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87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
"""
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Utils for fetching pretrained model parts. Currently, this relies on huggingface transformers' from_pretrained code.
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If necessary, one can rewrite this to implement a different behavior, such as:
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- loading files from a local data source (e.g. S3)
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- load files via BitTorrent ( https://pypi.org/project/libtorrent/ ) or IPFS( https://docs.ipfs.io/how-to )
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- fetch the weights over IPoAC, using a fleet of trained pigeons ( http://www.faqs.org/rfcs/rfc1149.html )
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"""
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from __future__ import annotations
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from typing import Optional, OrderedDict, Union
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import torch
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from hivemind.utils.logging import get_logger, use_hivemind_log_handler
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from transformers.modeling_utils import WEIGHTS_NAME
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from transformers.utils.hub import cached_path, hf_bucket_url
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from petals.bloom import BloomBlock, BloomConfig
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use_hivemind_log_handler("in_root_logger")
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logger = get_logger(__file__)
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CLIENT_BRANCH = "main"
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BLOCK_BRANCH_PREFIX = "block_"
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USER_AGENT = {"file_type": "model", "framework": "pytorch", "from_auto_class": False}
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FORCE_DOWNLOAD = False
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RESUME_DOWNLOAD = False
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LOCAL_FILES_ONLY = False
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def load_pretrained_block(
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converted_model_name_or_path: str,
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block_index: int,
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config: Optional[BloomConfig] = None,
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torch_dtype: Union[torch.dtype, str] = "auto",
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use_auth_token: Optional[str] = None,
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cache_dir: Optional[str] = None,
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) -> BloomBlock:
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"""Load one BloomBlock from a converted model. See convert_model.py (or README.md) on how to convert it."""
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if config is None:
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config = BloomConfig.from_pretrained(converted_model_name_or_path, use_auth_token=use_auth_token)
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block = BloomBlock(config, layer_number=block_index)
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state_dict = _load_state_dict(
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converted_model_name_or_path, block_index, use_auth_token=use_auth_token, cache_dir=cache_dir
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)
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block.load_state_dict(state_dict)
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if torch_dtype == "auto":
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with torch.no_grad():
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for name, param in block.named_parameters():
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assert name in state_dict, f"{name} not in state dict"
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param.data = param.data.to(state_dict[name].dtype)
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else:
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assert torch_dtype in DTYPE_MAP.values(), f"torch_dtype must be one of {list(DTYPE_MAP.values())}"
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block = block.to(dtype=torch_dtype)
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report = block.load_state_dict(state_dict, strict=True)
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logger.info(f"Loaded {converted_model_name_or_path} block {block_index}, {report}")
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return block
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def _load_state_dict(
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pretrained_model_name_or_path: str,
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block_index: Optional[int] = None,
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use_auth_token: Optional[str] = None,
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cache_dir: Optional[str] = None,
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) -> OrderedDict[str, torch.Tensor]:
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revision = BLOCK_BRANCH_PREFIX + str(block_index) if block_index is not None else CLIENT_BRANCH
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archive_file = hf_bucket_url(pretrained_model_name_or_path, filename=WEIGHTS_NAME, revision=revision, mirror=None)
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# Load from URL or cache if already cached
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resolved_archive_file = cached_path(
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archive_file,
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cache_dir=cache_dir,
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force_download=FORCE_DOWNLOAD,
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proxies=None,
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resume_download=RESUME_DOWNLOAD,
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local_files_only=LOCAL_FILES_ONLY,
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use_auth_token=use_auth_token,
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user_agent=USER_AGENT,
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)
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state_dict = torch.load(resolved_archive_file, map_location="cpu")
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return state_dict
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DTYPE_MAP = dict(bfloat16=torch.bfloat16, float16=torch.float16, float32=torch.float32, auto="auto")
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