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

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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
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test/registered/kernel/attention/test_kda_gate_beta_cumsum.py
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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
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@@ -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
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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
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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()