ViperEkura
3fa7e66676
- TaskCacheRegistry -> TaskCacheManager (independent, held by scheduler) - TaskCacheState co-locates 5 parallel dicts into one dataclass - AllocationStrategy base class + PagedStrategy subclass (page_size is a parameter) - _rollback() helper for unified cleanup (no duplicate free paths) - Task._kv_len + prefill_done property (explicit, no output_tokens proxy) - Steady-state detection single-sourced in TaskCacheManager.bind() - PagePool is now pure physical layer (no task knowledge) - Removed dead _page_to_hash dict in RadixCache
648 lines
21 KiB
Python
648 lines
21 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 — AllocationStrategy: slot/page allocation with ref-counting and LRU
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PagePool owns the physical buffers and bind (KVCache assembly); it does
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not know about tasks. TaskCacheManager owns task_id -> TaskCacheState
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mapping and delegates physical slot allocation to the strategy.
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KVCache is a pure dataclass passed to the model for direct buffer access.
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Two strategies (selected once at construction):
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- ContiguousStrategy: pre-allocated per-request blocks, no dynamic alloc
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- PagedStrategy: dynamic paged allocation; page_size is a parameter
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(1 = token-level, >1 = page-level with radix prefix)
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"""
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import threading
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from abc import ABC
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from dataclasses import dataclass, field
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from typing import Callable, Dict, List, Optional, OrderedDict
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import torch
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from torch import Tensor
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from astrai.inference.core.workspace import InferenceWorkspace
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@dataclass
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class _BindState:
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"""Cached bind metadata for steady-state decode increment detection."""
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sig: tuple
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seq_lens: List[int]
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@dataclass
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class TaskCacheState:
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"""Per-task cache allocation state.
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Co-locating all task-owned cache state in one object makes the
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alloc/free/extend lifecycle atomic.
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"""
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req_idx: int
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length: int = 0
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cached: int = 0
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pages: List[int] = field(default_factory=list)
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def _is_steady_increment(
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prev_sig: Optional[tuple],
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prev_vals: Optional[List[int]],
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cur_sig: tuple,
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cur_vals: List[int],
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) -> bool:
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"""True when the same ordered set has every value +1 from the previous step."""
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return (
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prev_sig is not None
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and prev_vals is not None
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and prev_sig == cur_sig
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and len(prev_vals) == len(cur_vals)
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and all(c == p + 1 for c, p in zip(cur_vals, prev_vals))
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)
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def page_hash(
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token_ids: List[int], page_idx: int, page_size: int, parent_hash: int = 0
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) -> 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 = parent_hash
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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 RadixNode:
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"""A page-aligned edge in the CPU-side prefix radix."""
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__slots__ = ("parent", "children", "page_idx", "tokens", "lock_ref")
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def __init__(self, parent=None, tokens=(), page_idx=None):
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self.parent = parent
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self.children: Dict[tuple, "RadixNode"] = {}
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self.page_idx = page_idx
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self.tokens = tuple(tokens)
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self.lock_ref = 0
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class RadixCache:
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"""Page-granular radix prefix index with exact token matching."""
