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# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional, Union
import torch
from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
from .runtime_config import _get_shared_batch_mask, _parse_config_pattern
from .layer_norm_rope import rotate_half
class Qwen3RingBufferCache:
"""
Ring buffer KV cache with summary support.
Two strategies based on per-layer sliding_chunk_num:
- Large window layers (is_large_window=True): append-only buffer storing only text KV.
Summary KV is NOT stored since text tokens attend to all text KV directly.
- Small window layers (is_large_window=False):
Three buffers:
1. key_cache: [ring(ws) | old_summaries(growing) | chunk_mirror(≤C)]
→ attention input, steady state is a single contiguous slice
2. new_summary_buf: ring buffer of size scn, stores summaries whose text
is still in the window (not needed for attention)
3. chunk_buf: size C, holds current chunk's text KV
RoPE position information is baked into KV, so physical order doesn't matter.
"""
is_compileable = False
_SUMMARY_INIT_CAP = 512
_APPEND_HEADROOM = 1024
def __init__(self, config: Qwen3Config, sliding_chunk_nums: list[int]):
super().__init__()
self.summary_chunk_size = getattr(config, "summary_chunk_size", 0)
self.summary_token_num = getattr(config, "summary_token_num", 0)
self.num_hidden_layers = config.num_hidden_layers
self.sliding_chunk_nums = sliding_chunk_nums
large_window_threshold = min(sliding_chunk_nums) * self.summary_chunk_size
self.is_large_window = [sv * self.summary_chunk_size > large_window_threshold for sv in sliding_chunk_nums]
self.window_sizes = [sv * self.summary_chunk_size for sv in sliding_chunk_nums]
pool_mode = getattr(config, "summary_meta_pool_mode", "none") or "none"
pool_groups = _parse_config_pattern(
getattr(config, "summary_meta_pool_group_size", 0) or 0
)
if not isinstance(pool_groups, list):
pool_groups = [int(pool_groups)] * config.num_hidden_layers
self.meta_pool_group_sizes = [int(v) for v in pool_groups]
self.meta_pool_enabled = [
pool_mode in ("mean", "scalar", "dimension_wise")
and group_size > 1
for group_size in self.meta_pool_group_sizes
]
self.meta_pool_mode = pool_mode
self.key_cache = [None for _ in range(config.num_hidden_layers)]
self.value_cache = [None for _ in range(config.num_hidden_layers)]
# Full-layer Lightning Indexer keys are not main-attention K/V. The
# summary-indexer variant stores one key per raw chunk; token-indexer
# mode stores one key per raw text token.
self._sksa_index_keys = [None for _ in range(config.num_hidden_layers)]
self._sparse_gather_storage = {}
# Independent chunk-Q/K mode needs the auxiliary K states for the
# currently accumulating raw chunk. They are separate from the
# backbone attention cache and are discarded once its summary token is
# emitted.
self._sksa_chunk_k = [None for _ in range(config.num_hidden_layers)]
self._sksa_chunk_v = [None for _ in range(config.num_hidden_layers)]
# Large window: append-only
self._text_len = [0] * config.num_hidden_layers
self._capacity = [0] * config.num_hidden_layers
# Small window: ring buffer + summary
self._window_write_ptr = [0] * config.num_hidden_layers
self._n_valid_window = [0] * config.num_hidden_layers
self._old_summary_len = [0] * config.num_hidden_layers # old summaries in key_cache
self._old_summary_cap = [0] * config.num_hidden_layers
self._old_summary_total = [0] * config.num_hidden_layers
# Hierarchical old-summary state. Complete groups are stored as post-RoPE
# meta KV; fewer than group_size evicted summaries remain residual KV.
self._meta_key_cache = [None for _ in range(config.num_hidden_layers)]
self._meta_value_cache = [None for _ in range(config.num_hidden_layers)]
self._meta_len = [0] * config.num_hidden_layers
self._meta_cap = [0] * config.num_hidden_layers
self._residual_pre_key_buf = [None for _ in range(config.num_hidden_layers)]
self._residual_score_buf = [None for _ in range(config.num_hidden_layers)]
self._residual_cos_buf = [None for _ in range(config.num_hidden_layers)]
self._residual_sin_buf = [None for _ in range(config.num_hidden_layers)]
self._residual_position_buf = [None for _ in range(config.num_hidden_layers)]
# New summary ring buffer (small window only): summaries whose text is still in window
self._new_sum_key_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_value_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_pre_key_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_score_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_cos_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_sin_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_position_buf = [None for _ in range(config.num_hidden_layers)]
self._new_sum_len = [0] * config.num_hidden_layers # how many filled (≤ scn)
self._new_sum_write_ptr = [0] * config.num_hidden_layers # ring write pointer
# Mirror region length (small window only): partial chunk text KV count in key_cache
self._chunk_buf_len = [0] * config.num_hidden_layers
# Common
self.cur_chunk_sizes = [0] * config.num_hidden_layers
self.true_tokens = [0] * config.num_hidden_layers
self._total_chunks = [0] * config.num_hidden_layers # completed chunks count
self._reorganized = [False] * config.num_hidden_layers
self._prefill_text_positions = None
self._prefill_summary_positions = None
def __len__(self):
return self.num_hidden_layers
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
"""Returns nonzero only when this layer's cache is in post-reorganize state."""
