ViperEkura
d033b2ef0f
- SamplingBatchInfo: sample params built once per task set (top_k int32, pinned async H2D) - position_ids advances by +1 on steady-state decode instead of re-building - DecodeBindCache: bind_tasks increments seq_lens/kv_indptr, reuses req_pool_indices - saves ~240us of python/launch overhead per decode step
542 lines
18 KiB
Python
542 lines
18 KiB
Python
"""KV cache architecture: three-layer separation (SGLang-inspired).
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Layer 1 — KVStorage: flat token-level K/V buffers [n_layers, size, H, D]
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Layer 2 — ReqToTokenPool: index table [req_idx, pos] → physical token slot
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Layer 3 — Allocator: slot/page allocation with ref-counting and LRU
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PagePool orchestrates all three plus PrefixCache (content addressing).
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KVCache is a pure dataclass passed to the model for direct buffer access.
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Two modes:
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- contiguous (default): pre-allocated per-request blocks, no dynamic alloc
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- paged: shared pool with on-demand allocation, prefix caching support
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"""
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import threading
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from collections import OrderedDict
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from dataclasses import dataclass
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from typing import Callable, Dict, List, Optional
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import torch
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from torch import Tensor
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def page_hash(token_ids: List[int], page_idx: int, page_size: int) -> int:
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start = page_idx * page_size
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end = min(start + page_size, len(token_ids))
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h = 0
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for i in range(start, end):
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h = (h * 31 + token_ids[i]) & 0xFFFFFFFFFFFFFFFF
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return h
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class Allocator:
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"""Bitmask-based page allocator with ref-counting and LRU eviction."""
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def __init__(self, n_pages: int):
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self._free_mask = (1 << n_pages) - 1
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self._refs: List[int] = [0] * n_pages
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self._lru: OrderedDict[int, None] = OrderedDict()
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self.on_evict: Optional[Callable[[int], None]] = None
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self._lock = threading.Lock()
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def alloc(self) -> int:
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with self._lock:
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if self._free_mask:
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lsb = self._free_mask & -self._free_mask
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idx = lsb.bit_length() - 1
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self._free_mask ^= lsb
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self._refs[idx] = 1
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return idx
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if self._lru:
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idx, _ = self._lru.popitem(last=False)
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if self.on_evict:
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self.on_evict(idx)
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self._refs[idx] = 1
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self._free_mask &= ~(1 << idx)
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return idx
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return -1
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def free(self, idx: int, keep_cached: bool = False):
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with self._lock:
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self._refs[idx] -= 1
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if self._refs[idx] == 0:
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if keep_cached:
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self._lru[idx] = None
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else:
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self._free_mask |= 1 << idx
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def inc_ref(self, idx: int):
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with self._lock:
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self._refs[idx] += 1
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self._lru.pop(idx, None)
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def ref_count(self, idx: int) -> int:
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with self._lock:
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return self._refs[idx]
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def touch(self, idx: int):
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with self._lock:
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if idx in self._lru:
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self._lru.move_to_end(idx)
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class PrefixCache:
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"""Hash-based prefix matching: maps page hashes to physical page indices."""
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def __init__(self, page_size: int):
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self._page_size = page_size
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self._page_to_hash: Dict[int, int] = {}
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self._hash_to_page: Dict[int, int] = {}
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self._lock = threading.Lock()
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def evict(self, idx: int):
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with self._lock:
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h = self._page_to_hash.pop(idx, None)
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if h is not None:
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self._hash_to_page.pop(h, None)
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def has_page(self, idx: int) -> bool:
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with self._lock:
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return idx in self._page_to_hash
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def lookup(self, token_ids: List[int]) -> List[int]:
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with self._lock:
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full_pages = len(token_ids) // self._page_size
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hits: List[int] = []
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for i in range(full_pages):
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h = page_hash(token_ids, i, self._page_size)
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p = self._hash_to_page.get(h)
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if p is None:
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break
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hits.append(p)
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return hits
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def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
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with self._lock:
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h = page_hash(token_ids, logical_page_idx, self._page_size)
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old_h = self._page_to_hash.pop(page_idx, None)
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if old_h is not None:
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self._hash_to_page.pop(old_h, None)
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self._page_to_hash[page_idx] = h
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self._hash_to_page[h] = page_idx
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class ReqToTokenPool:
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"""Maps [req_idx, pos] -> physical token slot in KV storage.
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Each row is one request; each column is a sequence position. The value
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at [req_idx, pos] is the flat index into the KV storage buffers.
