diff --git a/caddy/Caddyfile b/caddy/Caddyfile index 082319f8..28108d27 100644 --- a/caddy/Caddyfile +++ b/caddy/Caddyfile @@ -4,7 +4,7 @@ } } - https://gpu.nilai.sandbox.nilogy.xyz { + https://nilai.sandbox.nilogy.xyz { import ssl_config reverse_proxy api:8443 } diff --git a/docker-compose.gpu.yml b/docker-compose.gpu.yml index f2e69c7a..a649c1d3 100644 --- a/docker-compose.gpu.yml +++ b/docker-compose.gpu.yml @@ -32,10 +32,7 @@ services: llama_8b_gpu: build: context: . - dockerfile: docker/model.Dockerfile - target: nilai - args: - MODEL_NAME: "llama_8b_gpu" + dockerfile: docker/vllm.Dockerfile runtime: nvidia deploy: resources: @@ -44,28 +41,29 @@ services: - driver: nvidia count: all capabilities: [gpu] - env_file: - - .env depends_on: etcd: condition: service_healthy + command: > + --model meta-llama/Llama-3.1-8B-Instruct + --gpu-memory-utilization 0.5 + --max-model-len 10000 + --tensor-parallel-size 1 environment: - SVC_HOST=llama_8b_gpu - SVC_PORT=8000 - ETCD_HOST=etcd - ETCD_PORT=2379 + env_file: + - .env volumes: - hugging_face_models:/root/.cache/huggingface # cache models networks: - backend_net - llama_1b_gpu: build: context: . - dockerfile: docker/model.Dockerfile - target: nilai - args: - MODEL_NAME: "llama_1b_gpu" + dockerfile: docker/vllm.Dockerfile runtime: nvidia deploy: resources: @@ -74,16 +72,21 @@ services: - driver: nvidia count: all capabilities: [gpu] - env_file: - - .env depends_on: etcd: condition: service_healthy + command: > + --model meta-llama/Llama-3.2-1B-Instruct + --gpu-memory-utilization 0.2 + --max-model-len 10000 + --tensor-parallel-size 1 environment: - SVC_HOST=llama_1b_gpu - SVC_PORT=8000 - ETCD_HOST=etcd - ETCD_PORT=2379 + env_file: + - .env volumes: - hugging_face_models:/root/.cache/huggingface # cache models networks: diff --git a/docker/vllm.Dockerfile b/docker/vllm.Dockerfile new file mode 100644 index 00000000..23346053 --- /dev/null +++ b/docker/vllm.Dockerfile @@ -0,0 +1,29 @@ +FROM vllm/vllm-openai:latest + +# # Specify model name and path during build +# ARG MODEL_NAME=llama_1b_cpu +# ARG MODEL_PATH=meta-llama/Llama-3.1-8B-Instruct + +# # Set environment variables +# ENV MODEL_NAME=${MODEL_NAME} +# ENV MODEL_PATH=${MODEL_PATH} +# ENV EXEC_PATH=nilai_models.models.${MODEL_NAME}:app + +COPY --link . /daemon/ + +WORKDIR /daemon/nilai-models/ + +RUN apt-get update && \ + apt-get install build-essential -y && \ + pip install uv && \ + uv sync && \ + apt-get clean && \ + apt-get autoremove && \ + rm -rf /var/lib/apt/lists/* + +# Expose port 8000 for incoming requests +EXPOSE 8000 + +ENTRYPOINT ["bash", "run.sh"] + +CMD [""] \ No newline at end of file diff --git a/nilai-api/pyproject.toml b/nilai-api/pyproject.toml index b017fd65..9b225382 100644 --- a/nilai-api/pyproject.toml +++ b/nilai-api/pyproject.toml @@ -22,6 +22,7 @@ dependencies = [ "httpx>=0.27.2", "nilrag>=0.1.2", "nilql>=0.0.0a3", + "openai>=1.59.9", ] diff --git a/nilai-api/src/nilai_api/routers/private.py b/nilai-api/src/nilai_api/routers/private.py index 5aca5469..1b35226b 100644 --- a/nilai-api/src/nilai_api/routers/private.py +++ b/nilai-api/src/nilai_api/routers/private.py @@ -1,26 +1,25 @@ # Fast API and serving import logging import os -import asyncio from base64 import b64encode -from typing import AsyncGenerator, Union +from typing import AsyncGenerator, Union, List import numpy as np import nilql import nilrag -import httpx from fastapi import APIRouter, Body, Depends, HTTPException from fastapi.responses import StreamingResponse from nilai_api.auth import get_user from nilai_api.crypto import sign_message from nilai_api.db import UserManager from nilai_api.state import state +from openai import OpenAI # Internal libraries from nilai_common import ( AttestationResponse, ChatRequest, - ChatResponse, + SignedChatCompletion, Message, ModelMetadata, Usage, @@ -79,7 +78,7 @@ async def get_attestation(user: dict = Depends(get_user)) -> AttestationResponse @router.get("/v1/models", tags=["Model"]) -async def get_models(user: dict = Depends(get_user)) -> list[ModelMetadata]: +async def get_models(user: dict = Depends(get_user)) -> List[ModelMetadata]: """ List all available models in the system. @@ -94,13 +93,22 @@ async def get_models(user: dict = Depends(get_user)) -> list[ModelMetadata]: """ logger.info(f"Retrieving models for user {user['userid']} from pid {os.getpid()}") return [endpoint.metadata for endpoint in (await state.models).values()] + # result = [Model( + # id = endpoint.metadata.id, + # created = 0, + # object = "model", + # owned_by = endpoint.metadata.author, + # data = endpoint.metadata.dict(), + # ) for endpoint in (await state.models).values()] + + # return result[0] @router.post("/v1/chat/completions", tags=["Chat"], response_model=None) async def chat_completion( req: ChatRequest = Body( ChatRequest( - model="Llama-3.2-1B-Instruct", + model="meta-llama/Llama-3.2-1B-Instruct", messages=[ Message(role="system", content="You are a helpful assistant."), Message(role="user", content="What is your name?"), @@ -108,7 +116,7 @@ async def chat_completion( ) ), user: dict = Depends(get_user), -) -> Union[ChatResponse, StreamingResponse]: +) -> Union[SignedChatCompletion, StreamingResponse]: """ Generate a chat completion response from the AI model. @@ -144,7 +152,7 @@ async def chat_completion( ```python # Generate a chat completion request = ChatRequest( - model="Llama-3.2-1B-Instruct", + model="meta-llama/Llama-3.2-1B-Instruct", messages=[ {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Hello, who are you?"} @@ -157,10 +165,17 @@ async def chat_completion( endpoint = await state.get_model(model_name) if endpoint is None: raise HTTPException( - status_code=400, detail="Invalid model name, check /v1/models for options" + status_code=400, + detail=f"Invalid model name {model_name}, check /v1/models for options", ) - model_url = endpoint.url + model_url = endpoint.url + "/v1/" + + logger.info( + f"Chat completion request for model {model_name} from user {user['userid']} on url: {model_url}" + ) + + client = OpenAI(base_url=model_url, api_key="") if req.nilrag: """ @@ -282,60 +297,49 @@ async def chat_completion( if req.stream: # Forwarding Streamed Responses - async def stream_response() -> AsyncGenerator[str, None]: + async def chat_completion_stream_generator() -> AsyncGenerator[str, None]: try: - async with httpx.AsyncClient() as client: - async with client.stream( - "POST", - f"{model_url}/v1/chat/completions", - json=req.model_dump(), - timeout=None, - ) as response: - response.raise_for_status() # Raise an error for invalid status codes - - # Process the streamed response chunks - async for chunk in response.aiter_lines(): - if chunk: # Skip empty lines - yield f"{chunk}\n" - await asyncio.sleep( - 0 - ) # Add an await to return inmediately - except httpx.HTTPStatusError as e: - raise HTTPException( - status_code=e.response.status_code, - detail=e.response.json().get("detail", str(e)), - ) - except httpx.RequestError as e: - raise HTTPException( - status_code=503, - detail=f"Error connecting to model service: {str(e)}", + response = client.chat.completions.create( + model=req.model, + messages=req.messages, + stream=req.stream, + extra_body={ + "stream_options": { + "include_usage": True, + # "continuous_usage_stats": True, + } + }, ) + for