wehub-resource-sync
676 lines
23 KiB
Python
676 lines
23 KiB
Python
# --------------------------------------------------------
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# BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (https://arxiv.org/abs/2208.06366)
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# Github source: https://github.com/microsoft/unilm/tree/master/beitv2
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# By Zhiliang Peng
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# Based on BEiT, timm, DeiT and DINO code bases
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# https://github.com/microsoft/unilm/tree/master/beit
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# https://github.com/facebookresearch/deit/
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# https://github.com/facebookresearch/dino
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# --------------------------------------------------------'
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import io
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import os
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import math
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import time
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import json
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import glob
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from collections import defaultdict, deque
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import datetime
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import numpy as np
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from timm.utils import get_state_dict
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from pathlib import Path
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import argparse
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import torch
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import torch.distributed as dist
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from torch._six import inf
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from tensorboardX import SummaryWriter
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def bool_flag(s):
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"""
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Parse boolean arguments from the command line.
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"""
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FALSY_STRINGS = {"off", "false", "0"}
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TRUTHY_STRINGS = {"on", "true", "1"}
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if s.lower() in FALSY_STRINGS:
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return False
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elif s.lower() in TRUTHY_STRINGS:
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return True
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else:
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raise argparse.ArgumentTypeError("invalid value for a boolean flag")
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def get_model(model):
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if isinstance(model, torch.nn.DataParallel) \
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or isinstance(model, torch.nn.parallel.DistributedDataParallel):
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return model.module
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else:
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return model
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class SmoothedValue(object):
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size=20, fmt=None):
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if fmt is None:
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fmt = "{median:.4f} ({global_avg:.4f})"
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self.deque = deque(maxlen=window_size)
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self.total = 0.0
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self.count = 0
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self.fmt = fmt
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def update(self, value, n=1):
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self.deque.append(value)
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self.count += n
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self.total += value * n
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def synchronize_between_processes(self):
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"""
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Warning: does not synchronize the deque!
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"""
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if not is_dist_avail_and_initialized():
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return
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t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
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dist.barrier()
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dist.all_reduce(t)
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t = t.tolist()
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self.count = int(t[0])
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self.total = t[1]
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@property
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def median(self):
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d = torch.tensor(list(self.deque))
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return d.median().item()
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@property
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def avg(self):
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d = torch.tensor(list(self.deque), dtype=torch.float32)
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return d.mean().item()
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@property
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def global_avg(self):
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return self.total / self.count
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@property
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def max(self):
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return max(self.deque)
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@property
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def value(self):
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return self.deque[-1]
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def __str__(self):
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return self.fmt.format(
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median=self.median,
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avg=self.avg,
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global_avg=self.global_avg,
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max=self.max,
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value=self.value)
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class MetricLogger(object):
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def __init__(self, delimiter="\t"):
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self.meters = defaultdict(SmoothedValue)
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self.delimiter = delimiter
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def update(self, **kwargs):
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for k, v in kwargs.items():
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if v is None:
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continue
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.meters[k].update(v)
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def __getattr__(self, attr):
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if attr in self.meters:
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return self.meters[attr]
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if attr in self.__dict__:
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return self.__dict__[attr]
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raise AttributeError("'{}' object has no attribute '{}'".format(
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type(self).__name__, attr))
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def __str__(self):
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loss_str = []
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for name, meter in self.meters.items():
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loss_str.append(
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"{}: {}".format(name, str(meter))
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)
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return self.delimiter.join(loss_str)
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def synchronize_between_processes(self):
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for meter in self.meters.values():
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meter.synchronize_between_processes()
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def add_meter(self, name, meter):
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self.meters[name] = meter
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def log_every(self, iterable, print_freq, header=None):
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i = 0
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if not header:
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header = ''
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start_time = time.time()
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end = time.time()
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iter_time = SmoothedValue(fmt='{avg:.4f}')
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data_time = SmoothedValue(fmt='{avg:.4f}')
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
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log_msg = [
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header,
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'[{0' + space_fmt + '}/{1}]',
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'eta: {eta}',
