.items()}
    elif isinstance(t, (list, tuple)):
        return [get_shape(q) for q in t]
    elif isinstance(t, numbers.Number):
        return type(t)
    else:
        raise ValueError('unexpected type {}'.format(type(t)))


def get_has_ddp_rank():
    master_port = os.environ.get('MASTER_PORT', None)
    node_rank = os.environ.get('NODE_RANK', None)
    local_rank = os.environ.get('LOCAL_RANK', None)
    world_size = os.environ.get('WORLD_SIZE', None)
    has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None
    return has_rank


def handle_ddp_subprocess():
    def main_decorator(main_func):
        @functools.wraps(main_func)
        def new_main(*args, **kwargs):
            # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE
            parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
            has_parent = parent_cwd is not None
            has_rank = get_has_ddp_rank()
            assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'

            if has_parent:
                # we are in the worker
                sys.argv.extend([
                    f'hydra.run.dir={parent_cwd}',
                    # 'hydra/hydra_logging=disabled',
                    # 'hydra/job_logging=disabled'
                ])
            # do nothing if this is a top-level process
            # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization

            main_func(*args, **kwargs)
        return new_main
    return main_decorator


def handle_ddp_parent_process():
    parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
    has_parent = parent_cwd is not None
    has_rank = get_has_ddp_rank()
    assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'

    if parent_cwd is None:
        os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd()

    return has_parent