Fix coref size inference (#10916)

* Add explicit tok2vec_size parameter in clusterer

* Add tok2vec size to span predictor config

* Minor fixes

spacy/ml/models/coref.py

@@ -19,19 +19,15 @@ def build_wl_coref_model(
     # pairs to keep per mention after rough scoring
     antecedent_limit: int = 50,
     antecedent_batch_size: int = 512,
+    tok2vec_size: int = 768,  # tok2vec size
 ):
     # TODO add model return types
-    # TODO fix this
-    try:
-        dim = tok2vec.get_dim("nO")
-    except ValueError:
-        # happens with transformer listener
-        dim = 768
+    # dim = tok2vec.maybe_get_dim("n0")
 
     with Model.define_operators({">>": chain}):
         coref_clusterer = PyTorchWrapper(
             CorefClusterer(
-                dim,
+                tok2vec_size,
                 distance_embedding_size,
                 hidden_size,
                 depth,
@@ -56,7 +52,7 @@ def convert_coref_clusterer_inputs(model: Model, X: List[Floats2d], is_train: bo
     def backprop(args: ArgsKwargs) -> List[Floats2d]:
         # convert to xp and wrap in list
         gradients = torch2xp(args.args[0])
-        assert isinstance(gradients, Floats2d)
+        # assert isinstance(gradients, Floats2d)
         return [gradients]
 
     return ArgsKwargs(args=(word_features,), kwargs={}), backprop
@@ -89,7 +85,7 @@ class CorefClusterer(torch.nn.Module):
 
     def __init__(
         self,
-        dim: int,  # tok2vec size
+        dim: int,
         dist_emb_size: int,
         hidden_size: int,
         n_layers: int,
@@ -109,19 +105,19 @@ class CorefClusterer(torch.nn.Module):
         """
         self.dropout = torch.nn.Dropout(dropout)
         self.batch_size = batch_size
-        # Modules
         self.pw = DistancePairwiseEncoder(dist_emb_size, dropout)
+
         pair_emb = dim * 3 + self.pw.shape
-        self.a_scorer = AnaphoricityScorer(pair_emb, hidden_size, n_layers, dropout)
+        self.a_scorer = AnaphoricityScorer(
+            pair_emb, hidden_size, n_layers, dropout
+        )
         self.lstm = torch.nn.LSTM(
             input_size=dim,
             hidden_size=dim,
             batch_first=True,
         )
+
         self.rough_scorer = RoughScorer(dim, dropout, roughk)
-        self.pw = DistancePairwiseEncoder(dist_emb_size, dropout)
-        pair_emb = dim * 3 + self.pw.shape
-        self.a_scorer = AnaphoricityScorer(pair_emb, hidden_size, n_layers, dropout)
 
     def forward(self, word_features: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         """

spacy/ml/models/span_predictor.py

@@ -13,6 +13,7 @@ from .coref_util import get_sentence_ids
 @registry.architectures("spacy.SpanPredictor.v1")
 def build_span_predictor(
     tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_size: int = 768,
     hidden_size: int = 1024,
     distance_embedding_size: int = 64,
     conv_channels: int = 4,
@@ -21,17 +22,11 @@ def build_span_predictor(
     prefix: str = "coref_head_clusters",
 ):
     # TODO add model return types
-    # TODO fix this
-    try:
-        dim = tok2vec.get_dim("nO")
-    except ValueError:
-        # happens with transformer listener
-        dim = 768
 
     with Model.define_operators({">>": chain, "&": tuplify}):
         span_predictor = PyTorchWrapper(
             SpanPredictor(
-                dim,
+                tok2vec_size,
                 hidden_size,
                 distance_embedding_size,
                 conv_channels,

spacy/pipeline/coref.py

@@ -30,6 +30,7 @@ from ..coref_scorer import Evaluator, get_cluster_info, lea
 default_config = """
 [model]
 @architectures = "spacy.Coref.v1"
+tok2vec_size = 768
 distance_embedding_size = 20
 hidden_size = 1024
 depth = 1

spacy/pipeline/span_predictor.py

@@ -24,6 +24,7 @@ from ..ml.models.coref_util import (
 default_span_predictor_config = """
 [model]
 @architectures = "spacy.SpanPredictor.v1"
+tok2vec_size = 768
 hidden_size = 1024
 distance_embedding_size = 64
 conv_channels = 4