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"""
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def __init__(self, size: int, max_context_len: int, device: torch.device):
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self.size = size
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self.max_context_len = max_context_len
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self.req_to_token = torch.zeros(
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(size, max_context_len), dtype=torch.long, device=device
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)
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self.free_slots = list(range(size))
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self._lock = threading.Lock()
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def alloc(self, num_reqs: int) -> Optional[List[int]]:
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with self._lock:
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if num_reqs > len(self.free_slots):
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return None
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slots = self.free_slots[:num_reqs]
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self.free_slots = self.free_slots[num_reqs:]
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return slots
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def free(self, req_indices: List[int]):
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with self._lock:
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self.free_slots.extend(req_indices)
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def write(self, indices, values):
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self.req_to_token[indices] = values
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class KVStorage:
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"""Token-level KV cache storage.
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Buffers: [n_layers, size, n_kv_heads, head_dim]. Each token occupies
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one slot indexed by ReqToTokenPool.
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"""
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def __init__(
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self,
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size: int,
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n_layers: int,
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n_kv_heads: int,
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head_dim: int,
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device: torch.device,
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dtype: torch.dtype,
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):
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self.size = size
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self.k_buffer = torch.empty(
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(n_layers, size, n_kv_heads, head_dim), device=device, dtype=dtype
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)
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self.v_buffer = torch.empty(
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(n_layers, size, n_kv_heads, head_dim), device=device, dtype=dtype
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)
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def get_key_buffer(self, layer_id: int) -> Tensor:
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return self.k_buffer[layer_id]
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def get_value_buffer(self, layer_id: int) -> Tensor:
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return self.v_buffer[layer_id]
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def set_kv_buffer(self, layer_id: int, loc: Tensor, k: Tensor, v: Tensor) -> None:
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self.k_buffer[layer_id, loc] = k
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self.v_buffer[layer_id, loc] = v
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@dataclass
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class KVCache:
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"""Pure data struct passed to model for KV cache I/O.
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The attention layer does raw buffer indexing — no methods, no abstraction.
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Attributes:
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k_buffer: [n_layers, size, n_kv_heads, head_dim]
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v_buffer: [n_layers, size, n_kv_heads, head_dim]
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req_to_token: [num_reqs, max_ctx_len] — index table
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req_pool_indices: [batch_size] — row indices into req_to_token
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seq_lens: [batch_size] — per-request total sequence lengths
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out_cache_loc: [batch, new_seq_len] or [batch, 1] — write indices
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max_len: max(seq_lens) as Python int — avoids GPU sync in decode
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kv_indptr: [batch+1] int32 — prefix sum of seq_lens, precomputed once
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per step so the attention backend avoids rebuilding it per layer.
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"""
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k_buffer: Tensor
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v_buffer: Tensor
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req_to_token: Tensor
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req_pool_indices: Tensor
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seq_lens: Tensor
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out_cache_loc: Tensor
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max_len: int = 0
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kv_indptr: Optional[Tensor] = None
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@dataclass
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class DecodeBindCache:
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"""Cached KV-addressing state for steady-state decode.
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Valid for one ordered task set advancing every sequence by exactly one
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token per step. ``seq_lens`` is the Python mirror used to validate the
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+1 progression without a GPU round-trip; on any task-set change or
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non-monotonic seq_lens the whole entry is rebuilt.
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"""
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sig: tuple
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seq_lens: List[int]
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req_pool_indices: Tensor
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seq_lens_t: Tensor
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kv_indptr: Tensor
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inc: Tensor
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class PagePool:
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"""Top-level KV cache manager.
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Combines KVStorage + ReqToTokenPool + Allocator + PrefixCache.
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Args:
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n_layers: Number of transformer layers.
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n_kv_heads: Number of KV attention heads.
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head_dim: Dimension per head.
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max_batch_size: Maximum concurrent requests.
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max_seq_len: Maximum sequence length per request.
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device, dtype: Tensor device and dtype.
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page_size: Page size for paged mode (1 = token-level).
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n_tokens: Total token slots for paged mode. None = contiguous mode
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(pre-allocates max_batch_size * max_seq_len).