if layer_idx >= self.num_hidden_layers:
return 0
if not self._reorganized[layer_idx]:
return 0
if self.is_large_window[layer_idx]:
return self._text_len[layer_idx]
else:
return (self._n_valid_window[layer_idx] + self._chunk_buf_len[layer_idx]
+ self._old_summary_len[layer_idx] + self._new_sum_len[layer_idx])
def get_cur_chunk_size(self, layer_idx: Optional[int] = None) -> int:
if layer_idx is None:
layer_idx = self.num_hidden_layers - 1
return self.cur_chunk_sizes[layer_idx]
def get_true_token_num(self, layer_idx: Optional[int] = None) -> int:
if layer_idx is None:
layer_idx = self.num_hidden_layers - 1
return self.true_tokens[layer_idx]
def set_sksa_index_keys(
self, layer_idx: int, index_keys: torch.Tensor
) -> None:
if index_keys.ndim != 3:
raise ValueError("S-KSA index keys must use [B,M,D] layout")
self._sksa_index_keys[layer_idx] = index_keys.contiguous()
def append_sksa_index_keys(
self, layer_idx: int, index_keys: torch.Tensor
) -> None:
if index_keys.ndim != 3:
raise ValueError("S-KSA index keys must use [B,M,D] layout")
current = self._sksa_index_keys[layer_idx]
if current is None:
self._sksa_index_keys[layer_idx] = index_keys.contiguous()
else:
if current.shape[0] != index_keys.shape[0] or current.shape[2] != index_keys.shape[2]:
raise ValueError("S-KSA index key cache geometry changed during decode")
self._sksa_index_keys[layer_idx] = torch.cat(
(current, index_keys), dim=1
)
def get_sksa_index_keys(self, layer_idx: int) -> torch.Tensor:
index_keys = self._sksa_index_keys[layer_idx]
if index_keys is None:
raise RuntimeError(f"S-KSA index key cache for layer {layer_idx} is uninitialized")
return index_keys
def set_sksa_chunk_k_from_prefill(
self,
layer_idx: int,
chunk_key_states: Optional[torch.Tensor],
summary_mask: torch.Tensor,
) -> None:
"""Retain auxiliary K for the incomplete raw chunk after prefill."""
if chunk_key_states is None:
self._sksa_chunk_k[layer_idx] = None
return
if chunk_key_states.ndim != 4:
raise ValueError("S-KSA chunk K must use [B,H,S,D] layout")
shared = _get_shared_batch_mask(summary_mask, "summary_mask")
positions = self._prefill_text_positions
text_key = (chunk_key_states.index_select(2, positions) if positions is not None
else chunk_key_states[:, :, ~shared, :])
partial = text_key.shape[2] % self.summary_chunk_size
if partial:
self._sksa_chunk_k[layer_idx] = text_key[:, :, -partial:, :].contiguous()
else:
self._sksa_chunk_k[layer_idx] = None
def append_sksa_chunk_k(
self, layer_idx: int, chunk_key_states: Optional[torch.Tensor]
) -> None:
"""Append one decoded raw token's independent auxiliary K state."""
if chunk_key_states is None:
return
if chunk_key_states.ndim != 4 or chunk_key_states.shape[2] != 1:
raise ValueError("decoded S-KSA chunk K must use [B,H,1,D] layout")
current = self._sksa_chunk_k[layer_idx]
self._sksa_chunk_k[layer_idx] = (
chunk_key_states.contiguous()
if current is None
else torch.cat((current, chunk_key_states), dim=2).contiguous()
)
def get_current_sksa_chunk_k(self, layer_idx: int) -> Optional[torch.Tensor]:
return self._sksa_chunk_k[layer_idx]
def clear_sksa_chunk_k(self, layer_idx: int) -> None:
self._sksa_chunk_k[layer_idx] = None
def set_sksa_chunk_v_from_prefill(
self,
layer_idx: int,
chunk_value_states: Optional[torch.Tensor],
summary_mask: torch.Tensor,
) -> None:
"""Retain auxiliary V for the incomplete raw chunk after prefill."""
