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141 | class VLLM(ModelBase):
"""Class for vLLM endpoints.
Args:
model_name_or_path: Name of the model
base_url: Base URL for http requests
Examples:
```python
from blendsql.models import VLLM
model = VLLM("RedHatAI/gemma-3-12b-it-quantized.w4a16", base_url="http://localhost:8000/v1/")
```
"""
def __init__(
self,
model_name_or_path: str,
base_url: str,
api_key: str = "N/A",
tokenizer: "BaseTokenizer" = None,
extra_body: dict | None = None,
caching: bool = False,
**kwargs,
):
from openai import AsyncOpenAI
self.extra_body = extra_body or dict()
super().__init__(
model_name_or_path=model_name_or_path,
caching=caching,
_allows_parallel_requests=True,
**kwargs,
)
if tokenizer is None:
from huggingface_hub import hf_hub_download
import json
with open(
hf_hub_download(
repo_id=model_name_or_path, filename="tokenizer_config.json"
),
"r",
) as f:
config = json.load(f)
self.chat_template = config["chat_template"]
with open(
hf_hub_download(
repo_id=model_name_or_path, filename="special_tokens_map.json"
),
"r",
) as f:
special_tokens_map = json.load(f)
self.special_tokens_map = {
k: v["content"] if isinstance(v, dict) else v
for k, v in special_tokens_map.items()
}
self.tokenizer = tokenizer
self.client = AsyncOpenAI(base_url=base_url, api_key=api_key)
async def generate(
self, item: GenerationItem, cancel_event: asyncio.Event | None = None
):
buffer = ""
extra_body = (
DEFAULT_BODY
| {"max_tokens": int(os.getenv(MAX_TOKENS_KEY, DEFAULT_MAX_TOKENS))}
| self.extra_body
)
if item.grammar:
extra_body |= {
"guided_decoding_backend": "guidance",
"guided_grammar": item.grammar,
"structured_outputs": {"grammar": item.grammar},
}
messages = [{"role": "user", "content": item.prompt}]
if item.assistant_continuation is not None:
messages.append(
{"role": "assistant", "content": item.assistant_continuation}
)
if self.tokenizer is None:
from .tokenization import render_jinja_template
prompt_to_send = render_jinja_template(
messages=messages,
chat_template=self.chat_template,
continue_final_message=item.assistant_continuation is not None,
add_generation_prompt=item.assistant_continuation is None,
**self.special_tokens_map,
)
else:
prompt_to_send = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
continue_final_message=item.assistant_continuation is not None,
add_generation_prompt=item.assistant_continuation is None,
)
stream = await self.client.completions.create(
model=self.model_name_or_path,
prompt=prompt_to_send,
stream=True,
stream_options={"include_usage": True},
extra_body=extra_body,
)
self.num_generation_calls += 1
add_to_global_history(prompt_to_send)
try:
async for chunk in stream:
if cancel_event and cancel_event.is_set():
return GenerationResult(item.identifier, buffer, completed=False)
if chunk.choices and chunk.choices[0].text:
buffer += chunk.choices[0].text
if hasattr(chunk, "usage") and chunk.usage is not None:
self.prompt_tokens += chunk.usage.prompt_tokens
self.completion_tokens += chunk.usage.completion_tokens
if chunk.usage.prompt_tokens_details is not None:
self.cached_tokens += (
chunk.usage.prompt_tokens_details.cached_tokens
)
finally:
await stream.close()
return GenerationResult(item.identifier, buffer, completed=True)
|