Start bringin in wl-coref

This absolutely does not work. First step here is getting over most of
the code in roughly the files we want it in. After the code has been
pulled over it can be restructured to match spaCy and cleaned up.

spacy/ml/models/__init__.py

@@ -1,4 +1,4 @@
-from .coref import *
+from .coref import * #noqa
 from .entity_linker import *  # noqa
 from .multi_task import *  # noqa
 from .parser import *  # noqa

spacy/ml/models/coref.py

@@ -448,3 +448,434 @@ def pairwise_product(bilinear, dropout, vecs: Floats2d, is_train):
         return dX
 
     return pw_prod, backward
+
+
+# XXX here down is wl-coref
+from typing import List, Tuple
+
+import torch
+
+# TODO rename this to coref_util
+import .coref_util_wl as utils
+
+# TODO rename to plain coref
+@registry.architectures("spacy.WLCoref.v1")
+def build_wl_coref_model(
+        #TODO add other hyperparams
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    ):
+    
+    # TODO change to use passed in values for config
+    config = utils._load_config("/dev/null")
+    with Model.define_operators({">>": chain}):
+
+        coref_scorer, span_predictor = configure_pytorch_modules(config)
+        # TODO chain tok2vec with these models
+        coref_scorer = PyTorchWrapper(
+            CorefScorer(
+                config.device,
+                config.embedding_size,
+                config.hidden_size,
+                config.n_hidden_layers,
+                config.dropout_rate,
+                config.rough_k,
+                config.a_scoring_batch_size
+            ),
+            convert_inputs=convert_coref_scorer_inputs,
+            convert_outputs=convert_coref_scorer_outputs
+        )
+        span_predictor = PyTorchWrapper(
+            SpanPredictor(
+                1024,
+                config.sp_embedding_size,
+                config.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
+
+def convert_coref_scorer_inputs(
+    model: Model,
+    X: Floats2d,
+    is_train: bool
+):
+    word_features = xp2torch(X, requires_grad=False)
+    return ArgsKwargs(args=(word_features, ), kwargs={}), lambda dX: []
+
+
+def convert_coref_scorer_outputs(
+    model: Model,
+    inputs_outputs,
+    is_train: bool
+):
+    _, outputs = inputs_outputs
+    scores, indices = outputs
+
+    def convert_for_torch_backward(dY: Floats2d) -> ArgsKwargs:
+        dY_t = xp2torch(dY)
+        return ArgsKwargs(
+            args=([scores],),
+            kwargs={"grad_tensors": [dY_t]},
+        )
+
+    scores_xp = torch2xp(scores)
+    indices_xp = torch2xp(indices)
+    return (scores_xp, indices_xp), convert_for_torch_backward
+
+# TODO This probably belongs in the component, not the model.
+def predict_span_clusters(span_predictor: Model,
+                          sent_ids: Ints1d,
+                          words: Floats2d,
+                          clusters: List[Ints1d]):
+    """
+    Predicts span clusters based on the word clusters.
+
+    Args:
+        doc (Doc): the document data
+        words (torch.Tensor): [n_words, emb_size] matrix containing
+            embeddings for each of the words in the text
+        clusters (List[List[int]]): a list of clusters where each cluster
+            is a list of word indices
+
+    Returns:
+        List[List[Span]]: span clusters
+    """
+    if not clusters:
+        return []
+
+    xp = span_predictor.ops.xp
+    heads_ids = xp.asarray(sorted(i for cluster in clusters for i in cluster))
+    scores = span_predictor.predict((sent_ids, words, heads_ids))
+    starts = scores[:, :, 0].argmax(axis=1).tolist()
+    ends = (scores[:, :, 1].argmax(axis=1) + 1).tolist()
+
+    head2span = {
+        head: (start, end)
+        for head, start, end in zip(heads_ids.tolist(), starts, ends)
+    }
+
+    return [[head2span[head] for head in cluster]
+            for cluster in clusters]
+
+# TODO add docstring for this, maybe move to utils.
+# This might belong in the component.
+def _clusterize(
+        model,
+        scores: Floats2d,
+        top_indices: Ints2d
+):
+    xp = model.ops.xp
+    antecedents = scores.argmax(axis=1) - 1
+    not_dummy = antecedents >= 0
+    coref_span_heads = xp.arange(0, len(scores))[not_dummy]
