gguf_transformers_backend.py

#!/usr/bin/env python3
"""
GGUF Backend with Native Transformers Support
Uses transformers library's built-in GGUF loading (no llama-cpp-python needed)
"""

import os
import logging
from contextlib import asynccontextmanager
from typing import List, Dict, Any, Optional
import uuid
import time

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field, field_validator

# Import transformers with GGUF support
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Pydantic models for OpenAI-compatible API
class ChatMessage(BaseModel):
    role: str = Field(..., description="The role of the message author")
    content: str = Field(..., description="The content of the message")
    
    @field_validator('role')
    @classmethod
    def validate_role(cls, v: str) -> str:
        if v not in ["system", "user", "assistant"]:
            raise ValueError("Role must be one of: system, user, assistant")
        return v

class ChatCompletionRequest(BaseModel):
    model: str = Field(default="gemma-3n-e4b-it", description="The model to use for completion")
    messages: List[ChatMessage] = Field(..., description="List of messages in the conversation")
    max_tokens: Optional[int] = Field(default=256, ge=1, le=1024, description="Maximum tokens to generate")
    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0, description="Sampling temperature")
    top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0, description="Top-p sampling")
    stream: Optional[bool] = Field(default=False, description="Whether to stream responses")

class ChatCompletionChoice(BaseModel):
    index: int
    message: ChatMessage
    finish_reason: str

class ChatCompletionResponse(BaseModel):
    id: str
    object: str = "chat.completion"
    created: int
    model: str
    choices: List[ChatCompletionChoice]

class HealthResponse(BaseModel):
    status: str
    model: str
    version: str
    backend: str
    quantization: str

# Global variables for model management
current_model = os.environ.get("AI_MODEL", "unsloth/gemma-3n-E4B-it-GGUF")
gguf_filename = os.environ.get("GGUF_FILE", "gemma-3n-E4B-it-Q4_K_M.gguf")
tokenizer = None
model = None
text_pipeline = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager with GGUF model loading via transformers"""
    global tokenizer, model, text_pipeline
    
    logger.info("🚀 Starting GGUF Backend Service (Transformers Native)")
    
    if os.environ.get("DEMO_MODE", "").strip() not in ("", "0", "false", "False"):
        logger.info("🧪 DEMO_MODE enabled: skipping model load")
        yield
        logger.info("🔄 Shutting down GGUF Backend Service (demo mode)...")
        return
    
    try:
        logger.info(f"📥 Loading GGUF model: {current_model}")
        logger.info(f"🎯 GGUF file pattern: {gguf_filename}")
        
        # Load tokenizer first
        tokenizer = AutoTokenizer.from_pretrained(
            current_model,
            trust_remote_code=True,
            use_fast=True
        )
        
        # Ensure pad token exists
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
        
        # Load GGUF model using native transformers support
        logger.info("⚙️ Loading GGUF model with transformers native support...")
        model = AutoModelForCausalLM.from_pretrained(
            current_model,
            gguf_file=gguf_filename,  # Key parameter for GGUF loading
            torch_dtype=torch.float32,  # CPU-compatible
            device_map="auto",  # Let transformers handle device placement
            low_cpu_mem_usage=True,  # Memory optimization
            trust_remote_code=True,
        )
        
        # Create pipeline for efficient generation
        text_pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            max_new_tokens=256,
            do_sample=True,
            temperature=1.0,
            top_p=0.95,
            pad_token_id=tokenizer.eos_token_id,
        )
        
        logger.info("✅ Successfully loaded GGUF model with transformers")
        logger.info(f"📊 Model: {current_model}")
        logger.info(f"🔧 GGUF File: {gguf_filename}")
        logger.info(f"🧠 Backend: Transformers native GGUF support")
        
    except Exception as e:
        logger.error(f"❌ Failed to initialize GGUF model: {e}")
        logger.info("🔄 Starting service in demo mode")
        model = None
        tokenizer = None
        text_pipeline = None
    
    yield
    
    logger.info("🔄 Shutting down GGUF Backend Service...")
    # Clean up model resources
    if model:
        del model
    if tokenizer:
        del tokenizer
    if text_pipeline:
        del text_pipeline

