PagedAttention: Memory Optimization Revolution for LLM Inference

# Traditional approach - allocate full sequence upfront
class TraditionalKVCache:
    def __init__(self, max_length=4096):
        # Pre-allocate maximum possible memory
        self.cache = torch.zeros(
            (2,  # K and V
             max_length,
             num_heads,
             head_dim),
            dtype=torch.float16,
            device='cuda'
        )
        # Problem: Always allocates max_length, even for short sequences

class PagedAttentionKVCache:
    """
    PagedAttention-inspired KV cache management
    Inspired by vLLM implementation
    """
    
    def __init__(self, block_size=16, num_blocks=1024):
        self.block_size = block_size  # Tokens per block
        self.num_blocks = num_blocks
        
        # Physical page storage - allocate on demand
        self.block_tables = {}  # seq_id -> list of block indices
        
        # Pre-allocate physical blocks (not tied to any sequence)
        self.free_blocks = list(range(num_blocks))
        
    def allocate(self, seq_id, num_tokens):
        """Allocate pages for a new sequence"""
        num_blocks_needed = (num_tokens + self.block_size - 1) // self.block_size
        
        if len(self.free_blocks) < num_blocks_needed:
            raise MemoryError("No free blocks available")
        
        # Allocate blocks on-demand
        allocated_blocks = []
        for _ in range(num_blocks_needed):
            block_id = self.free_blocks.pop()
            allocated_blocks.append(block_id)
        
        self.block_tables[seq_id] = allocated_blocks
        return allocated_blocks
    
    def append_tokens(self, seq_id, new_tokens):
        """Append new tokens, allocating new blocks if needed"""
        current_blocks = self.block_tables[seq_id]
        current_tokens = len(current_blocks) * self.block_size
        
        num_new_blocks_needed = (
            current_tokens + len(new_tokens) - 1
        ) // self.block_size - len(current_blocks)
        
        if num_new_blocks_needed > 0:
            # Allocate additional blocks as needed
            for _ in range(num_new_blocks_needed):
                self.block_tables[seq_id].append(
                    self.free_blocks.pop()
                )
    
    def get_blocks_for_attention(self, seq_id):
        """Retrieve block indices for attention computation"""
        return self.block_tables[seq_id]

def paged_attention(
    query,           # [batch, num_heads, head_dim]
    key_cache,       # Physical block storage
    value_cache,     # Physical block storage
    block_tables,    # Mapping: seq -> physical blocks
    query_length,
    max_seq_length
):
    """
    Compute attention using paged KV cache
    """
    num_heads = query.size(1)
    head_dim = query.size(2)
    
    # Compute query @ key for all positions
    # But now we iterate over pages instead of full sequence
    attn_weights = torch.zeros(
        query.size(0),
        num_heads,
        query_length,
        max_seq_length,
        device=query.device
    )
    
    for seq_id in range(query.size(0)):
        block_indices = block_tables[seq_id]
        
        # Process each page
        for block_idx in block_indices:
            # Load K/V from this physical block
            k_block = key_cache[block_idx]  # [num_heads, block_size, head_dim]
            v_block = value_cache[block_idx]
            
            # Compute attention for this block
            q = query[seq_id]  # [num_heads, query_length, head_dim]
            
            # Attention computation
            attn_scores = torch.matmul(q, k_block.transpose(-2, -1))
            attn_scores = attn_scores / (head_dim ** 0.5)
            
            # Store in corresponding location
            block_start = block_idx * k_block.size(1)
            block_end = block_start + k_block.size(1)
            attn_weights[
                seq_id, :, :, block_start:block_end
            ] = attn_scores
    
    # Softmax and value multiplication
    attn_weights = F.softmax(attn_weights, dim=-1)
    
    # Final output computation (simplified)
    output = torch.matmul(attn_weights, value_cache)
    
    return output

# vLLM-style attention kernel structure
class PagedAttentionKernel:
    
    BLOCK_SIZE = 16  # Tokens per KV cache block
    
    @staticmethod
    def forward(
        query,           # [num_seqs, num_heads, head_dim]
        key,             # Pre-computed and stored in pages
        value,           # Pre-computed and stored in pages
        block_tables,    # [num_seqs, max_blocks_per_seq]
        context_lengths # [num_seqs] - actual sequence lengths
    ):
        """
        Fused attention kernel for PagedAttention
        - Combines block lookup, attention computation
        - Minimizes memory access
        - Utilizes tensor cores for acceleration
        """
        # Kernel launches thousands of threads
        # Each thread block processes one sequence
        # Shared memory stores current query block
        # Global memory access patterns optimized for cache

class KVCacheManager:
    """
    Complete KV cache memory manager
    """
    
    def __init__(self, gpu_memory: int, block_size: int = 16):
        self.block_size = block_size
        
