nano-vllm/docs/sparse_policy_architecture.md

SparsePolicy Architecture Guide

This document describes the SparsePolicy abstraction for chunked attention computation in CPU offload mode.

Overview

SparsePolicy is an abstract base class that defines how attention is computed during chunked prefill and decode phases. All attention computation logic is delegated to the policy, allowing different sparse attention strategies to be implemented without modifying the core attention layer.

attention.py                     SparsePolicy
    |                                 |
    | _chunked_prefill_attention      |
    | ────────────────────────────>   | compute_chunked_prefill()
    |                                 |
    | _chunked_decode_attention       |
    | ────────────────────────────>   | compute_chunked_decode()
    |                                 |

Key Design Principles

1. Delegation Pattern: attention.py only validates and delegates; all computation is in the policy
2. No Direct Imports: attention.py does not import flash_attn_with_lse or merge_attention_outputs
3. Pipeline Encapsulation: Ring buffer and cross-layer pipelines are internal to the policy
4. Phase Support Flags: Policies declare which phases they support via supports_prefill and supports_decode

---

SparsePolicy Base Class

File: nanovllm/kvcache/sparse/policy.py

Class Attributes

Attribute	Type	Description
supports_prefill	bool	Whether policy supports prefill phase
supports_decode	bool	Whether policy supports decode phase

Abstract Methods

@abstractmethod
def select_blocks(
    self,
    available_blocks: List[int],
    offload_engine: "OffloadEngine",
    ctx: PolicyContext,
) -> List[int]:
    """Select which KV blocks to load for the current query chunk."""
    pass

@abstractmethod
def compute_chunked_prefill(
    self,
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    layer_id: int,
    softmax_scale: float,
    offload_engine: "OffloadEngine",
    kvcache_manager: "KVCacheManager",
    current_chunk_idx: int,
    seq: "Sequence",
    num_tokens: int,
) -> torch.Tensor:
    """Compute chunked prefill attention (complete flow)."""
    pass

@abstractmethod
def compute_chunked_decode(
    self,
    q: torch.Tensor,
    layer_id: int,
    softmax_scale: float,
    offload_engine: "OffloadEngine",
    kvcache_manager: "KVCacheManager",
    seq: "Sequence",
) -> torch.Tensor:
    """Compute chunked decode attention (complete flow)."""
    pass

Hook Methods

Method	When Called	Purpose
initialize()	After KV cache allocation	Initialize policy resources (e.g., metadata)
on_prefill_offload()	Before GPU→CPU copy during prefill	Collect block metadata
on_decode_offload()	Before GPU→CPU copy during decode	Update block metadata
reset()	New sequence / clear state	Reset policy state

---

FullAttentionPolicy

File: nanovllm/kvcache/sparse/full_policy.py

The default policy that loads all blocks (no sparsity). Serves as the baseline implementation.

Flags

supports_prefill = True
supports_decode = True

Prefill Flow (compute_chunked_prefill)

1. Get historical blocks from kvcache_manager
   └── cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)

2. Apply select_blocks (returns all for FullPolicy)
   └── cpu_block_table = self.select_blocks(cpu_block_table, offload_engine, ctx)

3. Load and compute historical blocks via ring buffer
   └── For each block:
       a. load_to_slot_layer(slot, layer_id, cpu_block_id)
       b. wait_slot_layer(slot)
       c. prev_k, prev_v = get_kv_for_slot(slot)
       d. flash_attn_with_lse(q, prev_k, prev_v, causal=False)
       e. merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)

4. Compute current chunk attention (causal)
   └── k_curr, v_curr = offload_engine.get_prefill_buffer_slice(layer_id, num_tokens)
   └── flash_attn_with_lse(q, k_curr, v_curr, causal=True)

5. Merge historical and current attention
   └── merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)

Decode Flow (compute_chunked_decode)

1. Get prefilled CPU blocks
   └── cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)

2. Calculate last block valid tokens
   └── total_prefill_tokens = kvcache_manager.get_prefill_len(seq)
   └── last_block_valid_tokens = total_prefill_tokens % block_size

3. Apply select_blocks for block filtering
   └── cpu_block_table = self.select_blocks(cpu_block_table, offload_engine, ctx)

