mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 15:37:29 +03:00
Paul O'Leary McCann
GitHub
commit
57165f9631
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@ -1,14 +1,14 @@
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from typing import List, Tuple
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import torch
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from thinc.api import Model, chain, tuplify
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from thinc.api import Model, chain
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from thinc.api import PyTorchWrapper, ArgsKwargs
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from thinc.types import Floats2d, Ints1d, Ints2d
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from thinc.types import Floats2d, Ints2d, Ints1d
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from thinc.util import xp2torch, torch2xp
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from ...tokens import Doc
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from ...util import registry
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from .coref_util import add_dummy, get_sentence_ids
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from .coref_util import add_dummy
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@registry.architectures("spacy.Coref.v1")
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@ -19,7 +19,6 @@ def build_wl_coref_model(
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n_hidden_layers: int = 1, # TODO rename to "depth"?
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dropout: float = 0.3,
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# pairs to keep per mention after rough scoring
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# TODO change to meaningful name
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rough_k: int = 50,
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# TODO is this not a training loop setting?
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a_scoring_batch_size: int = 512,
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@ -34,7 +33,6 @@ def build_wl_coref_model(
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dim = 768
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with Model.define_operators({">>": chain}):
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# TODO chain tok2vec with these models
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coref_scorer = PyTorchWrapper(
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CorefScorer(
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dim,
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@ -49,22 +47,14 @@ def build_wl_coref_model(
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convert_outputs=convert_coref_scorer_outputs,
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)
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coref_model = tok2vec >> coref_scorer
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# XXX just ignore this until the coref scorer is integrated
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# span_predictor = PyTorchWrapper(
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# SpanPredictor(
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# TODO this was hardcoded to 1024, check
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# hidden_size,
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# sp_embedding_size,
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# ),
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# convert_inputs=convert_span_predictor_inputs
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# )
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# TODO combine models so output is uniform (just one forward pass)
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# It may be reasonable to have an option to disable span prediction,
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# and just return words as spans.
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return coref_model
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def convert_coref_scorer_inputs(model: Model, X: List[Floats2d], is_train: bool):
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def convert_coref_scorer_inputs(
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model: Model,
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X: List[Floats2d],
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is_train: bool
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):
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# The input here is List[Floats2d], one for each doc
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# just use the first
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# TODO real batching
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@ -76,7 +66,7 @@ def convert_coref_scorer_inputs(model: Model, X: List[Floats2d], is_train: bool)
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gradients = torch2xp(args.args[0])
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return [gradients]
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return ArgsKwargs(args=(word_features,), kwargs={}), backprop
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return ArgsKwargs(args=(word_features, ), kwargs={}), backprop
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def convert_coref_scorer_outputs(model: Model, inputs_outputs, is_train: bool):
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@ -95,46 +85,13 @@ def convert_coref_scorer_outputs(model: Model, inputs_outputs, is_train: bool):
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return (scores_xp, indices_xp), convert_for_torch_backward
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# TODO add docstring for this, maybe move to utils.
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# This might belong in the component.
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def _clusterize(model, scores: Floats2d, top_indices: Ints2d):
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xp = model.ops.xp
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antecedents = scores.argmax(axis=1) - 1
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not_dummy = antecedents >= 0
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coref_span_heads = xp.arange(0, len(scores))[not_dummy]
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antecedents = top_indices[coref_span_heads, antecedents[not_dummy]]
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n_words = scores.shape[0]
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nodes = [GraphNode(i) for i in range(n_words)]
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for i, j in zip(coref_span_heads.tolist(), antecedents.tolist()):
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nodes[i].link(nodes[j])
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assert nodes[i] is not nodes[j]
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clusters = []
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for node in nodes:
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if len(node.links) > 0 and not node.visited:
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cluster = []
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stack = [node]
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while stack:
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current_node = stack.pop()
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current_node.visited = True
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cluster.append(current_node.id)
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stack.extend(link for link in current_node.links if not link.visited)
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assert len(cluster) > 1
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clusters.append(sorted(cluster))
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return sorted(clusters)
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class CorefScorer(torch.nn.Module):
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"""Combines all coref modules together to find coreferent spans.
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Attributes:
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epochs_trained (int): number of epochs the model has been trained for
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"""
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Combines all coref modules together to find coreferent token pairs.
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Submodules (in the order of their usage in the pipeline):
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rough_scorer (RoughScorer)
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pw (PairwiseEncoder)
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a_scorer (AnaphoricityScorer)
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sp (SpanPredictor)
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- rough_scorer (RoughScorer) that prunes candidate pairs
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- pw (DistancePairwiseEncoder) that computes pairwise features
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- a_scorer (AnaphoricityScorer) produces the final scores
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"""
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def __init__(
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@ -149,50 +106,64 @@ class CorefScorer(torch.nn.Module):
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):
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super().__init__()
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"""
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A newly created model is set to evaluation mode.
