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76 lines
3.1 KiB
76 lines
3.1 KiB
"""
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saving utilities
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"""
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import json
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import os
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from os.path import abspath, dirname, exists, join
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import subprocess
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import torch
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from uniter_model.utils.logger import LOGGER
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def save_training_meta(args):
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if args.rank > 0:
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return
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os.makedirs(join(args.output_dir, 'log'), exist_ok=True)
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os.makedirs(join(args.output_dir, 'ckpt'), exist_ok=True)
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with open(join(args.output_dir, 'log', 'hps.json'), 'w') as writer:
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json.dump(vars(args), writer, indent=4)
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if False:
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model_config = json.load(open(args.model_config))
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with open(join(args.output_dir, 'log', 'model.json'), 'w') as writer:
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json.dump(model_config, writer, indent=4)
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# git info
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try:
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LOGGER.info("Waiting on git info....")
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c = subprocess.run(["git", "rev-parse", "--abbrev-ref", "HEAD"],
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timeout=10, stdout=subprocess.PIPE)
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git_branch_name = c.stdout.decode().strip()
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LOGGER.info("Git branch: %s", git_branch_name)
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c = subprocess.run(["git", "rev-parse", "HEAD"],
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timeout=10, stdout=subprocess.PIPE)
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git_sha = c.stdout.decode().strip()
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LOGGER.info("Git SHA: %s", git_sha)
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git_dir = abspath(dirname(__file__))
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git_status = subprocess.check_output(
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['git', 'status', '--short'],
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cwd=git_dir, universal_newlines=True).strip()
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with open(join(args.output_dir, 'log', 'git_info.json'),
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'w') as writer:
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json.dump({'branch': git_branch_name,
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'is_dirty': bool(git_status),
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'status': git_status,
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'sha': git_sha},
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writer, indent=4)
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except subprocess.TimeoutExpired as e:
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LOGGER.exception(e)
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LOGGER.warn("Git info not found. Moving right along...")
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class ModelSaver(object):
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def __init__(self, output_dir, prefix='model_step', suffix='pt'):
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self.output_dir = output_dir
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self.prefix = prefix
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self.suffix = suffix
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def save(self, model, step, optimizer=None):
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output_model_file = join(self.output_dir,
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f"{self.prefix}_{step}.{self.suffix}")
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state_dict = {k: v.cpu() if isinstance(v, torch.Tensor) else v
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for k, v in model.state_dict().items()}
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if hasattr(model, 'vocab_pad') and model.vocab_pad:
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# store vocab embeddings before padding
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emb_w = state_dict['bert.embeddings.word_embeddings.weight']
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emb_w = emb_w[:-model.vocab_pad, :]
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state_dict['bert.embeddings.word_embeddings.weight'] = emb_w
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state_dict['cls.predictions.decoder.weight'] = emb_w
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torch.save(state_dict, output_model_file)
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if optimizer is not None:
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dump = {'step': step, 'optimizer': optimizer.state_dict()}
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if hasattr(optimizer, '_amp_stash'):
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pass # TODO fp16 optimizer
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torch.save(dump, f'{self.output_dir}/train_state_{step}.pt')
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