247 lines
8.4 KiB
Python
247 lines
8.4 KiB
Python
from typing import Dict, List, Tuple
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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 PagedCache:
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"""Paged KV cache: page pool, prefix-cache lookup, LRU eviction, task-page mapping."""
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def __init__(
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self,
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n_layers: int,
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n_pages: int,
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page_size: 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.page_size = page_size
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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.k_cache = torch.empty(
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(n_layers, n_pages, page_size, n_kv_heads, head_dim),
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device=device,
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dtype=dtype,
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)
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self.v_cache = torch.empty(
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(n_layers, n_pages, page_size, n_kv_heads, head_dim),
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device=device,
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dtype=dtype,
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)
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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._lru: List[int] = []
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self._pin: List[bool] = [False] * n_pages
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self._task_pages: Dict[str, List[int]] = {}
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self._task_cached: Dict[str, int] = {}
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def pages_needed(self, n_tokens: int) -> int:
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return (n_tokens + self.page_size - 1) // self.page_size
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def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
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hit_pages = self.lookup_prefix(prompt_ids)
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cached_tokens = len(hit_pages) * self.page_size
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for p in hit_pages:
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self.inc_ref(p)
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remaining = len(prompt_ids) - cached_tokens
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n_new = self.pages_needed(remaining) if remaining > 0 else 0
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new_pages = self.alloc_n(n_new) if n_new > 0 else []
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if remaining > 0 and not new_pages:
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for p in hit_pages:
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self.free(p)
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return False
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page_table = hit_pages + new_pages
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self._task_pages[task_id] = page_table
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self._task_cached[task_id] = cached_tokens
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return True
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def task_free(self, task_id: str) -> None:
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page_table = self._task_pages.pop(task_id, None)
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self._task_cached.pop(task_id, None)
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if page_table:
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for idx in page_table:
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self.free(idx)
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def task_extend(self, task_id: str, pos: int) -> bool:
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needed = self.pages_needed(pos + 1)
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page_table = self._task_pages[task_id]
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while len(page_table) < needed:
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p = self.alloc()
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if p < 0:
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return False
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page_table.append(p)
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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_page_table(self, task_id: str) -> List[int]:
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return self._task_pages.get(task_id, [])
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def task_n_pages(self, task_id: str) -> int:
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return len(self._task_pages.get(task_id, []))
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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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) -> None:
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page_table = self._task_pages[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, full_pages):
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self.record_page(page_table[i], prompt_ids, i)
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def make_table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
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states = [self._task_pages.get(tid, []) for tid in task_ids]
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max_pages = max((len(s) for s in states), default=0)
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rows = [s + [-1] * (max_pages - len(s)) for s in states]
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return torch.tensor(rows, dtype=torch.long, device=device)
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def _touch(self, idx: int) -> None:
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if self._refs[idx] == 0 and idx in self._lru:
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self._lru.remove(idx)
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self._lru.append(idx)
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def _evict_one(self) -> int:
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while self._lru:
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idx = self._lru.pop(0)
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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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self._pin[idx] = False
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self._refs[idx] = 1
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return idx
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return -1
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def record_page(
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self, page_idx: int, token_ids: List[int], logical_page_idx: int
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) -> None:
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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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self._pin[page_idx] = True
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if page_idx in self._lru:
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self._lru.remove(page_idx)
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def lookup_prefix(self, token_ids: List[int]) -> List[int]:
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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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self._touch(p)
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hits.append(p)
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return hits
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def inc_ref(self, idx: int) -> None:
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self._refs[idx] += 1
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if self._refs[idx] == 1 and idx in self._lru:
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self._lru.remove(idx)
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def alloc(self) -> int:
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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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if idx in self._lru:
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self._lru.remove(idx)
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return idx
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return self._evict_one()
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def alloc_n(self, n: int) -> List[int]:
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pages = [self.alloc() for _ in range(n)]
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if any(p < 0 for p in pages):
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for p in pages:
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if p >= 0:
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self.free(p)
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return []
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return pages
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def free(self, idx: int) -> None:
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self._refs[idx] -= 1
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if self._refs[idx] == 0:
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h = self._page_to_hash.get(idx)
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if h is not None and self._pin[idx]:
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self._lru.append(idx)
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else:
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self._free_mask |= 1 << idx
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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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self._pin[idx] = False
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def bind(self, page_table: Tensor, total_len: int = 0) -> "CacheView":
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return CacheView(self, page_table, total_len)
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def write(
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self, layer_id: int, page_table: Tensor, start_pos: int, k: Tensor, v: Tensor
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) -> None:
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seq_len = k.size(1)
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if seq_len == 0:
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return
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page_size = self.page_size
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written = 0
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first_page = start_pos // page_size
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last_page = (start_pos + seq_len - 1) // page_size
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for pi in range(first_page, last_page + 1):
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phys_pages = page_table[:, pi]
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page_start = pi * page_size
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write_start = max(page_start, start_pos)
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write_end = min(page_start + page_size, start_pos + seq_len)
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offset = write_start - page_start
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chunk = write_end - write_start
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self.k_cache[layer_id, phys_pages, offset : offset + chunk] = k[
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:, written : written + chunk
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]
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self.v_cache[layer_id, phys_pages, offset : offset + chunk] = v[
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:, written : written + chunk
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]
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written += chunk
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def gather(
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self, layer_id: int, page_table: Tensor, total_len: int
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) -> Tuple[Tensor, Tensor]:
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safe = page_table.clamp(min=0)
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k = self.k_cache[layer_id, safe]
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v = self.v_cache[layer_id, safe]
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k = k.flatten(1, 2)
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v = v.flatten(1, 2)
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k = k[:, :total_len]
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v = v[:, :total_len]
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return k, v
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class CacheView:
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"""Bundles PagedCache + page_table + total_len for attention layers."""
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__slots__ = ("_cache", "_page_table", "_total_len")
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def __init__(self, cache: PagedCache, page_table: Tensor, total_len: int = 0):
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self._cache = cache
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self._page_table = page_table
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self._total_len = total_len
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def write(self, layer_id: int, start_pos: int, k: Tensor, v: Tensor) -> None:
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self._cache.write(layer_id, self._page_table, start_pos, k, v)
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def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
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return self._cache.gather(layer_id, self._page_table, self._total_len)
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