+    antecedents = top_indices[coref_span_heads, antecedents[not_dummy]]
+    n_words = scores.shape[0]
+    nodes = [GraphNode(i) for i in range(n_words)]
+    for i, j in zip(coref_span_heads.tolist(), antecedents.tolist()):
+        nodes[i].link(nodes[j])
+        assert nodes[i] is not nodes[j]
+
+    clusters = []
+    for node in nodes:
+        if len(node.links) > 0 and not node.visited:
+            cluster = []
+            stack = [node]
+            while stack:
+                current_node = stack.pop()
+                current_node.visited = True
+                cluster.append(current_node.id)
+                stack.extend(link for link in current_node.links if not link.visited)
+            assert len(cluster) > 1
+            clusters.append(sorted(cluster))
+    return sorted(clusters)
+
+
+class CorefScorer(torch.nn.Module):
+    """Combines all coref modules together to find coreferent spans.
+
+    Attributes:
+        config (coref.config.Config): the model's configuration,
+            see config.toml for the details
+        epochs_trained (int): number of epochs the model has been trained for
+
+    Submodules (in the order of their usage in the pipeline):
+        rough_scorer (RoughScorer)
+        pw (PairwiseEncoder)
+        a_scorer (AnaphoricityScorer)
+        sp (SpanPredictor)
+    """
+    def __init__(
+        self,
+        device: str,
+        dist_emb_size: int,
+        hidden_size: int,
+        n_layers: int,
+        dropout_rate: float,
+        roughk: int,
+        batch_size: int
+    ):
+        super().__init__()
+        """
+        A newly created model is set to evaluation mode.
+
+        Args:
+            config_path (str): the path to the toml file with the configuration
+            section (str): the selected section of the config file
+            epochs_trained (int): the number of epochs finished
+                (useful for warm start)
+        """
+        # device, dist_emb_size, hidden_size, n_layers, dropout_rate
+        self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate).to(device)
+        bert_emb = 1024
+        pair_emb = bert_emb * 3 + self.pw.shape
+        self.a_scorer = AnaphoricityScorer(
+            pair_emb,
+            hidden_size,
+            n_layers,
+            dropout_rate
+        ).to(device)
+        self.lstm = torch.nn.LSTM(
+            input_size=bert_emb,
+            hidden_size=bert_emb,
+            batch_first=True,
+        )
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.rough_scorer = RoughScorer(
+            bert_emb,
+            dropout_rate,
+            roughk
+        ).to(device)
+        self.batch_size = batch_size
+
+    def forward(
+        self,
+        word_features: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        This is a massive method, but it made sense to me to not split it into
+        several ones to let one see the data flow.
+
+        Args:
+            word_features: torch.Tensor containing word encodings
+        Returns:
+            coreference scores and top indices
+        """
+        # words           [n_words, span_emb]
+        # cluster_ids     [n_words]
+        word_features = torch.unsqueeze(word_features, dim=0)
+        words, _ = self.lstm(word_features)
+        words = words.squeeze()
+        words = self.dropout(words)
+        # Obtain bilinear scores and leave only top-k antecedents for each word
+        # top_rough_scores  [n_words, n_ants]
+        # top_indices       [n_words, n_ants]
+        top_rough_scores, top_indices = self.rough_scorer(words)
+        # Get pairwise features [n_words, n_ants, n_pw_features]
+        pw = self.pw(top_indices)
+        batch_size = self.batch_size
+        a_scores_lst: List[torch.Tensor] = []
+
+        for i in range(0, len(words), batch_size):
+            pw_batch = pw[i:i + batch_size]
+            words_batch = words[i:i + batch_size]
+            top_indices_batch = top_indices[i:i + batch_size]
+            top_rough_scores_batch = top_rough_scores[i:i + batch_size]
+
+            # a_scores_batch    [batch_size, n_ants]
+            a_scores_batch = self.a_scorer(
+                all_mentions=words, mentions_batch=words_batch,
