90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
import torch
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import torch.distributed
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from opentelemetry import trace
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from transformers import AutoTokenizer, AutoConfig
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from typing import Optional, List
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import json
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import os
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from huggingface_hub import hf_hub_download
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
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FlashSantacoderForCausalLM,
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)
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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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tracer = trace.get_tracer(__name__)
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class FlashSantacoderSharded(FlashCausalLM):
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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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use_medusa: 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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raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
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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=True,
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)
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config.quantize = quantize
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config.use_medusa = use_medusa
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config.transpose = config.architectures[0].startswith("GPT2")
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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,
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device=device,
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dtype=dtype,
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process_group=self.process_group,
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aliases={"transformer.wte.weight": ["lm_head.weight"]},
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)
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if config.quantize == "gptq":
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weights._set_gptq_params(model_id, revision)
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model = FlashSantacoderForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashSantacoderSharded, self).__init__(
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model=model.to(device),
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tokenizer=tokenizer,
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num_layers=len(model.transformer.h),
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num_kv_heads=1,
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head_size=model.transformer.head_size,
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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 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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