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'{meters}',
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'time: {time}',
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'data: {data}'
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]
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if torch.cuda.is_available():
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log_msg.append('max mem: {memory:.0f}')
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log_msg = self.delimiter.join(log_msg)
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MB = 1024.0 * 1024.0
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for obj in iterable:
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data_time.update(time.time() - end)
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yield obj
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iter_time.update(time.time() - end)
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if i % print_freq == 0 or i == len(iterable) - 1:
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eta_seconds = iter_time.global_avg * (len(iterable) - i)
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
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if torch.cuda.is_available():
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time),
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memory=torch.cuda.max_memory_allocated() / MB))
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else:
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time)))
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i += 1
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end = time.time()
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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print('{} Total time: {} ({:.4f} s / it)'.format(
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header, total_time_str, total_time / len(iterable)))
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class TensorboardLogger(object):
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def __init__(self, log_dir):
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self.writer = SummaryWriter(logdir=log_dir)
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self.step = 0
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def set_step(self, step=None):
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if step is not None:
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self.step = step
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else:
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self.step += 1
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def update(self, head='scalar', step=None, **kwargs):
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for k, v in kwargs.items():
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if v is None:
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continue
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)
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def update_image(self, head='images', step=None, **kwargs):
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for k, v in kwargs.items():
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if v is None:
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continue
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self.writer.add_image(head + "/" + k, v, self.step if step is None else step)
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def flush(self):
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self.writer.flush()
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def _load_checkpoint_for_ema(model_ema, checkpoint):
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"""
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Workaround for ModelEma._load_checkpoint to accept an already-loaded object
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"""
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mem_file = io.BytesIO()
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torch.save(checkpoint, mem_file)
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mem_file.seek(0)
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model_ema._load_checkpoint(mem_file)
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def setup_for_distributed(is_master):
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"""
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This function disables printing when not in master process
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"""
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import builtins as __builtin__
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builtin_print = __builtin__.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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if is_master or force:
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builtin_print(*args, **kwargs)
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__builtin__.print = print
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def is_dist_avail_and_initialized():
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if not dist.is_available():
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return False
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if not dist.is_initialized():
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return False
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return True
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def get_world_size():
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if not is_dist_avail_and_initialized():
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return 1
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return dist.get_world_size()
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def get_rank():
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if not is_dist_avail_and_initialized():
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return 0
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return dist.get_rank()
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def is_main_process():
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return get_rank() == 0
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def save_on_master(*args, **kwargs):
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if is_main_process():
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torch.save(*args, **kwargs)
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def all_reduce(tensor, op=dist.ReduceOp.SUM, async_op=False):
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world_size = get_world_size()
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if world_size == 1:
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return tensor
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dist.all_reduce(tensor, op=op, async_op=async_op)
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return tensor
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def all_gather_batch(tensors):
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"""
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Performs all_gather operation on the provided tensors.
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"""
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# Queue the gathered tensors
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world_size = get_world_size()
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# There is no need for reduction in the single-proc case
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if world_size == 1:
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return tensors
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tensor_list = []
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output_tensor = []
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for tensor in tensors:
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tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
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dist.all_gather(
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tensor_all,
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tensor,
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async_op=False # performance opt
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)
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tensor_list.append(tensor_all)
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for tensor_all in tensor_list:
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output_tensor.append(torch.cat(tensor_all, dim=0))
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return output_tensor
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class GatherLayer(torch.autograd.Function):
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"""
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Gather tensors from all workers with support for backward propagation:
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This implementation does not cut the gradients as torch.distributed.all_gather does.
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"""
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@staticmethod
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def forward(ctx, x):
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output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
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dist.all_gather(output, x)
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return tuple(output)
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@staticmethod
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def backward(ctx, *grads):
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all_gradients = torch.stack(grads)
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dist.all_reduce(all_gradients)
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return all_gradients[dist.get_rank()]
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def all_gather_batch_with_grad(tensors):
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"""
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Performs all_gather operation on the provided tensors.
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Graph remains connected for backward grad computation.
