format

Author: mayuyuace

csrc/moe/moe_ops.h

@@ -8,5 +8,4 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
     const int64_t n_topk_group, const c10::string_view scoring_func,
     const c10::optional<torch::Tensor>& bias);
 
-
 void moe_sum(torch::Tensor& input, torch::Tensor& output);

tests/register_ops.py

@@ -17,6 +17,7 @@ def register_fake(fn):
     except ImportError:
         from torch.library import impl_abstract as register_fake
 
+
 # layer norm ops
 def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
              epsilon: float) -> None:
@@ -127,6 +128,7 @@ def dynamic_per_token_scaled_fp8_quant(
 def moe_sum(input: torch.Tensor, output: torch.Tensor) -> None:
     torch.ops._moe_C.moe_sum(input, output)
 
+
 def grouped_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -162,4 +164,4 @@ def _grouped_topk_fake(
         topk_indices = torch.empty((gating_output.size(0), topk),
                                    dtype=torch.int32,
                                    device=hidden_states.device)
-        return topk_weights, topk_indices
+        return topk_weights, topk_indices