        # Reserve memory for model weights, etc.
        # Allocate remaining for KV cache
        self.kv_cache_dtype = torch.float16
        self.kv_cache_shape = (
            2,  # K and V
            self.num_blocks,
            self.num_kv_heads,
            self.head_dim,
            self.block_size
        )
        
        # Allocate pinned CPU memory for swapping
        self.cpu_fallback = torch.empty(
            self.kv_cache_shape,
            dtype=self.kv_cache_dtype,
            pin_memory=True
        )
        
        # GPU KV cache
        self.gpu_kv_cache = torch.empty(
            self.kv_cache_shape,
            dtype=self.kv_cache_dtype,
            device='cuda'
        )
        
    def allocate_sequence(self, seq_id: int, max_length: int):
        """Allocate KV cache pages for a new sequence"""
        num_blocks = (max_length + self.block_size - 1) // self.block_size
        
        block_table = []
        for _ in range(num_blocks):
            block_id = self._allocate_block()
            block_table.append(block_id)
        
        self.sequence_tables[seq_id] = block_table
        return block_table
    
    def evict_blocks(self, num_blocks_needed: int):
        """Evict blocks from least recently used sequences"""
        # Sort sequences by last access time
        lru_order = sorted(
            self.sequence_access_time.items(),
            key=lambda x: x[1]
        )
        
        evicted_blocks = []
        for seq_id, _ in lru_order:
            if len(evicted_blocks) >= num_blocks_needed:
                break
                
            # Move to CPU if needed for later use
            self._move_to_cpu(seq_id)
            evicted_blocks.extend(
                self.sequence_tables.pop(seq_id)
            )
        
        return evicted_blocks

# Physical layout of KV cache
# [num_blocks, num_kv_heads, head_size, block_size]
# Example: FP16, 8 heads, 128 head_dim, 16 block_size
# Per block: 8 * 128 * 16 * 2 bytes = 32 KB

def handle_variable_length(
    queries: List[torch.Tensor],
    block_tables: Dict[int, List[int]],
    context_lengths: List[int]
):
    """Handle different sequence lengths efficiently"""
    
    # Sort by length for optimal GPU utilization
    sorted_indices = sorted(
        range(len(queries)),
        key=lambda i: context_lengths[i],
        reverse=True
    )
    
    # Process in sorted order for better memory coalescing
    for idx in sorted_indices:
        query = queries[idx]
        blocks = block_tables[idx]
        length = context_lengths[idx]
        
        # Compute attention for this sequence
        yield compute_paged_attention(
            query, blocks, length
        )

from vllm import LLM, SamplingParams

# Initialize vLLM engine
llm = LLM(
    model="meta-llama/Llama-2-70b-hf",
    tensor_parallel_size=4,  # Use 4 GPUs
    max_num_seqs=256,        # Maximum concurrent sequences
)

# Sampling parameters
sampling_params = SamplingParams(
    temperature=0.8,
    max_tokens=512,
)

# Generate from multiple prompts
prompts = [
    "Write a story about artificial intelligence",
    "Explain quantum computing in simple terms",
    "What are the benefits of meditation?",
]

# vLLM automatically uses PagedAttention
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)

from vllm.engine.llm_engine import LLMEngine
from vllm.engine.Args_Model import ModelArgs
from vllm.engine.Args_Sampling import SamplingParams

# Fine-tune PagedAttention behavior
engine = LLMEngine(
    model_args=ModelArgs(
        model="meta-llama/Llama-2-70b-hf",
        kv_cache_dtype="auto",  # FP8 kv cache
        block_size=16,          # Tune block size
    ),
    cache_config=dict(
        gpu_memory_utilization=0.9,
        swap_space_gb=32,       # CPU swap for overflow
    ),
    parallel_config=dict(
        tensor_parallel_size=4,
        pipeline_parallel_size=2,
    ),
)