Store mamba prefix-cache checkpoints at the configured SSM state dtype (#34820)

python/sglang/kernels/ops/attention/fla/chunk_delta_h.py

@@ -63,6 +63,9 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
     stride_init_state,
     cu_seqlens,
     chunk_offsets,
+    track_state,
+    track_chunk_idx,
+    stride_track_state,
     T,
     H: tl.constexpr,
     Hg: tl.constexpr,
@@ -78,6 +81,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
     IS_VARLEN: tl.constexpr,
     NT_BUCKET: tl.constexpr,
     USE_EXP2: tl.constexpr,
+    TRACK_STATE: tl.constexpr,
 ):
     i_v, i_nh = tl.program_id(0), tl.program_id(1)
     i_n, i_h = i_nh // H, i_nh % H
@@ -130,6 +134,15 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
     if INPLACE_UPDATE:
         ht = ht + i_h * V * K
 
+    if TRACK_STATE:
+        i_track = tl.load(track_chunk_idx + i_n).to(tl.int32)
+        p_track_base = track_state + (i_n * stride_track_state + i_h * V * K).to(
+            tl.int64
+        )
+    else:
+        i_track = -1
+        p_track_base = track_state
+
     # load initial state
     if USE_INITIAL_STATE and valid_state:
         p_h0_1 = tl.make_block_ptr(h0, (V, K), (K, 1), (i_v * BV, 0), (BV, 64), (1, 0))
@@ -172,6 +185,27 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
             )
             tl.store(p_h4, b_h4.to(p_h4.dtype.element_ty), boundary_check=(0, 1))
 
+        if TRACK_STATE and i_t == i_track:
+            p_t1 = tl.make_block_ptr(
+                p_track_base, (V, K), (K, 1), (i_v * BV, 0), (BV, 64), (1, 0)
+            )
+            tl.store(p_t1, b_h1, boundary_check=(0, 1))
+            if K > 64:
+                p_t2 = tl.make_block_ptr(
+                    p_track_base, (V, K), (K, 1), (i_v * BV, 64), (BV, 64), (1, 0)
+                )
+                tl.store(p_t2, b_h2, boundary_check=(0, 1))
+            if K > 128:
+                p_t3 = tl.make_block_ptr(
+                    p_track_base, (V, K), (K, 1), (i_v * BV, 128), (BV, 64), (1, 0)
+                )
+                tl.store(p_t3, b_h3, boundary_check=(0, 1))
+            if K > 192:
+                p_t4 = tl.make_block_ptr(
+                    p_track_base, (V, K), (K, 1), (i_v * BV, 192), (BV, 64), (1, 0)
+                )
+                tl.store(p_t4, b_h4, boundary_check=(0, 1))
+
         p_w = tl.make_block_ptr(
             w, (T, K), (stride_w, 1), (i_t * BT, 0), (BT, 64), (1, 0)
         )
@@ -326,10 +360,21 @@ def chunk_gated_delta_rule_fwd_h(
     cu_seqlens: Optional[torch.LongTensor] = None,
     chunk_indices: Optional[torch.LongTensor] = None,
     use_exp2: bool = False,
+    track_state: Optional[torch.Tensor] = None,
+    track_chunk_idx: Optional[torch.Tensor] = None,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     assert not (use_exp2 and g is not None), (
         "use_exp2 covers only the per-channel gk path; scalar g stays natural-exp"
     )
+    assert (track_state is None) == (track_chunk_idx is None), (
+        "track_state and track_chunk_idx must be passed together"
+    )
+    if track_state is not None:
+        # The caller rounds once to the pool dtype; a narrower buffer would
+        # silently double-round the snapshot.
+        assert track_state.dtype == torch.float32, (
+            f"track_state must be fp32, got {track_state.dtype}"
+        )
     B, T, Hg, K, V = *k.shape, u.shape[-1]
     H = u.shape[-2]
     BT = CHUNK_SIZE
@@ -369,6 +414,9 @@ def chunk_gated_delta_rule_fwd_h(
         stride_init_state=(initial_state.stride(0) if initial_state is not None else 0),
         cu_seqlens=cu_seqlens,
         chunk_offsets=chunk_offsets,
+        track_state=track_state,
+        track_chunk_idx=track_chunk_idx,
+        stride_track_state=(track_state.stride(0) if track_state is not None else 0),
         T=T,
         H=H,
         Hg=Hg,
@@ -383,5 +431,6 @@ def chunk_gated_delta_rule_fwd_h(
         IS_VARLEN=cu_seqlens is not None,
         NT_BUCKET=(0 if NT <= 32 else (1 if NT <= 128 else 2)),
         USE_EXP2=use_exp2,
+        TRACK_STATE=track_state is not None,
     )
     return h, v_new

python/sglang/kernels/ops/attention/fla/kda.py

@@ -1094,6 +1094,8 @@ def chunk_kda_fwd(
     dt_bias: Optional[torch.Tensor] = None,
     lower_bound: Optional[float] = None,
     output_intermediate_states: bool = False,
+    track_state: Optional[torch.Tensor] = None,
+    track_chunk_idx: Optional[torch.Tensor] = None,
 ):
     chunk_size = 64
     # Pre-compute chunk indices once and thread through all downstream kernels.
@@ -1169,6 +1171,8 @@ def chunk_kda_fwd(
         cu_seqlens=cu_seqlens,
         chunk_indices=chunk_indices,
         use_exp2=True,
+        track_state=track_state,
+        track_chunk_idx=track_chunk_idx,
     )
     del w, u, kg
 
@@ -1210,6 +1214,8 @@ def chunk_kda(
     dt_bias: Optional[torch.Tensor] = None,
     lower_bound: Optional[float] = None,
     output_intermediate_states: bool = False,
+    track_state: Optional[torch.Tensor] = None,
+    track_chunk_idx: Optional[torch.Tensor] = None,
     beta_is_raw: bool = False,
     **kwargs,
 ):
@@ -1238,4 +1244,6 @@ def chunk_kda(
         dt_bias=dt_bias,
         lower_bound=lower_bound,
         output_intermediate_states=output_intermediate_states,
+        track_state=track_state,
+        track_chunk_idx=track_chunk_idx,
     )

python/sglang/kernels/ops/attention/helion/kda_prefill.py

@@ -1024,6 +1024,40 @@ _STATE_VARLEN_SMALL_HEAD_CONFIG = helion.Config(
     range_unroll_factors=[0, 0],
 )
 
