advanced_inference.py

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoProcessor,
    BitsAndBytesConfig,
    GenerationConfig
)
from PIL import Image
import json
from typing import Optional, List, Dict, Any, Union
import time
from dataclasses import dataclass
import logging

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


@dataclass
class InferenceMetrics:
    latency_ms: float
    tokens_generated: int
    tokens_per_second: float
    memory_used_gb: float
    input_tokens: int
    total_tokens: int


class AdvancedHelionInference:
    
    def __init__(
        self,
        model_name: str = "DeepXR/Helion-V2.0-Thinking",
        quantization: Optional[str] = None,
        device: str = "auto",
        use_flash_attention: bool = True,
        torch_compile: bool = False,
        optimization_mode: str = "balanced"
    ):
        logger.info(f"Initializing Helion-V2.0-Thinking with {optimization_mode} mode")
        
        self.model_name = model_name
        self.optimization_mode = optimization_mode
        self.metrics_history = []
        
        quantization_config = self._get_quantization_config(quantization)
        
        logger.info("Loading processor...")
        self.processor = AutoProcessor.from_pretrained(model_name)
        
        logger.info("Loading model...")
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            quantization_config=quantization_config,
            device_map=device,
            torch_dtype=torch.bfloat16 if quantization is None else None,
            use_flash_attention_2=use_flash_attention,
            trust_remote_code=True,
            low_cpu_mem_usage=True
        )
        
        if torch_compile and quantization is None:
            logger.info("Compiling model with torch.compile...")
            self.model = torch.compile(self.model, mode="reduce-overhead")
        
        self.model.eval()
        
        self.generation_configs = {
            "creative": GenerationConfig(
                do_sample=True,
                temperature=0.9,
                top_p=0.95,
                top_k=50,
                repetition_penalty=1.15,
                max_new_tokens=2048
            ),
            "precise": GenerationConfig(
                do_sample=True,
                temperature=0.3,
                top_p=0.85,
                top_k=40,
                repetition_penalty=1.05,
                max_new_tokens=1024
            ),
            "balanced": GenerationConfig(
                do_sample=True,
                temperature=0.7,
                top_p=0.9,
                top_k=50,
                repetition_penalty=1.1,
                max_new_tokens=1024
            ),
            "code": GenerationConfig(
                do_sample=True,
                temperature=0.2,
                top_p=0.9,
                top_k=40,
                repetition_penalty=1.05,
                max_new_tokens=2048
            )
        }
        
        logger.info("Model loaded successfully!")
    
    def _get_quantization_config(self, quantization: Optional[str]) -> Optional[BitsAndBytesConfig]:
        if quantization is None:
            return None
        
        quantization_configs = {
            "4bit": BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=torch.bfloat16,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type="nf4"
            ),
            "8bit": BitsAndBytesConfig(
                load_in_8bit=True
            )
        }
        
        return quantization_configs.get(quantization)
    
    def generate(
        self,
        prompt: str,
        images: Optional[Union[Image.Image, List[Image.Image]]] = None,
        mode: str = "balanced",
        max_new_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        stream: bool = False,
        return_metrics: bool = False,
        **kwargs
    ) -> Union[str, tuple[str, InferenceMetrics]]:
        
        if isinstance(images, Image.Image):
            images = [images]
        
        start_time = time.time()
        initial_memory = torch.cuda.memory_allocated() / (1024**3) if torch.cuda.is_available() else 0
        
        if images:
            inputs = self.processor(
                text=prompt,
                images=images,
                return_tensors="pt"
            ).to(self.model.device)
        else:
            inputs = self.processor(
                text=prompt,
                return_tensors="pt"
            ).to(self.model.device)
        
        input_length = inputs['input_ids'].shape[1]
        
        gen_config = self.generation_configs[mode].to_dict()
        
        if max_new_tokens:
            gen_config['max_new_tokens'] = max_new_tokens
        if temperature:
            gen_config['temperature'] = temperature
        
        gen_config.update(kwargs)
        
        with torch.no_grad():
            if stream:
                return self._generate_stream(inputs, gen_config, return_metrics)
            else:
                outputs = self.model.generate(
                    **inputs,
                    **gen_config,
                    pad_token_id=self.processor.tokenizer.eos_token_id
                )
        
