[NPU] Add Ascend NPU support for DeepSeek-V4 (#25144)

Co-authored-by: khalil2ji3mp6 <khalilzhk@gmail.com>
Co-authored-by: randgun <kelonlu@163.com>
Co-authored-by: t00937989 <tanlei33@huawei.com>

python/sglang/srt/arg_groups/deepseek_v4_hook.py

@@ -15,25 +15,36 @@ def apply_deepseek_v4_defaults(server_args: ServerArgs, model_arch: str) -> None
 
     server_args.attention_backend = "dsv4"
     server_args.page_size = 256
-    logger.info(
+    if server_args.kv_cache_dtype == "auto":
-        f"Use dsv4 attention backend for {model_arch}, setting page_size to 256."
+        server_args.kv_cache_dtype = "fp8_e4m3"
+        logger.warning(
+            f"Setting KV cache dtype to {server_args.kv_cache_dtype} for {model_arch}."
         )
 
+    if server_args.device == "npu":
+        # NPU keeps the device-aware "dsv4" backend (the registry routes it to
+        # the Ascend V4 subclass); only the pool geometry / dtype differ.
+        # set_default_server_args() pins all three backends to "ascend" for
+        # generic NPU models; undo that here so V4 stays consistently on dsv4.
+        server_args.prefill_attention_backend = "dsv4"
+        server_args.decode_attention_backend = "dsv4"
+        server_args.page_size = 128
+        server_args.kv_cache_dtype = "bfloat16"
+
+    logger.info(
+        f"Use dsv4 attention backend for {model_arch}, setting page_size to {server_args.page_size}."
+    )
+    assert server_args.kv_cache_dtype in [
+        "fp8_e4m3",
+        "bfloat16",
+    ], f"{server_args.kv_cache_dtype} is not supported for {model_arch}"
+
     if server_args.max_running_requests is None:
         server_args.max_running_requests = 256
         logger.warning(
             f"Setting max_running_requests to {server_args.max_running_requests} for {model_arch}."
         )
 
-    if server_args.kv_cache_dtype == "auto":
-        server_args.kv_cache_dtype = "fp8_e4m3"
-        logger.warning(
-            f"Setting KV cache dtype to {server_args.kv_cache_dtype} for {model_arch}."
-        )
-    assert server_args.kv_cache_dtype in [
-        "fp8_e4m3"
-    ], f"{server_args.kv_cache_dtype} is not supported for {model_arch}"
-
     if server_args.speculative_algorithm is not None:
         assert (
             server_args.speculative_algorithm == "EAGLE"

python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py

@@ -550,6 +550,16 @@ class AscendAttnBackend(AttentionBackend):
                 dtype=torch.int64,
                 device=self.device,
             )
+        # V4-specific extra graph buffers. Default no-op on the base class;
+        # DeepseekV4AscendAttnBackend overrides.
+        self._init_dsv4_graph_buffers(max_bs=max_bs, max_num_tokens=max_num_tokens)
+
+    def _init_dsv4_graph_buffers(self, *, max_bs: int, max_num_tokens: int) -> None:
+        """Hook for V4-Flash to preallocate dsv4-specific graph buffers.
+
+        Default no-op. Overridden by DeepseekV4AscendAttnBackend.
+        """
+        pass
 
     def _init_cuda_graph_metadata(
         self,

python/sglang/srt/hardware_backend/npu/dsv4/dsv4_allocator.py

@@ -0,0 +1,625 @@
+"""DSV4-NPU SWA + c4/c128 paged allocator.
+
+Subclasses :class:`SWATokenToKVPoolAllocator` and adds paged allocation for the
+c4/c128 compressed-KV pools and their tail-only compress-state pools, alongside
+the parent's full + SWA pools.
+
+Per ``alloc_extend`` / ``alloc_decode``:
+  1. super() allocates the full + SWA slots (``out_full_loc``).
+  2. Allocate c4/c128 KV slots — one compressed token per ``ratio`` raw tokens
+     (``seq_len // ratio - prefix_len // ratio``) — via the standard
+     :class:`NPUPagedTokenToKVPoolAllocator` over the pool's c4/c128 KV buffers.
+  3. Allocate the c4/c128 compress-state slots the same way, tail-only per req,
+     using the per-req lens the scheduler packed into ``DSV4StateLens``.
+  4. Return a :class:`DSV4OutCacheLoc` bundling all five slot families.
+
+State slots are paged because the NPU fused compressor runs ``cache_mode=1``; the
+base class' ``translate_kv_loc_to_compress_state_loc`` ring-hash is the CUDA-only
+path and is unused on NPU. The bundle is the explicit return value:
+mem_cache/common.py unpacks ``out_full_loc`` and stashes the bundle on
+``batch.out_cache_loc_dsv4``; ``DSV4NPUReqToTokenPool`` writes the per-req
+``req_to_token_c{4,128}[_state]`` tables that :meth:`free` and the last_loc
+lookups read back.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, List, Optional
+
+import torch
+
+from sglang.srt.hardware_backend.npu.allocator_npu import NPUPagedTokenToKVPoolAllocator
+from sglang.srt.mem_cache.allocator.swa import SWATokenToKVPoolAllocator
+from sglang.srt.model_executor.forward_batch_info import DSV4OutCacheLoc, DSV4StateLens
+
+if TYPE_CHECKING:
+    from sglang.srt.managers.schedule_batch import Req
+
+
+def get_last_loc(
+    req_to_token: torch.Tensor,
+    req_pool_indices: torch.Tensor,
+    prefix_lens: torch.Tensor,
+) -> torch.Tensor:
+    """Slot id of each req's last already-allocated token, or -1 when
+    ``prefix_lens[i] == 0`` (fresh req).
+
+    Looks up ``req_to_token[req, prefix_lens - 1]`` to anchor the paged
+    allocator's ``alloc_extend`` on the real previous tail slot, preserving the
+    intra-page slot continuity the kernel's ``cmp_block_table`` relies on (the
+    allocator debug-asserts ``(last_loc + 1) % page_size == prefix_lens %
+    page_size``). Result dtype matches ``prefix_lens``.
+    """
+    req_pool_indices = req_pool_indices.to(torch.int64)
+    safe_idx = (prefix_lens.to(torch.int64) - 1).clamp(min=0)
+    looked_up = req_to_token[req_pool_indices, safe_idx].to(prefix_lens.dtype)
+    return torch.where(
+        prefix_lens > 0,
+        looked_up,
+        torch.full_like(prefix_lens, -1),
+    )
+
+
+class DSV4NPUTokenToKVPoolAllocator(SWATokenToKVPoolAllocator):
+    """SWA allocator + c4/c128 KV and compress-state paged allocators for DSV4 on NPU."""
+
+    def __init__(
+        self,
+        size: int,
+        size_swa: int,
+        page_size: int,
+        dtype: torch.dtype,
+        device: str,
+        kvcache,
+        need_sort: bool,
+    ):
+        super().__init__(
+            size=size,
+            size_swa=size_swa,
+            page_size=page_size,
+            dtype=dtype,
+            device=device,
+            kvcache=kvcache,
+            need_sort=need_sort,
+        )
+
+        def mk(pool_size, pool):
+            # c4/c128 KV and state sub-pools implement KVCache, so they drop into
+            # the standard paged allocator. pool_size is in compressed-token units.
+            return NPUPagedTokenToKVPoolAllocator(
+                pool_size,
+                page_size=page_size,
+                dtype=dtype,
+                device=device,
+                kvcache=pool,
+                need_sort=need_sort,
+            )
+
+        self.c4_attn_allocator = mk(kvcache.c4_size, kvcache.c4_kv_pool)
+        self.c128_attn_allocator = mk(kvcache.c128_size, kvcache.c128_kv_pool)
+
+        # State allocators (paged, NPU-only). Any layer's pool works as KVCache
+        # pointer (slot alloc is layer-agnostic); None when no c{ratio} layers or
+        # zero budget.
+        self.c4_state_attn_allocator: Optional[NPUPagedTokenToKVPoolAllocator] = None
+        self.c128_state_attn_allocator: Optional[NPUPagedTokenToKVPoolAllocator] = None
+        state_pools = getattr(kvcache, "compress_state_pools", None)
+        if state_pools:
+
+            def first_state_pool(want_ratio):
+                return next(
+                    (
+                        p
+                        for r, p in zip(kvcache.compression_ratios, state_pools)
+                        if r == want_ratio and p is not None
+                    ),
+                    None,
+                )
+
+            c4_state_pool = first_state_pool(4)
+            c128_state_pool = first_state_pool(128)
+            if c4_state_pool is not None and kvcache.c4_state_pool_size > 0:
+                self.c4_state_attn_allocator = mk(
+                    kvcache.c4_state_pool_size, c4_state_pool
+                )
+            if c128_state_pool is not None and kvcache.c128_state_pool_size > 0:
+                self.c128_state_attn_allocator = mk(
+                    kvcache.c128_state_pool_size, c128_state_pool
+                )
+
+        # Returned by the c-pool helpers when a step adds no compressed tokens.
+        self._empty_loc = torch.empty((0,), dtype=torch.int64, device=device)
+
+        # Per-call handle to the DSV4NPUReqToTokenPool, stashed by alloc_extend/
+        # alloc_decode for last_loc lookups; avoids a permanent allocator->pool ref.
+        self._cur_req_to_token_pool = None
+
+    @staticmethod
+    def _compute_c_extend_counts(
+        prefix_lens_cpu: torch.Tensor,
+        seq_lens_cpu: torch.Tensor,
+        ratio: int,
+    ) -> int:
+        """New compressed-K tokens this extend produces across the batch:
+        ``sum_i (seq_lens[i] // ratio - prefix_lens[i] // ratio)``."""
+        if prefix_lens_cpu is None or seq_lens_cpu is None:
+            return 0
+        diff = ((seq_lens_cpu // ratio) - (prefix_lens_cpu // ratio)).clamp(min=0)
+        return int(diff.sum().item())
+
+    @staticmethod
+    def _pool_exhausted(
+        ratio: int, kind: str, need: int, available: int
+    ) -> RuntimeError:
+        return RuntimeError(
+            f"DSV4 c{ratio} {kind} pool exhausted: need {need} new slots, "
+            f"available={available}. Raise --mem-fraction-static, lower "
+            f"--max-running-requests, or check that "
+            f"DSV4NPUTokenToKVPoolAllocator.free(req=...) releases {kind} slots "
+            f"on req finish."
+        )
+
+    def _alloc_state_extend(
+        self,
+        allocator: Optional[NPUPagedTokenToKVPoolAllocator],
+        raw_prefix_lens: torch.Tensor,
+        state_prefix_lens: torch.Tensor,
+        state_prefix_lens_cpu: torch.Tensor,
+        state_seq_lens: torch.Tensor,
+        state_seq_lens_cpu: torch.Tensor,
+        req_pool_indices: torch.Tensor,
+        last_loc_dtype: torch.dtype,
+        state_extend_num_tokens: int,
+        ratio: int,
+    ) -> torch.Tensor:
+        """Allocate tail-only state-pool slots for an extend at ``ratio``.
+
+        The state pool is a separate paged slot space; each req allocates only
+        its trailing window (cumulative lens precomputed by
+        ``ScheduleBatch._compute_dsv4_state_lens_*`` and passed via
+        ``DSV4StateLens``). ``state_last_loc`` is looked up from
+        ``req_to_token_c{ratio}_state`` at the RAW position
+        ``raw_prefix_lens - 1`` (the last position the previous extend/decode
+        populated). Returns ``_empty_loc`` when the allocator is absent (no
+        c{ratio} layers) or there is nothing to add.
+        """
+        if allocator is None or state_extend_num_tokens == 0:
+            return self._empty_loc
+
+        assert self._cur_req_to_token_pool is not None, (
+            "alloc_extend/alloc_decode must be called with req_to_token_pool= "
+            "for the state-pool last_loc lookup."
+        )
+        state_table = (
+            self._cur_req_to_token_pool.req_to_token_c4_state
+            if ratio == 4
+            else self._cur_req_to_token_pool.req_to_token_c128_state
+        )
+        state_last_loc = get_last_loc(
+            state_table, req_pool_indices, raw_prefix_lens
+        ).to(last_loc_dtype)
+
+        result = allocator.alloc_extend(
+            state_prefix_lens,
+            state_prefix_lens_cpu,
+            state_seq_lens,
+            state_seq_lens_cpu,
+            state_last_loc,
+            state_extend_num_tokens,
+        )
+        if result is None:
+            raise self._pool_exhausted(
+                ratio, "state", state_extend_num_tokens, allocator.available_size()
+            )
+        return result
+
+    def _alloc_c_extend(
+        self,
+        allocator: NPUPagedTokenToKVPoolAllocator,
+        prefix_lens: torch.Tensor,
+        prefix_lens_cpu: torch.Tensor,
+        seq_lens: torch.Tensor,
+        seq_lens_cpu: torch.Tensor,
+        req_pool_indices: torch.Tensor,
+        last_loc_dtype: torch.dtype,
+        ratio: int,
+    ) -> torch.Tensor:
+        """Allocate compressed-KV slots for an extend at ``ratio``.
+
+        Prefix/seq lens are translated to compressed units (``// ratio``); the
+        c-pool last_loc comes from ``req_to_token_c{ratio}`` via
+        :func:`get_last_loc` so the paged allocator continues in-page (or opens
+        a fresh page at a ratio boundary), keeping the intra-page continuity the
+        ``cmp_block_table`` reader relies on. Returns ``_empty_loc`` when this
+        step closes no compressed token.
+        """
+        c_extend = self._compute_c_extend_counts(prefix_lens_cpu, seq_lens_cpu, ratio)
+        if c_extend == 0:
+            return self._empty_loc
+
+        assert self._cur_req_to_token_pool is not None, (
+            "alloc_extend/alloc_decode must be called with req_to_token_pool= "
+            "for the c-pool last_loc lookup."
+        )
+        c_table = (
+            self._cur_req_to_token_pool.req_to_token_c4
+            if ratio == 4
+            else self._cur_req_to_token_pool.req_to_token_c128
+        )
+        c_prefix = (prefix_lens // ratio).to(prefix_lens.dtype)
+        c_seq = (seq_lens // ratio).to(seq_lens.dtype)
+        c_last_loc = get_last_loc(c_table, req_pool_indices, c_prefix).to(
+            last_loc_dtype
+        )
+
+        result = allocator.alloc_extend(
+            c_prefix,
+            prefix_lens_cpu // ratio,
+            c_seq,
+            seq_lens_cpu // ratio,
+            c_last_loc,
+            c_extend,
+        )
+        if result is None:
+            raise self._pool_exhausted(
+                ratio, "KV", c_extend, allocator.available_size()
+            )
+        return result
+
+    def _alloc_c_and_state(
+        self,
+        out_full_loc: torch.Tensor,
+        out_swa_loc: torch.Tensor,
+        prefix_lens: torch.Tensor,
+        prefix_lens_cpu: torch.Tensor,
+        seq_lens: torch.Tensor,
+        seq_lens_cpu: torch.Tensor,
+        last_loc_dtype: torch.dtype,
+        req_pool_indices: Optional[torch.Tensor],
+        dsv4_state_lens: Optional[DSV4StateLens],
+    ) -> DSV4OutCacheLoc:
+        """Allocate c4/c128 KV + state slots and bundle them with full/swa loc.
+
+        Shared by alloc_extend / alloc_decode (which differ only in how
+        prefix_lens is derived). State lens are tail-only, precomputed by
+        ScheduleBatch._compute_dsv4_state_lens_*; raw prefix_lens drives the
+        state last_loc lookup.
+        """
+        assert req_pool_indices is not None, (
+            "DSV4NPUTokenToKVPoolAllocator requires req_pool_indices "
+            "(forwarded from batch.req_pool_indices)."
+        )
+        assert dsv4_state_lens is not None, (
+            "DSV4NPUTokenToKVPoolAllocator requires dsv4_state_lens "
+            "(ScheduleBatch._compute_dsv4_state_lens_*)."
+        )
+        out_c4_loc = self._alloc_c_extend(
+            self.c4_attn_allocator,
+            prefix_lens,
+            prefix_lens_cpu,
+            seq_lens,
+            seq_lens_cpu,
+            req_pool_indices,
+            last_loc_dtype,
+            ratio=4,
+        )
+        out_c128_loc = self._alloc_c_extend(
+            self.c128_attn_allocator,
+            prefix_lens,
+            prefix_lens_cpu,
+            seq_lens,
+            seq_lens_cpu,
+            req_pool_indices,
+            last_loc_dtype,
+            ratio=128,
+        )
+        out_c4_state_loc = self._alloc_state_extend(
+            self.c4_state_attn_allocator,
+            prefix_lens,
+            dsv4_state_lens.c4_prefix_lens,
+            dsv4_state_lens.c4_prefix_lens_cpu,
+            dsv4_state_lens.c4_seq_lens,
+            dsv4_state_lens.c4_seq_lens_cpu,
+            req_pool_indices,
+            last_loc_dtype,
+            dsv4_state_lens.c4_extend_num_tokens,
+            ratio=4,
+        )
+        out_c128_state_loc = self._alloc_state_extend(
+            self.c128_state_attn_allocator,
+            prefix_lens,
+            dsv4_state_lens.c128_prefix_lens,
+            dsv4_state_lens.c128_prefix_lens_cpu,
+            dsv4_state_lens.c128_seq_lens,
+            dsv4_state_lens.c128_seq_lens_cpu,
+            req_pool_indices,
+            last_loc_dtype,
+            dsv4_state_lens.c128_extend_num_tokens,
+            ratio=128,
+        )
+        return DSV4OutCacheLoc(
+            out_full_loc=out_full_loc,
+            out_swa_loc=out_swa_loc,
+            out_c4_loc=out_c4_loc,
+            out_c128_loc=out_c128_loc,
+            out_c4_state_loc=out_c4_state_loc,
+            out_c128_state_loc=out_c128_state_loc,
+        )
+
+    def compute_dsv4_state_lens_extend(
+        self, reqs: List[Req], seq_lens: List[int]
+    ) -> Optional[DSV4StateLens]:
+        """Per-req c{4,128}_state pool alloc lens for extend (tail-only).
+
+        State pool stores only the trailing portion of each sequence (the c{N}
+        compressor's read/write window); the tail length depends on raw
+        seq_len's alignment to the SWA page boundary (128)::
+
+            c4_alloc_len  = tail + 128 if (tail <= 3 and seq_len >= 128) else tail
+            c128_alloc_len = tail                  where tail = seq_len % 128
+
+        Long prefills allocate only the trailing partial window, not slots for
+        already-compressed positions, so the small paged state pool (~256
+        slots/req) stays sufficient even for 28k-token prompts.
+
+        Mutates per-req cumulative state via getattr/setattr so the community
+        ``Req`` needs no DSV4 field declarations:
+          * ``req.c{4,128}_state_kv_len`` — cumulative slot count (prefix for
+            the paged allocator; never decreases on eviction).
+          * ``req.c{4,128}_state_alloc_offset`` — low-water raw-position mark
+            for eviction (see ``dsv4_common_hooks.maybe_evict_dsv4_state``).
+
+        Returns None when this model has no paged state pools (CUDA / non-V4 /
+        zero budget) — callers pass that straight through as ``dsv4_state_lens``.
+        """
+        if self.c4_state_attn_allocator is None:
+            return None
+        c4_prefix: List[int] = []
+        c4_seq: List[int] = []
+        c128_prefix: List[int] = []
+        c128_seq: List[int] = []
+        for req, seq_len in zip(reqs, seq_lens):
+            tail = seq_len % 128
+            c4_alloc_len = tail + 128 if (tail <= 3 and seq_len >= 128) else tail
+            c128_alloc_len = tail
+
+            prev_c4 = getattr(req, "c4_state_kv_len", 0)
+            prev_c128 = getattr(req, "c128_state_kv_len", 0)
+            new_c4 = prev_c4 + c4_alloc_len
+            new_c128 = prev_c128 + c128_alloc_len
+
+            c4_prefix.append(prev_c4)
+            c4_seq.append(new_c4)
+            c128_prefix.append(prev_c128)
+            c128_seq.append(new_c128)
+
+            req.c4_state_kv_len = new_c4
+            req.c128_state_kv_len = new_c128
+            req.c4_state_alloc_offset = seq_len - c4_alloc_len
+            req.c128_state_alloc_offset = seq_len - c128_alloc_len
+
+        return self._pack_state_lens(
+            c4_prefix,
+            c4_seq,
+            c128_prefix,
+            c128_seq,
+            c4_extend_num_tokens=int(sum(s - p for s, p in zip(c4_seq, c4_prefix))),
+            c128_extend_num_tokens=int(
+                sum(s - p for s, p in zip(c128_seq, c128_prefix))
+            ),
+        )
+
+    def compute_dsv4_state_lens_decode(
+        self, reqs: List[Req]
+    ) -> Optional[DSV4StateLens]:
+        """Per-req c{4,128}_state pool alloc lens for decode: exactly 1 new
+        state slot per req per pool. ``c{N}_state_alloc_offset`` does NOT
+        advance here (only eviction advances it). Returns None when there are
+        no paged state pools."""
+        if self.c4_state_attn_allocator is None:
+            return None
+        c4_prefix: List[int] = []
+        c4_seq: List[int] = []
+        c128_prefix: List[int] = []
+        c128_seq: List[int] = []
+        for req in reqs:
+            prev_c4 = getattr(req, "c4_state_kv_len", 0)
+            prev_c128 = getattr(req, "c128_state_kv_len", 0)
+            c4_prefix.append(prev_c4)
+            c4_seq.append(prev_c4 + 1)
+            c128_prefix.append(prev_c128)
+            c128_seq.append(prev_c128 + 1)
+            req.c4_state_kv_len = prev_c4 + 1
+            req.c128_state_kv_len = prev_c128 + 1
+
+        bs = len(reqs)
+        return self._pack_state_lens(
+            c4_prefix,
+            c4_seq,
+            c128_prefix,
+            c128_seq,
+            c4_extend_num_tokens=bs,
+            c128_extend_num_tokens=bs,
+        )
+
+    def _pack_state_lens(
+        self,
+        c4_prefix: List[int],
+        c4_seq: List[int],
+        c128_prefix: List[int],
+        c128_seq: List[int],
+        *,
+        c4_extend_num_tokens: int,
+        c128_extend_num_tokens: int,
+    ) -> DSV4StateLens:
+        c4_prefix_cpu = torch.tensor(c4_prefix, dtype=torch.int64)
+        c4_seq_cpu = torch.tensor(c4_seq, dtype=torch.int64)
+        c128_prefix_cpu = torch.tensor(c128_prefix, dtype=torch.int64)
+        c128_seq_cpu = torch.tensor(c128_seq, dtype=torch.int64)
+        return DSV4StateLens(
+            c4_prefix_lens=c4_prefix_cpu.to(self.device, non_blocking=True),
+            c4_prefix_lens_cpu=c4_prefix_cpu,
+            c4_seq_lens=c4_seq_cpu.to(self.device, non_blocking=True),
+            c4_seq_lens_cpu=c4_seq_cpu,
+            c4_extend_num_tokens=c4_extend_num_tokens,
+            c128_prefix_lens=c128_prefix_cpu.to(self.device, non_blocking=True),
+            c128_prefix_lens_cpu=c128_prefix_cpu,
+            c128_seq_lens=c128_seq_cpu.to(self.device, non_blocking=True),
+            c128_seq_lens_cpu=c128_seq_cpu,
+            c128_extend_num_tokens=c128_extend_num_tokens,
+        )
+
+    def alloc_extend(
+        self,
+        prefix_lens: torch.Tensor,
+        prefix_lens_cpu: torch.Tensor,
+        seq_lens: torch.Tensor,
+        seq_lens_cpu: torch.Tensor,
+        last_loc: torch.Tensor,
+        extend_num_tokens: int,
+        *,
+        req_pool_indices: Optional[torch.Tensor] = None,
+        dsv4_state_lens: Optional[DSV4StateLens] = None,
+        req_to_token_pool=None,
+    ) -> Optional[DSV4OutCacheLoc]:
+        # Stash per-req tables for this call's last_loc lookups (read by
+        # _alloc_c_extend / _alloc_state_extend); no permanent allocator->pool ref.
+        self._cur_req_to_token_pool = req_to_token_pool
+        out_full_loc = super().alloc_extend(
+            prefix_lens,
+            prefix_lens_cpu,
+            seq_lens,
+            seq_lens_cpu,
+            last_loc,
+            extend_num_tokens,
+        )
+        if out_full_loc is None:
+            return None
+
+        out_swa_loc = self.translate_loc_from_full_to_swa(out_full_loc)
+        assert out_swa_loc is not None, (
+            "translate_loc_from_full_to_swa returned None — "
+            "full_to_swa_index_mapping not initialized?"
+        )
+        return self._alloc_c_and_state(
+            out_full_loc,
+            out_swa_loc,
+            prefix_lens,
+            prefix_lens_cpu,
+            seq_lens,
+            seq_lens_cpu,
+            last_loc.dtype,
+            req_pool_indices,
+            dsv4_state_lens,
+        )
+
+    def alloc_decode(
+        self,
+        seq_lens: torch.Tensor,
+        seq_lens_cpu: torch.Tensor,
+        last_loc: torch.Tensor,
+        *,
+        req_pool_indices: Optional[torch.Tensor] = None,
+        dsv4_state_lens: Optional[DSV4StateLens] = None,
+        req_to_token_pool=None,
+    ) -> Optional[DSV4OutCacheLoc]:
+        self._cur_req_to_token_pool = req_to_token_pool
+        out_full_loc = super().alloc_decode(seq_lens, seq_lens_cpu, last_loc)
+        if out_full_loc is None:
+            return None
+
+        out_swa_loc = self.translate_loc_from_full_to_swa(out_full_loc)
+        # One new token per req. Model as an extend from (seq_len-1)//ratio to
+        # seq_len//ratio so _alloc_c_extend anchors on the real c-pool last_loc.
+        prefix_lens = (seq_lens - 1).clamp(min=0)
+        prefix_lens_cpu = (seq_lens_cpu - 1).clamp(min=0)
+        return self._alloc_c_and_state(
+            out_full_loc,
+            out_swa_loc,
+            prefix_lens,
+            prefix_lens_cpu,
+            seq_lens,
+            seq_lens_cpu,
+            last_loc.dtype,
+            req_pool_indices,
+            dsv4_state_lens,
+        )
+
+    def free(
+        self,
+        free_index: Optional[torch.Tensor] = None,
+        *,
+        req=None,
+        req_to_token_pool=None,
+    ):
+        """Unified free for full/swa/c4/c128 pools. Two forms (may co-fire):
+
+          * ``free(free_index)`` — full + SWA only (tail/radix eviction; no req
+            identity, so c-pool free can't run).
+          * ``free(req=, req_to_token_pool=)`` — from DSV4NPUReqToTokenPool.free
+            on req finish: reads the per-req slot lists from
+            ``req_to_token_c{4,128}[_state]`` and returns them to the c-pools
+            (the paged allocator dedupes by page).
+
+        KV pools free ``[0, kv_len // ratio)``. State pools are 1-per-raw-token
+        and free only the tail ``[c{N}_state_alloc_offset, kv_len)`` — the prefix
+        was already returned by ScheduleBatch._evict_swa (state rides SWA
+        eviction); freeing it again would double-free (caught by the paged
+        allocator's debug_mode assert, corrupts the free list otherwise).
+        """
+        if free_index is not None:
+            super().free(free_index)
+
+        if req is None or req_to_token_pool is None:
+            return
+        kv_len = req.kv_committed_len
+        req_pool_idx = req.req_pool_idx
+        if kv_len <= 0 or req_pool_idx is None:
+            return
+
+        # KV pools: free the leading [0, kv_len // ratio) compressed slots.
+        for ratio, allocator, table_attr in (
+            (4, self.c4_attn_allocator, "req_to_token_c4"),
+            (128, self.c128_attn_allocator, "req_to_token_c128"),
+        ):
+            n = kv_len // ratio
+            if n > 0 and hasattr(req_to_token_pool, table_attr):
+                slots = getattr(req_to_token_pool, table_attr)[req_pool_idx, :n]
+                # to int64 — paged allocator's free does cpu()//page_size on it.
+                allocator.free(slots.to(torch.int64))
+
+        # State pools: free only the tail [c{N}_state_alloc_offset, kv_len).
+        for ratio, allocator, table_attr, off_attr in (
+            (
+                4,
+                self.c4_state_attn_allocator,
+                "req_to_token_c4_state",
+                "c4_state_alloc_offset",
+            ),
+            (
+                128,
+                self.c128_state_attn_allocator,
+                "req_to_token_c128_state",
+                "c128_state_alloc_offset",
+            ),
+        ):
+            if allocator is None or not hasattr(req_to_token_pool, table_attr):
+                continue
+            off = getattr(req, off_attr, 0)
+            if kv_len > off:
+                slots = getattr(req_to_token_pool, table_attr)[req_pool_idx, off:kv_len]
+                allocator.free(slots.to(torch.int64))
+
+    def clear(self):
+        super().clear()
+        # super().__init__ calls clear() before our sub-allocators exist;
+        # getattr(..., None) tolerates that and the always-None state allocators.
+        for attr in (
+            "c4_attn_allocator",
+            "c128_attn_allocator",
+            "c4_state_attn_allocator",
+            "c128_state_attn_allocator",
+        ):
+            allocator = getattr(self, attr, None)
+            if allocator is not None:
+                allocator.clear()