+                pw_batch=pw_batch, top_indices_batch=top_indices_batch,
+                top_rough_scores_batch=top_rough_scores_batch
+            )
+            a_scores_lst.append(a_scores_batch)
+
+        coref_scores = torch.cat(a_scores_lst, dim=0)
+        return coref_scores, top_indices
+
+
+class AnaphoricityScorer(torch.nn.Module):
+    """ Calculates anaphoricity scores by passing the inputs into a FFNN """
+    def __init__(self,
+                 in_features: int,
+                 hidden_size,
+                 n_hidden_layers,
+                 dropout_rate):
+        super().__init__()
+        hidden_size = hidden_size
+        if not n_hidden_layers:
+            hidden_size = in_features
+        layers = []
+        for i in range(n_hidden_layers):
+            layers.extend([torch.nn.Linear(hidden_size if i else in_features,
+                                           hidden_size),
+                           torch.nn.LeakyReLU(),
+                           torch.nn.Dropout(dropout_rate)])
+        self.hidden = torch.nn.Sequential(*layers)
+        self.out = torch.nn.Linear(hidden_size, out_features=1)
+
+    def forward(self, *,  # type: ignore  # pylint: disable=arguments-differ  #35566 in pytorch
+                all_mentions: torch.Tensor,
+                mentions_batch: torch.Tensor,
+                pw_batch: torch.Tensor,
+                top_indices_batch: torch.Tensor,
+                top_rough_scores_batch: torch.Tensor,
+                ) -> torch.Tensor:
+        """ Builds a pairwise matrix, scores the pairs and returns the scores.
+
+        Args:
+            all_mentions (torch.Tensor): [n_mentions, mention_emb]
+            mentions_batch (torch.Tensor): [batch_size, mention_emb]
+            pw_batch (torch.Tensor): [batch_size, n_ants, pw_emb]
+            top_indices_batch (torch.Tensor): [batch_size, n_ants]
+            top_rough_scores_batch (torch.Tensor): [batch_size, n_ants]
+
+        Returns:
+            torch.Tensor [batch_size, n_ants + 1]
+                anaphoricity scores for the pairs + a dummy column
+        """
+        # [batch_size, n_ants, pair_emb]
+        pair_matrix = self._get_pair_matrix(
+            all_mentions, mentions_batch, pw_batch, top_indices_batch)
+
+        # [batch_size, n_ants]
+        scores = top_rough_scores_batch + self._ffnn(pair_matrix)
+        scores = utils.add_dummy(scores, eps=True)
+
+        return scores
+
+    def _ffnn(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Calculates anaphoricity scores.
+
+        Args:
+            x: tensor of shape [batch_size, n_ants, n_features]
+
+        Returns:
+            tensor of shape [batch_size, n_ants]
+        """
+        x = self.out(self.hidden(x))
+        return x.squeeze(2)
+
+    @staticmethod
+    def _get_pair_matrix(all_mentions: torch.Tensor,
+                         mentions_batch: torch.Tensor,
+                         pw_batch: torch.Tensor,
+                         top_indices_batch: torch.Tensor,
+                         ) -> torch.Tensor:
+        """
+        Builds the matrix used as input for AnaphoricityScorer.
+
+        Args:
+            all_mentions (torch.Tensor): [n_mentions, mention_emb],
+                all the valid mentions of the document,
+                can be on a different device
+            mentions_batch (torch.Tensor): [batch_size, mention_emb],
+                the mentions of the current batch,
+                is expected to be on the current device
+            pw_batch (torch.Tensor): [batch_size, n_ants, pw_emb],
+                pairwise features of the current batch,
+                is expected to be on the current device
+            top_indices_batch (torch.Tensor): [batch_size, n_ants],
+                indices of antecedents of each mention
+
+        Returns:
+            torch.Tensor: [batch_size, n_ants, pair_emb]
+        """
+        emb_size = mentions_batch.shape[1]
+        n_ants = pw_batch.shape[1]
+
+        a_mentions = mentions_batch.unsqueeze(1).expand(-1, n_ants, emb_size)
+        b_mentions = all_mentions[top_indices_batch]
+        similarity = a_mentions * b_mentions
+
+        out = torch.cat((a_mentions, b_mentions, similarity, pw_batch), dim=2)
