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index 67c5859..be3c19c 100644
--- a/python/sglang/srt/layers/attention/fla/fused_recurrent.py
+++ b/python/sglang/srt/layers/attention/fla/fused_recurrent.py
@@ -251,7 +251,7 @@ def fused_recurrent_gated_delta_rule_packed_decode_kernel(
x = a_val + dt_bias_val
softplus_x = tl.where(x <= SOFTPLUS_THRESHOLD, tl.log(1.0 + tl.exp(x)), x)
g_val = -tl.exp(A_log_val) * softplus_x
- beta_val = tl.sigmoid(b_val).to(b.dtype.element_ty).to(tl.float32)
+ beta_val = tl.sigmoid(b_val)
b_h *= exp(g_val)
b_v -= tl.sum(b_h * b_k[None, :], 1)
diff --git a/python/sglang/srt/layers/attention/fla/fused_sigmoid_gating_recurrent.py b/python/sglang/srt/layers/attention/fla/fused_sigmoid_gating_recurrent.py
index b45738c..375dd6d 100644
--- a/python/sglang/srt/layers/attention/fla/fused_sigmoid_gating_recurrent.py
+++ b/python/sglang/srt/layers/attention/fla/fused_sigmoid_gating_recurrent.py
@@ -3,6 +3,7 @@ from typing import Optional
import torch
import triton
import triton.language as tl
+import triton.language.extra.libdevice as libdevice
@triton.jit(do_not_specialize=["T"])
@@ -184,7 +185,8 @@ def fused_sigmoid_gating_delta_rule_update_kernel(
if IS_KDA:
b_h *= tl.exp(b_g[:, None])
else:
- b_h *= tl.exp(b_g)
+ # Keep decay rounded before the delta-rule fused update.
+ b_h = libdevice.mul_rn(b_h, tl.exp(b_g))
# Delta rule: v -= sum(h * k, dim=0)
b_v -= tl.sum(b_h * b_k[:, None], 0)
@@ -193,7 +195,11 @@ def fused_sigmoid_gating_delta_rule_update_kernel(
b_v *= b_beta
# Update hidden state: h += k[:, None] * v[None, :]
- b_h += b_k[:, None] * b_v[None, :]
+ if IS_KDA:
+ b_h += b_k[:, None] * b_v[None, :]
+ else:
+ # Match the single-step state write-back arithmetic in verify.
+ b_h = libdevice.fma_rn(b_k[:, None], b_v[None, :], b_h)
# Compute output: o = sum(h * q, dim=0)
b_o = tl.sum(b_h * b_q[:, None], 0)
diff --git a/python/sglang/srt/layers/attention/flashattention_backend.py b/python/sglang/srt/layers/attention/flashattention_backend.py
index 3a385db..7eb4233 100644
--- a/python/sglang/srt/layers/attention/flashattention_backend.py
+++ b/python/sglang/srt/layers/attention/flashattention_backend.py
@@ -210,12 +210,12 @@ class FlashAttentionBackend(AttentionBackend):
# If num_splits == 0, we use a heuristic to automatically determine the number of splits.
# We set nums splits to 1 if deterministic inference is enabled.
# See https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ for more details.
- # Furthermore, FA4 does not support num_splits=0 with CUDA Graph, so we set num_splits to 1 if CUDA Graph is enabled.
+ # Keep FA3 graph/eager split arithmetic consistent; FA4 also requires this for graphs.
self.num_splits = (
1
if model_runner.server_args.enable_deterministic_inference
or (
- self.fa_impl_ver == 4
+ self.fa_impl_ver in (3, 4)
and not model_runner.server_args.disable_cuda_graph
)
else 0
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 8e32640..4c2a84e 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -1467,10 +1467,20 @@ class Scheduler(
if self._war_barrier_enabled:
self.schedule_stream.wait_stream(self.forward_stream)
+ # Mamba prefix insertion can free duplicate KV slots and replace
+ # req_to_token entries with canonical cached slots. Finish that
+ # transition before preparing a dependent decode/verify batch.
+ processed_prefill_cache = self._has_pending_mamba_cache_update()
+ if processed_prefill_cache:
+ pop_and_process()
+
# Get the next batch to run
batch = self.get_next_batch_to_run()
self.cur_batch = batch
- disable_overlap_for_batch = self.is_disable_overlap_for_batch(batch)
+ disable_overlap_for_batch = (
+ not processed_prefill_cache
+ and self.is_disable_overlap_for_batch(batch)
+ )
# If we do not need to overlap the current batch with the last batch,
# we can process the last batch immediately.
@@ -1486,7 +1496,7 @@ class Scheduler(
# Process the last batch
if self.last_batch:
- if not disable_overlap_for_batch:
+ if not disable_overlap_for_batch and not processed_prefill_cache:
pop_and_process()
elif batch is None:
# When the server is idle, do self-check and re-init some states
@@ -1503,6 +1513,24 @@ class Scheduler(
if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
self.invariant_checker.self_check_during_busy()
+ def _has_pending_mamba_cache_update(self) -> bool:
+ # DP/MLP-sync ranks need a collective decision; leave that path unchanged.