python/sglang/srt/hardware_backend/npu/dsv4/dsv4_common_hooks.py

@@ -0,0 +1,356 @@
+"""Helpers used by mem_cache/common.py to wire DSV4-NPU per-req tables.
+
+mem_cache/common.py runs platform-agnostic alloc flow. When the model is
+DSV4 on NPU, ``alloc_paged_token_slots_{extend,decode}`` already stashed the
+:class:`DSV4OutCacheLoc` the allocator returned onto
+``batch.out_cache_loc_dsv4``. After each ``alloc_extend`` / ``alloc_decode``
+these hooks then:
+
+  1. Read the bundle from ``batch.out_cache_loc_dsv4``.
+  2. Write the per-pool slot ids into the per-req tables on the
+     :class:`DSV4NPUReqToTokenPool`.
+
+Non-DSV4 paths leave ``batch.out_cache_loc_dsv4`` None, so this module is a
+no-op for them.
+
+TODO: the disagg DSV4 path bypasses these hooks — it calls
+``allocator.alloc_extend`` directly then ``req_to_token_pool.write`` without
+going through ``mem_cache/common.py`` (see ``disaggregation/decode.py``). The
+DSV4OutCacheLoc bundle is still produced but never written into the per-req
+tables, so disagg + DSV4 is unsupported here (c-pages leak). Fixing requires
+calling these hooks from disagg's per-req alloc loop, or moving the write
+into the allocator itself.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
+
+
+def maybe_write_dsv4_extend(
+    batch: ScheduleBatch,
+    req_pool_indices_cpu: torch.Tensor,
+    prefix_lens_cpu: torch.Tensor,
+    seq_lens_cpu: torch.Tensor,
+) -> None:
+    """Post-alloc_extend hook for DSV4. No-op when allocator/pool is not DSV4.
+
+    For each compressed pool (c4 / c128), spreads the flat
+    ``out_c{4,128}_loc`` tensor across requests using per-req extend
+    counts (``seq_lens[i] // ratio - prefix_lens[i] // ratio``) and writes
+    the resulting slot ids into ``req_to_token_c{4,128}[req, prefix:seq]``.
+
+    Also writes ``req_to_token_swa[req, prefix:seq]`` with the swa slots
+    derived from out_full_loc via the SWA index mapping.
+    """
+    # Bundle stashed on batch.out_cache_loc_dsv4 by mem_cache/common.py;
+    # None on CUDA / non-V4 paths → no-op.
+    bundle = batch.out_cache_loc_dsv4
+    if bundle is None:
+        return
+
+    req_to_token_pool = batch.req_to_token_pool
+    if not hasattr(req_to_token_pool, "write_c4"):
+        return  # non-DSV4 pool; skip defensively (shouldn't happen)
+
+    # SWA writes: prefix..seq token positions, one slot per raw token.
+    _write_per_req_slice(
+        req_to_token_pool.write_swa,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_swa_loc,
+        ratio=1,
+    )
+
+    # c4 / c128 writes: prefix//ratio .. seq//ratio compressed positions.
+    _write_per_req_slice(
+        req_to_token_pool.write_c4,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_c4_loc,
+        ratio=4,
+    )
+    _write_per_req_slice(
+        req_to_token_pool.write_c128,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_c128_loc,
+        ratio=128,
+    )
+
+    # c4_state / c128_state writes: tail-only. Bundle length is
+    # sum(c{N}_state_alloc_len_i), NOT total raw extend tokens; each req's slots
+    # go at raw positions [req.c{N}_state_alloc_offset, seq_len).
+    if bundle.out_c4_state_loc is not None and hasattr(
+        req_to_token_pool, "write_c4_state"
+    ):
+        _write_state_tail_per_req(
+            req_to_token_pool.write_c4_state,
+            req_pool_indices_cpu,
+            [getattr(r, "c4_state_alloc_offset", 0) for r in batch.reqs],
+            seq_lens_cpu,
+            bundle.out_c4_state_loc,
+        )
+    if bundle.out_c128_state_loc is not None and hasattr(
+        req_to_token_pool, "write_c128_state"
+    ):
+        _write_state_tail_per_req(
+            req_to_token_pool.write_c128_state,
+            req_pool_indices_cpu,
+            [getattr(r, "c128_state_alloc_offset", 0) for r in batch.reqs],
+            seq_lens_cpu,
+            bundle.out_c128_state_loc,
+        )
+
+
+def maybe_write_dsv4_decode(
+    batch: ScheduleBatch,
+    seq_lens_cpu: torch.Tensor,
+    token_per_req: int,
+) -> None:
+    """Post-alloc_decode hook for DSV4. Spreads the new token slot ids
+    (one per req for swa, gated by ratio boundary for c4/c128) into the
+    per-req tables on DSV4NPUReqToTokenPool.
+
+    ``seq_lens_cpu`` is the POST-decode seq len (already incremented by
+    ``token_per_req``); the new compressed tokens go at positions
+    ``[(old_seq) // ratio, (new_seq) // ratio)``.
+    """
+    # Bundle stashed on batch.out_cache_loc_dsv4 by mem_cache/common.py;
+    # None on CUDA / non-V4 paths → no-op.
+    bundle = batch.out_cache_loc_dsv4
+    if bundle is None:
+        return
+
+    req_to_token_pool = batch.req_to_token_pool
+    if not hasattr(req_to_token_pool, "write_c4"):
+        return
+
+    prefix_lens_cpu = (seq_lens_cpu - token_per_req).clamp(min=0)
+    req_pool_indices_cpu = batch.req_pool_indices.cpu()
+
+    _write_per_req_slice(
+        req_to_token_pool.write_swa,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_swa_loc,
+        ratio=1,
+    )
+    _write_per_req_slice(
+        req_to_token_pool.write_c4,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_c4_loc,
+        ratio=4,
+    )
+    _write_per_req_slice(
+        req_to_token_pool.write_c128,
+        req_pool_indices_cpu,
+        prefix_lens_cpu,
+        seq_lens_cpu,
+        bundle.out_c128_loc,
+        ratio=128,
+    )
+
+    # State table decode writes: one slot per raw decode token (ratio=1).
+    if bundle.out_c4_state_loc is not None and hasattr(
+        req_to_token_pool, "write_c4_state"
+    ):
+        _write_per_req_slice(
+            req_to_token_pool.write_c4_state,
+            req_pool_indices_cpu,
+            prefix_lens_cpu,
+            seq_lens_cpu,
+            bundle.out_c4_state_loc,
+            ratio=1,
+        )
+    if bundle.out_c128_state_loc is not None and hasattr(
+        req_to_token_pool, "write_c128_state"
+    ):
+        _write_per_req_slice(
+            req_to_token_pool.write_c128_state,
+            req_pool_indices_cpu,
+            prefix_lens_cpu,
+            seq_lens_cpu,
+            bundle.out_c128_state_loc,
+            ratio=1,
+        )
+
+
+def _write_per_req(
+    write_fn,
+    req_pool_indices_cpu: torch.Tensor,
+    flat_loc: torch.Tensor,
+    bounds_fn,
+) -> None:
+    """Distribute a flat ``[total_alloc]`` slot tensor across reqs.
+
+    ``bounds_fn(i) -> (lo, hi)`` gives req i's write window; the matching
+    ``hi - lo`` slots are sliced off ``flat_loc`` in order and written via
+    ``write_fn((req_idx, slice(lo, hi)), values)``. flat_loc may be None /
+    empty when the alloc path bypassed DSV4NPUTokenToKVPoolAllocator (e.g.
+    page_size=1 or HiSparse wrapper); skip then.
+    """
+    if flat_loc is None or flat_loc.numel() == 0:
+        return
+    pt = 0
+    for i in range(req_pool_indices_cpu.shape[0]):
+        lo, hi = bounds_fn(i)
+        alloc_len = max(0, hi - lo)
+        if alloc_len == 0:
+            continue
+        req_idx = int(req_pool_indices_cpu[i].item())
+        chunk = flat_loc[pt : pt + alloc_len].to(torch.int32)
+        write_fn((req_idx, slice(lo, hi)), chunk)
+        pt += alloc_len
+
+
+def _write_state_tail_per_req(
+    write_fn,
+    req_pool_indices_cpu: torch.Tensor,
+    state_alloc_offsets: list,
+    seq_lens_cpu: torch.Tensor,
+    flat_loc: torch.Tensor,
+) -> None:
+    """Tail-only state write: req i's slots go at ``[state_alloc_offsets[i],
+    seq_lens[i])`` in ``req_to_token_c{N}_state``."""
+    _write_per_req(
+        write_fn,
+        req_pool_indices_cpu,
+        flat_loc,
+        lambda i: (int(state_alloc_offsets[i]), int(seq_lens_cpu[i].item())),
+    )
+
+
+def _write_per_req_slice(
+    write_fn,
+    req_pool_indices_cpu: torch.Tensor,
+    prefix_lens_cpu: torch.Tensor,
+    seq_lens_cpu: torch.Tensor,
+    flat_loc: torch.Tensor,
+    ratio: int,
+) -> None:
+    """Compressed-position write: req i's slots go at
+    ``[prefix_lens[i] // ratio, seq_lens[i] // ratio)``."""
+    _write_per_req(
+        write_fn,
+        req_pool_indices_cpu,
+        flat_loc,
+        lambda i: (
+            int(prefix_lens_cpu[i].item()) // ratio,
+            int(seq_lens_cpu[i].item()) // ratio,
+        ),
+    )
+
+
+def maybe_evict_dsv4_state(batch: ScheduleBatch, req: Req, pre_len: int) -> None:
+    """Per-decode evict for the DSV4-NPU compress-state pools, independent of
+    SWA evict cadence. Called every decode step from ``ScheduleBatch``.
+
+    The state pool is small (~2 pages c4 / ~3 pages c128 of raw positions per
+    req) — with a large sliding_window (SWA evict fires every
+    ``eviction_interval`` and needs ``pre_len > sliding_window + page_size`` to
+    free anything) the pool exhausts before the first SWA frontier advance, so
+    we drain it here on its own cadence.
+
+    Retention windows (kernel read window + decode lookahead margin):
+    c4 = 8 + 16, c128 = 128 + 64 raw positions — intentionally smaller than one
+    SWA page so the first eviction fires before the small pool fills. Watermarks
+    are page-aligned so freed slots are whole pages reclaimable by the paged
+    allocator. ``req.c{4,128}_state_alloc_offset`` (read/written via getattr/
+    setattr) is the low-water mark. No-op on non-DSV4-NPU paths.
+    """
+    allocator = batch.token_to_kv_pool_allocator
+    pool = batch.req_to_token_pool
+    if not hasattr(allocator, "c4_state_attn_allocator") or (
+        allocator.c4_state_attn_allocator is None
+        and allocator.c128_state_attn_allocator is None
+    ):
+        return
+
+    page_size = batch.tree_cache.page_size
+    c4_watermark = ((max(0, pre_len - (8 + 16))) // page_size) * page_size
+    c128_watermark = ((max(0, pre_len - (128 + 64))) // page_size) * page_size
+
+    _free_state_range(
+        allocator.c4_state_attn_allocator,
+        pool,
+        "req_to_token_c4_state",
+        req,
+        "c4_state_alloc_offset",
+        c4_watermark,
+    )
+    _free_state_range(
+        allocator.c128_state_attn_allocator,
+        pool,
+        "req_to_token_c128_state",
+        req,
+        "c128_state_alloc_offset",
+        c128_watermark,
+    )
+
+
+def maybe_evict_dsv4_state_on_swa(
+    allocator, pool, req: Req, new_swa_evicted_seqlen: int
+) -> None:
+    """Free compress-state slots that ride along with SWA eviction.
+
+    State at raw positions < ``swa_evicted_seqlen`` is no longer readable (the
+    compressor only reads the trailing ``2*ratio`` window) and is returned to
+    its paged allocator to keep the small state pool from exhausting on long
+    generations. No-op when the DSV4-NPU state allocators are absent.
+
+    This path is needed for small-sliding-window models where
+    ``sliding_window < retention`` (e.g. c128 retention 192 > window 128):
+    in that case the watermark-based eviction alone may not free slots
+    fast enough, and the SWA-ride eviction is the primary reclaim mechanism.
+    For typical large-window models (DS-V4 with window >> 192), the
+    watermark eviction always runs first, making this path a no-op.
+    """
+    if not hasattr(allocator, "c4_state_attn_allocator"):
+        return
+    _free_state_range(
+        allocator.c4_state_attn_allocator,
+        pool,
+        "req_to_token_c4_state",
+        req,
+        "c4_state_alloc_offset",
+        new_swa_evicted_seqlen,
+    )
+    _free_state_range(
+        allocator.c128_state_attn_allocator,
+        pool,
+        "req_to_token_c128_state",
+        req,
+        "c128_state_alloc_offset",
+        new_swa_evicted_seqlen,
+    )
+
+
+def _free_state_range(
+    state_allocator,
+    pool,
+    table_attr: str,
+    req: Req,
+    offset_attr: str,
+    watermark: int,
+) -> None:
+    """Free ``[alloc_offset, watermark)`` raw-position state slots for ``req``
+    and advance its low-water mark. No-op when the allocator/table is absent or
+    the watermark hasn't advanced past the current offset."""
+    offset = getattr(req, offset_attr, 0)
+    if state_allocator is None or not hasattr(pool, table_attr) or watermark <= offset:
+        return
+    free_slots = getattr(pool, table_attr)[req.req_pool_idx, offset:watermark]
+    state_allocator.free(free_slots.to(torch.int64))
+    setattr(req, offset_attr, watermark)

