87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
import torch
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import torch.distributed
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from typing import Optional
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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)
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from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
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from text_generation_server.models import TransformersCausalLM
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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 OPTSharded(TransformersCausalLM):
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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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speculator: 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(f"cuda:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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else:
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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 = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code,
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)
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config.quantize = quantize
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config.speculator = speculator
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tokenizer.pad_token_id = config.pad_token_id
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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(
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filenames, device=device, dtype=dtype, process_group=self.process_group
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)
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if config.quantize in ["gptq", "marlin"]:
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weights._set_gptq_params(model_id, revision)
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model = OPTForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super().__init__(
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model_id=model_id,
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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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rank=rank,
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world_size=world_size,
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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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):
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outputs, speculative_logits = 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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use_cache=True,
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)
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return outputs.logits, speculative_logits, outputs.past_key_values
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