if chunk_value_states is None:
self._sksa_chunk_v[layer_idx] = None
return
if chunk_value_states.ndim != 4:
raise ValueError("S-KSA chunk V must use [B,H,S,D] layout")
shared = _get_shared_batch_mask(summary_mask, "summary_mask")
positions = self._prefill_text_positions
text_value = (chunk_value_states.index_select(2, positions) if positions is not None
else chunk_value_states[:, :, ~shared, :])
partial = text_value.shape[2] % self.summary_chunk_size
if partial:
self._sksa_chunk_v[layer_idx] = text_value[:, :, -partial:, :].contiguous()
else:
self._sksa_chunk_v[layer_idx] = None
def append_sksa_chunk_v(
self, layer_idx: int, chunk_value_states: Optional[torch.Tensor]
) -> None:
"""Append one decoded raw token's independent auxiliary V state."""
if chunk_value_states is None:
return
if chunk_value_states.ndim != 4 or chunk_value_states.shape[2] != 1:
raise ValueError("decoded S-KSA chunk V must use [B,H,1,D] layout")
current = self._sksa_chunk_v[layer_idx]
self._sksa_chunk_v[layer_idx] = (
chunk_value_states.contiguous()
if current is None
else torch.cat((current, chunk_value_states), dim=2).contiguous()
)
def get_current_sksa_chunk_v(self, layer_idx: int) -> Optional[torch.Tensor]:
return self._sksa_chunk_v[layer_idx]
def clear_sksa_chunk_v(self, layer_idx: int) -> None:
self._sksa_chunk_v[layer_idx] = None
# ── Prefill: per-layer one-shot init into ring buffer layout ──
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
cache_kwargs: Optional[dict[str, Any]] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Deprecated for prefill — use init_layer_from_prefill instead.
Kept only to surface accidental calls; the prefill path now skips this
intermediate full-length buffer entirely.
"""
raise NotImplementedError(
"Qwen3RingBufferCache.update() is no longer used; prefill goes through "
"init_layer_from_prefill, decode goes through update_text/update_summary."
)
def prepare_prefill(self, text_positions, summary_positions):
self._prefill_text_positions = text_positions
self._prefill_summary_positions = summary_positions
def init_layer_from_prefill(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
summary_mask: torch.Tensor,
layer_idx: int,
*,
pre_rope_key_states: Optional[torch.Tensor] = None,
summary_scores: Optional[torch.Tensor] = None,
summary_cos: Optional[torch.Tensor] = None,
summary_sin: Optional[torch.Tensor] = None,
summary_position_ids: Optional[torch.Tensor] = None,
meta_key_states: Optional[torch.Tensor] = None,
meta_value_states: Optional[torch.Tensor] = None,
sksa_chunk_key_states: Optional[torch.Tensor] = None,
):
"""Allocate this layer's cache directly into the post-reorganize ring buffer
layout from prefill K/V (already projected + RoPE applied), with NO intermediate
full-length buffer. Replaces the old update() + reorganize_after_prefill() pair
for the prefill path.
key_states / value_states: [bsz, kv_heads, prefill_len_expanded, head_dim]
summary_mask: [bsz, prefill_len_expanded] (bool, batch-uniform)
"""
assert not self._reorganized[layer_idx], f"layer {layer_idx} already reorganized"
prefill_k = key_states
prefill_v = value_states
bsz, heads, prefill_len, head_dim = prefill_k.shape
device, dtype = prefill_k.device, prefill_k.dtype
sm = _get_shared_batch_mask(summary_mask, "summary_mask")
text_mask = ~sm
text_positions = self._prefill_text_positions
summary_positions = self._prefill_summary_positions
if text_positions is None:
text_positions = torch.nonzero(text_mask, as_tuple=False).flatten()
summary_positions = torch.nonzero(sm, as_tuple=False).flatten()
n_text = text_positions.numel()
block = self.summary_chunk_size + self.summary_token_num
# Match what update() + reorganize_after_prefill() would have set:
# update(): cur_chunk_sizes += prefill_len; true_tokens += n_text
# reorganize: cur_chunk_sizes %= block
self.cur_chunk_sizes[layer_idx] = prefill_len % block
self.true_tokens[layer_idx] = n_text
if self.is_large_window[layer_idx]:
text_k = prefill_k.index_select(2, text_positions)
text_v = prefill_v.index_select(2, text_positions)
cap = n_text + self._APPEND_HEADROOM
new_k = torch.empty(bsz, heads, cap, head_dim, dtype=dtype, device=device)
new_v = torch.empty(bsz, heads, cap, head_dim, dtype=dtype, device=device)
if n_text > 0:
new_k[:, :, :n_text, :].copy_(text_k)
new_v[:, :, :n_text, :].copy_(text_v)
self.key_cache[layer_idx] = new_k
self.value_cache[layer_idx] = new_v
self._text_len[layer_idx] = n_text
self._capacity[layer_idx] = cap
self._total_chunks[layer_idx] = n_text // self.summary_chunk_size
else:
all_summary_k = prefill_k.index_select(2, summary_positions)
all_summary_v = prefill_v.index_select(2, summary_positions)
n_summary = all_summary_k.shape[2]
C = self.summary_chunk_size
ws = self.window_sizes[layer_idx]
scn = self.sliding_chunk_nums[layer_idx]
n_complete_chunks = n_text // C
n_partial = n_text % C
n_complete_text = n_complete_chunks * C
n_window_chunks = min(scn, n_complete_chunks)
