Secure optimization Hugging Face vs Mistral 2: A Practical Guide

# huggingface_secure_optimize.py
# Requirements: transformers==4.36.2, optimum==1.17.0, torch==2.1.2, gramine==1.4.0
# Hardware: AWS EC2 g5.2xlarge (A10G 24GB)
# Benchmark context: Mistral-2-7B-Instruct-v0.1 optimization for TEE deployment

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from optimum.onnxruntime import ORTModelForCausalLM
from optimum.onnxruntime.configuration import OptimizationConfig
import gramine.runtime as gr  # For TEE attestation
import logging
from typing import Optional, Dict

# Configure logging for audit trails (required for secure deployments)
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    handlers=[logging.FileHandler("hf_optimize.log"), logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

class SecureHFOptimizer:
    def __init__(self, model_id: str = "mistralai/Mistral-2-7B-Instruct-v0.1"):
        self.model_id = model_id
        self.tokenizer: Optional[AutoTokenizer] = None
        self.model: Optional[ORTModelForCausalLM] = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        logger.info(f"Initialized optimizer for {model_id} on {self.device}")

    def load_and_quantize(self, quantization_mode: str = "int4") -> None:
        """Load base model and apply Optimum quantization with error handling"""
        try:
            # Load tokenizer with safety checks
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.model_id,
                trust_remote_code=False,  # Disable untrusted remote code for security
                revision="v0.1"  # Pin model version
            )
            logger.info("Tokenizer loaded successfully")

            # Load base model with FP16 precision
            base_model = AutoModelForCausalLM.from_pretrained(
                self.model_id,
                torch_dtype=torch.float16,
                device_map=self.device,
                revision="v0.1",
                low_cpu_mem_usage=True
            )
            logger.info("Base model loaded, FP16 size: {:.2f} GB".format(
                sum(p.numel() * p.element_size() for p in base_model.parameters()) / 1e9
            ))

            # Configure ONNX optimization for quantization
            optimization_config = OptimizationConfig(
                optimization_level=2,  # Max ONNX optimization
                fp16=quantization_mode == "fp16",
                int8=quantization_mode == "int8",
                int4=quantization_mode == "int4",
                use_gpu=True
            )

            # Convert to ONNX with quantization
            self.model = ORTModelForCausalLM.from_pretrained(
                base_model,
                self.tokenizer,
                optimization_config=optimization_config,
                export=True,  # Export to ONNX
                provider="CUDAExecutionProvider"  # Use GPU runtime
            )
            logger.info(f"Model quantized to {quantization_mode}, ONNX size: {self.model.size / 1e9:.2f} GB")

        except Exception as e:
            logger.error(f"Failed to load/quantize model: {str(e)}", exc_info=True)
            raise  # Re-raise for deployment pipelines to catch

    def attest_tee(self) -> Dict[str, str]:
        """Attest TEE environment using Gramine for Hugging Face deployment"""
        try:
            # Gramine TEE attestation (required for regulated deployments)
            attestation_report = gr.get_attestation_report()
            logger.info(f"TEE attestation successful: {attestation_report['mrenclave']}")
            return {
                "mrenclave": attestation_report["mrenclave"],
                "mrsigner": attestation_report["mrsigner"],
                "status": "valid"
            }
        except ImportError:
            logger.warning("Gramine not available, skipping TEE attestation (non-enclave deployment)")
            return {"status": "non-tee"}
        except Exception as e:
            logger.error(f"TEE attestation failed: {str(e)}", exc_info=True)
            return {"status": "invalid", "error": str(e)}

    def infer(self, prompt: str, max_new_tokens: int = 128) -> str:
        """Run secure inference with input validation"""
        if not self.model or not self.tokenizer:
            raise RuntimeError("Model not loaded. Call load_and_quantize first.")
        try:
            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
            outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        except Exception as e:
            logger.error(f"Inference failed: {str(e)}", exc_info=True)
            raise

if __name__ == "__main__":
    # Example usage
    optimizer = SecureHFOptimizer()
    optimizer.load_and_quantize(quantization_mode="int4")
    tee_status = optimizer.attest_tee()
    print(f"TEE Status: {tee_status}")
    result = optimizer.infer("Explain TEE in 2 sentences.")
    print(f"Inference Result: {result}")

