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714cf46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | import copy
import torch
from huggingface_hub import HfApi, login
from transformers import AutoModelForMaskedLM, EsmTokenizer
from dplm2_fastplms.modeling_dplm2 import DPLM2Config as FastDPLM2Config, DPLM2ForMaskedLM
from weight_parity_utils import assert_model_parameters_fp32
MODEL_DICT = {
"Synthyra/DPLM2-150M": "airkingbd/dplm2_150m",
"Synthyra/DPLM2-650M": "airkingbd/dplm2_650m",
"Synthyra/DPLM2-3B": "airkingbd/dplm2_3b",
}
SHARDED_REPO_IDS = {"Synthyra/DPLM2-3B"}
SHARD_SIZE = "5GB"
def _delete_legacy_unsharded_weights_if_present(api: HfApi, repo_id: str) -> None:
if repo_id not in SHARDED_REPO_IDS:
return
repo_files = api.list_repo_files(repo_id=repo_id, repo_type="model")
if "model.safetensors" in repo_files:
print(f"Deleting legacy unified model.safetensors from {repo_id}")
api.delete_file(
path_in_repo="model.safetensors",
repo_id=repo_id,
repo_type="model",
)
def _assert_repo_has_sharded_weights(api: HfApi, repo_id: str) -> None:
if repo_id not in SHARDED_REPO_IDS:
return
repo_files = api.list_repo_files(repo_id=repo_id, repo_type="model")
has_index_file = "model.safetensors.index.json" in repo_files
has_shard_file = any(
repo_file.startswith("model-") and repo_file.endswith(".safetensors")
for repo_file in repo_files
)
assert has_index_file, f"{repo_id} is missing model.safetensors.index.json."
assert has_shard_file, f"{repo_id} has no model shard files."
assert "model.safetensors" not in repo_files, f"{repo_id} still has unified model.safetensors."
def _push_model_with_expected_format(model: DPLM2ForMaskedLM, api: HfApi, repo_id: str) -> None:
if repo_id in SHARDED_REPO_IDS:
print(f"Pushing sharded weights for {repo_id} with max_shard_size={SHARD_SIZE}")
model.push_to_hub(repo_id, max_shard_size=SHARD_SIZE)
_delete_legacy_unsharded_weights_if_present(api, repo_id)
_assert_repo_has_sharded_weights(api, repo_id)
return
model.push_to_hub(repo_id)
def _resolve_repo_items(repo_ids: list[str] | None) -> list[tuple[str, str]]:
if repo_ids is None or len(repo_ids) == 0:
return list(MODEL_DICT.items())
selected_items: list[tuple[str, str]] = []
for repo_id in repo_ids:
assert repo_id in MODEL_DICT, (
f"Unknown repo_id {repo_id}. "
f"Valid options: {sorted(MODEL_DICT.keys())}"
)
selected_items.append((repo_id, MODEL_DICT[repo_id]))
return selected_items
if __name__ == "__main__":
# py -m dplm2_fastplms.get_dplm2_weights
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--hf_token", type=str, default=None)
parser.add_argument("--repo_ids", nargs="*", type=str, default=None)
parser.add_argument("--skip-weights", action="store_true")
args = parser.parse_args()
api = HfApi()
if args.hf_token is not None:
assert len(args.hf_token) > 0, "--hf_token cannot be empty."
login(token=args.hf_token)
for repo_id, source_repo in _resolve_repo_items(args.repo_ids):
config = FastDPLM2Config.from_pretrained(source_repo)
config.auto_map = {
"AutoConfig": "modeling_dplm2.DPLM2Config",
"AutoModel": "modeling_dplm2.DPLM2Model",
"AutoModelForMaskedLM": "modeling_dplm2.DPLM2ForMaskedLM",
"AutoModelForSequenceClassification": "modeling_dplm2.DPLM2ForSequenceClassification",
"AutoModelForTokenClassification": "modeling_dplm2.DPLM2ForTokenClassification",
}
config.tie_word_embeddings = False
if args.skip_weights:
tokenizer = EsmTokenizer.from_pretrained(source_repo)
config.push_to_hub(repo_id)
tokenizer.push_to_hub(repo_id)
print(f"[skip-weights] uploaded config+tokenizer for {repo_id}")
continue
model = DPLM2ForMaskedLM.from_pretrained(source_repo, config=config).eval().cpu().to(torch.float32)
model.tokenizer = EsmTokenizer.from_pretrained(source_repo)
# Break any potential embedding/LM-head parameter aliasing before export.
model.lm_head.dense.weight = copy.deepcopy(model.lm_head.dense.weight)
model.lm_head.dense.bias = copy.deepcopy(model.lm_head.dense.bias)
model.lm_head.decoder.weight = copy.deepcopy(model.lm_head.decoder.weight)
model.lm_head.decoder.bias = copy.deepcopy(model.lm_head.decoder.bias)
model.lm_head.layer_norm.weight = copy.deepcopy(model.lm_head.layer_norm.weight)
model.lm_head.layer_norm.bias = copy.deepcopy(model.lm_head.layer_norm.bias)
assert_model_parameters_fp32(
model=model,
model_name=f"DPLM2 model ({source_repo})",
)
tokenizer = model.tokenizer
tokenizer.push_to_hub(repo_id)
_push_model_with_expected_format(model, api, repo_id)
api.upload_file(
path_or_fileobj="dplm2_fastplms/modeling_dplm2.py",
path_in_repo="modeling_dplm2.py",
repo_id=repo_id,
repo_type="model",
)
downloaded_model = AutoModelForMaskedLM.from_pretrained(
repo_id,
dtype=torch.float32,
device_map="cpu",
force_download=True,
trust_remote_code=True,
)
assert_model_parameters_fp32(
model=downloaded_model,
model_name=f"downloaded DPLM2 model ({repo_id})",
)
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