mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-20 10:44:41 +03:00
d7d317a1b5
This is now cleaner and significantly faster. There's still some messy parts in the code (particularly variable names), will get to that later.
447 lines
15 KiB
Python
447 lines
15 KiB
Python
from dataclasses import dataclass
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import warnings
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from thinc.api import Model, Linear, Relu, Dropout
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from thinc.api import chain, noop, Embed, add, tuplify
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from thinc.types import Floats2d, Floats1d, Ints2d, Ragged
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from typing import List, Callable, Tuple, Any
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from ...tokens import Doc
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from ...util import registry
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from .coref_util import get_candidate_mentions, select_non_crossing_spans, topk
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@registry.architectures("spacy.Coref.v1")
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def build_coref(
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tok2vec: Model[List[Doc], List[Floats2d]],
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get_mentions: Any = get_candidate_mentions,
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hidden: int = 1000,
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dropout: float = 0.3,
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mention_limit: int = 3900,
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#TODO this needs a better name. It limits the max mentions as a ratio of
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# the token count.
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mention_limit_ratio: float = 0.4,
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max_span_width: int = 20,
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antecedent_limit: int = 50
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):
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dim = tok2vec.get_dim("nO") * 3
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span_embedder = build_span_embedder(get_mentions, max_span_width)
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with Model.define_operators({">>": chain, "&": tuplify, "+": add}):
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mention_scorer = (
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Linear(nI=dim, nO=hidden)
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>> Relu(nI=hidden, nO=hidden)
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>> Dropout(dropout)
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>> Linear(nI=hidden, nO=1)
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)
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mention_scorer.initialize()
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#TODO make feature_embed_size a param
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feature_embed_size = 20
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width_scorer = build_width_scorer(max_span_width, hidden, feature_embed_size)
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bilinear = Linear(nI=dim, nO=dim) >> Dropout(dropout)
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bilinear.initialize()
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ms = (build_take_vecs() >> mention_scorer) + width_scorer
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model = (
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(tok2vec & noop())
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>> span_embedder
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>> (ms & noop())
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>> build_coarse_pruner(mention_limit, mention_limit_ratio)
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>> build_ant_scorer(bilinear, Dropout(dropout), antecedent_limit)
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)
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return model
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@dataclass
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class SpanEmbeddings:
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indices: Ints2d # Array with 2 columns (for start and end index)
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vectors: Ragged # Ragged[Floats2d] # One vector per span
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# NB: We assume that the indices refer to a concatenated Floats2d that
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# has one row per token in the *batch* of documents. This makes it unambiguous
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# which row is in which document, because if the lengths are e.g. [10, 5],
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# a span starting at 11 must be starting at token 2 of doc 1. A bug could
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# potentially cause you to have a span which crosses a doc boundary though,
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# which would be bad.
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# The lengths in the Ragged are not the tokens per doc, but the number of
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# mentions per doc.