n_window_text = n_window_chunks * C
window_start = n_complete_text - n_window_text
# Only retain the live window and partial chunk, avoiding a
# full-context text KV temporary on compressed layers.
live_positions = text_positions[window_start:]
text_k = prefill_k.index_select(2, live_positions)
text_v = prefill_v.index_select(2, live_positions)
n_old_total = max(0, n_summary - n_window_chunks)
n_new = n_summary - n_old_total
hierarchical = self.meta_pool_enabled[layer_idx]
group_size = self.meta_pool_group_sizes[layer_idx]
n_meta = n_old_total // group_size if hierarchical else 0
n_old = n_old_total % group_size if hierarchical else n_old_total
old_begin = n_meta * group_size if hierarchical else 0
old_s_cap = max(self._SUMMARY_INIT_CAP, (n_old + 1) * 2)
total_cap = ws + old_s_cap + C
new_k = torch.empty(bsz, heads, total_cap, head_dim, dtype=dtype, device=device)
new_v = torch.empty(bsz, heads, total_cap, head_dim, dtype=dtype, device=device)
if n_window_text > 0:
new_k[:, :, :n_window_text, :].copy_(text_k[:, :, :n_window_text, :])
new_v[:, :, :n_window_text, :].copy_(text_v[:, :, :n_window_text, :])
self._n_valid_window[layer_idx] = n_window_text
self._window_write_ptr[layer_idx] = n_window_text % ws
if n_old > 0:
new_k[:, :, ws:ws + n_old, :].copy_(
all_summary_k[:, :, old_begin:n_old_total, :]
)
new_v[:, :, ws:ws + n_old, :].copy_(
all_summary_v[:, :, old_begin:n_old_total, :]
)
self._old_summary_len[layer_idx] = n_old
self._old_summary_total[layer_idx] = n_old_total
self._old_summary_cap[layer_idx] = old_s_cap
if hierarchical:
required = (
pre_rope_key_states,
summary_scores,
summary_cos,
summary_sin,
summary_position_ids,
meta_key_states,
meta_value_states,
)
if any(item is None for item in required):
raise ValueError("hierarchical prefill cache requires pre-RoPE and meta state")
all_pre_k = pre_rope_key_states.index_select(2, summary_positions)
meta_cap = max(self._SUMMARY_INIT_CAP, (n_meta + 1) * 2)
meta_k_buf = torch.empty(
bsz, heads, meta_cap, head_dim, dtype=dtype, device=device
)
meta_v_buf = torch.empty_like(meta_k_buf)
if n_meta:
meta_k_buf[:, :, :n_meta, :].copy_(meta_key_states[:, :, :n_meta, :])
meta_v_buf[:, :, :n_meta, :].copy_(meta_value_states[:, :, :n_meta, :])
self._meta_key_cache[layer_idx] = meta_k_buf
self._meta_value_cache[layer_idx] = meta_v_buf
self._meta_len[layer_idx] = n_meta
self._meta_cap[layer_idx] = meta_cap
residual_pre = torch.empty(
bsz, heads, group_size, head_dim, dtype=dtype, device=device
)
score_width = (
heads * head_dim
if self.meta_pool_mode == "dimension_wise"
else 1
)
residual_score = torch.empty(
bsz,
group_size,
score_width,
dtype=summary_scores.dtype,
device=device,
)
residual_cos = torch.empty(
bsz, group_size, head_dim, dtype=summary_cos.dtype, device=device
)
residual_sin = torch.empty_like(residual_cos)
residual_position = torch.empty(
bsz, group_size, dtype=torch.long, device=device
)
if n_old:
residual_pre[:, :, :n_old, :].copy_(
all_pre_k[:, :, old_begin:n_old_total, :]
)
residual_score[:, :n_old, :].copy_(
summary_scores[:, old_begin:n_old_total]
)
residual_cos[:, :n_old, :].copy_(
summary_cos[:, old_begin:n_old_total, :]
)
residual_sin[:, :n_old, :].copy_(
summary_sin[:, old_begin:n_old_total, :]
