[Disagg][Qwen3.5] Fix heterogeneous attn-TP scatter transfer: GDN conv sub-block slice + GQA replicated-KV head map (#30997)

Co-authored-by: Xuwei Li <lixuwei.xy@gmail.com>

python/sglang/srt/configs/mamba_utils.py

@@ -148,6 +148,12 @@ class Mamba2StateShape:
     # GDN kernels infer from `mixed_qkv`). Used by the GDN ReplaySSM ring
     # buffer (k_cache) to size/stride exactly like the kernel expects.
     num_k_heads_per_tp: int = 1
+    # Full (unsharded) conv sub-block dims, e.g. GDN's [key_dim, key_dim,
+    # value_dim] for conv_state == cat([query, key, value]). Each sub-block is
+    # head-sharded INDEPENDENTLY across attn-TP, so PD transfer across different
+    # attn_tp_size must slice per sub-block. None when the single contiguous
+    # slice already matches the layout (e.g. standard Mamba2 conv order differs).
+    conv_shard_groups: Optional[List[int]] = None
 
     @staticmethod
     def create(
@@ -159,6 +165,7 @@ class Mamba2StateShape:
         head_dim: int,
         state_size: int,
         conv_kernel: int,
+        conv_shard_groups: Optional[List[int]] = None,
     ) -> "Mamba2StateShape":
         # The q/k projections are sharded by `num_k_heads // tp` heads (the
         # ORIGINAL n_groups, before the conv head-shard extension below), so the
@@ -196,6 +203,7 @@ class Mamba2StateShape:
             state_size=state_size,
             conv_kernel=conv_kernel,
             num_k_heads_per_tp=num_k_heads_per_tp,
+            conv_shard_groups=conv_shard_groups,
         )
 
 

python/sglang/srt/configs/qwen3_next.py

@@ -291,6 +291,12 @@ class Qwen3NextConfig(PretrainedConfig):
             world_size = get_parallel().attn_tp_size
             adjust_tp_num_heads_if_necessary(self, world_size, False)
 
+        # GDN conv_state == cat([query, key, value]); each sub-block is
+        # head-sharded independently across attn-TP, so record the full
+        # (unsharded) sub-block dims for correct PD transfer between prefill and
+        # decode with different attn_tp_size (see _send_mamba_state_slice).
+        key_dim = self.linear_key_head_dim * self.linear_num_key_heads
+        value_dim = self.linear_value_head_dim * self.linear_num_value_heads
         shape = Mamba2StateShape.create(
             tp_world_size=get_parallel().attn_tp_size,
             intermediate_size=self.linear_value_head_dim * self.linear_num_value_heads,
@@ -299,6 +305,7 @@ class Qwen3NextConfig(PretrainedConfig):
             head_dim=self.linear_value_head_dim,
             state_size=self.linear_key_head_dim,
             conv_kernel=self.linear_conv_kernel_dim,
+            conv_shard_groups=[key_dim, key_dim, value_dim],
         )
 
         return Mamba2CacheParams(

python/sglang/srt/disaggregation/base/conn.py

@@ -50,6 +50,10 @@ class KVArgs:
     # Per-tensor TP slice dim, used when prefill/decode attn_tp_size differ.
     state_dim_per_tensor: List[List[int]]
     is_hybrid_mla_backend: bool
+    # Per-tensor conv sub-block dims (GDN: [key_dim, key_dim, value_dim]) so the
+    # scatter transfer can slice each independently head-sharded sub-block; None
+    # per tensor when the single contiguous slice already matches the layout.
+    state_conv_shard_groups: List[List[Optional[List[int]]]]
     ib_device: str
     ib_traffic_class: str
     gpu_id: int

