hankcs/HanLP · error · ValueError

Unsupported dim: . Only 2d (T,C) or 3d (B,T,C) is supported

Error message

Unsupported dim: {x.dim()}. Only 2d (T,C) or 3d (B,T,C) is supported

What it means

HanLP's custom dropout (spatial dropout over feature columns) only supports 2-D (T,C) or 3-D (B,T,C) tensors: for 3-D it samples a per-feature mask broadcast over timesteps, for 2-D a plain elementwise mask. Any other rank is rejected because the masking scheme (masking whole channels) is undefined there.

Solutions

1. Reshape input to (B,T,C) or (T,C) before this dropout
2. For 4-D conv activations, use nn.Dropout2d / standard Dropout instead
3. Move the dropout after a view/reshape that yields 2-D or 3-D

Example fix

# before
y = dropout(x)  # x is (B, C, H, W) -> error
# after
B, C, H, W = x.shape
y = dropout(x.permute(0,2,3,1).reshape(B, H*W, C)).reshape(B, H, W, C).permute(0,3,1,2)

if x.dim() not in (2, 3):
    x = x.reshape(x.shape[0], -1, x.shape[-1])

def dropout_supported(x: torch.Tensor) -> bool:
    return x.dim() in (2, 3)

When it happens

Trigger: Calling this dropout module's forward on a 1-D vector, a 4-D tensor (e.g. conv feature maps), or a batched 4-D input from a CNN/transformer pipeline.

Common situations: Reusing the layer on image or audio tensors; applying it to flattened logits or scalars; inserting it before an unsqueeze/reshape that was expected but omitted.

Related errors

• mask of the first timestep must all be on
• self.model.config.pad_token_id has to be defined.
• the first two dimensions of emissions and mask must match…
• the first two dimensions of emissions and tags must match…
• activation must be callable: type=

AI-assisted analysis of hankcs/HanLP@ddb1299bdd (2026-08-27). Data as JSON: /api/errors/0bce53882838db44. Report an issue: GitHub.

Appendix: source

Thrown at hanlp/layers/dropout.py:158

        return items


class LockedDropout(nn.Module):
    def __init__(self, dropout_rate=0.5):
        super(LockedDropout, self).__init__()
        self.dropout_rate = dropout_rate

    def forward(self, x):
        if not self.training or not self.dropout_rate:
            return x

        if x.dim() == 3:
            mask = x.new(x.size(0), 1, x.size(2)).bernoulli_(1 - self.dropout_rate) / (1 - self.dropout_rate)
            mask = mask.expand_as(x)
        elif x.dim() == 2:
            mask = torch.empty_like(x).bernoulli_(1 - self.dropout_rate) / (1 - self.dropout_rate)
        else:
            raise ValueError(f'Unsupported dim: {x.dim()}. Only 2d (T,C) or 3d (B,T,C) is supported')
        return mask * x

View on GitHub (pinned to ddb1299bdd)