Merge pull request #125 from Stonelinks/app-server-module-importable
Make app server module importable
This commit is contained in:
Andrei
GitHub
commit
79ba9ed98d
6 changed files with 401 additions and 270 deletions
0
llama_cpp/server/__init__.py
Normal file
0
llama_cpp/server/__init__.py
Normal file
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@ -5,283 +5,29 @@ To run this example:
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```bash
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pip install fastapi uvicorn sse-starlette
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export MODEL=../models/7B/...
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uvicorn fastapi_server_chat:app --reload
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```
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Then run:
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```
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uvicorn llama_cpp.server.app:app --reload
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```
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or
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```
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python3 -m llama_cpp.server
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```
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Then visit http://localhost:8000/docs to see the interactive API docs.
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"""
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import os
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import json
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from threading import Lock
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from typing import List, Optional, Literal, Union, Iterator, Dict
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from typing_extensions import TypedDict
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import llama_cpp
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from fastapi import Depends, FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
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from sse_starlette.sse import EventSourceResponse
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class Settings(BaseSettings):
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model: str
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n_ctx: int = 2048
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n_batch: int = 512
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n_threads: int = max((os.cpu_count() or 2) // 2, 1)
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f16_kv: bool = True
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use_mlock: bool = False # This causes a silent failure on platforms that don't support mlock (e.g. Windows) took forever to figure out...
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use_mmap: bool = True
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embedding: bool = True
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last_n_tokens_size: int = 64
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logits_all: bool = False
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cache: bool = False # WARNING: This is an experimental feature
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app = FastAPI(
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title="🦙 llama.cpp Python API",
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version="0.0.1",
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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settings = Settings()
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llama = llama_cpp.Llama(
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settings.model,
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f16_kv=settings.f16_kv,
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use_mlock=settings.use_mlock,
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use_mmap=settings.use_mmap,
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embedding=settings.embedding,
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logits_all=settings.logits_all,
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n_threads=settings.n_threads,
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n_batch=settings.n_batch,
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n_ctx=settings.n_ctx,
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last_n_tokens_size=settings.last_n_tokens_size,
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)
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if settings.cache:
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cache = llama_cpp.LlamaCache()
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llama.set_cache(cache)
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llama_lock = Lock()
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def get_llama():
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with llama_lock:
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yield llama
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class CreateCompletionRequest(BaseModel):
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prompt: Union[str, List[str]]
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suffix: Optional[str] = Field(None)
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max_tokens: int = 16
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temperature: float = 0.8
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top_p: float = 0.95
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echo: bool = False
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stop: Optional[List[str]] = []
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stream: bool = False
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# ignored or currently unsupported
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model: Optional[str] = Field(None)
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n: Optional[int] = 1
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logprobs: Optional[int] = Field(None)
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presence_penalty: Optional[float] = 0
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frequency_penalty: Optional[float] = 0
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best_of: Optional[int] = 1
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logit_bias: Optional[Dict[str, float]] = Field(None)
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user: Optional[str] = Field(None)
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# llama.cpp specific parameters
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top_k: int = 40
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repeat_penalty: float = 1.1
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class Config:
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schema_extra = {
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"example": {
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"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
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"stop": ["\n", "###"],
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}
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}
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CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
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@app.post(
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"/v1/completions",
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response_model=CreateCompletionResponse,
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)
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def create_completion(
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request: CreateCompletionRequest, llama: llama_cpp.Llama = Depends(get_llama)
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):
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if isinstance(request.prompt, list):
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request.prompt = "".join(request.prompt)
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completion_or_chunks = llama(
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**request.dict(
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exclude={
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"model",
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"n",
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"frequency_penalty",
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"presence_penalty",
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"best_of",
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"logit_bias",
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"user",
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}
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)
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)
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if request.stream:
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chunks: Iterator[llama_cpp.CompletionChunk] = completion_or_chunks # type: ignore
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return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
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completion: llama_cpp.Completion = completion_or_chunks # type: ignore
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return completion
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class CreateEmbeddingRequest(BaseModel):
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model: Optional[str]
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input: str
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user: Optional[str]
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class Config:
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schema_extra = {
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"example": {
