import argparse import os import psutil import torch.backends.quantized import torch.nn as nn import transformers from hivemind.utils.logging import get_logger, use_hivemind_log_handler from huggingface_hub import Repository from tqdm.auto import tqdm from src import BloomModel from src.bloom.from_pretrained import BLOCK_BRANCH_PREFIX, CLIENT_BRANCH from src.client import DistributedBloomConfig use_hivemind_log_handler("in_root_logger") logger = get_logger(__file__) DTYPE_MAP = dict(bfloat16=torch.bfloat16, float16=torch.float16, float32=torch.float32, auto="auto") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Load bloom layers and convert to 8-bit using torch quantization.") parser.add_argument("--model", type=str, default="bigscience/bloom-6b3", help="Model name for from_pretrained") parser.add_argument("--revision", type=str, default=None, help="Optional commit id from HF hub") parser.add_argument("--torch_dtype", type=str, default="auto", help="Load initial model in this dtype") parser.add_argument("--output_path", type=str, default="./converted_model", help="Track output repo to this folder") parser.add_argument("--output_repo", type=str, default="bigscience/test-bloomd", help="Push to this HF hub repo") parser.add_argument("--client_branch", type=str, default=CLIENT_BRANCH, help="Save client version to this branch") parser.add_argument( "--block_branch_prefix", type=str, default=BLOCK_BRANCH_PREFIX, help="Save blocks to branches with this prefix" ) parser.add_argument( "--commit_message", type=str, default="push-o-matic", help="Use this commit message for all parts" ) parser.add_argument("--use_auth_token", type=str, default=None, help="auth token for from_pretrained") parser.add_argument("--resize_token_embeddings", type=int, default=None, help="change the vocabulary size") args = parser.parse_args() free_ram_gb = psutil.virtual_memory().available / 2**30 if args.model == "bigscience/bloom" and free_ram_gb < 400: logger.warning(f"ACHTUNG! converting bloom-176b will use up 350-400GB RAM, you have {free_ram_gb:.3f} free") assert args.torch_dtype in DTYPE_MAP, f"torch_dtype must be one of {list(DTYPE_MAP.keys())}" if os.path.exists(args.output_path) and ( len(os.listdir(args.output_path)) != 0 or not os.path.isdir(args.output_path) ): raise FileExistsError(f"Output path {args.output_path} already exists and is not an empty directory") logger.info(f"Loading source model {args.model} (this may take a few minutes)") config = DistributedBloomConfig.from_pretrained( args.model, use_auth_token=args.use_auth_token, revision=args.revision ) config.dht_prefix = args.output_repo model = BloomModel.from_pretrained( args.model, use_auth_token=args.use_auth_token, revision=args.revision, torch_dtype=DTYPE_MAP[args.torch_dtype] ) if args.resize_token_embeddings: logger.info(f"Resizing token embeddings, new size = {args.resize_token_embeddings}") model.resize_token_embeddings(args.resize_token_embeddings) config.vocab_size = args.resize_token_embeddings tokenizer = transformers.AutoTokenizer.from_pretrained( args.model, use_auth_token=args.use_auth_token, revision=args.revision ) os.makedirs(args.output_path, exist_ok=True) repo = Repository(args.output_path, clone_from=args.output_repo, use_auth_token=args.use_auth_token) repo.git_pull() transformer_blocks = model.h logger.info( f"Saving transformer blocks to {args.output_repo}@{args.block_branch_prefix}0" f" - {args.output_repo}@{args.block_branch_prefix}{len(transformer_blocks)}" ) for i, block in enumerate(tqdm(transformer_blocks)): repo.git_checkout(args.client_branch, create_branch_ok=True) with repo.commit( commit_message=args.commit_message, branch=args.block_branch_prefix + str(i), track_large_files=True ): torch.save(block.state_dict(), "./pytorch_model.bin") logger.info(f"Saving client-side modules to {args.output_repo}@{args.client_branch}") repo.git_checkout(args.client_branch, create_branch_ok=True) with repo.commit(commit_message=args.commit_message, branch=args.client_branch, track_large_files=True): model.h = nn.ModuleList() model.save_pretrained(".") tokenizer.save_pretrained(".") config.save_pretrained(".") logger.info(f"Converted {args.model} and pushed to {args.output_repo}")