spaCy/spacy/ml/models/coref.py

from typing import List, Tuple
import torch

from thinc.api import Model, chain, tuplify
from thinc.api import PyTorchWrapper, ArgsKwargs
from thinc.types import Floats2d, Ints1d, Ints2d
from thinc.util import xp2torch, torch2xp

from ...tokens import Doc
from ...util import registry
from .coref_util import add_dummy, get_sentence_ids


@registry.architectures("spacy.Coref.v1")
def build_wl_coref_model(
    tok2vec: Model[List[Doc], List[Floats2d]],
    embedding_size: int = 20,
    hidden_size: int = 1024,
    n_hidden_layers: int = 1,  # TODO rename to "depth"?
    dropout: float = 0.3,
    # pairs to keep per mention after rough scoring
    # TODO change to meaningful name
    rough_k: int = 50,
    # TODO is this not a training loop setting?
    a_scoring_batch_size: int = 512,
    # span predictor embeddings
    sp_embedding_size: int = 64,
):
    # TODO fix this
    try:
        dim = tok2vec.get_dim("nO")
    except ValueError:
        # happens with transformer listener
        dim = 768

    with Model.define_operators({">>": chain}):
        # TODO chain tok2vec with these models
        coref_scorer = PyTorchWrapper(
            CorefScorer(
                dim,
                embedding_size,
                hidden_size,
                n_hidden_layers,
                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
        #        hidden_size,
        #        sp_embedding_size,
        #    ),
        #    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_model



def convert_coref_scorer_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
    X = X[0]
    word_features = xp2torch(X, requires_grad=is_train)

    def backprop(args: ArgsKwargs) -> List[Floats2d]:
        # convert to xp and wrap in list
        gradients = torch2xp(args.args[0])
        return [gradients]

    return ArgsKwargs(args=(word_features,), kwargs={}), backprop


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[0])
        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 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:
        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,
        dim: int,  # tok2vec size
        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:
            epochs_trained (int): the number of epochs finished
                (useful for warm start)
        """
        self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate)
        # TODO clean this up
        bert_emb = dim
        pair_emb = bert_emb * 3 + self.pw.shape
        self.a_scorer = AnaphoricityScorer(
            pair_emb, hidden_size, n_layers, dropout_rate
        )
        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)
        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]
        self.lstm.flatten_parameters()  # XXX without this there's a warning
        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 = 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))
        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

    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))
        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)