minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

Fuse GLM-5.3-Flash KDA projections and prefill metadata (#39688)

Browse Source 
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
This commit is contained in:
Yuxuan Zhang
2026-09-19 23:46:33 -07:00
committed by GitHub
co-authored by Xinyuan Tong
parent c1a1eb5f66
commit c8eb54c41d
14 changed files with 1027 additions and 87 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/sglang/kernels/ops/attention/fla/kda.py
+58 -9
View File
@@ -917,6 +917,7 @@ def softplus_fwd(x):
@triton.heuristics(
{
"HAS_BIAS": lambda args: args["dt_bias"] is not None,
"HAS_BETA": lambda args: args["beta"] is not None,
"HAS_SCALE": lambda args: args["scale"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
"USE_LOWER_BOUND": lambda args: args["lower_bound"] is not None,
@@ -928,7 +929,7 @@ def softplus_fwd(x):
for BS in BS_LIST
for num_warps in [2, 4, 8]
],
key=["H", "S", "BT", "IS_VARLEN"],
key=["H", "S", "BT", "IS_VARLEN", "HAS_BETA"],
)
@triton.jit(do_not_specialize=["T"])
def kda_gate_chunk_cumsum_vector_kernel(
@@ -940,12 +941,18 @@ def kda_gate_chunk_cumsum_vector_kernel(
cu_seqlens,
chunk_indices,
lower_bound,
beta,
beta_out,
beta_stride_b: tl.constexpr,
beta_stride_t: tl.constexpr,
beta_stride_h: tl.constexpr,
T,
H: tl.constexpr,
S: tl.constexpr,
BT: tl.constexpr,
BS: tl.constexpr,
HAS_BIAS: tl.constexpr,
HAS_BETA: tl.constexpr,
HAS_SCALE: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_LOWER_BOUND: tl.constexpr,
@@ -1011,6 +1018,24 @@ def kda_gate_chunk_cumsum_vector_kernel(
b_o *= scale
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
if HAS_BETA:
if i_s == 0:
offsets_t = i_t * BT + tl.arange(0, BT)
if IS_VARLEN:
beta_offsets = (bos + offsets_t) * beta_stride_t
else:
beta_offsets = i_b * beta_stride_b + offsets_t * beta_stride_t
b_beta = tl.load(
beta + beta_offsets + i_h * beta_stride_h,
mask=offsets_t < T,
other=0.0,
).to(tl.float32)
tl.store(
beta_out + (bos + offsets_t) * H + i_h,
tl.sigmoid(b_beta),
mask=offsets_t < T,
)
def kda_gate_chunk_cumsum(
g: torch.Tensor,
@@ -1022,9 +1047,10 @@ def kda_gate_chunk_cumsum(
output_dtype: Optional[torch.dtype] = torch.float,
chunk_indices: Optional[torch.LongTensor] = None,
lower_bound: Optional[float] = None,
) -> torch.Tensor:
beta: Optional[torch.Tensor] = None,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
"""
Fused KDA gate activation + chunk-local cumulative sum.
Fused KDA gate activation + chunk-local cumulative sum, with optional beta.
Combines two memory-bound kernels into one:
1. Gate activation: g = -exp(A_log) * softplus(raw_g + dt_bias)
@@ -1040,9 +1066,11 @@ def kda_gate_chunk_cumsum(
output_dtype: Output dtype (default float32).
chunk_indices: Pre-computed chunk indices for varlen mode.
lower_bound: If set, use safe gate: lower_bound * sigmoid(exp(A_log) * g).
beta: Optional raw beta of shape [B, T, H], including strided projections.
Returns:
Cumulative-summed gated tensor of shape [B, T, H, K].
Cumulative-summed gated tensor of shape [B, T, H, K]. If beta is
supplied, also return its sigmoid in a contiguous float32 [B, T, H] tensor.
"""
if cu_seqlens is not None:
assert g.shape[0] == 1, (
@@ -1059,6 +1087,18 @@ def kda_gate_chunk_cumsum(
)
g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
if beta is not None:
assert beta.shape == (B, T, H)
assert beta.device == g.device
beta_out = torch.empty((B, T, H), dtype=torch.float32, device=beta.device)
beta_strides = beta.stride()
if cu_seqlens is not None:
beta_strides = (0, beta_strides[1], beta_strides[2])
else:
beta_out = None
beta_strides = (0, 0, 0)
if B * T == 0:
return g if beta is None else (g, beta_out)
def grid(meta):
return (cdiv(meta["S"], meta["BS"]), NT, B * H)
@@ -1072,12 +1112,17 @@ def kda_gate_chunk_cumsum(
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
lower_bound=lower_bound,
beta=beta,
beta_out=beta_out,
beta_stride_b=beta_strides[0],
beta_stride_t=beta_strides[1],
beta_stride_h=beta_strides[2],
T=T,
H=H,
S=S,
BT=BT,
)
return g
return g if beta is None else (g, beta_out)
def chunk_kda_fwd(
@@ -1096,6 +1141,7 @@ def chunk_kda_fwd(
output_intermediate_states: bool = False,
track_state: Optional[torch.Tensor] = None,
track_chunk_idx: Optional[torch.Tensor] = None,
beta_is_raw: bool = False,
):
chunk_size = 64
# Pre-compute chunk indices once and thread through all downstream kernels.
@@ -1118,9 +1164,14 @@ def chunk_kda_fwd(
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
lower_bound=lower_bound,
beta=beta if beta_is_raw else None,
)
if beta_is_raw:
g, beta = g
else:
# g is already gate-activated by caller; just do cumsum.
if beta_is_raw:
beta = beta.float().sigmoid().contiguous()
g = chunk_local_cumsum(
g,
chunk_size=chunk_size,
@@ -1226,16 +1277,13 @@ def chunk_kda(
q = l2norm_fwd(q.contiguous())
k = l2norm_fwd(k.contiguous())
if beta_is_raw:
beta = beta.float().sigmoid()
# Returns o [B, T, H, V] when output_intermediate_states=False, or (o, h [B, NT, H, V, K]) when output_intermediate_states=True.
return chunk_kda_fwd(
q=q,
k=k,
v=v.contiguous(),
g=g.contiguous(),
beta=beta.contiguous(),
beta=beta if beta_is_raw else beta.contiguous(),
scale=scale,
initial_state=initial_state,
initial_state_indices=initial_state_indices,
@@ -1246,4 +1294,5 @@ def chunk_kda(
output_intermediate_states=output_intermediate_states,
track_state=track_state,
track_chunk_idx=track_chunk_idx,
beta_is_raw=beta_is_raw,
)
python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py
+92 -7
View File
@@ -26,6 +26,9 @@ from sglang.srt.layers.attention.mamba.mamba2_metadata import (
ForwardMetadata,
Mamba2Metadata,
)
from sglang.srt.layers.attention.mamba.prefill_track_metadata import (
build_prefill_track_plan,
)
from sglang.srt.layers.attention.mamba.replay_state_indices_validator import (
validate_replay_state_indices_cpu,
)
@@ -36,6 +39,7 @@ from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.runtime_context import get_exec, get_memory, get_spec
from sglang.srt.speculative.eagle_info import EagleDraftInput, EagleVerifyInput
from sglang.srt.speculative.spec_info import SpecInput
from sglang.srt.utils import is_pin_memory_available
if TYPE_CHECKING:
from sglang.srt.layers.attention.verify_mask import VerifyMask
@@ -118,6 +122,22 @@ class MambaAttnBackendBase(AttentionBackend):
state ops, incl. the cuda-graph replay-prep copy into ``state_indices_list``."""