+ if (
+ self.require_mlp_sync
+ or not self.result_queue
+ or getattr(self.tree_cache, "disable", True)
+ or not getattr(self.tree_cache, "enable_mamba_extra_buffer", False)
+ ):
+ return False
+ pending_batch = self.result_queue[0][0]
+ return pending_batch.forward_mode.is_extend() and any(
+ req.mamba_last_track_seqlen is not None
+ and not req.finished()
+ and not req.is_retracted
+ and not getattr(req, "skip_radix_cache_insert", False)
+ for req in pending_batch.reqs
+ )
+
def is_disable_overlap_for_batch(self, batch: ScheduleBatch) -> bool:
# For two consecutive prefill batches, we disable overlap to improve the TTFT of the first batch.
# This might slightly hurt the throughput, so we use an environment variable to control it.
--- /dev/null
+++ b/test/registered/unit/mem_cache/test_mamba_prefill_boundary.py
@@ -0,0 +1,184 @@
+"""Regression for canonical KV publication before dependent overlap forwards."""
+
+import unittest
+from collections import deque
+from types import SimpleNamespace as NS
+from sglang.srt.managers.scheduler import Scheduler
+
+
+class StopLoop(Exception):
+ pass
+
+
+class Batch:
+ def __init__(self, name, req):
+ self.name = name
+ self.reqs = [req]
+ self.forward_mode = NS(is_extend=lambda: name == "prefill")
+
+ def copy(self):
+ return self
+
+
+class Harness:
+ event_loop_overlap = Scheduler.event_loop_overlap
+ _has_pending_mamba_cache_update = Scheduler._has_pending_mamba_cache_update
+
+ def __init__(self, finish=False, fail=False):
+ self.require_mlp_sync = False
+ self.tree_cache = NS(disable=False, enable_mamba_extra_buffer=True)
+ self.req = NS(
+ mamba_last_track_seqlen=384,
+ is_retracted=False,
+ skip_radix_cache_insert=False,
+ finished=lambda: False,
+ )
+ self.last_batch = None
+ self._engine_paused = False
+ self._war_barrier_enabled = False
+ self.is_generation = True
+ self.events = []
+ self.iteration = 0
+ self.sent = False
+ self.done = False
+ self.finish = finish
+ self.fail = fail
+ self.private_slots = [91, 92, 93]
+ self.canonical_slots = [1, 2, 3]
+ self.mapping = list(self.private_slots)
+ self.shared_values = [10, 20, 30]
+ self.other_request = list(self.canonical_slots)
+ self.refcount = 1
+ self.freed = []
+
+ def receive():
+ self.iteration += 1
+ if self.iteration > 3:
+ raise StopLoop()
+ return []
+
+ self.request_receiver = NS(recv_requests=receive)
+
+ def process_input_requests(self, reqs):
+ pass
+
+ def get_next_batch_to_run(self):
+ self.events.append("schedule")
+ if self.done:
+ return None
+ if not self.sent:
+ self.sent = True
+ return Batch("prefill", self.req)
+ return Batch("decode", self.req)
+
+ def is_disable_overlap_for_batch(self, batch):
+ return False
+
+ def run_batch(self, batch):
+ self.events.append("run:" + batch.name)
+ if batch.name == "decode":
+ assert self.mapping == self.canonical_slots, (
+ "dependent decode observed unpublished cache mapping"
+ )
+ assert not set(self.mapping) & set(self.freed), (
+ "dependent decode references released slots"
+ )
+ return object()
+
+ def process_batch_result(self, batch, result):
+ self.events.append("process:" + batch.name)
+ if batch.name == "prefill":
+ if self.fail:
+ raise RuntimeError("prefill failed")
+ if self.finish:
+ self.done = True
+ self.freed += self.private_slots
+ else:
+ self.freed += self.private_slots
+ self.mapping = list(self.canonical_slots)
+ self.refcount += 1
+ self.req.mamba_last_track_seqlen = None
+ else:
+ self.done = True
+ self.refcount -= 1
+
+ def launch_batch_sample_if_needed(self, result):
+ pass
+
+ def on_idle(self):
+ pass
+
+
+class TestPrefillBoundary(unittest.TestCase):
+ def test_publish_before_scheduling_and_consume_once(self):
+ h = Harness()
+ with self.assertRaises(StopLoop):
+ h.event_loop_overlap()
+ self.assertLess(h.events.index("process:prefill"), h.events.index("run:decode"))
+ self.assertEqual(h.events.count("process:prefill"), 1)
+ self.assertEqual(h.events.count("process:decode"), 1)
+ self.assertEqual(h.freed, [91, 92, 93])
+ self.assertEqual(h.refcount, 1)
+ self.assertEqual(h.other_request, [1, 2, 3])
+ self.assertEqual(h.shared_values, [10, 20, 30])
+
