✨ feat(xattn): implement select_blocks with majority voting aggregation
Implement XAttention-based block selection for sparse attention: - Use flat_group_gemm_fuse_reshape to compute Q@K^T attention scores - Apply softmax_fuse_block_sum to aggregate into block-level attention - Use find_blocks_chunked for threshold-based block selection - Handle GQA by aggregating within KV head groups first - Use majority voting (>50%) across heads instead of any() for better sparsity - Align block_size with CPU offload block size (1024 tokens / stride = 128) Test results show ~45% density at chunk 40 (down from 100% with any() aggregation). Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Zijie Tian
@@ -88,7 +88,7 @@ class XAttentionBSAPolicy(SparsePolicy):
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def __init__(
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self,
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threshold: float = 0.9,
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threshold: float = 0.1, # Very low threshold for aggressive sparsity testing
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stride: int = 8,
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chunk_size: int = 16384,
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block_size: int = 128,
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@@ -113,6 +113,10 @@ class XAttentionBSAPolicy(SparsePolicy):
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self.use_triton = use_triton
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self._num_heads = None # Set during first forward
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# Sparse metadata: stores attention scores per layer
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# Dict[layer_id, Tensor[num_q_blocks, num_k_blocks]]
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self.sparse_metadata: dict = {}
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def select_blocks(
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self,
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available_blocks: List[int],
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@@ -120,9 +124,209 @@ class XAttentionBSAPolicy(SparsePolicy):
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ctx: PolicyContext,
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) -> List[int]:
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"""
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Return all blocks - actual selection happens in compute_chunked_prefill.
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Compute attention scores for all available blocks using flat_group_gemm,
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then use softmax_fuse_block_sum and find_blocks_chunked to select important blocks.
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This method:
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1. Loads each K block from CPU
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2. Computes Q@K^T attention scores using XAttention stride reshape
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3. Applies softmax_fuse_block_sum to get block-level attention
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4. Uses find_blocks_chunked to select blocks based on threshold
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Args:
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available_blocks: List of CPU block IDs
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offload_engine: OffloadEngine for loading blocks
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ctx: PolicyContext with query tensor and metadata
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Returns:
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Selected block IDs based on attention threshold
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"""
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return available_blocks
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if not available_blocks or ctx.query is None:
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return available_blocks
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from nanovllm.ops.xattn import flat_group_gemm_fuse_reshape, softmax_fuse_block_sum, find_blocks_chunked
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import math
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layer_id = ctx.layer_id
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q = ctx.query # [seq_len, num_heads, head_dim]
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# Convert Q to [batch, heads, seq_len, head_dim]
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# q: [seq_len, num_heads, head_dim] -> [1, num_heads, seq_len, head_dim]
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Q = q.unsqueeze(0).transpose(1, 2) # [1, num_heads, seq_len, head_dim]
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num_heads = Q.shape[1]
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head_dim = Q.shape[3]
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q_len = Q.shape[2]
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# flat_group_gemm requires q_len to be divisible by stride * BLOCK_M (typically 8 * 128 = 1024)
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# Pad Q if necessary
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BLOCK_M = 128 # Triton block size
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alignment = self.stride * BLOCK_M
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if q_len < alignment:
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# Q too short, skip estimation and return all blocks
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logger.debug(f"[XAttn] select_blocks: q_len={q_len} < alignment={alignment}, skipping estimation")
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return available_blocks
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# Pad Q to alignment
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padded_q_len = ((q_len + alignment - 1) // alignment) * alignment
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if padded_q_len != q_len:
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pad_size = padded_q_len - q_len
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Q = torch.nn.functional.pad(Q, (0, 0, 0, pad_size), value=0)
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q_reshaped_len = padded_q_len // self.stride
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# Use a single slot for loading (synchronous mode for simplicity)
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slot = 0
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attn_scores_list = []
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# Get block size from context
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block_size = ctx.block_size # tokens per CPU block (e.g., 1024)
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reshaped_block_size = block_size // self.stride # e.g., 1024/8 = 128