return self.req_to_token_pool.translate_mamba_indices(mamba_indices)
@staticmethod
def _has_cpu_prefill_track_metadata(forward_batch: ForwardBatch) -> bool:
return (
forward_batch.forward_mode.is_extend()
and not forward_batch.forward_mode.is_target_verify()
and all(
values is not None and len(values) == forward_batch.batch_size
for values in (
forward_batch.mamba_prefill_track_mask_cpu,
forward_batch.mamba_track_seqlens_cpu,
forward_batch.extend_seq_lens_cpu,
forward_batch.extend_prefix_lens_cpu,
)
)
)
def _forward_metadata(self, forward_batch: ForwardBatch):
bs = forward_batch.batch_size
@@ -134,6 +154,8 @@ class MambaAttnBackendBase(AttentionBackend):
track_ssm_seq_idx = None
track_ssm_end_locs = None
track_ssm_recompute_dst = None
logical_num_tokens = None
track_mask_indices = None
mamba_cache_indices = self.req_to_token_pool.get_mamba_indices(
forward_batch.req_pool_indices
@@ -146,10 +168,20 @@ class MambaAttnBackendBase(AttentionBackend):
forward_batch.mamba_track_indices
)
# Resolve the tracked-row selection once per forward
has_mamba_track_mask = bool(
forward_batch.mamba_track_mask is not None
and forward_batch.mamba_track_mask.any()
)
cpu_track_metadata = self._has_cpu_prefill_track_metadata(forward_batch)
if cpu_track_metadata:
rows = [
i
for i, track in enumerate(forward_batch.mamba_prefill_track_mask_cpu)
if track
]
has_mamba_track_mask = bool(rows)
track_mask_indices = self._track_indices_to_device(rows) if rows else None
else:
has_mamba_track_mask = bool(
forward_batch.mamba_track_mask is not None
and forward_batch.mamba_track_mask.any()
)
_real_bs = forward_batch._original_batch_size
if _real_bs is not None and _real_bs < mamba_cache_indices.shape[0]:
mamba_cache_indices = mamba_cache_indices.clone()
@@ -258,9 +290,17 @@ class MambaAttnBackendBase(AttentionBackend):
forward_batch.extend_start_loc[-1]
+ forward_batch.extend_seq_lens[-1]
)
if (
forward_batch.extend_seq_lens_cpu is not None
and len(forward_batch.extend_seq_lens_cpu) == bs
and forward_batch.tbo_parent_token_range is None
):
logical_num_tokens = sum(forward_batch.extend_seq_lens_cpu)
else:
logical_num_tokens = int(query_start_loc[-1])
if has_mamba_track_mask:
track_conv_indices = self._init_track_conv_indices(
query_start_loc, forward_batch
query_start_loc, forward_batch, track_mask_indices
)
(
@@ -279,6 +319,8 @@ class MambaAttnBackendBase(AttentionBackend):
return ForwardMetadata(
query_start_loc=query_start_loc,
logical_num_tokens=logical_num_tokens,
mamba_track_mask_indices=track_mask_indices,
mamba_cache_indices=mamba_cache_indices,
# Physical track destinations (None when tracking off); cuda-graph
# supplies this via the static backend buffer in _replay_metadata.
@@ -347,7 +389,10 @@ class MambaAttnBackendBase(AttentionBackend):
)
def _init_track_conv_indices(
self, query_start_loc: torch.Tensor, forward_batch: ForwardBatch
self,
query_start_loc: torch.Tensor,
forward_batch: ForwardBatch,
track_mask_indices: Optional[torch.Tensor] = None,
):
"""Flattened input positions of conv states to track during extend (up to
the last complete chunk boundary, mamba_track_mask rows only)."""
@@ -361,7 +406,11 @@ class MambaAttnBackendBase(AttentionBackend):
"this path should only run when the track mask is set on an extend batch"
)
start_indices = query_start_loc[:-1] + aligned_len - conv_state_len
start_indices = start_indices[forward_batch.mamba_track_mask]
start_indices = (
start_indices.index_select(0, track_mask_indices)
if track_mask_indices is not None
else start_indices[forward_batch.mamba_track_mask]
)
indices = start_indices.unsqueeze(-1) + torch.arange(
conv_state_len,
@@ -379,6 +428,10 @@ class MambaAttnBackendBase(AttentionBackend):
chunk boundary. Also returns ``track_ssm_h_batch_src``: the batch rows of
the unaligned tracked seqs, used to integer-index the fp32 snapshot
buffer on the KDA path so the copy stays free of GPU syncs."""
