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