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"input": "The food was delicious and the waiter...",
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}
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}
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CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
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@app.post(
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"/v1/embeddings",
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response_model=CreateEmbeddingResponse,
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)
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def create_embedding(
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request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
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):
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return llama.create_embedding(**request.dict(exclude={"model", "user"}))
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class ChatCompletionRequestMessage(BaseModel):
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role: Union[Literal["system"], Literal["user"], Literal["assistant"]]
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content: str
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user: Optional[str] = None
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class CreateChatCompletionRequest(BaseModel):
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model: Optional[str]
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messages: List[ChatCompletionRequestMessage]
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temperature: float = 0.8
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top_p: float = 0.95
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stream: bool = False
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stop: Optional[List[str]] = []
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max_tokens: int = 128
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# ignored or currently unsupported
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model: Optional[str] = Field(None)
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n: Optional[int] = 1
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presence_penalty: Optional[float] = 0
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frequency_penalty: Optional[float] = 0
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logit_bias: Optional[Dict[str, float]] = Field(None)
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user: Optional[str] = Field(None)
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# llama.cpp specific parameters
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repeat_penalty: float = 1.1
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class Config:
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schema_extra = {
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"example": {
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"messages": [
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ChatCompletionRequestMessage(
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role="system", content="You are a helpful assistant."
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),
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ChatCompletionRequestMessage(
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role="user", content="What is the capital of France?"
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),
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]
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}
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}
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CreateChatCompletionResponse = create_model_from_typeddict(llama_cpp.ChatCompletion)
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@app.post(
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"/v1/chat/completions",
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response_model=CreateChatCompletionResponse,
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)
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def create_chat_completion(
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request: CreateChatCompletionRequest,
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llama: llama_cpp.Llama = Depends(get_llama),
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) -> Union[llama_cpp.ChatCompletion, EventSourceResponse]:
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completion_or_chunks = llama.create_chat_completion(
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**request.dict(
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exclude={
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"model",
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"n",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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}
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),
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)
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if request.stream:
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async def server_sent_events(
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chat_chunks: Iterator[llama_cpp.ChatCompletionChunk],
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):
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for chat_chunk in chat_chunks:
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yield dict(data=json.dumps(chat_chunk))
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yield dict(data="[DONE]")
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chunks: Iterator[llama_cpp.ChatCompletionChunk] = completion_or_chunks # type: ignore
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return EventSourceResponse(
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server_sent_events(chunks),
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)
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completion: llama_cpp.ChatCompletion = completion_or_chunks # type: ignore
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return completion
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class ModelData(TypedDict):
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id: str
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object: Literal["model"]
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owned_by: str
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permissions: List[str]
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class ModelList(TypedDict):
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object: Literal["list"]
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data: List[ModelData]
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GetModelResponse = create_model_from_typeddict(ModelList)
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@app.get("/v1/models", response_model=GetModelResponse)
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def get_models() -> ModelList:
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return {
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"object": "list",
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"data": [
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{
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"id": llama.model_path,
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"object": "model",
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"owned_by": "me",
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"permissions": [],
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}
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],
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}
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import uvicorn
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from llama_cpp.server.app import app, init_llama
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if __name__ == "__main__":
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import os
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import uvicorn
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init_llama()
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uvicorn.run(
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app, host=os.getenv("HOST", "localhost"), port=int(os.getenv("PORT", 8000))
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271
llama_cpp/server/app.py
Normal file
271
llama_cpp/server/app.py
Normal file
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@ -0,0 +1,271 @@
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import os
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import json
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from threading import Lock
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from typing import List, Optional, Literal, Union, Iterator, Dict
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from typing_extensions import TypedDict
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import llama_cpp
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from fastapi import Depends, FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
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from sse_starlette.sse import EventSourceResponse
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class Settings(BaseSettings):
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model: str = os.environ.get("MODEL", "null")
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n_ctx: int = 2048
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n_batch: int = 512
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n_threads: int = max((os.cpu_count() or 2) // 2, 1)
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f16_kv: bool = True
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use_mlock: bool = False # This causes a silent failure on platforms that don't support mlock (e.g. Windows) took forever to figure out...
