style refinement for hisparse

python/sglang/jit_kernel/csrc/hisparse.cuh

@@ -53,11 +53,24 @@ __device__ __forceinline__ int warp_inclusive_scan(int* s_data, int lane_id, int
   return accumulator;
 }
 
+// Shared memory size calculation for dynamic allocation.
+// Layout: int32_t region (4-byte aligned) followed by int16_t region (2-byte aligned).
+template <int NUM_TOP_K, int HOT_BUFFER_SIZE>
+struct SmemLayout {
+  static constexpr int HASH_SIZE = NUM_TOP_K * 2;
+  static constexpr int NUM_BUFFER_CHUNKS = (HOT_BUFFER_SIZE + WARP_SIZE - 1) / WARP_SIZE;
+  // int32_t region: top_k_tokens + chunk_offset + evict_chunk_offset + hash_keys + total_hits + newest_hit
+  static constexpr int TOTAL_INT32 = NUM_TOP_K + (NUM_BUFFER_CHUNKS + 1) + (NUM_BUFFER_CHUNKS + 1) + HASH_SIZE + 2;
+  // int16_t region: lru_slots_out + hash_vals
+  static constexpr int TOTAL_INT16 = HOT_BUFFER_SIZE + HASH_SIZE;
+  static constexpr size_t BYTES = TOTAL_INT32 * sizeof(int32_t) + TOTAL_INT16 * sizeof(int16_t);
+};
+
 // Each block processes one request
-// req_pool_indices are int64_t (pool indices can be large), seq_lens are int32_t
+// req_pool_indices are int64_t (pool indices can be large), seq_lens can be int32_t or int64_t
 // Layout: [HOT_BUFFER_SIZE slots for LRU] + [page_size slots for newest token]
 // newest_slot is at HOT_BUFFER_SIZE (first position of extra page)
-template <int BLOCK_SIZE, int NUM_TOP_K, int HOT_BUFFER_SIZE, bool IsMLA>
+template <int BLOCK_SIZE, int NUM_TOP_K, int HOT_BUFFER_SIZE, bool IsMLA, typename SeqLensT>
 __global__ void load_cache_to_device_buffer_kernel(
     const int32_t* __restrict__ top_k_tokens,
     int32_t* __restrict__ device_buffer_tokens,
@@ -69,7 +82,7 @@ __global__ void load_cache_to_device_buffer_kernel(
     void* __restrict__ device_buffer_v,
     int32_t* __restrict__ top_k_device_locs,
     const int64_t* __restrict__ req_pool_indices,
-    const int32_t* __restrict__ seq_lens,
+    const SeqLensT* __restrict__ seq_lens,
     int16_t* __restrict__ lru_slots,
     const int32_t* __restrict__ num_real_reqs,
     int64_t buffer_stride_0,
@@ -118,21 +131,29 @@ __global__ void load_cache_to_device_buffer_kernel(
     return;
   }
 
+  // Dynamic shared memory layout: int32_t arrays first, then int16_t arrays.
+  extern __shared__ char smem_raw[];
+  using Layout = SmemLayout<NUM_TOP_K, HOT_BUFFER_SIZE>;
+  constexpr int HASH_SIZE = Layout::HASH_SIZE;
+
+  int32_t* smem_i32 = reinterpret_cast<int32_t*>(smem_raw);
   // Top-k token positions; reused as miss-token scratch in the copy phase
-  __shared__ int32_t s_top_k_tokens[NUM_TOP_K];
+  int32_t* s_top_k_tokens = smem_i32;
   // Prefix-sum offsets for hit counting and miss counting
-  __shared__ int32_t s_chunk_offset[NUM_BUFFER_CHUNKS + 1];
+  int32_t* s_chunk_offset = s_top_k_tokens + NUM_TOP_K;
   // Prefix-sum offsets for evictable counting
-  __shared__ int32_t s_evict_chunk_offset[NUM_BUFFER_CHUNKS + 1];
+  int32_t* s_evict_chunk_offset = s_chunk_offset + (NUM_BUFFER_CHUNKS + 1);
+  // Open-addressing hash table: top-k token_id → top-k index (keys)
+  int32_t* s_hash_keys = s_evict_chunk_offset + (NUM_BUFFER_CHUNKS + 1);
+  // Scalar counters
+  int32_t& s_total_hits = s_hash_keys[HASH_SIZE];
+  int32_t& s_newest_hit = s_hash_keys[HASH_SIZE + 1];
+
+  int16_t* smem_i16 = reinterpret_cast<int16_t*>(smem_i32 + Layout::TOTAL_INT32);
   // Compacted slot ordering: [hits fwd→  ...  ←evictables bwd]
-  __shared__ int16_t s_lru_slots_out[HOT_BUFFER_SIZE];
-  // Open-addressing hash table: top-k token_id → top-k index
-  constexpr int HASH_SIZE = NUM_TOP_K * 2;
-  __shared__ int32_t s_hash_keys[HASH_SIZE];
-  __shared__ int16_t s_hash_vals[HASH_SIZE];
-
-  __shared__ int32_t s_total_hits;
-  __shared__ int32_t s_newest_hit;
+  int16_t* s_lru_slots_out = smem_i16;
+  // Open-addressing hash table: top-k token_id → top-k index (values)
+  int16_t* s_hash_vals = s_lru_slots_out + HOT_BUFFER_SIZE;
 
