First take at dimension inference

This follows the pattern used in the Biaffine Parser, which uses an init
function to get the size only after the tok2vec is available.

This works at first, but serialization fails with an error.

spacy/ml/models/coref.py

@@ -1,6 +1,6 @@
 from typing import List, Tuple
 
-from thinc.api import Model, chain
+from thinc.api import Model, chain, get_width
 from thinc.api import PyTorchWrapper, ArgsKwargs
 from thinc.types import Floats2d
 from thinc.util import torch, xp2torch, torch2xp
@@ -25,12 +25,48 @@ def build_wl_coref_model(
     tok2vec_size: int = 768,  # tok2vec size
 ):
     # TODO add model return types
-    # dim = tok2vec.maybe_get_dim("n0")
+
+    nI = None
 
     with Model.define_operators({">>": chain}):
-        coref_clusterer = PyTorchWrapper(
+        coref_clusterer = Model(
+            "coref_clusterer",
+            forward=coref_forward,
+            init=coref_init,
+            dims={"nI": nI},
+            attrs={
+                "distance_embedding_size": distance_embedding_size,
+                "hidden_size": hidden_size,
+                "depth": depth,
+                "dropout": dropout,
+                "antecedent_limit": antecedent_limit,
+                "antecedent_batch_size": antecedent_batch_size,
+            },
+        )
+
+        coref_model = tok2vec >> coref_clusterer
+    return coref_model
+
+
+def coref_init(model: Model, X=None, Y=None):
+    if model.layers:
+        return
+
+    if X is not None and model.has_dim("nI") is None:
+        model.set_dim("nI", get_width(X))
+
+    hidden_size = model.attrs["hidden_size"]
+    depth = model.attrs["depth"]
+    dropout = model.attrs["dropout"]
+    antecedent_limit = model.attrs["antecedent_limit"]
+    antecedent_batch_size = model.attrs["antecedent_batch_size"]
+    distance_embedding_size = model.attrs["distance_embedding_size"]
+
+    PyTorchWrapper = registry.get("layers", "PyTorchWrapper.v2")
+    model._layers = [
+        PyTorchWrapper(
             CorefClusterer(
-                tok2vec_size,
+                model.get_dim("nI"),
                 distance_embedding_size,
                 hidden_size,
                 depth,
@@ -41,13 +77,15 @@ def build_wl_coref_model(
             convert_inputs=convert_coref_clusterer_inputs,
             convert_outputs=convert_coref_clusterer_outputs,
         )
-        coref_model = tok2vec >> coref_clusterer
+        # TODO maybe we need mixed precision and grad scaling?
-    return coref_model
+    ]
 
 
-def convert_coref_clusterer_inputs(
+def coref_forward(model: Model, X, is_train: bool):
-        model: Model, X: List[Floats2d], is_train: bool
+    return model.layers[0](X, is_train)
-):
+
+
+def convert_coref_clusterer_inputs(model: Model, X: List[Floats2d], is_train: bool):
     # The input here is List[Floats2d], one for each doc
     # just use the first
     # TODO real batching
@@ -63,9 +101,7 @@ def convert_coref_clusterer_inputs(
     return ArgsKwargs(args=(word_features,), kwargs={}), backprop
 
 
-def convert_coref_clusterer_outputs(
+def convert_coref_clusterer_outputs(model: Model, inputs_outputs, is_train: bool):
-        model: Model, inputs_outputs, is_train: bool
-):
     _, outputs = inputs_outputs
     scores, indices = outputs
 
@@ -115,9 +151,7 @@ class CorefClusterer(torch.nn.Module):
         self.pw = DistancePairwiseEncoder(dist_emb_size, dropout)
 
         pair_emb = dim * 3 + self.pw.shape
-        self.a_scorer = AnaphoricityScorer(
+        self.a_scorer = AnaphoricityScorer(pair_emb, hidden_size, n_layers, dropout)
-            pair_emb, hidden_size, n_layers, dropout
-        )
         self.lstm = torch.nn.LSTM(
             input_size=dim,
             hidden_size=dim,