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import torch.nn as nn
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from ..modules import sparse as sp
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FP16_MODULES = (
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nn.Conv1d,
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nn.Conv2d,
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nn.Conv3d,
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nn.ConvTranspose1d,
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nn.ConvTranspose2d,
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nn.ConvTranspose3d,
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nn.Linear,
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sp.SparseConv3d,
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sp.SparseInverseConv3d,
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sp.SparseLinear,
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)
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def convert_module_to_f16(l):
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"""
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Convert primitive modules to float16.
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"""
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if isinstance(l, FP16_MODULES):
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for p in l.parameters():
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p.data = p.data.half()
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def convert_module_to_f32(l):
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"""
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Convert primitive modules to float32, undoing convert_module_to_f16().
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"""
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if isinstance(l, FP16_MODULES):
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for p in l.parameters():
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p.data = p.data.float()
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def scale_module(module, scale):
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"""
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Scale the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().mul_(scale)
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return module
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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