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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Dict
import torch
from transformers.utils import cached_file
from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PreTrainedModel
from ...hparams import ModelArguments
logger = get_logger(__name__)
def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
r"""
Loads value head parameters from Hugging Face Hub or local disk.
Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
"""
kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}
err_text = ""
try:
from safetensors import safe_open
vhead_file = cached_file(filename=V_HEAD_SAFE_WEIGHTS_NAME, **kwargs)
with safe_open(vhead_file, framework="pt", device="cpu") as f:
return {key: f.get_tensor(key) for key in f.keys()}
except Exception as err:
err_text = str(err)
try:
vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
return torch.load(vhead_file, map_location="cpu")
except Exception as err:
err_text = str(err)
logger.info("Provided path ({}) does not contain value head weights: {}.".format(path_or_repo_id, err_text))
logger.info("Ignore the above message if you are not resuming the training of a value head model.")
return None
def prepare_valuehead_model(model: "PreTrainedModel") -> None:
if getattr(model.config, "model_type", None) == "llava":
setattr(model, "lm_head", model.language_model.get_output_embeddings())
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
if getattr(model.config, "model_type", None) == "chatglm":
setattr(model, "lm_head", model.transformer.output_layer)
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
if getattr(model.config, "model_type", None) == "internlm2":
setattr(model, "lm_head", model.output)
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
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