utils/models.py

import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
import spaces

import torch
from transformers import pipeline, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
from .prompts import format_rag_prompt
from .shared import generation_interrupt

models = {
    "Qwen2.5-1.5b-Instruct": "qwen/qwen2.5-1.5b-instruct",
    "Qwen2.5-3b-Instruct": "qwen/qwen-2.5-3b-instruct",
    #"Llama-3.2-1b-Instruct": "meta-llama/llama-3.2-1b-instruct",
    #"Llama-3.2-3b-Instruct": "meta-llama/llama-3.2-3b-instruct",
    "Gemma-3-1b-it": "google/gemma-3-1b-it",
    "Gemma-3-4b-it": "google/gemma-3-4b-it",
    "Gemma-2-2b-it": "google/gemma-2-2b-it",
    "Phi-4-mini-instruct": "microsoft/phi-4-mini-instruct",
}

# List of model names for easy access
model_names = list(models.keys())

# Custom stopping criteria that checks the interrupt flag
class InterruptCriteria(StoppingCriteria):
    def __init__(self, interrupt_event):
        self.interrupt_event = interrupt_event
        
    def __call__(self, input_ids, scores, **kwargs):
        return self.interrupt_event.is_set()

@spaces.GPU
def generate_summaries(example, model_a_name, model_b_name):
    """
    Generates summaries for the given example using the assigned models sequentially.
    """
    if generation_interrupt.is_set():
        return "", ""

    context_text = ""
    context_parts = []
    if "full_contexts" in example:
        for ctx in example["full_contexts"]:
            if isinstance(ctx, dict) and "content" in ctx:
                context_parts.append(ctx["content"])
        context_text = "\n---\n".join(context_parts)
    else:
        raise ValueError("No context found in the example.")

    question = example.get("question", "")

    if generation_interrupt.is_set():
        return "", ""

    # Run model A
    summary_a = run_inference(models[model_a_name], context_text, question)
    
    if generation_interrupt.is_set():
        return summary_a, ""
    
    # Run model B
    summary_b = run_inference(models[model_b_name], context_text, question)
    
    return summary_a, summary_b

@spaces.GPU
def run_inference(model_name, context, question):
    """
    Run inference using the specified model.
    Returns the generated text or empty string if interrupted.
    """
    # Check interrupt at the beginning
    if generation_interrupt.is_set():
        return ""

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    result = ""

    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left", token=True)
        accepts_sys = (
            "System role not supported" not in tokenizer.chat_template
            if tokenizer.chat_template else False # Handle missing chat_template
        )

        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        # Check interrupt before loading the model
        if generation_interrupt.is_set():
            return ""
        
        pipe = pipeline(
            "text-generation",
            model=model_name,
            tokenizer=tokenizer,
            device_map='auto',
            max_length=512,
            do_sample=True,
            temperature=0.6,
            top_p=0.9,
        )

        text_input = format_rag_prompt(question, context, accepts_sys)

        # Check interrupt before generation
        if generation_interrupt.is_set():
            return ""

        outputs = pipe(text_input, max_new_tokens=512)
        result = outputs[0]['generated_text'][-1]['content']

    except Exception as e:
        print(f"Error in inference for {model_name}: {e}")
        result = f"Error generating response: {str(e)[:200]}..."

    finally:
        # Clean up resources
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    return result