if self._has_cpu_prefill_track_metadata(forward_batch):
return self._init_track_ssm_indices_from_cpu(
mamba_cache_indices, forward_batch
)
state_chunk_size = self.mamba_chunk_size
# CPU to avoid kernel launches for the masking ops
mamba_track_mask = forward_batch.mamba_track_mask.cpu()
@@ -454,6 +507,38 @@ class MambaAttnBackendBase(AttentionBackend):
to_device(track_ssm_recompute_dst),
)
def _track_indices_to_device(self, values, dtype=torch.int64):
return torch.tensor(
values, dtype=dtype, pin_memory=is_pin_memory_available(self.device)
).to(self.device, non_blocking=True)
def _init_track_ssm_indices_from_cpu(self, mamba_cache_indices, forward_batch):
is_mamba2 = isinstance(self, Mamba2AttnBackend)
plan = build_prefill_track_plan(
forward_batch.mamba_prefill_track_mask_cpu,
forward_batch.mamba_track_seqlens_cpu,
forward_batch.extend_seq_lens_cpu,
forward_batch.extend_prefix_lens_cpu,
self.mamba_chunk_size,
mamba2=is_mamba2,
)
to_device = self._track_indices_to_device
final_rows = to_device(plan.final_rows)
h_rows = to_device(plan.h_rows)
recompute_rows = to_device(plan.recompute_rows) if is_mamba2 else None
destinations = forward_batch.mamba_track_indices
return (
to_device(plan.chunk_indices, torch.int32),
to_device(plan.h_src),
destinations.index_select(0, h_rows),
to_device(plan.unaligned_rows),
mamba_cache_indices.index_select(0, final_rows),
destinations.index_select(0, final_rows),
recompute_rows,
to_device(plan.recompute_end_locs) if is_mamba2 else None,
destinations.index_select(0, recompute_rows) if is_mamba2 else None,
)
def init_forward_metadata_capture_cpu_graph(
self,
bs: int,
python/sglang/srt/layers/attention/linear/gdn_backend.py
+4 -3
View File
@@ -540,9 +540,10 @@ class GDNAttnBackend(MambaAttnBackendBase):
raise ValueError("GDN MIS metadata requires --enable-mis")
self.mis_metadata = build_gdn_mis_metadata(forward_batch)
if self.forward_metadata.has_mamba_track_mask:
self.forward_metadata.mamba_track_mask_indices = (
forward_batch.mamba_track_mask.nonzero(as_tuple=True)[0]
)
if getattr(self.forward_metadata, "mamba_track_mask_indices", None) is None:
self.forward_metadata.mamba_track_mask_indices = (
forward_batch.mamba_track_mask.nonzero(as_tuple=True)[0]
)
self.forward_metadata.conv_states_mask_indices = (
forward_batch.mamba_track_indices[
self.forward_metadata.mamba_track_mask_indices
python/sglang/srt/layers/attention/linear/kda_backend.py
+7 -4
View File
@@ -533,9 +533,10 @@ class KDAAttnBackend(MambaAttnBackendBase):
def init_forward_metadata(self, forward_batch: ForwardBatch):
super().init_forward_metadata(forward_batch)
if self.forward_metadata.has_mamba_track_mask:
self.forward_metadata.mamba_track_mask_indices = (
forward_batch.mamba_track_mask.nonzero(as_tuple=True)[0]
)
if self.forward_metadata.mamba_track_mask_indices is None:
self.forward_metadata.mamba_track_mask_indices = (
forward_batch.mamba_track_mask.nonzero(as_tuple=True)[0]
)
self.forward_metadata.conv_states_mask_indices = (
forward_batch.mamba_track_indices[
self.forward_metadata.mamba_track_mask_indices
@@ -822,7 +823,9 @@ class KDAAttnBackend(MambaAttnBackendBase):
has_initial_state = forward_batch.extend_prefix_lens > 0
physical_num_tokens = mixed_qkv.shape[0]
logical_num_tokens = int(query_start_loc[-1])
logical_num_tokens = self.forward_metadata.logical_num_tokens
if logical_num_tokens is None:
logical_num_tokens = int(query_start_loc[-1])
if logical_num_tokens < physical_num_tokens:
mixed_qkv = mixed_qkv[:logical_num_tokens]
a = a[:, :logical_num_tokens]
python/sglang/srt/layers/attention/mamba/mamba2_metadata.py
+1
View File
@@ -29,6 +29,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch
class ForwardMetadata:
query_start_loc: torch.Tensor
mamba_cache_indices: torch.Tensor
logical_num_tokens: Optional[int] = None
mamba_cache_indices_gdn: Optional[torch.Tensor] = None
# Mamba track DESTINATION slots (PHYSICAL, length == batch). Like
# mamba_cache_indices: a backend-owned static buffer under cuda-graph (translated
python/sglang/srt/layers/attention/mamba/prefill_track_metadata.py
+57
View File
@@ -0,0 +1,57 @@
"""Host-side row selection for prefill state snapshots.
Slot IDs deliberately stay on the device: unified pools translate virtual IDs
before these row indices gather physical source and destination slots.
"""
from dataclasses import dataclass
from itertools import accumulate
@dataclass
class PrefillTrackPlan:
tracked_rows: list[int]
final_rows: list[int]
unaligned_rows: list[int]
h_rows: list[int]
h_src: list[int]
recompute_rows: list[int]
recompute_end_locs: list[int]
chunk_indices: list[int]
def build_prefill_track_plan(
mask: list[bool],
track_lens: list[int],
extend_lens: list[int],
prefix_lens: list[int],
chunk_size: int,
*,
mamba2: bool,
) -> PrefillTrackPlan:
"""Use the backend's actual chunk size, including Mamba2's flat grid."""
assert len(mask) == len(track_lens) == len(extend_lens) == len(prefix_lens)
starts = list(accumulate(extend_lens, initial=0))
h_offsets = list(
accumulate(((n + chunk_size - 1) // chunk_size for n in extend_lens), initial=0)
)
plan = PrefillTrackPlan([], [], [], [], [], [], [], [-1] * len(mask))
for row, track in enumerate(mask):
if not track:
continue
plan.tracked_rows.append(row)
length = track_lens[row] - prefix_lens[row]
if length % chunk_size == 0:
plan.final_rows.append(row)
continue
chunk = length // chunk_size
plan.unaligned_rows.append(row)
plan.chunk_indices[row] = chunk
end = starts[row] + chunk * chunk_size
if mamba2 and end % chunk_size:
plan.recompute_rows.append(row)
plan.recompute_end_locs.append(end)
else:
plan.h_rows.append(row)
plan.h_src.append(end // chunk_size if mamba2 else h_offsets[row] + chunk)
return plan
python/sglang/srt/managers/schedule_batch.py
+13
View File
@@ -2411,6 +2411,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
mamba_track_buffer_indices: Optional[List[int]] = None # shape: [b], 0 or 1
mamba_track_mask: torch.Tensor = None # shape: [b], bool
mamba_track_seqlens: torch.Tensor = None # shape: [b], int64
# TBO rejects Mamba tracking; enabling it must also slice these CPU lists.
mamba_track_seqlens_cpu: Optional[List[int]] = None
mamba_prefill_track_mask_cpu: Optional[List[bool]] = None
mamba_track_mask_cpu: Optional[List[bool]] = None # shape: [b]
mamba_track_mask_next_cpu: Optional[List[bool]] = None # shape: [b]
mamba_decode_batch_idx_cpu: Optional[List[int]] = None # shape: [b]
@@ -2934,6 +2937,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
self.extend_input_logprob_token_ids = extend_input_logprob_token_ids
if get_exec().mamba.enable_mamba_extra_buffer:
self.mamba_prefill_track_mask_cpu = mamba_track_mask_cpu
self.mamba_track_seqlens_cpu = mamba_track_seqlens_cpu
self.mamba_track_indices = torch.tensor(
mamba_track_indices_cpu,
dtype=torch.int64,
@@ -3500,6 +3505,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
def prepare_for_decode(self):
