1class InferenceMetrics:
2    def __init__(self):
3        self.metrics = {
4            'throughput': 0,      # tokens/second
5            'latency_p50': 0,     # 50th percentile latency
6            'latency_p95': 0,     # 95th percentile latency  
7            'latency_p99': 0,     # 99th percentile latency
8            'gpu_utilization': 0, # GPU memory/utilization
9            'batch_efficiency': 0,# actual batch size / max batch
10            'cost_per_token': 0   # $/1K tokens
11        }
12    
13    def calculate_roi(self, original_cost, optimized_cost):
14        return (original_cost - optimized_cost) / original_cost * 100
15

1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3from accelerate import init_empty_weights, load_checkpoint_and_dispatch
4import bitsandbytes as bnb
5
6def gptq_quantization(model_name="meta-llama/Llama-2-7b"):
7    """
8    Post-training quantization using GPTQ algorithm
9    """
10    # Load base model
11    model = AutoModelForCausalLM.from_pretrained(
12        model_name,
13        torch_dtype=torch.float16,
14        device_map="auto"
15    )
16    
17    # Prepare calibration dataset
18    calibration_texts = [
19        "The transformer architecture revolutionized",
20        "Machine learning models require",
21        "Natural language processing applications"
22    ] * 100  # 300 examples for calibration
23    
24    tokenizer = AutoTokenizer.from_pretrained(model_name)
25    
26    # GPTQ quantization process
27    from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
28    
29    quantize_config = BaseQuantizeConfig(
30        bits=4,                    # 4-bit quantization
31        group_size=128,           # Group size for quantization
32        damp_percent=0.01,         # Damping percentage
33        desc_act=True,            # Whether to quantize with desc act
34        sym=True,                  # Symmetric quantization
35        true_sequential=True       # True sequential quantization
36    )
37    
38    # Quantize model
39    quantized_model = AutoGPTQForCausalLM.from_pretrained(
40        model_name,
41        quantize_config=quantize_config,
42        calibration_dataset=calibration_texts
43    )
44    
45    # Save quantized model
46    quantized_model.save_quantized("./quantized_llama")
47    
48    return quantized_model
49

1def awq_quantization():
2    """
3    Activation-aware quantization preserves important weights
4    """
5    from awq import AutoAWQForCausalLM
6    
7    quant_config = {
8        "zero_point": True,
9        "q_group_size": 128,
10        "w_bit": 4,
11        "version": "GEMM"
12    }
13    
14    # Load and quantize
15    model = AutoAWQForCausalLM.from_pretrained(
16        "TheBloke/Llama-2-7B-AWQ",
17        **quant_config
18    )
19    
20    # AWQ is particularly effective for:
21    # 1. Large models (70B+ parameters)
22    # 2. Models with significant activation outliers
23    # 3. Production serving with strict latency requirements
24    
25    return model
26

1import time
2from typing import List
3import numpy as np
4
5class DynamicBatcher:
6    def __init__(self, max_batch_size=32, max_wait_ms=100):
7        self.max_batch_size = max_batch_size
8        self.max_wait_ms = max_wait_ms
9        self.pending_requests = []
10        self.batch_counter = 0
11    
12    def add_request(self, request):
13        self.pending_requests.append({
14            "request": request,
15            "arrival_time": time.time(),
16            "processed": False
17        })
18    
19    def should_process_batch(self):
20        """
21        Decision criteria for batch processing:
22        1. Batch size reached
23        2. Max wait time exceeded
24        3. Sequence length similarity (for efficiency)
25        """
26        if len(self.pending_requests) >= self.max_batch_size:
27            return True
28        
29        if self.pending_requests:
30            oldest_wait = time.time() - self.pending_requests[0]["arrival_time"]
31            if oldest_wait * 1000 >= self.max_wait_ms:
32                return True
33        
34        return False
35    
36    def create_optimized_batch(self):
37        """
38        Group requests by sequence length for efficient processing
39        """
40        if not self.pending_requests:
41            return []
42        
43        # Sort by sequence length for padding efficiency
44        requests_with_length = []
45        for req in self.pending_requests:
46            seq_length = len(req["request"]["tokens"])
47            requests_with_length.append((seq_length, req))
48        
49        requests_with_length.sort(key=lambda x: x[0])
50        
51        # Create batches with similar sequence lengths
52        batches = []
53        current_batch = []
54        current_max_length = 0
55        
56        for seq_length, req in requests_with_length:
57            if len(current_batch) < self.max_batch_size and                abs(seq_length - current_max_length) < 50:  # Length similarity threshold
58                current_batch.append(req)
59                current_max_length = max(current_max_length, seq_length)
60            else:
61                if current_batch:
62                    batches.append(current_batch)
63                current_batch = [req]
64                current_max_length = seq_length
65        
66        if current_batch:
67            batches.append(current_batch)
68        
69        return batches
70