+# Track variants of the two varlen configs. The fp32 track snapshot adds one
+# load + one store mid-body, which would shift the positional indexing /
+# eviction lists above; separate kernels keep the non-track configs untouched.
+# Tracked batches are rare (prefix-cache checkpointing only), so these trade
+# the hand-tuned positional lists for plainly correct settings.
+_STATE_VARLEN_TRACK_CONFIG = helion.Config(
+    atomic_indexing=[],
+    block_sizes=[64],
+    indexing="pointer",
+    l2_groupings=[4],
+    loop_orders=[[0, 2, 1]],
+    num_stages=2,
+    num_warps=4,
+    pid_type="flat",
+    range_flattens=[None, None],
+    range_multi_buffers=[None, False],
+    range_num_stages=[],
+    range_unroll_factors=[0, 2],
+)
+_STATE_VARLEN_SMALL_HEAD_TRACK_CONFIG = helion.Config(
+    atomic_indexing=[],
+    block_sizes=[32],
+    indexing="pointer",
+    l2_groupings=[1],
+    loop_orders=[[1, 2, 0]],
+    num_stages=3,
+    num_warps=8,
+    pid_type="flat",
+    range_flattens=[None, None],
+    range_multi_buffers=[None, True],
+    range_num_stages=[],
+    range_unroll_factors=[0, 0],
+)
+
 
 @helion.kernel(
     static_shapes=False,
@@ -1040,6 +1074,9 @@ def _chunk_state(
     cu_seqlens: torch.Tensor,
     chunk_indices: torch.Tensor,
     chunk_offsets: torch.Tensor,
+    track_state: torch.Tensor,
+    track_chunk_idx: torch.Tensor,
+    has_track: hl.constexpr,  # pyrefly: ignore[bad-function-definition]
     is_varlen: hl.constexpr,  # pyrefly: ignore[bad-function-definition]
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """Propagate KDA state between chunks and update the state pool in place."""
@@ -1070,6 +1107,11 @@ def _chunk_state(
             initial_state.stride(2),
             initial_state.stride(3),
             initial_state_indices.stride(0),
+            track_state.stride(0),
+            track_state.stride(1),
+            track_state.stride(2),
+            track_state.stride(3),
+            track_chunk_idx.stride(0),
         )
     )
 
@@ -1108,6 +1150,9 @@ def _chunk_state(
             :,
         ].float()
 
+        if has_track:
+            i_track = track_chunk_idx[tile_sequence.id]
+
         for token_tile in hl.tile(sequence_length, block_size=64):
             global_chunk = output_offset + token_tile.id
             h_rows[
@@ -1115,6 +1160,17 @@ def _chunk_state(
                 tile_v,
                 :,
             ] = state.to(h.dtype)
+            if has_track:
+                # Snapshot the fp32 accumulator at the tracked chunk boundary
+                # (the h store above rounds to the activation dtype; -1 marks
+                # untracked sequences and never matches a chunk id).
+                if token_tile.id == i_track:
+                    track_state[
+                        tile_sequence.id,
+                        tile_h.id,
+                        tile_v.index,
+                        :,
+                    ] = state
             token = begin + token_tile.index
             valid = token < end
             row = token * H + tile_h.id
@@ -1163,13 +1219,29 @@ _chunk_state_varlen_small_head = helion.kernel(
     config=_STATE_VARLEN_SMALL_HEAD_CONFIG,
     ignore_warnings=_IGNORED_WARNINGS,
 )(_chunk_state.fn)
+_chunk_state_varlen_track = helion.kernel(
+    static_shapes=False,
+    config=_STATE_VARLEN_TRACK_CONFIG,
+    ignore_warnings=_IGNORED_WARNINGS,
+)(_chunk_state.fn)
+_chunk_state_varlen_small_head_track = helion.kernel(
+    static_shapes=False,
+    config=_STATE_VARLEN_SMALL_HEAD_TRACK_CONFIG,
+    ignore_warnings=_IGNORED_WARNINGS,
+)(_chunk_state.fn)
 
 
-def _select_state_kernel(*, is_varlen: bool, num_heads: int) -> helion.Kernel:
+def _select_state_kernel(
+    *, is_varlen: bool, num_heads: int, has_track: bool
+) -> helion.Kernel:
     if not is_varlen:
         return _chunk_state
     if num_heads <= _PREFILL_SMALL_HEAD_THRESHOLD:
+        if has_track:
+            return _chunk_state_varlen_small_head_track
         return _chunk_state_varlen_small_head
+    if has_track:
+        return _chunk_state_varlen_track
     return _chunk_state_varlen
 
 
@@ -1314,6 +1386,8 @@ def chunk_kda(
     dt_bias: torch.Tensor | None = None,
     lower_bound: float | None = None,
     output_intermediate_states: bool = False,
+    track_state: torch.Tensor | None = None,
+    track_chunk_idx: torch.Tensor | None = None,
     beta_is_raw: bool = False,
     **kwargs: object,
 ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
@@ -1322,6 +1396,13 @@ def chunk_kda(
         scale = k.shape[-1] ** -0.5
     if initial_state is None or initial_state_indices is None:
         raise ValueError("KDA prefill requires an indexed initial-state pool")
+    assert (track_state is None) == (track_chunk_idx is None), (
+        "track_state and track_chunk_idx must be passed together"
+    )
+    if track_state is not None:
+        assert track_state.dtype == torch.float32, (
+            f"track_state must be fp32, got {track_state.dtype}"
+        )
 
     num_tokens = q.shape[1]
     if g.shape[1] < num_tokens or beta.shape[1] < num_tokens:
@@ -1349,6 +1430,8 @@ def chunk_kda(
             dt_bias=dt_bias,
             lower_bound=lower_bound,
             output_intermediate_states=output_intermediate_states,
+            track_state=track_state,
+            track_chunk_idx=track_chunk_idx,
         )
 
