mirror of https://github.com/vladmandic/automatic
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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try:
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import xformers
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XFORMERS_AVAILABLE = True
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except ImportError:
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XFORMERS_AVAILABLE = False
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from .. import env
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from ..utils import compile_wrapper
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class SwiGLUTorch(nn.Module):
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def __init__(
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self, in_features, hidden_features, out_features, bias=True, _pack_weights=True
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):
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super().__init__()
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self.in_features = in_features
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self.hidden_features = hidden_features or in_features
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self.out_features = out_features or in_features
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if _pack_weights:
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self.w12 = torch.nn.Linear(in_features, 2 * hidden_features, bias=bias)
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else:
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self.w1 = torch.nn.Linear(in_features, hidden_features, bias=bias)
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self.w2 = torch.nn.Linear(in_features, hidden_features, bias=bias)
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self.w3 = torch.nn.Linear(hidden_features, out_features, bias=bias)
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@compile_wrapper
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def forward(self, x):
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if self.w12 is not None:
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x1, x2 = self.w12(x).chunk(2, dim=-1)
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else:
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x1 = self.w1(x)
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x2 = self.w2(x)
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return self.w3(F.silu(x1) * x2)
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if XFORMERS_AVAILABLE:
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from xformers.ops import SwiGLU
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else:
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SwiGLU = SwiGLUTorch
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if not env.USE_XFORMERS_LAYERS:
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SwiGLU = SwiGLUTorch
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if __name__ == "__main__":
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x = torch.randn(2, 16, 128)
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model1 = SwiGLU(128, 256, 128)
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model2 = SwiGLUTorch(128, 256, 128)
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model1.load_state_dict(model2.state_dict())
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print(F.mse_loss(model1(x), model2(x)), torch.norm(model1(x)))
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