1class ContinuousBatching:
2    """
3    Also known as iteration-level batching or flex batching
4    Processes requests at each generation step, not per request
5    """
6    def __init__(self, inference_engine):
7        self.inference_engine = inference_engine
8        self.active_requests = []
9        self.completed_requests = []
10    
11    def add_request(self, prompt, max_tokens):
12        request_id = len(self.active_requests)
13        self.active_requests.append({
14            "id": request_id,
15            "prompt": prompt,
16            "max_tokens": max_tokens,
17            "generated_tokens": [],
18            "completed": False,
19            "current_step": 0
20        })
21        return request_id
22    
23    def iteration_step(self):
24        """
25        Single generation step across all active requests
26        """
27        if not self.active_requests:
28            return
29        
30        # Prepare inputs for all active requests
31        batch_inputs = []
32        request_indices = []
33        
34        for i, req in enumerate(self.active_requests):
35            if not req["completed"]:
36                # Get next token position
37                current_tokens = req["generated_tokens"]
38                if not current_tokens:
39                    # First token after prompt
40                    input_tokens = req["prompt"]
41                else:
42                    input_tokens = req["prompt"] + current_tokens
43                
44                batch_inputs.append(input_tokens)
45                request_indices.append(i)
46        
47        # Batch inference
48        batch_outputs = self.inference_engine.batch_generate(batch_inputs)
49        
50        # Distribute results
51        for idx, output in zip(request_indices, batch_outputs):
52            req = self.active_requests[idx]
53            next_token = output[-1]  # Get newly generated token
54            
55            req["generated_tokens"].append(next_token)
56            req["current_step"] += 1
57            
58            # Check completion
59            if (len(req["generated_tokens"]) >= req["max_tokens"] or
60                next_token == self.inference_engine.eos_token_id):
61                req["completed"] = True
62                self.completed_requests.append(req)
63                self.active_requests[idx] = None
64        
65        # Clean completed requests
66        self.active_requests = [r for r in self.active_requests if r is not None]
67

1def vllm_serving_example():
2    """
3    vLLM implements continuous batching with PagedAttention
4    """
5    from vllm import LLM, SamplingParams
6    
7    # Initialize vLLM engine
8    llm = LLM(
9        model="meta-llama/Llama-2-7b-chat",
10        tensor_parallel_size=2,  # Model parallelism
11        gpu_memory_utilization=0.9,  # Aggressive memory use
12        max_num_batched_tokens=4096,  # Max concurrent tokens
13        max_num_seqs=256,  # Max concurrent sequences
14        quantization="awq",  # Quantization method
15        enforce_eager=True  # Disable graph capture for dynamic shapes
16    )
17    
18    # Sampling parameters
19    sampling_params = SamplingParams(
20        temperature=0.8,
21        top_p=0.95,
22        max_tokens=256,
23        stop=["\n", ".", "!"]
24    )
25    
26    # Batch inference
27    prompts = [
28        "Explain quantum computing in simple terms.",
29        "Write Python code for binary search.",
30        "Summarize the transformer architecture."
31    ] * 50  # 150 requests
32    
33    outputs = llm.generate(prompts, sampling_params)
34    
35    # Performance metrics
36    total_tokens = sum(len(output.outputs[0].token_ids) for output in outputs)
37    throughput = total_tokens / llm.engine.metrics.time_to_first_token
38    
39    return throughput
40