python/sglang/srt/hardware_backend/npu/dsv4/dsv4_memory_pool.py

@@ -0,0 +1,584 @@
+"""NPU-only KV pool variant for DeepSeek-V4.
+
+Subclasses :class:`DeepSeekV4TokenToKVPool` to swap the ring-buffered
+:class:`CompressStatePool` for the paged :class:`NPUCompressStatePool` that
+the on-NPU fused compressor kernel (``torch.ops.custom.compressor`` with
+``cache_mode=1``) requires. Atlas A3 rejects ``cache_mode=2`` (ring) entirely,
+so this is the only valid layout on that hardware.
+
+Selected at pool construction time by
+:meth:`ModelRunnerKVCacheMixin._init_pools` when the model is DSV4 AND the
+device is NPU. CUDA continues to use the unchanged base class.
+
+The subclass overrides only:
+
+  * ``_make_attn_state_pool`` / ``_make_indexer_state_pool`` — the per-ratio
+    state-pool factories the base ``_init_paged_compress_states`` loop calls.
+    Both return :class:`NPUCompressStatePool` (paged, ``cache_mode=1``)
+    instead of the base's ring-buffered :class:`CompressStatePool`.
+  * ``translate_kv_loc_to_compress_state_loc`` — raise loudly. The ring
+    hash this method implements is meaningless on the paged kernel; callers
+    must consume ``out_cache_loc_dsv4.out_c{4,128}_state_loc`` from the
+    allocator bundle instead. Currently the only NPU caller that still
+    invokes translate is the unfused Python compressor decode path
+    (``layers/attention/dsv4/compressor.py``); with USE_FUSED_COMPRESSOR=1
+    that path is dead. If someone disables the fused compressor, they hit
+    the raise with a clear message.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch_npu
+
+from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
+from sglang.srt.mem_cache.deepseek_v4_compress_state import CompressStatePool
+from sglang.srt.mem_cache.deepseek_v4_memory_pool import (
+    ONLINE_C128,
+    DeepSeekV4IndexerPool,
+    DeepSeekV4SingleKVPool,
+    DeepSeekV4TokenToKVPool,
+)
+
+
+class NPUDeepSeekV4SingleKVPool(DeepSeekV4SingleKVPool):
+    """NPU bf16 variant of the full / SWA / c4 / c128 single-KV pool.
+
+    ``npu_sparse_attn_sharedkv`` reads KV in PA_ND layout
+    ``(num_pages, kernel_page_size, num_kv_heads=1, dim)`` with ``dim`` packing
+    K_nope + K_rope as bf16, and requires ``cmp_kv.shape[1] == ori_kv.shape[1]``.
+    So the c4/c128 pools (whose token-level page_size is ``page_size // ratio``)
+    are allocated at the GLOBAL ``kernel_page_size`` rather than their own
+    per-ratio page_size; the SWA pool uses ``kernel_page_size == page_size``.
+    The CUDA fp8-packed-bytes layout (the base ``create_buffer``) is untouched.
+    """
+
+    def __init__(self, *args, kernel_page_size: int, **kwargs):
+        # Set before super().__init__ — it calls _create_buffers() ->
+        # create_buffer(), which reads self.kernel_page_size.
+        self.kernel_page_size = kernel_page_size
+        super().__init__(*args, **kwargs)
+
+    def create_buffer(self, *, num_pages: int):
+        # Non-bf16 store dtype (shouldn't happen here) falls back to base layout.
+        if self.store_dtype != torch.bfloat16:
+            return super().create_buffer(num_pages=num_pages)
+        kv_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+        self.kv_cache_total_dim = kv_dim
+        # GLOBAL kernel_page_size keeps cmp_kv.shape[1] == ori_kv.shape[1]; writes
+        # are flat-indexed by loc, so page granularity affects shape not location.
+        npu_num_pages = (self.size + self.kernel_page_size + 1) // self.kernel_page_size
+        return torch.zeros(
+            npu_num_pages,
+            self.kernel_page_size,
+            1,
+            kv_dim,
+            dtype=torch.bfloat16,
+            device=self.device,
+        )
+
+
+def npu_state_pool_size(
+    *,
+    ratio: int,
+    page_size: int,
+    max_num_reqs: int,
+) -> int:
+    """Per-pool state slot count for the NPU paged state pool's
+    :class:`NPUPagedTokenToKVPoolAllocator`.
+
+    Sizing formula::
+
+        max(2, ceil(1.8 * ratio / page_size) + 1) * max_num_reqs * page_size
+
+    Sized for steady-state during decode: each req keeps roughly the trailing
+    ``sliding_window_size`` worth of state slots live at any one time (SWA
+    eviction in :meth:`ScheduleBatch._evict_swa` frees state slots as it
+    advances), and the 1.8x factor adds headroom for the tail-only allocation
+    pattern across page boundaries.
+
+    Prefill no longer drives sizing because allocation is tail-only — long
+    prompts only allocate ``c{ratio}_alloc_len`` slots (``≤ tail + 128`` for
+    c4, ``≤ tail`` for c128, where ``tail = seq_len % 128``), not the full raw
+    seqlen. See :meth:`ScheduleBatch._compute_dsv4_state_lens_extend` for the
+    per-req formula.
+
+    Result is in TOKEN units (matches the SGLang allocator
+    ``PagedTokenToKVPoolAllocator(size, ...)`` convention where
+    ``num_pages = size // page_size`` is the count of USABLE pages handed out
+    by ``free_pages = arange(1, num_pages+1)``). The BUFFER allocates one extra
+    page (see :class:`NPUCompressStatePool`, sized ``(num_pages + 1) *
+    page_size`` — page 0 is the kernel's skip-sentinel).
+    """
+    blocks_per_req = max(2, math.ceil(1.8 * ratio / page_size) + 1)
+    num_usable_pages = blocks_per_req * max_num_reqs
+    return num_usable_pages * page_size
+
+
+class NPUCompressStatePool(CompressStatePool):
+    """Paged compress-state pool for the NPU fused compressor kernel.
+
+    ``torch.ops.custom.compressor`` (cache_mode=1) reads/writes the compress
+    state via ``state_cache`` shape ``(block_num, page_size, 2*coff*head_dim)``
+    indexed by a paged ``state_block_table`` (block ids from 1; value 0 means
+    "skip this slot"). The CUDA :class:`CompressStatePool` sizes itself
+    ring-style, which misaddresses slots under cache_mode=1 (ring is also
+    unsupported on Atlas A3). This subclass keeps the parent's buffer layout
+    (``(self._size, 2*coff*head_dim)`` flat; ``state_cache_3d`` reshapes to
+    ``(num_blocks, page_size, 2*coff*head_dim)``) but replaces the size formula
+    with a paged one derived from ``max_num_reqs``. Block 0 is reserved as the
+    kernel's skip-sentinel (zero kv / -inf score) so any ``state_block_table``
+    entry defaulting to 0 lands in a deterministic, attention-neutral place.
+
+    NPU-only; CUDA keeps using the unchanged :class:`CompressStatePool`.
+    """
+
+    def __init__(
+        self,
+        *,
+        size: int,
+        overlap: bool,
+        head_dim: int,
+        dtype: torch.dtype,
+        device: str,
+        enable_memory_saver: bool,
+        ratio: int,
+        page_size: int,
+    ):
+        # Bypass parent __init__ — its ring-based sizing is incompatible with the
+        # kernel's paged block-id contract. We redo buffer alloc and set the same
+        # fields so the parent API (state_cache_3d, kv_score_buffer) stays intact.
+        assert ratio in (
+            4,
+            128,
+        ), f"NPUCompressStatePool only supports ratio in (4, 128); got {ratio}"
+        assert page_size > 1, (
+            "NPUCompressStatePool requires page_size>1 (kernel's "
+            "state_cache_3d view is (block_num, page_size, slot_dim)). "
+            "Got page_size=%d." % page_size
+        )
+
+        # ``size`` is the ALLOCATOR's size (npu_state_pool_size output). Buffer
+        # needs one EXTRA page so the free list arange(1, num_pages+1) indexes it
+        # without OOB (page 0 = skip sentinel; pages 1..num_pages handed out).
+        num_usable_pages = (size + page_size - 1) // page_size
+        num_buffer_pages = num_usable_pages + 1
+        self._size = num_buffer_pages * page_size
+        self.page_size = page_size
+        # ring_size=0 marks "not ring-buffered" (paged allocator replaces the
+        # parent's ring hashing); kept so downstream hasattr probes don't break.
+        self.ring_size = 0
+        # online compress is a CUDA-only opt with no NPU fused-compressor support;
+        # force off so layout matches kernel expectations.
+        self.online = False
+
+        # Slot dim = 2 * coff * head_dim = [kv | score]; coff = 1 (no overlap) or
+        # 2 (overlap). Matches CompressStatePool non-online layout.
+        self.last_dim = 2 * (1 + int(overlap)) * head_dim
+
+        # Reuse parent's buffer-alloc helper; only self._size differs from the
+        # ring-based parent path.
+        self._alloc_kv_score_buffer(
+            dtype=dtype, device=device, enable_memory_saver=enable_memory_saver
+        )
+
+        # Block 0 = kernel skip-sentinel: kv zeroed, score -inf (softmax → 0).
+        # The free list excludes it; only stale state_block_table entries land here.
+        self.kv_score_buffer.kv[:page_size].zero_()
+        self.kv_score_buffer.score[:page_size].fill_(float("-inf"))
+
+
+class NPUDeepSeekV4IndexerPool(DeepSeekV4IndexerPool):
+    """NPU c4-indexer pool. Keeps the base packed CUDA buffer (read by
+    get_contiguous_buf_infos / NSA) and ADDS dedicated int8 K + float16 scale
+    buffers in PA_ND layout at the global ``kernel_page_size``, written by
+    ``torch_npu.npu_scatter_nd_update_`` and read by
+    ``torch.ops.custom.npu_quant_lightning_indexer``.
+    """
+
+    def __init__(self, *args, kernel_page_size: int, **kwargs):
+        # Set before super().__init__ — it calls _create_buffer().
+        self._kernel_page_size = kernel_page_size
+        super().__init__(*args, **kwargs)
+
+    def _create_buffer(self):
+        # Base allocates the packed CUDA index_k_with_scale_buffer (kept for
+        # get_contiguous_buf_infos / NSA compat); then add the NPU buffers.
+        super()._create_buffer()
+        kp = self._kernel_page_size
+        npu_num_pages = (self.size + kp + 1) // kp
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
+            self.index_k_buffer = [
+                torch.zeros(
+                    npu_num_pages,
+                    kp,
+                    1,
+                    self.index_head_dim,
+                    dtype=torch.int8,
+                    device=self.device,
+                )
+                for _ in range(self.layer_num)
+            ]
+            self.index_scale_buffer = [
+                torch.zeros(
+                    npu_num_pages,
+                    kp,
+                    1,
+                    1,
+                    dtype=torch.float16,
+                    device=self.device,
+                )
+                for _ in range(self.layer_num)
+            ]
+
+    @property
+    def has_npu_storage(self) -> bool:
+        return True
+
+    def get_index_k(self, layer_id: int) -> torch.Tensor:
+        return self.index_k_buffer[layer_id]
+
+    def get_index_scale(self, layer_id: int) -> torch.Tensor:
+        return self.index_scale_buffer[layer_id]
+
+    def set_index_k_scale(
+        self,
+        layer_id: int,
+        loc: torch.Tensor,
+        index_k: torch.Tensor,
+        index_k_scale: Optional[torch.Tensor],
+    ) -> None:
+        # int8 K + fp16 scale come from _compressor_epilog_npu's npu_dynamic_quant
+        # output (index_k: int8 [T, D], index_k_scale: fp16 [T, 1]).
+        d = self.index_head_dim
+        loc_long = loc.view(-1, 1).long()
+        torch_npu.npu_scatter_nd_update_(
+            self.index_k_buffer[layer_id].view(-1, 1, d),
+            loc_long,
+            index_k.to(torch.int8).view(-1, 1, d),
+        )
+        if index_k_scale is not None:
+            torch_npu.npu_scatter_nd_update_(
+                self.index_scale_buffer[layer_id].view(-1, 1, 1),
+                loc_long,
+                index_k_scale.to(torch.float16).view(-1, 1, 1),
+            )
+
+
+class DSV4NPUTokenToKVPool(DeepSeekV4TokenToKVPool):
+    """NPU-only DSV4 KV pool with paged compress-state buffers.
+
+    The full / SWA / c4 / c128 KV pools use the NPU bf16 PA_ND layout
+    (:class:`NPUDeepSeekV4SingleKVPool`); the compress-state pool is paged
+    (:class:`NPUCompressStatePool`) rather than ring-buffered; and the indexer
+    pool adds dedicated int8 K + fp16 scale buffers
+    (:class:`NPUDeepSeekV4IndexerPool`). The generic-accessor / port-hook
+    methods at the bottom of this class are the NPU equivalents of the CUDA
+    DSV4 store-cache chain — kept here, not in the community base, which raises
+    ``NotImplementedError`` for them (CUDA goes through the radix / store_cache
+    accessors instead).
+    """
+
+    def _make_kv_pool(
+        self,
+        *,
+        size: int,
+        page_size: int,
+        dtype: torch.dtype,
+        layer_num: int,
+        device: str,
+        enable_memory_saver: bool,
+        global_page_size: int,
+        cls: type = DeepSeekV4SingleKVPool,
+    ) -> NPUDeepSeekV4SingleKVPool:
+        # NPU does not use the HiSparse c4 device pool; fail loud if someone
+        # enables it so the silent layout mismatch surfaces at init.
+        assert cls is DeepSeekV4SingleKVPool, (
+            "enable_hisparse is not supported on the NPU DSV4 KV pool "
+            f"(got c4 pool class {cls.__name__})."
+        )
+        return NPUDeepSeekV4SingleKVPool(
+            size,
+            page_size,
+            dtype,
+            self.qk_nope_head_dim,
+            self.qk_rope_head_dim,
+            layer_num,
+            device,
+            enable_memory_saver,
+            kernel_page_size=global_page_size,
+        )
+
+    def _get_state_pool(self, layer_id: int, from_indexer: bool) -> CompressStatePool:
+        """Select this layer's attention vs c4-indexer compress-state pool.
+        Wraps the community getters so the NPU port hooks below don't index the
+        pool lists directly."""
+        if from_indexer:
+            return self.get_indexer_compress_states(layer_id)
+        return self.get_attention_compress_states(layer_id)
+
+    def _make_attn_state_pool(
+        self, ratio: int, enable_memory_saver: bool
+    ) -> NPUCompressStatePool:
+        # ONLINE_C128 (CUDA-only) collapses the c128 ring to size 1; the NPU fused
+        # compressor has no online mode, so assert the config mismatch early.
+        assert not (ratio == 128 and ONLINE_C128), (
+            "SGLANG_OPT_USE_ONLINE_COMPRESS is incompatible with the "
+            "NPU fused compressor (no online mode in the kernel)."
+        )
+        return NPUCompressStatePool(
+            size=self._state_pool_size(ratio),
+            overlap=ratio == 4,
+            head_dim=self.qk_nope_head_dim + self.qk_rope_head_dim,
+            dtype=self.c4_state_dtype if ratio == 4 else self.c128_state_dtype,
+            device=self.device,
+            enable_memory_saver=enable_memory_saver,
+            ratio=ratio,
+            page_size=self.swa_page_size,
+        )
+
+    def _make_indexer_state_pool(
+        self, ratio: int, enable_memory_saver: bool
+    ) -> NPUCompressStatePool:
+        # c4 indexer shares the c4 state pool size budget but has its own
+        # slot_dim (indexer_head_dim vs attention head_dim).
+        return NPUCompressStatePool(
+            size=self.c4_state_pool_size,
+            overlap=ratio == 4,
+            head_dim=self.indexer_head_dim,
+            device=self.device,
+            dtype=self.c4_state_dtype,
+            enable_memory_saver=enable_memory_saver,
+            ratio=ratio,
+            page_size=self.swa_page_size,
+        )
+
+    def _make_indexer_pool(
+        self,
+        size: int,
+        page_size: int,
+        dtype: torch.dtype,
+        index_head_dim: int,
+        layer_num: int,
+        device: str,
+        enable_memory_saver: bool,
+    ) -> NPUDeepSeekV4IndexerPool:
+        # NPU dedicated int8 K + fp16 scale buffers use the GLOBAL page_size