        if torch.cuda.is_available():
            torch.cuda.synchronize()
        
        end_time = time.time()
        latency = (end_time - start_time) * 1000
        
        response = self.processor.decode(outputs[0], skip_special_tokens=True)
        
        if response.startswith(prompt):
            response = response[len(prompt):].strip()
        
        tokens_generated = outputs.shape[1] - input_length
        tokens_per_second = tokens_generated / ((end_time - start_time) if (end_time - start_time) > 0 else 1)
        
        final_memory = torch.cuda.memory_allocated() / (1024**3) if torch.cuda.is_available() else 0
        memory_used = final_memory - initial_memory
        
        metrics = InferenceMetrics(
            latency_ms=latency,
            tokens_generated=tokens_generated,
            tokens_per_second=tokens_per_second,
            memory_used_gb=memory_used,
            input_tokens=input_length,
            total_tokens=outputs.shape[1]
        )
        
        self.metrics_history.append(metrics)
        
        if return_metrics:
            return response, metrics
        return response
    
    def _generate_stream(self, inputs, gen_config, return_metrics):
        from transformers import TextIteratorStreamer
        from threading import Thread
        
        streamer = TextIteratorStreamer(
            self.processor.tokenizer,
            skip_special_tokens=True,
            skip_prompt=True
        )
        
        gen_config['streamer'] = streamer
        
        thread = Thread(
            target=self.model.generate,
            kwargs={**inputs, **gen_config}
        )
        thread.start()
        
        for new_text in streamer:
            yield new_text
        
        thread.join()
    
    def batch_generate(
        self,
        prompts: List[str],
        images_list: Optional[List[Optional[Union[Image.Image, List[Image.Image]]]]] = None,
        mode: str = "balanced",
        **kwargs
    ) -> List[str]:
        
        if images_list is None:
            images_list = [None] * len(prompts)
        
        all_inputs = []
        for prompt, images in zip(prompts, images_list):
            if images:
                if isinstance(images, Image.Image):
                    images = [images]
                inputs = self.processor(
                    text=prompt,
                    images=images,
                    return_tensors="pt",
                    padding=True
                )
            else:
                inputs = self.processor(
                    text=prompt,
                    return_tensors="pt",
                    padding=True
                )
            all_inputs.append(inputs)
        
        batch_inputs = {
            k: torch.cat([inp[k] for inp in all_inputs], dim=0).to(self.model.device)
            for k in all_inputs[0].keys()
        }
        
        gen_config = self.generation_configs[mode].to_dict()
        gen_config.update(kwargs)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **batch_inputs,
                **gen_config,
                pad_token_id=self.processor.tokenizer.eos_token_id
            )
        
        responses = [
            self.processor.decode(output, skip_special_tokens=True)
            for output in outputs
        ]
        
        return responses
    
    def vision_qa(
        self,
        image: Image.Image,
        question: str,
        mode: str = "precise"
    ) -> str:
        
        prompt = f"Question: {question}\nAnswer:"
        return self.generate(prompt, images=image, mode=mode)
    
    def analyze_image(
        self,
        image: Image.Image,
        analysis_type: str = "detailed"
    ) -> str:
        
        prompts = {
            "detailed": "Provide a detailed description of this image, including objects, people, actions, setting, and any text visible.",
            "quick": "Briefly describe what you see in this image.",
            "technical": "Analyze this image from a technical perspective, including composition, lighting, colors, and quality.",
            "ocr": "Extract all text visible in this image and organize it clearly."
        }
        
        prompt = prompts.get(analysis_type, prompts["detailed"])
        return self.generate(prompt, images=image, mode="precise")
    
    def code_generation(
        self,
        task: str,
        language: str = "python",
        include_tests: bool = False
    ) -> str:
        
        prompt = f"Write {language} code for the following task:\n{task}"
        
        if include_tests:
            prompt += "\n\nInclude unit tests for the code."
        
        return self.generate(prompt, mode="code", max_new_tokens=2048)
    
    def function_call(
        self,
        user_query: str,
        available_tools: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        
        tools_str = json.dumps(available_tools, indent=2)
        
        prompt = f"""Available tools:
{tools_str}

User query: {user_query}

Respond with a JSON object specifying which tool to use and with what parameters:
{{"tool": "tool_name", "parameters": {{"param": "value"}}}}

