70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
import torch
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import torch.distributed
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from typing import Optional, List
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from text_generation_server.models import CausalLM
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FIM_PREFIX = "<fim-prefix>"
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FIM_MIDDLE = "<fim-middle>"
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FIM_SUFFIX = "<fim-suffix>"
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FIM_PAD = "<fim-pad>"
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EOD = "<|endoftext|>"
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class SantaCoder(CausalLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.float16
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else:
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if quantize:
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raise ValueError("quantization is not available on CPU")
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device = torch.device("cpu")
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dtype = torch.float32
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left", truncation_side="left"
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)
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tokenizer.add_special_tokens(
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{
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"additional_special_tokens": [
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EOD,
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FIM_PREFIX,
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FIM_MIDDLE,
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FIM_SUFFIX,
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FIM_PAD,
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],
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"pad_token": EOD,
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}
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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revision=revision,
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torch_dtype=dtype,
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load_in_8bit=quantize == "bitsandbytes",
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trust_remote_code=True, # required
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).to(device)
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super(CausalLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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dtype=dtype,
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device=device,
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)
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def decode(self, generated_ids: List[int]) -> str:
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# Do not skip special tokens as they are used for custom parsing rules of the generated text
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
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)
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