1class KVCacheManager:
2    def __init__(self, max_cache_size=2048, cache_dtype=torch.float16):
3        self.max_cache_size = max_cache_size
4        self.cache_dtype = cache_dtype
5        self.cache = {}  # request_id -> (keys, values)
6        self.hits = 0
7        self.misses = 0
8    
9    def get_or_create_cache(self, request_id, layer_idx, seq_length):
10        if request_id in self.cache and layer_idx in self.cache[request_id]:
11            cache_entry = self.cache[request_id][layer_idx]
12            if cache_entry["seq_length"] >= seq_length:
13                self.hits += 1
14                # Return cached keys/values up to seq_length
15                keys = cache_entry["keys"][:, :seq_length, :]
16                values = cache_entry["values"][:, :seq_length, :]
17                return keys, values
18        
19        self.misses += 1
20        return None, None
21    
22    def update_cache(self, request_id, layer_idx, keys, values, seq_length):
23        if request_id not in self.cache:
24            self.cache[request_id] = {}
25        
26        self.cache[request_id][layer_idx] = {
27            "keys": keys,
28            "values": values,
29            "seq_length": seq_length,
30            "last_used": time.time()
31        }
32    
33    def evict_old_entries(self, max_age_seconds=300):
34        """LRU eviction for cache management"""
35        current_time = time.time()
36        to_evict = []
37        
38        for req_id, layers in self.cache.items():
39            for layer_idx, entry in layers.items():
40                if current_time - entry["last_used"] > max_age_seconds:
41                    to_evict.append((req_id, layer_idx))
42        
43        for req_id, layer_idx in to_evict:
44            del self.cache[req_id][layer_idx]
45            if not self.cache[req_id]:
46                del self.cache[req_id]
47

1class PagedAttention:
2    """
3    Inspired by vLLM's PagedAttention
4    Treats KV cache as memory pages to reduce fragmentation
5    """
6    def __init__(self, page_size=256, num_layers=32):
7        self.page_size = page_size  # Tokens per page
8        self.num_layers = num_layers
9        self.physical_pages = []  # Actual GPU memory
10        self.logical_to_physical = {}  # Mapping
11        self.free_pages = set()
12    
13    def allocate_pages(self, num_tokens):
14        """Allocate pages for a sequence"""
15        num_pages = (num_tokens + self.page_size - 1) // self.page_size
16        allocated_pages = []
17        
18        for _ in range(num_pages):
19            if self.free_pages:
20                page_id = self.free_pages.pop()
21            else:
22                page_id = len(self.physical_pages)
23                self.physical_pages.append(self._create_page())
24            
25            allocated_pages.append(page_id)
26        
27        return allocated_pages
28    
29    def _create_page(self):
30        """Create a new physical page in GPU memory"""
31        # Shape: [num_layers, 2, page_size, hidden_size]
32        # 2 for keys and values
33        return torch.zeros(
34            (self.num_layers, 2, self.page_size, self.hidden_size),
35            dtype=self.cache_dtype,
36            device="cuda"
37        )
38    
39    def attention_with_paging(self, query, logical_pages, page_offsets):
40        """
41        Attention operation using paged KV cache
42        """
43        # Gather physical pages
44        physical_pages = [self.physical_pages[pid] for pid in logical_pages]
45        
46        # Perform attention across pages
47        # Implementation would use custom CUDA kernel in production
48        return self._paged_attention_kernel(query, physical_pages, page_offsets)
49

1def optimize_for_gpu_architecture(gpu_type="a100"):
2    """
3    Optimize based on specific GPU architecture
4    """
5    optimizations = {
6        "a100": {
7            "tensor_cores": True,
8            "memory_bandwidth": 1555,  # GB/s
9            "fp16_tflops": 312,
10            "int8_tflops": 624,
11            "recommended_batch": 64,
12            "flash_attention": True
13        },
14        "h100": {
15            "tensor_cores": True,
16            "memory_bandwidth": 3350,  # GB/s
17            "fp16_tflops": 989,
18            "fp8_tflops": 1978,
19            "recommended_batch": 128,
20            "flash_attention": True,
21            "transformer_engine": True  # NVIDIA's optimization
22        },
23        "rtx_4090": {
24            "tensor_cores": True,
25            "memory_bandwidth": 1008,  # GB/s
26            "fp16_tflops": 330,
27            "int8_tflops": 660,
28            "recommended_batch": 32,
29            "flash_attention": True
30        }
31    }
32    
33    config = optimizations.get(gpu_type, optimizations["a100"])
34    
35    # Apply architecture-specific optimizations
36    if config["flash_attention"]:
37        enable_flash_attention()
38    
39    if config.get("transformer_engine"):
40        enable_transformer_engine()
41    
42    return config
43