+        # (= self.page_size) as kernel_page_size, matching ori_kv for the kernel.
+        return NPUDeepSeekV4IndexerPool(
+            size,
+            page_size,
+            dtype,
+            index_head_dim,
+            layer_num,
+            device,
+            enable_memory_saver,
+            kernel_page_size=self.page_size,
+        )
+
+    def get_state_cache(self, layer_id: int, from_indexer: bool) -> torch.Tensor:
+        """fp32 ``[block_num, page_size, 2*coff*D]`` view of this layer's
+        kv+score buffer — the fused compressor op
+        (``torch.ops.custom.compressor``)'s ``state_cache`` argument."""
+        return self._get_state_pool(layer_id, from_indexer).state_cache_3d
+
+    # ------------------------------------------------------------------
+    # Generic KV accessors (community base raises NotImplementedError; CUDA uses
+    # store_cache). AscendAttnBackend reads KV through these, routed to the right
+    # sub-pool by compression ratio.
+    # ------------------------------------------------------------------
+
+    def get_key_buffer(self, layer_id: int) -> torch.Tensor:
+        item = self.layer_mapping[layer_id]
+        ratio = item.compress_ratio
+        if ratio == 0:
+            return self.swa_kv_pool.kv_buffer[item.compress_layer_id]
+        if ratio == 4:
+            return self.c4_kv_pool.kv_buffer[item.compress_layer_id]
+        if ratio == 128:
+            return self.c128_kv_pool.kv_buffer[item.compress_layer_id]
+        raise ValueError(f"unsupported compress_ratio={ratio} for get_key_buffer")
+
+    def get_value_buffer(self, layer_id: int) -> torch.Tensor:
+        # V4 uses MQA / latent attention — the K buffer doubles as V.
+        return self.get_key_buffer(layer_id)
+
+    def get_kv_buffer(self, layer_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        buf = self.get_key_buffer(layer_id)
+        return buf, buf
+
+    def get_swa_buffer(
+        self, layer_id: int, loc: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """Return the SWA layer's KV cache in PA_ND layout
+        (num_pages, page_size, num_kv_heads=1, dim). When ``loc`` is given,
+        flatten across (num_pages, page_size) and gather the matching tokens —
+        shape becomes (num_tokens, 1, dim).
+        """
+        # Index by RAW layer_id, not compress_layer_id (a per-bucket counter that
+        # would collide across ratios). swa_kv_pool is sized layer_num=total_layers.
+        kv = self.swa_kv_pool.kv_buffer[layer_id]
+        if loc is not None:
+            kv = kv.flatten(0, 1)[loc]
+        return kv
+
+    def get_compress_buffer(
+        self,
+        layer_id: int,
+        from_indexer: bool = False,
+        loc: Optional[torch.Tensor] = None,
+    ) -> Optional[torch.Tensor]:
+        """Return the compressed KV buffer for a c4 / c128 layer.
+
+        Routes to c4 / c128 kv_pool by layer compression ratio. Returns
+        ``None`` for ratio == 0 (no compress KV exists). The
+        from_indexer=True branch returns the dedicated int8 K buffer that
+        ``torch.ops.custom.npu_quant_lightning_indexer`` consumes.
+        """
+        item = self.layer_mapping[layer_id]
+        if item.compress_ratio == 4:
+            if from_indexer:
+                kv = self.c4_indexer_kv_pool.get_index_k(item.compress_layer_id)
+            else:
+                kv = self.c4_kv_pool.kv_buffer[item.compress_layer_id]
+        elif item.compress_ratio == 128:
+            assert not from_indexer, "c128 has no indexer pool"
+            kv = self.c128_kv_pool.kv_buffer[item.compress_layer_id]
+        else:
+            return None
+        if loc is not None:
+            kv = kv.flatten(0, 1)[loc]
+        return kv
+
+    def set_swa_buffer(
+        self,
+        layer_id: int,
+        loc: torch.Tensor,
+        cache: torch.Tensor,
+    ) -> None:
+        """Write ``cache`` into the SWA pool at flat token positions ``loc``.
+
+        ``cache`` shape: (num_tokens, num_kv_heads=1, dim). The buffer view is
+        (num_pages, page_size, 1, dim) so we flatten the first two dims and
+        index_put.
+        """
+        # Index by raw layer_id (see get_swa_buffer) to avoid bucket collision.
+        buf = self.swa_kv_pool.kv_buffer[layer_id]
+        buf_flat = buf.flatten(0, 1)  # (num_pages * page_size, 1, dim)
+        # Caller (V4 MQALayer) may hand us cache shaped (T, dim); the buffer has
+        # an explicit num_kv_heads=1 axis, so insert it.
+        if cache.ndim == buf_flat.ndim - 1:
+            cache = cache.unsqueeze(1)
+        buf_flat[loc] = cache.to(buf_flat.dtype)
+
+    # ------------------------------------------------------------------
+    # NPU port hooks — used by dsv4/{compressor,indexer}.py forward_npu.
+    # CompressStatePool stores a fused [kv | score] tensor; split is a last-dim slice.
+    # ------------------------------------------------------------------
+
+    def set_state_buffer(
+        self,
+        layer_id: int,
+        loc: torch.Tensor,
+        kv: torch.Tensor,
+        score: torch.Tensor,
+        from_indexer: bool,
+    ) -> None:
+        # KVAndScore.kv_score is [..., 2*coff*head_dim] = [kv | score].
+        kv_score = self._get_state_pool(layer_id, from_indexer).kv_score_buffer.kv_score
+        last_dim = kv_score.shape[-1]
+        half = last_dim // 2
+        kv_view = kv.reshape(-1, half).to(kv_score.dtype)
+        score_view = score.reshape(-1, half).to(kv_score.dtype)
+        kv_score[loc, :half] = kv_view
+        kv_score[loc, half:] = score_view
+
+    def get_state_buffer(
+        self,
+        layer_id: int,
+        from_indexer: bool,
+        kv_indices: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        kv_score = self._get_state_pool(layer_id, from_indexer).kv_score_buffer.kv_score
+        if kv_indices is not None:
+            kv_score = kv_score[kv_indices]
+        last_dim = kv_score.shape[-1]
+        half = last_dim // 2
+        kv = kv_score[..., :half].unsqueeze(-2)  # add num_kv_heads=1 axis
+        score = kv_score[..., half:].unsqueeze(-2)
+        return kv, score
+
+    def set_compress_buffer(
+        self,
+        layer_id: int,
+        loc: torch.Tensor,
+        kv: torch.Tensor,
+        kv_scale: Optional[torch.Tensor],
+        from_indexer: bool,
+    ) -> None:
+        # Routes to c4_indexer (from_indexer) / c4_kv (ratio 4) / c128_kv (ratio
+        # 128). NPU bypasses CUDA fused_store_cache with direct bf16 writes.
+        ratio, compress_layer_id, _ = self.layer_mapping[layer_id]
+        device_type = kv.device.type
+        if from_indexer:
+            assert ratio == 4, f"indexer only on c4 layers, got ratio={ratio}"
+            if device_type == "npu":
+                assert (
+                    self.c4_indexer_kv_pool.has_npu_storage
+                ), "NPU index buffers not allocated — pool was init'd on CUDA?"
+                self.c4_indexer_kv_pool.set_index_k_scale(
+                    compress_layer_id, loc, kv, kv_scale
+                )
+                return
+            if kv_scale is None:
+                self.c4_indexer_kv_pool.set_index_fused(compress_layer_id, loc, kv)
+                return
+            self.c4_indexer_kv_pool.set_index_k_scale_buffer(
+                compress_layer_id, loc, kv, kv_scale
+            )
+            return
+        compress_pool = self.c4_kv_pool if ratio == 4 else self.c128_kv_pool
+        if device_type == "npu":
+            # PA_ND layout: kv_buffer[layer_id] shape = (num_pages, page_size,
+            # 1, kv_dim). Flatten (num_pages, page_size) and index by `loc`.
+            buf = compress_pool.kv_buffer[compress_layer_id]
+            buf_flat = buf.flatten(0, 1)
+            kv_view = kv.to(buf_flat.dtype)
+            if kv_view.ndim == buf_flat.ndim - 1:
+                kv_view = kv_view.unsqueeze(1)
+            buf_flat[loc] = kv_view
+            return
+        compress_pool.set_key_buffer_fused(compress_layer_id, loc, kv)
+
+    def get_compress_dequant_scale_buffer(
+        self,
+        layer_id: int,
+        from_indexer: bool,
+    ) -> torch.Tensor:
+        # Returns the float16 dequant scale buffer (NPU indexer pool's dedicated
+        # scale buffer alongside the int8 K buffer).
+        assert from_indexer, "only indexer compress pool has dequant scale"
+        compress_layer_id = self.layer_mapping[layer_id].compress_layer_id
+        return self.c4_indexer_kv_pool.get_index_scale(compress_layer_id)
+
+    def translate_kv_loc_to_compress_state_loc(
+        self,
+        kv_loc: torch.Tensor,
+        compress_ratio: int,
+    ) -> torch.Tensor:
+        # Parent's ring-buffer hash is meaningless under the paged cache_mode=1
+        # contract; returning a stale value would silently corrupt state. Fail loud.
+        raise RuntimeError(
+            "DSV4NPUTokenToKVPool.translate_kv_loc_to_compress_state_loc was "
+            "called, but the NPU fused compressor kernel uses a paged state "
+            "pool (cache_mode=1) and does not support ring-buffer state "
+            "addressing (cache_mode=2 is explicitly unsupported on Atlas A3). "
+            "Callers must consume out_cache_loc_dsv4.out_c{4,128}_state_loc "
+            "from the allocator bundle (set during alloc_extend/alloc_decode) "
+            "and read state_page_table from req_to_token_c{4,128}_state on "
+            "the DSV4NPUReqToTokenPool instead. See "
+            "hardware_backend/npu/dsv4_memory_pool.py for the rationale."
+        )

python/sglang/srt/hardware_backend/npu/dsv4/dsv4_req_to_token_pool.py

@@ -0,0 +1,128 @@
+"""DSV4-NPU per-request mapping pool.
+
+Subclass of ``ReqToTokenPool`` that adds five auxiliary per-request tables
+needed by the DSV4 attention backend:
+
+  * ``req_to_token_swa``        — slot ids in the SWA full-pool view
+  * ``req_to_token_c4``         — slot ids in the c4 compressed-KV pool
+  * ``req_to_token_c128``       — slot ids in the c128 compressed-KV pool
+  * ``req_to_token_c4_state``   — c4 state-pool slot ids, 1 per raw token
+  * ``req_to_token_c128_state`` — c128 state-pool slot ids, 1 per raw token
+
+Compressed KV pools store 1 slot per ``ratio`` raw tokens, so their per-req
+table column count is ``max_context_len // ratio``. swa mirrors the raw
+token count. Elements are token-level slot ids; the attention backend
+converts to page ids via ``// page_size`` when constructing PA_ND block
+tables.
+
+The c4/c128 STATE pools also have per-req tables here: the NPU fused
+compressor uses a paged state pool (``cache_mode=1``), so each raw token's
+state slot id is recorded (1 column per raw token) and the backend builds
+``state_block_table = req_to_token_c{N}_state[req, ::page_size] // page_size``
+to feed the kernel. (The base class' ``translate_kv_loc_to_compress_state_loc``
+ring-hash is the CUDA-only path; it is disabled on NPU.)
+
+Memory cost example (size=64, max_context_len=32K): swa 8MB + c4 2MB +
+c128 64KB ≈ 10MB extra on top of the base req_to_token (8MB).
+
+The tables are populated by the ``dsv4_common_hooks`` writers (driven from
+``mem_cache/common.py``) immediately after a successful alloc_extend /
+alloc_decode, using the per-pool slot indices returned in ``DSV4OutCacheLoc``.
+"""
+
+from __future__ import annotations
+
+import torch
+
+from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
+from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
+from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
+
+
+class DSV4NPUReqToTokenPool(ReqToTokenPool):
+    """ReqToTokenPool extended with DSV4 SWA + c4/c128 per-req tables.
+
+    Drop-in replacement for ReqToTokenPool when the model is DeepSeek-V4 on
+    NPU. Selected by ``model_runner_kv_cache_mixin`` based on model arch +
+    device. Non-DSV4 and non-NPU paths continue to use the base class.
+
+    The auxiliary tables are intentionally NOT zeroed on ``clear()``: they are
+    indexed only by active rows (via req_pool_idx) and only each row's
+    ``[:seq_len]`` prefix is read, so stale entries past kv_committed_len are
+    unreachable by the attention metadata builder.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        max_context_len: int,
+        device: str,
+        enable_memory_saver: bool,
+    ):
+        super().__init__(size, max_context_len, device, enable_memory_saver)
+
+        memory_saver_adapter = TorchMemorySaverAdapter.create(
+            enable=enable_memory_saver
+        )
+
+        # Back-ref to DSV4NPUTokenToKVPoolAllocator, wired via
+        # register_dsv4_allocator after both exist, so free(req) can release
+        # c4/c128 pages. None at construction so base clear() runs safely.
+        self._dsv4_allocator = None
+
+        # (name, columns). swa + state tables: 1 slot per raw token; c4/c128:
+        # 1 slot per `ratio` raw tokens. Init zero so unallocated columns map to
+        # block 0 (kernel skip sentinel cleared by NPUCompressStatePool).
+        with memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
+            for name, cols in (
+                ("req_to_token_swa", max_context_len),
+                ("req_to_token_c4", max(1, max_context_len // 4)),
+                ("req_to_token_c128", max(1, max_context_len // 128)),
+                ("req_to_token_c4_state", max_context_len),
+                ("req_to_token_c128_state", max_context_len),
+            ):
+                setattr(
+                    self,
+                    name,
+                    torch.zeros(
+                        (self._alloc_size, cols),
+                        dtype=torch.int32,
+                        device=device,
+                    ),
+                )
+
+    # ------------------------------------------------------------------
+    # Per-pool write helpers, called by mem_cache/common.py after alloc, using
+    # slot indices from DSV4OutCacheLoc. Args: (req_pool_idx, token_offset), slot.
+    # ------------------------------------------------------------------
+
+    def write_swa(self, indices, values: torch.Tensor) -> None:
+        self.req_to_token_swa[indices] = values
+
+    def write_c4(self, indices, values: torch.Tensor) -> None:
+        self.req_to_token_c4[indices] = values
+
+    def write_c128(self, indices, values: torch.Tensor) -> None:
+        self.req_to_token_c128[indices] = values
+
+    def write_c4_state(self, indices, values: torch.Tensor) -> None:
+        self.req_to_token_c4_state[indices] = values
+
+    def write_c128_state(self, indices, values: torch.Tensor) -> None:
+        self.req_to_token_c128_state[indices] = values
+
+    def register_dsv4_allocator(self, allocator) -> None:
+        """Wire the DSV4NPUTokenToKVPoolAllocator ref so ``free(req)`` can
+        release c4/c128 pool pages alongside the req_pool_idx slot. This is a
+        one-way ref (pool -> allocator). The reverse direction (the allocator
+        reading these per-req tables for its c-pool / state last_loc lookup) is
+        no longer a stored back-ref: mem_cache/common.py passes this pool into
+        ``alloc_extend`` / ``alloc_decode`` per call instead."""
+        self._dsv4_allocator = allocator
+
+    def free(self, req):
+        # Trigger c4/c128 free via the allocator's unified free path. May be None
+        # between __init__ and register_dsv4_allocator — defensive None check.
+        if self._dsv4_allocator is not None:
+            self._dsv4_allocator.free(req=req, req_to_token_pool=self)
+        super().free(req)

python/sglang/srt/hardware_backend/npu/graph_runner/npu_graph_runner.py

@@ -35,7 +35,11 @@ from typing import TYPE_CHECKING, Dict, Optional, Union
 import numpy as np
 import torch
 
