♻️ refactor: migrate chunked prefill attention to SparsePolicy
Move all chunked prefill attention computation from attention.py to SparsePolicy.compute_chunked_attention(). This is the v4 architecture refactoring for sparse attention policies. Changes: - Add compute_chunked_attention abstract method to SparsePolicy base - Add offload_engine parameter to select_blocks for policies needing KV access during block selection - Implement compute_chunked_attention in FullAttentionPolicy with complete ring buffer pipeline logic - Simplify attention.py to delegate all chunked prefill to policy - Remove redundant _sync_load_previous_chunks and _ring_buffer_pipeline_load methods from Attention class Test: test_needle.py --enable-offload PASSED Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Zijie Tian
@@ -174,123 +174,45 @@ class Attention(nn.Module):
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"""
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Compute attention with per-layer prefill buffer for async offload.
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Optimized design:
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- Current chunk's KV is written to per-layer prefill buffer (not GPU slot)
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- Previous chunks' KV are loaded from CPU using GPU slots
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- Each layer offloads from its own buffer - no waiting required!
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Simplified design:
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- All computation logic is delegated to sparse_policy.compute_chunked_attention()
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- This method only handles async offload after computation
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For each layer:
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1. Current chunk's KV is in prefill_buffer[layer_id] (just written by model)
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2. Load previous chunks from CPU using available slots (pipeline)
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3. Compute attention against previous KV (no causal mask)
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4. Compute attention against current KV from prefill buffer (causal)
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5. Merge all results using online softmax
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6. Async offload prefill buffer to CPU (no waiting!)
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The policy handles:
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1. Loading historical blocks from CPU
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2. Computing attention against historical KV (no causal mask)
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3. Computing attention against current KV from prefill buffer (causal)
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4. Merging all results
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"""
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from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
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current_chunk_idx = context.current_chunk_idx
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torch.cuda.nvtx.range_push(f"ChunkedPrefill: L{self.layer_id} Chunk{current_chunk_idx}")
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# q shape: [total_tokens, num_heads, head_dim]
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q_batched = q.unsqueeze(0) # [1, total_tokens, heads, dim]
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num_tokens = k.shape[0]
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o_acc = None
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lse_acc = None
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kvcache_manager = context.kvcache_manager
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seq = context.chunked_seq if hasattr(context, 'chunked_seq') else None
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offload_engine = kvcache_manager.offload_engine if kvcache_manager is not None else None
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if kvcache_manager is not None and seq is not None and self.layer_id >= 0:
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# Get prefilled CPU blocks (blocks from previous chunks)
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cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
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# Get sparse policy - required for chunked prefill
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sparse_policy = kvcache_manager.sparse_policy
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if sparse_policy is None:
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raise RuntimeError("sparse_policy is required for chunked prefill")
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# Apply sparse policy if enabled
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sparse_policy = kvcache_manager.sparse_policy
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# [DEBUG] Verify execution path
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logger.debug(f"[DEBUG] Calling sparse_policy.compute_chunked_attention, "
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f"policy={sparse_policy}, layer={self.layer_id}, chunk={current_chunk_idx}")
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# === All sparse policies use select_blocks interface ===
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if cpu_block_table and sparse_policy is not None:
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num_chunks = getattr(context, 'num_chunks', current_chunk_idx + 1)
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policy_ctx = PolicyContext(
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query_chunk_idx=current_chunk_idx,
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num_query_chunks=num_chunks,
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layer_id=self.layer_id,
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query=None, # Prefill typically doesn't use query for selection
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is_prefill=True,
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block_size=kvcache_manager.block_size,
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total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
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)
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cpu_block_table = sparse_policy.select_blocks(
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cpu_block_table, policy_ctx
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)
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if cpu_block_table:
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# Get available load slots (all slots can be used since we use prefill buffer)
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load_slots = list(range(offload_engine.num_ring_slots))
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pipeline_depth = len(load_slots)
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if pipeline_depth == 0:
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# Only 1 slot total, cannot pipeline - use sync loading
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o_acc, lse_acc = self._sync_load_previous_chunks(
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q_batched, cpu_block_table, offload_engine
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)
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else:
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# Use ring buffer pipeline
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o_acc, lse_acc = self._ring_buffer_pipeline_load(
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q_batched, cpu_block_table, load_slots, offload_engine,
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current_chunk_idx
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)
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# Get compute stream for all attention operations
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compute_stream = offload_engine.compute_stream if offload_engine is not None else None
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# Compute attention against current chunk's KV from prefill buffer (with causal mask)
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needs_current_chunk_attention = True
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if needs_current_chunk_attention:
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if compute_stream is not None:
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with torch.cuda.stream(compute_stream):
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torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} CurrentChunk (causal)")
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# Get KV from per-layer prefill buffer
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k_batched, v_batched = offload_engine.get_prefill_buffer_slice(self.layer_id, num_tokens)
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current_o, current_lse = flash_attn_with_lse(
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q_batched,
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k_batched,
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v_batched,
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softmax_scale=self.scale,
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causal=True,
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)
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torch.cuda.nvtx.range_pop()
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else:
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torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} CurrentChunk (causal)")
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k_batched = k.unsqueeze(0)
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v_batched = v.unsqueeze(0)
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current_o, current_lse = flash_attn_with_lse(
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q_batched,
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k_batched,
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v_batched,
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softmax_scale=self.scale,
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causal=True,
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)
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torch.cuda.nvtx.range_pop()
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# Merge with accumulated (all on compute_stream for consistency)
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if o_acc is None:
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# No accumulated attention (no historical chunks processed)
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final_o = current_o
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else:
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# Has accumulated attention (historical chunks processed)
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if compute_stream is not None:
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with torch.cuda.stream(compute_stream):
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torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
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final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
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torch.cuda.nvtx.range_pop()
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else:
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torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
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final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
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torch.cuda.nvtx.range_pop()
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# Delegate all computation to policy (no flash_attn or merge calls here!)
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final_o = sparse_policy.compute_chunked_attention(
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q, k, v,
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self.layer_id,
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self.scale,
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offload_engine,
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kvcache_manager,
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current_chunk_idx,
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seq,
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num_tokens,
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)
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torch.cuda.nvtx.range_pop() # ChunkedPrefill
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@@ -305,181 +227,7 @@ class Attention(nn.Module):
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self.layer_id, cpu_block_id, num_tokens
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)
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# Sync default stream with compute_stream before returning
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# This ensures the result is ready for the rest of the model (layernorm, MLP)
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if compute_stream is not None:
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torch.cuda.default_stream().wait_stream(compute_stream)
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# Remove batch dimension: [1, total_tokens, heads, dim] -> [total_tokens, heads, dim]
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return final_o.squeeze(0)
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def _sync_load_previous_chunks(
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self,
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q_batched: torch.Tensor,
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cpu_block_table: list,
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offload_engine,
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):
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"""Synchronous loading fallback when pipeline_depth=0."""
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from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
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o_acc, lse_acc = None, None
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compute_stream = offload_engine.compute_stream
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for block_idx, cpu_block_id in enumerate(cpu_block_table):
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# Load to slot 0 (single slot)
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offload_engine.load_to_slot_layer(0, self.layer_id, cpu_block_id)
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offload_engine.wait_slot_layer(0)
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# IMPORTANT: Must use compute_stream to match wait_slot_layer
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with torch.cuda.stream(compute_stream):
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prev_k, prev_v = offload_engine.get_kv_for_slot(0)
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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=self.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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return o_acc, lse_acc
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def _ring_buffer_pipeline_load(
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self,
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q_batched: torch.Tensor,
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cpu_block_table: list,
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load_slots: list,
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offload_engine,
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current_chunk_idx: int = -1,
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):
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"""
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Ring buffer async pipeline loading with double buffering.
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Uses compute_done events to ensure safe buffer reuse:
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- Before loading to slot X, wait for previous compute on slot X to finish
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- Before computing on slot X, wait for load to slot X to finish
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Timeline with 2 slots (A, B):
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┌──────────────┐
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│ Load B0→A │
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└──────────────┘
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┌──────────────┐ ┌──────────────┐
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│ Load B1→B │ │ Load B2→A │ ...
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└──────────────┘ └──────────────┘
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↘ ↘
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┌──────────────┐ ┌──────────────┐
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│ Compute(A) │ │ Compute(B) │ ...