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"""
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# Queue the gathered tensors
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world_size = get_world_size()
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# There is no need for reduction in the single-proc case
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if world_size == 1:
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return tensors
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tensor_list = []
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output_tensor = []
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for tensor in tensors:
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tensor_all = GatherLayer.apply(tensor)
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tensor_list.append(tensor_all)
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for tensor_all in tensor_list:
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output_tensor.append(torch.cat(tensor_all, dim=0))
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return output_tensor
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def _get_rank_env():
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if "RANK" in os.environ:
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return int(os.environ["RANK"])
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else:
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return int(os.environ['OMPI_COMM_WORLD_RANK'])
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def _get_local_rank_env():
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if "LOCAL_RANK" in os.environ:
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return int(os.environ["LOCAL_RANK"])
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else:
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return int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
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def _get_world_size_env():
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if "WORLD_SIZE" in os.environ:
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return int(os.environ["WORLD_SIZE"])
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else:
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return int(os.environ['OMPI_COMM_WORLD_SIZE'])
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def init_distributed_mode(args):
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if args.dist_on_itp:
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args.rank = _get_rank_env()
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args.world_size = _get_world_size_env() # int(os.environ['OMPI_COMM_WORLD_SIZE'])
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args.gpu = _get_local_rank_env()
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args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
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os.environ['LOCAL_RANK'] = str(args.gpu)
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os.environ['RANK'] = str(args.rank)
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os.environ['WORLD_SIZE'] = str(args.world_size)
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# ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
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elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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args.rank = int(os.environ["RANK"])
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args.world_size = int(os.environ['WORLD_SIZE'])
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args.gpu = int(os.environ['LOCAL_RANK'])
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elif 'SLURM_PROCID' in os.environ:
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args.rank = int(os.environ['SLURM_PROCID'])
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args.gpu = args.rank % torch.cuda.device_count()
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else:
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print('Not using distributed mode')
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args.distributed = False
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return
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args.distributed = True
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torch.cuda.set_device(args.gpu)
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args.dist_backend = 'nccl'
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print('| distributed init (rank {}): {}, gpu {}'.format(
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args.rank, args.dist_url, args.gpu), flush=True)
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torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
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world_size=args.world_size, rank=args.rank)
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torch.distributed.barrier()
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setup_for_distributed(args.rank == 0)
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def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
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missing_keys = []
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unexpected_keys = []
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error_msgs = []
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# copy state_dict so _load_from_state_dict can modify it
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metadata = getattr(state_dict, '_metadata', None)
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state_dict = state_dict.copy()
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if metadata is not None:
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state_dict._metadata = metadata
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def load(module, prefix=''):
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local_metadata = {} if metadata is None else metadata.get(
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prefix[:-1], {})
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module._load_from_state_dict(
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state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
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for name, child in module._modules.items():
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if child is not None:
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load(child, prefix + name + '.')
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load(model, prefix=prefix)
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warn_missing_keys = []
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ignore_missing_keys = []
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for key in missing_keys:
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keep_flag = True
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for ignore_key in ignore_missing.split('|'):
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if ignore_key in key:
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keep_flag = False
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break
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if keep_flag:
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warn_missing_keys.append(key)
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else:
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ignore_missing_keys.append(key)
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missing_keys = warn_missing_keys
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if len(missing_keys) > 0:
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print("Weights of {} not initialized from pretrained model: {}".format(
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model.__class__.__name__, missing_keys))
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if len(unexpected_keys) > 0:
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print("Weights from pretrained model not used in {}: {}".format(
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model.__class__.__name__, unexpected_keys))
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if len(ignore_missing_keys) > 0:
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print("Ignored weights of {} not initialized from pretrained model: {}".format(
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model.__class__.__name__, ignore_missing_keys))
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if len(error_msgs) > 0:
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print('\n'.join(error_msgs))
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def get_grad_norm(parameters, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = list(filter(lambda p: p.grad is not None, parameters))
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norm_type = float(norm_type)
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total_norm = 0
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for p in parameters:
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param_norm = p.grad.data.norm(norm_type)
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total_norm += param_norm.item() ** norm_type
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total_norm = total_norm ** (1. / norm_type)
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return total_norm
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class NativeScalerWithGradNormCount:
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state_dict_key = "amp_scaler"
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def __init__(self):
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self._scaler = torch.cuda.amp.GradScaler()
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def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True, layer_names=None):
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self._scaler.scale(loss).backward(create_graph=create_graph)
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if update_grad:
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if clip_grad is not None:
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assert parameters is not None
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self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
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norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
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else:
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self._scaler.unscale_(optimizer)
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norm = get_grad_norm_(parameters, layer_names=layer_names)
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self._scaler.step(optimizer)
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self._scaler.update()
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else:
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norm = None
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return norm
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def state_dict(self):
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return self._scaler.state_dict()
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def load_state_dict(self, state_dict):
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self._scaler.load_state_dict(state_dict)
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def get_grad_norm_(parameters, norm_type: float = 2.0, layer_names=None) -> torch.Tensor:
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = [p for p in parameters if p.grad is not None]
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norm_type = float(norm_type)
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if len(parameters) == 0:
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return torch.tensor(0.)
