mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:12:07 +00:00
04e1af94d7
* feat: first draft load multiple lora * feat: load weights within layer and refactor lora pass * fix: refactor and reduce lora math * feat: baseline impl single request multi lora support * feat: prefer lorax implementation and port loading logic * fix: prefer adapter_data and refactors * feat: perfer loraxs custom punica kernels and add mlp loras * fix: adjust batch for bgmv * fix: adjust adapter_segments logic when in batch * fix: refactor and move changes to v3 proto * fix: pass model_id for all flash causal lms * fix: pass model_id for all causal and seq2seq lms * fix: add model_id to model test * feat: add lora support to mistral and refactors * feat: prefer model id in request * fix: include rust code for adapter id * feat: bump launcher and add new lora docs * feat: support base model generation and refactors * fix: rename doc to retry ci build * feat: support if vlm models * fix: add adapter_data param and avoid missing layers * fix: add adapter_data param to phi and neox * fix: update all models forwards to include adapter_data * fix: add model_id to IdeficsCausalLM * Update lora.md Fixed a typo * Update lora.md Fixing spam image * fix: add lora kernel to dockerfile, support running without kernels and refactors * fix: avoid dockerfile conflict * fix: refactors and adjust flash llama lora logic * fix: skip llama test due to CI issue (temp) * fix: skip llama test CI (temp) 2 * fix: revert skips and prefer updated ci token for tests * fix: refactors and helpful comments * fix: add noop in TensorParallelAdapterRowLinear too * fix: refactor and move shard_lora_weights logic * fix: exit early if no adapter_data --------- Co-authored-by: Derek <datavistics@gmail.com>
78 lines
2.3 KiB
Python
78 lines
2.3 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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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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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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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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with device:
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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=trust_remote_code,
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)
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super(CausalLM, self).__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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)
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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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