main.py

import torch
from typing import Any
from typing import Optional
from fastapi import FastAPI
from pydantic import BaseModel
from vllm import LLM, SamplingParams, RequestOutput


# Don't forget to set HF_TOKEN in the env during running

app = FastAPI()

# Initialize the LLM engine
# Replace 'your-model-path' with the actual path or name of your model

engine_llama_3_2: LLM = LLM(
    model='meta-llama/Llama-3.2-3B-Instruct',
    revision="0cb88a4f764b7a12671c53f0838cd831a0843b95",
    max_num_batched_tokens=512,    # Reduced for T4
    max_num_seqs=16,               # Reduced for T4
    gpu_memory_utilization=0.85,   # Slightly increased, adjust if needed
    # Llama-3.2-3B-Instruct max context length is 131072, but we reduce it to 32k.
    # 32k tokens, 3/4 of 32k is 24k words, each page average is 500 or 0.5k words,
    # so that's basically 24k / .5k = 24 x 2 =~48 pages.
    # Because when we use maximum token length, it will be slower and the memory is not enough for T4.
    max_model_len=32768,
    enforce_eager=True,            # Disable CUDA graph
    dtype='auto',                  # Use 'half' if you want half precision
)


@app.get("/")
def greet_json():
    cuda_info: dict[str, Any] = {}
    if torch.cuda.is_available():
        cuda_current_device: int = torch.cuda.current_device()
        cuda_info = {
            "device_count": torch.cuda.device_count(),
            "cuda_device": torch.cuda.get_device_name(cuda_current_device),
            "cuda_capability": torch.cuda.get_device_capability(cuda_current_device),
            "allocated":  f"{round(torch.cuda.memory_allocated(cuda_current_device) / 1024 ** 3, 1)} GB",
            "cached": f"{round(torch.cuda.memory_reserved(cuda_current_device) / 1024 ** 3, 1)} GB",
        }

    return {
        "message": f"CUDA availability is {torch.cuda.is_available()}",
        "cuda_info": cuda_info,
        "model": [
            {
                "name": "meta-llama/Llama-3.2-3B-Instruct",
                "revision": "0cb88a4f764b7a12671c53f0838cd831a0843b95",
            }
        ]
    }


class GenerationRequest(BaseModel):
    prompt: str
    max_tokens: int = 100
    temperature: float = 0.7
    logit_bias: Optional[dict[int, float]] = None


class GenerationResponse(BaseModel):
    text: Optional[str]
    error: Optional[str]


@app.post("/generate-llama3-2")
def generate_text(request: GenerationRequest) -> list[RequestOutput] | dict[str, str]:
    try:
        sampling_params: SamplingParams = SamplingParams(
            temperature=request.temperature,
            max_tokens=request.max_tokens,
            logit_bias=request.logit_bias,
        )

        # Generate text
        return engine_llama_3_2.generate(
            prompts=request.prompt,
            sampling_params=sampling_params
        )

    except Exception as e:
        return {
            "error": str(e)
        }