)
residual_position[:, :n_old].copy_(
summary_position_ids[:, old_begin:n_old_total]
)
self._residual_pre_key_buf[layer_idx] = residual_pre
self._residual_score_buf[layer_idx] = residual_score
self._residual_cos_buf[layer_idx] = residual_cos
self._residual_sin_buf[layer_idx] = residual_sin
self._residual_position_buf[layer_idx] = residual_position
if n_partial > 0:
mirror_start = ws + n_old
new_k[:, :, mirror_start:mirror_start + n_partial, :].copy_(
text_k[:, :, n_window_text:, :])
new_v[:, :, mirror_start:mirror_start + n_partial, :].copy_(
text_v[:, :, n_window_text:, :])
self.key_cache[layer_idx] = new_k
self.value_cache[layer_idx] = new_v
self._capacity[layer_idx] = total_cap
self._text_len[layer_idx] = 0
ns_buf_k = torch.empty(bsz, heads, scn, head_dim, dtype=dtype, device=device)
ns_buf_v = torch.empty(bsz, heads, scn, head_dim, dtype=dtype, device=device)
if n_new > 0:
ns_buf_k[:, :, :n_new, :].copy_(all_summary_k[:, :, n_old_total:, :])
ns_buf_v[:, :, :n_new, :].copy_(all_summary_v[:, :, n_old_total:, :])
self._new_sum_key_buf[layer_idx] = ns_buf_k
self._new_sum_value_buf[layer_idx] = ns_buf_v
if hierarchical:
ns_pre = torch.empty(bsz, heads, scn, head_dim, dtype=dtype, device=device)
score_width = (
heads * head_dim
if self.meta_pool_mode == "dimension_wise"
else 1
)
ns_score = torch.empty(
bsz,
scn,
score_width,
dtype=summary_scores.dtype,
device=device,
)
ns_cos = torch.empty(bsz, scn, head_dim, dtype=summary_cos.dtype, device=device)
ns_sin = torch.empty_like(ns_cos)
ns_position = torch.empty(
bsz, scn, dtype=torch.long, device=device
)
if n_new:
ns_pre[:, :, :n_new, :].copy_(all_pre_k[:, :, n_old_total:, :])
ns_score[:, :n_new, :].copy_(summary_scores[:, n_old_total:])
ns_cos[:, :n_new, :].copy_(summary_cos[:, n_old_total:, :])
ns_sin[:, :n_new, :].copy_(summary_sin[:, n_old_total:, :])
ns_position[:, :n_new].copy_(
summary_position_ids[:, n_old_total:]
)
self._new_sum_pre_key_buf[layer_idx] = ns_pre
self._new_sum_score_buf[layer_idx] = ns_score
self._new_sum_cos_buf[layer_idx] = ns_cos
self._new_sum_sin_buf[layer_idx] = ns_sin
self._new_sum_position_buf[layer_idx] = ns_position
self._new_sum_len[layer_idx] = n_new
self._new_sum_write_ptr[layer_idx] = n_new % scn
self._chunk_buf_len[layer_idx] = n_partial
self._total_chunks[layer_idx] = n_old_total + n_new
self._reorganized[layer_idx] = True
self.set_sksa_chunk_k_from_prefill(
layer_idx, sksa_chunk_key_states, summary_mask
)
# ── Reorganize after prefill ──
def reorganize_after_prefill(self, summary_mask: torch.Tensor):
"""No-op sanity check.
Prefill path now writes each layer directly into the ring buffer layout via
init_layer_from_prefill, so by the time Qwen3Model.forward calls this,
all layers must already be reorganized.
"""
for layer_idx in range(self.num_hidden_layers):
assert self._reorganized[layer_idx], (
f"layer {layer_idx} was not initialized via init_layer_from_prefill"
)
self._prefill_text_positions = None
self._prefill_summary_positions = None
# ── Decode: text token update ──
def update_text(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
*,
sksa_chunk_key_states: Optional[torch.Tensor] = None,
):
"""Write a single text token KV during decode."""