# mistral2_secure_optimize.py
# Requirements: mistral-optimize==0.2.1, mistral-inference==0.3.0, torch==2.1.2, aws-nitro-enclaves-sdk==1.0.0
# Hardware: AWS EC2 g5.2xlarge (A10G 24GB)
# Benchmark context: Native Mistral 2 optimization for Nitro Enclaves

import torch
from mistral_inference import MistralTokenizer, MistralForCausalLM
from mistral_optimize import Quantizer, EnclaveDeployer
from aws_nitro_enclaves import NitroEnclaveClient
import logging
from typing import Optional, Dict
import json

# Audit-compliant logging for regulated environments
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    handlers=[logging.FileHandler("mistral_optimize.log"), logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

class Mistral2SecureOptimizer:
    def __init__(self, model_id: str = "mistralai/Mistral-2-7B-Instruct-v0.1"):
        self.model_id = model_id
        self.tokenizer: Optional[MistralTokenizer] = None
        self.model: Optional[MistralForCausalLM] = None
        self.quantizer = Quantizer()
        self.enclave_deployer = EnclaveDeployer()
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        logger.info(f"Initialized Mistral 2 optimizer for {model_id} on {self.device}")

    def load_and_quantize(self, quantization_mode: str = "int4_nf4") -> None:
        """Load Mistral 2 model and apply native quantization with error handling"""
        try:
            # Load native Mistral tokenizer (no remote code risk)
            self.tokenizer = MistralTokenizer.from_pretrained(
                self.model_id,
                revision="v0.1"  # Pin model version
            )
            logger.info("Mistral 2 tokenizer loaded successfully")

            # Load native Mistral 2 model with FP16
            self.model = MistralForCausalLM.from_pretrained(
                self.model_id,
                torch_dtype=torch.float16,
                device_map=self.device,
                revision="v0.1"
            )
            logger.info("Base Mistral 2 model loaded, FP16 size: {:.2f} GB".format(
                sum(p.numel() * p.element_size() for p in self.model.parameters()) / 1e9
            ))

            # Apply native Mistral quantization (NF4 is Mistral-proprietary, better accuracy)
            if quantization_mode == "int4_nf4":
                self.model = self.quantizer.quantize_nf4(
                    self.model,
                    group_size=64,  # Mistral-recommended group size for 7B
                    device=self.device
                )
            elif quantization_mode == "int8":
                self.model = self.quantizer.quantize_int8(self.model)
            else:
                raise ValueError(f"Unsupported quantization mode: {quantization_mode}")

            logger.info(f"Model quantized to {quantization_mode}, size: {self.model.size / 1e9:.2f} GB")

        except Exception as e:
            logger.error(f"Failed to load/quantize Mistral 2 model: {str(e)}", exc_info=True)
            raise

    def deploy_to_nitro_enclave(self, enclave_id: str = "mistral-enclave-01") -> Dict[str, str]:
        """Deploy quantized model to AWS Nitro Enclave with native Mistral integration"""
        try:
            # Initialize Nitro Enclave client
            nitro_client = NitroEnclaveClient(region="us-east-1")
            logger.info(f"Deploying to Nitro Enclave {enclave_id}")

            # Use Mistral native enclave deployer (no 3rd party Gramine required)
            deploy_result = self.enclave_deployer.deploy(
                model=self.model,
                tokenizer=self.tokenizer,
                enclave_client=nitro_client,
                enclave_id=enclave_id,
                attestation_required=True  # Mandatory for regulated deployments
            )

            logger.info(f"Enclave deployment successful: {deploy_result['enclave_cid']}")
            return {
                "enclave_cid": deploy_result["enclave_cid"],
                "attestation_doc": deploy_result["attestation_doc"],
                "status": "deployed"
            }

        except Exception as e:
            logger.error(f"Enclave deployment failed: {str(e)}", exc_info=True)
            return {"status": "failed", "error": str(e)}

    def infer(self, prompt: str, max_new_tokens: int = 128) -> str:
        """Run inference on optimized Mistral 2 model with input validation"""
        if not self.model or not self.tokenizer:
            raise RuntimeError("Model not loaded. Call load_and_quantize first.")
        try:
            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
            outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        except Exception as e:
            logger.error(f"Inference failed: {str(e)}", exc_info=True)
            raise

if __name__ == "__main__":
    # Example usage
    optimizer = Mistral2SecureOptimizer()
    optimizer.load_and_quantize(quantization_mode="int4_nf4")
    deploy_status = optimizer.deploy_to_nitro_enclave()
    print(f"Enclave Deploy Status: {json.dumps(deploy_status, indent=2)}")
    result = optimizer.infer("Explain TEE in 2 sentences.")
    print(f"Inference Result: {result}")

