67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
import time
|
|
from typing import List
|
|
|
|
import torch
|
|
from transformers import (
|
|
AutoModelForCausalLM,
|
|
AutoTokenizer,
|
|
StoppingCriteria,
|
|
StoppingCriteriaList,
|
|
)
|
|
|
|
from ..models import Choice, CompletionRequest, CompletionResponse
|
|
from .language_presets import LanguagePresets
|
|
from .utils import random_completion_id, trim_with_stop_words
|
|
|
|
|
|
class PythonModelService:
|
|
def __init__(
|
|
self,
|
|
model_name,
|
|
):
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda")
|
|
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
|
|
else:
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32
|
|
|
|
self.device = device
|
|
self.tokenizer = AutoTokenizer.from_pretrained(
|
|
model_name, local_files_only=True
|
|
)
|
|
self.model = (
|
|
AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
torch_dtype=dtype,
|
|
device_map="auto" if torch.cuda.is_available() else None,
|
|
local_files_only=True,
|
|
)
|
|
.to(device)
|
|
.eval()
|
|
)
|
|
|
|
def generate(self, request: CompletionRequest) -> List[Choice]:
|
|
# FIXME(meng): read preset from request.
|
|
preset_name = "python"
|
|
preset = LanguagePresets[preset_name]
|
|
|
|
input_ids = self.tokenizer.encode(request.prompt, return_tensors="pt").to(
|
|
self.device
|
|
)
|
|
res = self.model.generate(
|
|
input_ids,
|
|
max_length=preset.max_length,
|
|
)
|
|
output_ids = res[0][len(input_ids[0]) :]
|
|
text = trim_with_stop_words(
|
|
self.tokenizer.decode(output_ids), preset.stop_words
|
|
)
|
|
return [Choice(index=0, text=text)]
|
|
|
|
def __call__(self, request: CompletionRequest) -> CompletionResponse:
|
|
choices = self.generate(request)
|
|
return CompletionResponse(
|
|
id=random_completion_id(), created=int(time.time()), choices=choices
|
|
)
|