chunk in response: + if chunk.usage is not None: + UserManager.update_token_usage( + user["userid"], + prompt_tokens=chunk.usage.prompt_tokens, + completion_tokens=chunk.usage.completion_tokens, + ) + else: + data = chunk.model_dump_json(exclude_unset=True) + yield f"data: {data}\n\n" + + except Exception as e: + logger.error(f"Error streaming response: {e}") + return + # Return the streaming response return StreamingResponse( - stream_response(), + chat_completion_stream_generator(), media_type="text/event-stream", # Ensure client interprets as Server-Sent Events ) - try: - async with httpx.AsyncClient() as client: - response = await client.post( - f"{model_url}/v1/chat/completions", json=req.model_dump(), timeout=None - ) - response.raise_for_status() - model_response = ChatResponse.model_validate_json(response.content) - except httpx.HTTPStatusError as e: - # Forward the original error from the model - raise HTTPException( - status_code=e.response.status_code, - detail=e.response.json().get("detail", str(e)), - ) - except httpx.RequestError as e: - # Handle connection/timeout errors - raise HTTPException( - status_code=503, detail=f"Error connecting to model service: {str(e)}" - ) + response = client.chat.completions.create( + model=req.model, messages=req.messages, stream=req.stream + ) + model_response = SignedChatCompletion( + **response.dict(), + signature="", + ) # Update token usage UserManager.update_token_usage( user["userid"], diff --git a/nilai-models/README.md b/nilai-models/README.md index e69de29b..6db00507 100644 --- a/nilai-models/README.md +++ b/nilai-models/README.md @@ -0,0 +1,19 @@ +# Running vLLM without docker + + +```shell +# For Llama 8B +uv run bash run.sh \ +--model meta-llama/Llama-3.1-8B-Instruct \ +--gpu-memory-utilization 0.5 \ +--max-model-len 10000 \ +--tensor-parallel-size 1 +``` + +```shell +# For Llama 1B +bash run.sh --model meta-llama/Llama-3.2-1B-Instruct \ +--gpu-memory-utilization 0.2 \ +--max-model-len 10000 \ +--tensor-parallel-size 1 +``` diff --git a/nilai-models/gunicorn.conf.py b/nilai-models/gunicorn.conf.py deleted file mode 100644 index 1c022b26..00000000 --- a/nilai-models/gunicorn.conf.py +++ /dev/null @@ -1,16 +0,0 @@ -# gunicorn.config.py - -# Bind to address and port -bind = "0.0.0.0:8000" - -# Set the number of workers (2) -workers = 1 - -# Set the number of threads per worker (16) -threads = 4 - -# Set the timeout (120 seconds) -timeout = 120 - -# Set the worker class to UvicornWorker for async handling -worker_class = "uvicorn.workers.UvicornWorker" diff --git a/nilai-models/pyproject.toml b/nilai-models/pyproject.toml index 9fde34c1..ba0695d1 100644 --- a/nilai-models/pyproject.toml +++ b/nilai-models/pyproject.toml @@ -8,15 +8,8 @@ authors = [ ] requires-python = ">=3.12" dependencies = [ - "accelerate>=1.1.1", - "cryptography>=44.0.0", - "fastapi[standard]>=0.115.5", - "gunicorn>=23.0.0", + "httpx>=0.27.2", "nilai-common", - "torch>=2.5.1", - "transformers>=4.46.3", - "uvicorn>=0.32.1", - "vllm>=0.6.6.post1", ] [build-system] diff --git a/nilai-models/run.sh b/nilai-models/run.sh new file mode 100644 index 00000000..3a7cff4b --- /dev/null +++ b/nilai-models/run.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +echo "Starting the primary process" +# Start the primary process and put it in the background +echo "Args: $*" +python3 -m vllm.entrypoints.openai.api_server $* & #--model $1 --gpu-memory-utilization 0.5 --max-model-len 10000 --tensor-parallel-size 1 & + +echo "Starting the secondary process" +# Start the helper process +uv run python3 -m nilai_models.daemon + +# Wait for any process to exit +wait -n + +# Exit with status of process that exited first +exit $? diff --git a/nilai-models/src/nilai_models/daemon.py b/nilai-models/src/nilai_models/daemon.py new file mode 100644 index 00000000..95e74d6a --- /dev/null +++ b/nilai-models/src/nilai_models/daemon.py @@ -0,0 +1,111 @@ +# nilai/models/model.py +import asyncio +import signal +import logging +import httpx + +from nilai_common import ( # Model service discovery and host settings + SETTINGS, + ModelServiceDiscovery, + ModelEndpoint, + ModelMetadata, +) + +logger = logging.getLogger(__name__) + + +async def get_metadata(num_retries=10): + """Fetch model metadata from model + service and return as ModelMetadata object""" + current_retries = 0 + while True: + try: + url = f"http://{SETTINGS['host']}:{SETTINGS['port']}/v1/models" + # Request model metadata from localhost:8000/v1/models + async with httpx.AsyncClient() as client: + response = await client.get(url) + response.raise_for_status() + response_data = response.json() + model_name = response_data["data"][0]["id"] + return ModelMetadata( + id=model_name, # Unique identifier + name=model_name, # Human-readable name + version="1.0", # Model version + description="", + author="", # Model creators + license="Apache 2.0", # Usage license + source=f"https://huggingface.co/{model_name}", # Model source + supported_features=["chat_completion"], # Capabilities + ) + + except Exception as e: + logger.warning(f"Failed to fetch model metadata from {url}: {e}") + current_retries += 1 + if current_retries >= num_retries: + raise e + await asyncio.sleep(10) + + +async def run_service(discovery_service, model_endpoint): + """Runs the model service and keeps it alive""" + try: + logger.info(f"Registering model: {model_endpoint.metadata.id}") + lease = await discovery_service.register_model(model_endpoint, prefix="/models") + logger.info(f"Model registered successfully: {model_endpoint}") + + await discovery_service.keep_alive(lease) + + except asyncio.CancelledError: + logger.info("Service shutdown requested") + raise + except Exception as e: + logger.error(f"Service error: {e}") + raise + finally: + try: + await discovery_service.unregister_model(model_endpoint.metadata.id) + logger.info(f"Model unregistered: {model_endpoint.metadata.id}") + except Exception as e: + logger.error(f"Error unregistering model: {e}") + + +async def main(): + discovery_service = None + model_endpoint = None + + try: + # Initialize discovery service + discovery_service = ModelServiceDiscovery( + host=SETTINGS["etcd_host"], port=SETTINGS["etcd_port"] + ) + + metadata = await get_metadata() + model_endpoint = ModelEndpoint( + url=f"http://{SETTINGS['host']}:{SETTINGS['port']}", metadata=metadata + ) + + # Setup signal handlers + loop = asyncio.get_running_loop() + for sig in (signal.SIGTERM, signal.SIGINT): + loop.add_signal_handler(sig, lambda: asyncio.create_task(shutdown())) + + # Run service + await run_service(discovery_service, model_endpoint) + + except Exception as e: + logger.error(f"Failed to initialize model service: {e}") + raise + + +async def shutdown(): + """Cleanup and shutdown""" + tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()] + [task.cancel() for task in tasks] + await asyncio.gather(*tasks, return_exceptions=True) + loop = asyncio.get_running_loop() + loop.stop() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + asyncio.run(main()) diff --git a/nilai-models/src/nilai_models/model.py b/nilai-models/src/nilai_models/model.py deleted file mode 100644 index 02eb5fd0..00000000 --- a/nilai-models/src/nilai_models/model.py +++ /dev/null @@ -1,223 +0,0 @@ -# nilai/models/model.py -import asyncio # For making HTTP requests -import logging -import time # For tracking uptime and time-related calculations -from abc import ABC, abstractmethod # Abstract Base Class to define interfaces -from contextlib import asynccontextmanager # For managing async context -from typing import Union # For type hinting - -from fastapi import FastAPI # Web framework for creating API endpoints -from fastapi.responses import StreamingResponse -from nilai_common import ChatRequest # Request type for chat interactions -from nilai_common import ChatResponse # Response type for chat completions -from nilai_common import HealthCheckResponse # Custom response type for health checks -from nilai_common import ModelEndpoint # Endpoint information for model registration -from nilai_common import ModelMetadata # Metadata about the model -from nilai_common import ( # Model service discovery and host settings - SETTINGS, - ModelServiceDiscovery, -) - -logger = logging.getLogger(__name__) - - -class Model(ABC): - """ - Abstract base class for AI models, providing a standardized interface - for model initialization, routing, and basic functionality. - - This class serves as a blueprint for creating different AI model - implementations with consistent API endpoints and behaviors. - """ - - def __init__(self, metadata: ModelMetadata, prefix="/models"): - """ - Initialize the model with its metadata and tracking information. - - Args: - metadata (ModelMetadata): Detailed information about the model. - prefix (str): Optional prefix for hiding the model behind a subpath. - """ - # Store the model's metadata for later retrieval - self.metadata = metadata - self.url = f"http://{SETTINGS['host']}:{SETTINGS['port']}" - self.endpoint = ModelEndpoint(url=self.url, metadata=self.metadata) - # Record the start time for uptime tracking - self._uptime = time.time() - self.app = self.setup_app() - self.prefix = prefix - - def setup_app(self): - @asynccontextmanager - async def lifespan(app: FastAPI): - discovery_service = None - keep_alive_task = None - - try: - # Load models - self.load_models() - # Initialize discovery service - discovery_service = ModelServiceDiscovery( - host=SETTINGS["etcd_host"], port=SETTINGS["etcd_port"] - ) - - # Validate model metadata - if not self.endpoint or not self.endpoint.metadata: - raise ValueError("Invalid model metadata") - - # Register model and start keepalive - logger.info(f"Registering model: {self.endpoint.metadata.id}") - lease = await discovery_service.register_model( - self.endpoint, self.prefix - ) - keep_alive_task = asyncio.create_task( - discovery_service.keep_alive(lease), - name=f"keepalive_{self.endpoint.metadata.id}", - ) - - logger.info(f"Model registered successfully: {self.endpoint}") - yield - - except Exception as e: - logger.error(f"Failed to initialize model service: {e}") - raise - finally: - # Cleanup - if keep_alive_task and not keep_alive_task.done(): - keep_alive_task.cancel() - try: - await keep_alive_task - except asyncio.CancelledError: - pass - - if discovery_service: - try: - await discovery_service.unregister_model( - self.endpoint.metadata.id - ) - logger.info(f"Model unregistered: {self.endpoint.metadata.id}") - except Exception as e: - logger.error(f"Error unregistering model: {e}") - - # Create a FastAPI application instance for the model - self.app = FastAPI(lifespan=lifespan) - self._setup_routes() - return self.app - - def load_models(self): - """ - Load the model(s) required for the service. - - This method should be overridden by child classes to load - the specific model(s) required for the service. - """ - pass - - def get_app(self) -> FastAPI: - """ - Retrieve the FastAPI application instance for the model. - - Returns: - FastAPI: The application instance associated with this model. - """ - return self.app - - def _setup_routes(self): - """ - Set up standard routes for the model's API. - - This method provides default implementations for common endpoints: - - /chat: For initiating chat completions - - /health: For checking the model's health status - - /model: For retrieving model information - - Intended to be called during model initialization or overridden - by child classes to add custom routes. - """ - - @self.app.post("/v1/chat/completions", response_model=None) - async def chat(req: ChatRequest) -> Union[ChatResponse, StreamingResponse]: - return await self.chat_completion(req) - - # Health check endpoint - @self.app.get("/v1/health") - async def health() -> HealthCheckResponse: - return await self.health_check() - - # Model information endpoint - @self.app.get("/v1/models") - async def model_info() -> ModelMetadata: - return await self.model_info() - - @abstractmethod - async def chat_completion( - self, req: ChatRequest - ) -> ChatResponse | StreamingResponse: - """ - Abstract method for generating chat completions. - - This method MUST be implemented by any child class. - It defines the core interaction mechanism for the model. - - Args: - req (ChatRequest): The chat request containing messages and parameters. - - Returns: - ChatResponse: The generated response from the model. - - Raises: - NotImplementedError: If not overridden by a child class. - """ - pass - - async def model_info(self) -> ModelMetadata: - """ - Retrieve the model's metadata. - - Returns: - ModelMetadata: Detailed information about the model. - """ - return self.metadata - - async def health_check(self) -> HealthCheckResponse: - """ - Perform a basic health check for the model. - - Returns: - HealthCheckResponse: Current health status and uptime. - """ - return HealthCheckResponse( - status="healthy", - uptime=self.uptime, - ) - - @property - def uptime(self): - """ - Calculate and format the model's uptime in a human-readable format. - - Returns: - str: A formatted string representing the model's uptime - (e.g., "2 days, 3 hours, 45 minutes"). - """ - # Calculate total elapsed time since initialization - elapsed_time = time.time() - self._uptime - - # Break down elapsed time into days, hours, minutes, seconds - days, remainder = divmod(elapsed_time, 86400) - hours, remainder = divmod(remainder, 3600) - minutes, seconds = divmod(remainder, 60) - - # Prepare parts of the uptime string - parts = [] - if days > 0: - parts.append(f"{int(days)} days") - if hours > 0: - parts.append(f"{int(hours)} hours") - if minutes > 0: - parts.append(f"{int(minutes)} minutes") - if seconds > 0: - parts.append(f"{int(seconds)} seconds") - - # Join the parts into a readable string - return ", ".join(parts) diff --git a/nilai-models/src/nilai_models/models/__init__.py b/nilai-models/src/nilai_models/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/nilai-models/src/nilai_models/models/llama_1b_cpu/__init__.py b/nilai-models/src/nilai_models/models/llama_1b_cpu/__init__.py deleted file mode 100644 index 1a5d2e5c..00000000 --- a/nilai-models/src/nilai_models/models/llama_1b_cpu/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from nilai_models.models.llama_1b_cpu.llama_1b_cpu import app - -__all__ = ["app"] diff --git a/nilai-models/src/nilai_models/models/llama_1b_cpu/llama_1b_cpu.py b/nilai-models/src/nilai_models/models/llama_1b_cpu/llama_1b_cpu.py deleted file mode 100644 index a537c09c..00000000 --- a/nilai-models/src/nilai_models/models/llama_1b_cpu/llama_1b_cpu.py +++ /dev/null @@ -1,55 +0,0 @@ -from llama_cpp import Llama -from nilai_common import ModelMetadata -from nilai_models.models.llama_model import LlamaCppModel - - -class Llama1BCpu(LlamaCppModel): - """ - A specific implementation of the Model base class for the Llama 1B CPU model. - - This class provides: - - Model initialization using llama_cpp - - Chat completion functionality (with streaming support) - - Metadata about the Llama model - """ - - def __init__(self): - """ - Initialize the Llama 1B model: - - Load pre-trained model using llama_cpp - - Configured for CPU inference with 16 threads - """ - - # Initialize model metadata - super().