+ def test_finish_in_prefill_is_processed_before_successor_allocation(self):
+ h = Harness(finish=True)
+ with self.assertRaises(StopLoop):
+ h.event_loop_overlap()
+ self.assertNotIn("run:decode", h.events)
+ self.assertEqual(h.freed, [91, 92, 93])
+
+ def test_failed_prefill_cannot_launch_dependent_decode(self):
+ h = Harness(fail=True)
+ with self.assertRaisesRegex(RuntimeError, "prefill failed"):
+ h.event_loop_overlap()
+ self.assertNotIn("run:decode", h.events)
+ self.assertEqual(h.freed, [])
+
+ def test_untracked_prefill_retains_overlap(self):
+ h = Harness()
+ h.req.mamba_last_track_seqlen = None
+ h.mapping = list(h.canonical_slots)
+ with self.assertRaises(StopLoop):
+ h.event_loop_overlap()
+ self.assertLess(h.events.index("run:decode"), h.events.index("process:prefill"))
+
+ def test_nonapplicable_paths_do_not_add_barrier(self):
+ for change in [
+ "dp",
+ "disabled",
+ "nonmamba",
+ "no_track",
+ "finished",
+ "retracted",
+ "skip",
+ "decode",
+ "empty",
+ ]:
+ with self.subTest(change=change):
+ h = Harness()
+ h.result_queue = deque([(Batch("prefill", h.req), object())])
+ if change == "dp":
+ h.require_mlp_sync = True
+ elif change == "disabled":
+ h.tree_cache.disable = True
+ elif change == "nonmamba":
+ h.tree_cache.enable_mamba_extra_buffer = False
+ elif change == "no_track":
+ h.req.mamba_last_track_seqlen = None
+ elif change == "finished":
+ h.req.finished = lambda: True
+ elif change == "retracted":
+ h.req.is_retracted = True
+ elif change == "skip":
+ h.req.skip_radix_cache_insert = True
+ elif change == "decode":
+ h.result_queue[0] = (Batch("decode", h.req), object())
+ elif change == "empty":
+ h.result_queue.clear()
+ self.assertFalse(h._has_pending_mamba_cache_update())
+
+
+if __name__ == "__main__":
+ unittest.main()
--- /dev/null
+++ b/test/registered/unit/mem_cache/test_mamba_shared_prefix_lifetime.py
@@ -0,0 +1,64 @@
+import unittest
+from array import array
+import torch
+import test_mamba_unittest as mamba_tests
+from sglang.srt.mem_cache.base_prefix_cache import (
+ InsertParams,
+ MatchPrefixParams,
+ EvictParams,
+)
+from sglang.srt.mem_cache.radix_cache import RadixKey
+
+
+class SharedPins(unittest.TestCase):
+ def test_shared_prefix_retained_until_both_consumers_release(self):
+ tree, alloc, pool, make_req = (
+ mamba_tests.TestMamba()._setup_tree_and_allocator()
+ )
+ initial = alloc.available_size()
+ ma = pool.mamba_allocator.available_size()
+ a = make_req()
+ av = alloc.alloc(3)
+ key = RadixKey(array("q", [1, 2, 3]))
+ tree.insert(
+ InsertParams(key=key, value=av, mamba_value=a.mamba_pool_idx.unsqueeze(0))
+ )
+ na = tree.match_prefix(MatchPrefixParams(key=key)).last_device_node
+ tree.inc_lock_ref(na)
+ kcache = alloc.get_kvcache().get_key_buffer(3)
+ marker = torch.full_like(kcache.index_select(0, av), 0.25)
+ kcache.index_copy_(0, av, marker)
+ b = make_req()
+ bv = alloc.alloc(7)
+ bkey = RadixKey(array("q", [1, 2, 3, 4, 5, 6, 7]))
+ tree.insert(
+ InsertParams(
+ key=bkey,
+ value=bv,
+ mamba_value=b.mamba_pool_idx.unsqueeze(0),
+ prev_prefix_len=0,
+ )
+ )
+ nb = tree.match_prefix(MatchPrefixParams(key=bkey)).last_device_node
+ tree.inc_lock_ref(nb)
+ free = torch.cat([alloc.free_pages, alloc.release_pages] + alloc.free_group)
+ self.assertTrue(bool(torch.isin(bv[:3], free).all()))
+ self.assertFalse(bool(torch.isin(av, free).any()))
+ self.assertEqual(na.full_lock_ref, 2)
+ self.assertTrue(torch.equal(na.value, av))
+ self.assertTrue(torch.equal(kcache.index_select(0, av), marker))
+ self.assertEqual(tree.evict(EvictParams(num_tokens=128)).num_tokens_evicted, 0)
+ tree.dec_lock_ref(nb)
+ tree.evict(EvictParams(num_tokens=128))
+ self.assertEqual(na.full_lock_ref, 1)
+ self.assertTrue(torch.equal(kcache.index_select(0, av), marker))
+ self.assertEqual(alloc.available_size(), initial - 3)
+ tree.dec_lock_ref(na)
+ tree.evict(EvictParams(num_tokens=128))
+ tree.sanity_check()
+ self.assertEqual(alloc.available_size(), initial)
+ self.assertEqual(pool.mamba_allocator.available_size(), ma)
+
+
+if __name__ == "__main__":
+ unittest.main()
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