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Args:
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epochs_trained (int): the number of epochs finished
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(useful for warm start)
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dim: Size of the input features.
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dist_emb_size: Size of the distance embeddings.
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hidden_size: Size of the coreference candidate embeddings.
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n_layers: Numbers of layers in the AnaphoricityScorer.
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dropout_rate: Dropout probability to apply across all modules.
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roughk: Number of candidates the RoughScorer returns.
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batch_size: Internal batch-size for the more expensive scorer.
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"""
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.batch_size = batch_size
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# Modules
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self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate)
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# TODO clean this up
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bert_emb = dim
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pair_emb = bert_emb * 3 + self.pw.shape
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pair_emb = dim * 3 + self.pw.shape
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self.a_scorer = AnaphoricityScorer(
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pair_emb,
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hidden_size,
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n_layers,
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dropout_rate
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)
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self.lstm = torch.nn.LSTM(
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input_size=dim,
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hidden_size=dim,
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batch_first=True,
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)
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self.rough_scorer = RoughScorer(dim, dropout_rate, roughk)
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self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate)
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pair_emb = dim * 3 + self.pw.shape
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self.a_scorer = AnaphoricityScorer(
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pair_emb, hidden_size, n_layers, dropout_rate
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)
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self.lstm = torch.nn.LSTM(
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input_size=bert_emb,
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hidden_size=bert_emb,
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batch_first=True,
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)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.rough_scorer = RoughScorer(bert_emb, dropout_rate, roughk)
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self.batch_size = batch_size
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def forward(self, word_features: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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def forward(
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self, word_features: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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This is a massive method, but it made sense to me to not split it into
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several ones to let one see the data flow.
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1. LSTM encodes the incoming word_features.
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2. The RoughScorer scores and prunes the candidates.
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3. The DistancePairwiseEncoder embeds the distances between pairs.
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4. The AnaphoricityScorer scores all pairs in mini-batches.
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Args:
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word_features: torch.Tensor containing word encodings
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Returns:
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coreference scores and top indices
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word_features: torch.Tensor containing word encodings
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returns:
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coref_scores: n_words x roughk floats.
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top_indices: n_words x roughk integers.
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"""
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# words [n_words, span_emb]
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# cluster_ids [n_words]
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self.lstm.flatten_parameters() # XXX without this there's a warning
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word_features = torch.unsqueeze(word_features, dim=0)
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words, _ = self.lstm(word_features)
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words = words.squeeze()
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# words: n_words x dim
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words = self.dropout(words)
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# Obtain bilinear scores and leave only top-k antecedents for each word
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# top_rough_scores [n_words, n_ants]
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# top_indices [n_words, n_ants]
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# top_rough_scores: (n_words x roughk)
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# top_indices: (n_words x roughk)
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top_rough_scores, top_indices = self.rough_scorer(words)
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# Get pairwise features [n_words, n_ants, n_pw_features]
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# Get pairwise features
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# (n_words x roughk x n_pw_features)
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pw = self.pw(top_indices)
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batch_size = self.batch_size
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a_scores_lst: List[torch.Tensor] = []
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@ -272,13 +243,8 @@ class AnaphoricityScorer(torch.nn.Module):
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def _ffnn(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Calculates anaphoricity scores.
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Args:
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x: tensor of shape [batch_size, n_ants, n_features]
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Returns:
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tensor of shape [batch_size, n_ants]
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x: tensor of shape (batch_size x roughk x n_features
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returns: tensor of shape (batch_size x rough_k)
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"""
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x = self.out(self.hidden(x))
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return x.squeeze(2)
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@ -293,21 +259,18 @@ class AnaphoricityScorer(torch.nn.Module):
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"""
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Builds the matrix used as input for AnaphoricityScorer.
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Args:
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all_mentions (torch.Tensor): [n_mentions, mention_emb],
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all the valid mentions of the document,
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can be on a different device
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mentions_batch (torch.Tensor): [batch_size, mention_emb],
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the mentions of the current batch,
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is expected to be on the current device
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pw_batch (torch.Tensor): [batch_size, n_ants, pw_emb],
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pairwise features of the current batch,
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is expected to be on the current device
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top_indices_batch (torch.Tensor): [batch_size, n_ants],
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indices of antecedents of each mention
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all_mentions: (n_mentions x mention_emb),
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all the valid mentions of the document,
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can be on a different device
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mentions_batch: (batch_size x mention_emb),
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the mentions of the current batch.
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pw_batch: (batch_size x roughk x pw_emb),
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pairwise distance features of the current batch.
