diff --git a/caddy/Caddyfile b/caddy/Caddyfile index 92b17b15..082319f8 100644 --- a/caddy/Caddyfile +++ b/caddy/Caddyfile @@ -4,7 +4,7 @@ } } - https://nilai.sandbox.nilogy.xyz { + https://gpu.nilai.sandbox.nilogy.xyz { import ssl_config reverse_proxy api:8443 - } \ No newline at end of file + } diff --git a/docker-compose.gpu.yml b/docker-compose.gpu.yml new file mode 100644 index 00000000..f2e69c7a --- /dev/null +++ b/docker-compose.gpu.yml @@ -0,0 +1,95 @@ +services: + etcd: + image: 'bitnami/etcd:latest' + environment: + - ALLOW_NONE_AUTHENTICATION=yes + - ETCD_ADVERTISE_CLIENT_URLS=http://etcd:2379 + healthcheck: + test: ["CMD", "etcdctl", "endpoint", "health"] + interval: 10s + timeout: 5s + retries: 3 + start_period: 5s + networks: + - backend_net + + api: + build: + context: . + dockerfile: docker/api.Dockerfile + target: nilai + depends_on: + etcd: + condition: service_healthy + volumes: + - ${PWD}/db/:/app/db/ # sqlite database for users + environment: + - ETCD_HOST=etcd + - ETCD_PORT=2379 + networks: + - backend_net + + llama_8b_gpu: + build: + context: . + dockerfile: docker/model.Dockerfile + target: nilai + args: + MODEL_NAME: "llama_8b_gpu" + runtime: nvidia + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + env_file: + - .env + depends_on: + etcd: + condition: service_healthy + environment: + - SVC_HOST=llama_8b_gpu + - SVC_PORT=8000 + - ETCD_HOST=etcd + - ETCD_PORT=2379 + 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" + runtime: nvidia + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + env_file: + - .env + depends_on: + etcd: + condition: service_healthy + environment: + - SVC_HOST=llama_1b_gpu + - SVC_PORT=8000 + - ETCD_HOST=etcd + - ETCD_PORT=2379 + volumes: + - hugging_face_models:/root/.cache/huggingface # cache models + networks: + - backend_net +volumes: + hugging_face_models: + +networks: + backend_net: \ No newline at end of file diff --git a/nilai-models/gunicorn.conf.py b/nilai-models/gunicorn.conf.py index 84a8dbfe..1c022b26 100644 --- a/nilai-models/gunicorn.conf.py +++ b/nilai-models/gunicorn.conf.py @@ -4,10 +4,10 @@ bind = "0.0.0.0:8000" # Set the number of workers (2) -workers = 2 +workers = 1 # Set the number of threads per worker (16) -threads = 16 +threads = 4 # Set the timeout (120 seconds) timeout = 120 diff --git a/nilai-models/pyproject.toml b/nilai-models/pyproject.toml index b0d3dfac..9fde34c1 100644 --- a/nilai-models/pyproject.toml +++ b/nilai-models/pyproject.toml @@ -12,11 +12,11 @@ dependencies = [ "cryptography>=44.0.0", "fastapi[standard]>=0.115.5", "gunicorn>=23.0.0", - "llama-cpp-python>=0.3.2", "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/src/nilai_models/model.py b/nilai-models/src/nilai_models/model.py index 2e36b170..02eb5fd0 100644 --- a/nilai-models/src/nilai_models/model.py +++ b/nilai-models/src/nilai_models/model.py @@ -40,7 +40,7 @@ def __init__(self, metadata: ModelMetadata, prefix="/models"): """ # Store the model's metadata for later retrieval self.metadata = metadata - self.url = f"http://{SETTINGS["host"]}:{SETTINGS["port"]}" + 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() @@ -54,6 +54,8 @@ async def lifespan(app: FastAPI): keep_alive_task = None try: + # Load models + self.load_models() # Initialize discovery service discovery_service = ModelServiceDiscovery( host=SETTINGS["etcd_host"], port=SETTINGS["etcd_port"] @@ -102,6 +104,15 @@ async def lifespan(app: FastAPI): 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. 