87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from typing import List, Optional, Tuple
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from text_generation_server.models import CausalLM
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class RW(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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trust_remote_code: bool = False,
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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,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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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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device_map="auto"
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if torch.cuda.is_available() and torch.cuda.device_count() > 1
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else None,
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load_in_8bit=quantize == "bitsandbytes",
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trust_remote_code=trust_remote_code,
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)
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if torch.cuda.is_available() and torch.cuda.device_count() == 1:
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model = model.cuda()
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if tokenizer.pad_token_id is None:
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if model.config.pad_token_id is not None:
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tokenizer.pad_token_id = model.config.pad_token_id
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elif model.config.eos_token_id is not None:
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tokenizer.pad_token_id = model.config.eos_token_id
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elif tokenizer.eos_token_id is not None:
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tokenizer.pad_token_id = tokenizer.eos_token_id
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else:
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tokenizer.add_special_tokens({"pad_token": "[PAD]"})
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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 forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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# Model Forward
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if past_key_values is not None:
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reshaped_past_key_values = []
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for layer in past_key_values:
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past_keys, past_values = layer
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reshaped_past_key_values.append(
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(
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past_keys.view(-1, *past_keys.shape[-2:]),
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past_values.view(-1, *past_values.shape[-2:]),
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)
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)
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past_key_values = reshaped_past_key_values
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outputs = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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)
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return outputs.logits, outputs.past_key_values
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