# Initialize FastAPI app
app = FastAPI(
    title="GGUF Backend Service (Transformers Native)",
    description="Memory-efficient GGUF model API using transformers native support",
    version="1.0.0",
    lifespan=lifespan
)

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

def convert_messages_to_prompt(messages: List[ChatMessage]) -> str:
    """Convert OpenAI messages format to Gemma 3n chat format."""
    prompt_parts = []
    
    for message in messages:
        role = message.role
        content = message.content.strip()
        
        if role == "system":
            prompt_parts.append(f"<start_of_turn>system\n{content}<end_of_turn>")
        elif role == "user":
            prompt_parts.append(f"<start_of_turn>user\n{content}<end_of_turn>")
        elif role == "assistant":
            prompt_parts.append(f"<start_of_turn>model\n{content}<end_of_turn>")
    
    # Add the start for model response
    prompt_parts.append("<start_of_turn>model\n")
    
    return "\n".join(prompt_parts)

def generate_response(messages: List[ChatMessage], max_tokens: int = 256, temperature: float = 1.0, top_p: float = 0.95) -> str:
    """Generate response using GGUF model via transformers pipeline."""
    if text_pipeline is None:
        return "🤖 Demo mode: GGUF model not loaded. This would be a real response from the Gemma 3n GGUF model."
    
    try:
        # Convert messages to prompt
        prompt = convert_messages_to_prompt(messages)
        
        # Limit max_tokens for memory efficiency
        max_tokens = min(max_tokens, 512)
        
        # Generate response
        result = text_pipeline(
            prompt,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            return_full_text=False,
            pad_token_id=tokenizer.eos_token_id,
        )
        
        # Extract generated text
        if result and len(result) > 0:
            response_text = result[0]['generated_text'].strip()
            # Clean up any unwanted tokens
            if "<end_of_turn>" in response_text:
                response_text = response_text.split("<end_of_turn>")[0].strip()
            return response_text
        else:
            return "I apologize, but I'm having trouble generating a response right now."
            
    except Exception as e:
        logger.error(f"GGUF generation failed: {e}")
        return "I apologize, but I'm having trouble generating a response right now. Please try again."

@app.get("/", response_class=JSONResponse)
async def root() -> Dict[str, Any]:
    """Root endpoint with service information"""
    return {
        "service": "GGUF Backend Service",
        "version": "1.0.0",
        "model": current_model,
        "gguf_file": gguf_filename,
        "backend": "transformers-native-gguf",
        "quantization": "Q4_K_M",
        "endpoints": {
            "health": "/health",
            "chat": "/v1/chat/completions",
            "docs": "/docs"
        }
    }

@app.get("/health", response_model=HealthResponse)
async def health_check():
    """Health check endpoint"""
    status = "healthy" if text_pipeline is not None else "demo_mode"
    
    return HealthResponse(
        status=status,
        model=current_model,
        version="1.0.0",
        backend="transformers-native-gguf",
        quantization="Q4_K_M"
    )

@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(request: ChatCompletionRequest) -> ChatCompletionResponse:
    """Create a chat completion (OpenAI-compatible) using GGUF model"""
    
    try:
        # Generate response
        response_text = generate_response(
            messages=request.messages,
            max_tokens=request.max_tokens or 256,
            temperature=request.temperature or 1.0,
            top_p=request.top_p or 0.95
        )
        
        # Create response message
        response_message = ChatMessage(role="assistant", content=response_text)
        
        # Create choice
        choice = ChatCompletionChoice(
            index=0,
            message=response_message,
            finish_reason="stop"
        )
        
        # Create completion response
        completion = ChatCompletionResponse(
            id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
            object="chat.completion",
            created=int(time.time()),
            model=request.model,
            choices=[choice]
        )
        
        return completion
        
    except Exception as e:
        logger.error(f"Chat completion failed: {e}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)