The coref model is able to be loaded

The span predictor component is initialized but not used at all now.
Plan is to work on it after the word level clustering part is trainable
end-to-end.

spacy/ml/models/coref.py

@@ -4,7 +4,8 @@ import warnings
 from thinc.api import Model, Linear, Relu, Dropout
 from thinc.api import chain, noop, Embed, add, tuplify, concatenate
 from thinc.api import reduce_first, reduce_last, reduce_mean
-from thinc.types import Floats2d, Floats1d, Ints2d, Ragged
+from thinc.api import PyTorchWrapper
+from thinc.types import Floats2d, Floats1d, Ints1d, Ints2d, Ragged
 from typing import List, Callable, Tuple, Any
 from ...tokens import Doc
 from ...util import registry
@@ -456,7 +457,7 @@ from typing import List, Tuple
 import torch
 
 # TODO rename this to coref_util
-import .coref_util_wl as utils
+from .coref_util_wl import add_dummy
 
 # TODO rename to plain coref
 @registry.architectures("spacy.WLCoref.v1")
@@ -478,20 +479,23 @@ def build_wl_coref_model(
     with Model.define_operators({">>": chain}):
         # TODO chain tok2vec with these models
         # TODO fix device - should be automatic
-        device = "gpu:0"
+        device = "cuda:0"
         coref_scorer = PyTorchWrapper(
             CorefScorer(
                 device,
                 embedding_size,
                 hidden_size,
                 n_hidden_layers,
-                dropout_rate,
+                dropout,
                 rough_k,
                 a_scoring_batch_size
             ),
             convert_inputs=convert_coref_scorer_inputs,
             convert_outputs=convert_coref_scorer_outputs
         )
+
+        coref_model = tok2vec >> coref_scorer
+        # XXX just ignore this until the coref scorer is integrated
         span_predictor = PyTorchWrapper(
             SpanPredictor(
                 # TODO this was hardcoded to 1024, check
@@ -499,12 +503,13 @@ def build_wl_coref_model(
                 sp_embedding_size,
                 device
             ),
+            
             convert_inputs=convert_span_predictor_inputs
         )
     # TODO combine models so output is uniform (just one forward pass)
     # It may be reasonable to have an option to disable span prediction,
     # and just return words as spans.
-    return coref_scorer
+    return coref_model
 
 def convert_coref_scorer_inputs(
     model: Model,
@@ -534,6 +539,17 @@ def convert_coref_scorer_outputs(
     indices_xp = torch2xp(indices)
     return (scores_xp, indices_xp), convert_for_torch_backward
 
+def convert_span_predictor_inputs(
+    model: Model,
+    X: Tuple[Ints1d, Floats2d, Ints1d],
+    is_train: bool
+):
+    sent_id = xp2torch(X[0], requires_grad=False)
+    word_features = xp2torch(X[1], requires_grad=False)
+    head_ids = xp2torch(X[2], requires_grad=False)
+    argskwargs = ArgsKwargs(args=(sent_id, word_features, head_ids), kwargs={})
+    return argskwargs, lambda dX: []
+
 # TODO This probably belongs in the component, not the model.
 def predict_span_clusters(span_predictor: Model,
                           sent_ids: Ints1d,
@@ -747,7 +763,7 @@ class AnaphoricityScorer(torch.nn.Module):
 
         # [batch_size, n_ants]
         scores = top_rough_scores_batch + self._ffnn(pair_matrix)
-        scores = utils.add_dummy(scores, eps=True)
+        scores = add_dummy(scores, eps=True)
 
         return scores