-from sglang.srt.configs.model_config import AttentionArch, is_deepseek_dsa
+from sglang.srt.configs.model_config import (
+    AttentionArch,
+    is_deepseek_dsa,
+    is_deepseek_v4,
+)
 from sglang.srt.distributed.parallel_state import GroupCoordinator
 from sglang.srt.environ import envs
 from sglang.srt.model_executor.runner import DecodeCudaGraphRunner
@@ -231,7 +235,10 @@ class NPUGraphRunner(DecodeCudaGraphRunner):
 
         graph_key = self._make_graph_key(self.bs)
 
-        if not is_deepseek_dsa(self.model_runner.model_config.hf_config):
+        if not (
+            is_deepseek_dsa(self.model_runner.model_config.hf_config)
+            or is_deepseek_v4(self.model_runner.model_config.hf_config)
+        ):
             if forward_batch.forward_mode.is_target_verify():
                 seq_lens_cpu = forward_batch.seq_lens.cpu() + self.num_tokens_per_bs
                 seq_lens = seq_lens_cpu.tolist() + [0] * (self.bs - self.raw_bs)

python/sglang/srt/hardware_backend/npu/moe/topk.py

@@ -41,6 +41,26 @@ def fused_topk_npu(
             )
         topk_weights = topk_weights.to(torch.float32)
 
+    # sqrtsoftplus (DSV4 noaux_tc): the NPU op only scores sigmoid/softmax, so use
+    # a torch path. top-k over (scores + bias); weights from un-biased scores.
+    elif topk_config.scoring_func == "sqrtsoftplus":
+        scores = torch.nn.functional.softplus(router_logits.float()).sqrt()
+        scores_for_choice = (
+            scores + correction_bias.unsqueeze(0).float()
+            if correction_bias is not None
+            else scores
+        )
+        _, topk_ids = torch.topk(
+            scores_for_choice, k=topk_config.top_k, dim=-1, sorted=False
+        )
+        topk_ids = topk_ids.to(torch.int32)
+        topk_weights = scores.gather(1, topk_ids)
+        if renormalize:
+            topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+        else:
+            topk_weights = topk_weights * topk_config.routed_scaling_factor
+        topk_weights = topk_weights.to(torch.float32)
+
     # Support grouped top-k or correction bias or sigmoid or routed_scaling_factor
     elif (
         correction_bias is not None
@@ -66,6 +86,7 @@ def fused_topk_npu(
             ),
             eps=float(1e-20),
         )
+        topk_weights = topk_weights.to(torch.float32)
 
     # torch native is not yet supported num_token_non_padded
     # Fallback to torch native implementation

python/sglang/srt/hardware_backend/npu/utils.py

@@ -99,7 +99,12 @@ def init_npu_backend():
 
     assert _is_npu, "NPU backend initialization called on non-NPU device."
 
+    try:
+        import custom_ops  # noqa: F401
         import sgl_kernel_npu  # noqa: F401
+    except ImportError as e:
+        logger.warning("NPU custom kernel packages unavailable: %s", e)
+
     import torch_npu
     from torch_npu.contrib import transfer_to_npu  # noqa: F401
 

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

@@ -6,9 +6,11 @@ from sglang.srt.configs.linear_attn_model_registry import (
     get_linear_attn_config,
     import_backend_class,
 )
-from sglang.srt.utils import get_device_capability, is_musa
+from sglang.srt.utils import get_device_capability, is_hip, is_musa, is_npu
 
 _is_musa = is_musa()
+_is_npu = is_npu()
+_is_hip = is_hip()
 
 logger = logging.getLogger(__name__)
 
@@ -126,9 +128,13 @@ def _create_nsa_compat(runner):
 
 @register_attention_backend("dsv4")
 def create_dsv4_backend(runner):
-    from sglang.srt.utils import is_hip
+    if _is_npu:
+        from sglang.srt.hardware_backend.npu.attention.ascend_dsv4_backend import (
+            DeepseekV4AscendAttnBackend,
+        )
 
-    if is_hip():
+        return DeepseekV4AscendAttnBackend(runner)
+    elif _is_hip:
         from sglang.srt.layers.attention.deepseek_v4_backend_hip_radix import (
             DeepseekV4HipRadixBackend,
         )

python/sglang/srt/layers/attention/dsv4/compressor.py

@@ -19,17 +19,21 @@ from sglang.srt.layers.attention.dsa.utils import dsa_use_prefill_cp
 from sglang.srt.layers.attention.dsv4.quant_k_cache import (
     quant_to_nope_fp8_rope_bf16_pack_triton,
 )
+from sglang.srt.layers.dp_attention import get_attention_cp_size
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import ReplicatedLinear
 from sglang.srt.layers.utils.cp_utils import cp_all_gather_rerange_output
+from sglang.srt.layers.utils.multi_platform import MultiPlatformOp
 from sglang.srt.mem_cache.deepseek_v4_compress_state import (
     CompressStatePool,
 )
 from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
+from sglang.srt.model_executor.forward_context import get_attn_backend
 from sglang.srt.models.deepseek_v2 import _is_hip
 from sglang.srt.runtime_context import get_parallel
-from sglang.srt.utils import add_prefix, get_bool_env_var, set_weight_attrs
+from sglang.srt.utils import add_prefix, get_bool_env_var, is_npu, set_weight_attrs
 
+_is_npu = is_npu()
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 _tgemm = None
 if _use_aiter:
@@ -341,7 +345,7 @@ def create_paged_compressor_data(
     return FusedCompressMetadata(write_loc=write_loc, extra_data=extra_data, plan=plan)
 
 
-class Compressor(nn.Module):
+class Compressor(MultiPlatformOp):
     def __init__(
         self,
         config: DeepSeekV4Config,
@@ -390,6 +394,9 @@ class Compressor(nn.Module):
     def _apply_ape_hotfix(self):
         self.ape_converted = True
 
+        if _is_npu:
+            return
+
         if self.overlap:
             ape = torch.chunk(self.ape.data, 2, dim=-1)
             ape = torch.cat([ape[0], ape[1]], dim=0)
@@ -433,11 +440,11 @@ class Compressor(nn.Module):
             )
         return kv_score
 
-    def forward(
+    def forward_native(
         self,
         x: torch.Tensor,
         forward_batch: ForwardBatch,
-        attn_backend: AttentionBackend,
+        attn_backend: Optional[AttentionBackend] = None,
     ) -> torch.Tensor:
         if forward_batch.forward_mode.is_idle():
             assert x.shape[0] == 0
@@ -461,6 +468,26 @@ class Compressor(nn.Module):
             is_paged=True,
         )
 
+    def forward_npu(
+        self,
+        x: torch.Tensor,
+        forward_batch: ForwardBatch,
+        attn_backend: Optional[AttentionBackend] = None,
+    ) -> torch.Tensor:
+        if forward_batch.forward_mode.is_idle():
+            assert x.shape[0] == 0
+            return x.new_empty(0, self.head_dim)
+
+        if dsa_use_prefill_cp(forward_batch):
+            x = cp_all_gather_rerange_output(
+                x,
+                get_attention_cp_size(),
+                forward_batch,
+                torch.cuda.current_stream(),
+            )
+
+        return get_attn_backend().forward_compress(self, x, forward_batch)
+
 
 if _is_hip and not envs.SGLANG_OPT_USE_COMPRESSOR_V2.get():
     from sglang.srt.layers.attention.dsv4.compress_hip import (  # noqa: F811

python/sglang/srt/layers/deepseek_v4_rope.py

@@ -1,3 +1,4 @@
+import logging
 import math
 from functools import lru_cache
 from typing import Optional
@@ -6,16 +7,29 @@ import torch
 import triton
 import triton.language as tl
 
+logger = logging.getLogger(__name__)
+
+# tilelang isn't shipped on every platform (e.g. Ascend NPU images) and the
+# only tilelang artifacts in this file are pass_configs that downstream
+# tilelang.jit decorators would consume — the kernels actually defined here
+# are Triton. Keep the import optional so this module loads on NPU.
 try:
     import tilelang
 
     tilelang.set_log_level("WARNING")
+
     pass_configs = {
         tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
         tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
     }
 except ImportError:
-    pass
+    logger.info(
+        "tilelang not installed; deepseek_v4_rope pass_configs unset. "
+        "Triton kernels in this module still run; only downstream tilelang.jit "
+        "consumers of pass_configs will need to handle the None."
+    )
+    tilelang = None
+    pass_configs = None
 
 FP8 = "float8_e4m3"
 BF16 = "bfloat16"
@@ -23,9 +37,21 @@ FP32 = "float32"
 INT32 = "int32"
 
 
+def _yarn_get_mscale(scale: float = 1.0, mscale: float = 1.0) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
 @lru_cache(2)
 def precompute_freqs_cis(
-    dim, seqlen, original_seq_len, base, factor, beta_fast, beta_slow
+    dim,
+    seqlen,
+    original_seq_len,
+    base,
+    factor,
+    beta_fast,
+    beta_slow,
 ) -> torch.Tensor:
 
     def find_correction_dim(num_rotations, dim, base, max_seq_len):
@@ -434,3 +460,123 @@ def fused_norm_rope_inplace_triton(
         HAS_WEIGHT=(weight is not None),
         USE_POS=(positions is not None),
     )
+
+
+# Cache contiguous real/imag halves of each freqs_cis (its .real/.imag are
+# strided views, stride=2 on the interleaved layout), keyed by id.
+_NPU_ROPE_CONTIG_CACHE: dict[int, tuple] = {}
+
+
+def _get_contig_freqs_real_imag(
+    freqs_cis: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Return contiguous (real, imag) halves of ``freqs_cis``, cached by id.
+
+    Used by NPU rope paths to avoid the per-call StridedSlice materialization
+    triggered by aclnnIndex over the strided ``.real`` / ``.imag`` views of
+    the complex ``freqs_cis`` buffer. First call per freqs_cis pays the
+    contiguous() once; later calls reuse the cached tensors.
+
+    All callers within a single MQALayer (outer rope, indexer inner rope,
+    compressor epilog rope) get the same freqs_cis instance, so each layer
+    materializes at most one (real, imag) pair.
+    """
+    cache_key = id(freqs_cis)
+    cached = _NPU_ROPE_CONTIG_CACHE.get(cache_key)
+    if cached is None:
+        cached = (freqs_cis.real.contiguous(), freqs_cis.imag.contiguous())
+        _NPU_ROPE_CONTIG_CACHE[cache_key] = cached
+    return cached
+
+
+def get_fused_compressor_rope_cos_sin(
+    freqs_cis: torch.Tensor,
+    positions_cmp: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Build (cos, sin) tensors shaped ``[T, rope_head_dim]`` for the fused
+    compressor op (``torch.ops.custom.compressor``).
+
+    The op consumes ``rope_cos`` / ``rope_sin`` of shape
+    ``[min(T, T//cmp_ratio + B), rope_head_dim]`` in bf16/fp16. We index
+    the cached contig real/imag halves of the complex ``freqs_cis`` and
+    interleave-double the last dim to match the kernel's expected layout
+    (matches dsv4_release ``ComplexExpRotaryEmbedding.cos_cache``, which
+    is built as ``complex_cache.real.repeat_interleave(2, dim=-1)``).
+
+    Safe to call from inside a captured aclgraph: both ``index_select`` and
+    ``repeat_interleave`` over a graph-input ``positions_cmp`` of fixed
+    capture-time shape produce static-shape outputs. Identical to what the
+    existing inplace_partial_rotary_mul fallback does at
+    :func:`v4_rope_inplace_npu`, just without the inverse / 4D-view step.
+    """
+    real_contig, imag_contig = _get_contig_freqs_real_imag(freqs_cis)
+    cos_half = real_contig.index_select(0, positions_cmp)
+    sin_half = imag_contig.index_select(0, positions_cmp)
+    cos = cos_half.repeat_interleave(2, dim=-1).to(dtype)
+    sin = sin_half.repeat_interleave(2, dim=-1).to(dtype)
+    return cos, sin
+
+
+def v4_rope_inplace_npu(
+    q_rope: torch.Tensor,
+    kv_rope: Optional[torch.Tensor],
+    freqs_cis: torch.Tensor,
+    positions: torch.Tensor,
+    inverse: bool = False,
+) -> None:
+    """In-place interleaved RoPE for V4 — torch fallback used on NPU.
+
+    Mirrors main's CUDA `fused_rope` kernel: consecutive (even, odd) pairs
+    of x form complex pairs, with `freqs_cis` a complex tensor where
+    `freqs_cis.real[t, k]` = cos(theta_{t,k}), `freqs_cis.imag` = sin(...)
+    indexed by frequency pair k in [0, rope_dim/2).
+
+    NOTE on V4-Flash YARN `mscale`: when the model was trained with the
+    YARN magnitude-scale `mscale` ≠ 1.0, the cos/sin values stored in
+    `freqs_cis` MUST already be pre-multiplied by `mscale` at precompute
+    time — see `precompute_freqs_cis`. This function
+    just reads what's stored; it does NOT apply mscale here.
+
+    Prefer the NPU-native `torch.ops.custom.inplace_partial_rotary_mul`:
+    the torch fallback differs by ~1 ULP per element vs the kernel because
+    torch does bf16*bf16 muls with bf16 accumulation while the NPU kernel
+    accumulates in fp32; 43 layers × (Q + K) = 86 rope calls compound that
+    drift enough to flip argmax on marginal prompts.
+    """
+    # Build cos/sin caches in the kernel's expected (T, 1, 1, rope_dim) layout,
+    # each freq value repeated twice for the interleaved pairing convention.
+    freqs_real_contig, freqs_imag_contig = _get_contig_freqs_real_imag(freqs_cis)
+    cos_half = freqs_real_contig[positions]  # (T, rope_dim/2)
+    sin_half = freqs_imag_contig[positions]
+    if inverse:
+        sin_half = -sin_half
+    cos_full = cos_half.repeat_interleave(2, dim=-1).to(q_rope.dtype)
+    sin_full = sin_half.repeat_interleave(2, dim=-1).to(q_rope.dtype)
+    rope_dim = cos_full.shape[-1]
+    # repeat_interleave produces a contiguous tensor, so the .view()
+    # below already returns a contiguous result — no .contiguous() needed.
+    cos4 = cos_full.view(-1, 1, 1, rope_dim)
+    sin4 = sin_full.view(-1, 1, 1, rope_dim)
+    # q_rope: (T, n_heads, rope_dim) → (T, 1, n_heads, rope_dim) view
+    # kv_rope: (T, 1, rope_dim) → (T, 1, 1, rope_dim) view
+    q_view = q_rope.unsqueeze(1)
+    torch.ops.custom.inplace_partial_rotary_mul(
+        q_view,
+        cos4,
+        sin4,
+        rotary_mode="interleave",
+        partial_slice=[0, rope_dim],
+    )
+    if kv_rope is not None:
+        if kv_rope.dim() == 3:
+            kv_view = kv_rope.unsqueeze(1)
+        else:
+            kv_view = kv_rope.view(-1, 1, 1, rope_dim)
+        torch.ops.custom.inplace_partial_rotary_mul(
+            kv_view,
+            cos4,
+            sin4,
+            rotary_mode="interleave",
+            partial_slice=[0, rope_dim],
+        )

python/sglang/srt/layers/mhc.py

@@ -3,8 +3,6 @@ import logging
 import math
 from typing import Tuple
 
-import tilelang
-import tilelang.language as T
 import torch
 
 from sglang.jit_kernel.utils import is_arch_support_pdl
@@ -14,6 +12,17 @@ from sglang.srt.layers.utils.common import strict_contiguous
 
 logger = logging.getLogger(__name__)
 
+# Tilelang isn't packaged on every platform (notably Ascend NPU images) but
+# this module is imported transitively from deepseek_v4.py — module-load
+# must succeed even when tilelang is missing. The kernels themselves still
+# require tilelang at runtime; we replace the package with a stub that lets
+# `@tilelang.jit` decorations and `tilelang.PassConfigKey.*` references parse
+# without ImportError, and any actual call into the kernels raises a clear
+# message at execution time instead of crashing on import.
+try:
+    import tilelang
+    import tilelang.language as T
+
     tilelang.set_log_level("WARNING")
 