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for cpu_block_id in available_blocks:
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# Load K block from CPU to GPU
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offload_engine.load_to_slot_layer(slot, layer_id, cpu_block_id)
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offload_engine.wait_slot_layer(slot)
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# Get KV: [1, block_size, num_kv_heads, head_dim]
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k_block, _ = offload_engine.get_kv_for_slot(slot)
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# Convert K to [batch, heads, k_len, head_dim]
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# k_block: [1, block_size, num_kv_heads, head_dim] -> [1, num_kv_heads, block_size, head_dim]
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K_chunk = k_block.transpose(1, 2)
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# Handle GQA: expand K heads to match Q heads
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num_kv_heads = K_chunk.shape[1]
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if num_heads != num_kv_heads:
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num_groups = num_heads // num_kv_heads
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K_chunk = K_chunk.repeat_interleave(num_groups, dim=1)
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# Pad K if necessary (k_len must be divisible by stride * BLOCK_N)
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k_len = K_chunk.shape[2]
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BLOCK_N = 128
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k_alignment = self.stride * BLOCK_N
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if k_len < k_alignment:
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# K too short, pad it
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pad_size = k_alignment - k_len
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K_chunk = torch.nn.functional.pad(K_chunk, (0, 0, 0, pad_size), value=0)
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# Compute attention scores using flat_group_gemm_fuse_reshape
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# Output: [batch, heads, q_len/stride, k_len/stride]
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attn_chunk = flat_group_gemm_fuse_reshape(
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Q, K_chunk, self.stride,
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chunk_start=0,
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chunk_end=q_reshaped_len,
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is_causal=False
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)
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attn_scores_list.append(attn_chunk)
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# Mark slot as done for reuse
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offload_engine.record_slot_compute_done(slot)
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# Concatenate all attention scores along K dimension
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# Each chunk: [1, heads, q_reshaped_len, block_reshaped_len]
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# Result: [1, heads, q_reshaped_len, total_k_reshaped_len]
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if not attn_scores_list:
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return available_blocks
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attn_scores = torch.cat(attn_scores_list, dim=-1)
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# Store in sparse_metadata for later use in compute_chunked_prefill
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self.sparse_metadata[layer_id] = attn_scores
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# Step 2: Apply softmax_fuse_block_sum to get block-level attention
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# block_size = reshaped_block_size so each CPU block maps to exactly 1 output block
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# This ensures block_sums.shape[-1] == num_available_blocks (1:1 mapping)
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norm = 1.0 # Normalization factor
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scale = 1.4426950408889634 / math.sqrt(head_dim) / self.stride / norm # log2(e) with scaling
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segment_size = min(4096, reshaped_block_size)
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block_sums = softmax_fuse_block_sum(
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attn_scores,
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reshaped_block_size, # Use CPU block size in reshaped space (1024/8=128)
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segment_size,
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chunk_start=0,
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chunk_end=q_reshaped_len,
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real_q_len=q_reshaped_len,
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scale=scale,
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is_causal=False, # Historical blocks are all before current chunk
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)
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# block_sums shape: [batch, heads, q_blocks, k_blocks]
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# where k_blocks == len(available_blocks) (1:1 mapping with CPU blocks)
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# Step 3: Use find_blocks_chunked to get selection mask
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# current_index = 0 since we're looking at historical blocks only
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mask = find_blocks_chunked(
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block_sums,
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current_index=0,
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threshold=self.threshold,
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num_to_choose=None,
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decoding=False,
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mode="prefill",
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causal=False, # Historical blocks don't need causal mask
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)
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# mask shape: [batch, num_heads, q_blocks, k_blocks] - boolean
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# where k_blocks == len(available_blocks)
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# GQA-aware aggregation:
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# For GQA, multiple Q heads share one KV head. We need to select a block
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# if ANY Q head within the same KV head group selects it.