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def __add__(self, right):
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out = self.vectors.data + right.vectors.data
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return SpanEmbeddings(self.indices, Ragged(out, self.vectors.lengths))
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def __iadd__(self, right):
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self.vectors.data += right.vectors.data
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return self
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def build_width_scorer(max_span_width, hidden_size, feature_embed_size=20):
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span_width_prior = (
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Embed(nV=max_span_width, nO=feature_embed_size)
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>> Linear(nI=feature_embed_size, nO=hidden_size)
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>> Relu(nI=hidden_size, nO=hidden_size)
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>> Dropout()
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>> Linear(nI=hidden_size, nO=1)
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)
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span_width_prior.initialize()
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return Model(
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"WidthScorer",
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forward=width_score_forward,
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layers=[span_width_prior])
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def width_score_forward(model, embeds: SpanEmbeddings, is_train) -> Tuple[Floats1d, Callable]:
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# calculate widths, subtracting 1 so it's 0-index
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w_ffnn = model.layers[0]
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idxs = embeds.indices
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widths = idxs[:,1] - idxs[:,0] - 1
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wscores, width_b = w_ffnn(widths, is_train)
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lens = embeds.vectors.lengths
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def width_score_backward(d_score: Floats1d) -> SpanEmbeddings:
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dX = width_b(d_score)
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vecs = Ragged(dX, lens)
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return SpanEmbeddings(idxs, vecs)
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return wscores, width_score_backward
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# model converting a Doc/Mention to span embeddings
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# get_mentions: Callable[Doc, Pairs[int]]
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def build_span_embedder(
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get_mentions: Callable,
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max_span_width: int = 20,
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) -> Model[Tuple[List[Floats2d], List[Doc]], SpanEmbeddings]:
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with Model.define_operators({">>": chain, "|": concatenate}):
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span_reduce = (extract_spans() >>
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(reduce_first() | reduce_last() | reduce_mean()))
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model = Model(
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"SpanEmbedding",
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forward=span_embeddings_forward,
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attrs={
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"get_mentions": get_mentions,
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# XXX might be better to make this an implicit parameter in the
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# mention generator
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"max_span_width": max_span_width,
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},
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layers=[span_reduce],
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)
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model.set_ref("span_reducer", span_reduce)
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return model
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def span_embeddings_forward(
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model, inputs: Tuple[List[Floats2d], List[Doc]], is_train
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) -> Tuple[SpanEmbeddings, Callable]:
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ops = model.ops
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xp = ops.xp
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tokvecs, docs = inputs
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# TODO fix this
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dim = tokvecs[0].shape[1]
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get_mentions = model.attrs["get_mentions"]
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max_span_width = model.attrs["max_span_width"]
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mentions = ops.alloc2i(0, 2)
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total_length = 0
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docmenlens = [] # number of mentions per doc
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for doc in docs:
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starts, ends = get_mentions(doc, max_span_width)
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docmenlens.append(len(starts))
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cments = ops.asarray2i([starts, ends]).transpose()
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mentions = xp.concatenate((mentions, cments + total_length))
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total_length += len(doc)
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# TODO support attention here
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tokvecs = xp.concatenate(tokvecs)
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tokvecs_r = Ragged(tokvecs, docmenlens)
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mentions_r = Ragged(mentions, docmenlens)
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span_reduce = model.get_ref("span_reducer")
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spanvecs, span_reduce_back = span_reduce( (tokvecs_r, mentions_r), is_train)
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embeds = Ragged(spanvecs, docmenlens)
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def backprop_span_embed(dY: SpanEmbeddings) -> Tuple[List[Floats2d], List[Doc]]:
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oweights = []
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offset = 0
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for mlen in dY.vectors.lengths:
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hi = offset + mlen
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vecs = dY.vectors.data[offset:hi]
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out, out_idx = span_reduce_back(vecs)
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oweights.append(out.data)
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offset = hi
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return oweights, docs
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return SpanEmbeddings(mentions, embeds), backprop_span_embed
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def build_coarse_pruner(
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mention_limit: int,
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mention_limit_ratio: float,
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) -> Model[SpanEmbeddings, SpanEmbeddings]:
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model = Model(
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"CoarsePruner",
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forward=coarse_prune,
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attrs={
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"mention_limit": mention_limit,
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"mention_limit_ratio": mention_limit_ratio,
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},
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)
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return model
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def coarse_prune(
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model, inputs: Tuple[Floats1d, SpanEmbeddings], is_train
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) -> Tuple[Tuple[Floats1d, SpanEmbeddings], Callable]:
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"""Given scores for mention, output the top non-crossing mentions.
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Mentions can contain other mentions, but candidate mentions cannot cross each other.
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"""
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rawscores, spanembeds = inputs
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scores = rawscores.flatten()
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mention_limit = model.attrs["mention_limit"]
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mention_limit_ratio = model.attrs["mention_limit_ratio"]
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# XXX: Issue here. Don't need docs to find crossing spans, but might for the limits.