4. Load prefilled blocks via ring buffer pipeline
   └── _decode_ring_buffer_pipeline()

5. Read accumulated decode tokens from decode buffer
   └── decode_k = offload_engine.decode_k_buffer[layer_id, start:end]
   └── decode_v = offload_engine.decode_v_buffer[layer_id, start:end]
   └── flash_attn_with_lse(q, decode_k, decode_v, causal=False)

6. Merge all results
   └── merge_attention_outputs(o_acc, lse_acc, decode_o, decode_lse)

---

Ring Buffer Pipeline

The ring buffer pipeline (_decode_ring_buffer_pipeline) loads blocks one by one using GPU ring buffer slots. This approach is memory-efficient and works well for both short and long sequences.

Slot[0]: Block A ──> Compute ──> Block C ──> Compute
Slot[1]: Block B ──> Compute ──> Block D ──> Compute

Advantages:

• Memory efficient (only needs a few GPU slots)
• Fine-grained overlap between H2D transfer and compute
• Works well for long sequences

Flow:

# Phase 1: Pre-load up to num_slots blocks
for i in range(num_preload):
    offload_engine.load_to_slot_layer(load_slots[i], layer_id, cpu_block_table[i])

# Phase 2: Process blocks with pipeline
for block_idx in range(num_blocks):
    current_slot = load_slots[block_idx % num_slots]

    # Wait for transfer
    offload_engine.wait_slot_layer(current_slot)

    # Compute attention
    prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
    prev_o, prev_lse = flash_attn_with_lse(q, prev_k, prev_v, causal=False)
    offload_engine.record_slot_compute_done(current_slot)

    # Pipeline: start loading next block
    if next_block_idx < num_blocks:
        offload_engine.load_to_slot_layer(current_slot, layer_id, cpu_block_table[next_block_idx])

    # Merge results
    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)

---

Code Conventions

Unsupported Phases Must Assert False

If a policy doesn't support a phase, the corresponding method must assert False:

class PrefillOnlyPolicy(SparsePolicy):
    supports_prefill = True
    supports_decode = False

    def compute_chunked_prefill(self, ...):
        # Normal prefill implementation
        ...

    def compute_chunked_decode(self, ...):
        assert False, "PrefillOnlyPolicy does not support decode phase"

Caller Must Check Support Flags

attention.py checks support flags before calling:

if not sparse_policy.supports_decode:
    raise RuntimeError(f"{sparse_policy} does not support decode phase")

This provides double protection:

1. Caller check → Clear error message
2. Method assert → Prevents bypassing the check

CPU-GPU Communication via OffloadEngine Only

All CPU-GPU data transfers must go through OffloadEngine methods:

# Correct: Use OffloadEngine methods
offload_engine.load_to_slot_layer(slot, layer_id, cpu_block_id)
offload_engine.wait_slot_layer(slot)
k, v = offload_engine.get_kv_for_slot(slot)

# Incorrect: Direct torch operations
gpu_tensor.copy_(cpu_tensor)  # DON'T DO THIS
gpu_tensor = cpu_tensor.to("cuda")  # DON'T DO THIS

---

File Structure

File	Purpose
nanovllm/kvcache/sparse/policy.py	Base class, PolicyContext, abstract methods
nanovllm/kvcache/sparse/full_policy.py	FullAttentionPolicy implementation
nanovllm/kvcache/sparse/quest.py	QuestPolicy (decode-only Top-K selection)
nanovllm/layers/attention.py	Attention layer, delegates to policy

---

Policy Implementations

Policy	supports_prefill	supports_decode	Description
FullAttentionPolicy	True	True	Loads all blocks (baseline)
QuestPolicy	False	True	Decode-only Top-K selection
XAttentionBSAPolicy	False	False	Placeholder for future BSA

---

Testing

Run needle-in-haystack test with offload:

CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/path/to/nano-vllm:$PYTHONPATH \
    python tests/test_needle.py --model ~/models/Llama-3.1-8B-Instruct --enable-offload

Expected output:

Needle-in-Haystack Test
Model: Llama-3.1-8B-Instruct
CPU offload: True
Sparse policy: FULL
Result: PASSED