step3.5 flash revise for graph mode and use triton activation (#27739)

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

@@ -2104,7 +2104,7 @@ class AscendAttnBackend(AttentionBackend):
                     v,
                 )
 
-        if sinks is not None:
+        if sinks is not None or self.is_hybrid_swa:
             # Use SWA block tables if hybrid SWA is enabled for this layer
             if self._is_swa_layer(layer):
                 block_tables = self.forward_metadata.block_tables_swa
@@ -2158,7 +2158,9 @@ class AscendAttnBackend(AttentionBackend):
                         if layer.sliding_window_size != -1
                         else FULL_ATTENTION_WINDOW
                     ),
-                    next_tokens=0,
+                    next_tokens=(
+                        0 if layer.sliding_window_size == -1 else FULL_ATTENTION_WINDOW
+                    ),
                     atten_mask=self.fia_mask.to(torch.int8),
                     sparse_mode=sparse_mode,
                     softmax_scale=layer.scaling,
@@ -2213,59 +2215,6 @@ class AscendAttnBackend(AttentionBackend):
             else:
                 actual_seq_len_kv = seq_lens_cpu_int.cpu().int().tolist()
 
-            if (layer.qk_head_dim != layer.v_head_dim) and (
-                self.is_hybrid_swa and layer.sliding_window_size == -1
-            ):
-                query_v2 = q.reshape(
-                    -1, layer.tp_q_head_num, layer.qk_head_dim
-                ).contiguous()
-                actual_seq_qlen = (
-                    torch.tensor([1] * len(actual_seq_len_kv), dtype=torch.int32)
-                    .cumsum(dim=0)
-                    .tolist()
-                )
-                common_kwargs = dict(
-                    num_query_heads=layer.tp_q_head_num,
-                    num_key_value_heads=layer.tp_k_head_num,
-                    input_layout="TND",
-                    pre_tokens=FULL_ATTENTION_WINDOW,
-                    next_tokens=0,
-                    atten_mask=self.fia_mask.to(torch.int8),
-                    sparse_mode=3,
-                    softmax_scale=layer.scaling,
-                    block_table=self.forward_metadata.block_tables,
-                    block_size=self.page_size,
-                    actual_seq_qlen=actual_seq_qlen,
-                    actual_seq_kvlen=actual_seq_len_kv,
-                )
-                workspace = (
-                    torch_npu._npu_fused_infer_attention_score_v2_get_max_workspace(
-                        query_v2,
-                        k_cache.contiguous(),
-                        v_cache.contiguous(),
-                        **common_kwargs,
-                    )
-                )
-                attn_output = torch.empty(
-                    (
-                        query_v2.shape[0],
-                        layer.tp_q_head_num,
-                        layer.v_head_dim,
-                    ),
-                    dtype=q.dtype,
-                    device=q.device,
-                )
-                softmax_lse = torch.empty(1, dtype=q.dtype, device=q.device)
-                torch_npu.npu_fused_infer_attention_score_v2.out(
-                    query_v2,
-                    k_cache.contiguous(),
-                    v_cache.contiguous(),
-                    **common_kwargs,
-                    workspace=workspace,
-                    out=[attn_output, softmax_lse],
-                )
-                return attn_output.view(-1, layer.tp_q_head_num * layer.v_head_dim)
-
             num_tokens = query.shape[0]
             workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
                 query,

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

@@ -108,7 +108,8 @@ class NPUGraphRunner(DecodeCudaGraphRunner):
         self._init_arch_map()
         self.use_fia = get_bool_env_var("ASCEND_USE_FIA", "False")
         self.if_use_v2 = any(
-            arch in ("MiMoV2ForCausalLM", "MiMoV2FlashForCausalLM")
+            arch
+            in ("MiMoV2ForCausalLM", "MiMoV2FlashForCausalLM", "Step3p5ForCausalLM")
             for arch in (model_runner.model_config.hf_config.architectures or [])
         )
 

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

@@ -69,20 +69,6 @@ except ImportError:
     flashinfer_cutlass_fused_moe = None
 
 
-def swiglustep_and_mul(x: torch.Tensor, limit: float = 7.0) -> torch.Tensor:
-    """Out-variant of swiglustep activation.
-
-    Writes into `out`:
-      silu(x[:d]).clamp(max=limit) * x[d:].clamp(-limit, limit)
-    """
-    gate, up = x.chunk(2, dim=-1)
-    gate = F.silu(gate)
-    gate = gate.clamp(max=limit)
-    up = up.clamp(min=-limit, max=limit)
-    out = gate * up
-    return out
-
-
 class UnquantizedEmbeddingMethod(QuantizeMethodBase):
     """Unquantized method for embeddings."""
 
@@ -729,8 +715,15 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, MultiPlatformOp):
             hidden_states = swiglu_oai(layer, hidden_states)
         elif self.moe_runner_config.activation == "silu":
             if self.moe_runner_config.gemm1_clamp_limit is not None:
-                hidden_states = swiglustep_and_mul(
-                    hidden_states, self.moe_runner_config.gemm1_clamp_limit
+                from sgl_kernel_npu.activation.swiglu_quant import swiglu_quant
+
+                hidden_states, _ = swiglu_quant(
+                    hidden_states,
+                    group_list=expert_tokens,
+                    group_list_type=1,
+                    need_quant=False,
+                    do_limit=True,
+                    limit=self.moe_runner_config.gemm1_clamp_limit,
                 )
             else:
                 hidden_states = torch.ops.npu.npu_swiglu(hidden_states)