python/sglang/srt/disaggregation/common/staging_buffer.py

@@ -708,9 +708,11 @@ def compute_head_slice_params(
         unique_head_idx = local_tp_rank // src_replication
         dst_head_start = (unique_head_idx * src_heads_per_rank) % dst_heads_per_rank
     else:
-        src_head_start = (
-            dst_tp_rank_in_group * dst_heads_per_rank
-        ) % src_heads_per_rank
+        # GQA replication: consecutive decode ranks share a KV head
+        # (tp_rank // num_kv_head_replicas), so map by integer division not modulo.
+        dst_replication = max(1, dst_attn_tp_size // total_kv_heads)
+        unique_dst_head_idx = dst_tp_rank_in_group // dst_replication
+        src_head_start = (unique_dst_head_idx * dst_heads_per_rank) % src_heads_per_rank
         num_heads_to_send = dst_heads_per_rank
         dst_head_start = 0
 

python/sglang/srt/disaggregation/mooncake/conn.py

@@ -39,7 +39,10 @@ from sglang.srt.disaggregation.common.utils import (
 from sglang.srt.disaggregation.mooncake.utils import (
     check_mooncake_custom_mem_pool_enabled,
 )
-from sglang.srt.disaggregation.utils import DisaggregationMode
+from sglang.srt.disaggregation.utils import (
+    DisaggregationMode,
+    compute_mamba_state_slice_blocks,
+)
 from sglang.srt.distributed.parallel_state import get_mooncake_transfer_engine
 from sglang.srt.environ import envs
 from sglang.srt.observability.mooncake_trace import (
@@ -760,8 +763,13 @@ class MooncakeKVManager(CommonKVManager):
             ) % dst_heads_per_rank
         else:
             # Send KVCache from 1 prefill instance to multiple decode instances
+            # GQA replication (total_kv_heads < dst_attn_tp_size): consecutive decode
+            # ranks share one KV head (QKVParallelLinear: tp_rank // num_kv_head_replicas),
+            # so map by integer division NOT modulo or ranks 1..r-1 fetch the wrong head.
+            dst_replication = max(1, dst_attn_tp_size // total_kv_heads)
+            unique_dst_head_idx = dst_tp_rank_in_group // dst_replication
             src_head_start_offset = (
-                dst_tp_rank_in_group * dst_heads_per_rank
+                unique_dst_head_idx * dst_heads_per_rank
             ) % src_heads_per_rank
             num_heads_to_send = dst_heads_per_rank
             dst_head_start_offset = 0
@@ -971,6 +979,10 @@ class MooncakeKVManager(CommonKVManager):
                 if i < len(self.kv_args.state_dim_per_tensor)
                 else []
             )
+            src_conv_shard_groups = getattr(self.kv_args, "state_conv_shard_groups", [])
+            src_conv_shard_groups = (
+                src_conv_shard_groups[i] if i < len(src_conv_shard_groups) else []
+            )
             if target_rank_registration_info is not None:
                 dst_data_ptrs = (
                     target_rank_registration_info.dst_state_data_ptrs[i]
@@ -1012,6 +1024,7 @@ class MooncakeKVManager(CommonKVManager):
                             dst_dim_per_tensor,
                             target_rank_registration_info.dst_tp_rank,
                             target_rank_registration_info.dst_attn_tp_size,
+                            src_conv_shard_groups,
                         )
                         or rc
                     )
@@ -1150,6 +1163,7 @@ class MooncakeKVManager(CommonKVManager):
         dst_state_dim_per_tensor: list[int],
         dst_tp_rank: int,
         dst_attn_tp_size: int,
+        src_state_conv_shard_groups: list = None,
     ):
         """Transfer Mamba states with TP slice support.
 