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use_mmap: bool = True
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embedding: bool = True
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last_n_tokens_size: int = 64
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logits_all: bool = False
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cache: bool = False # WARNING: This is an experimental feature
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vocab_only: bool = False
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app = FastAPI(
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title="🦙 llama.cpp Python API",
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version="0.0.1",
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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llama: llama_cpp.Llama = None
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def init_llama(settings: Settings = None):
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if settings is None:
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settings = Settings()
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global llama
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llama = llama_cpp.Llama(
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settings.model,
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f16_kv=settings.f16_kv,
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use_mlock=settings.use_mlock,
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use_mmap=settings.use_mmap,
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embedding=settings.embedding,
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logits_all=settings.logits_all,
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n_threads=settings.n_threads,
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n_batch=settings.n_batch,
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n_ctx=settings.n_ctx,
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last_n_tokens_size=settings.last_n_tokens_size,
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vocab_only=settings.vocab_only,
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)
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if settings.cache:
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cache = llama_cpp.LlamaCache()
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llama.set_cache(cache)
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llama_lock = Lock()
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def get_llama():
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with llama_lock:
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yield llama
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class CreateCompletionRequest(BaseModel):
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prompt: Union[str, List[str]]
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suffix: Optional[str] = Field(None)
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max_tokens: int = 16
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temperature: float = 0.8
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top_p: float = 0.95
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echo: bool = False
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stop: Optional[List[str]] = []
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stream: bool = False
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# ignored or currently unsupported
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model: Optional[str] = Field(None)
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n: Optional[int] = 1
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logprobs: Optional[int] = Field(None)
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presence_penalty: Optional[float] = 0
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frequency_penalty: Optional[float] = 0
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best_of: Optional[int] = 1
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logit_bias: Optional[Dict[str, float]] = Field(None)
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user: Optional[str] = Field(None)
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# llama.cpp specific parameters
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top_k: int = 40
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repeat_penalty: float = 1.1
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class Config:
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schema_extra = {
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"example": {
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"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
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"stop": ["\n", "###"],
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}
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}
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CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
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@app.post(
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"/v1/completions",
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response_model=CreateCompletionResponse,
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)
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def create_completion(
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request: CreateCompletionRequest, llama: llama_cpp.Llama = Depends(get_llama)
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):
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if isinstance(request.prompt, list):
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request.prompt = "".join(request.prompt)
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completion_or_chunks = llama(
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**request.dict(
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exclude={
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"model",
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"n",
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"frequency_penalty",
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"presence_penalty",
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"best_of",
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"logit_bias",
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"user",
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}
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)
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)
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if request.stream:
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chunks: Iterator[llama_cpp.CompletionChunk] = completion_or_chunks # type: ignore
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return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
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completion: llama_cpp.Completion = completion_or_chunks # type: ignore
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return completion
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class CreateEmbeddingRequest(BaseModel):
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model: Optional[str]
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input: str
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user: Optional[str]
|
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|
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class Config:
|
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schema_extra = {
|
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"example": {
|
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"input": "The food was delicious and the waiter...",
|
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}
|
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}
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CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
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@app.post(
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"/v1/embeddings",
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response_model=CreateEmbeddingResponse,
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)
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def create_embedding(
|
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request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
|
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):
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return llama.create_embedding(**request.dict(exclude={"model", "user"}))
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|
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|
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class ChatCompletionRequestMessage(BaseModel):
|
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role: Union[Literal["system"], Literal["user"], Literal["assistant"]]
|
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content: str
|
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user: Optional[str] = None
|
||||
|
||||
|
||||
class CreateChatCompletionRequest(BaseModel):
|
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model: Optional[str]
|
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messages: List[ChatCompletionRequestMessage]
|
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temperature: float = 0.8
|
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top_p: float = 0.95
|
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stream: bool = False
|
||||
stop: Optional[List[str]] = []
|
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max_tokens: int = 128
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|
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# ignored or currently unsupported
|
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model: Optional[str] = Field(None)
|
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n: Optional[int] = 1
|
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presence_penalty: Optional[float] = 0
|
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frequency_penalty: Optional[float] = 0
|
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logit_bias: Optional[Dict[str, float]] = Field(None)
|
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user: Optional[str] = Field(None)
|
||||
|
||||
# llama.cpp specific parameters
|
||||
repeat_penalty: float = 1.1
|
||||
|
||||
class Config:
|
||||
schema_extra = {
|
||||
"example": {
|
||||
"messages": [
|
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ChatCompletionRequestMessage(
|
||||
role="system", content="You are a helpful assistant."
