|
| 1 | +from typing import TYPE_CHECKING, Optional |
| 2 | + |
| 3 | +from .transformers import Transformer, TransformerTokenizer |
| 4 | + |
| 5 | +if TYPE_CHECKING: |
| 6 | + from transformers import PreTrainedModel, PreTrainedTokenizer |
| 7 | + |
| 8 | + |
| 9 | +class AWQModel(Transformer): |
| 10 | + """Represents a `transformers` model.""" |
| 11 | + |
| 12 | + def __init__( |
| 13 | + self, |
| 14 | + model: "PreTrainedModel", |
| 15 | + tokenizer: "PreTrainedTokenizer", |
| 16 | + ): |
| 17 | + self.device = model.model.device |
| 18 | + self.model = model |
| 19 | + self.tokenizer = tokenizer |
| 20 | + |
| 21 | + |
| 22 | +def awq( |
| 23 | + model_name: str, |
| 24 | + fuse_layers: bool = True, |
| 25 | + device: Optional[str] = None, |
| 26 | + model_kwargs: dict = {}, |
| 27 | + tokenizer_kwargs: dict = {}, |
| 28 | +): |
| 29 | + try: |
| 30 | + from awq import AutoAWQForCausalLM |
| 31 | + except ImportError: |
| 32 | + raise ImportError( |
| 33 | + "The `autoawq` and `transformers` library needs to be installed in order to use `AutoAWQ` models." |
| 34 | + ) |
| 35 | + |
| 36 | + model_kwargs["fuse_layers"] = fuse_layers |
| 37 | + model_kwargs["safetensors"] = True |
| 38 | + |
| 39 | + if device is not None: |
| 40 | + model_kwargs["device_map"] = device |
| 41 | + |
| 42 | + model = AutoAWQForCausalLM.from_quantized(model_name, **model_kwargs) |
| 43 | + tokenizer = TransformerTokenizer(model_name, trust_remote_code=True) |
| 44 | + |
| 45 | + return AWQModel(model, tokenizer) |
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