   // Initialize shared memory: counters, hash table, prefix-sum offsets.
   if (tid == 0) {
@@ -363,28 +384,47 @@ void load_cache_to_device_buffer(
   const int64_t top_k_device_locs_stride = top_k_device_locs.strides()[0];
   const auto device = LaunchKernel::resolve_device(top_k_tokens.device());
 
-  LaunchKernel(bs, BLOCK_SIZE, device)(
-      load_cache_to_device_buffer_kernel<BLOCK_SIZE, NUM_TOP_K, HOT_BUFFER_SIZE, IsMLA>,
-      static_cast<const int32_t*>(top_k_tokens.data_ptr()),
-      static_cast<int32_t*>(device_buffer_tokens.data_ptr()),
-      static_cast<const int64_t*>(host_cache_locs.data_ptr()),
-      static_cast<const int32_t*>(device_buffer_locs.data_ptr()),
-      host_cache_k.data_ptr(),
-      (IsMLA || host_cache_v.ndim() == 0) ? (const void*)nullptr : host_cache_v.data_ptr(),
-      device_buffer_k.data_ptr(),
-      (IsMLA || device_buffer_v.ndim() == 0) ? (void*)nullptr : device_buffer_v.data_ptr(),
-      static_cast<int32_t*>(top_k_device_locs.data_ptr()),
-      static_cast<const int64_t*>(req_pool_indices.data_ptr()),
-      static_cast<const int32_t*>(seq_lens.data_ptr()),
-      static_cast<int16_t*>(lru_slots.data_ptr()),
-      static_cast<const int32_t*>(num_real_reqs.data_ptr()),
-      buffer_stride_0,
-      host_stride,
-      lru_slot_stride_0,
-      top_k_tokens_stride,
-      top_k_device_locs_stride,
-      page_size,
-      item_size_bytes);
+  // Generic lambda: both int32 and int64 kernel variants are compiled;
+  // the correct one is selected at runtime based on seq_lens dtype.
+  auto launch = [&](auto kernel_fn, const auto* seq_lens_ptr) {
+    constexpr size_t smem_bytes = SmemLayout<NUM_TOP_K, HOT_BUFFER_SIZE>::BYTES;
+    if constexpr (smem_bytes > 48u * 1024u) {
+      cudaFuncSetAttribute(kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_bytes);
+    }
+    LaunchKernel(bs, BLOCK_SIZE, device, smem_bytes)(
+        kernel_fn,
+        static_cast<const int32_t*>(top_k_tokens.data_ptr()),
+        static_cast<int32_t*>(device_buffer_tokens.data_ptr()),
+        static_cast<const int64_t*>(host_cache_locs.data_ptr()),
+        static_cast<const int32_t*>(device_buffer_locs.data_ptr()),
+        host_cache_k.data_ptr(),
+        (IsMLA || host_cache_v.ndim() == 0) ? (const void*)nullptr : host_cache_v.data_ptr(),
+        device_buffer_k.data_ptr(),
+        (IsMLA || device_buffer_v.ndim() == 0) ? (void*)nullptr : device_buffer_v.data_ptr(),
+        static_cast<int32_t*>(top_k_device_locs.data_ptr()),
+        static_cast<const int64_t*>(req_pool_indices.data_ptr()),
+        seq_lens_ptr,
+        static_cast<int16_t*>(lru_slots.data_ptr()),
+        static_cast<const int32_t*>(num_real_reqs.data_ptr()),
+        buffer_stride_0,
+        host_stride,
+        lru_slot_stride_0,
+        top_k_tokens_stride,
+        top_k_device_locs_stride,
+        page_size,
+        item_size_bytes);
+  };
+
+  const auto dtype = seq_lens.dtype();
+  if (dtype.code == kDLInt && dtype.bits == 64) {
+    launch(
+        load_cache_to_device_buffer_kernel<BLOCK_SIZE, NUM_TOP_K, HOT_BUFFER_SIZE, IsMLA, int64_t>,
+        static_cast<const int64_t*>(seq_lens.data_ptr()));
+  } else {
+    launch(
+        load_cache_to_device_buffer_kernel<BLOCK_SIZE, NUM_TOP_K, HOT_BUFFER_SIZE, IsMLA, int32_t>,
+        static_cast<const int32_t*>(seq_lens.data_ptr()));
+  }
 }
 