@@ -1158,7 +1172,10 @@ class MooncakeKVManager(CommonKVManager):
         - temporal_state: [num_layers, size+1, num_heads/tp, head_dim, state_size]
 
         The 3rd dimension is sliced by TP. When prefill and decode have different
-        attn_tp_size, we need to slice the state accordingly.
+        attn_tp_size, we slice the state accordingly. GDN conv_state is the
+        concatenation [query | key | value] with each sub-block head-sharded
+        independently, so on the scatter path it is sliced per sub-block via
+        ``src_state_conv_shard_groups`` (see compute_mamba_state_slice_blocks).
         """
         logger.warning_once(
             "Using Mamba state slice transfer for different TP sizes between prefill and decode. "
@@ -1192,33 +1209,42 @@ class MooncakeKVManager(CommonKVManager):
             src_bytes_per_dim = src_item_len // src_dim
             dst_bytes_per_dim = dst_item_len // dst_dim
 
-            if self.attn_tp_size > dst_attn_tp_size:
-                # Multiple prefill ranks send to 1 decode rank
-                src_dim_start = 0
-                num_dims_to_send = src_dim
-                writers_per_decode = self.attn_tp_size // dst_attn_tp_size
-                local_writer_idx = local_tp_rank_in_group % writers_per_decode
-                dst_dim_start = local_writer_idx * src_dim
-            else:
-                # 1 prefill rank sends to multiple decode ranks
-                src_dim_start = (dst_tp_rank_in_group * dst_dim) % src_dim
-                num_dims_to_send = dst_dim
-                dst_dim_start = 0
-
-            src_dim_offset = src_dim_start * src_bytes_per_dim
-            dst_dim_offset = dst_dim_start * dst_bytes_per_dim
-            bytes_to_send = num_dims_to_send * src_bytes_per_dim
-
-            src_addr = (
-                src_state_data_ptrs[i]
-                + src_item_len * int(prefill_mamba_index[0])
-                + src_dim_offset
-            )
-            dst_addr = (
-                dst_state_ptr + dst_item_len * int(dst_mamba_index[0]) + dst_dim_offset
+            conv_shard_groups = (
+                src_state_conv_shard_groups[i]
+                if src_state_conv_shard_groups and i < len(src_state_conv_shard_groups)
+                else None
             )
+            # One block for single-axis states; three (q/k/v) for GDN conv_state
+            # on the scatter path.
+            for (
+                src_dim_start,
+                dst_dim_start,
+                num_dims_to_send,
+            ) in compute_mamba_state_slice_blocks(
+                src_dim=src_dim,
+                dst_dim=dst_dim,
+                src_attn_tp_size=self.attn_tp_size,
+                dst_attn_tp_size=dst_attn_tp_size,
+                dst_tp_rank_in_group=dst_tp_rank_in_group,
+                local_tp_rank_in_group=local_tp_rank_in_group,
+                conv_shard_groups=conv_shard_groups,
+            ):
+                src_dim_offset = src_dim_start * src_bytes_per_dim
+                dst_dim_offset = dst_dim_start * dst_bytes_per_dim
+                bytes_to_send = num_dims_to_send * src_bytes_per_dim
 
-            transfer_blocks.append((src_addr, dst_addr, bytes_to_send))
+                src_addr = (
+                    src_state_data_ptrs[i]
+                    + src_item_len * int(prefill_mamba_index[0])
+                    + src_dim_offset
+                )
+                dst_addr = (
+                    dst_state_ptr
+                    + dst_item_len * int(dst_mamba_index[0])
+                    + dst_dim_offset
+                )
+
+                transfer_blocks.append((src_addr, dst_addr, bytes_to_send))
 
         return self._transfer_data(req.mooncake_session_id, transfer_blocks)
 

python/sglang/srt/disaggregation/nixl/conn.py

@@ -32,7 +32,10 @@ from sglang.srt.disaggregation.common.utils import (
     pack_int_lists,
     unpack_int_lists,
 )
-from sglang.srt.disaggregation.utils import DisaggregationMode
+from sglang.srt.disaggregation.utils import (
+    DisaggregationMode,
+    compute_mamba_state_slice_blocks,
+)
 from sglang.srt.environ import envs
 from sglang.srt.server_args import ServerArgs
 
@@ -1868,12 +1871,16 @@ class NixlKVManager(CommonKVManager):
         notif: str,
         decode_tp_size: int,
         decode_tp_rank: int,
+        src_state_conv_shard_groups: list = None,
     ):
         """Transfer Mamba states with TP slice support via RDMA.
 