Response:"""
        
        response = self.generate(prompt, mode="precise", temperature=0.2)
        
        try:
            import re
            json_match = re.search(r'\{.*\}', response, re.DOTALL)
            if json_match:
                return json.loads(json_match.group())
            return {"error": "No valid JSON found", "raw": response}
        except json.JSONDecodeError as e:
            return {"error": str(e), "raw": response}
    
    def multi_modal_rag(
        self,
        query: str,
        documents: List[str],
        images: Optional[List[Image.Image]] = None
    ) -> str:
        
        context = "\n\n".join([f"Document {i+1}:\n{doc}" for i, doc in enumerate(documents)])
        
        prompt = f"""Context:\n{context}\n\nQuestion: {query}\n\nAnswer based on the provided context:"""
        
        return self.generate(prompt, images=images, mode="precise", max_new_tokens=1024)
    
    def get_metrics_summary(self) -> Dict[str, float]:
        
        if not self.metrics_history:
            return {}
        
        return {
            "avg_latency_ms": sum(m.latency_ms for m in self.metrics_history) / len(self.metrics_history),
            "avg_tokens_per_second": sum(m.tokens_per_second for m in self.metrics_history) / len(self.metrics_history),
            "avg_memory_used_gb": sum(m.memory_used_gb for m in self.metrics_history) / len(self.metrics_history),
            "total_tokens_generated": sum(m.tokens_generated for m in self.metrics_history),
            "num_requests": len(self.metrics_history)
        }
    
    def clear_cache(self):
        
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        self.model.clear_cache() if hasattr(self.model, 'clear_cache') else None
        logger.info("Cache cleared")


def main():
    
    import argparse
    
    parser = argparse.ArgumentParser(description="Advanced Helion-V2.0-Thinking Inference")
    parser.add_argument("--model", type=str, default="DeepXR/Helion-V2.0-Thinking")
    parser.add_argument("--quantization", type=str, choices=["4bit", "8bit", None], default=None)
    parser.add_argument("--mode", type=str, default="balanced", choices=["creative", "precise", "balanced", "code"])
    parser.add_argument("--prompt", type=str, help="Text prompt")
    parser.add_argument("--image", type=str, help="Path to image file")
    parser.add_argument("--stream", action="store_true", help="Enable streaming output")
    parser.add_argument("--torch-compile", action="store_true", help="Use torch.compile")
    parser.add_argument("--benchmark", action="store_true", help="Run benchmark")
    
    args = parser.parse_args()
    
    model = AdvancedHelionInference(
        model_name=args.model,
        quantization=args.quantization,
        torch_compile=args.torch_compile
    )
    
    if args.benchmark:
        print("Running benchmark...")
        
        test_prompts = [
            "Explain quantum computing in simple terms.",
            "Write a Python function to calculate fibonacci numbers.",
            "What are the main causes of climate change?"
        ]
        
        for prompt in test_prompts:
            response, metrics = model.generate(
                prompt,
                mode=args.mode,
                return_metrics=True
            )
            print(f"\nPrompt: {prompt}")
            print(f"Response: {response[:100]}...")
            print(f"Metrics: {metrics}")
        
        summary = model.get_metrics_summary()
        print(f"\nBenchmark Summary:")
        for key, value in summary.items():
            print(f"  {key}: {value:.2f}")
    
    elif args.prompt:
        image = Image.open(args.image) if args.image else None
        
        if args.stream:
            print("Streaming response:")
            for text in model.generate(args.prompt, images=image, mode=args.mode, stream=True):
                print(text, end="", flush=True)
            print()
        else:
            response, metrics = model.generate(
                args.prompt,
                images=image,
                mode=args.mode,
                return_metrics=True
            )
            print(f"Response: {response}")
            print(f"\nMetrics:")
            print(f"  Latency: {metrics.latency_ms:.2f}ms")
            print(f"  Tokens/sec: {metrics.tokens_per_second:.2f}")
            print(f"  Tokens generated: {metrics.tokens_generated}")
    else:
        print("Please provide --prompt or use --benchmark")


if __name__ == "__main__":
    main()