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transformers-can-do-bayesian-inference

like 21

Running

• "collections.OrderedDict",
• "bar_distribution.FullSupportBarDistribution",
• "torch.nn.modules.container.ModuleList",
• "torch.nn.modules.container.Sequential",
• "torch.nn.modules.transformer.TransformerEncoder",
• "torch.nn.modules.normalization.LayerNorm",
• "torch.nn.modules.activation.MultiheadAttention",
• "transformer.TransformerModel",
• "torch.nn.functional.gelu",
• "torch._utils._rebuild_tensor_v2",
• "torch.FloatStorage",
• "torch.nn.modules.linear.NonDynamicallyQuantizableLinear",
• "torch.nn.modules.transformer.TransformerEncoderLayer",
• "positional_encodings.NoPositionalEncoding",
• "torch._utils._rebuild_parameter",
• "torch.nn.modules.linear.Linear",
• "torch.nn.modules.activation.GELU",
• "torch.nn.modules.dropout.Dropout",
• "__builtin__.set"

How to fix it?

• "torch.nn.functional.gelu",
• "torch.nn.modules.normalization.LayerNorm",
• "collections.OrderedDict",
• "positional_encodings.NoPositionalEncoding",
• "bar_distribution.FullSupportBarDistribution",
• "torch.nn.modules.linear.NonDynamicallyQuantizableLinear",
• "torch._utils._rebuild_tensor_v2",
• "__builtin__.set",
• "torch.nn.modules.dropout.Dropout",
• "torch.FloatStorage",
• "torch.nn.modules.container.ModuleList",
• "torch.nn.modules.transformer.TransformerEncoderLayer",
• "torch.nn.modules.linear.Linear",
• "torch.nn.modules.container.Sequential",
• "torch.nn.modules.transformer.TransformerEncoder",
• "torch.nn.modules.activation.GELU",
• "torch.nn.modules.activation.MultiheadAttention",
• "torch._utils._rebuild_parameter",
• "transformer.TransformerModel"

How to fix it?