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"""
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def __init__(
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self,
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n_layers: int,
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n_kv_heads: int,
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head_dim: int,
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max_batch_size: int,
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max_seq_len: int,
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device: torch.device,
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dtype: torch.dtype,
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page_size: int = 1,
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n_tokens: Optional[int] = None,
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):
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self.page_size = page_size
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self.max_batch_size = max_batch_size
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self.max_seq_len = max_seq_len
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self.device = device
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self.dtype = dtype
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self.n_layers = n_layers
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self.n_kv_heads = n_kv_heads
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self.head_dim = head_dim
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self.contiguous = n_tokens is None
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if self.contiguous:
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self.n_tokens = max_batch_size * max_seq_len
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else:
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self.n_tokens = n_tokens
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self._storage = KVStorage(
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self.n_tokens, n_layers, n_kv_heads, head_dim, device, dtype
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)
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self._req_pool = ReqToTokenPool(max_batch_size, max_seq_len, device)
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if self.contiguous:
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for i in range(max_batch_size):
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self._req_pool.req_to_token[i] = torch.arange(
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i * max_seq_len, (i + 1) * max_seq_len, device=device
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)
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self._alloc: Optional[Allocator] = None
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self._prefix: Optional[PrefixCache] = None
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else:
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n_pages = self.n_tokens // page_size
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self._alloc = Allocator(n_pages)
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self._prefix = PrefixCache(page_size) if page_size > 1 else None
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if self._prefix is not None:
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self._alloc.on_evict = self._prefix.evict
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self._task_req: Dict[str, int] = {}
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self._task_len: Dict[int, int] = {}
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self._task_cached: Dict[str, int] = {}
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self._task_slots: Dict[str, List[int]] = {}
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self._task_pages: Dict[str, List[int]] = {}
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self._lock = threading.Lock()
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# Single-slot incremental cache for steady-state decode: the same
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# ordered task set advances every sequence by exactly one token per
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# step, so seq_lens_t and kv_indptr can be updated in-place instead
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# of re-allocating + re-cumsumming. Any task-set change is a miss.
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self._bind_cache: Optional[DecodeBindCache] = None
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# ---- task lifecycle ----
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def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
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req_slots = self._req_pool.alloc(1)
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if req_slots is None:
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return False
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req_idx = req_slots[0]
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self._task_req[task_id] = req_idx
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if self.contiguous:
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self._task_len[req_idx] = len(prompt_ids)
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self._task_cached[task_id] = 0
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return True
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n_tokens_needed = len(prompt_ids)
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cached = 0
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if self._prefix is not None:
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hits = self._prefix.lookup(prompt_ids)
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cached = len(hits) * self.page_size
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for p in hits:
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self._alloc.inc_ref(p)
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self._task_pages[task_id] = list(hits)
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self._task_slots[task_id] = []
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else:
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self._task_pages[task_id] = []
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self._task_slots[task_id] = []
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remaining = n_tokens_needed - cached
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if remaining > 0:
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if self.page_size == 1:
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slots = self._alloc_tokens(remaining)
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if slots is None:
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for p in self._task_pages[task_id]:
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self._alloc.free(p)
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self._req_pool.free([req_idx])
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del self._task_req[task_id]
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return False
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self._task_slots[task_id] = slots
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else:
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n_new_pages = (remaining + self.page_size - 1) // self.page_size
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new_pages = []
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for _ in range(n_new_pages):
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p = self._alloc.alloc()
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if p < 0:
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for hp in self._task_pages[task_id]:
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self._alloc.free(hp)
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for np_ in new_pages:
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self._alloc.free(np_)
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self._req_pool.free([req_idx])
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del self._task_req[task_id]
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return False
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new_pages.append(p)
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self._task_pages[task_id].extend(new_pages)
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self._write_req_to_token(task_id, prompt_ids, cached)
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self._task_len[req_idx] = len(prompt_ids)
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self._task_cached[task_id] = cached
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return True
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def task_free(self, task_id: str):
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req_idx = self._task_req.pop(task_id, None)
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if req_idx is None:
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return
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self._task_len.pop(req_idx, None)
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self._task_cached.pop(task_id, None)