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# In old code the limit can be:
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# - hard number per doc
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# - ratio of tokens in the doc
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offset = 0
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selected = []
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sellens = []
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for menlen in spanembeds.vectors.lengths:
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hi = offset + menlen
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cscores = scores[offset:hi]
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# negate it so highest numbers come first
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tops = (model.ops.xp.argsort(-1 * cscores)).tolist()
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starts = spanembeds.indices[offset:hi, 0].tolist()
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ends = spanembeds.indices[offset:hi:, 1].tolist()
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# calculate the doc length
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doclen = ends[-1] - starts[0]
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# XXX seems to make more sense to use menlen than doclen here?
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#mlimit = min(mention_limit, int(mention_limit_ratio * doclen))
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mlimit = min(mention_limit, int(mention_limit_ratio * menlen))
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# csel is a 1d integer list
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csel = select_non_crossing_spans(tops, starts, ends, mlimit)
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# add the offset so these indices are absolute
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csel = [ii + offset for ii in csel]
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# this should be constant because short choices are padded
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sellens.append(len(csel))
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selected += csel
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offset += menlen
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selected = model.ops.asarray1i(selected)
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top_spans = spanembeds.indices[selected]
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top_vecs = spanembeds.vectors.data[selected]
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out = SpanEmbeddings(top_spans, Ragged(top_vecs, sellens))
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# save some variables so the embeds can be garbage collected
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idxlen = spanembeds.indices.shape[0]
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vecshape = spanembeds.vectors.data.shape
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indices = spanembeds.indices
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veclens = out.vectors.lengths
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def coarse_prune_backprop(
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dY: Tuple[Floats1d, SpanEmbeddings]
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) -> Tuple[Floats1d, SpanEmbeddings]:
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dYscores, dYembeds = dY
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dXscores = model.ops.alloc1f(idxlen)
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dXscores[selected] = dYscores.flatten()
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dXvecs = model.ops.alloc2f(*vecshape)
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dXvecs[selected] = dYembeds.vectors.data
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rout = Ragged(dXvecs, veclens)
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dXembeds = SpanEmbeddings(indices, rout)
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# inflate for mention scorer
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dXscores = model.ops.xp.expand_dims(dXscores, 1)
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return (dXscores, dXembeds)
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return (scores[selected], out), coarse_prune_backprop
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def build_take_vecs() -> Model[SpanEmbeddings, Floats2d]:
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# this just gets vectors out of spanembeddings
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# XXX Might be better to convert SpanEmbeddings to a tuple and use with_getitem
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return Model("TakeVecs", forward=take_vecs_forward)
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def take_vecs_forward(model, inputs: SpanEmbeddings, is_train) -> Floats2d:
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idxs = inputs.indices
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lens = inputs.vectors.lengths
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def backprop(dY: Floats2d) -> SpanEmbeddings:
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vecs = Ragged(dY, lens)
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return SpanEmbeddings(idxs, vecs)
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return inputs.vectors.data, backprop
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def build_ant_scorer(
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bilinear, dropout, ant_limit=50
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) -> Model[Tuple[Floats1d, SpanEmbeddings], List[Floats2d]]:
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return Model(
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"AntScorer",
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forward=ant_scorer_forward,
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layers=[bilinear, dropout],
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attrs={
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"ant_limit": ant_limit,
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},
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)
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def ant_scorer_forward(
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model, inputs: Tuple[Floats1d, SpanEmbeddings], is_train
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) -> Tuple[Tuple[List[Tuple[Floats2d, Ints2d]], Ints2d], Callable]:
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ops = model.ops
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xp = ops.xp
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ant_limit = model.attrs["ant_limit"]
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# this contains the coarse bilinear in coref-hoi
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# coarse bilinear is a single layer linear network
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# TODO make these proper refs
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bilinear = model.layers[0]
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dropout = model.layers[1]
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# XXX Note on dimensions: This won't work as a ragged because the floats2ds
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# are not all the same dimensions. Each floats2d is a square in the size of
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# the number of antecedents in the document. Actually, that will have the
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# same size if antecedents are padded... Needs checking.
