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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ | |
| Utilities to save and load models. | |
| """ | |
| from contextlib import contextmanager | |
| import functools | |
| import hashlib | |
| import inspect | |
| import io | |
| from pathlib import Path | |
| import warnings | |
| from omegaconf import OmegaConf | |
| from diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state | |
| import torch | |
| def get_quantizer(model, args, optimizer=None): | |
| """Return the quantizer given the XP quantization args.""" | |
| quantizer = None | |
| if args.diffq: | |
| quantizer = DiffQuantizer( | |
| model, min_size=args.min_size, group_size=args.group_size) | |
| if optimizer is not None: | |
| quantizer.setup_optimizer(optimizer) | |
| elif args.qat: | |
| quantizer = UniformQuantizer( | |
| model, bits=args.qat, min_size=args.min_size) | |
| return quantizer | |
| def load_model(path_or_package, strict=False): | |
| """Load a model from the given serialized model, either given as a dict (already loaded) | |
| or a path to a file on disk.""" | |
| if isinstance(path_or_package, dict): | |
| package = path_or_package | |
| elif isinstance(path_or_package, (str, Path)): | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore") | |
| path = path_or_package | |
| package = torch.load(path, 'cpu') | |
| else: | |
| raise ValueError(f"Invalid type for {path_or_package}.") | |
| klass = package["klass"] | |
| args = package["args"] | |
| kwargs = package["kwargs"] | |
| if strict: | |
| model = klass(*args, **kwargs) | |
| else: | |
| sig = inspect.signature(klass) | |
| for key in list(kwargs): | |
| if key not in sig.parameters: | |
| warnings.warn("Dropping inexistant parameter " + key) | |
| del kwargs[key] | |
| model = klass(*args, **kwargs) | |
| state = package["state"] | |
| set_state(model, state) | |
| return model | |
| def get_state(model, quantizer, half=False): | |
| """Get the state from a model, potentially with quantization applied. | |
| If `half` is True, model are stored as half precision, which shouldn't impact performance | |
| but half the state size.""" | |
| if quantizer is None: | |
| dtype = torch.half if half else None | |
| state = {k: p.data.to(device='cpu', dtype=dtype) for k, p in model.state_dict().items()} | |
| else: | |
| state = quantizer.get_quantized_state() | |
| state['__quantized'] = True | |
| return state | |
| def set_state(model, state, quantizer=None): | |
| """Set the state on a given model.""" | |
| if state.get('__quantized'): | |
| if quantizer is not None: | |
| quantizer.restore_quantized_state(model, state['quantized']) | |
| else: | |
| restore_quantized_state(model, state) | |
| else: | |
| model.load_state_dict(state) | |
| return state | |
| def save_with_checksum(content, path): | |
| """Save the given value on disk, along with a sha256 hash. | |
| Should be used with the output of either `serialize_model` or `get_state`.""" | |
| buf = io.BytesIO() | |
| torch.save(content, buf) | |
| sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8] | |
| path = path.parent / (path.stem + "-" + sig + path.suffix) | |
| path.write_bytes(buf.getvalue()) | |
| def serialize_model(model, training_args, quantizer=None, half=True): | |
| args, kwargs = model._init_args_kwargs | |
| klass = model.__class__ | |
| state = get_state(model, quantizer, half) | |
| return { | |
| 'klass': klass, | |
| 'args': args, | |
| 'kwargs': kwargs, | |
| 'state': state, | |
| 'training_args': OmegaConf.to_container(training_args, resolve=True), | |
| } | |
| def copy_state(state): | |
| return {k: v.cpu().clone() for k, v in state.items()} | |
| def swap_state(model, state): | |
| """ | |
| Context manager that swaps the state of a model, e.g: | |
| # model is in old state | |
| with swap_state(model, new_state): | |
| # model in new state | |
| # model back to old state | |
| """ | |
| old_state = copy_state(model.state_dict()) | |
| model.load_state_dict(state, strict=False) | |
| try: | |
| yield | |
| finally: | |
| model.load_state_dict(old_state) | |
| def capture_init(init): | |
| def __init__(self, *args, **kwargs): | |
| self._init_args_kwargs = (args, kwargs) | |
| init(self, *args, **kwargs) | |
| return __init__ | |