         When prefill and decode have different attn_tp_size, we slice the
         TP-sharded dimension (3rd dim) of conv_state and temporal_state
-        accordingly, mirroring Mooncake's _send_mamba_state_slice.
+        accordingly, mirroring Mooncake's _send_mamba_state_slice. GDN
+        conv_state is [query | key | value] with each sub-block head-sharded
+        independently, so on the scatter path it is sliced per sub-block via
+        ``src_state_conv_shard_groups`` (see compute_mamba_state_slice_blocks).
         """
         logger.warning_once(
             "Using Mamba state slice transfer for different TP sizes. "
@@ -1911,33 +1918,42 @@ class NixlKVManager(CommonKVManager):
             src_bytes_per_dim = src_item_len // src_dim
             dst_bytes_per_dim = dst_item_len // dst_dim
 
-            if self.attn_tp_size > decode_tp_size:
-                src_dim_start = 0
-                num_dims_to_send = src_dim
-                writers_per_decode = self.attn_tp_size // decode_tp_size
-                local_writer_idx = local_tp_rank_in_group % writers_per_decode
-                dst_dim_start = local_writer_idx * src_dim
-            else:
-                src_dim_start = (dst_tp_rank_in_group * dst_dim) % src_dim
-                num_dims_to_send = dst_dim
-                dst_dim_start = 0
-
-            src_dim_offset = src_dim_start * src_bytes_per_dim
-            dst_dim_offset = dst_dim_start * dst_bytes_per_dim
-            bytes_to_send = num_dims_to_send * src_bytes_per_dim
-
-            src_addr = (
-                src_state_data_ptrs[i]
-                + src_item_len * int(prefill_state_indices[0])
-                + src_dim_offset
+            conv_shard_groups = (
+                src_state_conv_shard_groups[i]
+                if src_state_conv_shard_groups and i < len(src_state_conv_shard_groups)
+                else None
             )
-            dst_addr = (
-                dst_state_ptr
-                + dst_item_len * int(dst_state_indices[0])
-                + dst_dim_offset
-            )
-            src_addrs.append((src_addr, bytes_to_send, self.kv_args.gpu_id))
-            dst_addrs.append((dst_addr, bytes_to_send, dst_gpu_id))
+            # One block for single-axis states; three (q/k/v) for GDN conv_state
+            # on the scatter path.
+            for (
+                src_dim_start,
+                dst_dim_start,
+                num_dims_to_send,
+            ) in compute_mamba_state_slice_blocks(
+                src_dim=src_dim,
+                dst_dim=dst_dim,
+                src_attn_tp_size=self.attn_tp_size,
+                dst_attn_tp_size=decode_tp_size,
+                dst_tp_rank_in_group=dst_tp_rank_in_group,
+                local_tp_rank_in_group=local_tp_rank_in_group,
+                conv_shard_groups=conv_shard_groups,
+            ):
+                src_dim_offset = src_dim_start * src_bytes_per_dim
+                dst_dim_offset = dst_dim_start * dst_bytes_per_dim
+                bytes_to_send = num_dims_to_send * src_bytes_per_dim
+
+                src_addr = (
+                    src_state_data_ptrs[i]
+                    + src_item_len * int(prefill_state_indices[0])
+                    + src_dim_offset
+                )
+                dst_addr = (
+                    dst_state_ptr
+                    + dst_item_len * int(dst_state_indices[0])
+                    + dst_dim_offset
+                )
+                src_addrs.append((src_addr, bytes_to_send, self.kv_args.gpu_id))
+                dst_addrs.append((dst_addr, bytes_to_send, dst_gpu_id))
 
         src_descs = self.agent.get_xfer_descs(src_addrs, "VRAM")
         dst_descs = self.agent.get_xfer_descs(dst_addrs, "VRAM")
@@ -1976,6 +1992,9 @@ class NixlKVManager(CommonKVManager):
         src_state_dim_per_tensor = (
             getattr(self.kv_args, "state_dim_per_tensor", []) or []
         )
+        src_state_conv_shard_groups = (
+            getattr(self.kv_args, "state_conv_shard_groups", []) or []
+        )
         dst_state_item_lens = dst_state_item_lens or []
         dst_state_dim_per_tensor = dst_state_dim_per_tensor or []
 