# benchmark_secure_optimization.py
# Requirements: transformers==4.36.2, optimum==1.17.0, mistral-optimize==0.2.1, mistral-inference==0.3.0, prometheus-client==0.19.0
# Hardware: AWS EC2 p4d.24xlarge (A100 80GB)
# Benchmark context: Side-by-side comparison of Hugging Face vs Mistral 2 optimization

import time
import torch
import statistics
from huggingface_secure_optimize import SecureHFOptimizer
from mistral2_secure_optimize import Mistral2SecureOptimizer
from prometheus_client import start_http_server, Summary, Gauge
import logging
from typing import List, Dict
import json

# Prometheus metrics for benchmark tracking
LATENCY_SUMMARY = Summary("inference_latency_ms", "Inference latency in milliseconds")
MEMORY_GAUGE = Gauge("model_memory_gb", "Model memory footprint in GB")
ACCURACY_GAUGE = Gauge("model_accuracy", "0-shot Hellaswag accuracy")

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

class OptimizationBenchmark:
    def __init__(self, num_samples: int = 1000, rpm: int = 1000):
        self.num_samples = num_samples
        self.rpm = rpm  # Requests per minute
        self.results: Dict[str, Dict] = {
            "huggingface": {"latencies": [], "memory": 0, "accuracy": 0},
            "mistral2": {"latencies": [], "memory": 0, "accuracy": 0}
        }
        logger.info(f"Initialized benchmark: {num_samples} samples, {rpm} RPM")

    def run_hf_benchmark(self) -> None:
        """Benchmark Hugging Face optimized model"""
        logger.info("Starting Hugging Face benchmark...")
        hf_optimizer = SecureHFOptimizer()
        try:
            # Load and quantize with INT4 (HF Optimum)
            hf_optimizer.load_and_quantize(quantization_mode="int4")
            self.results["huggingface"]["memory"] = hf_optimizer.model.size / 1e9

            # Warmup: 100 requests
            for _ in range(100):
                hf_optimizer.infer("Warmup prompt.")

            # Sustained load test: 1000 requests at target RPM
            interval = 60 / self.rpm  # Seconds between requests
            for i in range(self.num_samples):
                start = time.perf_counter()
                hf_optimizer.infer(f"Benchmark prompt {i}")
                end = time.perf_counter()
                latency_ms = (end - start) * 1000
                LATENCY_SUMMARY.observe(latency_ms)
                self.results["huggingface"]["latencies"].append(latency_ms)
                if i % 100 == 0:
                    logger.info(f"HF benchmark progress: {i}/{self.num_samples}")

            # Calculate p99 latency
            p99 = statistics.quantiles(self.results["huggingface"]["latencies"], n=100)[98]
            logger.info(f"Hugging Face p99 latency: {p99:.2f} ms")

            # Calculate accuracy (simplified 0-shot Hellaswag check)
            # In real benchmark, use full Hellaswag validation set
            correct = 0
            for _ in range(100):  # Sample 100 for speed
                prompt = "Complete the sentence: The cat sat on the..."
                result = hf_optimizer.infer(prompt)
                if "mat" in result.lower():  # Simplified check
                    correct += 1
            self.results["huggingface"]["accuracy"] = correct / 100
            ACCURACY_GAUGE.set(self.results["huggingface"]["accuracy"])

        except Exception as e:
            logger.error(f"HF benchmark failed: {str(e)}", exc_info=True)
            raise

    def run_mistral2_benchmark(self) -> None:
        """Benchmark Mistral 2 native optimized model"""
        logger.info("Starting Mistral 2 benchmark...")
        mistral_optimizer = Mistral2SecureOptimizer()
        try:
            # Load and quantize with NF4 (Mistral native)
            mistral_optimizer.load_and_quantize(quantization_mode="int4_nf4")
            self.results["mistral2"]["memory"] = mistral_optimizer.model.size / 1e9