|
||||
),
|
||||
ChatCompletionRequestMessage(
|
||||
role="user", content="What is the capital of France?"
|
||||
),
|
||||
]
|
||||
}
|
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}
|
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|
||||
|
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CreateChatCompletionResponse = create_model_from_typeddict(llama_cpp.ChatCompletion)
|
||||
|
||||
|
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@app.post(
|
||||
"/v1/chat/completions",
|
||||
response_model=CreateChatCompletionResponse,
|
||||
)
|
||||
def create_chat_completion(
|
||||
request: CreateChatCompletionRequest,
|
||||
llama: llama_cpp.Llama = Depends(get_llama),
|
||||
) -> Union[llama_cpp.ChatCompletion, EventSourceResponse]:
|
||||
completion_or_chunks = llama.create_chat_completion(
|
||||
**request.dict(
|
||||
exclude={
|
||||
"model",
|
||||
"n",
|
||||
"presence_penalty",
|
||||
"frequency_penalty",
|
||||
"logit_bias",
|
||||
"user",
|
||||
}
|
||||
),
|
||||
)
|
||||
|
||||
if request.stream:
|
||||
|
||||
async def server_sent_events(
|
||||
chat_chunks: Iterator[llama_cpp.ChatCompletionChunk],
|
||||
):
|
||||
for chat_chunk in chat_chunks:
|
||||
yield dict(data=json.dumps(chat_chunk))
|
||||
yield dict(data="[DONE]")
|
||||
|
||||
chunks: Iterator[llama_cpp.ChatCompletionChunk] = completion_or_chunks # type: ignore
|
||||
|
||||
return EventSourceResponse(
|
||||
server_sent_events(chunks),
|
||||
)
|
||||
completion: llama_cpp.ChatCompletion = completion_or_chunks # type: ignore
|
||||
return completion
|
||||
|
||||
|
||||
class ModelData(TypedDict):
|
||||
id: str
|
||||
object: Literal["model"]
|
||||
owned_by: str
|
||||
permissions: List[str]
|
||||
|
||||
|
||||
class ModelList(TypedDict):
|
||||
object: Literal["list"]
|
||||
data: List[ModelData]
|
||||
|
||||
|
||||
GetModelResponse = create_model_from_typeddict(ModelList)
|
||||
|
||||
|
||||
@app.get("/v1/models", response_model=GetModelResponse)
|
||||
def get_models() -> ModelList:
|
||||
return {
|
||||
"object": "list",
|
||||
"data": [
|
||||
{
|
||||
"id": llama.model_path,
|
||||
"object": "model",
|
||||
"owned_by": "me",
|
||||
"permissions": [],
|
||||
}
|
||||
],
|
||||
}
|
||||
95
poetry.lock
generated
95
poetry.lock
generated
|
|
@ -1,4 +1,25 @@
|
|||
# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "3.6.2"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.6.2"
|
||||
files = [
|
||||
{file = "anyio-3.6.2-py3-none-any.whl", hash = "sha256:fbbe32bd270d2a2ef3ed1c5d45041250284e31fc0a4df4a5a6071842051a51e3"},
|
||||
{file = "anyio-3.6.2.tar.gz", hash = "sha256:25ea0d673ae30af41a0c442f81cf3b38c7e79fdc7b60335a4c14e05eb0947421"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
idna = ">=2.8"
|
||||
sniffio = ">=1.1"
|
||||
|
||||
[package.extras]
|
||||
doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
|
||||
test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
|
||||
trio = ["trio (>=0.16,<0.22)"]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
|
|
@ -398,6 +419,64 @@ colorama = ">=0.4"
|
|||
[package.extras]
|
||||
async = ["aiofiles (>=0.7,<1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.14.0"
|
||||
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
|
||||
{file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "0.17.0"
|
||||
description = "A minimal low-level HTTP client."
