66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import torch
|
|
import torch.distributed
|
|
|
|
from typing import Optional, List, Tuple
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
|
from text_generation.models import CausalLM
|
|
|
|
FIM_PREFIX = "<fim-prefix>"
|
|
FIM_MIDDLE = "<fim-middle>"
|
|
FIM_SUFFIX = "<fim-suffix>"
|
|
FIM_PAD = "<fim-pad>"
|
|
EOD = "<|endoftext|>"
|
|
|
|
|
|
class SantaCoder(CausalLM):
|
|
def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda")
|
|
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
|
|
else:
|
|
if quantize:
|
|
raise ValueError("quantization is not available on CPU")
|
|
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id, revision=revision, padding_side="left"
|
|
)
|
|
tokenizer.add_special_tokens(
|
|
{
|
|
"additional_special_tokens": [
|
|
EOD,
|
|
FIM_PREFIX,
|
|
FIM_MIDDLE,
|
|
FIM_SUFFIX,
|
|
FIM_PAD,
|
|
],
|
|
"pad_token": EOD,
|
|
}
|
|
)
|
|
|
|
self.model = (
|
|
AutoModelForCausalLM.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
torch_dtype=dtype,
|
|
load_in_8bit=quantize,
|
|
trust_remote_code=True, # required
|
|
)
|
|
.to(device)
|
|
.eval()
|
|
)
|
|
|
|
super(CausalLM, self).__init__(
|
|
tokenizer=tokenizer,
|
|
device=device,
|
|
)
|
|
|
|
def decode(self, generated_ids: List[int]) -> str:
|
|
# Do not skip special tokens as they are used for custom parsing rules of the generated text
|
|
return self.tokenizer.decode(
|
|
generated_ids, skip_special_tokens=False, cleanup_tokenization_spaces=False
|
|
)
|