7d340e5d642a594df7fed1679576751807cadaaf,thinc/layers/hashembed.py,,forward,#Any#Any#Any#,40

Before Change

    column: int = model.attrs["column"]
    nV = E.shape[0]
    input_shape = tuple(ids.shape)
    if ids.ndim >= 2:
        ids1d = model.ops.as_contig(ids[:, column], dtype="uint64")  // type: ignore
    else:
        ids1d = cast(Ints1d, ids)
    keys = model.ops.hash(ids1d, seed) % nV
    vectors = E[keys].sum(axis=1)
    drop_mask = cast(Floats2d, model.ops.get_dropout_mask((vectors.shape[1],), dropout))
    vectors *= drop_mask

After Change

    nN = ids.shape[0]
    seed: int = model.attrs["seed"]
    keys = model.ops.hash(ids, seed) % nV
    dropout: Optional[float] = model.attrs.get("dropout_rate")
    drop_mask = cast(Floats1d, model.ops.get_dropout_mask((nO,), dropout))
    dropout = model.attrs.get("dropout_rate")
    output = vectors[keys]
    output *= drop_mask

Instances