FlashAttention-3: Next-Generation Transformer Optimization

# Simplified FlashAttention V1 algorithm
def flash_attention_v1(Q, K, V, softmax_scale):
    """
    Standard FlashAttention with tiling
    """
    BATCH, N_CTX, H, D = Q.shape
    D_HEAD = D  # head dimension
    
    # Initialize output and statistics
    O = torch.zeros_like(Q)
    l = torch.zeros((BATCH, H, N_CTX), device=Q.device)
    m = torch.full((BATCH, H, N_CTX), -float('inf'), device=Q.device)
    
    # Process in tiles (blocks along sequence dimension)
    for j in range(0, N_CTX, BLOCK_M):
        # Load Q block
        q_block = Q[:, j:j+BLOCK_M]
        
        for i in range(0, N_CTX, BLOCK_N):
            # Load K, V blocks
            k_block = K[:, i:i+BLOCK_N]
            v_block = V[:, i:i+BLOCK_N]
            
            # Compute attention for this tile
            qk = torch.matmul(q_block, k_block.transpose(-2, -1)) * softmax_scale
            
            # Online softmax computation
            m_new = torch.maximum(m, qk.max(-1).values)
            l_new = torch.exp(m - m_new.unsqueeze(-1)) @ torch.ones_like(qk[0]) + \
                    torch.exp(qk - m_new.unsqueeze(-1)).sum(-1)
            
            # Scale and accumulate output
            p = torch.exp(qk - m_new.unsqueeze(-1))
            O_block = (p @ v_block).type_as(O)
            
            O[:, j:j+BLOCK_M] = O[:, j:j+BLOCK_M] * \
                torch.exp(m[:, j:j+BLOCK_M] - m_new).unsqueeze(-1) + O_block
            l[:, j:j+BLOCK_M] = l[:, j:j+BLOCK_M] * \
                torch.exp(m[:, j:j+BLOCK_M] - m_new) + l_new
            
            m = m_new
    
    O = O / l.unsqueeze(-1)
    return O

# Conceptual warp specialization in FlashAttention-3
class WarpSpecializedAttention:
    """
    FlashAttention-3 uses different warp groups for different tasks
    """
    
    WARP_GROUPS = {
        'LOAD_KV': 0,      # Warps 0-1: Load K, V from global to shared
        'COMPUTE_QK': 2,    # Warps 2-3: Compute Q @ K^T
        'SOFTMAX': 4,       # Warp 4: Softmax computation
        'LOAD_V_OUTPUT': 5, # Warp 5: Load V, compute final output
        'STORE_RESULT': 7   # Warp 7: Store to global memory
    }
    
    @cuda.jit
    def forward_kernel(
        Q, K, V, O,
        stride_q, stride_k, stride_v, stride_o,
        B, H, N, D
    ):
        # Get cluster and block identifiers
        cluster_id = cuda.cluster.group_index()
        block_id = cuda.block_thread_index()
        
        # Each warp group operates independently but synchronizes
        # Warp groups can overlap their work
        warp_id = cuda.warp_id()
        
        if warp_id in [0, 1]:
            # TMA async load for K, V
            tma_async_load_kv(K, V, cluster_id)
        elif warp_id in [2, 3]:
            # Compute QK^T while K,V loads happen
            qk_result = compute_qk_matmul(Q, K)
        # ... other warp groups

def interleaved_attention_fused(
    Q, K, V, O,
    BLOCK_M=128, BLOCK_N=64
):
    """
    Interleaved matmul and softmax computation
    Key innovation: begin softmax before full QK^T completes
    """
    
    # Initialize online softmax statistics
    m_i = torch.full(..., -float('inf'))  # max so far
    l_i = torch.zeros(...)                # sum of exp
    
    for i in range(0, N, BLOCK_N):
        # Start loading next K,V block
        k_next = K[:, i:i+BLOCK_N]
        v_next = V[:, i:i+BLOCK_N]
        
        # Compute partial QK^T for current Q block
        qk_partial = Q[:, :BLOCK_M] @ k_next.T
        
        # BEGIN SOFTMAX BEFORE FULL BLOCK COMPLETES
        # Compute partial softmax incrementally
        m_new = torch.maximum(m_i[:, :, :BLOCK_M], 
                             qk_partial.max(-1).values)
        
        # Scale previous output
        scale_prev = torch.exp(m_i[:, :, :BLOCK_M] - m_new.unsqueeze(-1))
        
        # Scale current exp values
        scale_curr = torch.exp(qk_partial - m_new.unsqueeze(-1))
        
        # Accumulate into output
        p_curr = scale_curr @ v_next
        O_new = O * scale_prev + p_curr
        
        # Update statistics
        l_i_new = l_i * scale_prev.squeeze(-1) + scale_curr.sum(-1)
        
        # Update for next iteration
        O[:, :BLOCK_M] = O_new
        m_i[:, :, :BLOCK_M] = m_new
        l_i[:, :BLOCK_M] = l_i_new
    