     q = q.contiguous()
@@ -1395,7 +1478,10 @@ def chunk_kda(
     else:
         metadata = torch.empty(0, device=q.device, dtype=torch.int32)
         chunk_offsets = torch.empty(0, device=q.device, dtype=torch.long)
-    state_kernel = _select_state_kernel(is_varlen=is_varlen, num_heads=q.size(2))
+    has_track = track_state is not None
+    state_kernel = _select_state_kernel(
+        is_varlen=is_varlen, num_heads=q.size(2), has_track=has_track
+    )
     h, v_new = state_kernel(
         kg,
         w,
@@ -1410,6 +1496,11 @@ def chunk_kda(
             else torch.empty(0, 2, device=q.device, dtype=torch.long)
         ),
         chunk_offsets,
+        # Unused when has_track is False; bind in-place tensors as stand-ins so
+        # the kernel signature always sees real tensors.
+        track_state if has_track else initial_state,
+        track_chunk_idx if has_track else initial_state_indices,
+        has_track,
         is_varlen,
     )
     if chunk_indices is None:

python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py

@@ -114,7 +114,9 @@ class MambaAttnBackendBase(AttentionBackend):
         retrieve_parent_token = None
         track_conv_indices = None
         track_ssm_h_src = None
+        track_chunk_idx = None
         track_ssm_h_dst = None
+        track_ssm_h_batch_src = None
         track_ssm_final_src = None
         track_ssm_final_dst = None
         track_ssm_seq_idx = None
@@ -250,8 +252,10 @@ class MambaAttnBackendBase(AttentionBackend):
                     )
 
                     (
+                        track_chunk_idx,
                         track_ssm_h_src,
                         track_ssm_h_dst,
+                        track_ssm_h_batch_src,
                         track_ssm_final_src,
                         track_ssm_final_dst,
                         track_ssm_seq_idx,
@@ -273,8 +277,10 @@ class MambaAttnBackendBase(AttentionBackend):
             track_conv_indices=track_conv_indices,
             track_ssm_h_src=track_ssm_h_src,
             track_ssm_h_dst=track_ssm_h_dst,
+            track_ssm_h_batch_src=track_ssm_h_batch_src,
             track_ssm_final_src=track_ssm_final_src,
             track_ssm_final_dst=track_ssm_final_dst,
+            track_chunk_idx=track_chunk_idx,
             track_ssm_seq_idx=track_ssm_seq_idx,
             track_ssm_end_locs=track_ssm_end_locs,
             track_ssm_recompute_dst=track_ssm_recompute_dst,
@@ -358,7 +364,9 @@ class MambaAttnBackendBase(AttentionBackend):
     ):
         """src/dst indices to track SSM states for prefix caching: aligned seqs
         cache last_recurrent_state, unaligned cache intermediate `h` at the last
-        chunk boundary."""
+        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."""
         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()
@@ -416,9 +424,17 @@ class MambaAttnBackendBase(AttentionBackend):
         def to_device(t):
             return None if t is None else t.to(self.device, non_blocking=True)
 
+        track_chunk_idx = torch.full((lens_to_track.shape[0],), -1, dtype=torch.int32)
+        tracked_seqs = mamba_track_mask.nonzero(as_tuple=True)[0][not_aligned]
+        track_chunk_idx[tracked_seqs] = (
+            lens_masked[not_aligned] // state_chunk_size
+        ).to(torch.int32)
+
         return (
+            to_device(track_chunk_idx),
             to_device(track_ssm_h_src),
             to_device(track_ssm_h_dst),
+            to_device(tracked_seqs),
             to_device(track_ssm_final_src),
             to_device(track_ssm_final_dst),
             to_device(track_ssm_seq_idx),
@@ -887,18 +903,31 @@ class MambaAttnBackendBase(AttentionBackend):
         ssm_states: torch.Tensor,
         forward_metadata: ForwardMetadata,
         track_states: Optional[torch.Tensor] = None,
+        *,
+        h_track_buf: Optional[torch.Tensor] = None,
     ):
         """Copy extend SSM state at the last chunk boundary to track slots (source
-        depends on chunk alignment; see `_init_track_ssm_indices`)."""
+        depends on chunk alignment; see `_init_track_ssm_indices`).
+
+        Unaligned rows read the fp32 ``h_track_buf`` snapshot written in-kernel
+        when given (its rows follow the batch, selected by the integer index
+        ``track_ssm_h_batch_src`` — a boolean mask would nonzero() and sync the
+        stream once per layer); otherwise they fall back to the per-chunk
+        states ``h`` (already rounded to the activation dtype)."""
         if forward_metadata.has_mamba_track_mask:
             # Triton always returns h; FlashInfer returns it only when checkpoints
             # were requested. Aligned-only tracking reads the final state below.
             if forward_metadata.track_ssm_h_src.numel() > 0:
-                assert h is not None
+                if h_track_buf is not None:
-                h = h.squeeze(0)
+                    ssm_states[forward_metadata.track_ssm_h_dst] = h_track_buf[
-                ssm_states[forward_metadata.track_ssm_h_dst] = h[
+                        forward_metadata.track_ssm_h_batch_src
-                    forward_metadata.track_ssm_h_src
+                    ].to(ssm_states.dtype, copy=False)
-                ].to(ssm_states.dtype, copy=False)
+                else:
+                    assert h is not None
+                    h = h.squeeze(0)
+                    ssm_states[forward_metadata.track_ssm_h_dst] = h[
+                        forward_metadata.track_ssm_h_src
+                    ].to(ssm_states.dtype, copy=False)
             if (
                 forward_metadata.track_ssm_recompute_dst is not None
                 and forward_metadata.track_ssm_recompute_dst.numel() > 0

python/sglang/srt/layers/attention/linear/kda_backend.py

@@ -330,6 +330,14 @@ class KDAKernelDispatcher:
             **kwargs,
         )
 