            # Warmup: 100 requests
            for _ in range(100):
                mistral_optimizer.infer("Warmup prompt.")

            # Sustained load test
            interval = 60 / self.rpm
            for i in range(self.num_samples):
                start = time.perf_counter()
                mistral_optimizer.infer(f"Benchmark prompt {i}")
                end = time.perf_counter()
                latency_ms = (end - start) * 1000
                LATENCY_SUMMARY.observe(latency_ms)
                self.results["mistral2"]["latencies"].append(latency_ms)
                if i % 100 == 0:
                    logger.info(f"Mistral 2 benchmark progress: {i}/{self.num_samples}")

            # Calculate p99 latency
            p99 = statistics.quantiles(self.results["mistral2"]["latencies"], n=100)[98]
            logger.info(f"Mistral 2 p99 latency: {p99:.2f} ms")

            # Accuracy check
            correct = 0
            for _ in range(100):
                prompt = "Complete the sentence: The cat sat on the..."
                result = mistral_optimizer.infer(prompt)
                if "mat" in result.lower():
                    correct += 1
            self.results["mistral2"]["accuracy"] = correct / 100
            ACCURACY_GAUGE.set(self.results["mistral2"]["accuracy"])

        except Exception as e:
            logger.error(f"Mistral 2 benchmark failed: {str(e)}", exc_info=True)
            raise

    def save_results(self, output_path: str = "benchmark_results.json") -> None:
        """Save benchmark results to JSON"""
        # Calculate p99 for both
        for tool in ["huggingface", "mistral2"]:
            if self.results[tool]["latencies"]:
                self.results[tool]["p99_latency_ms"] = statistics.quantiles(
                    self.results[tool]["latencies"], n=100
                )[98]
        with open(output_path, "w") as f:
            json.dump(self.results, f, indent=2)
        logger.info(f"Results saved to {output_path}")

if __name__ == "__main__":
    # Start Prometheus metrics server
    start_http_server(8000)
    logger.info("Prometheus metrics server started on port 8000")

    # Run benchmarks
    benchmark = OptimizationBenchmark(num_samples=1000, rpm=1000)
    benchmark.run_hf_benchmark()
    benchmark.run_mistral2_benchmark()
    benchmark.save_results()

    # Print summary
    print("\n=== Benchmark Summary ===")
    for tool in ["huggingface", "mistral2"]:
        res = benchmark.results[tool]
        print(f"{tool.upper()}:")
        print(f"  p99 Latency: {res.get('p99_latency_ms', 0):.2f} ms")
        print(f"  Memory: {res['memory']:.2f} GB")
        print(f"  Accuracy: {res['accuracy']:.2%}")

# requirements-hf.txt (Hugging Face secure optimization)
transformers==4.36.2 \
  --hash=sha256:7a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcdef12345
optimum==1.17.0 \
  --hash=sha256:8b1c2d3e4f5678901234567890123456789abcdef1234567890abcdef123456
torch==2.1.2 \
  --hash=sha256:9c1d2e3f45678901234567890123456789abcdef1234567890abcdef1234567
gramine==1.4.0 \
  --hash=sha256:0d1e2f3a4b5c678901234567890123456789abcdef1234567890abcdef12345

# Mistral native enclave attestation check (middleware snippet)
def validate_enclave_attestation(enclave_cid: str) -> bool:
    client = NitroEnclaveClient()
    attestation = client.get_attestation(enclave_cid)
    # Pin expected mrenclave hash for Mistral 2 optimized model
    expected_mrenclave = "a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcdef12345"
    if attestation["mrenclave"] != expected_mrenclave:
        logger.error(f"Invalid enclave: {attestation['mrenclave']}")
        return False
    return True

# Domain-specific accuracy check snippet (fintech)
def check_fintech_accuracy(model, tokenizer, num_samples: int = 500) -> float:
    correct = 0
    # Load fintech compliance benchmark (500 samples)
    with open("fintech_qa_benchmark.json") as f:
        benchmark = json.load(f)
    for sample in benchmark[:num_samples]:
        prompt = sample["prompt"]
        expected = sample["answer"]
        result = model.infer(prompt)
        if expected.lower() in result.lower():
            correct += 1
    accuracy = correct / num_samples
    if accuracy < 0.95:  # Fail if <95% accuracy
        raise ValueError(f"Fintech accuracy too low: {accuracy:.2%}")
    return accuracy