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def __init__(self, page_size: int):
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self._page_size = page_size
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self._root = RadixNode()
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self._page_to_node: Dict[int, RadixNode] = {}
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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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node = self._page_to_node.pop(idx, None)
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if node is None:
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return
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node.page_idx = None
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parent = node.parent
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if parent is not None:
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parent.children.pop(node.tokens, 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_node
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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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node = self._root
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for i in range(full_pages):
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start = i * self._page_size
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page_tokens = tuple(token_ids[start : start + self._page_size])
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child = node.children.get(page_tokens)
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if child is None or child.page_idx is None:
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break
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hits.append(child.page_idx)
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node = child
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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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full_pages = len(token_ids) // self._page_size
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if logical_page_idx >= full_pages:
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return
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old = self._page_to_node.pop(page_idx, None)
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if old is not None and old.parent is not None:
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old.parent.children.pop(old.tokens, None)
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node = self._root
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for i in range(logical_page_idx + 1):
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start = i * self._page_size
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page_tokens = tuple(token_ids[start : start + self._page_size])
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child = node.children.get(page_tokens)
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if child is None:
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child = RadixNode(node, page_tokens)
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node.children[page_tokens] = child
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node = child
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if node.page_idx is not None and node.page_idx != page_idx:
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replaced = node.page_idx
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self._page_to_node.pop(replaced, None)
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node.page_idx = page_idx
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self._page_to_node[page_idx] = node
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def release(self, pages: List[int]) -> None:
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with self._lock:
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for page_idx in pages:
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node = self._page_to_node.get(page_idx)
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if node is not None and node.lock_ref:
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node.lock_ref -= 1
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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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"""
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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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qo_indptr: Optional[Tensor] = None
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decode_o_part: Optional[Tensor] = None
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decode_ml_part: Optional[Tensor] = None
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decode_out: Optional[Tensor] = None
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class AllocationStrategy(ABC):
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"""Physical slot allocation policy.
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The base class provides contiguous-mode defaults (all no-ops): req_to_token
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is pre-filled at PagePool init, so no dynamic allocation is needed.
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PagedStrategy overrides every method to add dynamic allocation.
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"""
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def alloc(self, state: TaskCacheState, prompt_ids: List[int]) -> bool:
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return True
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def free(self, state: TaskCacheState) -> None:
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pass
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def extend(self, state: TaskCacheState, pos: int) -> bool:
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return True
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def write_indices(self, state: TaskCacheState, prompt_ids: List[int]) -> None:
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pass
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def record_hashes(
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self, state: TaskCacheState, prompt_ids: List[int], start: int
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) -> None:
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pass
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class PagedStrategy(AllocationStrategy):
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"""Dynamic paged allocation from a shared bitmask pool.
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page_size is a parameter, not a separate strategy: at page_size=1 each
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allocated page *is* one token slot (``page * 1 + 0``), and prefix
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caching is simply disabled (``prefix=None``). The unified page
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formula ``pages[page_idx] * page_size + offset`` holds for both.
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"""
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def __init__(
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self,
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alloc: Allocator,
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prefix: Optional[RadixCache],
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page_size: int,
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req_pool: ReqToTokenPool,
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device,
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):
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self._alloc = alloc
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self._prefix = prefix
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self._page_size = page_size
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self._req_pool = req_pool
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self._device = device
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def alloc(self, state: TaskCacheState, prompt_ids: List[int]) -> bool:
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if self._prefix is not None:
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hits = self._prefix.lookup(prompt_ids)
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state.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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state.pages = list(hits)
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remaining = len(prompt_ids) - state.cached
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if remaining <= 0:
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return True
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n_new = (remaining + self._page_size - 1) // self._page_size
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for _ in range(n_new):
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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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state.pages.append(p)
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return True
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def free(self, state: TaskCacheState) -> None:
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if self._prefix is not None:
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for p in state.pages:
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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 state.pages:
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self._alloc.free(p)
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def extend(self, state: TaskCacheState, pos: int) -> bool:
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page_idx = pos // self._page_size
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if page_idx >= len(state.pages):
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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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state.pages.append(p)
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offset = pos % self._page_size
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self._req_pool.req_to_token[state.req_idx, pos] = (
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state.pages[page_idx] * self._page_size + offset
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)
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return True
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def write_indices(self, state: TaskCacheState, prompt_ids: List[int]) -> None:
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total = len(prompt_ids)
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for pos in range(state.cached, total):
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page_idx = pos // self._page_size
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offset = pos % self._page_size
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if page_idx < len(state.pages):
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self._req_pool.req_to_token[state.req_idx, pos] = (
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state.pages[page_idx] * self._page_size + offset
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)
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def record_hashes(
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self, state: TaskCacheState, prompt_ids: List[int], start: int
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) -> None:
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if self._prefix is None:
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return
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full = len(prompt_ids) // self._page_size
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for i in range(start, min(full, len(state.pages))):
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self._prefix.record(state.pages[i], prompt_ids, i)
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class PagePool:
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"""Physical KV cache: buffers + req-pool + allocation strategy + bind.