if self.is_large_window[layer_idx]:
cur = self._text_len[layer_idx]
new_len = cur + 1
if new_len > self._capacity[layer_idx]:
cap = max(new_len + self._APPEND_HEADROOM, self._capacity[layer_idx] * 2)
old_k, old_v = self.key_cache[layer_idx], self.value_cache[layer_idx]
bsz, heads, _, head_dim = old_k.shape
new_k = torch.empty(bsz, heads, cap, head_dim, dtype=old_k.dtype, device=old_k.device)
new_v = torch.empty(bsz, heads, cap, head_dim, dtype=old_v.dtype, device=old_v.device)
new_k[:, :, :cur, :].copy_(old_k[:, :, :cur, :])
new_v[:, :, :cur, :].copy_(old_v[:, :, :cur, :])
self.key_cache[layer_idx] = new_k
self.value_cache[layer_idx] = new_v
self._capacity[layer_idx] = cap
self.key_cache[layer_idx][:, :, cur:new_len, :].copy_(key_states)
self.value_cache[layer_idx][:, :, cur:new_len, :].copy_(value_states)
self._text_len[layer_idx] = new_len
else:
# Write only to key_cache mirror region (chunk_buf eliminated)
ws = self.window_sizes[layer_idx]
n_old = self._old_summary_len[layer_idx]
pos = self._chunk_buf_len[layer_idx]
mirror_pos = ws + n_old + pos
self.key_cache[layer_idx][:, :, mirror_pos:mirror_pos+1, :].copy_(key_states)
self.value_cache[layer_idx][:, :, mirror_pos:mirror_pos+1, :].copy_(value_states)
self._chunk_buf_len[layer_idx] = pos + 1
self.cur_chunk_sizes[layer_idx] += 1
self.true_tokens[layer_idx] += 1
self.append_sksa_chunk_k(layer_idx, sksa_chunk_key_states)
# ── Decode: summary token update ──
def update_summary(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
*,
pre_rope_key_states: Optional[torch.Tensor] = None,
summary_scores: Optional[torch.Tensor] = None,
summary_cos: Optional[torch.Tensor] = None,
summary_sin: Optional[torch.Tensor] = None,
summary_position_ids: Optional[torch.Tensor] = None,
position_bias: Optional[torch.Tensor] = None,
):
"""Write summary token KV during decode (chunk boundary).
Large window: skip.
Small window (order matters — flush mirror before evict to avoid clobbering):
1. Flush mirror region → ring
2. Evict oldest new_summary → old_summary in key_cache (if full)
3. Write new summary → new_summary_buf
"""
n_summary = key_states.shape[2]
if self.is_large_window[layer_idx]:
self.cur_chunk_sizes[layer_idx] += n_summary
self._total_chunks[layer_idx] += n_summary
return
C = self.summary_chunk_size
ws = self.window_sizes[layer_idx]
scn = self.sliding_chunk_nums[layer_idx]
hierarchical = self.meta_pool_enabled[layer_idx]
group_size = self.meta_pool_group_sizes[layer_idx]
cbl = self._chunk_buf_len[layer_idx]
ptr = self._window_write_ptr[layer_idx]
n_old = self._old_summary_len[layer_idx]
# Step 1: Flush mirror region → ring (must happen before evict touches mirror[0])
mirror_start = ws + n_old
if ptr + cbl <= ws:
self.key_cache[layer_idx][:, :, ptr:ptr + cbl, :].copy_(
self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
self.value_cache[layer_idx][:, :, ptr:ptr + cbl, :].copy_(
self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
else:
first = ws - ptr
self.key_cache[layer_idx][:, :, ptr:ws, :].copy_(
self.key_cache[layer_idx][:, :, mirror_start:mirror_start + first, :])
self.value_cache[layer_idx][:, :, ptr:ws, :].copy_(
self.value_cache[layer_idx][:, :, mirror_start:mirror_start + first, :])
rest = cbl - first
self.key_cache[layer_idx][:, :, :rest, :].copy_(
self.key_cache[layer_idx][:, :, mirror_start + first:mirror_start + cbl, :])
self.value_cache[layer_idx][:, :, :rest, :].copy_(
self.value_cache[layer_idx][:, :, mirror_start + first:mirror_start + cbl, :])
self._window_write_ptr[layer_idx] = (ptr + cbl) % ws
if self._n_valid_window[layer_idx] < ws:
self._n_valid_window[layer_idx] = min(ws, self._n_valid_window[layer_idx] + cbl)
self._chunk_buf_len[layer_idx] = 0
# Step 2: Evict oldest new_summary → old_summary (now safe — mirror already flushed)
if self._new_sum_len[layer_idx] >= scn:
read_ptr = self._new_sum_write_ptr[layer_idx]
old_dst = ws + n_old # == mirror_start, but mirror data is already in ring
