format

tests/kernels/test_moe.py

@@ -38,6 +38,7 @@ def torch_moe(a, w1, w2, score, topk):
     return (out.view(B, -1, w2.shape[1]) *
             topk_weight.view(B, -1, 1).to(out.dtype)).sum(dim=1)
 
+
 def torch_moe_single(a, w, score, topk):
     B, D = a.shape
     a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
@@ -51,6 +52,7 @@ def torch_moe_single(a, w, score, topk):
             out[mask] = a[mask] @ w[i].transpose(0, 1)
     return (out.view(B, -1, w.shape[1])).sum(dim=1)
 
+
 @pytest.mark.parametrize("m", [1024 * 128, 512, 222, 33, 1])
 @pytest.mark.parametrize("n", [2048, 256, 1024])
 @pytest.mark.parametrize("k", [128, 511, 1024])

vllm/model_executor/layers/fused_moe/__init__.py

@@ -1,6 +1,6 @@
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts, fused_marlin_moe, fused_moe, fused_topk,
-    get_config_file_name, single_marlin_moe, grouped_topk)
+    get_config_file_name, grouped_topk, single_marlin_moe)
 
 __all__ = [
     "fused_moe",

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -367,6 +367,7 @@ def fused_topk(
         topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
     return topk_weights, topk_ids
 
+
 # This is used by the Deepseek-V2 model
 def grouped_topk(
     hidden_states: torch.Tensor,
@@ -412,6 +413,7 @@ def get_expert_config(w1: torch.Tensor, w2: torch.Tensor, topk: int, M: int,
         return get_default_config(M, E, N, w1.shape[2], topk,
                                   "float8" if use_fp8 else None)
 
+
 def fused_experts(hidden_states: torch.Tensor,
                   w1: torch.Tensor,
                   w2: torch.Tensor,