+    def effective_extend_kernel(self, lower_bound: Optional[float]):
+        """The kernel ``extend`` will actually run: safe-gate models reroute
+        kernels without ``supports_safe_gate`` to Triton."""
+        kernel = self.extend_kernel
+        if lower_bound is not None and not getattr(kernel, "supports_safe_gate", True):
+            kernel = self.triton_kernel
+        return kernel
+
     def extend(
         self,
         q: torch.Tensor,
@@ -343,11 +351,7 @@ class KDAKernelDispatcher:
         query_start_loc: torch.Tensor,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
-        kernel = self.extend_kernel
+        kernel = self.effective_extend_kernel(kwargs.get("lower_bound"))
-        if kwargs.get("lower_bound") is not None and not getattr(
-            kernel, "supports_safe_gate", True
-        ):
-            kernel = self.triton_kernel
         return kernel.extend(
             q,
             k,
@@ -857,6 +861,34 @@ class KDAAttnBackend(MambaAttnBackendBase):
             a = a.unflatten(-1, (-1, layer.head_k_dim))
 
         track_ssm = self.forward_metadata.has_mamba_track_mask
+        track_chunk_idx = self.forward_metadata.track_chunk_idx
+        h_track_buf = None
+        if (
+            track_ssm
+            and track_chunk_idx is not None
+            # Same rows as track_ssm_h_batch_src, but known without a GPU sync.
+            and self.forward_metadata.track_ssm_h_src.numel() > 0
+        ):
+            # fp32 scratch the kernel snapshots the tracked chunk-boundary
+            # states into (rows follow the batch; untracked rows stay unread).
+            # A kernel that does not declare support would leave the buffer
+            # unwritten and corrupt prefix-cache restores — fail loudly here.
+            # Check the kernel the dispatcher will actually run (safe-gate
+            # reroute included), not just the configured one.
+            extend_kernel = self.kernel_dispatcher.effective_extend_kernel(
+                layer.lower_bound
+            )
+            assert extend_kernel.supports_track_state_snapshot, (
+                f"{type(extend_kernel).__name__} cannot write the fp32 track "
+                f"snapshot required by the mamba track path; use "
+                f"--linear-attn-prefill-backend triton or "
+                f"--mamba-radix-cache-strategy no_buffer"
+            )
+            h_track_buf = torch.empty(
+                (track_chunk_idx.shape[0], *ssm_states.shape[1:]),
+                dtype=torch.float32,
+                device=ssm_states.device,
+            )
         core_attn_out = self.kernel_dispatcher.extend(
             q=q,
             k=k,
@@ -881,6 +913,8 @@ class KDAAttnBackend(MambaAttnBackendBase):
             track_ssm_h_src=(
                 self.forward_metadata.track_ssm_h_src if track_ssm else None
             ),
+            track_state=h_track_buf,
+            track_chunk_idx=(track_chunk_idx if h_track_buf is not None else None),
         )
         if track_ssm:
             # Snapshot the SSM state at the last track-aligned chunk boundary
@@ -888,7 +922,11 @@ class KDAAttnBackend(MambaAttnBackendBase):
             # ping-pong track slots (see _init_track_ssm_indices).
             core_attn_out, h = core_attn_out
             self._track_mamba_state_extend(
-                forward_batch, h, ssm_states, self.forward_metadata
+                forward_batch,
+                h,
+                ssm_states,
+                self.forward_metadata,
+                h_track_buf=h_track_buf,
             )
 
         if logical_num_tokens < physical_num_tokens:

python/sglang/srt/layers/attention/linear/kernels/kda_flashkda.py

@@ -41,6 +41,8 @@ def _triton_fallback(
     lower_bound=None,
     beta_is_raw=False,
     return_intermediate_states=False,
+    track_state=None,
+    track_chunk_idx=None,
 ):
     """Fall back to the Triton chunk_kda kernel (handles all preprocessing).
 
@@ -67,6 +69,8 @@ def _triton_fallback(
         lower_bound=lower_bound,
         beta_is_raw=beta_is_raw,
         output_intermediate_states=return_intermediate_states,
+        track_state=track_state,
+        track_chunk_idx=track_chunk_idx,
     )
 
 
@@ -83,6 +87,10 @@ class FlashKDAKernel(LinearAttnKernelBase):
     Requires an SM90+ GPU with the ``flash_kda`` package.
     """
 
+    # Tracked batches always take the Triton fallback, which forwards the
+    # fp32 snapshot arguments (see _triton_fallback).
+    supports_track_state_snapshot: bool = True
+
     def decode(
         self,
         q: torch.Tensor,
@@ -141,6 +149,8 @@ class FlashKDAKernel(LinearAttnKernelBase):
                 lower_bound=lower_bound,
                 beta_is_raw=beta_is_raw,
                 return_intermediate_states=return_intermediate_states,
+                track_state=kwargs.get("track_state"),
+                track_chunk_idx=kwargs.get("track_chunk_idx"),
             )
 
         return (

python/sglang/srt/layers/attention/linear/kernels/kda_helion.py

@@ -20,6 +20,7 @@ class HelionKDAKernel(LinearAttnKernelBase):
     """
 
     supports_packed_decode = True
+    supports_track_state_snapshot: bool = True
 
     def __init__(
         self,
@@ -188,4 +189,6 @@ class HelionKDAKernel(LinearAttnKernelBase):
             dt_bias=dt_bias,
             lower_bound=lower_bound,
             output_intermediate_states=return_intermediate_states,
+            track_state=kwargs.get("track_state"),
+            track_chunk_idx=kwargs.get("track_chunk_idx"),
         )

python/sglang/srt/layers/attention/linear/kernels/kda_nvidia.py

@@ -76,6 +76,10 @@ def _from_nvidia_kda_state_layout(
 
 
 class NvidiaKDAKernel(LinearAttnKernelBase):
+    # Tracked batches route to the embedded Triton fallback, which forwards
+    # the fp32 snapshot arguments (see _triton_extend).
+    supports_track_state_snapshot: bool = True
+
     def __init__(self):
         # This kernel uses tcgen05 + TMEM, which are available on datacenter
         # Blackwell (SM100/SM103, reported as capability major 10), but not on

python/sglang/srt/layers/attention/linear/kernels/kda_ptx.py

@@ -9,9 +9,11 @@ serving shape: K = V = 128, chunk 64.
 Scope: ordinary extend batches satisfying the kernel's fixed tensor contract.
 Correctness-sensitive cases stay on Triton:
 
-- track batches receive dense intermediate SSM states directly from the kernel
+- track batches carrying the fp32 snapshot buffer (``track_state``, the mamba
-  when the cache checkpoint stride is also 64 tokens. Other interior snapshots
+  extra_buffer track path) stay on Triton — the kernel cannot write it.
-  stay on Triton; boundary-only tracking can still use the final state;
+  Interior snapshots consumed as dense ``h`` still come from the kernel when
+  the cache checkpoint stride is also 64 tokens; boundary-only tracking uses
+  the final state either way;
 - spec-decode extends, which must stay rollback-able.
 
