[Kernel] Support DCP for Triton backend (#25132)

Signed-off-by: Wei Wei <wwei6@meta.com>

tests/kernels/attention/test_triton_decode_attention.py

@@ -46,6 +46,8 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
     # o will have the same shape as q
     o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
 
+    lse = torch.zeros(B, H_Q, dtype=dtype, device="cuda")
+
     b_seq_len = torch.full((B, ), seq_len, device="cuda")
 
     attn_logits = torch.empty(
@@ -60,6 +62,7 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
         k_buffer,
         v_buffer,
         o,
+        lse,
         req_to_token,
         b_seq_len,
         attn_logits,
@@ -72,12 +75,14 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
     v_buffer = v_buffer.view(CACHE_SIZE // PAGE_SIZE, PAGE_SIZE, H_KV, D_V)
 
     o1 = torch.zeros_like(o)
+    lse1 = torch.zeros_like(lse)
 
     decode_attention_fwd(
         q,
         k_buffer,
         v_buffer,
         o1,
+        lse1,
         req_to_page,
         b_seq_len,
         attn_logits,

vllm/attention/ops/triton_decode_attention.py

@@ -474,12 +474,14 @@ def _decode_grouped_att_m_fwd(
 def _fwd_kernel_stage2(
     Mid_O,
     o,
+    lse,
     B_Seqlen,
     stride_mid_ob,
     stride_mid_oh,
     stride_mid_os,
     stride_obs,
     stride_oh,
+    stride_lse_bs,
     NUM_KV_SPLITS: tl.constexpr,
     BLOCK_DV: tl.constexpr,
     Lv: tl.constexpr,
@@ -525,12 +527,18 @@ def _fwd_kernel_stage2(
         acc / e_sum,
         mask=mask_d,
     )
+    lse_val = e_max + tl.log(e_sum)
+    tl.store(
+        lse + cur_batch * stride_lse_bs + cur_head,
+        lse_val,
+    )
 
 
 def _decode_softmax_reducev_fwd(
     logits,
     q,
     o,
+    lse,
     v_buffer,
     b_seq_len,
     num_kv_splits,
@@ -555,12 +563,14 @@ def _decode_softmax_reducev_fwd(
     _fwd_kernel_stage2[grid](
         logits,
         o,
+        lse,
         b_seq_len,
         logits.stride(0),
         logits.stride(1),
         logits.stride(2),
         o.stride(0),
         o.stride(1),
+        lse.stride(0),
         NUM_KV_SPLITS=NUM_KV_SPLITS,
         BLOCK_DV=BLOCK_DV,
         Lv=Lv,
@@ -575,6 +585,7 @@ def decode_attention_fwd_normal(
     k_buffer,
     v_buffer,
     o,
+    lse,
     req_to_token,
     b_seq_len,
     attn_logits,
@@ -595,7 +606,7 @@ def decode_attention_fwd_normal(
         page_size,
         logit_cap,
     )
-    _decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, b_seq_len,
+    _decode_softmax_reducev_fwd(attn_logits, q, o, lse, v_buffer, b_seq_len,
                                 num_kv_splits)
 
 
@@ -604,6 +615,7 @@ def decode_attention_fwd_grouped(
     k_buffer,
     v_buffer,
     o,
+    lse,
     req_to_token,
     b_seq_len,
     attn_logits,
@@ -624,7 +636,7 @@ def decode_attention_fwd_grouped(
         page_size,
         logit_cap,
     )
-    _decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, b_seq_len,
+    _decode_softmax_reducev_fwd(attn_logits, q, o, lse, v_buffer, b_seq_len,
                                 num_kv_splits)
 
 
@@ -633,6 +645,7 @@ def decode_attention_fwd(
     k_buffer,
     v_buffer,
     o,
+    lse,
     req_to_token,
     b_seq_len,
     attn_logits,
@@ -651,6 +664,7 @@ def decode_attention_fwd(
             k_buffer,
             v_buffer,
             o,
+            lse,
             req_to_token,
             b_seq_len,
             attn_logits,
@@ -666,6 +680,7 @@ def decode_attention_fwd(
             k_buffer,
             v_buffer,
             o,
+            lse,
             req_to_token,
             b_seq_len,
             attn_logits,

vllm/model_executor/models/deepseek_v2.py

@@ -685,7 +685,7 @@ class DeepseekV2DecoderLayer(nn.Module):
     ) -> torch.Tensor:
         # Self Attention
         if residual is None:
-            residual = hidden_states
+            residual = hidden_states.clone()
             hidden_states = self.input_layernorm(hidden_states)
         else:
             hidden_states, residual = self.input_layernorm(

vllm/v1/attention/backends/mla/triton_mla.py

@@ -32,6 +32,7 @@ class TritonMLABackend(MLACommonBackend):
 
 
 class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
+    can_return_lse_for_decode: bool = True
 
     def __init__(
             self,
@@ -139,19 +140,20 @@ class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
 
         assert isinstance(q, torch.Tensor)
         B = q.shape[0]
+        q_num_heads = q.shape[1]
         o = torch.zeros(B,
-                        self.num_heads,
+                        q_num_heads,
                         self.kv_lora_rank,
                         dtype=q.dtype,
                         device=q.device)
-
+        lse = torch.zeros(B, q_num_heads, dtype=q.dtype, device=q.device)
         num_kv_splits = 4  # TODO: heuristic
 
         # TODO(lucas) Allocate ahead of time
         attn_logits = torch.empty(
             (
                 B,
-                self.num_heads,
+                q_num_heads,
                 num_kv_splits,
                 # NOTE(lucas) idk why the +1 is here but sglang has it so we
                 # just mirror that
@@ -167,9 +169,9 @@ class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
         PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
 
         # Run MQA
-        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
+        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o, lse,
                              attn_metadata.decode.block_table,
                              attn_metadata.decode.seq_lens, attn_logits,
                              num_kv_splits, self.scale, PAGE_SIZE)
 
-        return o, None
+        return o, lse