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device = parameters[0].grad.device
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if norm_type == inf:
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total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
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else:
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# total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
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layer_norm = torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters])
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|
total_norm = torch.norm(layer_norm, norm_type)
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|
# print(layer_norm.max(dim=0))
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|
|
|
if layer_names is not None:
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|
if torch.isnan(total_norm) or torch.isinf(total_norm) or total_norm > 1.0:
|
|
value_top, name_top = torch.topk(layer_norm, k=5)
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|
print(f"Top norm value: {value_top}")
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|
print(f"Top norm name: {[layer_names[i][7:] for i in name_top.tolist()]}")
|
|
|
|
return total_norm
|
|
|
|
|
|
def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
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|
start_warmup_value=0, warmup_steps=-1):
|
|
warmup_schedule = np.array([])
|
|
warmup_iters = warmup_epochs * niter_per_ep
|
|
if warmup_steps > 0:
|
|
warmup_iters = warmup_steps
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|
print("Set warmup steps = %d" % warmup_iters)
|
|
if warmup_epochs > 0:
|
|
warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
|
|
|
|
iters = np.arange(epochs * niter_per_ep - warmup_iters)
|
|
schedule = np.array(
|
|
[final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])
|
|
|
|
schedule = np.concatenate((warmup_schedule, schedule))
|
|
|
|
assert len(schedule) == epochs * niter_per_ep
|
|
return schedule
|
|
|
|
|
|
def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None, save_ckpt_freq=1):
|
|
output_dir = Path(args.output_dir)
|
|
epoch_name = str(epoch)
|
|
|
|
if not getattr(args, 'enable_deepspeed', False):
|
|
checkpoint_paths = [output_dir / 'checkpoint.pth']
|
|
if epoch == 'best':
|
|
checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name),]
|
|
elif (epoch + 1) % save_ckpt_freq == 0:
|
|
checkpoint_paths.append(output_dir / ('checkpoint-%s.pth' % epoch_name))
|
|
|
|
for checkpoint_path in checkpoint_paths:
|
|
to_save = {
|
|
'model': model_without_ddp.state_dict(),
|
|
'optimizer': optimizer.state_dict(),
|
|
'epoch': epoch,
|
|
# 'scaler': loss_scaler.state_dict(),
|
|
'args': args,
|
|
}
|
|
if loss_scaler is not None:
|
|
to_save['scaler'] = loss_scaler.state_dict()
|
|
|
|
if model_ema is not None:
|
|
to_save['model_ema'] = get_state_dict(model_ema)
|
|
|
|
if optimizer_disc is not None:
|
|
to_save['optimizer_disc'] = optimizer_disc.state_dict()
|
|
|
|
save_on_master(to_save, checkpoint_path)
|
|
else:
|
|
client_state = {'epoch': epoch}
|
|
if model_ema is not None:
|
|
client_state['model_ema'] = get_state_dict(model_ema)
|
|
model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