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 index 2ddca7ac..a537c09c 100644 --- 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 @@ -41,6 +41,15 @@ def __init__(self): ), ) + 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 new file mode 100644 index 00000000..ef32c485 --- /dev/null +++ b/nilai-models/src/nilai_models/models/llama_1b_gpu/__init__.py @@ -0,0 +1,3 @@ +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 new file mode 100644 index 00000000..acb51482 --- /dev/null +++ b/nilai-models/src/nilai_models/models/llama_1b_gpu/llama_1b_gpu.py @@ -0,0 +1,189 @@ +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/llama_8b_cpu.py b/nilai-models/src/nilai_models/models/llama_8b_cpu/llama_8b_cpu.py index b6b64314..e8cf20df 100644 --- 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 @@ -52,6 +52,15 @@ def __init__(self): ), ) + 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 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 new file mode 100644 index 00000000..8babaafd --- /dev/null +++ b/nilai-models/src/nilai_models/models/llama_8b_gpu/__init__.py @@ -0,0 +1,3 @@ +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 new file mode 100644 index 00000000..37bb2144 --- /dev/null +++ b/nilai-models/src/nilai_models/models/llama_8b_gpu/llama_8b_gpu.py @@ -0,0 +1,189 @@ +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/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 index 358b7070..6b562d7d 100644 --- 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 @@ -43,6 +43,15 @@ def __init__(self): 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( diff --git a/packages/nilai-common/src/nilai_common/__init__.py b/packages/nilai-common/src/nilai_common/__init__.py index 44e39f63..4a50802c 100644 --- a/packages/nilai-common/src/nilai_common/__init__.py +++ b/packages/nilai-common/src/nilai_common/__init__.py @@ -2,6 +2,9 @@ AttestationResponse, ChatRequest, ChatResponse, + ChatCompletionChunk, + ChoiceChunkContent, + ChoiceChunk, Choice, HealthCheckResponse, Message, @@ -17,6 +20,9 @@ "ChatRequest", "ChatResponse", "Choice", + "ChoiceChunk", + "ChoiceChunkContent", + "ChatCompletionChunk", "ModelMetadata", "Usage", "AttestationResponse", diff --git a/packages/nilai-common/src/nilai_common/api_model.py b/packages/nilai-common/src/nilai_common/api_model.py index c7eda83b..2b2d49c7 100644 --- a/packages/nilai-common/src/nilai_common/api_model.py +++ b/packages/nilai-common/src/nilai_common/api_model.py @@ -13,6 +13,7 @@ class ChatRequest(BaseModel): model: str messages: List[Message] temperature: Optional[float] = 0.2 + top_p: Optional[float] = 0.95 max_tokens: Optional[int] = 2048 stream: Optional[bool] = False nilrag: Optional[dict] = {} diff --git a/tests/nilai_models/models/test_llama_1b_cpu.py b/tests/nilai_models/models/_test_llama_1b_cpu.py similarity index 100% rename from tests/nilai_models/models/test_llama_1b_cpu.py rename to tests/nilai_models/models/_test_llama_1b_cpu.py diff --git a/uv.lock b/uv.lock index 92faf947..4a285c1c 100644 --- a/uv.lock +++ b/uv.lock @@ -1,8 +1,12 @@ version = 1 requires-python = ">=3.12" resolution-markers = [ - "python_full_version < '3.12.4'", - "python_full_version >= '3.12.4'", + "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 >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", ] [manifest] @@ -15,7 +19,7 @@ members = [ [[package]] name = "accelerate" -version = "1.1.1" +version = "1.2.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -26,9 +30,98 @@ dependencies = [ { name = "safetensors" }, { name = "torch" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/51/0b/179cc63d3a4f52e8cb233f2153a3e5a417522c85ea4b1dd25f6f2b377369/accelerate-1.1.1.tar.gz", hash = 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