__init__( - model=Llama.from_pretrained( - repo_id="bartowski/Llama-3.2-1B-Instruct-GGUF", - filename="Llama-3.2-1B-Instruct-Q5_K_S.gguf", - n_threads=16, - n_ctx=128 * 1024, - verbose=False, - ), - metadata=ModelMetadata( - id="Llama-3.2-1B-Instruct", - name="Llama-3.2-1B-Instruct", - version="1.0", - description="Llama is a large language model trained on supervised and unsupervised data.", - author="Meta-Llama", - license="Apache 2.0", - source="https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF", - supported_features=["chat_completion", "streaming"], # Added streaming - ), - ) - - def load_models(self): - """ - Load the model(s) required for the service. - - This method is called during model initialization to load the - specific model(s) required for the service at service startup. - """ - pass - - -# FastAPI app instance -app = Llama1BCpu().get_app() diff --git a/nilai-models/src/nilai_models/models/llama_1b_gpu/__init__.py b/nilai-models/src/nilai_models/models/llama_1b_gpu/__init__.py deleted file mode 100644 index ef32c485..00000000 --- a/nilai-models/src/nilai_models/models/llama_1b_gpu/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from nilai_models.models.llama_1b_gpu.llama_1b_gpu import app - -__all__ = ["app"] diff --git a/nilai-models/src/nilai_models/models/llama_1b_gpu/llama_1b_gpu.py b/nilai-models/src/nilai_models/models/llama_1b_gpu/llama_1b_gpu.py deleted file mode 100644 index acb51482..00000000 --- a/nilai-models/src/nilai_models/models/llama_1b_gpu/llama_1b_gpu.py +++ /dev/null @@ -1,189 +0,0 @@ -import uuid -import time -import torch -import logging -import json -import asyncio -from typing import AsyncGenerator -from vllm.engine.async_llm_engine import AsyncLLMEngine -from vllm.engine.arg_utils import AsyncEngineArgs -from fastapi import HTTPException -from transformers import AutoTokenizer -from vllm import SamplingParams, RequestOutput -from fastapi.responses import StreamingResponse -from nilai_common import ( - ChatRequest, - ChatResponse, - Message, - ModelMetadata, - Usage, - Choice, - ChatCompletionChunk, - ChoiceChunk, - ChoiceChunkContent, -) -from nilai_models.model import Model - - -class Llama1BGpu(Model): - """ - A specific implementation of the Model base class for the Llama 8B GPU model. - """ - - def __init__(self, load=True) -> None: - if not torch.cuda.is_available(): - raise ValueError("Attempted to initialize GPU model on non-GPU machine") - super().__init__( - ModelMetadata( - id="Llama-3.2-1B-Instruct", # Unique identifier - name="Llama-3.2-1B-Instruct", # Human-readable name - version="1.0", # Model version - description="Llama is a large language model trained on supervised and unsupervised data.", - author="Meta-Llama", # Model creators - license="Apache 2.0", # Usage license - source="https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct", # Model source - supported_features=["chat_completion"], # Capabilities - ), - ) - - def load_models(self): - """ - Load the model(s) required for the service. - - This method is called during model initialization to load the - specific model(s) required for the service at service startup. - """ - engine_args = AsyncEngineArgs( - model="meta-llama/Llama-3.2-1B-Instruct", - gpu_memory_utilization=0.3, - max_model_len=60624, - tensor_parallel_size=torch.cuda.device_count(), - ) - self.llm_engine = AsyncLLMEngine.from_engine_args(engine_args) - self.tokenizer = AutoTokenizer.from_pretrained( - "meta-llama/Llama-3.2-1B-Instruct" - ) - - async def chat_completion( - self, - req: ChatRequest = ChatRequest( - # Default request with sample messages for documentation - model="meta-llama/Llama-3.2-1B-Instruct", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ), - ) -> StreamingResponse | ChatResponse: - """ - Generate a chat completion using the Llama model, with optional streaming. - - Args: - req (ChatRequest): The chat request containing conversation messages. - stream (bool): Whether to return a streamed response. - - Returns: - ChatResponse or StreamingResponse: Either a full response or a streaming response. - """ - if not req.messages or len(req.messages) == 0: - raise HTTPException( - status_code=400, detail="The 'messages' field is required." - ) - if not req.model: - raise HTTPException( - status_code=400, detail="The 'model' field is required." - ) - - # Transform incoming messages to llama_cpp-compatible format - conversation = [ - { - "role": msg.role, # Preserve message role (system/user/assistant) - "content": msg.content, # Preserve message content - } - for msg in req.messages - ] - - prompt = self.tokenizer.apply_chat_template( - conversation, tokenize=False, add_generation_prompt=True - ) - - sampling_params = SamplingParams( - temperature=req.temperature if req.temperature else 0.7, - top_p=req.top_p if req.top_p else 0.95, - max_tokens=req.max_tokens if req.max_tokens else 1024, - ) - - if req.stream: - - async def generate() -> AsyncGenerator[str, None]: - try: - previous_generated_len = 0 - async for chunk in self.llm_engine.generate( - prompt, - sampling_params=sampling_params, - request_id=str(uuid.uuid4()), - ): # Generate chunks - current_text = chunk.outputs[0].text - - # Get only new tokens by slicing from previous length - new_text = current_text[previous_generated_len:] - # print(new_tokens, end='', flush=True) - previous_generated_len = len(current_text) - chunk = ChoiceChunk( - index=0, - delta=ChoiceChunkContent(content=new_text), - ) # Create a ChoiceChunk - completion_chunk = ChatCompletionChunk(choices=[chunk]) - yield f"data: {completion_chunk.model_dump_json()}\n\n" # Stream the chunk - await asyncio.sleep(0) # Add an await to return inmediately - - yield "data: [DONE]\n\n" - except Exception as e: - logging.error("An error occurred: %s", str(e)) - yield f"data: {json.dumps({'error': 'Internal error occurred!'