1class MemoryOptimizer:
2    def __init__(self, model, gpu_memory_gb):
3        self.model = model
4        self.total_memory = gpu_memory_gb * 1024**3  # Convert to bytes
5        self.allocated = 0
6    
7    def calculate_memory_usage(self):
8        """Calculate model memory requirements"""
9        memory_breakdown = {
10            "parameters": self._get_parameter_size(),
11            "activations": self._estimate_activation_memory(),
12            "kv_cache": self._estimate_kv_cache_memory(),
13            "optimizer": 0,  # Not needed for inference
14            "gradients": 0   # Not needed for inference
15        }
16        
17        total = sum(memory_breakdown.values())
18        utilization = total / self.total_memory
19        
20        return memory_breakdown, utilization
21    
22    def optimize_memory(self):
23        """Apply memory optimization techniques"""
24        techniques = []
25        
26        # 1. Activation checkpointing (recompute instead of store)
27        if self.memory_breakdown["activations"] > 0.3 * self.total_memory:
28            techniques.append("activation_checkpointing")
29        
30        # 2. Offloading to CPU (for very large models)
31        if self.memory_breakdown["parameters"] > 0.8 * self.total_memory:
32            techniques.append("cpu_offloading")
33        
34        # 3. Parameter sharing (tied embeddings)
35        techniques.append("tied_embeddings")
36        
37        # 4. Sparse attention patterns
38        techniques.append("sparse_attention")
39        
40        return techniques
41

1def model_parallel_inference():
2    """
3    Distribute model across multiple GPUs
4    """
5    import torch.distributed as dist
6    from torch.nn.parallel import DistributedDataParallel
7    
8    # Initialize distributed environment
9    dist.init_process_group(backend="nccl")
10    
11    # Split model layers across GPUs
12    world_size = dist.get_world_size()
13    rank = dist.get_rank()
14    
15    # Example: 32 layers across 4 GPUs
16    layers_per_gpu = 32 // world_size
17    start_layer = rank * layers_per_gpu
18    end_layer = start_layer + layers_per_gpu
19    
20    # Each GPU processes its subset of layers
21    # Communication between GPUs for layer outputs
22    
23    # Pipeline parallelism for sequential processing
24    # Tensor parallelism for within-layer distribution
25

1class InferenceAutoScaler:
2    def __init__(self, min_replicas=1, max_replicas=10):
3        self.min_replicas = min_replicas
4        self.max_replicas = max_replicas
5        self.current_replicas = min_replicas
6        self.metrics_window = []
7    
8    def scaling_decision(self, metrics, window_size=10):
9        """
10        Make scaling decision based on metrics
11        """
12        self.metrics_window.append(metrics)
13        if len(self.metrics_window) > window_size:
14            self.metrics_window.pop(0)
15        
16        avg_latency_p95 = np.mean([m["latency_p95"] for m in self.metrics_window])
17        avg_throughput = np.mean([m["throughput"] for m in self.metrics_window])
18        avg_utilization = np.mean([m["gpu_utilization"] for m in self.metrics_window])
19        
20        # Scale up if latency high and utilization high
21        if avg_latency_p95 > 2000 and avg_utilization > 0.8:
22            if self.current_replicas < self.max_replicas:
23                self.current_replicas += 1
24                return "scale_up"
25        
26        # Scale down if utilization low
27        if avg_utilization < 0.3 and self.current_replicas > self.min_replicas:
28            self.current_replicas -= 1
29            return "scale_down"
30        
31        return "maintain"
32

1def calculate_optimization_roi(base_cost, optimized_cost, implementation_cost):
2    """
3    Calculate return on investment for optimization efforts
4    """
5    annual_savings = (base_cost - optimized_cost) * 365
6    payback_period = implementation_cost / annual_savings  # Years
7    
8    if payback_period < 0.5:  # 6 months
9        recommendation = "Strongly recommended"
10    elif payback_period < 1:  # 1 year
11        recommendation = "Recommended"
12    elif payback_period < 2:  # 2 years
13        recommendation = "Consider if scaling"
14    else:
15        recommendation = "Defer until scale justifies"
16    
17    return {
18        "annual_savings": annual_savings,
19        "payback_period_years": payback_period,
20        "recommendation": recommendation
21    }
22

1KPIS = {
2    "latency": {
3        "p95_threshold": 2000,  # 2 seconds
4        "p99_threshold": activity 3000,  # 3 seconds
5        "alert_window": 300  # 5 minutes
6    },
7    "throughput": {
8        "minimum": 100,  # tokens/second
9        "degradation_threshold": 0.8  # 80% of baseline
10    },
11    "cost": {
12        "increase_threshold": 1.2,  # 20% increase
13        "budget_alert": 0.9  # 90% of monthly budget
14    },
15    "accuracy": {
16        "acceptable_drop": 0.02,  # 2% accuracy drop
17        "eval_frequency": 1000  # every 1000 requests
18    }
19}
20