Real-time Inference Optimization: Lightning-fast LLM Responses

Flash Attention Optimization

import torch
import triton
import triton.language as tl

@triton.jit
def flash_attention_kernel(
    Q, K, V, Out,
    stride_qz, stride_qh, stride_qm, stride_qk,
    stride_kz, stride_kh, stride_kn, stride_kk,
    stride_vz, stride_vh, stride_vn, stride_vk,
    stride_oz, stride_oh, stride_om, stride_ok,
    Z, H, M, N, K,
    BLOCK_M: tl.constexpr, 
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    """Flash Attention optimization kernel"""
    # 1. Block-wise attention computation
    # 2. Fused softmax
    # 3. Reduce HBM access
    
    start_m = tl.program_id(0)
    off_hz = tl.program_id(1)
    
    # Initialize local variables
    m_prev = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
    l_prev = tl.zeros([BLOCK_M], dtype=tl.float32)
    acc = tl.zeros([BLOCK_M, BLOCK_K], dtype=tl.float32)
    
    # Iterate over K,V blocks
    for start_n in range(0, N, BLOCK_N):
        # Load Q, K, V tiles
        q = tl.load(Q + ...)
        k = tl.load(K + ...)
        v = tl.load(V + ...)
        
        # Compute QK^T
        qk = tl.dot(q, tl.trans(k))
        qk = qk * (1.0 / tl.sqrt(K))
        
        # Online softmax update
        m_curr = tl.maximum(m_prev, tl.max(qk, 1))
        l_curr = tl.exp(m_prev - m_curr) * l_prev + tl.sum(tl.exp(qk - m_curr[:, None]), 1)
        
        # Update accumulator
        acc = acc * tl.exp(m_prev - m_curr)[:, None] + tl.dot(tl.exp(qk - m_curr[:, None]), v)
        
        m_prev = m_curr
        l_prev = l_curr
    
    # Output result
    acc = acc / l_prev[:, None]
    tl.store(Out + ..., acc)

class OptimizedLLMInference:
    """Optimized LLM inference class"""
    
    def __init__(self, model_path, optimization_config):
        self.model = self.load_optimized_model(model_path)
        self.config = optimization_config
        
        # KV cache manager
        self.kv_cache = KVCacheManager(
            max_batch_size=optimization_config['max_batch_size'],
            max_seq_length=optimization_config['max_seq_length']
        )
        
        # Dynamic batching
        self.batch_scheduler = DynamicBatchScheduler(
            max_batch_size=optimization_config['max_batch_size'],
            max_wait_time=optimization_config['max_wait_time']
        )
        
    def continuous_batching(self, requests):
        """Continuous batching optimization"""
        batches = []
        current_batch = []
        
        for req in requests:
            # Decide whether the request can be added to current batch
            if self.can_add_to_batch(current_batch, req):
                current_batch.append(req)
            else:
                if current_batch:
                    batches.append(current_batch)
                current_batch = [req]
        
        if current_batch:
            batches.append(current_batch)
            
        return batches
    
    def paged_attention(self, query, key_cache, value_cache):
        """Paged attention mechanism"""
        # Store KV cache in pages
        page_size = 16
        num_pages = (key_cache.size(1) + page_size - 1) // page_size
        
        # Compute attention per page
        attn_output = torch.zeros_like(query)
        
        for page_idx in range(num_pages):
            start_idx = page_idx * page_size
            end_idx = min((page_idx + 1) * page_size, key_cache.size(1))
            
            # Attention for current page
            page_attn = self.compute_attention(
                query,
                key_cache[:, start_idx:end_idx],
                value_cache[:, start_idx:end_idx]
            )
            
            attn_output += page_attn
            
        return attn_output