forked from ccf-ai-infra/GPUCodeForces
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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"""
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合理优化的PyTorch L1 Loss实现
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使用PyTorch内置函数，避免不必要的中间张量创建
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"""
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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使用PyTorch内置的l1_loss函数
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L1 Loss = |input - target|
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Args:
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input (torch.Tensor): 预测值
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target (torch.Tensor): 真实值
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Returns:
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torch.Tensor: L1 Loss标量值
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"""
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# 直接使用内置函数，让PyTorch处理优化
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return torch.nn.functional.l1_loss(
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input,
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target,
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reduction='sum'
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)
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batch_size = 128
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num_features = 2000
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def get_inputs():
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"""
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生成合理的测试数据
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"""
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input_vals = torch.randn(batch_size, num_features)
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target_vals = torch.randn(batch_size, num_features)
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return [input_vals, target_vals]
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def get_init_inputs():
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return [] # 没有特殊的初始化输入需求
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