 }  // namespace

python/sglang/srt/managers/hisparse_coordinator.py

@@ -123,7 +123,7 @@ def set_decode_producer_stream(self, stream) -> None:
         self.decode_producer_stream = stream
 
     def admit_request_into_staging(self, req: Req) -> None:
-        req.staging = True
+        req.hisparse_staging = True
         logical_indices = self.req_to_token_pool.req_to_token[
             req.req_pool_idx, : len(req.fill_ids)
         ]
@@ -224,7 +224,7 @@ def collect_ready_reqs(self) -> List[Req]:
             _, _, req = self.ack_staging_queue.pop(0)
             # prepare device buffer and update req
             self.alloc_device_buffer(req)
-            req.staging = False
+            req.hisparse_staging = False
             self._skip_first_backup[req.req_pool_idx] = True
             finish_count -= 1
             ready_reqs.append(req)
@@ -494,7 +494,7 @@ def abort_staging_request(self, req: Req) -> None:
         """Remove a request from the staging queue and free its host resources.
 
         Must be called when aborting a request that has been admitted into staging
-        but has not yet completed (i.e. req.staging is True).
+        but has not yet completed (i.e. req.hisparse_staging is True).
         """
         # Remove from staging queue
         self.ack_staging_queue = [
@@ -510,10 +510,10 @@ def abort_staging_request(self, req: Req) -> None:
             self.mem_pool_host.free(host_indices)
         self.req_to_host_pool[req.req_pool_idx, :] = -1
         self._skip_first_backup[req.req_pool_idx] = False
-        req.staging = False
+        req.hisparse_staging = False
 
     def retract_req(self, req: Req) -> None:
-        if req.staging:
+        if req.hisparse_staging:
             self.abort_staging_request(req)
         else:
             self.request_finished(req)
@@ -556,14 +556,14 @@ def swap_in_selected_pages(
         layer_id: int,
     ) -> torch.Tensor:
         """Swap selected top-k tokens into device memory and return their indices."""
-        # The CUDA kernel expects req_pool_indices as int64 and seq_lens as int32.
+        # The CUDA kernel expects req_pool_indices as int64 and seq_lens as int32 or int64.
         if req_pool_indices.dtype != torch.int64:
             raise ValueError(
                 f"req_pool_indices dtype {req_pool_indices.dtype} is not int64 as expected"
             )
-        if seq_lens.dtype != torch.int32:
+        if seq_lens.dtype not in (torch.int32, torch.int64):
             raise ValueError(
-                f"seq_lens dtype {seq_lens.dtype} is not int32 as expected"
+                f"seq_lens dtype {seq_lens.dtype} is not int32 or int64 as expected"
             )
         if top_k_result.dtype != torch.int32:
             raise ValueError(

python/sglang/srt/managers/schedule_batch.py

@@ -805,7 +805,7 @@ def __init__(
         self.init_diffusion_llm(dllm_config)
 
         # For hisparse
-        self.staging = False
+        self.hisparse_staging = False
 
     @property
     def seqlen(self) -> int:

python/sglang/srt/managers/scheduler.py

@@ -2146,6 +2146,7 @@ def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
             chunked_req_to_exclude.add(self.chunked_req)
             self.stash_chunked_request(self.chunked_req)
 
