79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import torch
|
|
|
|
from transformers import AutoConfig
|
|
from typing import Optional
|
|
|
|
from text_generation.models.model import Model
|
|
from text_generation.models.causal_lm import CausalLM
|
|
from text_generation.models.bloom import BLOOM, BLOOMSharded
|
|
from text_generation.models.seq2seq_lm import Seq2SeqLM
|
|
from text_generation.models.galactica import Galactica, GalacticaSharded
|
|
from text_generation.models.santacoder import SantaCoder
|
|
from text_generation.models.gpt_neox import GPTNeox, GPTNeoxSharded
|
|
from text_generation.models.t5 import T5Sharded
|
|
|
|
__all__ = [
|
|
"Model",
|
|
"BLOOM",
|
|
"BLOOMSharded",
|
|
"CausalLM",
|
|
"Galactica",
|
|
"GalacticaSharded",
|
|
"GPTNeox",
|
|
"GPTNeoxSharded",
|
|
"Seq2SeqLM",
|
|
"SantaCoder",
|
|
"T5Sharded",
|
|
"get_model",
|
|
]
|
|
|
|
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
|
|
# in PyTorch 1.12 and later.
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
|
|
torch.backends.cudnn.allow_tf32 = True
|
|
|
|
# Disable gradients
|
|
torch.set_grad_enabled(False)
|
|
|
|
|
|
def get_model(
|
|
model_id: str, revision: Optional[str], sharded: bool, quantize: bool
|
|
) -> Model:
|
|
if model_id.startswith("facebook/galactica"):
|
|
if sharded:
|
|
return GalacticaSharded(model_id, revision, quantize=quantize)
|
|
else:
|
|
return Galactica(model_id, revision, quantize=quantize)
|
|
|
|
if "santacoder" in model_id:
|
|
return SantaCoder(model_id, revision, quantize)
|
|
|
|
config = AutoConfig.from_pretrained(model_id, revision=revision)
|
|
|
|
if config.model_type == "bloom":
|
|
if sharded:
|
|
return BLOOMSharded(model_id, revision, quantize=quantize)
|
|
else:
|
|
return BLOOM(model_id, revision, quantize=quantize)
|
|
|
|
if config.model_type == "gpt_neox":
|
|
if sharded:
|
|
return GPTNeoxSharded(model_id, revision, quantize=quantize)
|
|
else:
|
|
return GPTNeox(model_id, revision, quantize=quantize)
|
|
|
|
if config.model_type == "t5":
|
|
if sharded:
|
|
return T5Sharded(model_id, revision, quantize=quantize)
|
|
else:
|
|
return Seq2SeqLM(model_id, revision, quantize=quantize)
|
|
|
|
if sharded:
|
|
raise ValueError("sharded is not supported for AutoModel")
|
|
try:
|
|
return CausalLM(model_id, revision, quantize=quantize)
|
|
except Exception:
|
|
return Seq2SeqLM(model_id, revision, quantize=quantize)
|