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Does not know about tasks — task lifecycle is managed by
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:class:`TaskCacheManager`, which holds a reference to this pool.
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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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self.n_tokens = max_batch_size * max_seq_len if self.contiguous else 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._strategy = AllocationStrategy()
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else:
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n_pages = self.n_tokens // page_size
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alloc = Allocator(n_pages)
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prefix = RadixCache(page_size) if page_size > 1 else None
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if prefix is not None:
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alloc.on_evict = prefix.evict
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self._strategy = PagedStrategy(
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alloc, prefix, page_size, self._req_pool, device
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)
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def bind_tasks(
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self,
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req_indices: List[int],
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seq_lens: List[int],
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workspace: InferenceWorkspace,
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device: Optional[torch.device] = None,
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start_pos: Optional[int] = None,
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incremental: bool = False,
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) -> KVCache:
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if device is None:
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device = workspace.device
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b = len(req_indices)
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rpi_buf = workspace.req_pool_indices
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sl_buf = workspace.seq_lens
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kvp_buf = workspace.kv_indptr
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inc_buf = workspace.inc
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ocl_buf = workspace.out_cache_loc
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if incremental:
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sl_buf[:b] += 1
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kvp_buf[: b + 1] += inc_buf[: b + 1]
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else:
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rpi_buf[:b].copy_(
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torch.tensor(req_indices, dtype=torch.long, device=device)
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)
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sl_buf[:b].copy_(torch.tensor(seq_lens, dtype=torch.long, device=device))
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kvp_buf[: b + 1].zero_()
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kvp_buf[1 : b + 1] = sl_buf[:b].cumsum(0).to(torch.int32)
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req_pool_indices = rpi_buf[:b]
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seq_lens_t = sl_buf[:b]
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kv_indptr = kvp_buf[: b + 1]
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if start_pos is not None:
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seq_len = seq_lens[0]
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out_cache_loc = self._req_pool.req_to_token[
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req_pool_indices, start_pos:seq_len
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]
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q_len = seq_len - start_pos
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workspace.qo_indptr[: b + 1].copy_(
|
|
torch.arange(b + 1, dtype=torch.int32, device=device) * q_len
|
|
)
|
|
qo_indptr = workspace.qo_indptr[: b + 1]
|
|
decode_o_part = decode_ml_part = decode_out = None
|
|
else:
|
|
write_pos = seq_lens_t - 1
|
|
loc = self._req_pool.req_to_token[req_pool_indices, write_pos].unsqueeze(-1)
|
|
ocl_buf[:b].copy_(loc)
|
|
out_cache_loc = ocl_buf[:b]
|
|
qo_indptr = None
|
|
decode_o_part = getattr(workspace, "decode_o_part", None)
|
|
decode_ml_part = getattr(workspace, "decode_ml_part", None)
|
|
decode_out = getattr(workspace, "decode_out", None)
|
|
|
|
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,
|
|
qo_indptr=qo_indptr,
|
|
decode_o_part=decode_o_part,
|
|
decode_ml_part=decode_ml_part,
|
|
decode_out=decode_out,
|
|
)
|
|
|
|
|
|
class TaskCacheManager:
|
|
"""Task <-> KV slot lifecycle manager.
