STT: Add sliding window and attention sink cache

[Blaizzy]

1. Sliding‑window (token‑wise) trimming

Keep only the most‑recent N tokens in each layer’s key/value tensors.

MAX_CACHE_TOKENS = 256          #  ≃ 12 s of speech for tiny/small
def trim_past_kv(past_kv, keep=MAX_CACHE_TOKENS):
"""past_kv is an EncoderDecoderCache.
Returns a view that keeps only the last keep positions."""
for layer in range(len(past_kv.self_attention_cache.key_cache)):
k = past_kv.self_attention_cache.key_cache[layer]
v = past_kv.self_attention_cache.value_cache[layer]
# k, v: (batch, heads, seq_len, head_dim)
past_kv.self_attention_cache.key_cache[layer]   = k[:, :, -keep:, :]
past_kv.self_attention_cache.value_cache[layer] = v[:, :, -keep:, :]
# Tell the cache how many positions we just dropped
past_kv.offset -= max(0, past_kv.seq_len - keep)
return past_kv

Insert this right after you append each new token:

past_kv = trim_past_kv(past_kv)

Memory saved – 2 × L × H × D × keep × bytes_per_elem
Whisper‑small fp16 ⇒ 2 × 12 × 6 × 64 × 256 × 2 ≈ 3 MiB instead of 6 MiB with a 512‑token cache.

Accuracy hit – Up to ~1 s before the window edge you’ll almost never notice; beyond that you might lose punctuation or long‑range re‑writes. With keep=256 most users report no audible quality drop in interactive dictation.

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2. Sliding‑window with attention sink

Keep the first SINK_SIZE global “sink” tokens plus the newest WINDOW_SIZE tokens. These sink positions act as a stable anchor and restore most of the quality you’d lose with a pure sliding window — at essentially the same memory cost.

SINK_SIZE   = 32    # first tokens kept forever
WINDOW_SIZE = 256   # recent tokens kept as usual
def trim_kv_with_sink(past_kv, sink=SINK_SIZE, window=WINDOW_SIZE):
"""Return a view that keeps <sink> head tokens and the last <window> ones."""
seq_len = past_kv.get_seq_length()
if seq_len <= sink + window:
return past_kv  # nothing to trim yet
for layer in range(len(past_kv.self_attention_cache.key_cache)):
    k = past_kv.self_attention_cache.key_cache[layer]
    v = past_kv.self_attention_cache.value_cache[layer]
    past_kv.self_attention_cache.key_cache[layer] = torch.cat(
        [k[:, :, :sink, :], k[:, :, -window:, :]], dim=2)
    past_kv.self_attention_cache.value_cache[layer] = torch.cat(
        [v[:, :, :sink, :], v[:, :, -window:, :]], dim=2)

past_kv.offset += seq_len - (sink + window)  # align cache_position
return past_kv

After generating each new token:

past_kv = trim_kv_with_sink(past_kv)

Memory saved – (SINK_SIZE + WINDOW_SIZE) × 2 × L × H × D × bytes_per_elem.
With the values above on Whisper‑small fp16 this is ≈ 3.4 MiB.

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3. Layer‑wise half‑caching

Keep all tokens, but only in every second decoder layer:

def drop_even_layers(past_kv):
    for layer in range(0, len(past_kv.self_attention_cache.key_cache), 2):
        past_kv.self_attention_cache.key_cache[layer]   = None
        past_kv.self_attention_cache.value_cache[layer] = None
    return past_kv

When the model reaches a layer whose cache is None it just recomputes K/V on the fly. This roughly halves memory and keeps long‑range context, but adds ~8–12 % extra compute because the skipped layers do some re‑work.

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4. Which one should you pick?

Goal	Recommended trick
Lowest latency (GPU)	Sliding‑window, keep≈128–256
Tiny VRAM (≤ 4 GB)	Window keep≈128 and drop even layers
CPU‑only laptop	Layer‑wise drop (compute‑bound > memory‑bound)
No quality loss allowed	Keep sink tokens and leave first 2–4 decoder layers fully cached

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Implementation notes & gotchas

1. Adjust cache_position – after you slice tokens you must subtract the number you removed (past_kv.offset above) so the causal mask lines up.

2. When you restart generation (new sentence, VAD gap, …) just set past_kv = None rather than trying to “flush” it.

3. All tricks work for attn_implementation="sdpa"; they’re just plain tensor views.

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TL;DR

Yes, trim away. A 256‑token sliding window is the cleanest 50 % memory win with negligible impact; adding sink tokens restores accuracy, and drop‑every‑other‑layer buys another ~25 %, paid for in a small speed tax. The core code is literally a single tensor[:, :, -N:] or torch.cat() line.