61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import torch
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from transformers import AutoConfig
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from typing import Optional
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from text_generation.models.model import Model
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from text_generation.models.causal_lm import CausalLM
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from text_generation.models.bloom import BLOOM, BLOOMSharded
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from text_generation.models.seq2seq_lm import Seq2SeqLM
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from text_generation.models.galactica import Galactica, GalacticaSharded
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from text_generation.models.santacoder import SantaCoder
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from text_generation.models.gpt_neox import GPTNeox, GPTNeoxSharded
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__all__ = [
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"Model",
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"BLOOM",
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"BLOOMSharded",
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"CausalLM",
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"Seq2SeqLM",
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"SantaCoder",
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"get_model",
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]
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# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
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# in PyTorch 1.12 and later.
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torch.backends.cuda.matmul.allow_tf32 = True
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# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
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torch.backends.cudnn.allow_tf32 = True
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def get_model(
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model_name: str, revision: Optional[str], sharded: bool, quantize: bool
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) -> Model:
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config = AutoConfig.from_pretrained(model_name, revision=revision)
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if config.model_type == "bloom":
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if sharded:
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return BLOOMSharded(model_name, revision, quantize=quantize)
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else:
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return BLOOM(model_name, revision, quantize=quantize)
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elif config.model_type == "gpt_neox":
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if sharded:
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return GPTNeoxSharded(model_name, revision, quantize=quantize)
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else:
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return GPTNeox(model_name, revision, quantize=quantize)
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elif model_name.startswith("facebook/galactica"):
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if sharded:
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return GalacticaSharded(model_name, revision, quantize=quantize)
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else:
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return Galactica(model_name, revision, quantize=quantize)
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elif "santacoder" in model_name:
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return SantaCoder(model_name, revision, quantize)
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else:
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if sharded:
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raise ValueError("sharded is not supported for AutoModel")
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try:
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return CausalLM(model_name, revision, quantize=quantize)
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except Exception:
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return Seq2SeqLM(model_name, revision, quantize=quantize)
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