from dataclasses import dataclass 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.api import PyTorchWrapper, ArgsKwargs from thinc.types import Floats2d, Floats1d, Ints1d, Ints2d, Ragged from typing import List, Callable, Tuple, Any from ...tokens import Doc from ...util import registry from ..extract_spans import extract_spans from .coref_util import get_candidate_mentions, select_non_crossing_spans, topk @registry.architectures("spacy.Coref.v1") def build_coref( tok2vec: Model[List[Doc], List[Floats2d]], get_mentions: Any = get_candidate_mentions, hidden: int = 1000, dropout: float = 0.3, mention_limit: int = 3900, # TODO this needs a better name. It limits the max mentions as a ratio of # the token count. mention_limit_ratio: float = 0.4, max_span_width: int = 20, antecedent_limit: int = 50, ): dim = tok2vec.get_dim("nO") * 3 span_embedder = build_span_embedder(get_mentions, max_span_width) with Model.define_operators({">>": chain, "&": tuplify, "+": add}): mention_scorer = ( Linear(nI=dim, nO=hidden) >> Relu(nI=hidden, nO=hidden) >> Dropout(dropout) >> Linear(nI=hidden, nO=hidden) >> Relu(nI=hidden, nO=hidden) >> Dropout(dropout) >> Linear(nI=hidden, nO=1) ) mention_scorer.initialize() # TODO make feature_embed_size a param feature_embed_size = 20 width_scorer = build_width_scorer(max_span_width, hidden, feature_embed_size) bilinear = Linear(nI=dim, nO=dim) >> Dropout(dropout) bilinear.initialize() ms = (build_take_vecs() >> mention_scorer) + width_scorer model = ( (tok2vec & noop()) >> span_embedder >> (ms & noop()) >> build_coarse_pruner(mention_limit, mention_limit_ratio) >> build_ant_scorer(bilinear, Dropout(dropout), antecedent_limit) ) return model @dataclass class SpanEmbeddings: indices: Ints2d # Array with 2 columns (for start and end index) vectors: Ragged # Ragged[Floats2d] # One vector per span # NB: We assume that the indices refer to a concatenated Floats2d that # has one row per token in the *batch* of documents. This makes it unambiguous # which row is in which document, because if the lengths are e.g. [10, 5], # a span starting at 11 must be starting at token 2 of doc 1. A bug could # potentially cause you to have a span which crosses a doc boundary though, # which would be bad. # The lengths in the Ragged are not the tokens per doc, but the number of # mentions per doc. def __add__(self, right): out = self.vectors.data + right.vectors.data return SpanEmbeddings(self.indices, Ragged(out, self.vectors.lengths)) def __iadd__(self, right): self.vectors.data += right.vectors.data return self def build_width_scorer(max_span_width, hidden_size, feature_embed_size=20): span_width_prior = ( Embed(nV=max_span_width, nO=feature_embed_size) >> Linear(nI=feature_embed_size, nO=hidden_size) >> Relu(nI=hidden_size, nO=hidden_size) >> Dropout() >> Linear(nI=hidden_size, nO=1) ) span_width_prior.initialize() model = Model("WidthScorer", forward=width_score_forward, layers=[span_width_prior]) model.set_ref("width_prior", span_width_prior) return model def width_score_forward( model, embeds: SpanEmbeddings, is_train ) -> Tuple[Floats1d, Callable]: # calculate widths, subtracting 1 so it's 0-index w_ffnn = model.get_ref("width_prior") idxs = embeds.indices widths = idxs[:, 1] - idxs[:, 0] - 1 wscores, width_b = w_ffnn(widths, is_train) lens = embeds.vectors.lengths def width_score_backward(d_score: Floats1d) -> SpanEmbeddings: dX = width_b(d_score) vecs = Ragged(dX, lens) return SpanEmbeddings(idxs, vecs) return wscores, width_score_backward # model converting a Doc/Mention to span embeddings # get_mentions: Callable[Doc, Pairs[int]] def build_span_embedder( get_mentions: Callable, max_span_width: int = 20, ) -> Model[Tuple[List[Floats2d], List[Doc]], SpanEmbeddings]: with Model.define_operators({">>": chain, "|": concatenate}): span_reduce = extract_spans() >> ( reduce_first() | reduce_last() | reduce_mean() ) model = Model( "SpanEmbedding", forward=span_embeddings_forward, attrs={ "get_mentions": get_mentions, # XXX might be better to make this an implicit parameter in the # mention generator "max_span_width": max_span_width, }, layers=[span_reduce], ) model.set_ref("span_reducer", span_reduce) return model def span_embeddings_forward( model, inputs: Tuple[List[Floats2d], List[Doc]], is_train ) -> Tuple[SpanEmbeddings, Callable]: ops = model.ops xp = ops.xp tokvecs, docs = inputs # TODO fix this dim = tokvecs[0].shape[1] get_mentions = model.attrs["get_mentions"] max_span_width = model.attrs["max_span_width"] mentions = ops.alloc2i(0, 2) docmenlens = [] # number of mentions per doc for doc in docs: starts, ends = get_mentions(doc, max_span_width) docmenlens.append(len(starts)) cments = ops.asarray2i([starts, ends]).transpose() mentions = xp.concatenate((mentions, cments)) # TODO support attention here tokvecs = xp.concatenate(tokvecs) doclens = [len(doc) for doc in docs] tokvecs_r = Ragged(tokvecs, doclens) mentions_r = Ragged(mentions, docmenlens) span_reduce = model.get_ref("span_reducer") spanvecs, span_reduce_back = span_reduce((tokvecs_r, mentions_r), is_train) embeds = Ragged(spanvecs, docmenlens) def backprop_span_embed(dY: SpanEmbeddings) -> Tuple[List[Floats2d], List[Doc]]: grad, idxes = span_reduce_back(dY.vectors.data) oweights = [] offset = 0 for doclen in doclens: hi = offset + doclen oweights.append(grad.data[offset:hi]) offset = hi return oweights, docs return SpanEmbeddings(mentions, embeds), backprop_span_embed def build_coarse_pruner( mention_limit: int, mention_limit_ratio: float, ) -> Model[SpanEmbeddings, SpanEmbeddings]: model = Model( "CoarsePruner", forward=coarse_prune, attrs={ "mention_limit": mention_limit, "mention_limit_ratio": mention_limit_ratio, }, ) return model def coarse_prune( model, inputs: Tuple[Floats1d, SpanEmbeddings], is_train ) -> Tuple[Tuple[Floats1d, SpanEmbeddings], Callable]: """Given scores for mention, output the top non-crossing mentions. Mentions can contain other mentions, but candidate mentions cannot cross each other. """ rawscores, spanembeds = inputs scores = rawscores.flatten() mention_limit = model.attrs["mention_limit"] mention_limit_ratio = model.attrs["mention_limit_ratio"] # XXX: Issue here. Don't need docs to find crossing spans, but might for the limits. # In old code the limit can be: # - hard number per doc # - ratio of tokens in the doc offset = 0 selected = [] sellens = [] for menlen in spanembeds.vectors.lengths: hi = offset + menlen cscores = scores[offset:hi] # negate it so highest numbers come first # This is relatively slow but can't be skipped. tops = (model.ops.xp.argsort(-1 * cscores)).tolist() starts = spanembeds.indices[offset:hi, 0].tolist() ends = spanembeds.indices[offset:hi:, 1].tolist() # calculate the doc length doclen = ends[-1] - starts[0] # XXX seems to make more sense to use menlen than doclen here? # coref-hoi uses doclen (number of words). mlimit = min(mention_limit, int(mention_limit_ratio * doclen)) # csel is a 1d integer list csel = select_non_crossing_spans(tops, starts, ends, mlimit) # add the offset so these indices are absolute csel = [ii + offset for ii in csel] # this should be constant because short choices are padded sellens.append(len(csel)) selected += csel offset += menlen selected = model.ops.asarray1i(selected) top_spans = spanembeds.indices[selected] top_vecs = spanembeds.vectors.data[selected] out = SpanEmbeddings(top_spans, Ragged(top_vecs, sellens)) # save some variables so the embeds can be garbage collected idxlen = spanembeds.indices.shape[0] vecshape = spanembeds.vectors.data.shape indices = spanembeds.indices veclens = out.vectors.lengths def coarse_prune_backprop( dY: Tuple[Floats1d, SpanEmbeddings] ) -> Tuple[Floats1d, SpanEmbeddings]: dYscores, dYembeds = dY dXscores = model.ops.alloc1f(idxlen) dXscores[selected] = dYscores.flatten() dXvecs = model.ops.alloc2f(*vecshape) dXvecs[selected] = dYembeds.vectors.data rout = Ragged(dXvecs, veclens) dXembeds = SpanEmbeddings(indices, rout) # inflate for mention scorer dXscores = model.ops.xp.expand_dims(dXscores, 1) return (dXscores, dXembeds) return (scores[selected], out), coarse_prune_backprop def build_take_vecs() -> Model[SpanEmbeddings, Floats2d]: # this just gets vectors out of spanembeddings # XXX Might be better to convert SpanEmbeddings to a tuple and use with_getitem return Model("TakeVecs", forward=take_vecs_forward) def take_vecs_forward(model, inputs: SpanEmbeddings, is_train) -> Floats2d: idxs = inputs.indices lens = inputs.vectors.lengths def backprop(dY: Floats2d) -> SpanEmbeddings: vecs = Ragged(dY, lens) return SpanEmbeddings(idxs, vecs) return inputs.vectors.data, backprop def build_ant_scorer( bilinear, dropout, ant_limit=50 ) -> Model[Tuple[Floats1d, SpanEmbeddings], List[Floats2d]]: model = Model( "AntScorer", forward=ant_scorer_forward, layers=[bilinear, dropout], attrs={ "ant_limit": ant_limit, }, ) model.set_ref("bilinear", bilinear) model.set_ref("dropout", dropout) return model def ant_scorer_forward( model, inputs: Tuple[Floats1d, SpanEmbeddings], is_train ) -> Tuple[Tuple[List[Tuple[Floats2d, Ints2d]], Ints2d], Callable]: ops = model.ops xp = ops.xp ant_limit = model.attrs["ant_limit"] # this contains the coarse bilinear in coref-hoi # coarse bilinear is a single layer linear network # TODO make these proper refs bilinear = model.get_ref("bilinear") dropout = model.get_ref("dropout") mscores, sembeds = inputs vecs = sembeds.vectors # ragged offset = 0 backprops = [] out = [] for ll in vecs.lengths: hi = offset + ll # each iteration is one doc # first calculate the pairwise product scores cvecs = vecs.data[offset:hi] pw_prod, prod_back = pairwise_product(bilinear, dropout, cvecs, is_train) # now calculate the pairwise mention scores ms = mscores[offset:hi].flatten() pw_sum, pw_sum_back = pairwise_sum(ops, ms) # make a mask so antecedents precede referrents ant_range = xp.arange(0, cvecs.shape[0]) # This will take the log of 0, which causes a warning, but we're doing # it on purpose so we can just ignore the warning. with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=RuntimeWarning) mask = xp.log( (xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1 ).astype("f") scores = pw_prod + pw_sum + mask top_limit = min(ant_limit, len(scores)) top_scores, top_scores_idx = topk(xp, scores, top_limit) # now add the placeholder placeholder = ops.alloc2f(scores.shape[0], 1) top_scores = xp.concatenate((placeholder, top_scores), 1) out.append((top_scores, top_scores_idx)) # In the full model these scores can be further refined. In the current # state of this model we're done here, so this pruning is less important, # but it's still helpful for reducing memory usage (since scores can be # garbage collected when the loop exits). offset += ll backprops.append((prod_back, pw_sum_back)) # save vars for gc vecshape = vecs.data.shape veclens = vecs.lengths scoreshape = mscores.shape idxes = sembeds.indices def backprop( dYs: Tuple[List[Tuple[Floats2d, Ints2d]], Ints2d] ) -> Tuple[Floats2d, SpanEmbeddings]: dYscores, dYembeds = dYs dXembeds = Ragged(ops.alloc2f(*vecshape), veclens) dXscores = ops.alloc1f(*scoreshape) offset = 0 for dy, (prod_back, pw_sum_back), ll in zip(dYscores, backprops, veclens): hi = offset + ll dyscore, dyidx = dy # remove the placeholder dyscore = dyscore[:, 1:] # the full score grid is square fullscore = ops.alloc2f(ll, ll) for ii, (ridx, rscores) in enumerate(zip(dyidx, dyscore)): fullscore[ii][ridx] = rscores dXembeds.data[offset:hi] = prod_back(fullscore) dXscores[offset:hi] = pw_sum_back(fullscore) offset = hi # make it fit back into the linear dXscores = xp.expand_dims(dXscores, 1) return (dXscores, SpanEmbeddings(idxes, dXembeds)) return (out, sembeds.indices), backprop def pairwise_sum(ops, mention_scores: Floats1d) -> Tuple[Floats2d, Callable]: """Find the most likely mention-antecedent pairs.""" # This doesn't use multiplication because two items with low mention scores # don't make a good candidate pair. pw_sum = ops.xp.expand_dims(mention_scores, 1) + ops.xp.expand_dims( mention_scores, 0 ) def backward(d_pwsum: Floats2d) -> Floats1d: # For the backward pass, the gradient is distributed over the whole row and # column, so pull it all in. out = d_pwsum.sum(axis=0) + d_pwsum.sum(axis=1) return out return pw_sum, backward def pairwise_product(bilinear, dropout, vecs: Floats2d, is_train): # A neat side effect of this is that we don't have to pass the backprops # around separately because the closure handles them. source, source_b = bilinear(vecs, is_train) target, target_b = dropout(vecs.T, is_train) pw_prod = source @ target def backward(d_prod: Floats2d) -> Floats2d: dS = source_b(d_prod @ target.T) dT = target_b(source.T @ d_prod) dX = dS + dT.T return dX return pw_prod, backward # XXX here down is wl-coref from typing import List, Tuple import torch from thinc.util import xp2torch, torch2xp # TODO rename this to coref_util from .coref_util_wl import add_dummy # TODO rename to plain coref @registry.architectures("spacy.WLCoref.