#
Optimizing Local Inference

In this chapter, we’ll explore various techniques and strategies for optimizing local inference with Large Language Models. We’ll cover topics like quantization, caching, attention optimizations, and hardware utilization.

##
Model Optimization Techniques

###
1. Quantization

Quantization reduces model size and memory usage by converting weights to lower precision formats.

####
4-bit Quantization

    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    # Configure 4-bit quantization
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype="float16",
        bnb_4bit_quant_type="nf4"
    )

    # Load quantized model
    model = AutoModelForCausalLM.from_pretrained(
        "HuggingFaceTB/SmolLM2-1.7B-Instruct",
        quantization_config=quantization_config,
        device_map="auto"
    )

####
8-bit Quantization

    from transformers import AutoModelForCausalLM

    # Load model in 8-bit
    model = AutoModelForCausalLM.from_pretrained(
        "HuggingFaceTB/SmolLM2-1.7B-Instruct",
        load_in_8bit=True,
        device_map="auto"
    )

###
2. Attention Optimizations

####
Flash Attention

Flash Attention reduces memory usage and increases speed by optimizing attention computation.

    from transformers import AutoModelForCausalLM

    model = AutoModelForCausalLM.from_pretrained(
        "HuggingFaceTB/SmolLM2-1.7B-Instruct",
        use_flash_attention_2=True,
        device_map="auto"
    )

####
Sliding Window Attention

Reduces memory usage for long sequences by using local attention windows.

    config = AutoConfig.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct")
    config.sliding_window = 4096  # Set window size
    model = AutoModelForCausalLM.from_pretrained(
        "HuggingFaceTB/SmolLM2-1.7B-Instruct",
        config=config
    )

##
Memory Management

###
1. GPU Memory Optimization

####
Memory Tracking

    import torch

    def print_gpu_memory():
        print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f}MB")
        print(f"Cached: {torch.cuda.memory_reserved() / 1024**2:.2f}MB")

    # Monitor memory usage
    print_gpu_memory()
    torch.cuda.empty_cache()  # Clear unused memory
    print_gpu_memory()

####
Gradient Checkpointing

    model.gradient_checkpointing_enable()  # Enable gradient checkpointing

###
2. CPU Offloading

Offload parts of the model to CPU when GPU memory is limited.

    from transformers import AutoModelForCausalLM

    # Configure CPU offloading
    model = AutoModelForCausalLM.from_pretrained(
        "HuggingFaceTB/SmolLM2-1.7B-Instruct",
        device_map="auto",
        offload_folder="offload"
    )

##
Inference Optimization

###
1. Batching

####
Static Batching

    def batch_inference(prompts, batch_size=4):
        results = []
        for i in range(0, len(prompts), batch_size):
            batch = prompts[i:i + batch_size]
            inputs = tokenizer(batch, padding=True, return_tensors="pt")
            outputs = model.generate(**inputs)
            decoded = tokenizer.batch_decode(outputs)
            results.extend(decoded)
        return results

####
Dynamic Batching

    class DynamicBatcher:
        def __init__(self, model, tokenizer, max_batch_size=8):
            self.model = model
            self.tokenizer = tokenizer
            self.max_batch_size = max_batch_size
            self.queue = []

        async def add_request(self, prompt):
            self.queue.append(prompt)
            if len(self.queue) >= self.max_batch_size:
                return await self.process_batch()
            return None

###
2. Caching

####
KV Cache Management

    class KVCache:
        def __init__(self, max_size=1000):
            self.cache = {}
            self.max_size = max_size

        def get(self, key_tokens):
            key = tuple(key_tokens.tolist())
            return self.cache.get(key)

        def put(self, key_tokens, value):
            if len(self.cache) >= self.max_size:
                self.cache.pop(next(iter(self.cache)))
            key = tuple(key_tokens.tolist())
            self.cache[key] = value

##
Hardware Utilization

###
1. Multi-GPU Support

####
Model Parallelism

    from accelerate import dispatch_model

    # Distribute model across GPUs
    model = dispatch_model(model, device_map="balanced")

####
Data Parallelism

    import torch.nn as nn

    # Wrap model for data parallel processing
    if torch.cuda.device_count() > 1:
        model = nn.DataParallel(model)

###
2. CPU Threading

    import torch

    # Set number of threads for CPU operations
    torch.set_num_threads(8)

##
Performance Monitoring

###
1. Latency Tracking

    import time

    class LatencyTracker:
        def __init__(self):
            self.times = []

        def __enter__(self):
            self.start = time.perf_counter()
            return self

        def __exit__(self, *args):
            self.times.append(time.perf_counter() - self.start)

        @property
        def average(self):
            return sum(self.times) / len(self.times)

###
2. Throughput Monitoring

    class ThroughputMonitor:
        def __init__(self, window_size=100):
            self.window_size = window_size
            self.timestamps = []

        def add_request(self):
            self.timestamps.append(time.time())
            if len(self.timestamps) > self.window_size:
                self.timestamps.pop(0)

        @property
        def requests_per_second(self):
            if len(self.timestamps) < 2:
                return 0
            time_diff = self.timestamps[-1] - self.timestamps[0]
            return len(self.timestamps) / time_diff

##
Best Practices

1. Model Selection

   - Choose appropriate model size
   - Consider hardware constraints
   - Balance quality and speed

2. Memory Management

   - Monitor memory usage
   - Use appropriate quantization
   - Implement caching strategies

3. Performance Optimization

   - Enable hardware optimizations
   - Use batching when possible
   - Monitor and profile performance

##
Resources

- Hugging Face Performance Optimization Guide
- PyTorch Memory Management
- Flash Attention Paper
- Quantization Guide
- Model Parallelism Tutorial

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