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└──────────────┘ └──────────────┘
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The load_to_slot_layer internally waits for compute_done[slot] before
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starting the transfer, ensuring no data race.
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"""
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from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
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num_blocks = len(cpu_block_table)
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if num_blocks == 0:
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return None, None
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pipeline_depth = len(load_slots)
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if pipeline_depth == 0:
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return None, None
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o_acc, lse_acc = None, None
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if pipeline_depth == 1:
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# Only 1 slot available, cannot pipeline - use synchronous mode
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# IMPORTANT: Must use compute_stream to match synchronization in
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# load_to_slot_layer (waits for compute_done) and wait_slot_layer
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slot = load_slots[0]
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compute_stream = offload_engine.compute_stream
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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, self.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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# Debug: call hooks on compute_stream (synchronized with transfer)
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if offload_engine.debug_mode:
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offload_engine._call_debug_hooks(slot, self.layer_id, cpu_block_id)
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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=self.scale,
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causal=False,
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)
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# Record compute done so next load can safely reuse this slot
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offload_engine.record_slot_compute_done(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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return o_acc, lse_acc
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# N-way pipeline: use ALL available slots for maximum overlap
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# Pipeline depth = num_slots - 1 (num_slots blocks in flight)
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num_slots = len(load_slots)
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# Phase 1: Pre-load up to num_slots blocks to fill the pipeline
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# This starts all transfers in parallel, utilizing full PCIe bandwidth
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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], self.layer_id, cpu_block_table[i])
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# Phase 2: Main loop - compute and immediately reuse slot for next transfer
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# Use dedicated compute_stream (not default stream) to enable overlap with transfers
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compute_stream = offload_engine.compute_stream
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for block_idx in range(num_blocks):
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torch.cuda.nvtx.range_push(f"PipelineBlock: L{self.layer_id} B{block_idx}")
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# Cycle through slots: slot[block_idx % num_slots]
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current_slot = load_slots[block_idx % num_slots]
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cpu_block_id = cpu_block_table[block_idx]
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# Wait for current slot's transfer to complete (on compute_stream)
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offload_engine.wait_slot_layer(current_slot)
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# Compute attention on current slot's data
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# IMPORTANT: Use dedicated compute_stream to avoid implicit sync with default stream
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with torch.cuda.stream(compute_stream):
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# Debug: call hooks on compute_stream (synchronized with transfer)
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if offload_engine.debug_mode:
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offload_engine._call_debug_hooks(current_slot, self.layer_id, cpu_block_id)
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torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} PrevBlock{block_idx}")
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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=self.scale,
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causal=False,
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)
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torch.cuda.nvtx.range_pop()
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# Record compute done - this allows the next transfer to safely overwrite this slot
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offload_engine.record_slot_compute_done(current_slot)
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# Immediately start loading the NEXT block into this slot (if more blocks remain)
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# Key insight: reuse current_slot immediately after compute is done!
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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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offload_engine.load_to_slot_layer(current_slot, self.layer_id, cpu_block_table[next_block_idx])
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# Merge with accumulated (also on compute_stream for consistency)
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with torch.cuda.stream(compute_stream):
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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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torch.cuda.nvtx.range_pop() # PipelineBlock
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return o_acc, lse_acc
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return final_o
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def _chunked_decode_attention(
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self,
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@@ -524,6 +272,8 @@ class Attention(nn.Module):
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if last_block_valid_tokens == 0 and total_prefill_tokens > 0:
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last_block_valid_tokens = block_size # Last block was exactly full
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offload_engine = kvcache_manager.offload_engine
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# Apply sparse policy if enabled (Quest does Top-K selection for decode)
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sparse_policy = kvcache_manager.sparse_policy
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if sparse_policy is not None:
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@@ -537,11 +287,9 @@ class Attention(nn.Module):
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total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
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)
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cpu_block_table = sparse_policy.select_blocks(
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cpu_block_table, policy_ctx
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cpu_block_table, offload_engine, policy_ctx
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)
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offload_engine = kvcache_manager.offload_engine
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# Use cross-layer pipeline if active (initialized in model_runner)
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if offload_engine.is_pipeline_active():
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o_acc, lse_acc = self._decode_with_layer_pipeline(
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