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top_indices_batch: (batch_size x n_ants),
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indices of antecedents of each mention
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Returns:
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torch.Tensor: [batch_size, n_ants, pair_emb]
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out: pairwise features (batch_size x n_ants x pair_emb)
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"""
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emb_size = mentions_batch.shape[1]
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n_ants = pw_batch.shape[1]
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@ -322,16 +285,15 @@ class AnaphoricityScorer(torch.nn.Module):
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class RoughScorer(torch.nn.Module):
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"""
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Is needed to give a roughly estimate of the anaphoricity of two candidates,
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only top scoring candidates are considered on later steps to reduce
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computational complexity.
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Cheaper module that gives a rough estimate of the anaphoricity of two
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candidates, only top scoring candidates are considered on later
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steps to reduce computational cost.
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"""
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def __init__(self, features: int, dropout_rate: float, rough_k: float):
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super().__init__()
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.bilinear = torch.nn.Linear(features, features)
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self.k = rough_k
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def forward(
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@ -348,29 +310,27 @@ class RoughScorer(torch.nn.Module):
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pair_mask = torch.log((pair_mask > 0).to(torch.float))
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bilinear_scores = self.dropout(self.bilinear(mentions)).mm(mentions.T)
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rough_scores = pair_mask + bilinear_scores
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return self._prune(rough_scores)
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def _prune(self, rough_scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Selects top-k rough antecedent scores for each mention.
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Args:
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rough_scores: tensor of shape [n_mentions, n_mentions], containing
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rough antecedent scores of each mention-antecedent pair.
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Returns:
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FloatTensor of shape [n_mentions, k], top rough scores
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LongTensor of shape [n_mentions, k], top indices
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"""
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top_scores, indices = torch.topk(
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rough_scores, k=min(self.k, len(rough_scores)), dim=1, sorted=False
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)
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return top_scores, indices
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class DistancePairwiseEncoder(torch.nn.Module):
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def __init__(self, embedding_size, dropout_rate):
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"""
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Takes the top_indices indicating, which is a ranked
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list for each word and its most likely corresponding
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anaphora candidates. For each of these pairs it looks
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up a distance embedding from a table, where the distance
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corresponds to the log-distance.
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embedding_size: int,
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Dimensionality of the distance-embeddings table.
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dropout_rate: float,
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Dropout probability.
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"""
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super().__init__()
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emb_size = embedding_size
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self.distance_emb = torch.nn.Embedding(9, emb_size)
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@ -378,11 +338,12 @@ class DistancePairwiseEncoder(torch.nn.Module):
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self.shape = emb_size
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def forward(
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self, # type: ignore # pylint: disable=arguments-differ #35566 in pytorch
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top_indices: torch.Tensor,
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self,
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top_indices: torch.Tensor
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) -> torch.Tensor:
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word_ids = torch.arange(0, top_indices.size(0))
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distance = (word_ids.unsqueeze(1) - word_ids[top_indices]).clamp_min_(min=1)
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distance = (word_ids.unsqueeze(1) - word_ids[top_indices]
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).clamp_min_(min=1)
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log_distance = distance.to(torch.float).log2().floor_()
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log_distance = log_distance.clamp_max_(max=6).to(torch.long)
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distance = torch.where(distance < 5, distance - 1, log_distance + 2)
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|
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@ -3,7 +3,7 @@ import torch
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from thinc.api import Model, chain, tuplify
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from thinc.api import PyTorchWrapper, ArgsKwargs
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from thinc.types import Floats2d, Ints1d, Ints2d
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from thinc.types import Floats2d, Ints1d
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from thinc.util import xp2torch, torch2xp
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from ...tokens import Doc
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|
|
@ -40,10 +40,9 @@ def convert_span_predictor_inputs(
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model: Model, X: Tuple[Ints1d, Floats2d, Ints1d], is_train: bool
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):
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tok2vec, (sent_ids, head_ids) = X
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# Normally we shoudl use the input is_train, but for these two it's not relevant
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# Normally we should use the input is_train, but for these two it's not relevant
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def backprop(args: ArgsKwargs) -> List[Floats2d]:
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# convert to xp and wrap in list
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gradients = torch2xp(args.args[1])
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return [[gradients], None]
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@ -55,7 +54,6 @@ def convert_span_predictor_inputs(
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head_ids = xp2torch(head_ids[0], requires_grad=False)
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argskwargs = ArgsKwargs(args=(sent_ids, word_features, head_ids), kwargs={})
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# TODO actually support backprop
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return argskwargs, backprop
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|
|
@ -66,15 +64,13 @@ def predict_span_clusters(
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"""
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Predicts span clusters based on the word clusters.