self.forward_mode = ForwardMode.DECODE
self.mamba_track_seqlens_cpu = None
self.mamba_prefill_track_mask_cpu = None
# Decode embeds the last output token via embed_tokens; clear the stale
# prefill-time tensor so it doesn't leak into ForwardBatch.
self.input_embeds = None
@@ -3653,6 +3660,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
self.mamba_track_buffer_indices = None
self.mamba_track_mask = None
self.mamba_track_seqlens = None
self.mamba_track_seqlens_cpu = None
self.mamba_prefill_track_mask_cpu = None
self.mamba_track_mask_cpu = None
self.mamba_track_mask_next_cpu = None
self.mamba_decode_batch_idx_cpu = None
@@ -3719,6 +3728,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
self.mamba_track_buffer_indices = None
self.mamba_track_mask = None
self.mamba_track_seqlens = None
self.mamba_track_seqlens_cpu = None
self.mamba_prefill_track_mask_cpu = None
self.mamba_track_mask_cpu = None
self.mamba_track_mask_next_cpu = None
self.mamba_decode_batch_idx_cpu = None
@@ -3782,6 +3793,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
mamba_track_buffer_indices=self.mamba_track_buffer_indices,
mamba_track_mask=self.mamba_track_mask,
mamba_track_seqlens=self.mamba_track_seqlens,
mamba_track_seqlens_cpu=self.mamba_track_seqlens_cpu,
mamba_prefill_track_mask_cpu=self.mamba_prefill_track_mask_cpu,
mamba_track_mask_cpu=self.mamba_track_mask_cpu,
mamba_track_mask_next_cpu=self.mamba_track_mask_next_cpu,
mamba_decode_batch_idx_cpu=self.mamba_decode_batch_idx_cpu,
python/sglang/srt/model_executor/forward_batch_info.py
+22 -2
View File
@@ -503,6 +503,8 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
mamba_track_mask: Optional[torch.Tensor] = None # shape: [b], bool
# The seqlens to track mamba state if masked, prefill only.
mamba_track_seqlens: Optional[torch.Tensor] = None # shape: [b], int64
mamba_prefill_track_mask_cpu: Optional[List[bool]] = None
mamba_track_seqlens_cpu: Optional[List[int]] = None
# Deferred mamba init ops: COW pairs and clear indices (performed on forward stream)
mamba_cow_src_indices: Optional[torch.Tensor] = None
mamba_cow_dst_indices: Optional[torch.Tensor] = None
@@ -912,6 +914,16 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
mamba_track_indices=batch.mamba_track_indices,
mamba_track_mask=batch.mamba_track_mask,
mamba_track_seqlens=batch.mamba_track_seqlens,
mamba_prefill_track_mask_cpu=(
list(batch.mamba_prefill_track_mask_cpu)
if batch.mamba_prefill_track_mask_cpu is not None
else None
),
mamba_track_seqlens_cpu=(
list(batch.mamba_track_seqlens_cpu)
if batch.mamba_track_seqlens_cpu is not None
else None
),
mamba_cow_src_indices=batch.mamba_cow_src_indices,
mamba_cow_dst_indices=batch.mamba_cow_dst_indices,
mamba_clear_indices=batch.mamba_clear_indices,
@@ -1035,8 +1047,8 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
ret.extend_prefix_lens = torch.tensor(
extend_prefix_lens, dtype=torch.int32, pin_memory=pin_memory
).to(device, non_blocking=True)
ret.extend_prefix_lens_cpu = extend_prefix_lens
ret.extend_seq_lens_cpu = extend_seq_lens
ret.extend_prefix_lens_cpu = list(extend_prefix_lens)
ret.extend_seq_lens_cpu = list(extend_seq_lens)
else:
# gpu_only: device tensors handed in directly; leave *_cpu unset.
assert isinstance(extend_seq_lens, torch.Tensor)
@@ -1689,6 +1701,14 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
self.mamba_track_indices = self._pad_tensor_to_size(
self.mamba_track_indices, bs
)
if self.mamba_prefill_track_mask_cpu is not None:
self.mamba_prefill_track_mask_cpu = self.mamba_prefill_track_mask_cpu + [
False
] * (bs - len(self.mamba_prefill_track_mask_cpu))
if self.mamba_track_seqlens_cpu is not None:
self.mamba_track_seqlens_cpu = self.mamba_track_seqlens_cpu + [0] * (
bs - len(self.mamba_track_seqlens_cpu)
)
if self.mamba_track_mask is not None:
self.mamba_track_mask = self._pad_tensor_to_size(self.mamba_track_mask, bs)
if self.mamba_track_seqlens is not None:
python/sglang/srt/models/glm5_next.py
+149 -62
View File
@@ -35,6 +35,7 @@ from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
ColumnParallelBatchedLinear,
ColumnParallelLinear,
LinearBase,
MergedColumnParallelLinear,
MergedColumnParallelRepeatedLinear,
QKVParallelLinear,
@@ -48,6 +49,7 @@ from sglang.srt.layers.moe.utils import (
is_shared_experts_fusion_disabled,
)
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.radix_linear_attention import RadixLinearAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils.common import PPMissingLayer
@@ -98,7 +100,13 @@ from sglang.srt.multimodal.mm_utils import (
run_dp_presharded_mrope_vision_model,
run_dp_sharded_mrope_vision_model,
)
from sglang.srt.runtime_context import get_forward, get_mm, get_parallel, get_spec
from sglang.srt.runtime_context import (
get_forward,
get_lora,
get_mm,
get_parallel,
get_spec,
)
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.utils.common import (
BumpAllocator,
@@ -303,6 +311,55 @@ class Glm5NextVisionModel(GlmOcrVisionModel):
class Glm5NextLinearAttention(nn.Module):
_PACKED_MODULES_MAPPING = {
"fused_qkvbfg_a_proj": [
"q_proj",
"k_proj",
"v_proj",
"b_proj",
"f_a_proj",
"g_a_proj",
],
"fused_bfg_a_proj": ["b_proj", "f_a_proj", "g_a_proj"],
"fused_fg_b_proj": ["f_b_proj", "g_b_proj"],
}
@classmethod
def _can_fuse_proj(
cls,
quant_config: Optional[QuantizationConfig],
prefix: str,
*fused_projs: str,
) -> bool:
if get_lora().enable_lora or get_lora().lora_paths:
return False
if quant_config is None:
return True
if quant_config.get_name() not in {
"fp8",
"mxfp8",
"modelopt_fp8",
"modelopt_fp4",
"modelopt_mixed",
}:
return False
probe = LinearBase(1, 1)
source_projs = [
proj
for fused_proj in fused_projs
for proj in cls._PACKED_MODULES_MAPPING[fused_proj]
]
if "fused_qkvbfg_a_proj" in fused_projs:
source_projs.append("qkv_proj")
return all(
isinstance(
quant_config.get_quant_method(probe, prefix=f"{prefix}.{proj}"),
UnquantizedLinearMethod,
)
for proj in source_projs
)
def __init__(
self,
layer_idx: int,
@@ -336,7 +393,12 @@ class Glm5NextLinearAttention(nn.Module):
projection_size = self.head_dim * self.num_heads
self.conv_size = config.linear_attn_config["short_conv_kernel_size"]
self.do_fuse_qkvbfg = quant_config is None and head_shard_size == self.tp_size
self.do_fuse_qkvbfg = self._can_fuse_proj(
quant_config, prefix, "fused_qkvbfg_a_proj", "fused_fg_b_proj"
)
self.fuse_bfg = not self.do_fuse_qkvbfg and self._can_fuse_proj(
quant_config, prefix, "fused_bfg_a_proj", "fused_fg_b_proj"
)
if self.do_fuse_qkvbfg:
self.qkvb_sizes = [
projection_size,