     # Set once mhc_pre() has compiled every n_splits bucket at startup.
@@ -23,6 +32,35 @@ pass_configs = {
         tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
         tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
     }
+except ImportError:
+
+    class _TilelangMissing:
+        """Stub so module-level @tilelang.jit and PassConfigKey accesses parse."""
+
+        def __getattr__(self, name):
+            if name == "jit":
+
+                def _jit(*_args, **_kwargs):
+                    def _wrap(fn):
+                        def _raise(*a, **k):
+                            raise RuntimeError(
+                                "tilelang is not installed; this kernel cannot run "
+                                "on the current platform"
+                            )
+
+                        return _raise
+
+                    return _wrap
+
+                return _jit
+            return _TilelangMissing()
+
+        def __call__(self, *_args, **_kwargs):
+            return _TilelangMissing()
+
+    tilelang = _TilelangMissing()
+    T = _TilelangMissing()
+    pass_configs = None
 
 FP8 = "float8_e4m3"
 BF16 = "bfloat16"
@@ -1515,3 +1553,60 @@ def mhc_fused_post_pre(
         comb_mix_cur.view(*outer_shape, hc_mult, hc_mult),
         layer_input_cur.view(*outer_shape, hidden_size),
     )
+
+
+def npu_hc_pre(
+    x: torch.Tensor,
+    hc_fn: torch.Tensor,
+    hc_scale: torch.Tensor,
+    hc_base: torch.Tensor,
+    hc_mult: int,
+    hc_sinkhorn_iters: int,
+    rms_norm_eps: float,
+    hc_eps: float,
+    forward_batch=None,
+) -> tuple:
+    """NPU-accelerated hc_pre via the custom_ops kernel.
+
+    Returns (y, post, comb, norm_fused).  norm_fused is always False
+    because npu_hc_pre does not fold input_layernorm — the caller must
+    apply it separately.
+    """
+    shape, dtype = x.size(), x.dtype
+
+    # IDLE / empty short-circuit, mirroring the dsv4-flash source.
+    # The kernel emits post/comb in fp32 (sinkhorn iterates in fp32),
+    # so the dummies must too — otherwise downstream comb/post-aware
+    # ops see a silent fp32 ↔ bf16 split between idle and non-idle
+    # batches.
+    is_idle = forward_batch is not None and forward_batch.forward_mode.is_idle()
+    if is_idle or x.shape[0] == 0:
+        bs = x.shape[0]
+        y = torch.empty((bs, shape[-1]), dtype=dtype, device=x.device)
+        post = torch.empty((bs, hc_mult), dtype=torch.float32, device=x.device)
+        comb = torch.empty(
+            (bs, hc_mult, hc_mult),
+            dtype=torch.float32,
+            device=x.device,
+        )
+        return y, post, comb, False
+
+    # Note the return order: (y, post, comb) — y is the (T, hidden)
+    # mixed activation, post / comb are the hc_post inputs. The
+    # fused kernel emits y in fp32 (sinkhorn iterates in fp32), so
+    # cast back to the input dtype before the downstream
+    # aclnnRmsNorm (which has no x=fp32 / gamma=bf16 overload).
+    y, post, comb = torch.ops.custom.npu_hc_pre(
+        x,
+        hc_fn,
+        hc_scale,
+        hc_base,
+        hc_mult=hc_mult,
+        hc_sinkhorn_iters=hc_sinkhorn_iters,
+        norm_eps=rms_norm_eps,
+        hc_eps=hc_eps,
+    )
+    # npu_hc_pre uses norm_eps for sinkhorn's internal RMS only; it does
+    # not fold input_layernorm. Return norm_fused=False so the caller
+    # applies the layernorm itself, matching the deepgemm/torch paths.
+    return y.to(dtype), post, comb, False

python/sglang/srt/layers/moe/hash_topk.py

@@ -19,10 +19,13 @@ from sglang.srt.layers.moe.topk import (
     _mask_topk_ids_padded_region,
     _zero_topk_weights_padded_region,
 )
-from sglang.srt.utils import is_hip
+from sglang.srt.utils import is_hip, is_npu
 
 logger = logging.getLogger(__name__)
 
+_is_hip = is_hip()
+_is_npu = is_npu()
+
 
 class HashTopK(nn.Module):
     def __init__(
@@ -182,8 +185,7 @@ class HashTopK(nn.Module):
             )
         else:
             topk_weights, topk_ids = self._forward_torch(router_logits, input_ids)
-
+        if _is_hip or _is_npu:
-        if is_hip():
             topk_weights = topk_weights.to(torch.float32)
 
         log2phy_prob = None

python/sglang/srt/layers/moe/topk.py

@@ -1165,6 +1165,13 @@ def _mask_topk_ids_padded_region(
     # TODO: let the kernel support other dtypes
     if _is_cuda and topk_ids.dtype == torch.int32 and fill_value == -1:
         mask_topk_ids(topk_ids, num_token_non_padded)
+    elif _is_npu:
+        # On NPU, bool-indexed scatter `topk_ids[bool_mask, :] = -1` lowers
+        # to aclnnNonzeroV2 and can trigger an aicore timeout under long
+        # workloads; `torch.where` avoids that nonzero scan.
+        indices = torch.arange(0, topk_ids.shape[0], device=topk_ids.device)
+        mask = (indices >= num_token_non_padded).unsqueeze(-1)
+        topk_ids = torch.where(mask, torch.full_like(topk_ids, -1), topk_ids)
     else:
         indices = torch.arange(0, topk_ids.shape[0], device=topk_ids.device)
         topk_ids[indices >= num_token_non_padded, :] = fill_value

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8_moe.py

@@ -132,3 +132,23 @@ class NPUCompressedTensorsW8A8Int8DynamicMoE(CompressedTensorsMoEScheme):
     ) -> CombineInput:
 
         return self.kernel.apply(layer, dispatch_output)
+
+    def apply_without_routing_weights(
+        self,
+        layer,
+        hidden_states,
+        hidden_states_scale,
+        group_list_type,
+        group_list,
+        output_dtype,
+    ):
+        # NPU MoE bypasses MoeRunner: expose the kernel's existing
+        # apply_without_routing_weights directly through the scheme.
+        return self.kernel.apply_without_routing_weights(
+            layer,
+            hidden_states,
+            hidden_states_scale,
+            group_list_type,
+            group_list,
+            output_dtype,
+        )

python/sglang/srt/layers/quantization/modelslim/modelslim.py

@@ -88,6 +88,17 @@ class ModelSlimConfig(QuantizationConfig):
 
     def __init__(self, quant_config: Dict[str, Any] = {}):
         super().__init__()
+        keys = [k for k in quant_config if isinstance(k, str)]
+        is_dsv4 = any(k.startswith("hc_head_") for k in keys)
+        if is_dsv4:
+            from sglang.srt.models.deepseek_v4 import DeepseekV4ForCausalLM
+
+            remap = DeepseekV4ForCausalLM.remap_weight_name_to_dpsk_hf_format
+            quant_config = {
+                (remap(k) if isinstance(k, str) else k): v
+                for k, v in quant_config.items()
+            }
+
         self.quant_description = quant_config
         ignore = cast(List[str], quant_config.get("ignore", []))
         self.ignore = ignore if ignore is not None else []

python/sglang/srt/managers/schedule_batch.py

@@ -68,6 +68,9 @@ from sglang.srt.disaggregation.utils import FAKE_BOOTSTRAP_HOST, DisaggregationM
 from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_rank
 from sglang.srt.dllm.mixin.req import ReqDllmMixin
 from sglang.srt.environ import envs
+from sglang.srt.hardware_backend.npu.dsv4.dsv4_common_hooks import (
+    maybe_evict_dsv4_state,
+)
 from sglang.srt.managers.embed_types import PositionalEmbeds
 from sglang.srt.managers.scheduler_components.new_token_ratio_tracker import (
     NewTokenRatioTracker,
@@ -1743,6 +1746,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
 
     # The output locations of the KV cache
     out_cache_loc: torch.Tensor = None  # shape: [b], int64
+    # DSV4-NPU: per-pool slot bundle from DSV4NPUTokenToKVPoolAllocator (None
+    # elsewhere); c4/c128 state lens ride on ``batch.dsv4_state_lens``.
+    out_cache_loc_dsv4: Optional[Any] = None
 
     # For hybrid GDN prefix cache
     mamba_track_indices: torch.Tensor = None  # shape: [b], int64
@@ -2624,7 +2630,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         if self.model_config.is_encoder_decoder:
             self.prepare_encoder_info_decode()
 
-        # Allocate memory
+        # Allocate memory (DSV4-NPU c{4,128}_state alloc lens are computed inside
+        # the allocator, triggered from mem_cache/common.py.)
         self.out_cache_loc = alloc_for_decode(self, token_per_req=1)
 
         # Update req-level memory management fields
@@ -2887,6 +2894,10 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                     if req.decode_batch_idx % eviction_interval == 1:
                         self._evict_swa(req, req.seqlen - 1)
 
+                    # DSV4-NPU only (no-op elsewhere): the small paged compress-state
+                    # pool must drain every decode step, independent of SWA cadence.
+                    maybe_evict_dsv4_state(self, req, req.seqlen - 1)
+
                     # Once the decode position has moved past the sliding window,
                     # the SWA portion of the prefill-time tree lock is no longer
                     # needed by this request. Convert it from protected to

python/sglang/srt/mem_cache/common.py

@@ -6,6 +6,11 @@ from typing import TYPE_CHECKING, Optional
 import numpy as np
 import torch
 
+from sglang.srt.hardware_backend.npu.dsv4.dsv4_common_hooks import (
+    maybe_evict_dsv4_state_on_swa,
+    maybe_write_dsv4_decode,
+    maybe_write_dsv4_extend,
+)
 from sglang.srt.mem_cache.allocator.swa import SWATokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache, EvictParams
 from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
@@ -21,14 +26,17 @@ from sglang.srt.mem_cache.triton_ops.common import (
     write_req_to_token_pool_triton,
 )
 from sglang.srt.server_args import ServerArgs, get_global_server_args
-from sglang.srt.utils import is_hip, support_triton
+from sglang.srt.utils import is_hip, is_npu, support_triton
 from sglang.srt.utils.common import ceil_align, is_pin_memory_available
 
+_is_npu = is_npu()
+
 _is_hip = is_hip()
 
 if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
     from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
+    from sglang.srt.model_executor.forward_batch_info import DSV4StateLens
 
 # Needs 2 + 1 slots for mamba request with prefix cache. 2 for ping pong cache, 1 for running mamba state.
 MAMBA_STATE_PER_REQ_PREFIX_CACHE = 3
@@ -95,6 +103,9 @@ def free_swa_out_of_window_slots(
             req.req_pool_idx, req.swa_evicted_seqlen : new_swa_evicted_seqlen
         ]
         token_to_kv_pool_allocator.free_swa(free_slots)
+        maybe_evict_dsv4_state_on_swa(
+            token_to_kv_pool_allocator, req_to_token_pool, req, new_swa_evicted_seqlen
+        )
         req.swa_evicted_seqlen = new_swa_evicted_seqlen
 
 
@@ -309,6 +320,25 @@ def evict_from_tree_cache(tree_cache: BasePrefixCache | None, num_tokens: int):
             tree_cache.evict(EvictParams(num_tokens=num_tokens))
 
 
+def _compute_dsv4_state_lens(batch, *, is_decode: bool):
+    """Per-req c{4,128}_state pool alloc lens (a ``DSV4StateLens``) for this
+    alloc step. The DSV4-NPU allocator owns the computation (it also mutates the
+    per-req cumulative state on each ``Req``); we just trigger it here, right
+    before the paged alloc that consumes the result.
+
+    None on CUDA / non-V4 paths (allocator has no ``compute_dsv4_state_lens_*``)
+    so the ``alloc_paged_token_slots_*`` forwarding stays a no-op.
+    """
+    allocator = batch.token_to_kv_pool_allocator
+    if not hasattr(allocator, "compute_dsv4_state_lens_extend"):
+        return None
+    if is_decode:
+        return allocator.compute_dsv4_state_lens_decode(batch.reqs)
+    return allocator.compute_dsv4_state_lens_extend(
+        batch.reqs, batch.seq_lens_cpu.tolist()
+    )
+
+
 def alloc_paged_token_slots_extend(
     tree_cache: BasePrefixCache,
     prefix_lens: torch.Tensor,
@@ -318,6 +348,9 @@ def alloc_paged_token_slots_extend(
     last_loc: torch.Tensor,
     extend_num_tokens: int,
     backup_state: bool = False,
+    req_pool_indices: Optional[torch.Tensor] = None,
+    dsv4_state_lens: Optional[DSV4StateLens] = None,
+    batch=None,
 ):
     # Over estimate the number of tokens: assume each request needs a new page.
     allocator = tree_cache.token_to_kv_pool_allocator
@@ -328,15 +361,35 @@ def alloc_paged_token_slots_extend(
     if backup_state:
         state = allocator.backup_state()
 
-    out_cache_loc = allocator.alloc_extend(
+    is_dsv4 = req_pool_indices is not None and hasattr(allocator, "c4_attn_allocator")
+    extra_alloc_kwargs = {}
+    if is_dsv4:
+        extra_alloc_kwargs["req_pool_indices"] = req_pool_indices
+        # Pass the per-req tables in per call for the c-pool / state last_loc
+        # lookup; the allocator holds no reference to the pool.
+        if batch is not None:
+            extra_alloc_kwargs["req_to_token_pool"] = batch.req_to_token_pool
+        if dsv4_state_lens is not None:
+            extra_alloc_kwargs["dsv4_state_lens"] = dsv4_state_lens
+
+    out = allocator.alloc_extend(
         prefix_lens,
         prefix_lens_cpu,
         seq_lens,
         seq_lens_cpu,
         last_loc,
         extend_num_tokens,
+        **extra_alloc_kwargs,
     )
 
+    if is_dsv4:
+        bundle = out
+        out_cache_loc = None if bundle is None else bundle.out_full_loc
+        if batch is not None:
+            batch.out_cache_loc_dsv4 = bundle
+    else:
+        out_cache_loc = out
+
     if out_cache_loc is None:
         error_msg = (
             f"Prefill out of memory. Try to lower your batch size.\n"
@@ -431,6 +484,9 @@ def alloc_for_extend(
             seq_lens_cpu=batch.seq_lens_cpu,
             last_loc=torch.cat(last_loc),
             extend_num_tokens=batch.extend_num_tokens,
+            req_pool_indices=req_pool_indices_device,
+            dsv4_state_lens=_compute_dsv4_state_lens(batch, is_decode=False),
+            batch=batch,
         )
 
     # Write to req_to_token_pool
@@ -448,6 +504,16 @@ def alloc_for_extend(
         batch.req_to_token_pool,
     )
 
+    # DSV4-NPU hook: write c4/c128/swa per-req tables from the stashed bundle.
+    # No-op on non-DSV4 paths (out_cache_loc_dsv4 stays None there).
+    if _is_npu:
+        maybe_write_dsv4_extend(
+            batch,
+            req_pool_indices_cpu,
+            prefix_lens_cpu,
+            batch.seq_lens_cpu,
+        )
+
     return out_cache_loc, req_pool_indices_device, req_pool_indices_cpu
 
 
@@ -457,6 +523,9 @@ def alloc_paged_token_slots_decode(
     seq_lens_cpu: torch.Tensor,
     last_loc: torch.Tensor,
     token_per_req: int = 1,
+    req_pool_indices: Optional[torch.Tensor] = None,
+    dsv4_state_lens: Optional[DSV4StateLens] = None,
+    batch=None,
 ) -> torch.Tensor:
     """Allocate paged KV cache for decode batch."""
     allocator = tree_cache.token_to_kv_pool_allocator
@@ -464,7 +533,28 @@ def alloc_paged_token_slots_decode(
     num_tokens = len(seq_lens) * allocator.page_size
     evict_from_tree_cache(tree_cache, num_tokens)
 
-    out_cache_loc = allocator.alloc_decode(seq_lens, seq_lens_cpu, last_loc)
+    # DSV4-NPU allocator also needs req_pool_indices + per-req state lens and
+    # returns a DSV4OutCacheLoc bundle; hasattr-gated so others stay unchanged.
+    is_dsv4 = req_pool_indices is not None and hasattr(allocator, "c4_attn_allocator")
+    extra_alloc_kwargs = {}
+    if is_dsv4:
+        extra_alloc_kwargs["req_pool_indices"] = req_pool_indices
+        # Per-call per-req tables for the last_loc lookup; the allocator holds
+        # no reference to the pool.
+        if batch is not None:
+            extra_alloc_kwargs["req_to_token_pool"] = batch.req_to_token_pool
+        if dsv4_state_lens is not None:
+            extra_alloc_kwargs["dsv4_state_lens"] = dsv4_state_lens
+
+    out = allocator.alloc_decode(seq_lens, seq_lens_cpu, last_loc, **extra_alloc_kwargs)
+
+    if is_dsv4:
+        bundle = out
+        out_cache_loc = None if bundle is None else bundle.out_full_loc
+        if batch is not None:
+            batch.out_cache_loc_dsv4 = bundle
+    else:
+        out_cache_loc = out
 
     if out_cache_loc is None:
         error_msg = (
@@ -508,6 +598,9 @@ def alloc_for_decode(batch: ScheduleBatch, token_per_req: int) -> torch.Tensor:
             seq_lens_cpu=batch.seq_lens_cpu + token_per_req,
             last_loc=last_loc,
             token_per_req=token_per_req,
+            req_pool_indices=batch.req_pool_indices,
+            dsv4_state_lens=_compute_dsv4_state_lens(batch, is_decode=True),
+            batch=batch,
         )
 
     # Write to req_to_token_pool
@@ -520,6 +613,15 @@ def alloc_for_decode(batch: ScheduleBatch, token_per_req: int) -> torch.Tensor:
         (batch.req_pool_indices, locs), out_cache_loc.to(torch.int32)
     )
 
+    # DSV4-NPU hook: post-decode write of c4/c128/swa per-req tables from the
+    # stashed bundle. No-op on non-DSV4 paths (out_cache_loc_dsv4 stays None).
+    if _is_npu:
+        maybe_write_dsv4_decode(
+            batch,
+            batch.seq_lens_cpu + token_per_req,
+            token_per_req,
+        )
+
     return out_cache_loc
 
 
@@ -576,6 +678,8 @@ def release_kv_cache(req: Req, tree_cache: BasePrefixCache, is_insert: bool = Tr
             req.mamba_pool_idx is not None
         ), "mamba state is freed while the tree cache does not manage mamba states"
         tree_cache.req_to_token_pool.free_mamba_cache(req)
+    # The DSV4-NPU ReqToTokenPool subclass's free() additionally releases the
+    # c4/c128 state pages; other ReqToTokenPool subclasses are a no-op here.
     tree_cache.req_to_token_pool.free(req)
 
 

python/sglang/srt/mem_cache/deepseek_v4_compress_state.py

@@ -2,15 +2,21 @@ from __future__ import annotations
 
 import dataclasses
 from contextlib import nullcontext
+from math import gcd
 
 import torch
 
 from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
 from sglang.srt.mem_cache.utils import maybe_init_custom_mem_pool
-from sglang.srt.utils import is_hip
+from sglang.srt.utils import is_hip, is_npu
 from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
 
 _is_hip = is_hip()
+_is_npu = is_npu()
+
+
+def _lcm(a: int, b: int) -> int:
+    return a // gcd(a, b) * b
 
 
 @dataclasses.dataclass
@@ -109,16 +115,39 @@ class CompressStatePool:
             last_dim = 3 * head_dim
         else:
             self._size = size + self.ring_size + 1
-            self._size = (self._size + ratio - 1) // ratio * ratio
+            # Pad to lcm(ratio, page_size) so the flat buffer reshapes cleanly into
+            # [block_num, page_size, last_dim] for the fused compressor op; page_size=1 falls back to ratio-only padding.
+            pad_to = (
+                _lcm(ratio, swa_page_size) if (swa_page_size > 1 and _is_npu) else ratio
+            )
+            self._size = (self._size + pad_to - 1) // pad_to * pad_to
             self._logical_size = self._size
             last_dim = 2 * (1 + overlap) * head_dim
 
-        if _is_hip:
+        self.last_dim = last_dim
-            self.kv_score_buffer = KVAndScore(
+        self._alloc_kv_score_buffer(
-                torch.empty((self._size, last_dim), dtype=dtype, device=device)
+            dtype=dtype, device=device, enable_memory_saver=enable_memory_saver
         )
         if not online:
             self.kv_score_buffer[-1].clear()
+
+    def _alloc_kv_score_buffer(
+        self, *, dtype: torch.dtype, device: str, enable_memory_saver: bool
+    ) -> None:
+        """Allocate the flat ``(self._size, self.last_dim)`` kv+score buffer
+        under the memory-saver / custom-mem-pool context and wrap it in
+        :class:`KVAndScore`. Sets ``self.memory_saver_adapter``,
+        ``self.custom_mem_pool`` and ``self.kv_score_buffer``.
+
+        Subclasses (e.g. :class:`NPUCompressStatePool`) that compute a
+        different ``self._size`` reuse this instead of duplicating the
+        allocation boilerplate. Requires ``self._size`` and ``self.last_dim``
+        to be set already.
+        """
+        if _is_hip:
+            self.kv_score_buffer = KVAndScore(
+                torch.empty((self._size, self.last_dim), dtype=dtype, device=device)
+            )
         else:
             self.memory_saver_adapter = TorchMemorySaverAdapter.create(
                 enable=enable_memory_saver
@@ -126,7 +155,6 @@ class CompressStatePool:
             self.enable_custom_mem_pool, self.custom_mem_pool, _ = (
                 maybe_init_custom_mem_pool(device=device)
             )
-
             with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
                 with (
                     torch.cuda.use_mem_pool(self.custom_mem_pool)
@@ -135,13 +163,31 @@ class CompressStatePool:
                 ):
                     self.kv_score_buffer = KVAndScore(
                         torch.empty(
-                            (self._size, last_dim),
+                            (self._size, self.last_dim),
                             dtype=dtype,
                             device=device,
                         )
                     )
-                    if not online:
+
-                        self.kv_score_buffer[-1].clear()
+    @property
+    def state_cache_3d(self) -> torch.Tensor:
+        """``[block_num, page_size, last_dim]`` view of the flat kv+score
+        buffer. ``last_dim = 2*(1+overlap)*head_dim`` — exactly the
+        ``2*coff*D`` layout the fused compressor op wants for its
+        ``state_cache`` argument (kv at ``[:, :, :coff*D]``, score at
+        ``[:, :, coff*D:]``). Only valid for the non-online buffer; the
+        online layout has ``last_dim = 3*head_dim`` which the fused path
+        doesn't use.
+        """
+        assert not self.online, (
+            "state_cache_3d is for the fused compressor path; "
+            "online (3*head_dim) buffer is indexer-only."
+        )
+        assert self.page_size > 1, (
+            "state_cache_3d requires page_size>1; pool was constructed "
+            "with the default page_size=1 (flat 2D layout)."
+        )
+        return self.kv_score_buffer.kv_score.view(-1, self.page_size, self.last_dim)
 
     def translate_from_swa_loc_to_state_loc(
         self, swa_loc: torch.Tensor

python/sglang/srt/mem_cache/deepseek_v4_memory_pool.py

@@ -568,40 +568,38 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
             self.unified_swa_pages = self.unified_kv_pool.swa_pages
         else:
             self.unified_kv_pool = None
-            self.swa_kv_pool = DeepSeekV4SingleKVPool(
+            self.swa_kv_pool = self._make_kv_pool(
-                swa_size,
+                size=swa_size,
-                swa_page_size,
+                page_size=swa_page_size,
-                dtype,
+                dtype=dtype,
-                qk_nope_head_dim,
+                layer_num=layer_num,
-                qk_rope_head_dim,
+                device=device,
-                layer_num,
+                enable_memory_saver=enable_memory_saver,
-                device,
+                global_page_size=swa_page_size,
-                enable_memory_saver,
             )
 
         c4_kv_pool_type = DeepSeekV4SingleKVPool
         if enable_hisparse:
             c4_kv_pool_type = HiSparseC4DevicePool
-            self.c4_kv_pool = c4_kv_pool_type(
+        self.c4_kv_pool = self._make_kv_pool(
-                c4_size,
+            size=c4_size,
-                c4_page_size,
+            page_size=c4_page_size,
-                dtype,
+            dtype=dtype,
-                qk_nope_head_dim,
+            layer_num=c4_layer_num,
-                qk_rope_head_dim,
+            device=device,
-                c4_layer_num,
+            enable_memory_saver=enable_memory_saver,
-                device,
+            global_page_size=page_size,
-                enable_memory_saver,
+            cls=c4_kv_pool_type,
         )
 
-            self.c128_kv_pool = DeepSeekV4SingleKVPool(
+        self.c128_kv_pool = self._make_kv_pool(
-                c128_size,
+            size=c128_size,
-                c128_page_size,
+            page_size=c128_page_size,
-                dtype,
+            dtype=dtype,
-                qk_nope_head_dim,
+            layer_num=c128_layer_num,
-                qk_rope_head_dim,
+            device=device,
-                c128_layer_num,
+            enable_memory_saver=enable_memory_saver,
-                device,
+            global_page_size=page_size,
-                enable_memory_saver,
         )
 
         indexer_size = (
@@ -609,7 +607,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
             if (not _is_hip or envs.SGLANG_OPT_USE_COMPRESSOR_V2.get())
             else c4_size
         )
-        self.c4_indexer_kv_pool = DeepSeekV4IndexerPool(
+        self.c4_indexer_kv_pool = self._make_indexer_pool(
             indexer_size,
             c4_page_size,
             dtype,
@@ -741,6 +739,99 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
 
         return data_ptrs, data_lens, item_lens
 
+    def _make_kv_pool(
+        self,
+        *,
+        size: int,
+        page_size: int,
+        dtype: torch.dtype,
+        layer_num: int,
+        device: str,
+        enable_memory_saver: bool,
+        global_page_size: int,
+        cls: type = DeepSeekV4SingleKVPool,
+    ) -> DeepSeekV4SingleKVPool:
+        """Build a full / SWA / c4 / c128 single-KV pool. ``global_page_size``
+        is the model-wide page_size (== ``page_size`` for the SWA pool, larger
+        for the per-ratio c4/c128 pools); the default CUDA pool ignores it.
+        Overridden by :class:`DSV4NPUTokenToKVPool` to swap in the NPU bf16
+        PA_ND variant, which needs ``global_page_size`` for its kernel view."""
+        del global_page_size  # CUDA pools key only off their own page_size
+        return cls(
+            size,
+            page_size,
+            dtype,
+            self.qk_nope_head_dim,
+            self.qk_rope_head_dim,
+            layer_num,
+            device,
+            enable_memory_saver,
+        )
+
+    def _make_indexer_pool(
+        self,
+        size: int,
+        page_size: int,
+        dtype: torch.dtype,
+        index_head_dim: int,
+        layer_num: int,
+        device: str,
+        enable_memory_saver: bool,
+    ) -> DeepSeekV4IndexerPool:
+        """Build the c4 lightning-indexer K pool (packed CUDA layout).
+        Overridden by :class:`DSV4NPUTokenToKVPool` to swap in the
+        dedicated-buffer NPU variant (int8 K + fp16 scale)."""
+        return DeepSeekV4IndexerPool(
+            size,
+            page_size,
+            dtype,
+            index_head_dim,
+            layer_num,
+            device,
+            enable_memory_saver,
+        )
+
+    def _state_pool_size(self, ratio: int) -> int:
+        return self.c4_state_pool_size if ratio == 4 else self.c128_state_pool_size
+
+    def _make_attn_state_pool(
+        self, ratio: int, enable_memory_saver: bool
+    ) -> CompressStatePool:
+        """Build the per-layer attention compress-state pool for ``ratio``
+        (4 or 128). Overridden by :class:`DSV4NPUTokenToKVPool` to swap the
+        ring-buffered pool for the NPU paged one."""
+        return CompressStatePool(
+            size=self._state_pool_size(ratio),
+            ring_size=self.get_ring_size(ratio),
+            overlap=ratio == 4,
+            head_dim=self.qk_nope_head_dim + self.qk_rope_head_dim,
+            dtype=self.c4_state_dtype if ratio == 4 else self.c128_state_dtype,
+            device=self.device,
+            enable_memory_saver=enable_memory_saver,
+            ratio=ratio,
+            online=(ratio == 128 and ONLINE_C128),
+            swa_page_size=self.swa_page_size,
+            online_mtp_max_draft_tokens=(
+                self.online_mtp_max_draft_tokens if ratio == 128 else 0
+            ),
+        )
+
+    def _make_indexer_state_pool(
+        self, ratio: int, enable_memory_saver: bool
+    ) -> CompressStatePool:
+        """Build the per-layer indexer compress-state pool (c4 only)."""
+        return CompressStatePool(
+            size=self._state_pool_size(ratio),
+            ring_size=self.get_ring_size(ratio),
+            overlap=ratio == 4,
+            head_dim=self.indexer_head_dim,
+            device=self.device,
+            dtype=self.c4_state_dtype,
+            enable_memory_saver=enable_memory_saver,
+            ratio=ratio,
+            swa_page_size=self.swa_page_size,
+        )
+
     def _init_paged_compress_states(self, enable_memory_saver: bool):
         c4_state_pool_size = self.c4_state_pool_size
         c128_state_pool_size = self.c128_state_pool_size
@@ -754,37 +845,14 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
             ratio = self.compression_ratios[idx]
             if ratio == 0:
                 continue
-            overlap = ratio == 4
-            size = c4_state_pool_size if ratio == 4 else c128_state_pool_size
-            ring_size = self.get_ring_size(ratio)
 
-            self.compress_state_pools[idx] = CompressStatePool(
+            self.compress_state_pools[idx] = self._make_attn_state_pool(
-                size=size,
+                ratio, enable_memory_saver
-                ring_size=ring_size,
-                overlap=overlap,
-                head_dim=self.qk_nope_head_dim + self.qk_rope_head_dim,
-                dtype=self.c4_state_dtype if ratio == 4 else self.c128_state_dtype,
-                device=self.device,
-                enable_memory_saver=enable_memory_saver,
-                ratio=ratio,
-                online=(ratio == 128 and ONLINE_C128),
-                swa_page_size=self.swa_page_size,
-                online_mtp_max_draft_tokens=(
-                    self.online_mtp_max_draft_tokens if ratio == 128 else 0
-                ),
             )
 
             if ratio == 4:
-                self.indexer_compress_state_pools[idx] = CompressStatePool(
+                self.indexer_compress_state_pools[idx] = self._make_indexer_state_pool(
-                    size=size,
+                    ratio, enable_memory_saver
-                    ring_size=ring_size,
-                    overlap=overlap,
-                    head_dim=self.indexer_head_dim,
-                    device=self.device,
-                    dtype=self.c4_state_dtype,
-                    enable_memory_saver=enable_memory_saver,
-                    ratio=ratio,
-                    swa_page_size=self.swa_page_size,
                 )
 
     def _init_compressed_layer_mapping(self):

python/sglang/srt/model_executor/forward_batch_info.py

@@ -217,6 +217,65 @@ def compute_local_num_token_non_padded(
     )
 
 
+@dataclass
+class DSV4OutCacheLoc:
+    """Per-forward-pass KV cache allocation for DeepSeek-V4 on NPU.
+
+    Bundles slot indices for full/SWA pools, the two compressed-KV pools
+    (c4/c128), and the two compressed-state pools (c4_state/c128_state).
+    Populated by the NPU V4 allocator (DSV4NPUTokenToKVPoolAllocator) when
+    the model is DeepSeek-V4 on NPU; left as ``None`` on ForwardBatch
+    otherwise. CUDA's DSV4 path doesn't construct this bundle (state is
+    derived via translate_kv_loc_to_compress_state_loc there).
+
+    All fields are token-level slot ids in their respective pools (NOT page
+    ids). Attention backends convert to page ids via ``// page_size`` when
+    constructing PA_ND block tables.
+
+    State fields default to ``None`` so the bundle is constructible from
+    paths that allocate KV but not state (or vice versa); the NPU allocator
+    fills all six on real alloc, CUDA paths leave state ones None and use
+    the ring-hash translation instead.
+    """
+
+    out_full_loc: torch.Tensor
+    out_swa_loc: torch.Tensor
+    out_c4_loc: torch.Tensor
+    out_c128_loc: torch.Tensor
+    out_c4_state_loc: Optional[torch.Tensor] = None
+    out_c128_state_loc: Optional[torch.Tensor] = None
+
+
+@dataclass
+class DSV4StateLens:
+    """Per-extend/decode c4/c128 compress-state pool allocation lens (DSV4-NPU).
+
+    Built by ``ScheduleBatch._compute_dsv4_state_lens_{extend,decode}`` and
+    threaded through ``mem_cache/common.py`` to
+    ``DSV4NPUTokenToKVPoolAllocator.alloc_{extend,decode}``, which consumes:
+
+      * ``c{4,128}_prefix_lens`` / ``..._cpu`` — per-req prev cumulative
+        state-slot count (the paged allocator's ``prefix`` contract).
+      * ``c{4,128}_seq_lens`` / ``..._cpu`` — per-req new cumulative count.
+      * ``c{4,128}_extend_num_tokens`` — total new state slots this step.
+
+    Replaces the 10 loose ``c{4,128}_state_*`` kwargs the allocator used to
+    take: scheduler only produces this object, common only forwards it, the
+    allocator only consumes it.
+    """
+
+    c4_prefix_lens: torch.Tensor
+    c4_prefix_lens_cpu: torch.Tensor
+    c4_seq_lens: torch.Tensor
+    c4_seq_lens_cpu: torch.Tensor
+    c4_extend_num_tokens: int
+    c128_prefix_lens: torch.Tensor
+    c128_prefix_lens_cpu: torch.Tensor
+    c128_seq_lens: torch.Tensor
+    c128_seq_lens_cpu: torch.Tensor
+    c128_extend_num_tokens: int
+
+
 @dataclass
 class NgramEmbeddingInfo:
     """Ngram embedding state for LongCat models."""
@@ -286,6 +345,9 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
     # The original sequence length without being chunked. Qwen-1M related.
     orig_seq_lens: Optional[torch.Tensor] = None
 
+    # DSV4-NPU only: per-pool slot bundle from DSV4NPUTokenToKVPoolAllocator,
+    # consumed by the Ascend backend for PA_ND block tables. None elsewhere.
+    out_cache_loc_dsv4: Optional[DSV4OutCacheLoc] = None
     # The indices to track mamba state with
     mamba_track_indices: Optional[torch.Tensor] = None  # shape: [b], int64
     # The mask to track mamba state if needed
@@ -615,6 +677,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
             # Inputs aliased by reference from ScheduleBatch
             seq_lens_cpu=seq_lens_cpu,
             orig_seq_lens=batch.orig_seq_lens,
+            out_cache_loc_dsv4=batch.out_cache_loc_dsv4,
             mamba_track_indices=batch.mamba_track_indices,
             mamba_track_mask=batch.mamba_track_mask,
             mamba_track_seqlens=batch.mamba_track_seqlens,

python/sglang/srt/model_executor/model_runner_kv_cache_mixin.py

@@ -391,7 +391,17 @@ class ModelRunnerKVCacheMixin:
                     start_layer=self.start_layer,
                 )
             else:
-                self.req_to_token_pool = ReqToTokenPool(
+                # DSV4 on NPU needs an extended ReqToTokenPool holding per-req
+                # swa/c4/c128/c{4,128}_state tables; others stay on the stock one.
+                req_to_token_pool_cls = ReqToTokenPool
+                if _is_npu and is_deepseek_v4(self.model_config.hf_config):
+                    from sglang.srt.hardware_backend.npu.dsv4.dsv4_req_to_token_pool import (
+                        DSV4NPUReqToTokenPool,
+                    )
+
+                    req_to_token_pool_cls = DSV4NPUReqToTokenPool
+
+                self.req_to_token_pool = req_to_token_pool_cls(
                     size=max_num_reqs,
                     max_context_len=self.model_config.context_len
                     + extra_max_context_len,
@@ -412,6 +422,7 @@ class ModelRunnerKVCacheMixin:
 
         if is_dsv4_model:
             swa_page_size = self.page_size
+            if not _is_npu:
                 assert swa_page_size == 256, "In paged swa mode, page_size must be 256."
 
             if self.is_draft_worker:
@@ -424,7 +435,40 @@ class ModelRunnerKVCacheMixin:
                 ] * self.num_effective_layers
             else:
                 compression_ratios = self.model_config.compress_ratios
-            self.token_to_kv_pool = DeepSeekV4TokenToKVPool(
+
+            # NPU + DSV4 → paged-state subclass: the fused compressor kernel
+            # needs cache_mode=1 (paged); Atlas A3 rejects cache_mode=2 (ring),
+            # so the CUDA ring-buffer state path can't be shared. CUDA keeps
+            # DeepSeekV4TokenToKVPool unchanged; NPU recomputes state sizes below.
+            if _is_npu:
+                from sglang.srt.hardware_backend.npu.dsv4.dsv4_memory_pool import (
+                    DSV4NPUTokenToKVPool,
+                    npu_state_pool_size,
+                )
+
+                pool_cls = DSV4NPUTokenToKVPool
+                # Recompute state pool sizes for the NPU paged formula (CUDA's
+                # ring sizes are dropped here). Tail-only allocation keeps the
+                # per-req-budget formula sufficient at any prefill length: long
+                # prompts allocate only ``tail+128`` (c4) / ``tail`` (c128)
+                # slots (tail = seq_len % 128), and decode is drained by
+                # sliding eviction in ``ScheduleBatch._evict_swa``.
+                c4_state_pool_size = npu_state_pool_size(
+                    ratio=4,
+                    page_size=self.page_size,
+                    max_num_reqs=self.max_running_requests,
+                )
+                c128_state_pool_size = npu_state_pool_size(
+                    ratio=128,
+                    page_size=self.page_size,
+                    max_num_reqs=self.max_running_requests,
+                )
+            else:
+                pool_cls = DeepSeekV4TokenToKVPool
+                c4_state_pool_size = self.c4_state_pool_size
+                c128_state_pool_size = self.c128_state_pool_size
+
+            self.token_to_kv_pool = pool_cls(
                 max_num_reqs=self.max_running_requests,
                 # SWA ring is indexed by req_pool_idx; PD decode inflates req_to_token
                 # past max_running_requests (pre-alloc), so size to the real capacity.
@@ -432,8 +476,8 @@ class ModelRunnerKVCacheMixin:
                 swa_size=self.swa_max_total_num_tokens,
                 c4_size=self.c4_max_total_num_tokens,
                 c128_size=self.c128_max_total_num_tokens,
-                c4_state_pool_size=self.c4_state_pool_size,
+                c4_state_pool_size=c4_state_pool_size,
-                c128_state_pool_size=self.c128_state_pool_size,
+                c128_state_pool_size=c128_state_pool_size,
                 page_size=self.page_size,
                 swa_page_size=swa_page_size,
                 sliding_window=self.model_config.window_size,
@@ -763,10 +807,21 @@ class ModelRunnerKVCacheMixin:
                 )
             elif _is_npu and (
                 self.server_args.attention_backend == "ascend"
+                or is_dsv4_model
                 or self.hybrid_gdn_config is not None
             ):
                 if self.is_hybrid_swa:
-                    self.token_to_kv_pool_allocator = SWATokenToKVPoolAllocator(
+                    # DSV4 on NPU: SWA allocator subclass that also drives the
+                    # c4/c128 allocators, producing a DSV4OutCacheLoc per alloc.
+                    if is_dsv4_model:
+                        from sglang.srt.hardware_backend.npu.dsv4.dsv4_allocator import (
+                            DSV4NPUTokenToKVPoolAllocator,
+                        )
+
+                        swa_allocator_cls = DSV4NPUTokenToKVPoolAllocator
+                    else:
+                        swa_allocator_cls = SWATokenToKVPoolAllocator
+                    self.token_to_kv_pool_allocator = swa_allocator_cls(
                         self.full_max_total_num_tokens,
                         self.swa_max_total_num_tokens,
                         page_size=self.page_size,
@@ -843,6 +898,13 @@ class ModelRunnerKVCacheMixin:
                     )
                 )
 
+            # DSV4-NPU: wire allocator back-ref into req_to_token_pool so its
+            # free(req) can release c4/c128 pool pages alongside the slot.
+            if hasattr(self.req_to_token_pool, "register_dsv4_allocator"):
+                self.req_to_token_pool.register_dsv4_allocator(
+                    self.token_to_kv_pool_allocator
+                )
+
         else:
             assert self.is_draft_worker
             if self.is_hybrid_swa:

python/sglang/srt/model_executor/runner/decode_cuda_graph_runner.py

@@ -156,6 +156,7 @@ def build_replay_fb_view(
         seq_lens_cpu=buffers.seq_lens_cpu[:bs],
         encoder_lens=buffers.encoder_lens[:bs] if is_encoder_decoder else None,
         out_cache_loc=getattr(forward_batch, "out_cache_loc", None),
+        out_cache_loc_dsv4=getattr(forward_batch, "out_cache_loc_dsv4", None),
         spec_info=forward_batch.spec_info,
     )
 

python/sglang/srt/models/deepseek_v2.py

@@ -385,6 +385,14 @@ class DeepseekV2MLP(nn.Module):
 
         # Fallback: fused silu+clamp kernel (still faster than unfused)
         elif self.swiglu_limit is not None:
+            if _is_npu:
+                _g, _u = gate_up.chunk(2, dim=-1)
+                _lim = float(self.swiglu_limit)
+                gate_up = torch.cat(
+                    [_g.clamp(max=_lim), _u.clamp(min=-_lim, max=_lim)], dim=-1
+                )
+                x = self.act_fn(gate_up)
+            else:
                 M, N = gate_up.shape
                 x = gate_up.new_empty((M, N // 2))
                 silu_and_mul_clamp(gate_up, x, float(self.swiglu_limit))
@@ -493,6 +501,8 @@ class MoEGate(nn.Module):
 
             elif _use_aiter:
                 logits = aiter_dsv3_router_gemm(hidden_states, self.weight)
+            elif _is_npu:
+                logits = F.linear(hidden_states, self.weight, None)
             else:
                 if self.is_deepseek_v4:
                     from sglang.jit_kernel.dsv4 import linear_bf16_fp32

python/sglang/srt/models/deepseek_v4.py

@@ -29,6 +29,7 @@ from sglang.srt.compilation.compilation_config import register_split_op
 from sglang.srt.configs.deepseek_v4 import DeepSeekV4Config
 from sglang.srt.distributed import (
     get_pp_group,
+    get_tensor_model_parallel_world_size,
     get_tp_group,
 )
 from sglang.srt.environ import envs
@@ -47,10 +48,15 @@ from sglang.srt.layers.communicator_dsa_cp import (
     dsa_cp_gather_hidden_states,
     dsa_cp_reduce_scatter_hidden_states,
 )
+from sglang.srt.layers.deepseek_v4_rope import (
+    v4_rope_inplace_npu,
+)
 from sglang.srt.layers.dp_attention import (
     _DpGatheredBufferWrapper,
     attn_tp_all_gather,
+    attn_tp_all_reduce,
     dp_gather_partial,
+    dp_gather_replicate,
     dp_scatter,
     get_dp_global_num_tokens,
     get_global_dp_buffer,
@@ -61,7 +67,7 @@ from sglang.srt.layers.dp_attention import (
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.mhc import mhc_fused_post_pre
+from sglang.srt.layers.mhc import mhc_fused_post_pre, npu_hc_pre
 from sglang.srt.layers.moe import get_moe_a2a_backend, should_use_dp_reduce_scatterv
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.quantization.fp8_kernel import sglang_per_token_group_quant_fp8
@@ -129,6 +135,11 @@ from sglang.srt.utils import (
 from sglang.srt.utils.custom_op import register_custom_op
 from sglang.srt.utils.hf_transformers_utils import get_rope_config
 
+# NPU-only: bind torch_npu here so _compute_q_b / _forward_prepare can call
+# torch_npu.npu_rms_norm directly (imports elsewhere aren't visible in this module).
+if _is_npu:
+    import torch_npu
+
 logger = logging.getLogger(__name__)
 
 _FP8_WO_A_GEMM = envs.SGLANG_OPT_FP8_WO_A_GEMM.get()
@@ -293,7 +304,12 @@ class MQALayer(nn.Module):
             if compress_ratio_override is not None
             else config.compress_ratios[layer_id]
         )
-        assert compress_ratio in [0, 4, 128]
+
+        assert compress_ratio in (
+            0,
+            4,
+            128,
+        ), f"V4 compress_ratio: expected one of (0, 4, 128), got {compress_ratio}"
         self.compress_ratio: Literal[0, 4, 128] = compress_ratio
 
         assert self.head_dim == config.head_dim
@@ -317,11 +333,9 @@ class MQALayer(nn.Module):
 
         from sglang.srt.layers.deepseek_v4_rope import precompute_freqs_cis
 
-        assert self.compress_ratio in {0, 4, 128}
+        # YARN correction applies to ALL layers (dense and compressed share the same
-        if self.compress_ratio:
+        # YARN-corrected inv_freq); only the rope base differs (rope_theta vs compress_rope_theta).
         original_seq_len = rope_scaling["original_max_position_embeddings"]
-        else:
-            original_seq_len = 0
 
         freqs_cis = precompute_freqs_cis(
             dim=self.qk_rope_head_dim,
@@ -354,7 +368,7 @@ class MQALayer(nn.Module):
 
         self.compressor = None
         self.indexer = None
-        if self.compress_ratio:
+        if self.compress_ratio in (4, 128):
             self.compressor = Compressor(
                 config,
                 layer_id=self.layer_id,
@@ -436,7 +450,8 @@ class MQALayer(nn.Module):
             self.hidden_size,
             bias=False,
             quant_config=quant_config,
-            reduce_results=attn_tp_size > 1,
+            reduce_results=attn_tp_size == get_tensor_model_parallel_world_size()
+            and attn_tp_size > 1,
             prefix=add_prefix("wo_b", prefix),
             tp_rank=attn_tp_rank,
             tp_size=attn_tp_size,
@@ -811,6 +826,33 @@ class MQALayer(nn.Module):
                     forward_batch,
                     torch.cuda.current_stream(),
                 )
+        elif _is_npu:
+            q_lora = self.q_norm(q_lora)
+            q, _ = self.wq_b(q_lora)
+            q = q.view(-1, self.n_local_heads, self.head_dim)
+            _dummy = q.new_ones(q.shape[-1])
+            q = torch_npu.npu_rms_norm(q, _dummy, self.eps)[0]
+
+            if qkv_a is not None:
+                kv = qkv_a[..., self.q_lora_rank :]
+            else:
+                kv, _ = self.wkv(x)
+            kv = self.kv_norm(kv)
+
+            v4_rope_inplace_npu(
+                q[..., -self.qk_rope_head_dim :],
+                kv[..., -self.qk_rope_head_dim :].unsqueeze(1),
+                self.freqs_cis,
+                positions,
+            )
+            attn_backend.store_cache(
+                layer_id=self.layer_id,
+                swa_k=kv,
+                forward_batch=forward_batch,
+            )
+            kv = None
+            if q_out is not None:
+                q_out.copy_(q)
         else:
             q_lora = self.q_norm(q_lora)
             q = self._compute_q_b(q_lora, positions, q_out)
@@ -879,9 +921,6 @@ class MQALayer(nn.Module):
         x_quant=None,
     ) -> torch.Tensor:
         if not get_attn_tp_context().input_scattered and x.shape[0] == 0:
-            assert (
-                not self.wo_b.reduce_results
-            ), "short-circuiting allreduce will lead to hangs"
             return x
 
         attn_backend = get_attn_backend()
@@ -1000,6 +1039,15 @@ class MQALayer(nn.Module):
                     save_kv_cache=save_kv_cache,
                 )
             o = o[:, tp_slice, :]
+        if _is_npu:
+            v4_rope_inplace_npu(
+                o[..., -self.qk_rope_head_dim :],
+                None,
+                self.freqs_cis,
+                positions,
+                inverse=True,
+            )
+        else:
             fused_rope_inplace(
                 o[..., -self.qk_rope_head_dim :],
                 None,
@@ -1034,6 +1082,8 @@ class MQALayer(nn.Module):
             o = torch.einsum("tgd,grd->tgr", o, wo_a)
 
         o, _ = self.wo_b(o.flatten(1))
+        if self.tp_size > 1 and self.tp_size < get_tensor_model_parallel_world_size():
+            o = attn_tp_all_reduce(o)
 
         return o
 
@@ -1233,6 +1283,7 @@ class DeepseekV4DecoderLayer(nn.Module):
         hc_scale: torch.Tensor,
         hc_base: torch.Tensor,
         norm: Optional[nn.Module] = None,
+        forward_batch: Optional[ForwardBatch] = None,
     ):
         """If *norm* is given and the TileLang path is active, the returned
         hidden_states are already post-norm (the norm is fused into the kernel)."""
@@ -1248,6 +1299,19 @@ class DeepseekV4DecoderLayer(nn.Module):
 
         shape, dtype = x.size(), x.dtype
 
+        if _is_npu:
+            return npu_hc_pre(
+                x,
+                hc_fn,
+                hc_scale,
+                hc_base,
+                hc_mult=self.hc_mult,
+                hc_sinkhorn_iters=self.hc_sinkhorn_iters,
+                rms_norm_eps=self.rms_norm_eps,
+                hc_eps=self.hc_eps,
+                forward_batch=forward_batch,
+            )
+
         if x.shape[0] == 0:
             y = torch.empty((0, shape[-1]), dtype=dtype, device=x.device)
             post = torch.empty((0, self.hc_mult), dtype=torch.float32, device=x.device)
@@ -1339,6 +1403,9 @@ class DeepseekV4DecoderLayer(nn.Module):
                 (0, self.hc_mult, x.shape[-1]), dtype=x.dtype, device=x.device
             )
 
+        if _is_npu:
+            return torch.ops.custom.npu_hc_post(x, residual, post, comb)
+
         if envs.SGLANG_OPT_USE_TILELANG_MHC_POST.get():
             from sglang.srt.layers.mhc import mhc_post
 
@@ -1412,6 +1479,7 @@ class DeepseekV4DecoderLayer(nn.Module):
                 self.hc_attn_scale,
                 self.hc_attn_base,
                 norm=self.input_layernorm,
+                forward_batch=forward_batch,
             )
             if not norm_fused:
                 if _use_aiter and _is_gfx95_supported:
@@ -1482,6 +1550,7 @@ class DeepseekV4DecoderLayer(nn.Module):
                 self.hc_ffn_scale,
                 self.hc_ffn_base,
                 norm=self.post_attention_layernorm,
+                forward_batch=forward_batch,
             )
             if not norm_fused:
                 hidden_states = self.post_attention_layernorm(hidden_states)
@@ -1695,7 +1764,9 @@ class DeepseekV4Model(nn.Module):
                 dtype=input_ids.dtype,
                 device=input_ids.device,
             )
-            dp_gather_partial(input_ids_global, input_ids[:, None], forward_batch)
+            # Token ids are replicated within an attention-TP group. Use replicate
+            # gather here to avoid summing duplicated ids when attention_tp_size > 1.
+            dp_gather_replicate(input_ids_global, input_ids[:, None], forward_batch)
             input_ids_global = input_ids_global.squeeze(-1)
         else:
             input_ids_global = input_ids
@@ -1886,6 +1957,7 @@ class DeepseekV4ForCausalLM(nn.Module):
         if self.capture_aux_hidden_states:
             hidden_states, aux_hidden_states = hidden_states
         hidden_states, pre_hc_head = hidden_states
+
         return self.logits_processor(
             input_ids,
             hidden_states,
@@ -1930,7 +2002,10 @@ class DeepseekV4ForCausalLM(nn.Module):
         for layer_id in range(self.model.start_layer, self.model.end_layer):
             layer = self.model.layers[layer_id]
             self_attn = layer.self_attn
-            if self_attn.compress_ratio != 0 and not self_attn.compressor.ape_converted:
+            if (
+                self_attn.compress_ratio in (4, 128)
+                and not self_attn.compressor.ape_converted
+            ):
                 self_attn.compressor.apply_ape_hotfix()
             if (
                 self_attn.compress_ratio == 4
@@ -1941,7 +2016,9 @@ class DeepseekV4ForCausalLM(nn.Module):
 
     @staticmethod
     def remap_weight_name_to_dpsk_hf_format(
-        name: str, is_nextn: bool = False, num_hidden_layers: Optional[int] = None
+        name: str,
+        is_nextn: bool = False,
+        num_hidden_layers: Optional[int] = None,
     ) -> str:
         if name == "embed.weight":
             return "model.embed_tokens.weight"
@@ -2339,8 +2416,10 @@ class DeepseekV4ForCausalLM(nn.Module):
         del self.lm_head.weight
         self.model.embed_tokens.weight = embed
         self.lm_head.weight = head
-        torch.cuda.empty_cache()
+        # Hot weight reload (RL workflows). Use the device-agnostic module
-        torch.cuda.synchronize()
+        # accessor so this works on both CUDA/HIP and NPU.
+        torch.get_device_module().empty_cache()
+        torch.get_device_module().synchronize()
 
     @classmethod
     def get_model_config_for_expert_location(cls, config):

python/sglang/srt/speculative/draft_utils.py

@@ -1,7 +1,7 @@
 import logging
 
 from sglang.srt.server_args import ServerArgs, get_global_server_args
-from sglang.srt.utils.common import is_blackwell, is_hip, is_musa
+from sglang.srt.utils.common import is_blackwell, is_hip, is_musa, is_npu
 
 logger = logging.getLogger(__name__)
 
@@ -236,7 +236,11 @@ class DraftBackendFactory:
         )
 
     def _create_dsv4_decode_backend(self):
-        if is_hip():
+        # On NPU the "dsv4" backend resolves to the Ascend V4 subclass; its
+        # draft path reuses the Ascend multi-step draft backend.
+        if is_npu():
+            return self._create_ascend_decode_backend()
+        elif is_hip():
             from sglang.srt.layers.attention.deepseek_v4_backend_hip_radix import (
                 DeepseekV4MultiStepBackend,
             )
@@ -333,7 +337,11 @@ class DraftBackendFactory:
         return None
 
     def _create_dsv4_prefill_backend(self):
-        if is_hip():
+        # On NPU the "dsv4" backend resolves to the Ascend V4 subclass; its
+        # draft-extend path reuses the Ascend prefill draft backend.
+        if is_npu():
+            return self._create_ascend_prefill_backend()
+        elif is_hip():
             from sglang.srt.layers.attention.deepseek_v4_backend_hip_radix import (
                 DeepseekV4HipRadixBackend,
             )