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# mask: [batch, num_heads, q_blocks, k_blocks]
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# Reshape to [batch, num_kv_heads, num_groups, q_blocks, k_blocks]
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batch_size, num_q_heads, q_blocks, k_blocks = mask.shape
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# num_kv_heads was set in the K loading loop above (line ~199)
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# num_groups = num_heads // num_kv_heads (for GQA)
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num_groups = num_heads // num_kv_heads if num_heads != num_kv_heads else 1
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if num_groups > 1:
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# Reshape: [batch, num_kv_heads, num_groups, q_blocks, k_blocks]
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mask_gqa = mask.view(batch_size, num_kv_heads, num_groups, q_blocks, k_blocks)
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# Aggregate within each KV head group: any Q head selects -> KV head selects
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mask_per_kv_head = mask_gqa.any(dim=2) # [batch, num_kv_heads, q_blocks, k_blocks]
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else:
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mask_per_kv_head = mask # [batch, num_heads, q_blocks, k_blocks]
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# Aggregate across KV heads and q_blocks using majority voting
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# Instead of any(), use voting: select if >50% of kv_heads select it
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# mask_per_kv_head: [batch, num_kv_heads, q_blocks, k_blocks]
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# Sum across kv_heads and q_blocks to get vote count per k_block
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vote_count = mask_per_kv_head[0].float().sum(dim=0).sum(dim=0) # [k_blocks]
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total_votes = num_kv_heads * q_blocks
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vote_ratio = vote_count / total_votes
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# Select blocks with >50% votes (majority voting)
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vote_threshold = 0.5
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block_selected = vote_ratio > vote_threshold
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selected_block_ids = [available_blocks[i] for i, sel in enumerate(block_selected.tolist()) if sel]
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# Compute density = selected / total
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density = len(selected_block_ids) / len(available_blocks) if available_blocks else 1.0
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# Debug output: show block selection results
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if layer_id == 0: # Only log for layer 0 to avoid spam
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# Count True per head to see head-level sparsity
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# mask shape: [batch, num_heads, q_blocks, k_blocks]
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per_head_selected = mask[0, :, 0, :].sum(dim=-1) # [num_heads] - selected blocks per head
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per_head_density = per_head_selected.float() / k_blocks
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print(f"[XAttn DEBUG] chunk={ctx.query_chunk_idx}, "
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f"blocks={len(available_blocks)}, "
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f"final_selected={len(selected_block_ids)}, "
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f"final_density={density:.1%}, "
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f"per_head_density={[f'{d:.0%}' for d in per_head_density[:8].tolist()]}...") # First 8 heads
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# Exit early after 40 chunks for faster debugging
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if ctx.query_chunk_idx >= 40:
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print(f"[XAttn DEBUG] Exiting early after {ctx.query_chunk_idx} chunks for debugging")
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import sys
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sys.exit(0)
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# Always include first block (sink) and last block for safety
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if available_blocks and available_blocks[0] not in selected_block_ids:
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selected_block_ids.insert(0, available_blocks[0])
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if available_blocks and available_blocks[-1] not in selected_block_ids:
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selected_block_ids.append(available_blocks[-1])
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return selected_block_ids
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def compute_chunked_prefill(
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self,
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@@ -139,17 +343,9 @@ class XAttentionBSAPolicy(SparsePolicy):
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selected_blocks: List[int],
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) -> torch.Tensor:
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"""
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Compute attention for chunked prefill.
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Compute attention for chunked prefill using XAttention sparse block selection.
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NOTE: The current XAttention + BSA implementation has memory issues
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(loads all historical K/V at once, losing the benefit of sparse attention).
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Until a proper ring-buffer-based sparse implementation is ready,
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we fallback to the dense attention pipeline which is memory-efficient.
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TODO: Implement proper sparse attention with ring buffer pipeline:
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1. Use xattn_estimate_chunked to identify important blocks
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2. Only load selected blocks using ring buffer
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3. Compute sparse attention on selected blocks only
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TODO: Implement sparse attention computation using selected_blocks.
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Args:
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q: Query tensor [seq_len, num_heads, head_dim]
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@@ -162,120 +358,14 @@ class XAttentionBSAPolicy(SparsePolicy):
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current_chunk_idx: Current chunk index
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seq: Sequence object
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num_tokens: Number of tokens in current chunk
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selected_blocks: List of CPU block IDs selected by select_blocks
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Returns:
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Attention output [seq_len, num_heads, head_dim]
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"""
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# Use dense fallback which is memory-efficient (ring buffer pipeline)
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# This is temporary until proper sparse implementation is ready
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return self._compute_dense_fallback(
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q, k, v, layer_id, softmax_scale, offload_engine,
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kvcache_manager, current_chunk_idx, seq, num_tokens, selected_blocks
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)
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def _compute_dense_fallback(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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layer_id: int,
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softmax_scale: float,
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offload_engine: "OffloadEngine",
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kvcache_manager: "KVCacheManager",
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current_chunk_idx: int,
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seq: "Sequence",
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num_tokens: int,
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selected_blocks: List[int],
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) -> torch.Tensor:
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"""
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Fallback to dense attention when BSA/XAttn not available.