@@ -350,21 +412,23 @@ class Glm5NextLinearAttention(nn.Module):
self.hidden_size,
self.qkvb_sizes,
self.fg_sizes,
quant_config=quant_config,
quant_config=None,
prefix=f"{prefix}.fused_qkvbfg_a_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.split_sizes = [
3 * projection_size // head_shard_size,
self.num_heads // head_shard_size,
2 * self.head_dim,
]
fused_dtype = (
getattr(config, "dtype", None)
or getattr(config, "torch_dtype", None)
or torch.get_default_dtype()
)
self.fused_fg_b_proj = ColumnParallelBatchedLinear(
2, self.head_dim, projection_size, dtype=fused_dtype
2,
self.head_dim,
projection_size,
dtype=self.fused_qkvbfg_a_proj.params_dtype,
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
else:
self.qkv_proj = QKVParallelLinear(
@@ -379,50 +443,70 @@ class Glm5NextLinearAttention(nn.Module):
prefix=f"{prefix}.qkv_proj",
)
self.f_a_proj = ReplicatedLinear(
self.hidden_size,
self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.f_a_proj",
)
if self.fuse_bfg:
self.fused_bfg_a_proj = MergedColumnParallelRepeatedLinear(
self.hidden_size,
[self.num_heads],
[self.head_dim, self.head_dim],
quant_config=None,
prefix=f"{prefix}.fused_bfg_a_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.bfg_split_sizes = [self.local_num_heads, 2 * self.head_dim]
self.fused_fg_b_proj = ColumnParallelBatchedLinear(
2,
self.head_dim,
projection_size,
dtype=self.fused_bfg_a_proj.params_dtype,
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
else:
self.f_a_proj = ReplicatedLinear(
self.hidden_size,
self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.f_a_proj",
)
self.f_b_proj = ColumnParallelLinear(
self.head_dim,
projection_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.f_b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.f_b_proj = ColumnParallelLinear(
self.head_dim,
projection_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.f_b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.b_proj = ColumnParallelLinear(
self.hidden_size,
self.num_heads,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.b_proj = ColumnParallelLinear(
self.hidden_size,
self.num_heads,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.g_a_proj = ReplicatedLinear(
self.hidden_size,
self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.g_a_proj",
)
self.g_b_proj = ColumnParallelLinear(
self.head_dim,
projection_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.g_b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.g_a_proj = ReplicatedLinear(
self.hidden_size,
self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.g_a_proj",
)
self.g_b_proj = ColumnParallelLinear(
self.head_dim,
projection_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.g_b_proj",
tp_rank=head_shard_rank,
tp_size=head_shard_size,
)
self.dt_bias = nn.Parameter(
torch.empty(divide(projection_size, head_shard_size), dtype=torch.float32)
@@ -490,9 +574,16 @@ class Glm5NextLinearAttention(nn.Module):
def forward_qkvbfg(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
qkv, _ = self.qkv_proj(hidden_states)
beta = self.b_proj(hidden_states)[0]
forget_gate = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
if self.fuse_bfg:
fused_states = self.fused_bfg_a_proj(hidden_states)
beta, fg_a_states = torch.split(fused_states, self.bfg_split_sizes, dim=-1)
forget_gate, g_proj_states = self.fused_fg_b_proj(
fg_a_states.view(-1, 2, self.head_dim).transpose(0, 1)
)
else:
beta = self.b_proj(hidden_states)[0]
forget_gate = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
return (
qkv,
@@ -1089,15 +1180,7 @@ class Glm5NextForConditionalGeneration(nn.Module):
packed_modules_mapping = {
"fused_qkv_a_proj_with_mqa": ["q_a_proj", "kv_a_proj_with_mqa"],
"fused_qkvbfg_a_proj": [
"q_proj",
"k_proj",
"v_proj",
"b_proj",
"f_a_proj",
"g_a_proj",
],
"fused_fg_b_proj": ["f_b_proj", "g_b_proj"],
**Glm5NextLinearAttention._PACKED_MODULES_MAPPING,
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
"qkv_conv1d": ["q_conv1d", "k_conv1d", "v_conv1d"],
"gate_up_proj": ["gate_proj", "up_proj"],
@@ -1391,6 +1474,9 @@ class Glm5NextForConditionalGeneration(nn.Module):
(".fused_qkvbfg_a_proj", ".g_a_proj", 5),
(".fused_fg_b_proj", ".f_b_proj", 0),
(".fused_fg_b_proj", ".g_b_proj", 1),
(".fused_bfg_a_proj", ".b_proj", 0),
(".fused_bfg_a_proj", ".f_a_proj", 1),
(".fused_bfg_a_proj", ".g_a_proj", 2),
(".qkv_proj", ".q_proj", "q"),
(".qkv_proj", ".k_proj", "k"),
(".qkv_proj", ".v_proj", "v"),
@@ -1491,6 +1577,7 @@ class Glm5NextForConditionalGeneration(nn.Module):
param_name
in {
".fused_qkvbfg_a_proj",
".fused_bfg_a_proj",
".fused_fg_b_proj",
".qkv_proj",
".qkv_conv1d",
python/sglang/srt/speculative/spec_utils.py
+2
View File
@@ -816,6 +816,8 @@ def prepare_mamba_track_for_verify(batch: ScheduleBatch) -> None:
set_mamba_track_indices_from_reqs(batch, track_positions)
batch.mamba_track_mask = None
batch.mamba_track_seqlens = None
batch.mamba_prefill_track_mask_cpu = None
batch.mamba_track_seqlens_cpu = None
def _verify_commit_step_indices(
test/registered/kernel/attention/test_kda_gate_beta_cumsum.py
+130
View File
@@ -0,0 +1,130 @@
import unittest
import torch
from sglang.kernels.ops.attention.fla.kda import chunk_kda, kda_gate_chunk_cumsum
from sglang.test.ci.ci_register import register_cuda_ci
register_cuda_ci(est_time=60, stage="base-b-kernel-unit", runner_config="1-gpu-large")
@unittest.skipUnless(torch.cuda.is_available(), "requires CUDA")
class TestKDAGateBetaCumsum(unittest.TestCase):
@torch.inference_mode()
def test_gate_cumsum_beta_matches_separate_sigmoid(self):
torch.manual_seed(42)
for cu_seqlens, chunks, batch, tokens in (
(None, None, 2, 65),
(
torch.tensor([0, 0, 1, 65, 130], device="cuda", dtype=torch.int32),
torch.tensor(
[[1, 0], [2, 0], [3, 0], [3, 1]], device="cuda", dtype=torch.int32
),
1,
130,
),
):
heads, dim = 3, 128
gate = torch.randn(
batch, tokens, heads, dim, device="cuda", dtype=torch.bfloat16
)
a_log = torch.randn(heads, device="cuda")
bias = torch.randn(heads * dim, device="cuda")
packed = torch.randn(
batch, tokens, 4 * heads + 7, device="cuda", dtype=torch.bfloat16
)
beta = packed[..., 2 : 2 + heads]
for lower_bound in (None, -5.0):
with self.subTest(
varlen=cu_seqlens is not None, lower_bound=lower_bound
):
kwargs = dict(
A_log=a_log,
chunk_size=64,
dt_bias=bias,
cu_seqlens=cu_seqlens,
chunk_indices=chunks,
lower_bound=lower_bound,
)
expected_gate = kda_gate_chunk_cumsum(gate, **kwargs)
actual_gate, actual_beta = kda_gate_chunk_cumsum(
gate, beta=beta, **kwargs
)