 Single-sequence token counts that are not a multiple of the kernel's 64-token
@@ -49,6 +51,12 @@ _PAD_GATE = -1000.0
 
 
 class PtxKDAKernel(LinearAttnKernelBase):
+    # Batches carrying the fp32 track snapshot buffer (track_state) route to
+    # the embedded Triton fallback, which forwards the snapshot arguments
+    # (the track_state check in extend -> _triton_extend); boundary-only
+    # tracking stays native.
+    supports_track_state_snapshot: bool = True
+
     def __init__(self):
         # tcgen05 + TMEM with sm_103a-only encodings: GB300 (SM103) only.
         self.supports_prefill = torch.cuda.is_available() and (
@@ -217,6 +225,11 @@ class PtxKDAKernel(LinearAttnKernelBase):
             )
         eligible = (
             not kwargs.get("is_spec_decode")
+            # The native kernel cannot write the fp32 track snapshot buffer;
+            # a batch carrying one must take the Triton fallback, which
+            # forwards the snapshot arguments (see _triton_extend). Leaving
+            # the buffer unwritten would corrupt prefix-cache track slots.
+            and kwargs.get("track_state") is None
             and intermediate_stride_supported
             and shape_known
             and supported_shape

python/sglang/srt/layers/attention/linear/kernels/kda_triton.py

@@ -28,6 +28,7 @@ class TritonKDAKernel(LinearAttnKernelBase):
     # the same fallback CPU/NPU use. Batched decode is handled via query_start_loc.
     supports_packed_decode: bool = not is_cpu() and not is_npu() and not is_xpu()
     supports_fused_chain_verify: bool = not is_cpu() and not is_npu()
+    supports_track_state_snapshot: bool = True
 
     def packed_decode(
         self,
@@ -248,4 +249,6 @@ class TritonKDAKernel(LinearAttnKernelBase):
             lower_bound=lower_bound,
             beta_is_raw=beta_is_raw,
             output_intermediate_states=return_intermediate_states,
+            track_state=kwargs.get("track_state"),
+            track_chunk_idx=kwargs.get("track_chunk_idx"),
         )

python/sglang/srt/layers/attention/linear/kernels/kernel_backend.py

@@ -13,6 +13,14 @@ class LinearAttnKernelBase(ABC):
     uses_state_checkpoints: bool = False
     supports_fused_chain_verify: bool = False
 
+    # True when extend() honors the fp32 track snapshot (track_state /
+    # track_chunk_idx), natively or by routing tracked batches to a kernel
+    # that does. KDAAttnBackend asserts this before allocating the snapshot
+    # buffer: a kernel that silently ignores those arguments leaves the buffer
+    # unwritten and corrupts prefix-cache restores. Kernels that reject
+    # tracked batches loudly (NotImplementedError) keep the default False.
+    supports_track_state_snapshot: bool = False
+
     @abstractmethod
     def decode(
         self,

python/sglang/srt/layers/attention/mamba/mamba2_metadata.py

@@ -55,6 +55,11 @@ class ForwardMetadata:
     track_ssm_h_dst: Optional[torch.Tensor] = None
     track_ssm_final_src: Optional[torch.Tensor] = None
     track_ssm_final_dst: Optional[torch.Tensor] = None
+    track_chunk_idx: Optional[torch.Tensor] = None
+    # Batch rows of the chunk-unaligned tracked seqs; indexes the fp32
+    # h_track_buf snapshot (KDA path) with plain integer indexing, so the
+    # copy into the track slots does not nonzero()-sync the stream.
+    track_ssm_h_batch_src: Optional[torch.Tensor] = None
     state_checkpoint_cu_starts: Optional[torch.Tensor] = None
     num_state_checkpoints: int = 0
     state_checkpoint_every_n_tokens: int = 0

