Refine naming (#8868)

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

@@ -686,7 +686,7 @@ class TritonAttnBackend(AttentionBackend):
         layer: RadixAttention,
         forward_batch: ForwardBatch,
         save_kv_cache=True,
-        sk=None,
+        sinks=None,
     ):
         # TODO: reuse the buffer across layers
         if layer.qk_head_dim != layer.v_head_dim:
@@ -731,7 +731,7 @@ class TritonAttnBackend(AttentionBackend):
             layer.scaling,
             layer.logit_cap,
             sliding_window_size=sliding_window_size,
-            sk=sk,
+            sinks=sinks,
         )
         return o
 
@@ -743,7 +743,7 @@ class TritonAttnBackend(AttentionBackend):
         layer: RadixAttention,
         forward_batch: ForwardBatch,
         save_kv_cache=True,
-        sk=None,
+        sinks=None,
     ):
         # During torch.compile, there is a bug in rotary_emb that causes the
         # output value to have a 3D tensor shape. This reshapes the output correctly.
@@ -780,7 +780,7 @@ class TritonAttnBackend(AttentionBackend):
             self.max_kv_splits,
             layer.scaling,
             layer.logit_cap,
-            sk=sk,
+            sinks=sinks,
         )
         return o
 

python/sglang/srt/layers/attention/triton_ops/decode_attention.py

@@ -495,7 +495,7 @@ def _fwd_kernel_stage2(
     O,
     kv_indptr,
     num_kv_splits,
-    sk_ptr,
+    sink_ptr,
     stride_mid_ob,
     stride_mid_oh,
     stride_mid_os,
@@ -505,7 +505,7 @@ def _fwd_kernel_stage2(
     MIN_BLOCK_KV: tl.constexpr,
     BLOCK_DV: tl.constexpr,
     Lv: tl.constexpr,
-    HAS_SK: tl.constexpr,
+    HAS_SINK: tl.constexpr,
 ):
     cur_batch = tl.program_id(0)
     cur_head = tl.program_id(1)
@@ -547,9 +547,9 @@ def _fwd_kernel_stage2(
             e_sum = e_sum * old_scale + exp_logic
             e_max = n_e_max
 
-    if HAS_SK:
-        cur_sk = tl.load(sk_ptr + cur_head)
-        e_sum += tl.exp(cur_sk - e_max)
+    if HAS_SINK:
+        cur_sink = tl.load(sink_ptr + cur_head)
+        e_sum += tl.exp(cur_sink - e_max)
 
     tl.store(
         O + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
@@ -567,14 +567,14 @@ def _decode_softmax_reducev_fwd(
     kv_indptr,
     num_kv_splits,
     max_kv_splits,
-    sk=None,
+    sinks=None,
 ):
     batch, head_num = q.shape[0], q.shape[1]
     Lv = v_buffer.shape[-1]
     BLOCK_DV = triton.next_power_of_2(Lv)
 
     MAX_KV_SPLITS = max_kv_splits
-    HAS_SK = sk is not None
+    HAS_SINK = sinks is not None
 
     extra_kargs = {}
     if _is_hip:
@@ -589,7 +589,7 @@ def _decode_softmax_reducev_fwd(
         o,
         kv_indptr,
         num_kv_splits,
-        sk,
+        sinks,
         logits.stride(0),
         logits.stride(1),
         logits.stride(2),
@@ -599,7 +599,7 @@ def _decode_softmax_reducev_fwd(
         MIN_BLOCK_KV=_MIN_BLOCK_KV,
         BLOCK_DV=BLOCK_DV,
         Lv=Lv,
-        HAS_SK=HAS_SK,
+        HAS_SINK=HAS_SINK,
         num_warps=4,
         num_stages=2,
         **extra_kargs,
@@ -619,7 +619,7 @@ def decode_attention_fwd_normal(
     max_kv_splits,
     sm_scale,
     logit_cap=0.0,
-    sk=None,
+    sinks=None,
 ):
     _decode_att_m_fwd(
         q,
@@ -643,7 +643,7 @@ def decode_attention_fwd_normal(
         kv_indptr,
         num_kv_splits,
         max_kv_splits,
-        sk,
+        sinks,
     )
 
