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