})}\n\n" - - # Return the streamed response with headers - return StreamingResponse(generate(), media_type="text/event-stream") - # Non-streaming (regular) chat completion - try: - request_output: RequestOutput = None # type: ignore - async for chunk in self.llm_engine.generate( - prompt, sampling_params=sampling_params, request_id=str(uuid.uuid4()) - ): - request_output = chunk - generation = request_output.outputs[0].text - except ValueError: - raise HTTPException( - status_code=400, - detail="The prompt size exceeds the maximum limit of 2048 tokens.", - ) - if not generation or len(generation) == 0: - raise ValueError("The model returned no output.") - - response = ChatResponse( - signature="", - id="chatcmpl-" + str(uuid.uuid4()), - object="chat.completion", - created=int(time.time()), - model=req.model, - choices=[ - Choice( - index=0, - message=Message(role="assistant", content=generation), - finish_reason="complete", - logprobs=None, - ) - ], - usage=Usage( - prompt_tokens=len(prompt.split()), - completion_tokens=len(generation.split()), - total_tokens=len(prompt.split()) + len(generation.split()), - ), - ) - return response - - -# Create and expose the FastAPI app for this Llama model -# - Calls get_app() from the base Model class -# - Allows easy integration with ASGI servers like uvicorn -app = Llama1BGpu().get_app() diff --git a/nilai-models/src/nilai_models/models/llama_8b_cpu/__init__.py b/nilai-models/src/nilai_models/models/llama_8b_cpu/__init__.py deleted file mode 100644 index 1c95b44e..00000000 --- a/nilai-models/src/nilai_models/models/llama_8b_cpu/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from nilai_models.models.llama_8b_cpu.llama_8b_cpu import app - -__all__ = ["app"] diff --git a/nilai-models/src/nilai_models/models/llama_8b_cpu/llama_8b_cpu.py b/nilai-models/src/nilai_models/models/llama_8b_cpu/llama_8b_cpu.py deleted file mode 100644 index e8cf20df..00000000 --- a/nilai-models/src/nilai_models/models/llama_8b_cpu/llama_8b_cpu.py +++ /dev/null @@ -1,68 +0,0 @@ -from llama_cpp import Llama -from nilai_common import ModelMetadata -from nilai_models.models.llama_model import LlamaCppModel - - -class Llama8BCpu(LlamaCppModel): - """ - A specific implementation of the Model base class for the Llama 1B CPU model. - - This class provides: - - Model initialization using llama_cpp - - Chat completion functionality - - Metadata about the Llama model - """ - - def __init__(self): - """ - Initialize the Llama 1B model: - 1. Load the pre-trained model using llama_cpp - 2. Set up model metadata - - Configuration details: - - Uses a specific quantized model from Hugging Face - - Configured for CPU inference - - Uses 16 threads for improved performance on CPU - """ - # Load the pre-trained Llama model - # - repo_id: Source of the model - # - filename: Specific model file (quantized version) - # - n_threads: Number of CPU threads for inference - # - verbose: Disable detailed logging - - # Initialize the base Model class with model metadata - # Provides comprehensive information about the model - super().__init__( - model=Llama.from_pretrained( - repo_id="bartowski/Meta-Llama-3-8B-Instruct-GGUF", - filename="Meta-Llama-3-8B-Instruct-Q5_K_M.gguf", - n_threads=16, - n_ctx=8 * 1024, - verbose=False, - ), - metadata=ModelMetadata( - id="Llama-3.1-8B-Instruct", # Unique identifier - name="Llama-3.1-8B-Instruct", # Human-readable name - version="1.0", # Model version - description="Llama is a large language model trained on supervised and unsupervised data.", - author="Meta-Llama", # Model creators - license="Apache 2.0", # Usage license - source="https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF", # Model source - supported_features=["chat_completion"], # Capabilities - ), - ) - - def load_models(self): - """ - Load the model(s) required for the service. - - This method is called during model initialization to load the - specific model(s) required for the service at service startup. - """ - pass - - -# Create and expose the FastAPI app for this Llama model -# - Calls get_app() from the base Model class -# - Allows easy integration with ASGI servers like uvicorn -app = Llama8BCpu().get_app() diff --git a/nilai-models/src/nilai_models/models/llama_8b_gpu/__init__.py b/nilai-models/src/nilai_models/models/llama_8b_gpu/__init__.py deleted file mode 100644 index 8babaafd..00000000 --- a/nilai-models/src/nilai_models/models/llama_8b_gpu/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from nilai_models.models.llama_8b_gpu.llama_8b_gpu import app - -__all__ = ["app"] diff --git a/nilai-models/src/nilai_models/models/llama_8b_gpu/llama_8b_gpu.py b/nilai-models/src/nilai_models/models/llama_8b_gpu/llama_8b_gpu.py deleted file mode 100644 index 37bb2144..00000000 --- a/nilai-models/src/nilai_models/models/llama_8b_gpu/llama_8b_gpu.py +++ /dev/null @@ -1,189 +0,0 @@ -import uuid -import time -import torch -import logging -import json -import asyncio -from typing import AsyncGenerator -from vllm.engine.async_llm_engine import AsyncLLMEngine -from vllm.engine.arg_utils import AsyncEngineArgs -from fastapi import HTTPException -from transformers import AutoTokenizer -from vllm import SamplingParams, RequestOutput -from fastapi.responses import StreamingResponse -from nilai_common import ( - ChatRequest, - ChatResponse, - Message, - ModelMetadata, - Usage, - Choice, - ChatCompletionChunk, - ChoiceChunk, - ChoiceChunkContent, -) -from nilai_models.model import Model - - -class Llama8BGpu(Model): - """ - A specific implementation of the Model base class for the Llama 8B GPU model. - """ - - def __init__(self, load=True) -> None: - if not torch.cuda.is_available(): - raise ValueError("Attempted to initialize GPU model on non-GPU machine") - super().__init__( - ModelMetadata( - id="Llama-3.1-8B-Instruct", # Unique identifier - name="Llama-3.1-8B-Instruct", # Human-readable name - version="1.0", # Model version - description="Llama is a large language model trained on supervised and unsupervised data.", - author="Meta-Llama", # Model creators - license="Apache 2.0", # Usage license - source="https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct", # Model source - supported_features=["chat_completion"], # Capabilities - ), - ) - - def load_models(self): - """ - Load the model(s) required for the service. - - This method is called during model initialization to load the - specific model(s) required for the service at service startup. - """ - engine_args = AsyncEngineArgs( - model="meta-llama/Llama-3.1-8B-Instruct", - gpu_memory_utilization=0.6, - max_model_len=60624, - tensor_parallel_size=torch.cuda.device_count(), - ) - self.llm_engine = AsyncLLMEngine.from_engine_args(engine_args) - self.tokenizer = AutoTokenizer.from_pretrained( - "meta-llama/Llama-3.1-8B-Instruct" - ) - - async def chat_completion( - self, - req: ChatRequest = ChatRequest( - # Default request with sample messages for documentation - model="meta-llama/Llama-3.1-8B-Instruct", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ), - ) -> StreamingResponse | ChatResponse: - """ - Generate a chat completion using the Llama model, with optional streaming. - - Args: - req (ChatRequest): The chat request containing conversation messages. - stream (bool): Whether to return a streamed response. - - Returns: - ChatResponse or StreamingResponse: Either a full response or a streaming response. - """ - if not req.messages or len(req.messages) == 0: - raise HTTPException( - status_code=400, detail="The 'messages' field is required." - ) - if not req.model: - raise HTTPException( - status_code=400, detail="The 'model' field is required." - ) - - # Transform incoming messages to llama_cpp-compatible format - conversation = [ - { - "role": msg.role, # Preserve message role (system/user/assistant) - "content": msg.content, # Preserve message content - } - for msg in req.messages - ] - - prompt = self.tokenizer.apply_chat_template( - conversation, tokenize=False, add_generation_prompt=True - ) - - sampling_params = SamplingParams( - temperature=req.temperature if req.temperature else 0.7, - top_p=req.top_p if req.top_p else 0.95, - max_tokens=req.max_tokens if req.max_tokens else 1024, - ) - - if req.stream: - - async def generate() -> AsyncGenerator[str, None]: - try: - previous_generated_len = 0 - async for chunk in self.llm_engine.generate( - prompt, - sampling_params=sampling_params, - request_id=str(uuid.uuid4()), - ): # Generate chunks - current_text = chunk.outputs[0].text - - # Get only new tokens by slicing from previous length - new_text = current_text[previous_generated_len:] - # print(new_tokens, end='', flush=True) - previous_generated_len = len(current_text) - chunk = ChoiceChunk( - index=0, - delta=ChoiceChunkContent(content=new_text), - ) # Create a ChoiceChunk - completion_chunk = ChatCompletionChunk(choices=[chunk]) - yield f"data: {completion_chunk.model_dump_json()}\n\n" # Stream the chunk - await asyncio.sleep(0) # Add an await to return inmediately - - yield "data: [DONE]\n\n" - except Exception as e: - logging.error("An error occurred: %s", str(e)) - yield f"data: {json.dumps({'error': 'Internal error occurred!'})}\n\n" - - # Return the streamed response with headers - return StreamingResponse(generate(), media_type="text/event-stream") - # Non-streaming (regular) chat completion - try: - generation: RequestOutput = None - async for request_output in self.llm_engine.generate( - prompt, sampling_params=sampling_params, request_id=str(uuid.uuid4()) - ): - generation = request_output - generation = generation.outputs[0].text - except ValueError: - raise HTTPException( - status_code=400, - detail="The prompt size exceeds the maximum limit of 2048 tokens.", - ) - if not generation or len(generation) == 0: - raise ValueError("The model returned no output.") - - response = ChatResponse( - signature="", - id="chatcmpl-" + str(uuid.uuid4()), - object="chat.completion", - created=int(time.time()), - model=req.model, - choices=[ - Choice( - index=0, - message=Message(role="assistant", content=generation), - finish_reason="complete", - logprobs=None, - ) - ], - usage=Usage( - prompt_tokens=len(prompt.split()), - completion_tokens=len(generation.split()), - total_tokens=len(prompt.split()) + len(generation.split()), - ), - ) - return response - - -# Create and expose the FastAPI app for this Llama model -# - Calls get_app() from the base Model class -# - Allows easy integration with ASGI servers like uvicorn -app = Llama8BGpu().get_app() diff --git a/nilai-models/src/nilai_models/models/llama_model.py b/nilai-models/src/nilai_models/models/llama_model.py deleted file mode 100644 index 4f969e99..00000000 --- a/nilai-models/src/nilai_models/models/llama_model.py +++ /dev/null @@ -1,131 +0,0 @@ -import asyncio -import json -import logging -from typing import AsyncGenerator - -from fastapi import HTTPException -from fastapi.responses import StreamingResponse -from nilai_common import ChatRequest, ChatResponse, Message -from nilai_common.api_model import ChatCompletionChunk, ChoiceChunk, ChoiceChunkContent -from nilai_models.model import Model - - -class LlamaCppModel(Model): - """ - A specific implementation of the Model base class for the Llama CPU model. - - This class provides: - - Model initialization using llama_cpp - - Chat completion functionality (with streaming support) - - Metadata about the Llama model - """ - - def __init__(self, model, metadata, prefix="/models"): - """ - Initialize the Llama 1B model: - - Load pre-trained model using llama_cpp - - Configured for CPU inference with 16 threads - """ - self.model = model - - # Initialize model metadata - super().__init__(metadata, prefix) - - async def chat_completion( - self, - req: ChatRequest = ChatRequest( - model="bartowski/Llama-3.2-1B-Instruct-GGUF", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ), - ) -> StreamingResponse | ChatResponse: - """ - Generate a chat completion using the Llama model, with optional streaming. - - Args: - req (ChatRequest): The chat request containing conversation messages. - stream (bool): Whether to return a streamed response. - - Returns: - ChatResponse or StreamingResponse: Either a full response or a streaming response. - """ - if not req.messages or len(req.messages) == 0: - raise HTTPException( - status_code=400, detail="The 'messages' field is required." - ) - if not req.model: - raise HTTPException( - status_code=400, detail="The 'model' field is required." - ) - - # Transform incoming messages to llama_cpp-compatible format - prompt = [ - { - "role": msg.role, - "content": msg.content, - } - for msg in req.messages - ] - - # Streaming response logic - if req.stream: - - async def generate() -> AsyncGenerator[str, None]: - try: - # Create a generator for the streamed output - loop = asyncio.get_event_loop() - output_generator = await loop.run_in_executor( - None, - lambda: self.model.create_chat_completion( - prompt, # type: ignore - stream=True, - temperature=req.temperature if req.temperature else 0.2, - max_tokens=req.max_tokens if req.max_tokens else 2048, - ), - ) - for output in output_generator: - # Extract delta content from output - choices = output.get("choices", []) # type: ignore - if not choices or "delta" not in choices[0]: - continue # Skip invalid chunks - # Extract delta - delta = choices[0]["delta"] - chunk = ChoiceChunk( - index=delta.get("index", 0), - delta=ChoiceChunkContent(content=delta.get("content", "")), - ) # Create a ChoiceChunk - completion_chunk = ChatCompletionChunk(choices=[chunk]) - yield f"data: {completion_chunk.model_dump_json()}\n\n" # Stream the chunk - await asyncio.sleep(0) # Add an await to return inmediately - - yield "data: [DONE]\n\n" - except Exception as e: - logging.error("An error occurred: %s", str(e)) - yield f"data: {json.dumps({'error': 'Internal error occurred!'})}\n\n" - - # Return the streamed response with headers - return StreamingResponse(generate(), media_type="text/event-stream") - - # Non-streaming (regular) chat completion - try: - generation: dict = self.model.create_chat_completion( - prompt, - temperature=req.temperature if req.temperature else 0.2, - max_tokens=req.max_tokens if req.max_tokens else 2048, - ) # type: ignore - except ValueError: - raise HTTPException( - status_code=400, - detail="The prompt size exceeds the maximum limit of 2048 tokens.", - ) - if not generation or len(generation) == 0: - raise ValueError("The model returned no output.") - - response = ChatResponse( - signature="", - **generation, - ) - response.model = self.metadata.name - return response diff --git a/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/__init__.py b/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/__init__.py deleted file mode 100644 index 390c7363..00000000 --- a/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from nilai_models.models.secret_llama_1b_cpu.secret_llama_1b_cpu import app - -__all__ = ["app"] diff --git a/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/secret_llama_1b_cpu.py b/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/secret_llama_1b_cpu.py deleted file mode 100644 index 6b562d7d..00000000 --- a/nilai-models/src/nilai_models/models/secret_llama_1b_cpu/secret_llama_1b_cpu.py +++ /dev/null @@ -1,78 +0,0 @@ -from fastapi.responses import StreamingResponse -from llama_cpp import Llama -from nilai_common import ModelMetadata -from nilai_common.api_model import ChatRequest, ChatResponse, Message -from nilai_models.models.llama_model import LlamaCppModel - - -class SecretLlama1BCpu(LlamaCppModel): - """ - A specific implementation of the Model base class for the Llama 1B CPU model. - - This class provides: - - Model initialization using llama_cpp - - Chat completion functionality (with streaming support) - - Metadata about the Llama model - """ - - def __init__(self): - """ - Initialize the Llama 1B model: - - Load pre-trained model using llama_cpp - - Configured for CPU inference with 16 threads - """ - # Initialize model metadata - super().