 
@@ -660,7 +660,7 @@ def decode_attention_fwd_grouped(
     max_kv_splits,
     sm_scale,
     logit_cap=0.0,
-    sk=None,
+    sinks=None,
 ):
     _decode_grouped_att_m_fwd(
         q,
@@ -684,7 +684,7 @@ def decode_attention_fwd_grouped(
         kv_indptr,
         num_kv_splits,
         max_kv_splits,
-        sk,
+        sinks,
     )
 
 
@@ -701,7 +701,7 @@ def decode_attention_fwd(
     max_kv_splits,
     sm_scale,
     logit_cap=0.0,
-    sk=None,
+    sinks=None,
 ):
     assert max_kv_splits == attn_logits.shape[2]
     assert q.shape[0] <= kv_indptr.shape[0] - 1
@@ -724,7 +724,7 @@ def decode_attention_fwd(
             max_kv_splits,
             sm_scale,
             logit_cap=logit_cap,
-            sk=sk,
+            sinks=sinks,
         )
     else:
         # GQA/MQA/MLA
@@ -741,5 +741,5 @@ def decode_attention_fwd(
             max_kv_splits,
             sm_scale,
             logit_cap=logit_cap,
-            sk=sk,
+            sinks=sinks,
         )

python/sglang/srt/layers/attention/triton_ops/extend_attention.py

@@ -51,7 +51,7 @@ def _fwd_kernel(
     kv_indices,
     mask_ptr,
     mask_indptr,
-    sk_ptr,
+    sink_ptr,
     sm_scale,
     kv_group_num,
     stride_qbs,
@@ -79,7 +79,7 @@ def _fwd_kernel(
     IS_CAUSAL: tl.constexpr,
     SKIP_PREFIX_CUSTOM_MASK: tl.constexpr,
     STORE_TRANSPOSE: tl.constexpr,
-    HAS_SK: tl.constexpr,
+    HAS_SINK: tl.constexpr,
 ):
     cur_seq = tl.program_id(0)
     cur_head = tl.program_id(1)
@@ -302,9 +302,9 @@ def _fwd_kernel(
 
         e_max = n_e_max
 
-    if HAS_SK:
-        cur_sk = tl.load(sk_ptr + cur_head)
-        deno += tl.exp(cur_sk - e_max)
+    if HAS_SINK:
+        cur_sink = tl.load(sink_ptr + cur_head)
+        deno += tl.exp(cur_sink - e_max)
 
     offs_o = (
         (cur_seq_extend_start_idx + cur_block_m * BLOCK_M + offs_m[:, None])
@@ -344,7 +344,7 @@ def extend_attention_fwd(
     logit_cap=0.0,
     skip_prefix_custom_mask=True,
     sliding_window_size=-1,
-    sk=None,
+    sinks=None,
 ):
     """
     q_extend, k_extend, v_extend, o_extend: contiguous tensors
@@ -410,7 +410,7 @@ def extend_attention_fwd(
     # Skip custom mask for prefix part
     SKIP_PREFIX_CUSTOM_MASK = skip_prefix_custom_mask
 
-    HAS_SK = sk is not None
+    HAS_SINK = sinks is not None
 
     grid = (batch_size, head_num, triton.cdiv(max_len_extend, BLOCK_M))
     num_stages = 1
@@ -431,7 +431,7 @@ def extend_attention_fwd(
         kv_indices,
         custom_mask,
         mask_indptr,
-        sk,
+        sinks,
         sm_scale,
         kv_group_num,
         q_extend.stride(0),
@@ -458,7 +458,7 @@ def extend_attention_fwd(
         USE_CUSTOM_MASK=USE_CUSTOM_MASK,
         IS_CAUSAL=is_causal,
         SKIP_PREFIX_CUSTOM_MASK=SKIP_PREFIX_CUSTOM_MASK,
-        HAS_SK=HAS_SK,
+        HAS_SINK=HAS_SINK,
         STORE_TRANSPOSE=_is_hip,
         num_warps=num_warps,
         num_stages=num_stages,

python/sglang/srt/models/gpt_oss.py

@@ -301,7 +301,7 @@ class GptOssAttention(nn.Module):
         hidden_states, forward_batch, inner_state = intermediate_state
         if inner_state is None:
             return hidden_states
-        attn_output = self.attn(*inner_state, sk=self.sinks)
+        attn_output = self.attn(*inner_state, sinks=self.sinks)
         output, _ = self.o_proj(attn_output)
         return output