|
|
|
|
Sole owner of task state. Owns the task_id -> TaskCacheState map and
|
|
delegates physical slot allocation to the strategy, and KV bind to
|
|
PagePool. Held directly by the scheduler — not as a PagePool attribute.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
strategy: AllocationStrategy,
|
|
req_pool: ReqToTokenPool,
|
|
max_seq_len: int,
|
|
pool: PagePool,
|
|
):
|
|
self._strategy = strategy
|
|
self._req_pool = req_pool
|
|
self._max_seq_len = max_seq_len
|
|
self._pool = pool
|
|
self._states: Dict[str, TaskCacheState] = {}
|
|
self._bind_state: Optional[_BindState] = None
|
|
|
|
def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
|
|
self._bind_state = None
|
|
req_slots = self._req_pool.alloc(1)
|
|
if req_slots is None:
|
|
return False
|
|
state = TaskCacheState(req_idx=req_slots[0])
|
|
self._states[task_id] = state
|
|
if not self._strategy.alloc(state, prompt_ids):
|
|
self._rollback(state, task_id)
|
|
return False
|
|
self._strategy.write_indices(state, prompt_ids)
|
|
state.length = len(prompt_ids)
|
|
return True
|
|
|
|
def _rollback(self, state: TaskCacheState, task_id: str):
|
|
self._strategy.free(state)
|
|
self._req_pool.free([state.req_idx])
|
|
self._states.pop(task_id, None)
|
|
|
|
def task_free(self, task_id: str):
|
|
self._bind_state = None
|
|
state = self._states.pop(task_id, None)
|
|
if state is None:
|
|
return
|
|
self._strategy.free(state)
|
|
self._req_pool.free([state.req_idx])
|
|
|
|
def task_extend(self, task_id: str, pos: int) -> bool:
|
|
state = self._states.get(task_id)
|
|
if state is None or pos >= self._max_seq_len:
|
|
return False
|
|
if not self._strategy.extend(state, pos):
|
|
return False
|
|
state.length = pos + 1
|
|
return True
|
|
|
|
def task_cached(self, task_id: str) -> int:
|
|
state = self._states.get(task_id)
|
|
return state.cached if state is not None else 0
|
|
|
|
def task_record_hashes(
|
|
self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
|
|
):
|
|
state = self._states.get(task_id)
|
|
if state is not None:
|
|
self._strategy.record_hashes(state, prompt_ids, start_logical_page)
|
|
|
|
@staticmethod
|
|
def task_cacheable_ids(task_id: str, prompt_ids: List[int], output_ids: List[int]):
|
|
"""Return the sequence whose KV entries are already materialized.
|
|
|
|
The first sampled output is produced by prompt prefill, and the last
|
|
sampled output has not been decoded into KV yet.
|
|
"""
|
|
return list(prompt_ids) + list(output_ids[:-1])
|
|
|
|
def bind(
|
|
self,
|
|
task_ids: List[str],
|
|
workspace: InferenceWorkspace,
|
|
device: Optional[torch.device] = None,
|
|
start_pos: Optional[int] = None,
|
|
) -> KVCache:
|
|
states = [self._states[tid] for tid in task_ids]
|
|
req_indices = [s.req_idx for s in states]
|
|
seq_lens = [s.length for s in states]
|
|
sig = tuple(req_indices)
|
|
|
|
prev = self._bind_state
|
|
incremental = (
|
|
start_pos is None
|
|
and prev is not None
|
|
and _is_steady_increment(prev.sig, prev.seq_lens, sig, seq_lens)
|
|
)
|
|
self._bind_state = _BindState(sig, list(seq_lens))
|
|
self._bind_was_steady = incremental
|
|
|
|
return self._pool.bind_tasks(
|
|
req_indices,
|
|
seq_lens,
|
|
workspace,
|
|
device=device,
|
|
start_pos=start_pos,
|
|
incremental=incremental,
|
|
)
|
|
|
|
@property
|
|
def bind_was_steady(self) -> bool:
|
|
"""Whether the most recent ``bind()`` was a steady-state increment."""
|
|
return self._bind_was_steady
|