# Check capacity for old_summary growth
needed = old_dst + 1 + C # +1 for new old_sum, +C for future chunk mirror
if needed > self._capacity[layer_idx]:
new_s_cap = max(self._old_summary_cap[layer_idx] * 2, n_old + self._SUMMARY_INIT_CAP)
new_total = ws + new_s_cap + C
old_k, old_v = self.key_cache[layer_idx], self.value_cache[layer_idx]
bsz, heads, _, head_dim = old_k.shape
nk = torch.empty(bsz, heads, new_total, head_dim, dtype=old_k.dtype, device=old_k.device)
nv = torch.empty(bsz, heads, new_total, head_dim, dtype=old_v.dtype, device=old_v.device)
copy_len = ws + n_old
nk[:, :, :copy_len, :].copy_(old_k[:, :, :copy_len, :])
nv[:, :, :copy_len, :].copy_(old_v[:, :, :copy_len, :])
self.key_cache[layer_idx] = nk
self.value_cache[layer_idx] = nv
self._old_summary_cap[layer_idx] = new_s_cap
self._capacity[layer_idx] = new_total
self.key_cache[layer_idx][:, :, old_dst:old_dst+1, :].copy_(
self._new_sum_key_buf[layer_idx][:, :, read_ptr:read_ptr+1, :])
self.value_cache[layer_idx][:, :, old_dst:old_dst+1, :].copy_(
self._new_sum_value_buf[layer_idx][:, :, read_ptr:read_ptr+1, :])
n_old += 1
self._old_summary_total[layer_idx] += 1
if hierarchical:
self._residual_pre_key_buf[layer_idx][:, :, n_old-1:n_old, :].copy_(
self._new_sum_pre_key_buf[layer_idx][:, :, read_ptr:read_ptr+1, :]
)
self._residual_score_buf[layer_idx][:, n_old-1:n_old, :].copy_(
self._new_sum_score_buf[layer_idx][:, read_ptr:read_ptr+1, :]
)
self._residual_cos_buf[layer_idx][:, n_old-1:n_old, :].copy_(
self._new_sum_cos_buf[layer_idx][:, read_ptr:read_ptr+1, :]
)
self._residual_sin_buf[layer_idx][:, n_old-1:n_old, :].copy_(
self._new_sum_sin_buf[layer_idx][:, read_ptr:read_ptr+1, :]
)
self._residual_position_buf[layer_idx][:, n_old-1:n_old].copy_(
self._new_sum_position_buf[layer_idx][
:, read_ptr:read_ptr+1
]
)
if n_old == group_size:
scores = self._residual_score_buf[layer_idx][
:, :group_size, :
]
if self.meta_pool_mode == "dimension_wise":
if position_bias is None:
weights = torch.full_like(scores, 1.0 / group_size)
else:
weights = torch.softmax(
(
scores
+ position_bias.view(
1, group_size, scores.shape[-1]
)
).float(),
dim=1,
).to(key_states.dtype)
bsz, heads, _, head_dim = self._residual_pre_key_buf[
layer_idx
].shape
weights = weights.view(
bsz, group_size, heads, head_dim
).permute(0, 2, 1, 3)
else:
scalar_scores = scores.squeeze(-1)
if position_bias is None:
scalar_weights = torch.full_like(
scalar_scores, 1.0 / group_size
)
else:
scalar_weights = torch.softmax(
(
scalar_scores
+ position_bias.view(1, group_size)
).float(),
dim=1,
).to(key_states.dtype)
weights = scalar_weights[:, None, :, None]
meta_pre_k = (
self._residual_pre_key_buf[layer_idx][:, :, :group_size, :]
* weights
).sum(dim=2)
meta_v = (
self.value_cache[layer_idx][:, :, ws:ws + group_size, :]
* weights
).sum(dim=2)
last_cos = self._residual_cos_buf[layer_idx][:, group_size - 1, :]
last_sin = self._residual_sin_buf[layer_idx][:, group_size - 1, :]
meta_k = (
meta_pre_k * last_cos[:, None, :]
+ rotate_half(meta_pre_k) * last_sin[:, None, :]
)
meta_dst = self._meta_len[layer_idx]
if meta_dst >= self._meta_cap[layer_idx]:
old_meta_k = self._meta_key_cache[layer_idx]
old_meta_v = self._meta_value_cache[layer_idx]
new_cap = max(meta_dst + self._SUMMARY_INIT_CAP, self._meta_cap[layer_idx] * 2)
bsz, heads, _, head_dim = old_meta_k.shape
new_meta_k = torch.empty(
bsz, heads, new_cap, head_dim,
dtype=old_meta_k.dtype, device=old_meta_k.device,
)
new_meta_v = torch.empty_like(new_meta_k)
new_meta_k[:, :, :meta_dst, :].copy_(old_meta_k[:, :, :meta_dst, :])
new_meta_v[:, :, :meta_dst, :].copy_(old_meta_v[:, :, :meta_dst, :])
self._meta_key_cache[layer_idx] = new_meta_k
self._meta_value_cache[layer_idx] = new_meta_v
self._meta_cap[layer_idx] = new_cap
self._meta_key_cache[layer_idx][:, :, meta_dst:meta_dst+1, :].copy_(
meta_k.unsqueeze(2)
)
self._meta_value_cache[layer_idx][:, :, meta_dst:meta_dst+1, :].copy_(
