fixed softmarginloss

S1/35/prompt.txt

@@ -27,6 +27,7 @@ The goal is to create a single, highly-efficient CUDA kernel pass that addresses
 This strategy transforms a multi-stage, memory-bound operation into a single-pass, compute-efficient, and numerically stable kernel, yielding substantial performance improvements.
 
 You are given the following architecture:
+
 import torch
 import torch.nn as nn
 

S1/35/run_code.py

@@ -21,6 +21,7 @@ def run_benchmark():
     torch_model.eval()
     cuda_model.eval()
 
+
     print("-------------------- 精度对齐验证 --------------------")
     with torch.no_grad():
         # 预热GPU

S1/35/softmarginloss_cuda.py

@@ -85,7 +85,6 @@ __global__ void soft_margin_loss_reduce_kernel_stage1(
     }
 }
 
-
 torch::Tensor soft_margin_loss_cuda_forward(
     const torch::Tensor& input, 
     const torch::Tensor& target,

S1/35/softmarginloss_torch.py

@@ -18,6 +18,7 @@ class Model(nn.Module):
         return self.loss_fn(input_tensor, target_tensor)
 
 def get_inputs():
+    
     """
     生成用于测试的输入张量。
     """