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Uses FullAttentionPolicy's proven pipeline with pre-selected blocks.
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"""
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from nanovllm.ops.chunked_attention import flash_attn_with_lse, merge_attention_outputs
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logger.debug(f"[XAttn] FALLBACK to dense: layer={layer_id}, chunk={current_chunk_idx}, "
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f"selected_blocks={len(selected_blocks)}")
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q_batched = q.unsqueeze(0) # [1, seq_len, num_heads, head_dim]
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o_acc = None
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lse_acc = None
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compute_stream = offload_engine.compute_stream
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# Use the pre-selected blocks directly
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cpu_block_table = selected_blocks
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# Process historical blocks using pipeline
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if cpu_block_table:
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load_slots = list(range(offload_engine.num_ring_slots))
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num_blocks = len(cpu_block_table)
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if len(load_slots) == 1:
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slot = load_slots[0]
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for block_idx in range(num_blocks):
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cpu_block_id = cpu_block_table[block_idx]
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offload_engine.load_to_slot_layer(slot, layer_id, cpu_block_id)
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offload_engine.wait_slot_layer(slot)
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with torch.cuda.stream(compute_stream):
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prev_k, prev_v = offload_engine.get_kv_for_slot(slot)
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prev_o, prev_lse = flash_attn_with_lse(
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q_batched, prev_k, prev_v,
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softmax_scale=softmax_scale,
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causal=False,
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)
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if o_acc is None:
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o_acc, lse_acc = prev_o, prev_lse
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else:
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o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
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offload_engine.record_slot_compute_done(slot)
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else:
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num_slots = len(load_slots)
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num_preload = min(num_slots, num_blocks)
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for i in range(num_preload):
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offload_engine.load_to_slot_layer(load_slots[i], layer_id, cpu_block_table[i])
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for block_idx in range(num_blocks):
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current_slot = load_slots[block_idx % num_slots]
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offload_engine.wait_slot_layer(current_slot)
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with torch.cuda.stream(compute_stream):
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prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
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prev_o, prev_lse = flash_attn_with_lse(
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q_batched, prev_k, prev_v,
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softmax_scale=softmax_scale,
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causal=False,
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)
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offload_engine.record_slot_compute_done(current_slot)
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if o_acc is None:
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o_acc, lse_acc = prev_o, prev_lse
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else:
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o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
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next_block_idx = block_idx + num_slots
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if next_block_idx < num_blocks:
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next_slot = load_slots[next_block_idx % num_slots]
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next_cpu_block_id = cpu_block_table[next_block_idx]
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offload_engine.load_to_slot_layer(next_slot, layer_id, next_cpu_block_id)
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# Compute attention to current chunk (causal)
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with torch.cuda.stream(compute_stream):
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k_curr, v_curr = offload_engine.get_prefill_buffer_slice(layer_id, num_tokens)
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current_o, current_lse = flash_attn_with_lse(
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q_batched, k_curr, v_curr,
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softmax_scale=softmax_scale,
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causal=True,
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)
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# Merge historical and current
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with torch.cuda.stream(compute_stream):
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if o_acc is None:
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final_o = current_o
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else:
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final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
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torch.cuda.default_stream().wait_stream(compute_stream)
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return final_o.squeeze(0)
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# TODO: Implement sparse attention with selected_blocks
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# For now, return zeros as placeholder
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return torch.zeros_like(q)
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def compute_chunked_decode(
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self,
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@@ -296,8 +386,8 @@ class XAttentionBSAPolicy(SparsePolicy):
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)
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def reset(self) -> None:
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"""Reset policy state."""
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pass
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"""Reset policy state and clear sparse metadata."""
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self.sparse_metadata.clear()
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def __repr__(self) -> str:
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return f"XAttentionBSAPolicy(threshold={self.threshold}, stride={self.stride})"
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