Opt MHA chunked prefix: merge prefix and extend kv cache to run mha once (#10953)

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

@@ -855,14 +855,24 @@ class FlashAttentionBackend(AttentionBackend):
                     )
                 else:
                     # MHA for extend part of sequence without attending prefix kv cache
+                    cu_seqlens_k = (
+                        metadata.cu_seqlens_q
+                        if not forward_batch.mha_one_shot
+                        else metadata.cu_seqlens_k
+                    )
+                    max_seqlen_k = (
+                        metadata.max_seq_len_q
+                        if not forward_batch.mha_one_shot
+                        else metadata.max_seq_len_k
+                    )
                     output = flash_attn_varlen_func(
                         q=q.view(-1, layer.tp_q_head_num, layer.head_dim),
                         k=k.view(-1, layer.tp_k_head_num, layer.head_dim).to(q.dtype),
                         v=v.view(-1, layer.tp_k_head_num, layer.v_head_dim).to(q.dtype),
                         cu_seqlens_q=metadata.cu_seqlens_q,
-                        cu_seqlens_k=metadata.cu_seqlens_q,
+                        cu_seqlens_k=cu_seqlens_k,
                         max_seqlen_q=metadata.max_seq_len_q,
-                        max_seqlen_k=metadata.max_seq_len_q,
+                        max_seqlen_k=max_seqlen_k,
                         softmax_scale=layer.scaling,
                         causal=True,
                         return_softmax_lse=forward_batch.mha_return_lse,

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

@@ -82,6 +82,7 @@ class FlashInferMhaChunkKVRunner:
 
         # Buffers and wrappers
         self.qo_indptr = attn_backend.qo_indptr
+        self.kv_indptr = attn_backend.kv_indptr
         self.workspace_buffer = attn_backend.workspace_buffer
         self.fmha_backend = attn_backend.fmha_backend
 
@@ -132,9 +133,14 @@ class FlashInferMhaChunkKVRunner:
             )
         # ragged prefill
         if not disable_flashinfer_ragged:
+            kv_indptr = (
+                qo_indptr
+                if not forward_batch.mha_one_shot
+                else self.kv_indptr[: bs + 1]
+            )
             self.ragged_wrapper.begin_forward(
                 qo_indptr=qo_indptr,
-                kv_indptr=qo_indptr,
+                kv_indptr=kv_indptr,
                 num_qo_heads=self.num_local_heads,
                 num_kv_heads=self.num_local_heads,
                 head_dim_qk=self.qk_nope_head_dim + self.qk_rope_head_dim,
@@ -156,7 +162,7 @@ class FlashInferMhaChunkKVRunner:
             chunk_idx = forward_batch.prefix_chunk_idx
             assert chunk_idx >= 0
             wrapper = self.chunk_ragged_wrappers[chunk_idx]
-            o1, s1 = wrapper.forward_return_lse(
+            o = wrapper.forward_return_lse(
                 q.view(-1, layer.tp_q_head_num, layer.head_dim),
                 k.view(-1, layer.tp_k_head_num, layer.head_dim).to(q.dtype),
                 v.view(-1, layer.tp_v_head_num, layer.v_head_dim).to(q.dtype),
@@ -165,7 +171,12 @@ class FlashInferMhaChunkKVRunner:
                 logits_soft_cap=logits_soft_cap,
             )
         else:
-            o1, s1 = self.ragged_wrapper.forward_return_lse(
+            forward = (
+                self.ragged_wrapper.forward_return_lse
+                if forward_batch.mha_return_lse
+                else self.ragged_wrapper.forward
+            )
+            o = forward(
                 q.view(-1, layer.tp_q_head_num, layer.head_dim),
                 k.view(-1, layer.tp_k_head_num, layer.head_dim).to(q.dtype),
                 v.view(-1, layer.tp_v_head_num, layer.v_head_dim).to(q.dtype),
@@ -173,8 +184,7 @@ class FlashInferMhaChunkKVRunner:
                 sm_scale=layer.scaling,
                 logits_soft_cap=logits_soft_cap,
             )
-
+        return o
-        return o1, s1
 
 
 class FlashInferMLAAttnBackend(AttentionBackend):
@@ -512,15 +522,13 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         q_rope: Optional[torch.Tensor] = None,
         k_rope: Optional[torch.Tensor] = None,
     ):
-        if (
+        if forward_batch.attn_attend_prefix_cache is not None and any(
-            forward_batch.attn_attend_prefix_cache is not None
+            forward_batch.extend_prefix_lens_cpu
-            and forward_batch.mha_return_lse
         ):  # MHA Chunk
             assert self.enable_chunk_kv
             assert q_rope is None
             assert k_rope is None
-            o1, s1 = self.mha_chunk_kv_cache.forward(q, k, v, layer, forward_batch)
+            return self.mha_chunk_kv_cache.forward(q, k, v, layer, forward_batch)
-            return o1, s1
 
         cache_loc = forward_batch.out_cache_loc
         logits_soft_cap = layer.logit_cap

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

@@ -1,3 +1,4 @@
+import torch
 import triton
 import triton.language as tl
 
@@ -101,3 +102,80 @@ def create_flashmla_kv_indices_triton(
             data // PAGED_SIZE,
             mask=mask_out,
         )
+
+
+@triton.jit
+def concat_and_cast_mha_k_kernel(
+    k_ptr,
+    k_nope_ptr,
+    k_rope_ptr,
+    head_cnt: tl.constexpr,
+    k_stride0: tl.constexpr,
+    k_stride1: tl.constexpr,
+    nope_stride0: tl.constexpr,
+    nope_stride1: tl.constexpr,
+    rope_stride0: tl.constexpr,
+    nope_dim: tl.constexpr,
+    rope_dim: tl.constexpr,
+):
+    pid_loc = tl.program_id(0)
+    head_range = tl.arange(0, head_cnt)
+
+    k_head_ptr = k_ptr + pid_loc * k_stride0 + head_range[:, None] * k_stride1
+
+    nope_offs = tl.arange(0, nope_dim)
+
+    src_nope_ptr = (
+        k_nope_ptr
+        + pid_loc * nope_stride0
+        + head_range[:, None] * nope_stride1
+        + nope_offs[None, :]
+    )
+    dst_nope_ptr = k_head_ptr + nope_offs[None, :]
+
+    src_nope = tl.load(src_nope_ptr)
+    tl.store(dst_nope_ptr, src_nope)
+
+    rope_offs = tl.arange(0, rope_dim)
+    src_rope_ptr = k_rope_ptr + pid_loc * rope_stride0 + rope_offs[None, :]
+    dst_rope_ptr = k_head_ptr + nope_dim + rope_offs[None, :]
+    src_rope = tl.load(src_rope_ptr)
+    tl.store(dst_rope_ptr, src_rope)
+
+
+def concat_and_cast_mha_k_triton(
+    k: torch.Tensor,
+    k_nope: torch.Tensor,
+    k_rope: torch.Tensor,
+):
+    # The source data type will be implicitly converted to the target data type.
+    assert (
+        len(k.shape) == 3 and len(k_nope.shape) == 3 and len(k_rope.shape) == 3
+    ), f"shape should be 3d, but got {k.shape=}, {k_nope.shape=}, {k_rope.shape=}"
+    assert (
+        k.shape[0] == k_nope.shape[0] and k.shape[0] == k_rope.shape[0]
+    ), f"invalid shape, got {k.shape=}, {k_nope.shape=}, {k_rope.shape=}"
+    assert (
+        k.shape[1] == k_nope.shape[1] and 1 == k_rope.shape[1]
+    ), f"invalid shape, got {k.shape=}, {k_nope.shape=}, {k_rope.shape=}"
+    assert (
+        k.shape[-1] == k_nope.shape[-1] + k_rope.shape[-1]
+    ), f"invalid shape, got {k.shape=}, {k_nope.shape=}, {k_rope.shape=}"
+
+    nope_dim = k_nope.shape[-1]
+    rope_dim = k_rope.shape[-1]
+    grid = (k.shape[0],)
+
+    concat_and_cast_mha_k_kernel[grid](
+        k,
+        k_nope,
+        k_rope,
+        k.shape[1],
+        k.stride(0),
+        k.stride(1),
+        k_nope.stride(0),
+        k_nope.stride(1),
+        k_rope.stride(0),
+        nope_dim,
+        rope_dim,
+    )

python/sglang/srt/mem_cache/memory_pool.py

@@ -1213,6 +1213,65 @@ def set_mla_kv_buffer_triton(
     )
 
 
+@triton.jit
+def get_mla_kv_buffer_kernel(
+    kv_buffer_ptr,
+    cache_k_nope_ptr,
+    cache_k_rope_ptr,
+    loc_ptr,
+    buffer_stride: tl.constexpr,
+    nope_stride: tl.constexpr,
+    rope_stride: tl.constexpr,
+    nope_dim: tl.constexpr,
+    rope_dim: tl.constexpr,
+):
+    pid_loc = tl.program_id(0)
+    loc = tl.load(loc_ptr + pid_loc)
+    loc_src_ptr = kv_buffer_ptr + loc * buffer_stride
+
+    nope_offs = tl.arange(0, nope_dim)
+    nope_src_ptr = loc_src_ptr + nope_offs
+    nope_src = tl.load(nope_src_ptr)
+
+    tl.store(
+        cache_k_nope_ptr + pid_loc * nope_stride + nope_offs,
+        nope_src,
+    )
+
+    rope_offs = tl.arange(0, rope_dim)
+    rope_src_ptr = loc_src_ptr + nope_dim + rope_offs
+    rope_src = tl.load(rope_src_ptr)
+    tl.store(
+        cache_k_rope_ptr + pid_loc * rope_stride + rope_offs,
+        rope_src,
+    )
+
+
+def get_mla_kv_buffer_triton(
+    kv_buffer: torch.Tensor,
+    loc: torch.Tensor,
+    cache_k_nope: torch.Tensor,
+    cache_k_rope: torch.Tensor,
+):
+    # The source data type will be implicitly converted to the target data type.
+    nope_dim = cache_k_nope.shape[-1]  # 512
+    rope_dim = cache_k_rope.shape[-1]  # 64
+    n_loc = loc.numel()
+    grid = (n_loc,)
+
+    get_mla_kv_buffer_kernel[grid](
+        kv_buffer,
+        cache_k_nope,
+        cache_k_rope,
+        loc,
+        kv_buffer.stride(0),
+        cache_k_nope.stride(0),
+        cache_k_rope.stride(0),
+        nope_dim,
+        rope_dim,
+    )
+
+
 class MLATokenToKVPool(KVCache):
     def __init__(
         self,
@@ -1363,6 +1422,29 @@ class MLATokenToKVPool(KVCache):
                 cache_k_rope,
             )
 
+    def get_mla_kv_buffer(
+        self,
+        layer: RadixAttention,
+        loc: torch.Tensor,
+        dst_dtype: Optional[torch.dtype] = None,
+    ):
+        # get k nope and k rope from the kv buffer, and optionally cast them to dst_dtype.
+        layer_id = layer.layer_id
+        kv_buffer = self.get_key_buffer(layer_id)
+        dst_dtype = dst_dtype or self.dtype
+        cache_k_nope = torch.empty(
+            (loc.shape[0], 1, self.kv_lora_rank),
+            dtype=dst_dtype,
+            device=kv_buffer.device,
+        )
+        cache_k_rope = torch.empty(
+            (loc.shape[0], 1, self.qk_rope_head_dim),
+            dtype=dst_dtype,
+            device=kv_buffer.device,
+        )
+        get_mla_kv_buffer_triton(kv_buffer, loc, cache_k_nope, cache_k_rope)
+        return cache_k_nope, cache_k_rope
+
     def get_cpu_copy(self, indices):
         torch.cuda.synchronize()
         kv_cache_cpu = []

python/sglang/srt/model_executor/forward_batch_info.py

@@ -39,6 +39,7 @@ import triton
 import triton.language as tl
 
 from sglang.srt.distributed.parallel_state import get_moe_expert_parallel_world_size
+from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
 from sglang.srt.layers.dp_attention import (
     DpPaddingMode,
     get_attention_dp_rank,
@@ -250,6 +251,8 @@ class ForwardBatch:
     # For MLA chunked prefix cache used in chunked prefill
     # Tell attention backend whether lse needs to be returned
     mha_return_lse: Optional[bool] = None
+    mha_one_shot_kv_indices: Optional[torch.Tensor] = None
+    mha_one_shot: Optional[bool] = None
 
     # For multimodal
     mm_inputs: Optional[List[MultimodalInputs]] = None
@@ -863,6 +866,10 @@ class ForwardBatch:
             self.token_to_kv_pool, MLATokenToKVPool
         ), "Currently chunked prefix cache can only be used by Deepseek models"
 
+        if not any(self.extend_prefix_lens_cpu):
+            self.num_prefix_chunks = 0
+            return
+
         if self.prefix_chunk_len is not None:
             # Chunked kv cache info already prepared by prior modules
             return
@@ -917,6 +924,34 @@ class ForwardBatch:
     def can_run_tbo(self):
         return self.tbo_split_seq_index is not None
 
+    def fetch_mha_one_shot_kv_indices(self):
+        if self.mha_one_shot_kv_indices is not None:
+            return self.mha_one_shot_kv_indices
+        batch_size = self.batch_size
+        paged_kernel_lens_sum = sum(self.seq_lens_cpu)
+        kv_indices = torch.empty(
+            paged_kernel_lens_sum,
+            dtype=torch.int32,
+            device=self.req_pool_indices.device,
+        )
+        kv_indptr = torch.zeros(
+            batch_size + 1,
+            dtype=torch.int32,
+            device=self.req_pool_indices.device,
+        )
+        kv_indptr[1:] = torch.cumsum(self.seq_lens, dim=0)
+        create_flashinfer_kv_indices_triton[(self.batch_size,)](
+            self.req_to_token_pool.req_to_token,
+            self.req_pool_indices,
+            self.seq_lens,
+            kv_indptr,
+            None,
+            kv_indices,
+            self.req_to_token_pool.req_to_token.shape[1],
+        )
+        self.mha_one_shot_kv_indices = kv_indices
+        return kv_indices
+
 
 def enable_num_token_non_padded(server_args):
     return get_moe_expert_parallel_world_size() > 1

python/sglang/srt/models/deepseek_v2.py

@@ -57,6 +57,7 @@ from sglang.srt.layers.attention.npu_ops.mla_preprocess import (
     is_mla_preprocess_enabled,
 )
 from sglang.srt.layers.attention.nsa.nsa_indexer import Indexer
+from sglang.srt.layers.attention.utils import concat_and_cast_mha_k_triton
 from sglang.srt.layers.communicator import (
     LayerCommunicator,
     LayerScatterModes,
@@ -241,6 +242,10 @@ class AttnForwardMethod(IntEnum):
     # This method can avoid OOM when prefix lengths are long.
     MHA_CHUNKED_KV = auto()
 
+    # Use multi-head attention, execute the MHA for prefix and extended kv in one shot
+    # when the sequence lengths are below the threshold.
+    MHA_ONE_SHOT = auto()
+
     # Use MLA but with fused RoPE
     MLA_FUSED_ROPE = auto()
 
@@ -306,6 +311,14 @@ def _is_extend_without_speculative(forward_batch):
     )
 
 
+def _support_mha_one_shot(attn: DeepseekV2AttentionMLA, forward_batch, backend_name):
+    attn_supported = backend_name in ["fa3", "flashinfer", "flashmla"]
+    sum_seq_lens = (
+        sum(forward_batch.seq_lens_cpu) if forward_batch.seq_lens_cpu is not None else 0
+    )
+    return attn_supported and sum_seq_lens <= forward_batch.get_max_chunk_capacity()
+
+
 def _handle_attention_backend(
     attn: DeepseekV2AttentionMLA, forward_batch, backend_name
 ):
@@ -325,6 +338,8 @@ def _handle_attention_backend(
             or sum_extend_prefix_lens == 0
         )
     ):
+        if _support_mha_one_shot(attn, forward_batch, backend_name):
+            return AttnForwardMethod.MHA_ONE_SHOT
         return AttnForwardMethod.MHA_CHUNKED_KV
     else:
         return _dispatch_mla_subtype(attn, forward_batch)
@@ -1062,6 +1077,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         self.scaling = self.qk_head_dim**-0.5
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
+        self.kv_cache_dtype = get_global_server_args().kv_cache_dtype
 
         # NOTE modification to rope_scaling must be done early enough, b/c e.g. Indexer needs it
         if rope_scaling:
@@ -1359,6 +1375,10 @@ class DeepseekV2AttentionMLA(nn.Module):
             inner_state = self.forward_normal_chunked_kv_prepare(
                 positions, hidden_states, forward_batch, zero_allocator
             )
+        elif attn_forward_method == AttnForwardMethod.MHA_ONE_SHOT:
+            inner_state = self.forward_normal_one_shot_prepare(
+                positions, hidden_states, forward_batch, zero_allocator
+            )
         elif attn_forward_method == AttnForwardMethod.MLA:
             if not self.is_mla_preprocess_enabled:
                 inner_state = self.forward_absorb_prepare(
@@ -1410,6 +1430,8 @@ class DeepseekV2AttentionMLA(nn.Module):
             return self.forward_normal_core(*inner_state)
         elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV:
             return self.forward_normal_chunked_kv_core(*inner_state)
+        elif attn_forward_method == AttnForwardMethod.MHA_ONE_SHOT:
+            return self.forward_normal_one_shot_core(*inner_state)
         elif attn_forward_method == AttnForwardMethod.MLA:
             return self.forward_absorb_core(*inner_state)
         elif attn_forward_method == AttnForwardMethod.NPU_MLA_SPARSE:
@@ -1444,41 +1466,24 @@ class DeepseekV2AttentionMLA(nn.Module):
         kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
         latent_cache = latent_cache.unsqueeze(1)
         kv_a = self.kv_a_layernorm(kv_a)
+        k_pe = latent_cache[:, :, self.kv_lora_rank :]
+        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
+        q[..., self.qk_nope_head_dim :] = q_pe
+
+        self._set_mla_kv_buffer(latent_cache, kv_a, k_pe, forward_batch)
+        if (
+            forward_batch.mha_one_shot
+            and sum(forward_batch.extend_prefix_lens_cpu) != 0
+        ):
+            kv_a, k_pe = self._get_mla_kv_buffer(
+                forward_batch.fetch_mha_one_shot_kv_indices(), q.dtype, forward_batch
+            )
         kv = self.kv_b_proj(kv_a)[0]
         kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
         k_nope = kv[..., : self.qk_nope_head_dim]
         v = kv[..., self.qk_nope_head_dim :]
-        k_pe = latent_cache[:, :, self.kv_lora_rank :]
-        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
-        q[..., self.qk_nope_head_dim :] = q_pe
-        k = torch.empty_like(q)
-
-        # Temporary for DeepSeek V3/R1 only, but can generalize if needed
-        if (
-            _is_cuda
-            and (self.num_local_heads == 128)
-            and (self.qk_nope_head_dim == 128)
-            and (self.qk_rope_head_dim == 64)
-        ):
-            concat_mla_k(k=k, k_nope=k_nope, k_rope=k_pe)
-        else:
-            k[..., : self.qk_nope_head_dim] = k_nope
-            k[..., self.qk_nope_head_dim :] = k_pe
-
-        if not _is_npu:
-            latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
-            latent_cache[:, :, self.kv_lora_rank :] = k_pe
-
-            # Save latent cache
-            forward_batch.token_to_kv_pool.set_kv_buffer(
-                self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
-            )
-        else:
-            # To reduce a time-costing split operation
-            forward_batch.token_to_kv_pool.set_kv_buffer(
-                self.attn_mha, forward_batch.out_cache_loc, kv_a.unsqueeze(1), k_pe
-            )
 
+        k = self._concat_and_cast_mha_k(k_nope, k_pe, forward_batch)
         return q, k, v, forward_batch
 
     def forward_normal_core(self, q, k, v, forward_batch):
@@ -2288,20 +2293,11 @@ class DeepseekV2AttentionMLA(nn.Module):
         for i in range(forward_batch.num_prefix_chunks):
             forward_batch.set_prefix_chunk_idx(i)
 
+            kv_indices = forward_batch.prefix_chunk_kv_indices[i]
             # Fetch latent cache from memory pool with precomputed chunked kv indices
-            latent_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer(
+            kv_a_normed, k_pe = self._get_mla_kv_buffer(
-                self.attn_mha.layer_id
+                kv_indices, q.dtype, forward_batch
             )
-            latent_cache = (
-                latent_cache_buf[forward_batch.prefix_chunk_kv_indices[i]]
-                .contiguous()
-                .to(q.dtype)
-            )
-
-            kv_a_normed, k_pe = latent_cache.split(
-                [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-            )
-            kv_a_normed = kv_a_normed.squeeze(1).contiguous()
             kv = self.kv_b_proj(kv_a_normed)[0]
             kv = kv.view(
                 -1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim
@@ -2376,6 +2372,107 @@ class DeepseekV2AttentionMLA(nn.Module):
         output, _ = self.o_proj(attn_output)
         return output
 
+    def forward_normal_one_shot_prepare(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        zero_allocator: BumpAllocator,
+    ):
+        forward_batch.mha_one_shot = True
+        return self.forward_normal_prepare(
+            positions, hidden_states, forward_batch, zero_allocator
+        )
+
+    def forward_normal_one_shot_core(self, q, k, v, forward_batch):
+        has_extend_prefix = any(forward_batch.extend_prefix_lens_cpu)
+        # Only initialize the info once
+        if has_extend_prefix and forward_batch.num_prefix_chunks is None:
+            forward_batch.num_prefix_chunks = 0
+            if hasattr(forward_batch.attn_backend, "init_mha_chunk_metadata"):
+                forward_batch.attn_backend.init_mha_chunk_metadata(forward_batch)
+        forward_batch.mha_return_lse = False
+        # Do mha for extended part without prefix
+        forward_batch.set_attn_attend_prefix_cache(False)
+        return self.forward_normal_core(q, k, v, forward_batch)
+
+    def _set_mla_kv_buffer(
+        self,
+        latent_cache: torch.Tensor,
+        kv_a: torch.Tensor,
+        k_pe: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ):
+        if _is_cuda:
+            # Save latent cache
+            forward_batch.token_to_kv_pool.set_mla_kv_buffer(
+                self.attn_mha, forward_batch.out_cache_loc, kv_a.unsqueeze(1), k_pe
+            )
+        elif _is_npu:
+            # To reduce a time-costing split operation
+            forward_batch.token_to_kv_pool.set_kv_buffer(
+                self.attn_mha, forward_batch.out_cache_loc, kv_a.unsqueeze(1), k_pe
+            )
+        else:
+            latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
+            latent_cache[:, :, self.kv_lora_rank :] = k_pe
+
+            # Save latent cache
+            forward_batch.token_to_kv_pool.set_kv_buffer(
+                self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
+            )
+
+    def _get_mla_kv_buffer(
+        self,
+        kv_indices: torch.Tensor,
+        dst_dtype: torch.dtype,
+        forward_batch: ForwardBatch,
+    ):
+        if _is_cuda:
+            kv_a, k_pe = forward_batch.token_to_kv_pool.get_mla_kv_buffer(
+                self.attn_mha, kv_indices, dst_dtype
+            )
+            kv_a = kv_a.squeeze(1)
+        else:
+            latent_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer(
+                self.attn_mha.layer_id
+            )
+            latent_cache = latent_cache_buf[kv_indices].contiguous().to(dst_dtype)
+
+            kv_a, k_pe = latent_cache.split(
+                [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+            )
+            kv_a = kv_a.squeeze(1).contiguous()
+        return kv_a, k_pe
+
+    def _concat_and_cast_mha_k(self, k_nope, k_pe, forward_batch):
+        # Temporary for DeepSeek V3/R1 only, but can generalize if needed
+        k_shape = (k_nope.shape[0], self.num_local_heads, self.qk_head_dim)
+        if (
+            _is_cuda
+            and (self.num_local_heads == 128)
+            and (self.qk_nope_head_dim == 128)
+            and (self.qk_rope_head_dim == 64)
+        ):
+            k = k_nope.new_empty(*k_shape)
+            concat_mla_k(k=k, k_nope=k_nope, k_rope=k_pe)
+        elif _is_cuda:
+            # fa3 mha support fp8 inputs
+            if (
+                self.current_attention_backend == "fa3"
+                and self.kv_cache_dtype != "auto"
+            ):
+                attn_dtype = forward_batch.token_to_kv_pool.dtype
+            else:
+                attn_dtype = k_nope.dtype
+            k = k_nope.new_empty(*k_shape, dtype=attn_dtype)
+            concat_and_cast_mha_k_triton(k, k_nope, k_pe)
+        else:
+            k = k_nope.new_empty(*k_shape)
+            k[..., : self.qk_nope_head_dim] = k_nope
+            k[..., self.qk_nope_head_dim :] = k_pe
+        return k
+
     @staticmethod
     def _get_q_b_proj_quant_config(quant_config):
         if get_bool_env_var("SGLANG_NVFP4_CKPT_FP8_GEMM_IN_ATTN"):