torch.testing.assert_close(
actual_gate, expected_gate, atol=1e-4, rtol=1e-6
)
torch.testing.assert_close(
actual_beta, beta.float().sigmoid(), atol=1.2e-7, rtol=1e-6
)
self.assertEqual(actual_beta.dtype, torch.float32)
@torch.inference_mode()
def test_chunk_raw_beta_matches_activated_beta_and_final_state(self):
torch.manual_seed(17)
tokens, heads, dim = 68, 2, 64
shape = (1, tokens, heads, dim)
q, k, v, gate = [
torch.randn(shape, device="cuda", dtype=torch.bfloat16) for _ in range(4)
]
packed = torch.randn(
1,
tokens,
3 * heads * dim + heads + 2 * dim,
device="cuda",
dtype=torch.bfloat16,
)
layouts = (
torch.randn(1, tokens, heads, device="cuda", dtype=torch.bfloat16),
torch.randn(
1, heads, tokens, device="cuda", dtype=torch.bfloat16
).transpose(1, 2),
packed[..., 3 * heads * dim : 3 * heads * dim + heads],
)
a_log = torch.zeros(heads, device="cuda")
bias = torch.randn(heads * dim, device="cuda")
cu_seqlens = torch.tensor([0, 3, tokens], device="cuda", dtype=torch.int32)
state = torch.randn(2, heads, dim, dim, device="cuda") * 0.01
indices = torch.arange(2, device="cuda", dtype=torch.int32)
for beta in layouts:
for fused_gate in (False, True):
with self.subTest(stride=beta.stride(), fused_gate=fused_gate):
kwargs = dict(
q=q,
k=k,
scale=dim**-0.5,
use_qk_l2norm_in_kernel=True,
initial_state_indices=indices,
cu_seqlens=cu_seqlens,
A_log=a_log if fused_gate else None,
dt_bias=bias if fused_gate else None,
lower_bound=-5.0 if fused_gate else None,
)
g = (
gate
if fused_gate
else -torch.nn.functional.softplus(gate.float())
)
expected_state, actual_state = state.clone(), state.clone()
expected = chunk_kda(
v=v.clone(),
g=g.clone(),
beta=beta.float().sigmoid(),
initial_state=expected_state,
**kwargs,
)
actual = chunk_kda(
v=v.clone(),
g=g.clone(),
beta=beta,
beta_is_raw=True,
initial_state=actual_state,
**kwargs,
)
torch.testing.assert_close(actual, expected, atol=2e-3, rtol=2e-3)
torch.testing.assert_close(
actual_state, expected_state, atol=2e-4, rtol=2e-3
)
if __name__ == "__main__":
unittest.main()
test/registered/unit/layers/test_mamba2_track_ssm_indices.py
+8
View File
@@ -25,11 +25,19 @@ def _backend():
def _forward_batch(extend_lens, prefix_lens, track_seqlens, track_mask):
return SimpleNamespace(
forward_mode=SimpleNamespace(
is_extend=lambda: True, is_target_verify=lambda: False
),
extend_seq_lens=torch.tensor(extend_lens),
extend_prefix_lens=torch.tensor(prefix_lens),
mamba_track_seqlens=torch.tensor(track_seqlens),
mamba_track_mask=torch.tensor(track_mask),
mamba_track_indices=torch.arange(100, 100 + len(extend_lens)),
# Exercise the legacy GPU planner, not the CPU-metadata fast path.
mamba_prefill_track_mask_cpu=None,
mamba_track_seqlens_cpu=None,
extend_seq_lens_cpu=None,
extend_prefix_lens_cpu=None,
)
test/registered/unit/layers/test_mamba_prefill_track_metadata.py
+302
View File
@@ -0,0 +1,302 @@
import random
import unittest
from types import SimpleNamespace
from unittest.mock import patch
import torch
from sglang.srt.layers.attention.hybrid_linear_attn_backend import (
Mamba2AttnBackend,
MambaAttnBackendBase,
)
from sglang.srt.managers.schedule_batch import ScheduleBatch
from sglang.srt.model_executor.forward_batch_info import (
CaptureHiddenMode,
ForwardBatch,
ForwardMode,
)
from sglang.srt.runtime_context import get_context
from sglang.srt.speculative import spec_utils
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=5, suite="base-a-test-cpu")
class NoHostRead(torch.Tensor):
def cpu(self, *args, **kwargs):
raise AssertionError("CPU tracking must not copy device metadata to the host")
def make_batch(lengths, prefix, track_lens, mask, mirrored):
def tensor(values):
return torch.tensor(values).as_subclass(
NoHostRead if mirrored else torch.Tensor
)
return SimpleNamespace(
batch_size=len(lengths),
forward_mode=ForwardMode.EXTEND,
extend_seq_lens=tensor(lengths),
extend_prefix_lens=tensor(prefix),
mamba_track_seqlens=tensor(track_lens),
mamba_track_mask=tensor(mask),
mamba_track_indices=tensor([37 + i * 17 for i in range(len(lengths))]),
extend_seq_lens_cpu=lengths,
extend_prefix_lens_cpu=prefix,
mamba_track_seqlens_cpu=track_lens if mirrored else None,
mamba_prefill_track_mask_cpu=mask if mirrored else None,
)
def make_forward_batch(lengths, starts, cpu_lengths, mode=ForwardMode.EXTEND):
return ForwardBatch(
forward_mode=mode,
batch_size=len(lengths),
input_ids=torch.zeros(sum(lengths), dtype=torch.int64),
req_pool_indices=torch.arange(len(lengths)),
seq_lens=torch.tensor(lengths),
seq_lens_sum=sum(lengths),
out_cache_loc=torch.zeros(sum(lengths), dtype=torch.int64),
extend_start_loc=torch.tensor(starts, dtype=torch.int32),
extend_seq_lens=torch.tensor(lengths, dtype=torch.int32),
extend_seq_lens_cpu=cpu_lengths,
)
def make_metadata_backend():
backend = object.__new__(MambaAttnBackendBase)
backend.device = "cpu"
backend.topk = 1
backend.req_to_token_pool = SimpleNamespace(
get_mamba_indices=lambda rows: rows,
translate_mamba_indices=lambda slots: slots,
)
return backend
class TestMambaPrefillTrackMetadata(unittest.TestCase):
def test_cpu_plan_matches_existing_tensor_planner(self):
rng = random.Random(2026)
for backend_type in (MambaAttnBackendBase, Mamba2AttnBackend):
for chunk in (16, 64, 128):
backend = object.__new__(backend_type)
backend.device = "cpu"
backend._mamba_chunk_size = chunk
cases = [
(
[chunk + 6, 2 * chunk + 1, chunk],
[0, 2 * chunk, 0],
[chunk + 1, 3 * chunk + 1, chunk],
[True, True, True],
),
(
[2 * chunk + 1, 1, 1],
[chunk, 0, 0],
[2 * chunk + 1, 0, 0],
[True, False, False],
),
([1, chunk], [0, 0], [0, 0], [False, False]),
]
for _ in range(10):
lengths = [rng.randrange(1, chunk * 6) for _ in range(5)]
prefix = [rng.randrange(4) * chunk for _ in lengths]
cases.append(
(
lengths,
prefix,
[
p + rng.randrange(1, n + 1)
for p, n in zip(prefix, lengths)
],
[bool(rng.randrange(2)) for _ in lengths],
)
)
for lengths, prefix, track, mask in cases:
slots = torch.tensor([111 - i * 5 for i in range(len(lengths))])
with self.subTest(
backend=backend_type.__name__, chunk=chunk, mask=mask
):
expected = backend._init_track_ssm_indices(
slots, make_batch(lengths, prefix, track, mask, False)
)
actual = backend._init_track_ssm_indices(
slots.as_subclass(NoHostRead),
make_batch(lengths, prefix, track, mask, True),
)
for result, reference in zip(actual, expected):
if reference is None:
self.assertIsNone(result)
else:
torch.testing.assert_close(result, reference)
def test_verify_and_incomplete_mirrors_use_existing_planner(self):
batch = make_batch([64], [0], [64], [True], True)
