64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
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import torch
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import torch.distributed
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from transformers import AutoConfig, AutoTokenizer
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from typing import Optional, List, Tuple
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from text_generation_server.models import CausalLM
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from text_generation_server.models.custom_modeling.phi_modeling import PhiConfig, PhiForCausalLM
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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class Phi(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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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, _rank, _world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.float16 if dtype is None else dtype
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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 if dtype is None else dtype
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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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config = PhiConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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tokenizer.bos_token_id = config.bos_token_id
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tokenizer.eos_token_id = config.eos_token_id
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tokenizer.pad_token = tokenizer.eos_token
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config.quantize = quantize
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(filenames, device, dtype, process_group=self.process_group)
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model = PhiForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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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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