__init__( - model=Llama.from_pretrained( - repo_id="bartowski/Llama-3.2-1B-Instruct-GGUF", - filename="Llama-3.2-1B-Instruct-Q5_K_S.gguf", - n_threads=16, - n_ctx=128 * 1024, - verbose=False, - ), - metadata=ModelMetadata( - id="CheesyLlama", # Unique identifier - name="CheesyLlama", # Human-readable name - version="1.0", # Model version - description="Llama is a large language model trained on supervised and unsupervised cheese.", - author="Meta-Llama", # Model creators - license="Apache 2.0", # Usage license - source="https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF", # Model source - supported_features=["chat_completion"], # Capabilities - ), - prefix="nillion/", - ) - - def load_models(self): - """ - Load the model(s) required for the service. - - This method is called during model initialization to load the - specific model(s) required for the service at service startup. - """ - pass - - async def chat_completion( - self, - req: ChatRequest = ChatRequest( - model="bartowski/Llama-3.2-1B-Instruct-GGUF", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ), - ) -> StreamingResponse | ChatResponse: - req.messages.append( - Message( - role="system", - content="In addition to the previous. You are a cheese expert. You use cheese for all your answers. Whatever the user asks, you respond with a cheese-related answer or analogy.", - ) - ) - return await super().chat_completion(req) - - -# FastAPI app instance -# Create and expose the FastAPI app for this Llama model -# - Calls get_app() from the base Model class -# - Allows easy integration with ASGI servers like uvicorn -app = SecretLlama1BCpu().get_app() diff --git a/packages/nilai-common/pyproject.toml b/packages/nilai-common/pyproject.toml index 19a554a4..8c9d3110 100644 --- a/packages/nilai-common/pyproject.toml +++ b/packages/nilai-common/pyproject.toml @@ -9,6 +9,7 @@ authors = [ requires-python = ">=3.12" dependencies = [ "etcd3gw>=2.4.2", + "openai>=1.59.9", "pydantic>=2.10.1", "tenacity>=9.0.0", ] diff --git a/packages/nilai-common/src/nilai_common/__init__.py b/packages/nilai-common/src/nilai_common/__init__.py index 4a50802c..731c69c0 100644 --- a/packages/nilai-common/src/nilai_common/__init__.py +++ b/packages/nilai-common/src/nilai_common/__init__.py @@ -1,16 +1,13 @@ from nilai_common.api_model import ( AttestationResponse, ChatRequest, - ChatResponse, - ChatCompletionChunk, - ChoiceChunkContent, - ChoiceChunk, + SignedChatCompletion, Choice, HealthCheckResponse, Message, ModelEndpoint, ModelMetadata, - Usage, + CompletionUsage as Usage, ) from nilai_common.config import SETTINGS from nilai_common.discovery import ModelServiceDiscovery @@ -18,7 +15,7 @@ __all__ = [ "Message", "ChatRequest", - "ChatResponse", + "SignedChatCompletion", "Choice", "ChoiceChunk", "ChoiceChunkContent", diff --git a/packages/nilai-common/src/nilai_common/api_model.py b/packages/nilai-common/src/nilai_common/api_model.py index 2b2d49c7..324bcccb 100644 --- a/packages/nilai-common/src/nilai_common/api_model.py +++ b/packages/nilai-common/src/nilai_common/api_model.py @@ -1,12 +1,31 @@ import uuid -from typing import List, Optional +from typing import List, Optional, Literal +from openai.types.chat import ChatCompletion, ChatCompletionMessage +from openai.types.chat.chat_completion import Choice as OpenaAIChoice +from openai.types.chat.chat_completion import CompletionUsage from pydantic import BaseModel, Field -class Message(BaseModel): - role: str - content: str +__all__ = [ + "Message", + "Choice", + "ChatRequest", + "CompletionUsage", + "SignedChatCompletion", + "AttestationResponse", + "ModelMetadata", + "ModelEndpoint", + "HealthCheckResponse", +] + + +class Message(ChatCompletionMessage): + role: Literal["system", "user", "assistant"] + + +class Choice(OpenaAIChoice): + pass class ChatRequest(BaseModel): @@ -19,43 +38,7 @@ class ChatRequest(BaseModel): nilrag: Optional[dict] = {} -class ChoiceChunkContent(BaseModel): - content: Optional[str] - - -class ChoiceChunk(BaseModel): - """Choice chunk.""" - - index: int - delta: ChoiceChunkContent - - -class ChatCompletionChunk(BaseModel): - """Chat completion chunk.""" - - choices: List[ChoiceChunk] - - -class Choice(BaseModel): - index: int - message: Message - finish_reason: str - logprobs: Optional[dict] - - -class Usage(BaseModel): - prompt_tokens: int - completion_tokens: int - total_tokens: int - - -class ChatResponse(BaseModel): - id: str - object: str - created: int - model: str - choices: List[Choice] - usage: Usage +class SignedChatCompletion(ChatCompletion): signature: str diff --git a/tests/__init__.py b/tests/__init__.py index 694d9aba..88bbc534 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,10 +1,10 @@ from nilai_common import ( - ChatResponse, - Choice, + SignedChatCompletion, Message, ModelEndpoint, ModelMetadata, Usage, + Choice, ) model_metadata: ModelMetadata = ModelMetadata( @@ -22,16 +22,16 @@ url="http://test-model-url", metadata=model_metadata ) -response: ChatResponse = ChatResponse( +response: SignedChatCompletion = SignedChatCompletion( id="test-id", - object="test-object", + object="chat.completion", model="test-model", created=123456, choices=[ Choice( index=0, - message=Message(role="test-role", content="test-content"), - finish_reason="test-finish-reason", + message=Message(role="assistant", content="test-content"), + finish_reason="stop", logprobs={"test-logprobs": "test-value"}, ) ], diff --git a/tests/functional_tests/openai.py b/tests/functional_tests/openai.py new file mode 100644 index 00000000..3fd3b68f --- /dev/null +++ b/tests/functional_tests/openai.py @@ -0,0 +1,53 @@ +from openai import OpenAI + + +def test_stream(): + # Initialize OpenAI client + client = OpenAI( + base_url="http://localhost:8080/v1/", + api_key="1b509260-bdfc-4628-9bae-dd9814e902a1", + ) + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello, how are you?"}, + ] + print(messages) + response = client.chat.completions.create( + model="meta-llama/Llama-3.1-8B-Instruct", messages=messages, stream=True + ) + + content = "" + for chunk in response: + print(chunk) + if chunk.choices[0].delta.content is not None: + print(chunk.choices[0].delta.content) + content += chunk.choices[0].delta.content + + print(content) + if content: + print("Final response: ", content) + else: + raise Exception("No response received: ", content) + + +def test_non_stream(): + # Initialize OpenAI client + client = OpenAI( + base_url="http://localhost:8080/v1/", + api_key="1b509260-bdfc-4628-9bae-dd9814e902a1", + ) + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello, how are you?"