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mscores, sembeds = inputs
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vecs = sembeds.vectors # ragged
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offset = 0
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backprops = []
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out = []
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for ll in vecs.lengths:
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hi = offset + ll
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# each iteration is one doc
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# first calculate the pairwise product scores
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cvecs = vecs.data[offset:hi]
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pw_prod, prod_back = pairwise_product(bilinear, dropout, cvecs, is_train)
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# now calculate the pairwise mention scores
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ms = mscores[offset:hi].flatten()
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pw_sum, pw_sum_back = pairwise_sum(ops, ms)
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# make a mask so antecedents precede referrents
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ant_range = xp.arange(0, cvecs.shape[0])
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# This will take the log of 0, which causes a warning, but we're doing
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# it on purpose so we can just ignore the warning.
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=RuntimeWarning)
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mask = xp.log(
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(xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1
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).astype('f')
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scores = pw_prod + pw_sum + mask
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top_limit = min(ant_limit, len(scores))
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top_scores, top_scores_idx = topk(xp, scores, top_limit)
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# now add the placeholder
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placeholder = ops.alloc2f(scores.shape[0], 1)
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top_scores = xp.concatenate( (placeholder, top_scores), 1)
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#top_scores = ops.softmax(top_scores, axis=1)
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out.append((top_scores, top_scores_idx))
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# In the full model these scores can be further refined. In the current
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# state of this model we're done here, so this pruning is less important,
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# but it's still helpful for reducing memory usage (since scores can be
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# garbage collected when the loop exits).
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offset += ll
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backprops.append((prod_back, pw_sum_back))
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# save vars for gc
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vecshape = vecs.data.shape
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veclens = vecs.lengths
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scoreshape = mscores.shape
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idxes = sembeds.indices
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def backprop(
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dYs: Tuple[List[Tuple[Floats2d, Ints2d]], Ints2d]
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) -> Tuple[Floats2d, SpanEmbeddings]:
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dYscores, dYembeds = dYs
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dXembeds = Ragged(ops.alloc2f(*vecshape), veclens)
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dXscores = ops.alloc1f(*scoreshape)
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offset = 0
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for dy, (prod_back, pw_sum_back), ll in zip(dYscores, backprops, veclens):
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dyscore, dyidx = dy
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# remove the placeholder
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dyscore = dyscore[:, 1:]
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# the full score grid is square
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fullscore = ops.alloc2f(ll, ll)
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for ii, (ridx, rscores) in enumerate(zip(dyidx, dyscore)):
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fullscore[ii][ridx] = rscores
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dXembeds.data[offset : offset + ll] = prod_back(fullscore)
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dXscores[offset : offset + ll] = pw_sum_back(fullscore)
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offset += ll
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# make it fit back into the linear
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dXscores = xp.expand_dims(dXscores, 1)
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return (dXscores, SpanEmbeddings(idxes, dXembeds))
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return (out, sembeds.indices), backprop
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def pairwise_sum(ops, mention_scores: Floats1d) -> Tuple[Floats2d, Callable]:
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"""Find the most likely mention-antecedent pairs."""
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# This doesn't use multiplication because two items with low mention scores
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# don't make a good candidate pair.
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pw_sum = ops.xp.expand_dims(mention_scores, 1) + ops.xp.expand_dims(
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mention_scores, 0
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)
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def backward(d_pwsum: Floats2d) -> Floats1d:
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# For the backward pass, the gradient is distributed over the whole row and
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# column, so pull it all in.
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dim = d_pwsum.shape[0]
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out = ops.alloc1f(dim)
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for ii in range(dim):
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out[ii] = d_pwsum[:, ii].sum() + d_pwsum[ii, :].sum()
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return out
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return pw_sum, backward
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def pairwise_product(bilinear, dropout, vecs: Floats2d, is_train):
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# A neat side effect of this is that we don't have to pass the backprops
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# around separately because the closure handles them.
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source, source_b = bilinear(vecs, is_train)
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target, target_b = dropout(vecs.T, is_train)
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pw_prod = source @ target
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def backward(d_prod: Floats2d) -> Floats2d:
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dS = source_b(d_prod @ target.T)
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dT = target_b(source.T @ d_prod)
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dX = dS + dT.T
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return dX
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return pw_prod, backward
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