@@ -1991,6 +2010,11 @@ class NixlKVManager(CommonKVManager):
             src_dims = (
                 src_state_dim_per_tensor[i] if i < len(src_state_dim_per_tensor) else []
             )
+            src_conv = (
+                src_state_conv_shard_groups[i]
+                if i < len(src_state_conv_shard_groups)
+                else []
+            )
             dst_ptrs = dst_state_data_ptrs[i] if i < len(dst_state_data_ptrs) else []
             dst_indices = dst_state_indices[i] if i < len(dst_state_indices) else []
             dst_lens = dst_state_item_lens[i] if i < len(dst_state_item_lens) else []
@@ -2015,6 +2039,7 @@ class NixlKVManager(CommonKVManager):
                         comp_notif,
                         decode_tp_size,
                         decode_tp_rank,
+                        src_conv,
                     )
                 else:
                     h = self._send_mamba_state(

python/sglang/srt/disaggregation/utils.py

@@ -737,6 +737,78 @@ def is_mla_backend(target_kv_pool) -> bool:
     return isinstance(target_kv_pool, (MLATokenToKVPool, DeepSeekV4TokenToKVPool))
 
 
+def compute_mamba_state_slice_blocks(
+    src_dim: int,
+    dst_dim: int,
+    src_attn_tp_size: int,
+    dst_attn_tp_size: int,
+    dst_tp_rank_in_group: int,
+    local_tp_rank_in_group: int,
+    conv_shard_groups: Optional[List[int]] = None,
+) -> List[Tuple[int, int, int]]:
+    """Blocks to copy one mamba state item across differing attn-TP sizes.
+
+    Returns ``(src_dim_start, dst_dim_start, num_dims)`` triples in units of the
+    sliceable (3rd) dimension. Single-axis states (temporal_state, or when
+    ``conv_shard_groups`` is None) return one contiguous block -- byte-identical to
+    the legacy behavior.
+
+    GDN conv_state is ``cat([query | key | value])`` where each sub-block (full
+    dims == ``conv_shard_groups``, e.g. ``[key_dim, key_dim, value_dim]``) is
+    head-sharded INDEPENDENTLY across attn-TP. In the SCATTER direction
+    (1 prefill rank -> several decode ranks) a single contiguous slice straddles
+    the q/k/v boundaries and delivers wrong channels. The AGGREGATION direction
+    (several prefill ranks -> 1 decode rank) has the symmetric problem: a single
+    contiguous write interleaves the sub-blocks by writer. Both directions emit one
+    block per sub-block for conv_state; temporal_state and non-GDN states (when
+    ``conv_shard_groups`` is None) keep the single contiguous slice.
+    """
+    use_subdims = (
+        conv_shard_groups is not None
+        and sum(conv_shard_groups) == src_dim * src_attn_tp_size
+    )
+
+    if src_attn_tp_size > dst_attn_tp_size:
+        # Aggregation: several prefill ranks each write their shard into one decode slot.
+        writers_per_decode = src_attn_tp_size // dst_attn_tp_size
+        local_writer_idx = local_tp_rank_in_group % writers_per_decode
+        if not use_subdims:
+            return [(0, local_writer_idx * src_dim, src_dim)]
+        # conv_state: a plain contiguous write would interleave the sub-blocks by
+        # writer ([q0,k0,v0,q1,k1,v1,...]); place this writer's shard of each
+        # independently head-sharded sub-block at its grouped offset so the decode
+        # buffer is [q0,q1,...,k0,k1,...,v0,v1,...].
+        blocks: List[Tuple[int, int, int]] = []
+        src_off = 0
+        dst_off = 0
+        for full_sd in conv_shard_groups:
+            src_sub = full_sd // src_attn_tp_size
+            dst_sub = full_sd // dst_attn_tp_size
+            blocks.append((src_off, dst_off + local_writer_idx * src_sub, src_sub))
