update

src/fastserve/models/vllm.py

@@ -1,46 +1,78 @@
+import logging
 import os
-from typing import List
+from typing import Any, List, Optional
 
-from fastapi import FastAPI
+from llama_cpp import Llama
 from pydantic import BaseModel
-from vllm import LLM, SamplingParams
 
-tensor_parallel_size = int(os.environ.get("DEVICES", "1"))
-print("tensor_parallel_size: ", tensor_parallel_size)
+from fastserve.core import FastServe
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL = "openhermes-2-mistral-7b.Q6_K.gguf"
 
-llm = LLM("meta-llama/Llama-2-7b-hf", tensor_parallel_size=tensor_parallel_size)
 
 
 class PromptRequest(BaseModel):
-    prompt: str
-    temperature: float = 1
+    prompt: str = "Llamas are cute animal"
+    temperature: float = 0.8
+    top_p: float = 0.0
     max_tokens: int = 200
     stop: List[str] = []
 
 
 class ResponseModel(BaseModel):
     prompt: str
-    prompt_token_ids: List  # The token IDs of the prompt.
-    outputs: List[str]  # The output sequences of the request.
+    prompt_token_ids: Optional[List] = None  # The token IDs of the prompt.
+    text: str  # The output sequences of the request.
     finished: bool  # Whether the whole request is finished.
 
 
-app = FastAPI()
+class ServeVLLM(FastServe):
+    def __init__(
+        self,
+        model_path=DEFAULT_MODEL,
+        batch_size=1,
+        timeout=0.0,
+        *args,
+        **kwargs,
+    ):
+        from vllm import LLM, SamplingParams
+
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"{model_path} not found.")
+
+        self.llm = LLM(model_path)
+        self.model_path = model_path
+        self.args = args
+        self.kwargs = kwargs
+        super().__init__(
+            batch_size,
+            timeout,
+            input_schema=PromptRequest,
+            response_schema=ResponseModel,
+        )
+
+    def __call__(self, request: PromptRequest) -> Any:
+        from vllm import SamplingParams
+
+        sampling_params = SamplingParams(temperature=request.temperature, top_p=request.top_p)
+        result = self.llm(request.prompt, sampling_params=sampling_params)
+        logger.info(result)
+        return result
 
+    def handle(self, batch: List[PromptRequest]) -> List[ResponseModel]:
+        responses = []
+        for request in batch:
+            output = self(request)
 
-@app.post("/serve", response_model=ResponseModel)
-def serve(request: PromptRequest):
-    sampling_params = SamplingParams(
-        max_tokens=request.max_tokens,
-        temperature=request.temperature,
-        stop=request.stop,
-    )
+            response = ResponseModel(
+                **{
+                    "prompt": request.prompt,
+                    "text": output["choices"][0]["text"],
+                    "finished": True,
+                }
+            )
+            responses.append(response)
 
-    result = llm.generate(request.prompt, sampling_params=sampling_params)[0]
-    response = ResponseModel(
-        prompt=request.prompt,
-        prompt_token_ids=result.prompt_token_ids,
-        outputs=result.outputs,
-        finished=result.finished,
-    )
-    return response
+        return responses

src/fastserve/utils.py

@@ -1,6 +1,5 @@
 import os
 from typing import Any
-
 from pydantic import BaseModel
 
 
@@ -23,3 +22,29 @@ def get_ui_folder():
     path = os.path.join(os.path.dirname(__file__), "../ui")
     path = os.path.abspath(path)
     return path
+
+
+def download_file(url:str, dest:str):
+    import requests
+    from tqdm import tqdm
+    from huggingface_hub import HfApi, ModelFilter
+
+
+    if dest is None:
+        dest = os.path.abspath(os.path.basename(dest))
+
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+    total_size = int(response.headers.get('content-length', 0))
+    block_size = 1024
+    with open(dest, 'wb') as file, tqdm(
+        desc=dest,
+        total=total_size,
+        unit='iB',
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as bar:
+        for data in response.iter_content(block_size):
+            file.write(data)
+            bar.update(len(data))
+    return dest