29 lines
867 B
Python
29 lines
867 B
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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def __init__(self, scale: torch.Tensor, bias: torch.Tensor, alpha: float, beta: float):
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super(Model, self).__init__()
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self.register_buffer("scale", scale)
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self.register_buffer("bias", bias)
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self.register_buffer("alpha", torch.tensor(float(alpha), dtype=torch.float32))
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self.register_buffer("beta", torch.tensor(float(beta), dtype=torch.float32))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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z = x * self.scale + self.bias
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m = z / (1.0 + z * z)
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g = torch.sigmoid(self.alpha * m + self.beta)
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return x * g
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batch_size = 16
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dim = 16384
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def get_inputs():
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x = torch.randn(batch_size, dim)
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return [x]
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def get_init_inputs():
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scale = torch.randn(dim)
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bias = torch.randn(dim)
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return [scale, bias, 1.0, 0.0]
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