test/registered/kernel/ops/attention/test_kda_track_state.py

@@ -0,0 +1,183 @@
+import unittest
+
+import torch
+
+from sglang.kernels.ops.attention.fla.kda import chunk_kda
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cuda_ci(est_time=180, stage="base-b-kernel-unit", runner_config="4-gpu-b200")
+
+CHUNK_SIZE = 64
+
+_BACKENDS = {"triton": chunk_kda}
+HELION_AVAILABLE = True
+try:
+    import helion  # noqa: F401
+except ModuleNotFoundError as error:
+    # A broken install (transitive import failure) must stay loud; only the
+    # absent package downgrades the run to triton-only.
+    if error.name != "helion":
+        raise
+    HELION_AVAILABLE = False
+if HELION_AVAILABLE:
+    from sglang.kernels.ops.attention.helion.kda_prefill import (
+        chunk_kda as helion_chunk_kda,
+    )
+
+    _BACKENDS["helion"] = helion_chunk_kda
+
+
+def _make_varlen_inputs(seed, lens, num_heads=2, head_dim=128):
+    """Packed varlen KDA inputs: [1, sum(lens), H, D] plus a zero fp32 state pool."""
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    total = sum(lens)
+
+    def randn(*shape, dtype=torch.bfloat16):
+        return torch.randn(*shape, generator=generator, device="cuda", dtype=dtype)
+
+    q = randn(1, total, num_heads, head_dim)
+    k = randn(1, total, num_heads, head_dim)
+    v = (0.1 * randn(1, total, num_heads, head_dim, dtype=torch.float32)).to(
+        torch.bfloat16
+    )
+    gate = randn(1, total, num_heads, head_dim)
+    beta = torch.sigmoid(randn(1, total, num_heads, dtype=torch.float32)).to(
+        torch.bfloat16
+    )
+    a_log = randn(num_heads, dtype=torch.float32)
+    dt_bias = randn(num_heads * head_dim, dtype=torch.float32)
+    state = torch.zeros(
+        len(lens), num_heads, head_dim, head_dim, device="cuda", dtype=torch.float32
+    )
+    cu_seqlens = torch.tensor(
+        [0, *torch.tensor(lens).cumsum(0).tolist()], dtype=torch.int32, device="cuda"
+    )
+    return q, k, v, gate, beta, a_log, dt_bias, state, cu_seqlens
+
+
+def _run_chunk_kda(
+    chunk_kda_fn, q, k, v, gate, beta, a_log, dt_bias, state, cu_seqlens, **kwargs
+):
+    return chunk_kda_fn(
+        # chunk_kda writes in place (the attention output lands in v, the gate
+        # cumsum in g); hand every run fresh copies so runs stay independent.
+        q=q.clone(),
+        k=k.clone(),
+        v=v.clone(),
+        g=gate.clone(),
+        beta=beta.clone(),
+        scale=q.shape[-1] ** -0.5,
+        initial_state=state,
+        initial_state_indices=torch.arange(
+            state.shape[0], device="cuda", dtype=torch.int32
+        ),
+        use_qk_l2norm_in_kernel=True,
+        cu_seqlens=cu_seqlens,
+        A_log=a_log,
+        dt_bias=dt_bias,
+        lower_bound=-5.0,
+        **kwargs,
+    )
+
+
+class TestKdaTrackState(CustomTestCase):
+    def test_helion_backend_ran(self):
+        """Visibility hook: without helion installed the snapshot check above
+        runs triton-only and the Helion track configs go untested — surface
+        that as an explicit skip instead of a silent pass."""
+        if not HELION_AVAILABLE:
+            self.skipTest("helion is not installed; triton backend only")
+
+    @torch.inference_mode()
+    def test_track_state_snapshots_fp32_accumulator(self):
+        """Bug regression: the mamba radix track path snapshots the SSM state at
+        the last chunk boundary of unaligned sequences into the fp32 state pool.
+        It used to read the per-chunk states `h` (activation dtype, bf16), so a
+        prefix-cache hit restored a bf16-rounded state while a cache miss kept
+        fp32. `track_state` must carry the in-kernel fp32 accumulator: identical
+        to the fp32 final state of a run truncated at the boundary, and strictly
+        more precise than the bf16 `h` row for the same boundary.
+        """
+        if not torch.cuda.is_available():
+            self.skipTest("requires CUDA")
+        for backend, chunk_kda_fn in _BACKENDS.items():
+            # num_heads=2 exercises the Helion small-head track config; 16
+            # crosses _PREFILL_SMALL_HEAD_THRESHOLD (12) to exercise the
+            # large-head varlen track config that real models take.
+            for num_heads in (2, 16):
+                with self.subTest(backend=backend, num_heads=num_heads):
+                    self._check_track_state(chunk_kda_fn, num_heads)
+
+    def _check_track_state(self, chunk_kda_fn, num_heads):
+        # seq0: 100 tokens, unaligned -> snapshot at the 64-token boundary
+        # (start of chunk 1). seq1: 64 tokens, aligned -> not tracked.
+        lens = [100, 64]
+        q, k, v, gate, beta, a_log, dt_bias, state, cu_seqlens = _make_varlen_inputs(
+            0, lens, num_heads=num_heads
+        )
+        num_heads, head_dim = q.shape[2], q.shape[3]
+
+        track_state = torch.full(
+            (len(lens), num_heads, head_dim, head_dim),
+            float("nan"),
+            device="cuda",
+            dtype=torch.float32,
+        )
+        track_chunk_idx = torch.tensor([1, -1], dtype=torch.int32, device="cuda")
+        _, h = _run_chunk_kda(
+            chunk_kda_fn,
+            q,
+            k,
+            v,
+            gate,
+            beta,
+            a_log,
+            dt_bias,
+            state,
+            cu_seqlens,
+            output_intermediate_states=True,
+            track_state=track_state,
+            track_chunk_idx=track_chunk_idx,
+        )
+
+        # The untracked row must stay untouched; the tracked row must be finite.
+        self.assertTrue(torch.all(torch.isnan(track_state[1])))
+        self.assertFalse(torch.any(torch.isnan(track_state[0])))
+
+        # Reference: truncate seq0 at the boundary; the pool's fp32 row then
+        # receives the in-place final state for the same prefix — the
+        # established fp32 path the snapshot must agree with.
+        ref_state = torch.zeros(
+            1, num_heads, head_dim, head_dim, device="cuda", dtype=torch.float32
+        )
+        ref_cu_seqlens = torch.tensor([0, CHUNK_SIZE], dtype=torch.int32, device="cuda")
+        _run_chunk_kda(
+            chunk_kda_fn,
+            q[:, :CHUNK_SIZE],
+            k[:, :CHUNK_SIZE],
+            v[:, :CHUNK_SIZE],
+            gate[:, :CHUNK_SIZE],
+            beta[:, :CHUNK_SIZE],
+            a_log,
+            dt_bias,
+            ref_state,
+            ref_cu_seqlens,
+        )
+        torch.testing.assert_close(track_state[0], ref_state[0], rtol=1e-5, atol=1e-5)
+
+        # The guard: h packs one row per (seq, chunk); row 1 is seq0's state at
+        # the boundary, rounded to bf16. If the snapshot were re-routed through
+        # h, it could not match the fp32 reference above.
+        self.assertTrue(
+            torch.equal(h[0, 1].float(), track_state[0].to(torch.bfloat16).float()),
+            "h row should be exactly the bf16 rounding of the fp32 snapshot",
+        )
+        self.assertFalse(
+            torch.equal(track_state[0], track_state[0].to(torch.bfloat16).float()),
+            "test inputs must make bf16 rounding lossy",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/attention/linear/kernels/test_kda_ptx.py