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "httpcore-0.17.0-py3-none-any.whl", hash = "sha256:0fdfea45e94f0c9fd96eab9286077f9ff788dd186635ae61b312693e4d943599"},
|
||||
{file = "httpcore-0.17.0.tar.gz", hash = "sha256:cc045a3241afbf60ce056202301b4d8b6af08845e3294055eb26b09913ef903c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.0,<5.0"
|
||||
certifi = "*"
|
||||
h11 = ">=0.13,<0.15"
|
||||
sniffio = ">=1.0.0,<2.0.0"
|
||||
|
||||
[package.extras]
|
||||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (>=1.0.0,<2.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.24.0"
|
||||
description = "The next generation HTTP client."
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "httpx-0.24.0-py3-none-any.whl", hash = "sha256:447556b50c1921c351ea54b4fe79d91b724ed2b027462ab9a329465d147d5a4e"},
|
||||
{file = "httpx-0.24.0.tar.gz", hash = "sha256:507d676fc3e26110d41df7d35ebd8b3b8585052450f4097401c9be59d928c63e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = "*"
|
||||
httpcore = ">=0.15.0,<0.18.0"
|
||||
idna = "*"
|
||||
sniffio = "*"
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli", "brotlicffi"]
|
||||
cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"]
|
||||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (>=1.0.0,<2.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.4"
|
||||
|
|
@ -1232,6 +1311,18 @@ files = [
|
|||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.0"
|
||||
description = "Sniff out which async library your code is running under"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
|
||||
{file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
|
|
@ -1367,4 +1458,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.8.1"
|
||||
content-hash = "cc9babcdfdc3679a4d84f68912408a005619a576947b059146ed1b428850ece9"
|
||||
content-hash = "aa15e57300668bd23c051b4cd87bec4c1a58dcccd2f2b4767579fea7f2c5fa41"
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ mkdocs = "^1.4.2"
|
|||
mkdocstrings = {extras = ["python"], version = "^0.20.0"}
|
||||
mkdocs-material = "^9.1.4"
|
||||
pytest = "^7.2.2"
|
||||
httpx = "^0.24.0"
|
||||
|
||||
[build-system]
|
||||
requires = [
|
||||
|
|
|
|||
|
|
@ -128,3 +128,25 @@ def test_utf8(monkeypatch):
|
|||
n = 0 # reset
|
||||
completion = llama.create_completion("", max_tokens=1)
|
||||
assert completion["choices"][0]["text"] == ""
|
||||
|
||||
|
||||
def test_llama_server():
|
||||
from fastapi.testclient import TestClient
|
||||
from llama_cpp.server.app import app, init_llama, Settings
|
||||
s = Settings()
|
||||
s.model = MODEL
|
||||
s.vocab_only = True
|
||||
init_llama(s)
|
||||
client = TestClient(app)
|
||||
response = client.get("/v1/models")
|
||||
assert response.json() == {
|
||||
"object": "list",
|
||||
"data": [
|
||||
{
|
||||
"id": MODEL,
|
||||
"object": "model",
|
||||
"owned_by": "me",
|
||||
"permissions": [],
|
||||
}
|
||||
],
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue