Update extend/decode attention kernel for CPU in sgl-kernel and add UTs (#6405)

Co-authored-by: mingfeima <mingfei.ma@intel.com>

sgl-kernel/csrc/cpu/decode.cpp

@@ -34,6 +34,19 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ acc,
   }
 }
 
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ src, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  int64_t d = 0;
+  for (; d <= size - bVec::size(); d += bVec::size()) {
+    bVec out_bvec = bVec::loadu(src + d);
+    out_bvec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = src[d];
+  }
+}
+
 // GEMM handles query @ key (indexed) x scale
 //   A : [M, K]
 //   B : [N, K] indexed
@@ -611,8 +624,11 @@ void decode_attention_kernel_impl(
     scalar_t* __restrict__ output,
     float* __restrict__ attn_logits,
     const scalar_t* __restrict__ query,
-    const scalar_t* __restrict__ k_buffer,
+    scalar_t* __restrict__ k_buffer,
-    const scalar_t* __restrict__ v_buffer,
+    scalar_t* __restrict__ v_buffer,
+    const scalar_t* __restrict__ key,
+    const scalar_t* __restrict__ value,
+    const int64_t* __restrict__ loc,
     const index_t* __restrict__ req_to_token,
     const int64_t* __restrict__ req_pool_indices,
     const int64_t* __restrict__ seq_lens,
@@ -625,11 +641,33 @@ void decode_attention_kernel_impl(
     int64_t k_strideH,
     int64_t v_strideN,
     int64_t v_strideH,
+    int64_t nk_strideN,
+    int64_t nk_strideH,
+    int64_t nv_strideN,
+    int64_t nv_strideH,
     float scaling,
     float logit_cap,
     int64_t max_num_reqs,
     int64_t max_context_len,
     int64_t max_total_num_tokens) {
+  at::parallel_for(0, batches * num_heads, 0, [&](int64_t begin, int64_t end) {
+    int64_t bs{0}, head_id{0};
+    data_index_init(begin, bs, batches, head_id, num_heads);
+
+    for (int64_t i = begin; i < end; i++) {
+      int64_t loc_val = loc[bs];
+      scalar_t* k_buffer_ptr = k_buffer + loc_val * k_strideN + head_id * k_strideH;
+      scalar_t* v_buffer_ptr = v_buffer + loc_val * v_strideN + head_id * v_strideH;
+      const scalar_t* new_key_ptr = key + bs * nk_strideN + head_id * nk_strideH;
+      const scalar_t* new_value_ptr = value + bs * nv_strideN + head_id * nv_strideH;
+      copy_stub<scalar_t>(k_buffer_ptr, new_key_ptr, head_size);
+      copy_stub<scalar_t>(v_buffer_ptr, new_value_ptr, head_size_v);
+
+      // move to the next index
+      data_index_step(bs, batches, head_id, num_heads);
+    }
+  });
+
   using Vec = at::vec::Vectorized<float>;
 
   // block length for k_buffer and v_buffer
@@ -791,8 +829,11 @@ void decode_attention_grouped_kernel_impl(
     scalar_t* __restrict__ output,
     float* __restrict__ attn_logits,
     const scalar_t* __restrict__ query,
-    const scalar_t* __restrict__ k_buffer,
+    scalar_t* __restrict__ k_buffer,
-    const scalar_t* __restrict__ v_buffer,
+    scalar_t* __restrict__ v_buffer,
+    const scalar_t* __restrict__ key,
+    const scalar_t* __restrict__ value,
+    const int64_t* __restrict__ loc,
     const index_t* __restrict__ req_to_token,
     const int64_t* __restrict__ req_pool_indices,
     const int64_t* __restrict__ seq_lens,
@@ -806,11 +847,33 @@ void decode_attention_grouped_kernel_impl(
     int64_t k_strideH,
     int64_t v_strideN,
     int64_t v_strideH,
+    int64_t nk_strideN,
+    int64_t nk_strideH,
+    int64_t nv_strideN,
+    int64_t nv_strideH,
     float scaling,
     float logit_cap,
     int64_t max_num_reqs,
     int64_t max_context_len,
     int64_t max_total_num_tokens) {
+  at::parallel_for(0, batches * num_heads_kv, 0, [&](int64_t begin, int64_t end) {
+    int64_t bs{0}, head_kv_id{0};
+    data_index_init(begin, bs, batches, head_kv_id, num_heads_kv);
+
+    for (int64_t i = begin; i < end; i++) {
+      int64_t loc_val = loc[bs];
+      scalar_t* k_buffer_ptr = k_buffer + loc_val * k_strideN + head_kv_id * k_strideH;
+      scalar_t* v_buffer_ptr = v_buffer + loc_val * v_strideN + head_kv_id * v_strideH;
+      const scalar_t* new_key_ptr = key + bs * nk_strideN + head_kv_id * nk_strideH;
+      const scalar_t* new_value_ptr = value + bs * nv_strideN + head_kv_id * nv_strideH;
+      copy_stub<scalar_t>(k_buffer_ptr, new_key_ptr, head_size);
+      copy_stub<scalar_t>(v_buffer_ptr, new_value_ptr, head_size_v);
+
+      // move to the next index
+      data_index_step(bs, batches, head_kv_id, num_heads_kv);
+    }
+  });
+
   using Vec = at::vec::Vectorized<float>;
 
   // block length for k_buffer and v_buffer
@@ -833,14 +896,12 @@ void decode_attention_grouped_kernel_impl(
 
   // partition the heads into blocks for parallel
   const int64_t num_groups = num_heads / num_heads_kv;
-  const int64_t num_blocks = div_up(num_heads, std::min(BLOCK_H, num_groups));
+  const int64_t num_blocks = div_up(num_groups, BLOCK_H);
-  const int64_t num_groups_per_block = div_up(num_groups, BLOCK_H);
-  const int64_t num_heads_per_block = std::min(num_groups, BLOCK_H);
 
-  // parallel on [batches, num_blocks, num_kv_splits]
+  // parallel on [batches, num_heads_kv, num_blocks, num_kv_splits]
-  at::parallel_for(0, batches * num_blocks * num_kv_splits, 0, [&](int64_t begin, int64_t end) {
+  at::parallel_for(0, batches * num_heads_kv * num_blocks * num_kv_splits, 0, [&](int64_t begin, int64_t end) {
-    int64_t bs{0}, head_id{0}, kv_id{0};
+    int64_t bs{0}, head_kv_id{0}, block_id{0}, kv_id{0};
-    data_index_init(begin, bs, batches, head_id, num_blocks, kv_id, num_kv_splits);
+    data_index_init(begin, bs, batches, head_kv_id, num_heads_kv, block_id, num_blocks, kv_id, num_kv_splits);
 
     alignas(64) float s_i[BLOCK_H * BLOCK_N];
     float* __restrict__ s_delta = s_i;
@@ -850,15 +911,13 @@ void decode_attention_grouped_kernel_impl(
     alignas(64) float m_delta[BLOCK_H];
 
     for (int64_t i = begin; i < end; ++i) {
-      const int64_t h_start = head_id * num_heads_per_block;
+      const int64_t h_start = head_kv_id * num_groups + block_id * BLOCK_H;
-      const int64_t h_end = std::min(h_start + num_heads_per_block, num_heads);
+      const int64_t h_end = head_kv_id * num_groups + std::min(block_id * BLOCK_H + BLOCK_H, num_groups);
       const int64_t h_size = h_end - h_start;
 
       // get query
       const scalar_t* __restrict__ q_ptr = query + bs * q_strideM + h_start * q_strideH;
 
-      // kv head id and valid block head size
-      int64_t head_kv_id = head_id / num_groups_per_block;
       int64_t seq_len_kv = seq_lens[bs];
       int64_t req_pool_id = req_pool_indices[bs];
       TORCH_CHECK(seq_len_kv <= max_context_len, "seq_len_kv out of scope!");
@@ -952,7 +1011,7 @@ void decode_attention_grouped_kernel_impl(
       }
 
       // move to the next index
-      data_index_step(bs, batches, head_id, num_blocks, kv_id, num_kv_splits);
+      data_index_step(bs, batches, head_kv_id, num_heads_kv, block_id, num_blocks, kv_id, num_kv_splits);
     }
   });
 
@@ -1004,9 +1063,12 @@ void decode_attention_grouped_kernel_impl(
 //
 void decode_attention_cpu(
     at::Tensor& query,
-    at::Tensor& output,
     at::Tensor& k_buffer,
     at::Tensor& v_buffer,
+    at::Tensor& output,
+    at::Tensor& key,
+    at::Tensor& value,
+    at::Tensor& loc,
     at::Tensor& attn_logits,
     at::Tensor& req_to_token,
     at::Tensor& req_pool_indices,
@@ -1021,9 +1083,15 @@ void decode_attention_cpu(
   CHECK_INPUT(query);
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(k_buffer);
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(v_buffer);
+  // for MLA, key and value shares the same storage and value could be non-contiguous
+  CHECK_LAST_DIM_CONTIGUOUS_INPUT(key);
+  CHECK_LAST_DIM_CONTIGUOUS_INPUT(value);
   CHECK_DIM(3, query);
   CHECK_DIM(3, k_buffer);
   CHECK_DIM(3, v_buffer);
+  CHECK_DIM(3, key);
+  CHECK_DIM(3, value);
+  CHECK_DIM(1, loc);
 
   int64_t num_seqs = seq_lens.size(0);
   int64_t max_num_reqs = req_to_token.size(0);
@@ -1037,6 +1105,7 @@ void decode_attention_cpu(
 
   int64_t num_kv_splits = attn_logits.size(2);
 
+  CHECK_EQ(loc.numel(), num_seqs);
   CHECK_EQ(attn_logits.size(0), num_seqs);
   CHECK_EQ(attn_logits.size(1), num_heads);
   CHECK_EQ(attn_logits.size(3), head_size_v + 1);
@@ -1047,6 +1116,11 @@ void decode_attention_cpu(
   int64_t k_strideH = k_buffer.stride(1);
   int64_t v_strideN = v_buffer.stride(0);
   int64_t v_strideH = v_buffer.stride(1);
+  // strides for new key and value
+  int64_t nk_strideN = key.stride(0);
+  int64_t nk_strideH = key.stride(1);
+  int64_t nv_strideN = value.stride(0);
+  int64_t nv_strideH = value.stride(1);
 
   // check index data types
   const auto index_dtype = req_to_token.scalar_type();
@@ -1070,6 +1144,9 @@ void decode_attention_cpu(
             query.data_ptr<scalar_t>(),
             k_buffer.data_ptr<scalar_t>(),
             v_buffer.data_ptr<scalar_t>(),
+            key.data_ptr<scalar_t>(),
+            value.data_ptr<scalar_t>(),
+            loc.data_ptr<int64_t>(),
             req_to_token.data_ptr<index_t>(),
             req_pool_indices.data_ptr<int64_t>(),
             seq_lens.data_ptr<int64_t>(),
@@ -1082,6 +1159,10 @@ void decode_attention_cpu(
             k_strideH,
             v_strideN,
             v_strideH,
+            nk_strideN,
+            nv_strideH,
+            nv_strideN,
+            nv_strideH,
             sm_scale,
             logit_cap,
             max_num_reqs,
@@ -1095,6 +1176,9 @@ void decode_attention_cpu(
             query.data_ptr<scalar_t>(),
             k_buffer.data_ptr<scalar_t>(),
             v_buffer.data_ptr<scalar_t>(),
+            key.data_ptr<scalar_t>(),
+            value.data_ptr<scalar_t>(),
+            loc.data_ptr<int64_t>(),
             req_to_token.data_ptr<index_t>(),
             req_pool_indices.data_ptr<int64_t>(),
             seq_lens.data_ptr<int64_t>(),
@@ -1108,6 +1192,10 @@ void decode_attention_cpu(
             k_strideH,
             v_strideN,
             v_strideH,
+            nk_strideN,
+            nk_strideH,
+            nv_strideN,
+            nv_strideH,
             sm_scale,
             logit_cap,
             max_num_reqs,

sgl-kernel/csrc/cpu/torch_extension_cpu.cpp

@@ -49,9 +49,12 @@ std::tuple<at::Tensor, at::Tensor> biased_grouped_topk_cpu(
 // attention
 void decode_attention_cpu(
     at::Tensor& query,
-    at::Tensor& output,
     at::Tensor& k_cache,
-    at::Tensor& v_cahce,
+    at::Tensor& v_cache,
+    at::Tensor& output,
+    at::Tensor& key,
+    at::Tensor& value,
+    at::Tensor& loc,
     at::Tensor& attn_logits,
     at::Tensor& req_to_token,
     at::Tensor& req_pool_indices,

test/srt/cpu/test_decode.py

@@ -0,0 +1,167 @@
+import unittest
+
+import torch
+from sgl_kernel.common_ops import decode_attention_cpu as decode_attention
+from torch.nn.functional import scaled_dot_product_attention
+
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestDecodeAttention(CustomTestCase):
+    def _run_sdpa_forward_decode(
+        self,
+        query: torch.Tensor,
+        output: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        req_to_token: torch.Tensor,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        scaling=None,
+        enable_gqa=False,
+        causal=False,
+    ):
+        # [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
+        query = query.movedim(0, query.dim() - 2)
+
+        start_q, start_kv = 0, 0
+        for seq_idx in range(seq_lens.shape[0]):
+            seq_len_q = 1
+            seq_len_kv = seq_lens[seq_idx]
+            end_q = start_q + seq_len_q
+            end_kv = start_kv + seq_len_kv
+
+            per_req_query = query[:, start_q:end_q, :]
+
+            # get key and value from cache. per_req_tokens contains the kv cache
+            # index for each token in the sequence.
+            req_pool_idx = req_pool_indices[seq_idx]
+            per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]
+            per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
+            per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
+
+            per_req_out = (
+                scaled_dot_product_attention(
+                    per_req_query.unsqueeze(0),
+                    per_req_key.unsqueeze(0),
+                    per_req_value.unsqueeze(0),
+                    enable_gqa=enable_gqa,
+                    scale=scaling,
+                    is_causal=causal,
+                )
+                .squeeze(0)
+                .movedim(query.dim() - 2, 0)
+            )
+            output[start_q:end_q, :, :] = per_req_out
+            start_q, start_kv = end_q, end_kv
+
+        return output
+
+    def _test_grouped_decode_attention_once(self, B, H_Q, H_KV, D, D_V, device):
+        dtype = torch.bfloat16
+        # This represents the number of tokens already in the sequence
+        seq_len = 1024
+        total_tokens = B * seq_len
+        sm_scale = 1.0 / (D**0.5)
+        logit_cap = 0.0
+        num_kv_splits = 8
+        enable_gqa = H_Q != H_KV
+
+        # q represents the new token being generated, one per batch
+        q = torch.randn(B, H_Q, D, dtype=dtype, device=device)
+
+        # k_buffer and v_buffer represent all previous tokens
+        k_buffer = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=device)
+        v_buffer = torch.randn(total_tokens, H_KV, D_V, dtype=dtype, device=device)
+
+        key = torch.randn(B, H_KV, D, dtype=dtype)
+        value = torch.randn(B, H_KV, D_V, dtype=dtype)
+        loc = torch.randint(0, 10, (B,)).to(torch.int64)
+
+        # set kv cache
+        k_buffer[loc] = key
+        v_buffer[loc] = value
+
+        # o will have the same shape as q
+        o = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)
+        o_grouped = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)
+
+        req_to_token = (
+            torch.arange(total_tokens, device=device)
+            .reshape(B, seq_len)
+            .to(torch.int32)
+        )
+        b_req_idx = torch.arange(B, device=device).to(torch.int64)
+        b_seq_len = torch.full((B,), seq_len, device=device).to(torch.int64)
+
+        attn_logits = torch.empty(
+            (B, H_Q, num_kv_splits, D_V + 1),
+            dtype=torch.float32,
+            device=device,
+        )
+
+        # k_buffer, v_buffer, key and value supports non-contiguous tensors
+        k_buffer = k_buffer.transpose(0, 1).contiguous().transpose(0, 1)
+        v_buffer = v_buffer.transpose(0, 1).contiguous().transpose(0, 1)
+        key = key.transpose(0, 1).contiguous().transpose(0, 1)
+        value = value.transpose(0, 1).contiguous().transpose(0, 1)
+        decode_attention(
+            q,
+            k_buffer,
+            v_buffer,
+            o,
+            key,
+            value,
+            loc,
+            attn_logits,
+            req_to_token,
+            b_req_idx,
+            b_seq_len,
+            sm_scale,
+            logit_cap,
+        )
+
+        self._run_sdpa_forward_decode(
+            q,
+            o_grouped,
+            k_buffer,
+            v_buffer,
+            req_to_token,
+            b_req_idx,
+            b_seq_len,
+            scaling=sm_scale,
+            enable_gqa=enable_gqa,
+        )
+
+        cos_sim = torch.nn.functional.cosine_similarity(
+            o.flatten(), o_grouped.flatten(), dim=0
+        )
+        self.assertGreater(cos_sim.item(), 0.99)
+        torch.testing.assert_close(o, o_grouped, atol=3e-2, rtol=1e-6)
+
+    def _test_grouped_decode_attention(self, device="cuda"):
+        configs = [
+            (2, 16, 16, 64, 64),
+            (2, 16, 1, 16, 16),
+            (2, 32, 8, 33, 55),
+            (2, 16, 1, 64, 64),
+            (2, 64, 1, 13, 13),
+            (2, 128, 1, 80, 80),
+            (2, 128, 2, 512, 512),
+            (1, 16, 1, 576, 512),
+            (1, 16, 16, 576, 512),
+            (1, 22, 1, 576, 512),
+            (1, 40, 8, 128, 128),
+        ]
+
+        for B, H_Q, H_KV, D, D_V in configs:
+            self._test_grouped_decode_attention_once(
+                B, H_Q, H_KV, D, D_V, device=device
+            )
+
+    def test_grouped_decode_attention(self):
+        self._test_grouped_decode_attention("cpu")
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/cpu/test_extend.py

@@ -0,0 +1,187 @@
+import unittest
+
+import torch
+from sgl_kernel.common_ops import extend_attention_cpu as extend_attention
+from torch.nn.functional import scaled_dot_product_attention
+
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestExtendAttention(CustomTestCase):
+
+    def _run_sdpa_forward_extend(
+        self,
+        query: torch.Tensor,
+        output: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        req_to_token: torch.Tensor,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        extend_prefix_lens: torch.Tensor,
+        extend_seq_lens: torch.Tensor,
+        scaling=None,
+        enable_gqa=False,
+        causal=False,
+    ):
+
+        assert seq_lens.shape[0] == extend_prefix_lens.shape[0]
+        assert seq_lens.shape[0] == extend_seq_lens.shape[0]
+
+        # [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
+        query = query.movedim(0, query.dim() - 2)
+
+        start_q, start_kv = 0, 0
+        for seq_idx in range(seq_lens.shape[0]):
+
+            extend_seq_len_q = extend_seq_lens[seq_idx]
+            prefill_seq_len_q = extend_prefix_lens[seq_idx]
+
+            seq_len_kv = seq_lens[seq_idx]
+            end_q = start_q + extend_seq_len_q
+            end_kv = start_kv + seq_len_kv
+
+            per_req_query = query[:, start_q:end_q, :]
+            per_req_query_redudant = torch.empty(
+                (per_req_query.shape[0], seq_len_kv, per_req_query.shape[2]),
+                dtype=per_req_query.dtype,
+                device=per_req_query.device,
+            )
+
+            per_req_query_redudant[:, prefill_seq_len_q:, :] = per_req_query
+
+            # get key and value from cache. per_req_tokens contains the kv cache
+            # index for each token in the sequence.
+            req_pool_idx = req_pool_indices[seq_idx]
+            per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]
+            per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
+            per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
+
+            per_req_out_redudant = (
+                scaled_dot_product_attention(
+                    per_req_query_redudant.unsqueeze(0),
+                    per_req_key.unsqueeze(0),
+                    per_req_value.unsqueeze(0),
+                    enable_gqa=enable_gqa,
+                    scale=scaling,
+                    is_causal=causal,
+                )
+                .squeeze(0)
+                .movedim(query.dim() - 2, 0)
+            )
+            output[start_q:end_q, :, :] = per_req_out_redudant[prefill_seq_len_q:, :, :]
+            start_q, start_kv = end_q, end_kv
+        return output
+
+    def _test_extend_attention_once(self, B, N_CTX, H_Q, H_KV, D, DV, mla=False):
+        dtype = torch.bfloat16
+
+        b_seq_len_prefix = torch.randint(1, N_CTX // 2, (B,), dtype=torch.int32)
+        if mla:
+            b_seq_len_prefix.zero_()
+        b_seq_len_extend = torch.randint(1, N_CTX // 2, (B,), dtype=torch.int32)
+        b_seq_len = b_seq_len_prefix + b_seq_len_extend
+        max_len_in_batch = torch.max(b_seq_len, 0)[0].item()
+
+        b_req_idx = torch.arange(B, dtype=torch.int32)
+        req_to_tokens = torch.empty((B, max_len_in_batch), dtype=torch.int32)
+        b_start_loc = torch.zeros((B,), dtype=torch.int32)
+        b_start_loc[1:] = torch.cumsum(b_seq_len[:-1], 0)
+        b_start_loc_extend = torch.zeros((B,), dtype=torch.int32)
+        b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
+
+        for i in range(B):
+            req_to_tokens[i, : b_seq_len[i]] = torch.arange(
+                b_start_loc[i], b_start_loc[i] + b_seq_len[i]
+            )
+
+        total_token_num = torch.sum(b_seq_len).item()
+        extend_token_num = torch.sum(b_seq_len_extend).item()
+
+        H_BUF = 1 if mla else H_KV
+        k_buffer = torch.randn((total_token_num, H_BUF, D), dtype=dtype)
+        v_buffer = torch.randn((total_token_num, H_BUF, DV), dtype=dtype)
+
+        k_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype)
+        v_extend = torch.empty((extend_token_num, H_KV, DV), dtype=dtype)
+        q_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype)
+
+        for i in range(B):
+            extend_start_in_buffer = b_start_loc[i] + b_seq_len_prefix[i]
+            extend_end_in_buffer = b_start_loc[i] + b_seq_len[i]
+            extend_start = b_start_loc_extend[i]
+            extend_end = b_start_loc_extend[i] + b_seq_len_extend[i]
+            k_extend[extend_start:extend_end] = k_buffer[
+                extend_start_in_buffer:extend_end_in_buffer
+            ]
+            v_extend[extend_start:extend_end] = v_buffer[
+                extend_start_in_buffer:extend_end_in_buffer
+            ]
+            q_extend[extend_start:extend_end] = torch.randn(
+                (b_seq_len_extend[i], H_Q, D), dtype=dtype
+            )
+
+        # k_extend, v_extend, k_buffer and v_buffer supports non-contiguous tensors
+        k_extend = k_extend.transpose(0, 1).contiguous().transpose(0, 1)
+        v_extend = v_extend.transpose(0, 1).contiguous().transpose(0, 1)
+        k_buffer = k_buffer.transpose(0, 1).contiguous().transpose(0, 1)
+        v_buffer = v_buffer.transpose(0, 1).contiguous().transpose(0, 1)
+
+        b_seq_len_extend = b_seq_len - b_seq_len_prefix
+        b_start_loc_extend = torch.zeros_like(b_seq_len)
+        b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
+        max_len_extend = torch.max(b_seq_len_extend, 0)[0].item()
+
+        sm_scale = 1.0 / (D**0.5)
+        logit_cap = 0.0
+
+        # handle index type
+        b_req_idx = b_req_idx.to(torch.int64)
+        b_seq_len = b_seq_len.to(torch.int64)
+
+        enable_gqa = H_Q != H_KV
+        o_ref = torch.empty((extend_token_num, H_Q, DV), dtype=dtype)
+        self._run_sdpa_forward_extend(
+            q_extend,
+            o_ref,
+            k_buffer,
+            v_buffer,
+            req_to_tokens,
+            b_req_idx,
+            b_seq_len,
+            b_seq_len_prefix,
+            b_seq_len_extend,
+            scaling=sm_scale,
+            enable_gqa=enable_gqa,
+            causal=True,
+        )
+
+        o_extend = torch.empty((extend_token_num, H_Q, DV), dtype=dtype)
+        extend_attention(
+            q_extend,
+            k_extend,
+            v_extend,
+            o_extend,
+            k_buffer,
+            v_buffer,
+            req_to_tokens,
+            b_req_idx,
+            b_seq_len,
+            b_seq_len_extend,
+            b_start_loc_extend,
+            max_len_extend,
+            sm_scale,
+            logit_cap,
+        )
+
+        torch.testing.assert_close(o_ref, o_extend, atol=1e-2, rtol=1e-2)
+
+    def test_extend_attention(self):
+        for is_mla in [True, False]:
+            self._test_extend_attention_once(1, 123, 1, 1, 128, 96, is_mla)
+            self._test_extend_attention_once(1, 123, 16, 1, 128, 96, is_mla)
+            self._test_extend_attention_once(4, 1230, 16, 4, 128, 96, is_mla)
+
+
+if __name__ == "__main__":
+    unittest.main()