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Args:
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doc (Doc): the document data
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words (torch.Tensor): [n_words, emb_size] matrix containing
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embeddings for each of the words in the text
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clusters (List[List[int]]): a list of clusters where each cluster
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is a list of word indices
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span_predictor: a SpanPredictor instance
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sent_ids: For each word indicates, which sentence it appears in.
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words: Features for words.
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clusters: Clusters inferred by the CorefScorer.
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Returns:
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List[List[Span]]: span clusters
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List[List[Tuple[int, int]]: span clusters
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"""
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if not clusters:
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return []
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@ -141,29 +137,29 @@ class SpanPredictor(torch.nn.Module):
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# this use of dist_emb_size looks wrong but it was 64...?
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torch.nn.Linear(256, dist_emb_size),
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)
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# TODO make the Convs also parametrizeable
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self.conv = torch.nn.Sequential(
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torch.nn.Conv1d(64, 4, 3, 1, 1), torch.nn.Conv1d(4, 2, 3, 1, 1)
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)
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# TODO make embeddings size a parameter
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self.emb = torch.nn.Embedding(128, dist_emb_size) # [-63, 63] + too_far
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def forward(
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self, # type: ignore # pylint: disable=arguments-differ #35566 in pytorch
|
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self,
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sent_id,
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words: torch.Tensor,
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heads_ids: torch.Tensor,
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) -> torch.Tensor:
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"""
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Calculates span start/end scores of words for each span head in
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heads_ids
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Calculates span start/end scores of words for each span
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for each head.
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|
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Args:
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doc (Doc): the document data
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words (torch.Tensor): contextual embeddings for each word in the
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document, [n_words, emb_size]
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heads_ids (torch.Tensor): word indices of span heads
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sent_id: Sentence id of each word.
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words: features for each word in the document.
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heads_ids: word indices of span heads
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Returns:
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torch.Tensor: span start/end scores, [n_heads, n_words, 2]
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torch.Tensor: span start/end scores, (n_heads x n_words x 2)
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"""
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# If we don't receive heads, return empty
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if heads_ids.nelement() == 0:
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@ -176,13 +172,13 @@ class SpanPredictor(torch.nn.Module):
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emb_ids = relative_positions + 63
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# "too_far"
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emb_ids[(emb_ids < 0) + (emb_ids > 126)] = 127
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# Obtain "same sentence" boolean mask, [n_heads, n_words]
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# Obtain "same sentence" boolean mask: (n_heads x n_words)
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heads_ids = heads_ids.long()
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same_sent = sent_id[heads_ids].unsqueeze(1) == sent_id.unsqueeze(0)
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# To save memory, only pass candidates from one sentence for each head
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# pair_matrix contains concatenated span_head_emb + candidate_emb + distance_emb
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# for each candidate among the words in the same sentence as span_head
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# [n_heads, input_size * 2 + distance_emb_size]
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# (n_heads x input_size * 2 x distance_emb_size)
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rows, cols = same_sent.nonzero(as_tuple=True)
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pair_matrix = torch.cat(
|
||||
(
|
||||
|
|
@ -194,17 +190,17 @@ class SpanPredictor(torch.nn.Module):
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)
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||||
lengths = same_sent.sum(dim=1)
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||||
padding_mask = torch.arange(0, lengths.max().item()).unsqueeze(0)
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||||
padding_mask = padding_mask < lengths.unsqueeze(1) # [n_heads, max_sent_len]
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||||
# [n_heads, max_sent_len, input_size * 2 + distance_emb_size]
|
||||
# (n_heads x max_sent_len)
|
||||
padding_mask = padding_mask < lengths.unsqueeze(1)
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||||
# (n_heads x max_sent_len x 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])
|
||||
padded_pairs[padding_mask] = pair_matrix
|
||||
|
||||
res = self.ffnn(padded_pairs) # [n_heads, n_candidates, last_layer_output]
|
||||
res = self.ffnn(padded_pairs) # (n_heads x n_candidates x last_layer_output)
|
||||
res = self.conv(res.permute(0, 2, 1)).permute(
|
||||
0, 2, 1
|
||||
) # [n_heads, n_candidates, 2]
|
||||
) # (n_heads x n_candidates, 2)
|
||||
|
||||
scores = torch.full((heads_ids.shape[0], words.shape[0], 2), float("-inf"))
|
||||
scores[rows, cols] = res[padding_mask]
|
||||
|
|
|
|||
|
|
@ -350,9 +350,7 @@ class CoreferenceResolver(TrainablePipe):
|
|||
|
||||
def score(self, examples, **kwargs):
|
||||
"""Score a batch of examples using LEA.
|
||||
|
||||
For details on how LEA works and why to use it see the paper:
|
||||
|
||||
Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric
|
||||
Moosavi and Strube, 2016
|
||||
https://api.semanticscholar.org/CorpusID:17606580
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user