@@ -0,0 +1,131 @@
+"""Unit tests for the PTX KDA prefill routing wrapper."""
+
+import unittest
+from unittest.mock import Mock, patch
+
+import torch
+
+from sglang.srt.layers.attention.linear.kernels.kda_ptx import PtxKDAKernel
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=5, suite="base-a-test-cpu")
+
+
+class _RejectTriton:
+    def extend(self, *args, **kwargs):
+        raise AssertionError("native-eligible batch unexpectedly fell back to Triton")
+
+
+class TestPtxKDATrackRouting(CustomTestCase):
+    """Regression: a batch carrying the fp32 track snapshot buffer must not
+    take the native PTX path — the kernel cannot write the buffer, and the
+    backend copies it into the prefix-cache track slots unconditionally, so an
+    unwritten buffer silently corrupts later cache restores.
+    """
+
+    def _make_kernel(self):
+        kernel = PtxKDAKernel()
+        kernel._ensure_loaded = lambda: None
+        kernel._fwd = Mock(side_effect=AssertionError("native path must not run"))
+        return kernel
+
+    @staticmethod
+    def _inputs(seq_lens=(64, 100)):
+        total = sum(seq_lens)
+        H, D = 2, 128
+
+        def vals(offset):
+            return torch.full((1, total, H, D), offset, dtype=torch.bfloat16)
+
+        return {
+            "q": vals(0.1),
+            "k": vals(0.2),
+            "v": vals(0.3),
+            "g": vals(0.4),
+            "beta": torch.zeros(1, total, H, dtype=torch.bfloat16),
+            "ssm_states": torch.zeros(8, H, D, D, dtype=torch.float32),
+            "cache_indices": torch.tensor([1, 3], dtype=torch.int32),
+            "query_start_loc": torch.tensor(
+                [0] + list(torch.tensor(seq_lens).cumsum(0).tolist()),
+                dtype=torch.int32,
+            ),
+            "A_log": torch.zeros(H, dtype=torch.float32),
+            "dt_bias": torch.zeros(H * D, dtype=torch.float32),
+            "extend_seq_lens_cpu": list(seq_lens),
+        }
+
+    def test_batch_with_track_state_routes_to_triton(self):
+        kernel = self._make_kernel()
+        kernel._triton.extend = Mock(return_value="triton-out")
+        x = self._inputs()
+        track_state = torch.zeros(2, 2, 128, 128, dtype=torch.float32)
+        track_chunk_idx = torch.tensor([1, -1], dtype=torch.int32)
+
+        with patch(
+            "sglang.srt.layers.attention.linear.kernels.kda_ptx.mamba_cache_chunk_size",
+            return_value=64,
+        ):
+            out = kernel.extend(
+                x["q"],
+                x["k"],
+                x["v"],
+                x["g"],
+                x["beta"],
+                ssm_states=x["ssm_states"],
+                cache_indices=x["cache_indices"],
+                query_start_loc=x["query_start_loc"],
+                A_log=x["A_log"],
+                dt_bias=x["dt_bias"],
+                return_intermediate_states=True,
+                track_ssm_h_src=torch.tensor([1], dtype=torch.long),
+                track_state=track_state,
+                track_chunk_idx=track_chunk_idx,
+                extend_seq_lens_cpu=x["extend_seq_lens_cpu"],
+            )
+
+        self.assertEqual(out, "triton-out")
+        kernel._fwd.assert_not_called()
+        kernel._triton.extend.assert_called_once()
+        forwarded = kernel._triton.extend.call_args.kwargs
+        self.assertIs(forwarded["track_state"], track_state)
+        self.assertIs(forwarded["track_chunk_idx"], track_chunk_idx)
+
+    def test_batch_without_track_state_stays_native(self):
+        kernel = self._make_kernel()
+        kernel._triton = _RejectTriton()
+        h = torch.zeros(3, 2, 128, 128, dtype=torch.float32)
+
+        def fake_fwd(*args, **kwargs):
+            return [
+                args[2].clone(),  # out == v
+                kwargs["initial_state"].clone(),  # final_state
+                *([None] * 8),
+                h,  # result[10]
+            ]
+
+        kernel._fwd = fake_fwd
+        x = self._inputs()
+
+        out, h_out = kernel.extend(
+            x["q"],
+            x["k"],
+            x["v"],
+            x["g"],
+            x["beta"],
+            ssm_states=x["ssm_states"],
+            cache_indices=x["cache_indices"],
+            query_start_loc=x["query_start_loc"],
+            A_log=x["A_log"],
+            dt_bias=x["dt_bias"],
+            return_intermediate_states=True,
+            track_ssm_h_src=torch.empty(0, dtype=torch.long),
+            extend_seq_lens_cpu=x["extend_seq_lens_cpu"],
+        )
+
+        self.assertEqual(tuple(out.shape), (1, 164, 2, 128))
+        self.assertIs(h_out, h)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/attention/test_kda_helion_dispatcher.py

@@ -194,5 +194,56 @@ class TestHelionKDADispatcher(unittest.TestCase):
         self.assertEqual(args.linear_attn_backend, "helion")
 
 
+class TestKDATrackStateSnapshotDeclaration(unittest.TestCase):
+    """Bookkeeping: every KDA prefill kernel must declare whether extend()
+    honors the fp32 track snapshot (``supports_track_state_snapshot``).
+
+    KDAAttnBackend allocates the snapshot buffer whenever a tracked batch has
+    chunk-unaligned sequences and asserts the flag before use. A kernel that
+    serves extend() without the flag must reject tracked batches loudly
+    (NotImplementedError); a missing declaration used to mean the buffer was
+    silently left unwritten and prefix-cache restores read garbage (the
+    FlashKDA fallback once dropped the track arguments exactly this way).
+    """
+
+    def test_every_kda_prefill_kernel_declares_the_contract(self):
+        from sglang.srt.layers.attention.linear.kernels.kda_cutedsl import (
+            CuteDSLKDAKernel,
+        )
+        from sglang.srt.layers.attention.linear.kernels.kda_flashinfer import (
+            FlashInferKDAKernel,
+        )
+        from sglang.srt.layers.attention.linear.kernels.kda_flashkda import (
+            FlashKDAKernel,
+        )
+        from sglang.srt.layers.attention.linear.kernels.kda_nvidia import (
+            NvidiaKDAKernel,
+        )
+        from sglang.srt.layers.attention.linear.kernels.kda_ptx import (
+            PtxKDAKernel,
+        )
+
+        # Native support or fallback that forwards the snapshot arguments.
+        for cls in (
+            TritonKDAKernel,
+            HelionKDAKernel,
+            NvidiaKDAKernel,
+            PtxKDAKernel,
+            FlashKDAKernel,
+        ):
+            self.assertTrue(
+                cls.supports_track_state_snapshot,
+                f"{cls.__name__} must declare supports_track_state_snapshot "
+                f"(native support or a fallback that forwards track_state)",
+            )
+        # Reject tracked batches loudly instead (extend() raises).
+        for cls in (CuteDSLKDAKernel, FlashInferKDAKernel):
+            self.assertFalse(
+                cls.supports_track_state_snapshot,
+                f"{cls.__name__} rejects tracked batches; it must not claim "
+                f"snapshot support it does not have",
+            )
+
+
 if __name__ == "__main__":
     unittest.main()