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if not self.contiguous:
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if self._prefix is not None:
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for p in self._task_pages.get(task_id, []):
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keep = self._prefix.has_page(p)
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self._alloc.free(p, keep_cached=keep)
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if not keep:
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self._prefix.evict(p)
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else:
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for p in self._task_pages.get(task_id, []):
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self._alloc.free(p)
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self._task_pages.pop(task_id, None)
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self._task_slots.pop(task_id, None)
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self._req_pool.free([req_idx])
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def task_extend(self, task_id: str, pos: int) -> bool:
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req_idx = self._task_req.get(task_id)
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if req_idx is None:
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return False
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if self.contiguous:
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return pos < self.max_seq_len
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if self.page_size == 1:
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slots = self._alloc_tokens(1)
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if slots is None:
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return False
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self._task_slots.setdefault(task_id, []).extend(slots)
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self._req_pool.req_to_token[req_idx, pos] = slots[0]
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else:
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page_idx = pos // self.page_size
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existing = self._task_pages.get(task_id, [])
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if page_idx >= len(existing):
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p = self._alloc.alloc()
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if p < 0:
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return False
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existing.append(p)
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self._task_pages[task_id] = existing
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page_offset = pos % self.page_size
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page = existing[page_idx]
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token_slot = page * self.page_size + page_offset
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self._req_pool.req_to_token[req_idx, pos] = token_slot
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self._task_len[req_idx] = pos + 1
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return True
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def task_cached(self, task_id: str) -> int:
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return self._task_cached.get(task_id, 0)
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def task_record_hashes(
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self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
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):
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if self._prefix is None or self.contiguous:
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return
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pages = self._task_pages.get(task_id, [])
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full_pages = len(prompt_ids) // self.page_size
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for i in range(start_logical_page, min(full_pages, len(pages))):
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self._prefix.record(pages[i], prompt_ids, i)
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# ---- bind for forward ----
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def bind_tasks(
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self,
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task_ids: List[str],
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seq_lens: List[int],
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device: torch.device,
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start_pos: Optional[int] = None,
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) -> KVCache:
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req_indices = [self._task_req[tid] for tid in task_ids]
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sig = tuple(task_ids)
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cache = self._bind_cache
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incremental = (
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start_pos is None
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and cache is not None
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and cache.sig == sig
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and len(cache.seq_lens) == len(seq_lens)
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and all(s == p + 1 for s, p in zip(seq_lens, cache.seq_lens))
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)
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if incremental:
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req_pool_indices = cache.req_pool_indices
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seq_lens_t = cache.seq_lens_t + 1
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kv_indptr = cache.kv_indptr + cache.inc
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inc = cache.inc
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else:
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req_pool_indices = torch.tensor(
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req_indices, dtype=torch.long, device=device
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)
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seq_lens_t = torch.tensor(seq_lens, dtype=torch.long, device=device)
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kv_indptr = torch.zeros(len(seq_lens) + 1, dtype=torch.int32, device=device)
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kv_indptr[1:] = seq_lens_t.cumsum(0).to(torch.int32)
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inc = torch.arange(len(seq_lens) + 1, dtype=torch.int32, device=device)
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self._bind_cache = DecodeBindCache(
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sig=sig,
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seq_lens=list(seq_lens),
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req_pool_indices=req_pool_indices,
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seq_lens_t=seq_lens_t,
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kv_indptr=kv_indptr,
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inc=inc,
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)
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if start_pos is not None:
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seq_len = seq_lens[0]
|
|
out_cache_loc = self._req_pool.req_to_token[
|
|
req_pool_indices, start_pos:seq_len
|
|
]
|
|
else:
|
|
write_pos = seq_lens_t - 1
|
|
out_cache_loc = self._req_pool.req_to_token[
|
|
req_pool_indices, write_pos
|
|
].unsqueeze(-1)
|
|
|
|
return KVCache(
|
|
k_buffer=self._storage.k_buffer,
|
|
v_buffer=self._storage.v_buffer,
|
|
req_to_token=self._req_pool.req_to_token,
|
|
req_pool_indices=req_pool_indices,
|
|
seq_lens=seq_lens_t,
|
|
out_cache_loc=out_cache_loc,
|
|
max_len=max(seq_lens),
|
|
kv_indptr=kv_indptr,
|
|
)
|
|
|
|
# ---- internals ----
|
|
|
|
def _alloc_tokens(self, n: int) -> Optional[List[int]]:
|
|
if self.page_size != 1:
|
|
raise RuntimeError("_alloc_tokens is for page_size=1 only")
|
|
slots = []
|
|
for _ in range(n):
|
|
p = self._alloc.alloc()
|
|
if p < 0:
|
|
for s in slots:
|
|
self._alloc.free(s)
|
|
return None
|
|
slots.append(p)
|
|
return slots
|
|
|
|
def _write_req_to_token(self, task_id: str, prompt_ids: List[int], cached: int):
|
|
req_idx = self._task_req[task_id]
|
|
total = len(prompt_ids)
|
|
|
|
if self.contiguous:
|
|
return
|
|
|
|
if self.page_size == 1:
|
|
slots = self._task_slots.get(task_id, [])
|
|
all_slots = slots[: total - cached]
|
|
if all_slots:
|
|
self._req_pool.req_to_token[req_idx, cached:total] = torch.tensor(
|
|
all_slots, dtype=torch.long, device=self.device
|
|
)
|
|
else:
|
|
pages = self._task_pages.get(task_id, [])
|
|
for pos in range(cached, total):
|
|
page_idx = pos // self.page_size
|
|
page_offset = pos % self.page_size
|
|
if page_idx < len(pages):
|
|
token_slot = pages[page_idx] * self.page_size + page_offset
|
|
self._req_pool.req_to_token[req_idx, pos] = token_slot
|