meta_v.unsqueeze(2)
)
self._meta_len[layer_idx] += 1
n_old = 0
self._old_summary_len[layer_idx] = n_old
# Step 3: Write new summary to new_summary_buf (overwrite oldest slot)
w_ptr = self._new_sum_write_ptr[layer_idx]
self._new_sum_key_buf[layer_idx][:, :, w_ptr:w_ptr+1, :].copy_(key_states)
self._new_sum_value_buf[layer_idx][:, :, w_ptr:w_ptr+1, :].copy_(value_states)
if hierarchical:
if any(
item is None
for item in (
pre_rope_key_states,
summary_scores,
summary_cos,
summary_sin,
summary_position_ids,
)
):
raise ValueError("hierarchical decode requires pre-RoPE summary state")
self._new_sum_pre_key_buf[layer_idx][:, :, w_ptr:w_ptr+1, :].copy_(
pre_rope_key_states
)
self._new_sum_score_buf[layer_idx][:, w_ptr:w_ptr+1, :].copy_(
summary_scores
)
self._new_sum_cos_buf[layer_idx][:, w_ptr:w_ptr+1, :].copy_(summary_cos)
self._new_sum_sin_buf[layer_idx][:, w_ptr:w_ptr+1, :].copy_(summary_sin)
self._new_sum_position_buf[layer_idx][
:, w_ptr:w_ptr+1
].copy_(summary_position_ids)
self._new_sum_write_ptr[layer_idx] = (w_ptr + 1) % scn
if self._new_sum_len[layer_idx] < scn:
self._new_sum_len[layer_idx] += 1
self.cur_chunk_sizes[layer_idx] += n_summary
self._total_chunks[layer_idx] += n_summary
# ── Decode: get KV for attention ──
def get_attention_kv(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor, int]:
"""Get full KV for text token attention.
Large window: buffer[:text_len]
Small window (steady state): key_cache[:ws + n_old + cbl] — single slice, zero cat
"""
if self.is_large_window[layer_idx]:
tl = self._text_len[layer_idx]
return (self.key_cache[layer_idx][:, :, :tl, :],
self.value_cache[layer_idx][:, :, :tl, :], 0)
ws = self.window_sizes[layer_idx]
nv = self._n_valid_window[layer_idx]
cbl = self._chunk_buf_len[layer_idx]
n_old = self._old_summary_len[layer_idx]
# Steady state: ring full → [ring(ws) | old_sums(n_old) | chunk_mirror(cbl)] contiguous
if nv == ws:
end = ws + n_old + cbl
base_k = self.key_cache[layer_idx][:, :, :end, :]
base_v = self.value_cache[layer_idx][:, :, :end, :]
else:
# Warmup: ring not full, [nv:ws] is gap → cat
parts_k, parts_v = [], []
if nv > 0:
parts_k.append(self.key_cache[layer_idx][:, :, :nv, :])
parts_v.append(self.value_cache[layer_idx][:, :, :nv, :])
if cbl > 0:
mirror_start = ws + n_old
parts_k.append(self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
parts_v.append(self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
if n_old > 0:
parts_k.append(self.key_cache[layer_idx][:, :, ws:ws + n_old, :])
parts_v.append(self.value_cache[layer_idx][:, :, ws:ws + n_old, :])
base_k = parts_k[0] if len(parts_k) == 1 else torch.cat(parts_k, dim=2)
base_v = parts_v[0] if len(parts_v) == 1 else torch.cat(parts_v, dim=2)
meta_len = self._meta_len[layer_idx]
if meta_len:
base_k = torch.cat(
(base_k, self._meta_key_cache[layer_idx][:, :, :meta_len, :]), dim=2
)
base_v = torch.cat(
(base_v, self._meta_value_cache[layer_idx][:, :, :meta_len, :]), dim=2
)
return base_k, base_v, meta_len
def get_current_chunk_kv(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
"""Get KV of the current chunk's C text tokens for summary token attention."""
C = self.summary_chunk_size
if self.is_large_window[layer_idx]:
tl = self._text_len[layer_idx]
return (self.key_cache[layer_idx][:, :, tl - C:tl, :],
self.value_cache[layer_idx][:, :, tl - C:tl, :])
else:
ws = self.window_sizes[layer_idx]
n_old = self._old_summary_len[layer_idx]
cbl = self._chunk_buf_len[layer_idx]
mirror_start = ws + n_old
return (self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :],
self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
def reset_chunk_counter(self):
"""Reset chunk counters after a chunk boundary step completes."""
block = self.summary_chunk_size + self.summary_token_num
for layer_idx in range(self.num_hidden_layers):
if self.cur_chunk_sizes[layer_idx] >= block:
self.cur_chunk_sizes[layer_idx] %= block
|