test/registered/unit/layers/attention/test_mamba_track_state_dtype.py

@@ -0,0 +1,94 @@
+import unittest
+
+import torch
+
+from sglang.srt.layers.attention.hybrid_linear_attn_backend import (
+    MambaAttnBackendBase,
+)
+from sglang.srt.layers.attention.mamba.mamba2_metadata import ForwardMetadata
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=5, suite="base-a-test-cpu")
+
+# Above the fp16 midpoint 1 + 2^-11 (single-rounds up to 1 + 2^-10) but below
+# the bf16 midpoint 1 + 2^-8 (rounds to 1.0, which then stays 1.0 in fp16):
+# any fp32 -> bf16 -> fp16 double rounding loses the increment. The 2^-23 tail
+# is the last fp32 mantissa bit at 1.x, so the probe is fp32-exact (a 2^-24
+# tail would round back to the midpoint itself).
+DOUBLE_ROUND_PROBE = 1.0 + 2.0**-11 + 2.0**-23
+
+
+class TestTrackMambaStateDtype(CustomTestCase):
+    """The fp32 track snapshot is cast to the pool dtype exactly once.
+
+    ``_track_mamba_state_extend`` reads the in-kernel fp32 snapshot
+    (``h_track_buf``) and casts it to ``ssm_states.dtype`` in a single ``.to``.
+    This must hold for every ``--mamba-ssm-dtype``: fp32 keeps full precision,
+    bf16 matches the (already correct) legacy path, and fp16 must not inherit
+    the old double rounding through the bf16 per-chunk states ``h``.
+    """
+
+    @staticmethod
+    def _run_track_copy(pool_dtype, h_track_buf, dst_slots, batch_rows):
+        metadata = ForwardMetadata(
+            has_mamba_track_mask=True,
+            # Only numel() gates the copy on this path; the h-row values
+            # themselves are unused when h_track_buf is given.
+            track_ssm_h_src=torch.zeros(len(dst_slots), dtype=torch.long),
+            track_ssm_h_dst=torch.tensor(dst_slots),
+            track_ssm_h_batch_src=torch.tensor(batch_rows),
+            track_ssm_final_src=torch.empty(0, dtype=torch.long),
+            track_ssm_final_dst=torch.empty(0, dtype=torch.long),
+            # Required by the dataclass; unused on this path.
+            query_start_loc=torch.zeros(1, dtype=torch.int32),
+            mamba_cache_indices=torch.zeros(1, dtype=torch.long),
+        )
+        ssm_states = torch.zeros(8, *h_track_buf.shape[1:], dtype=pool_dtype)
+        # The method touches no `self` state; call it unbound so this stays a
+        # pure bookkeeping test.
+        MambaAttnBackendBase._track_mamba_state_extend(
+            None, None, None, ssm_states, metadata, h_track_buf=h_track_buf
+        )
+        return ssm_states
+
+    def test_snapshot_cast_once_to_pool_dtype(self):
+        torch.manual_seed(0)
+        h_track_buf = torch.randn(3, 2, 4, 4, dtype=torch.float32)
+        h_track_buf[0, 0, 0, 0] = DOUBLE_ROUND_PROBE
+        for pool_dtype in (torch.float32, torch.bfloat16, torch.float16):
+            with self.subTest(pool_dtype=pool_dtype):
+                ssm_states = self._run_track_copy(
+                    pool_dtype, h_track_buf, dst_slots=[5, 2], batch_rows=[0, 2]
+                )
+                # Single rounding of the fp32 snapshot, in batch-row order.
+                self.assertTrue(
+                    torch.equal(ssm_states[5], h_track_buf[0].to(pool_dtype))
+                )
+                self.assertTrue(
+                    torch.equal(ssm_states[2], h_track_buf[2].to(pool_dtype))
+                )
+                untouched = torch.ones(8, dtype=torch.bool)
+                untouched[[5, 2]] = False
+                self.assertTrue(torch.all(ssm_states[untouched] == 0))
+
+    def test_fp16_pool_is_not_double_rounded_through_bf16(self):
+        h_track_buf = torch.full((1, 1, 1, 1), DOUBLE_ROUND_PROBE)
+        ssm_states = self._run_track_copy(
+            torch.float16, h_track_buf, dst_slots=[3], batch_rows=[0]
+        )
+        # fp32 -> fp16 rounds the probe UP to 1 + 2^-10; the legacy path
+        # (fp32 -> bf16 h -> fp16) collapsed it to exactly 1.0.
+        self.assertEqual(ssm_states[3, 0, 0, 0].item(), 1.0 + 2.0**-10)
+
+    def test_no_unaligned_rows_leaves_pool_untouched(self):
+        # Aligned-only tracking: the h branch is gated off entirely.
+        h_track_buf = torch.randn(2, 1, 1, 1, dtype=torch.float32)
+        ssm_states = self._run_track_copy(
+            torch.float16, h_track_buf, dst_slots=[], batch_rows=[]
+        )
+        self.assertTrue(torch.all(ssm_states == 0))
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/test_mamba2_track_ssm_indices.py

@@ -37,8 +37,10 @@ def _split(extend_lens, prefix_lens, track_seqlens, track_mask):
     backend = _backend()
     cache_indices = torch.arange(len(extend_lens))
     (
+        _track_chunk_idx,
         h_src,
         h_dst,
+        _h_batch_src,
         _final_src,
         _final_dst,
         seq_idx,