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, ): dim = tok2vec.get_dim("nO") with Model.define_operators({">>": chain}): # TODO chain tok2vec with these models # TODO fix device - should be automatic device = "cuda:0" coref_scorer = PyTorchWrapper( CorefScorer( device, 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, 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_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 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, 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: 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, 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) """ # device, dist_emb_size, hidden_size, n_layers, dropout_rate self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate).to(device) #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 ).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] 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)) 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 SpanPredictor(torch.nn.Module): def __init__(self, input_size: int, distance_emb_size: int, device): super().__init__() self.ffnn = torch.nn.Sequential( torch.nn.Linear(input_size * 2 + 64, input_size), torch.nn.ReLU(), torch.nn.Dropout(0.3), torch.nn.Linear(input_size, 256), torch.nn.ReLU(), torch.nn.Dropout(0.3), torch.nn.Linear(256, 64), ) self.device = device self.conv = torch.nn.Sequential( torch.nn.Conv1d(64, 4, 3, 1, 1), torch.nn.Conv1d(4, 2, 3, 1, 1) ) self.emb = torch.nn.Embedding(128, distance_emb_size) # [-63, 63] + too_far def forward(self, # type: ignore # pylint: disable=arguments-differ #35566 in pytorch sent_id, words: torch.Tensor, heads_ids: torch.Tensor) -> torch.Tensor: """ Calculates span start/end scores of words for each span head in heads_ids Args: doc (Doc): the document data words (torch.Tensor): contextual embeddings for each word in the document, [n_words, emb_size] heads_ids (torch.Tensor): word indices of span heads Returns: torch.Tensor: span start/end scores, [n_heads, n_words, 2] """ # Obtain distance embedding indices, [n_heads, n_words] relative_positions = (heads_ids.unsqueeze(1) - torch.arange(words.shape[0], device=words.device).unsqueeze(0)) # make all valid distances positive emb_ids = relative_positions + 63 # "too_far" emb_ids[(emb_ids < 0) + (emb_ids > 126)] = 127 # Obtain "same sentence" boolean mask, [n_heads, n_words] sent_id = torch.tensor(sent_id, device=words.device) same_sent = (sent_id[heads_ids].unsqueeze(1) == sent_id.unsqueeze(0)) # To save memory, only pass candidates from one sentence for each head # pair_matrix contains concatenated span_head_emb + candidate_emb + distance_emb # for each candidate among the words in the same sentence as span_head # [n_heads, input_size * 2 + distance_emb_size] rows, cols = same_sent.nonzero(as_tuple=True) pair_matrix = torch.cat(( words[heads_ids[rows]], words[cols], self.emb(emb_ids[rows, cols]), ), dim=1) lengths = same_sent.sum(dim=1) padding_mask = torch.arange(0, lengths.max(), device=words.device).unsqueeze(0) padding_mask = (padding_mask < lengths.unsqueeze(1)) # [n_heads, max_sent_len] # [n_heads, max_sent_len, input_size * 2 + distance_emb_size] # This is necessary to allow the convolution layer to look at several # word scores padded_pairs = torch.zeros(*padding_mask.shape, pair_matrix.shape[-1], device=words.device) padded_pairs[padding_mask] = pair_matrix res = self.ffnn(padded_pairs) # [n_heads, n_candidates, last_layer_output] res = self.conv(res.permute(0, 2, 1)).permute(0, 2, 1) # [n_heads, n_candidates, 2] scores = torch.full((heads_ids.shape[0], words.shape[0], 2), float('-inf'), device=words.device) scores[rows, cols] = res[padding_mask] # Make sure that start <= head <= end during inference if not self.training: valid_starts = torch.log((relative_positions >= 0).to(torch.float)) valid_ends = torch.log((relative_positions <= 0).to(torch.float)) valid_positions = torch.stack((valid_starts, valid_ends), dim=2) return scores + valid_positions return scores 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)