|
|
|
|
def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None):
|
|
output_dir = Path(args.output_dir)
|
|
|
|
if not getattr(args, 'enable_deepspeed', False):
|
|
# torch.amp
|
|
if args.auto_resume and len(args.resume) == 0:
|
|
all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint.pth'))
|
|
if len(all_checkpoints) > 0:
|
|
args.resume = os.path.join(output_dir, 'checkpoint.pth')
|
|
else:
|
|
all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
|
|
latest_ckpt = -1
|
|
for ckpt in all_checkpoints:
|
|
t = ckpt.split('-')[-1].split('.')[0]
|
|
if t.isdigit():
|
|
latest_ckpt = max(int(t), latest_ckpt)
|
|
if latest_ckpt >= 0:
|
|
args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
|
|
print("Auto resume checkpoint: %s" % args.resume)
|
|
|
|
if args.resume:
|
|
if args.resume.startswith('https'):
|
|
checkpoint = torch.hub.load_state_dict_from_url(
|
|
args.resume, map_location='cpu', check_hash=True)
|
|
else:
|
|
checkpoint = torch.load(args.resume, map_location='cpu')
|
|
model_without_ddp.load_state_dict(checkpoint['model']) # strict: bool=True, , strict=False
|
|
print("Resume checkpoint %s" % args.resume)
|
|
if 'optimizer' in checkpoint and 'epoch' in checkpoint:
|
|
optimizer.load_state_dict(checkpoint['optimizer'])
|
|
print(f"Resume checkpoint at epoch {checkpoint['epoch']}")
|
|
args.start_epoch = checkpoint['epoch'] + 1
|
|
if hasattr(args, 'model_ema') and args.model_ema:
|
|
_load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
|
|
if 'scaler' in checkpoint:
|
|
loss_scaler.load_state_dict(checkpoint['scaler'])
|
|
print("With optim & sched!")
|
|
if 'optimizer_disc' in checkpoint:
|
|
optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
|
|
else:
|
|
# deepspeed, only support '--auto_resume'.
|
|
if args.auto_resume:
|
|
all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
|
|
latest_ckpt = -1
|
|
for ckpt in all_checkpoints:
|
|
t = ckpt.split('-')[-1].split('.')[0]
|
|
if t.isdigit():
|
|
latest_ckpt = max(int(t), latest_ckpt)
|
|
if latest_ckpt >= 0:
|
|
args.resume = os.path.join(output_dir, 'checkpoint-%d' % latest_ckpt)
|
|
print("Auto resume checkpoint: %d" % latest_ckpt)
|
|
_, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
|
|
args.start_epoch = client_states['epoch'] + 1
|
|
if model_ema is not None:
|
|
if args.model_ema:
|
|
_load_checkpoint_for_ema(model_ema, client_states['model_ema'])
|
|
|
|
def create_ds_config(args):
|
|
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(os.path.join(args.output_dir, "latest"), mode="w") as f:
|
|
pass
|
|
|
|
args.deepspeed_config = os.path.join(args.output_dir, "deepspeed_config.json")
|
|
with open(args.deepspeed_config, mode="w") as writer:
|
|
ds_config = {
|
|
"train_batch_size": args.batch_size * args.update_freq * get_world_size(),
|
|
"train_micro_batch_size_per_gpu": args.batch_size,
|
|
"steps_per_print": 1000,
|
|
"optimizer": {
|
|
"type": "Adam",
|
|
"adam_w_mode": True,
|
|
"params": {
|
|
"lr": args.lr,
|
|
"weight_decay": args.weight_decay,
|
|
"bias_correction": True,
|
|
"betas": [
|
|
0.9,
|
|
0.999
|
|
],
|
|
"eps": 1e-8
|
|
}
|
|
},
|
|
"fp16": {
|
|
"enabled": True,
|
|
"loss_scale": 0,
|
|
"initial_scale_power": 7,
|
|
"loss_scale_window": 128
|
|
}
|
|
}
|
|
|
|
writer.write(json.dumps(ds_config, indent=2))
|