    # Final scaling
    O = O / l_i.unsqueeze(-1)

# FP8 format options in Hopper
class FP8Formats:
    """
    Hopper supports multiple FP8 formats
    """
    E4M3 = "e4m3"  # 4 exponent, 3 mantissa - more precision
    E5M2 = "e5m2"  # 5 exponent, 2 mantissa - wider range

def flash_attention_fp8(Q, K, V):
    """
    FlashAttention-3 with FP8 computation
    Uses E4M3 for most operations, E5M2 for accumulation
    """
    # Convert to FP8
    Q_fp8 = Q.to(dtype=torch.float8_e4m3fn)
    K_fp8 = K.to(dtype=torch.float8_e4m3fn)
    V_fp8 = V.to(dtype=torch.float8_e4m3fn)
    
    # FP8 matmul for QK^T
    # Uses Tensor Cores at 4x throughput
    QK_fp8 = matmul_fp8(Q_fp8, K_fp8.transpose(-2, -1))
    
    # Convert to BF16 for softmax (more precision for softmax)
    QK_bf16 = QK_fp8.to(torch.bfloat16)
    
    # Softmax in BF16
    S = F.softmax(QK_bf16 * scale, dim=-1)
    
    # Convert back to FP8 for final matmul
    S_fp8 = S.to(torch.float8_e4m3fn)
    
    # FP8 matmul for attention scores times V
    O_fp8 = matmul_fp8(S_fp8, V_fp8)
    
    # Final output in BF16
    return O_fp8.to(torch.bfloat16)

# Memory access comparison
memory_access = {
    'Standard Attention': 'O(N² × d) reads/writes',
    'FlashAttention': 'O(N × d) reads, O(N × d) writes',
    'FlashAttention-3': 'O(N × d) reads, O(N × d) writes + async prefetch'
}

# Install with pip
# pip install flash-attn --prefer-binary

import torch
from flash_attn import flash_attn_func
from flash_attn.bert_padding import index_first_axis

def example_usage():
    # Input tensors: [batch, seqlen, nheads, headdim]
    q = torch.randn(2, 512, 16, 64, dtype=torch.float16, device='cuda')
    k = torch.randn(2, 512, 16, 64, dtype=torch.float16, device='cuda')
    v = torch.randn(2, 512, 16, 64, dtype=torch.float16, device='cuda')
    
    # FlashAttention-3 automatically uses optimizations
    output = flash_attn_func(
        q, k, v,
        softmax_scale=None,  #自动计算
        causal=True,         #下三角mask
    )
    
    return output

# Check if FP8 is available
def check_fp8_support():
    import subprocess
    result = subprocess.run(
        ['nvidia-smi', '--query-gpu=name,compute_9', '--format=csv,noheader'],
        capture_output=True, text=True
    )
    print(result.stdout)

# Simplified custom kernel demonstrating FA3 principles
import torch.cuda.nvtx as nvtx

@torch.cuda.jit
def flash_attention_fused_kernel(
    Q, K, V, O,
    softmax_scale,
    cu_seqlens_q,
    cu_seqlens_kv
):
    """
    Simplified FA3-style fused kernel
    """
    # Shared memory sizes
    Q_SZ = BLOCK_M * D
    KV_SZ = BLOCK_N * D * 2
    
    # Get thread and block indices
    batch_id = cuda.blockIdx.x
    head_id = cuda.blockIdx.y
    chunk_id = cuda.blockIdx.z
    
    # Warp-specialized operations
    # ... (complex synchronization logic)
    
    # Interleaved computation
    # 1. Load K,V asynchronously via TMA
    # 2. Compute partial QK while loading
    # 3. Partial softmax
    # 4. Continue with more K,V

def flash_decoding_with_fa3(
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    block_tables: torch.Tensor,
    context_lengths: torch.Tensor
):
    """
    Combine FlashAttention-3 with Flash-Decoding for long contexts
    """
    batch_size, num_heads, num_kv_heads = query.shape[0], query.shape[1], key_cache.shape[2]
    head_dim = query.shape[2]
    
    # Split query into chunks for parallel processing
    num_query_chunks = (num_heads + WARP_GROUPS - 1) // WARP_GROUPS
    
    # Each warp group processes different query chunk
    # Using FA3 optimizations for each chunk
    outputs = []
    for chunk_idx in range(num_query_chunks):
        q_chunk = query[:, chunk_idx*WARP_GROUPS:(chunk_idx+1)*WARP_GROUPS]
        
        # FA3 computation
        o_chunk = flash_attention_func(q_chunk, key_cache, value_cache)
        outputs.append(o_chunk)
    
    # Merge chunks
    return torch.cat(outputs, dim=1)

def fa3_training_step(model, batch):
    """
    Using FP8 with FlashAttention-3 for training
    """
    # Enable FP8
    with torch.cuda.amp.autocast(dtype=torch.float8_e4m3fn):
        # Forward pass with FA3
        outputs = []
        for layer in model.layers:
            q, k, v = layer.compute_qkv(hidden_states)
            
            # FA3 handles FP8 internally
            attn_output = flash_attn_func(q, k, v, causal=True)
            
            # Continue forward
            hidden_states = layer(attn_output)
    
    # Backward pass also uses FA3 (with recomputation)
    loss.backward()

# Optimal settings for different scenarios
configs = {
    'short_context': {
        'block_size': 128,
        'softmax_scale': None,  # auto
    },
    'long_context': {
        'block_size': 64,       # smaller for more granularity
        'return_softmax': False,
    },
    'memory_efficient': {
        'dropout_p': 0.0,       # No dropout for memory
        'alibi_slopes': None,   # No ALiBi if not needed
    }
}