minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 13 Packages Projects Releases Wiki Activity

[Apple Silicon][MLX] Cache seq_lens-derived tensors in BatchedDecodeContext (#23470)

Browse Source 
Signed-off-by: Xiaodong Ye <yeahdongcn@gmail.com>
This commit is contained in:
R0CKSTAR
2026-04-23 18:12:26 -07:00
committed by GitHub
parent 74c2e5bacd
commit 87e50f20f6
1 changed files with 26 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/sglang/srt/hardware_backend/mlx/kv_cache/attention_wrapper.py
+26 -11
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
import threading
from dataclasses import dataclass
from dataclasses import dataclass, field
from typing import Any, Optional
import mlx.core as mx
@@ -23,6 +23,24 @@ class BatchedDecodeContext:
# layer_caches[layer_idx][req_idx] = ContiguousKVCache
layer_caches: list[list[ContiguousKVCache]]
# Derived tensors/metadata, shared across all layers in one forward pass.
offsets: mx.array = field(init=False)
max_len: int = field(init=False)
valid_lens: mx.array = field(init=False)
needs_padding: bool = field(init=False)
pad_sizes: list[int] = field(init=False)
positions: Optional[mx.array] = field(init=False)
def __post_init__(self) -> None:
seq_lens = self.seq_lens
max_seq_len = max(seq_lens)
self.offsets = mx.array(seq_lens, dtype=mx.int32)
self.max_len = max_seq_len + 1
self.valid_lens = self.offsets + 1
self.needs_padding = min(seq_lens) < max_seq_len
self.pad_sizes = [max_seq_len - s for s in seq_lens]
self.positions = mx.arange(self.max_len) if self.needs_padding else None
def set_context(ctx: Optional[BatchedDecodeContext]) -> None:
_thread_local.batched_ctx = ctx
@@ -78,12 +96,13 @@ class MLXAttentionWrapper(nn.Module):
values = values.transpose(0, 2, 1, 3)
# Vectorized RoPE with per-batch offsets
offsets = mx.array(ctx.seq_lens, dtype=mx.int32)
offsets = ctx.offsets
queries = inner.rope(queries, offset=offsets)
keys = inner.rope(keys, offset=offsets)
layer_caches = ctx.layer_caches[layer_idx]
max_len = max(ctx.seq_lens) + 1
max_len = ctx.max_len
pad_sizes = ctx.pad_sizes
# TODO: replace per-request loop with native batched/ragged
# attention once mx.fast.scaled_dot_product_attention supports
@@ -95,10 +114,9 @@ class MLXAttentionWrapper(nn.Module):
layer_caches[i].write_token(keys[i : i + 1], values[i : i + 1])
k_all, v_all = layer_caches[i].get_kv()
curr_len = layer_caches[i].offset
if curr_len < max_len:
pad = max_len - curr_len
pad = pad_sizes[i]
if pad > 0:
k_pad = mx.zeros(
(1, inner.n_kv_heads, pad, head_dim), dtype=k_all.dtype
)
@@ -115,11 +133,8 @@ class MLXAttentionWrapper(nn.Module):
values_b = mx.concatenate(all_v, axis=0)
attn_mask = None
seq_lens_plus1 = [s + 1 for s in ctx.seq_lens]
if min(seq_lens_plus1) < max_len:
positions = mx.arange(max_len)
valid_lens = mx.array(seq_lens_plus1, dtype=mx.int32)
mask_bool = positions[None, :] >= valid_lens[:, None]
if ctx.needs_padding:
mask_bool = ctx.positions[None, :] >= ctx.valid_lens[:, None]
attn_mask = mx.where(
mask_bool[:, None, None, :],
mx.array(mx.finfo(queries.dtype).min, dtype=queries.dtype),