修复部分代码

vllm/model_executor/layers/quantization/slimquant_w4a8.py

@@ -158,11 +158,14 @@ class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
         layer: torch.nn.Module,
         x: torch.Tensor,
         bias: Optional[torch.Tensor] = None,
-        input_quant_args: Optional[list[torch.Tensor]] = None
+        input_quant_args: Optional[list[torch.Tensor]] = None,
+        silu_quant_args: Optional[list[torch.Tensor]] = None
     ):
         if envs.USE_FUSED_RMS_QUANT and input_quant_args is not None:
             assert len(input_quant_args) == 2
             x_q, x_scale = input_quant_args
+        elif envs.USE_FUSED_SILU_MUL_QUANT and silu_quant_args is not None:
+            x_q, x_scale = silu_quant_args
         else:
             x_q, x_scale = per_token_quant_int8(x)
 
@@ -373,7 +376,7 @@ class SlimQuantW4A8Int8MoEMethod:
                 )
 
 
-    def apply(# tp
+    def apply(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,
@@ -394,6 +397,7 @@ class SlimQuantW4A8Int8MoEMethod:
         use_nn_moe: Optional[bool] = False,
         routed_scaling_factor: Optional[float] = None,
         use_fused_gate: Optional[bool] = False,
+        shared_output: Optional[torch.Tensor] = None,
         **_  
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
@@ -434,4 +438,6 @@ class SlimQuantW4A8Int8MoEMethod:
             a1_scale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,
             use_nn_moe=use_nn_moe,
+            shared_output=shared_output,
+            routed_scaling_factor=routed_scaling_factor,
         )

vllm/model_executor/models/deepseek_v2.py

@@ -103,8 +103,12 @@ class DeepseekV2MLP(nn.Module):
                 ):
         if envs.USE_FUSED_RMS_QUANT:
             gate_up, new_resi, _  = self.gate_up_proj(x, rms_weight, residual, update_hd=update_hd)
+            if envs.USE_FUSED_SILU_MUL_QUANT:
+                x, _ = self.down_proj(gate_up, use_fused_silu_mul_quant=True)
+            else:
                 x = self.act_fn(gate_up)
                 x, _ = self.down_proj(x)
+
             return x, new_resi
         else:
             gate_up, _ = self.gate_up_proj(x)
@@ -574,6 +578,9 @@ class DeepseekV2MLAAttention(nn.Module):
             kv_c, k_pe = self.kv_a_proj_with_mqa(hidden_states, quant_args=input_quant_args, update_hd=False)[0].split(
                                 [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
 
+            if envs.VLLM_USE_LIGHTOP:
+                kv_c_normed = self.kv_a_layernorm.forward_cuda_opt(kv_c)
+            else:
                 kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
 
             q = q.view(-1, self.num_local_heads, self.qk_head_dim)