+            src_off += src_sub
+            dst_off += dst_sub
+        return blocks
+
+    # Scatter: 1 prefill rank feeds several decode ranks.
+    if not use_subdims:
+        src_dim_start = (dst_tp_rank_in_group * dst_dim) % src_dim
+        return [(src_dim_start, 0, dst_dim)]
+
+    # conv_state: gather the decode rank's [q | k | v] shard from the three
+    # independently head-sharded sub-blocks of the src tensor. dst is contiguous.
+    blocks: List[Tuple[int, int, int]] = []
+    src_off = 0
+    dst_off = 0
+    for full_sd in conv_shard_groups:
+        src_sub = full_sd // src_attn_tp_size  # this prefill rank's shard of sub-block
+        dst_sub = full_sd // dst_attn_tp_size  # this decode rank's shard of sub-block
+        src_start = src_off + (dst_tp_rank_in_group * dst_sub) % src_sub
+        blocks.append((src_start, dst_off, dst_sub))
+        src_off += src_sub
+        dst_off += dst_sub
+    return blocks
+
+
 def append_state_component(
     kv_args: KVArgs,
     state_type: StateType,
@@ -744,6 +816,7 @@ def append_state_component(
     data_lens: List[int],
     item_lens: List[int],
     dim_per_tensor: Optional[List[int]] = None,
+    conv_shard_groups: Optional[List[Optional[List[int]]]] = None,
 ) -> None:
     """Append one state component. Caller orders state_types consistently
     on prefill and decode sides."""
@@ -752,6 +825,7 @@ def append_state_component(
     kv_args.state_data_lens.append(data_lens)
     kv_args.state_item_lens.append(item_lens)
     kv_args.state_dim_per_tensor.append(dim_per_tensor or [])
+    kv_args.state_conv_shard_groups.append(conv_shard_groups or [])
 
 
 def setup_state_kv_args(
@@ -781,6 +855,7 @@ def setup_state_kv_args(
     kv_args.state_item_lens = []
     kv_args.state_dim_per_tensor = []
     kv_args.is_hybrid_mla_backend = False
+    kv_args.state_conv_shard_groups = []
 
     if isinstance(token_to_kv_pool, MiniMaxSparseKVPool):
         if token_to_kv_pool.index_kv_pool is not None:
@@ -837,8 +912,19 @@ def setup_state_kv_args(
             kv_args.is_hybrid_mla_backend = is_mla_backend(
                 token_to_kv_pool.full_kv_pool
             )
+            conv_shard_groups = (
+                token_to_kv_pool.get_state_conv_shard_groups()
+                if hasattr(token_to_kv_pool, "get_state_conv_shard_groups")
+                else None
+            )
             append_state_component(
-                kv_args, StateType.MAMBA, data_ptrs, data_lens, item_lens, dim
+                kv_args,
+                StateType.MAMBA,
+                data_ptrs,
+                data_lens,
+                item_lens,
+                dim,
+                conv_shard_groups,
             )
         elif isinstance(token_to_kv_pool, (DSATokenToKVPool, NPUMLATokenToKVPool)):
             if draft_token_to_kv_pool is not None and isinstance(
@@ -950,8 +1036,19 @@ def setup_state_kv_args(
                 if hasattr(req_to_token_pool, "get_state_dim_per_tensor")
                 else None
             )
+            conv_shard_groups = (
+                req_to_token_pool.get_state_conv_shard_groups()
+                if hasattr(req_to_token_pool, "get_state_conv_shard_groups")
+                else None
+            )
             append_state_component(
-                kv_args, StateType.MAMBA, data_ptrs, data_lens, item_lens, dim
+                kv_args,
+                StateType.MAMBA,
+                data_ptrs,
+                data_lens,
+                item_lens,
+                dim,
+                conv_shard_groups,
             )
 