}, + ] + print(messages) + response = client.chat.completions.create( + model="meta-llama/Llama-3.1-8B-Instruct", messages=messages, stream=False + ) + print(response) + # raise Exception(f"Response: {response}") + + +if __name__ == "__main__": + test_stream() + test_non_stream() diff --git a/tests/nilai_api/routers/test_private.py b/tests/nilai_api/routers/test_private.py index 6a431481..93b7b53c 100644 --- a/tests/nilai_api/routers/test_private.py +++ b/tests/nilai_api/routers/test_private.py @@ -6,9 +6,7 @@ from nilai_api.app import app from nilai_api.db import UserManager from nilai_api.state import state -from nilai_common.api_model import ChatResponse, Choice, Message, Usage - -from tests import model_endpoint, model_metadata +from tests import model_endpoint, model_metadata, response as RESPONSE client = TestClient(app) @@ -102,6 +100,9 @@ def test_get_usage(mock_user, mock_user_manager): "prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150, + "completion_tokens_details": None, + "prompt_tokens_details": None, + "queries": 10, } @@ -126,26 +127,18 @@ def test_get_models(mock_user, mock_user_manager, mock_state): def test_chat_completion(mock_user, mock_state, mock_user_manager, mocker): - response = ChatResponse( - id="test-id", - object="test-object", - model="test-model", - created=123456, - choices=[ - Choice( - index=0, - message=Message(role="test-role", content="test-content"), - finish_reason="test-finish-reason", - logprobs={"test-logprobs": "test-value"}, - ) - ], - usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150), - signature="test-signature", - ) + mocker.patch("openai.api_key", new="test-api-key") + # Mock the response from the OpenAI API + from openai.types.chat import ChatCompletion + + data = RESPONSE.dict() + data.pop("signature") + response_data = ChatCompletion(**data) mocker.patch( - "httpx.AsyncClient.post", - return_value=mocker.Mock(status_code=200, content=response.model_dump_json()), + "openai._base_client.SyncAPIClient._request", return_value=response_data ) + + # Mock client.post behavior response = client.post( "/v1/chat/completions", json={ @@ -157,10 +150,14 @@ def test_chat_completion(mock_user, mock_state, mock_user_manager, mocker): }, headers={"Authorization": "Bearer test-api-key"}, ) + + # Assertions assert response.status_code == 200 assert "usage" in response.json() assert response.json()["usage"] == { "prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150, + "completion_tokens_details": None, + "prompt_tokens_details": None, } diff --git a/tests/nilai_models/models/_test_llama_1b_cpu.py b/tests/nilai_models/models/_test_llama_1b_cpu.py deleted file mode 100644 index 0731d073..00000000 --- a/tests/nilai_models/models/_test_llama_1b_cpu.py +++ /dev/null @@ -1,69 +0,0 @@ -from unittest.mock import AsyncMock - -import pytest -from fastapi import HTTPException -from nilai_common import ChatRequest, ChatResponse, Message -from nilai_models.models.llama_1b_cpu.llama_1b_cpu import Llama1BCpu - -from tests import response as RESPONSE - -model = Llama1BCpu() - - -@pytest.fixture -def llama_model(mocker): - """Fixture to provide a Llama1BCpu instance for testing.""" - mocker.patch.object(model, "chat_completion", new_callable=AsyncMock) - return model - - -@pytest.mark.asyncio -async def test_chat_completion_valid_request(llama_model): - """Test chat completion with a valid request.""" - req = ChatRequest( - model="bartowski/Llama-3.2-1B-Instruct-GGUF", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ) - llama_model.chat_completion.return_value = RESPONSE - response = await llama_model.chat_completion(req) - assert isinstance(response, ChatResponse) - assert response.model == "test-model" - assert response.choices is not None - - -@pytest.mark.asyncio -async def test_chat_completion_empty_messages(llama_model): - """Test chat completion with an empty messages list.""" - req = ChatRequest( - model="bartowski/Llama-3.2-1B-Instruct-GGUF", - messages=[], - ) - llama_model.chat_completion.side_effect = HTTPException( - status_code=400, detail="The 'messages' field is required." - ) - with pytest.raises(HTTPException) as exc_info: - await llama_model.chat_completion(req) - assert exc_info.value.status_code == 400 - assert exc_info.value.detail == "The 'messages' field is required." - - -@pytest.mark.asyncio -async def test_chat_completion_missing_model(llama_model): - """Test chat completion with a missing model field.""" - req = ChatRequest( - model="", - messages=[ - Message(role="system", content="You are a helpful assistant."), - Message(role="user", content="What is your name?"), - ], - ) - llama_model.chat_completion.side_effect = HTTPException( - status_code=400, detail="The 'model' field is required." - ) - with pytest.raises(HTTPException) as exc_info: - await llama_model.chat_completion(req) - assert exc_info.value.status_code == 400 - assert exc_info.value.detail == "The 'model' field is required." diff --git a/tests/nilai_models/test_model.py b/tests/nilai_models/test_model.py deleted file mode 100644 index a041047f..00000000 --- a/tests/nilai_models/test_model.py +++ /dev/null @@ -1,59 +0,0 @@ -import pytest -from fastapi.testclient import TestClient -from nilai_common import ChatRequest, ChatResponse -from nilai_common.api_model import Message -from nilai_models.model import Model - -from tests import model_metadata, response - - -class MyModel(Model): - async def chat_completion(self, req: ChatRequest) -> ChatResponse: - return response - - -@pytest.fixture -def model_instance(): - metadata = model_metadata - return MyModel(metadata) - - -@pytest.fixture -def client(model_instance): - return TestClient(model_instance.get_app()) - - -def test_model_info(client): - response = client.get("/v1/models") - assert response.status_code == 200 - data = response.json() - assert data["id"] == "ABC" - assert data["name"] == "ABC" - assert data["description"] == "Description" - - -def test_health_check(client): - response = client.get("/v1/health") - assert response.status_code == 200 - data = response.json() - assert data["status"] == "healthy" - assert "uptime" in data - - -def test_chat_completion(client): - request = ChatRequest( - model="ABC", - messages=[ - Message(role="system", content="You are a helpful assistant"), - Message(role="user", content="Hello, who are you?"), - ], - ) - response = client.post("/v1/chat/completions", json=request.model_dump()) - assert response.status_code == 200 - data = response.json() - assert "choices" in data - assert data["choices"][0]["finish_reason"] == "test-finish-reason" - assert data["usage"]["prompt_tokens"] == 100 - assert data["usage"]["completion_tokens"] == 50 - assert data["usage"]["total_tokens"] == 150 - assert data["signature"] == "test-signature" diff --git a/uv.lock b/uv.lock index 4a285c1c..4418320e 100644 --- a/uv.lock +++ b/uv.lock @@ -1,13 +1,5 @@ version = 1 requires-python = ">=3.12" -resolution-markers = [ - "python_full_version >= '3.13' and sys_platform == 'darwin'", - "python_full_version < '3.13' and sys_platform == 'darwin'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version 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