success

modules/module_lib/linear.py

@@ -0,0 +1,30 @@
+import torch
+import numpy as np
+
+
+def weight_init(shape, mode, fan_in, fan_out):
+    if mode == 'xavier_uniform':
+        return np.sqrt(6 / (fan_in + fan_out)) * (torch.rand(*shape) * 2 - 1)
+    if mode == 'xavier_normal':
+        return np.sqrt(2 / (fan_in + fan_out)) * torch.randn(*shape)
+    if mode == 'kaiming_uniform':
+        return np.sqrt(3 / fan_in) * (torch.rand(*shape) * 2 - 1)
+    if mode == 'kaiming_normal':
+        return np.sqrt(1 / fan_in) * torch.randn(*shape)
+    raise ValueError(f'Invalid init mode "{mode}"')
+
+
+class Linear(torch.nn.Module):
+    def __init__(self, in_features, out_features, bias=True, init_mode='kaiming_normal', init_weight=1, init_bias=0):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        init_kwargs = dict(mode=init_mode, fan_in=in_features, fan_out=out_features)
+        self.weight = torch.nn.Parameter(weight_init([out_features, in_features], **init_kwargs) * init_weight)
+        self.bias = torch.nn.Parameter(weight_init([out_features], **init_kwargs) * init_bias) if bias else None
+
+    def forward(self, x):
+        x = x @ self.weight.to(x.dtype).t()
+        if self.bias is not None:
+            x = x.add_(self.bias.to(x.dtype))
+        return x