Span predictor leftovers

spacy/ml/models/span_predictor.py

@@ -1,6 +1,6 @@
 from typing import List, Tuple
 
-from thinc.api import Model, chain, tuplify
+from thinc.api import Model, chain, tuplify, get_width
 from thinc.api import PyTorchWrapper, ArgsKwargs
 from thinc.types import Floats2d, Ints1d
 from thinc.util import torch, xp2torch, torch2xp
@@ -22,6 +22,8 @@ def build_span_predictor(
 ):
     # TODO add model return types
 
+    nI = None
+
     with Model.define_operators({">>": chain, "&": tuplify}):
         span_predictor = Model(
             "span_predictor",
@@ -34,7 +36,6 @@ def build_span_predictor(
                 "conv_channels": conv_channels,
                 "window_size": window_size,
                 "max_distance": max_distance,
-                "prefix": prefix,
             },
         )
         head_info = build_get_head_metadata(prefix)
@@ -55,7 +56,6 @@ def span_predictor_init(model: Model, X=None, Y=None):
     conv_channels = model.attrs["conv_channels"]
     window_size = model.attrs["window_size"]
     max_distance = model.attrs["max_distance"]
-    prefix = model.attrs["prefix"]
 
     model._layers = [
         PyTorchWrapper(
@@ -66,7 +66,6 @@ def span_predictor_init(model: Model, X=None, Y=None):
                 conv_channels,
                 window_size,
                 max_distance,
-                prefix,
             ),
             convert_inputs=convert_span_predictor_inputs,
         )

spacy/pipeline/span_predictor.py

@@ -275,6 +275,12 @@ class SpanPredictor(TrainablePipe):
         assert len(X) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(X=X, Y=Y)
 
+        # Store the input dimensionality. nI and nO are not stored explicitly
+        # for PyTorch models. This makes it tricky to reconstruct the model
+        # during deserialization. So, besides storing the labels, we also
+        # store the number of inputs.
+        span_predictor = self.model.get_ref("span_predictor")
+        self.cfg["nI"] = span_predictor.get_dim("nI")
 
     def from_bytes(self, bytes_data, *, exclude=tuple()):
         deserializers = {