+        return out
+
+
+
+class RoughScorer(torch.nn.Module):
+    """
+    Is needed to give a roughly estimate of the anaphoricity of two candidates,
+    only top scoring candidates are considered on later steps to reduce
+    computational complexity.
+    """
+    def __init__(
+            self,
+            features: int, 
+            dropout_rate: float, 
+            rough_k: float
+    ):
+        super().__init__()
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.bilinear = torch.nn.Linear(features, features)
+
+        self.k = rough_k
+
+    def forward(
+        self,  # type: ignore  # pylint: disable=arguments-differ  #35566 in pytorch
+        mentions: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Returns rough anaphoricity scores for candidates, which consist of
+        the bilinear output of the current model summed with mention scores.
+        """
+        # [n_mentions, n_mentions]
+        pair_mask = torch.arange(mentions.shape[0])
+        pair_mask = pair_mask.unsqueeze(1) - pair_mask.unsqueeze(0)
+        pair_mask = torch.log((pair_mask > 0).to(torch.float))
+        pair_mask = pair_mask.to(mentions.device)
+        bilinear_scores = self.dropout(self.bilinear(mentions)).mm(mentions.T)
+        rough_scores = pair_mask + bilinear_scores
+
+        return self._prune(rough_scores)
+
+    def _prune(self,
+               rough_scores: torch.Tensor
+               ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Selects top-k rough antecedent scores for each mention.
+
+        Args:
+            rough_scores: tensor of shape [n_mentions, n_mentions], containing
+                rough antecedent scores of each mention-antecedent pair.
+
+        Returns:
+            FloatTensor of shape [n_mentions, k], top rough scores
+            LongTensor of shape [n_mentions, k], top indices
+        """
+        top_scores, indices = torch.topk(rough_scores,
+                                         k=min(self.k, len(rough_scores)),
+                                         dim=1, sorted=False)
+        return top_scores, indices
+
+
+class DistancePairwiseEncoder(torch.nn.Module):
+
+    def __init__(self, embedding_size, dropout_rate):
+        super().__init__()
+        emb_size = embedding_size
+        self.distance_emb = torch.nn.Embedding(9, emb_size)
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.shape = emb_size
+
+    @property
+    def device(self) -> torch.device:
+        """ A workaround to get current device (which is assumed to be the
+        device of the first parameter of one of the submodules) """
+        return next(self.distance_emb.parameters()).device
+
+
+    def forward(self,  # type: ignore  # pylint: disable=arguments-differ  #35566 in pytorch
+                top_indices: torch.Tensor
+        ) -> torch.Tensor:
+        word_ids = torch.arange(0, top_indices.size(0), device=self.device)
+        distance = (word_ids.unsqueeze(1) - word_ids[top_indices]
+                    ).clamp_min_(min=1)
+        log_distance = distance.to(torch.float).log2().floor_()
+        log_distance = log_distance.clamp_max_(max=6).to(torch.long)
+        distance = torch.where(distance < 5, distance - 1, log_distance + 2)
+        distance = self.distance_emb(distance)
+        return self.dropout(distance)

spacy/tests/test_models.py

@@ -6,7 +6,7 @@ from numpy.testing import assert_array_equal, assert_array_almost_equal
 import numpy
 from spacy.ml.models import build_Tok2Vec_model, MultiHashEmbed, MaxoutWindowEncoder
 from spacy.ml.models import build_bow_text_classifier, build_simple_cnn_text_classifier
-from spacy.ml.models import build_spancat_model
+from spacy.ml.models import build_spancat_model, build_wl_coref_model
 from spacy.ml.staticvectors import StaticVectors
 from spacy.ml.extract_spans import extract_spans, _get_span_indices
 from spacy.lang.en import English
@@ -269,3 +269,8 @@ def test_spancat_model_forward_backward(nO=5):
     Y, backprop = model((docs, spans), is_train=True)
     assert Y.shape == (spans.dataXd.shape[0], nO)
     backprop(Y)
+
+#TODO expand this
+def test_coref_model_init():
+    tok2vec = build_Tok2Vec_model(**get_tok2vec_kwargs())
+    model = build_wl_coref_model(tok2vec)