+        # HiSparse has its own prefill-to-decode transition; skip last_batch merge.
         if self.enable_hisparse:
             ready_reqs = self.hisparse_coordinator.collect_ready_reqs()
             if len(ready_reqs) > 0:
@@ -2155,31 +2156,35 @@ def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
                 else:
                     self.running_batch.merge_batch(new_batch)
                 self.running_batch.hisparse_coordinator = self.hisparse_coordinator
-        else:
-            if self.last_batch and self.last_batch.forward_mode.is_extend():
-                if self.last_batch.chunked_req is not None:
-                    # In the context pipeline parallelism, after the last chunk, the current microbatch still track outdated chunked_req.
-                    # We need to discard it.
-                    chunked_req_to_exclude.add(self.last_batch.chunked_req)
-
-                if self.dllm_config is not None and self.last_batch.reqs:
-                    chunked_req_to_exclude.update(self.last_batch.reqs)
-
-                # Filter batch
-                last_bs = self.last_batch.batch_size()
-                self.last_batch.filter_batch(
-                    chunked_req_to_exclude=list(chunked_req_to_exclude)
-                )
-                if self.last_batch.batch_size() < last_bs:
-                    self.running_batch.batch_is_full = False
 
-                # Merge the new batch into the running batch.
-                if not self.last_batch.is_empty():
-                    if self.running_batch.is_empty():
-                        self.running_batch = self.last_batch
-                    else:
-                        # Merge running_batch with prefill batch
-                        self.running_batch.merge_batch(self.last_batch)
+        if (
+            not self.enable_hisparse
+            and self.last_batch
+            and self.last_batch.forward_mode.is_extend()
+        ):
+            if self.last_batch.chunked_req is not None:
+                # In the context pipeline parallelism, after the last chunk, the current microbatch still track outdated chunked_req.
+                # We need to discard it.
+                chunked_req_to_exclude.add(self.last_batch.chunked_req)
+
+            if self.dllm_config is not None and self.last_batch.reqs:
+                chunked_req_to_exclude.update(self.last_batch.reqs)
+
+            # Filter batch
+            last_bs = self.last_batch.batch_size()
+            self.last_batch.filter_batch(
+                chunked_req_to_exclude=list(chunked_req_to_exclude)
+            )
+            if self.last_batch.batch_size() < last_bs:
+                self.running_batch.batch_is_full = False
+
+            # Merge the new batch into the running batch.
+            if not self.last_batch.is_empty():
+                if self.running_batch.is_empty():
+                    self.running_batch = self.last_batch
+                else:
+                    # Merge running_batch with prefill batch
+                    self.running_batch.merge_batch(self.last_batch)
 
         # For prefill-only batch, filter out finished requests since they
         # won't go through the decode step. This keeps running_batch accurate

python/sglang/srt/server_args.py

@@ -1427,6 +1427,18 @@ def _set_default_nsa_backends(self, kv_cache_dtype: str, major: int) -> str:
         user_set_prefill = self.nsa_prefill_backend is not None
         user_set_decode = self.nsa_decode_backend is not None
 
+        # HiSparse requires flashmla_sparse for both prefill and decode
+        if self.enable_hisparse:
+            if not user_set_prefill:
+                self.nsa_prefill_backend = "flashmla_sparse"
+            if not user_set_decode:
+                self.nsa_decode_backend = "flashmla_sparse"
+            logger.warning(
+                f"HiSparse enabled: using flashmla_sparse NSA backends "
+                f"(prefill={self.nsa_prefill_backend}, decode={self.nsa_decode_backend})."
+            )
+            return
+
         if not user_set_prefill and not user_set_decode and is_hip():
             self.nsa_prefill_backend = "tilelang"
             self.nsa_decode_backend = "tilelang"
@@ -6088,6 +6100,17 @@ def check_server_args(self):
             assert (
                 self.disable_radix_cache
             ), "Hierarchical sparse attention currently requires --disable-radix-cache."
+            for attr, label in [
+                ("nsa_prefill_backend", "prefill"),
+                ("nsa_decode_backend", "decode"),
+            ]:
+                backend = getattr(self, attr)
+                if backend is not None and backend != "flashmla_sparse":
+                    raise ValueError(
+                        f"HiSparse requires flashmla_sparse NSA {label} backend, "
+                        f"but got --nsa-{label}-backend={backend}. "
+                        f"Please use --nsa-{label}-backend=flashmla_sparse or omit it."
+                    )
 
         assert (
             self.schedule_conservativeness >= 0