 

python/sglang/srt/mem_cache/memory_pool.py

@@ -677,6 +677,9 @@ class MambaPool:
                 )
             self.mem_usage = mem_usage_bytes / GB
             self.num_mamba_layers = num_mamba_layers
+        # Full (unsharded) conv sub-block dims for PD transfer across different
+        # attn_tp_size (GDN: [key_dim, key_dim, value_dim]); None otherwise.
+        self.conv_shard_groups = getattr(cache_params.shape, "conv_shard_groups", None)
 
     def get_speculative_mamba2_params_all_layers(self) -> SpeculativeState:
         assert isinstance(self.mamba_cache, self.SpeculativeState)
@@ -832,6 +835,43 @@ class MambaPool:
             dim_per_tensor += [sliceable_dim] * self.num_mamba_layers
         return dim_per_tensor
 
+    def get_state_conv_shard_groups(self):
+        """Per-tensor conv sub-block dims, aligned element-wise with
+        get_state_dim_per_tensor().
+
+        For GDN, conv_state's sliceable axis is cat([query, key, value]) with
+        each sub-block head-sharded independently across attn-TP; the full
+        (unsharded) sub-block dims are returned so PD transfer across different
+        attn_tp_size can slice each sub-block. Returns None for temporal_state
+        (single head-sharded axis) and whenever no descriptor is available, so
+        those tensors keep the single contiguous slice.
+        """
+        subdims_per_tensor = []
+        for field in vars(self.mamba_cache):
+            # Mirror the exclusions in get_state_dim_per_tensor so the returned
+            # sub-dims line up element-wise with the RDMA buffer list.
+            if field in (
+                "intermediate_ssm",
+                "intermediate_conv_window",
+                "replayssm_d",
+                "replayssm_k",
+                "replayssm_g",
+            ):
+                continue
+            value = getattr(self.mamba_cache, field)
+            if value is None:
+                continue
+            tensors = value if isinstance(value, list) else [value]
+            for _ in tensors:
+                # Only conv_state carries a q/k/v decomposition.
+                subdims = (
+                    list(self.conv_shard_groups)
+                    if field == "conv" and self.conv_shard_groups is not None
+                    else None
+                )
+                subdims_per_tensor += [subdims] * self.num_mamba_layers
+        return subdims_per_tensor
+
 
 class HybridReqToTokenPool(ReqToTokenPool):
     """A memory pool that maps a request to its token locations."""
@@ -1022,6 +1062,9 @@ class HybridReqToTokenPool(ReqToTokenPool):
     def get_state_dim_per_tensor(self):
         return self.mamba_pool.get_state_dim_per_tensor()
 
+    def get_state_conv_shard_groups(self):
+        return self.mamba_pool.get_state_conv_shard_groups()
+
     def get_mamba_ping_pong_other_idx(self, mamba_next_track_idx: int) -> int:
         if self.mamba_ping_pong_track_buffer_size == 2:
             return 1 - mamba_next_track_idx
@@ -2598,6 +2641,10 @@ class HybridLinearKVPool(KVCache):
         """Get the sliceable dimension size for each mamba state tensor."""
         return self.mamba_pool.get_state_dim_per_tensor()
 
+    def get_state_conv_shard_groups(self):
+        """Per-tensor conv sub-block dims (GDN) aligned with the state list."""
+        return self.mamba_pool.get_state_conv_shard_groups()
+
     def maybe_get_custom_mem_pool(self):
         return self.full_kv_pool.maybe_get_custom_mem_pool()