eligible = MambaAttnBackendBase._has_cpu_prefill_track_metadata
self.assertTrue(eligible(batch))
batch.forward_mode = ForwardMode.TARGET_VERIFY
self.assertFalse(eligible(batch))
batch.forward_mode = ForwardMode.EXTEND
batch.mamba_track_seqlens_cpu = None
self.assertFalse(eligible(batch))
batch.mamba_track_seqlens_cpu = [64, 0]
self.assertFalse(eligible(batch))
def test_logical_token_extent_avoids_scalar_reads_with_valid_cpu_lengths(self):
backend = make_metadata_backend()
original_int = torch.Tensor.__int__
for lengths, starts, cpu_lengths, tbo_range, expected, scalar_reads in (
([3, 5], [0, 3], [3, 5], None, 8, 0),
([3, 5, 0], [0, 3, 8], [3, 5, 0], None, 8, 0),
([3, 5], [4, 7], None, None, 12, 1),
([3, 5], [4, 7], [8], None, 12, 1),
([3, 5], [4, 7], [3, 5], (4, 12), 12, 1),
):
with self.subTest(cpu_lengths=cpu_lengths, tbo_range=tbo_range):
batch = make_forward_batch(lengths, starts, cpu_lengths)
batch.tbo_parent_token_range = tbo_range
reads = []
def read_scalar(tensor):
if scalar_reads == 0:
raise AssertionError(
"Valid CPU lengths must avoid scalar reads"
)
reads.append(tensor.clone())
return original_int(tensor)
with patch.object(torch.Tensor, "__int__", read_scalar):
metadata = backend._forward_metadata(batch)
self.assertEqual(metadata.logical_num_tokens, expected)
self.assertEqual(len(reads), scalar_reads)
self.assertEqual(metadata.query_start_loc[-1].item(), expected)
def test_verify_decode_and_idle_ignore_stale_cpu_token_lengths(self):
backend = make_metadata_backend()
for mode, lengths, expected_starts in (
(ForwardMode.TARGET_VERIFY, [3, 3], [0, 3, 6]),
(ForwardMode.DECODE, [1, 1], [0, 1, 2]),
(ForwardMode.IDLE, [], [0]),
):
with self.subTest(mode=mode):
batch = make_forward_batch(
lengths, [0, 3][: len(lengths)], [100, 200], mode
)
if mode == ForwardMode.TARGET_VERIFY:
batch.spec_info = SimpleNamespace(
ragged_verify_layout=None, draft_token_num=3
)
with patch.object(
torch.Tensor,
"__int__",
side_effect=AssertionError("This mode must not read token scalars"),
):
metadata = backend._forward_metadata(batch)
self.assertIsNone(metadata.logical_num_tokens)
torch.testing.assert_close(
metadata.query_start_loc,
torch.tensor(expected_starts, dtype=torch.int32),
)
def test_forward_snapshot_and_padding_do_not_mutate_scheduler_lists(self):
override = get_context().override_server_args(device="cpu")
override.install()
self.addCleanup(override.restore)
batch = ScheduleBatch(
reqs=[SimpleNamespace(rid="one", lora_id=None, token_type_ids=None)],
device="cpu",
forward_mode=ForwardMode.EXTEND,
input_ids=torch.tensor([3]),
req_pool_indices=torch.tensor([2]),
seq_lens=torch.tensor([65]),
seq_lens_cpu=torch.tensor([65]),
seq_lens_sum=65,
out_cache_loc=torch.tensor([1]),
extend_lens=[1],
prefix_lens=[64],
extend_num_tokens=1,
mamba_track_mask=torch.tensor([True]),
mamba_track_seqlens=torch.tensor([65]),
mamba_prefill_track_mask_cpu=[True],
mamba_track_seqlens_cpu=[65],
)
runner = SimpleNamespace(
device="cpu",
model_config=SimpleNamespace(
requires_mm_token_modalities=False, model_is_mrope=False
),
kv_index_translator=SimpleNamespace(rebind_write_loc=lambda forward: None),
prefill_attention_backend_str="torch_native",
ngram_embedding_manager=SimpleNamespace(enabled=False),
lora_manager=None,
ps=SimpleNamespace(attn_dcp_size=1),
attn_backend=SimpleNamespace(
get_cpu_graph_seq_len_fill_value=lambda: 1,
get_cuda_graph_seq_len_fill_value=lambda: 1,
),
)
forward = ForwardBatch.init_new(
batch,
runner,
capture_hidden_mode=CaptureHiddenMode.NULL,
return_hidden_states_before_norm=False,
)
for target, source in (
("mamba_prefill_track_mask_cpu", "mamba_prefill_track_mask_cpu"),
("mamba_track_seqlens_cpu", "mamba_track_seqlens_cpu"),
("extend_seq_lens_cpu", "extend_lens"),
("extend_prefix_lens_cpu", "prefix_lens"),
):
self.assertEqual(getattr(forward, target), getattr(batch, source))
self.assertIsNot(getattr(forward, target), getattr(batch, source))
forward._pad_inputs_to_size(runner, num_tokens=3, bs=3)
self.assertEqual(batch.mamba_prefill_track_mask_cpu, [True])
self.assertEqual(batch.mamba_track_seqlens_cpu, [65])
self.assertEqual(batch.extend_lens, [1])
self.assertEqual(batch.prefix_lens, [64])
for host, device, expected in (
("mamba_prefill_track_mask_cpu", "mamba_track_mask", [True, False, False]),
("mamba_track_seqlens_cpu", "mamba_track_seqlens", [65, 0, 0]),
("extend_seq_lens_cpu", "extend_seq_lens", [1, 0, 0]),
("extend_prefix_lens_cpu", "extend_prefix_lens", [64, 0, 0]),
):
self.assertEqual(getattr(forward, host), expected)
self.assertEqual(getattr(forward, device).tolist(), expected)
def test_decode_and_verify_clear_prefill_lists_without_losing_snapshot(self):
for verify in (False, True):
with self.subTest(verify=verify):
batch = ScheduleBatch(
reqs=[],
spec_algorithm=SimpleNamespace(is_none=lambda: False),
mamba_track_mask=torch.tensor([True]),
mamba_track_seqlens=torch.tensor([65]),
mamba_prefill_track_mask_cpu=[True],
mamba_track_seqlens_cpu=[65],
)
snapshot = batch.copy()
if verify:
settings = SimpleNamespace(
mamba=SimpleNamespace(
enable_mamba_extra_buffer=True,
enable_mamba_extra_buffer_lazy=False,
)
)
with (
patch.object(spec_utils, "get_exec", return_value=settings),
patch.object(spec_utils, "set_mamba_track_indices_from_reqs"),
):
spec_utils.prepare_mamba_track_for_verify(batch)
self.assertIsNone(batch.mamba_track_mask)
self.assertIsNone(batch.mamba_track_seqlens)
else:
with patch.object(spec_utils, "spec_prepare_for_decode"):
batch.prepare_for_decode()
self.assertIsNone(batch.mamba_prefill_track_mask_cpu)
self.assertIsNone(batch.mamba_track_seqlens_cpu)
self.assertEqual(snapshot.mamba_prefill_track_mask_cpu, [True])
self.assertEqual(snapshot.mamba_track_seqlens_cpu, [65])
self.assertIsNone(snapshot.mamba_track_mask_cpu)
if __name__ == "__main__":
unittest.main()
test/registered/unit/models/test_glm5_next_bfg_fusion.py
+182
View File
@@ -0,0 +1,182 @@
import unittest
from types import SimpleNamespace
from unittest.mock import patch
import torch
import torch.nn.functional as F
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.models import glm5_next
from sglang.srt.runtime_context import get_context, get_parallel
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=5, suite="base-a-test-cpu")
PREFIX = "model.layers.0.self_attn"
QKV = ("q_proj", "k_proj", "v_proj")
BFG = ("b_proj", "f_a_proj", "g_a_proj", "f_b_proj", "g_b_proj")
class MockQuantizedLinearMethod:
"""Keep dense storage so the test isolates routing and checkpoint loading."""
create_weights = UnquantizedLinearMethod.create_weights
def apply(self, layer, x, bias=None):
return F.linear(x, layer.weight, bias)
class MockFp8Config:
def __init__(self, ignored):
self.ignored_layers = {f"{PREFIX}.{name}" for name in ignored}
def get_name(self):
return "fp8"
def get_quant_method(self, layer, prefix):
names = (
[prefix.replace("qkv_proj", name) for name in QKV]
if prefix.endswith(".qkv_proj")
else [prefix]
)
if all(name in self.ignored_layers for name in names):
return UnquantizedLinearMethod()
return MockQuantizedLinearMethod()
class TestGlm5NextBfgFusion(unittest.TestCase):
def setUp(self):
self.addCleanup(torch.set_default_dtype, torch.get_default_dtype())
torch.set_default_dtype(torch.float32)
override = get_context().override_server_args(
device="cpu", enable_lora=False, lora_paths=None
)
override.install()
self.addCleanup(override.restore)
patcher = patch.object(
UnquantizedLinearMethod,
"apply",
MockQuantizedLinearMethod.apply,
)
patcher.start()
self.addCleanup(patcher.stop)
@torch.no_grad()
def test_projection_loading_matches_unfused_reference(self):
torch.manual_seed(42)
hidden, heads, dim = 16, 4, 8
shapes = {name: (heads * dim, hidden) for name in QKV}
shapes.update(
b_proj=(heads, hidden),
f_a_proj=(dim, hidden),
g_a_proj=(dim, hidden),
f_b_proj=(heads * dim, dim),
g_b_proj=(heads * dim, dim),
)
weights = {name: torch.randn(shape) for name, shape in shapes.items()}
x = torch.randn(7, hidden)
for ignored, expected_route in (
(QKV + BFG, (True, False)),
(BFG, (False, True)),
((), (False, False)),
):
for attn_tp, rank in ((1, 0), (2, 0), (2, 1)):
with (
self.subTest(route=expected_route, attn_tp=attn_tp, rank=rank),
get_parallel().override(
tp_size=4, tp_rank=3, attn_tp_size=attn_tp, attn_tp_rank=rank
),
):
quant = MockFp8Config(ignored)
attention = glm5_next.Glm5NextLinearAttention(
layer_idx=0,
hidden_size=hidden,
config=SimpleNamespace(
linear_attn_config={
"head_dim": dim,
"num_heads": heads,
"short_conv_kernel_size": 4,
}
),
quant_config=quant,
prefix=PREFIX,
)
self.assertEqual(
(attention.do_fuse_qkvbfg, attention.fuse_bfg), expected_route
)
for parameter in attention.parameters():
parameter.fill_(torch.nan)
model = SimpleNamespace(
config=SimpleNamespace(n_routed_experts=0),
num_fused_shared_experts=0,
quant_config=quant,
named_parameters=lambda: (
(f"{PREFIX}.{name}", param)
for name, param in attention.named_parameters()
),
)
with patch.object(
glm5_next.DeepseekV2WeightLoaderMixin, "post_load_weights"
):
glm5_next.Glm5NextForConditionalGeneration.load_weights(
model,
[
(f"{PREFIX}.{name}.weight", w)
for name, w in weights.items()
],
)
def linear(value, name):
weight = weights[name]
if name not in ("f_a_proj", "g_a_proj"):
weight = weight.chunk(attn_tp, dim=0)[rank]
return F.linear(value, weight)
expected = (
torch.cat([linear(x, name) for name in QKV], dim=-1),
linear(x, "b_proj"),
linear(linear(x, "f_a_proj"), "f_b_proj"),
linear(linear(x, "g_a_proj"), "g_b_proj"),
)
forward = (
attention.forward_qkvbfg_fused
if attention.do_fuse_qkvbfg
else attention.forward_qkvbfg
)
for actual, reference in zip(forward(x, None), expected):
torch.testing.assert_close(
actual, reference, atol=1e-5, rtol=1e-5
)
def test_each_quantized_gate_projection_disables_fusion(self):
for quantized in BFG:
quant = MockFp8Config(name for name in QKV + BFG if name != quantized)
for packed in ("fused_qkvbfg_a_proj", "fused_bfg_a_proj"):
with self.subTest(quantized=quantized, packed=packed):
self.assertFalse(
glm5_next.Glm5NextLinearAttention._can_fuse_proj(
quant, PREFIX, packed, "fused_fg_b_proj"
)
)
def test_lora_disables_full_and_bfg_fusion(self):
for enable_lora, paths in ((True, None), (False, ["adapter"])):
with patch.object(
glm5_next,
"get_lora",
return_value=SimpleNamespace(enable_lora=enable_lora, lora_paths=paths),
):
for quant in (None, MockFp8Config(QKV + BFG)):
for packed in ("fused_qkvbfg_a_proj", "fused_bfg_a_proj"):
with self.subTest(
enabled=enable_lora, paths=paths, packed=packed
):
self.assertFalse(
glm5_next.Glm5NextLinearAttention._can_fuse_proj(
quant, PREFIX, packed, "fused_fg_b_proj"
)
)
if __name__ == "__main__":
unittest.main()