mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
472 lines
20 KiB
Cython
472 lines
20 KiB
Cython
# cython: infer_types=True, bounds_check=False, profile=True
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from cymem.cymem cimport Pool
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from libc.stdlib cimport free, malloc
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from libc.string cimport memcpy, memset
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import numpy
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from thinc.api import get_array_module
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from ..attrs cimport MORPH, NORM
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from ..lexeme cimport EMPTY_LEXEME, Lexeme
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from ..structs cimport LexemeC, TokenC
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from ..vocab cimport Vocab
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from .doc cimport Doc, set_children_from_heads, token_by_end, token_by_start
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from .span cimport Span
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from .token cimport Token
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from ..attrs import intify_attrs
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from ..errors import Errors
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from ..strings import get_string_id
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from ..util import SimpleFrozenDict
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from .underscore import is_writable_attr
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cdef class Retokenizer:
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"""Helper class for doc.retokenize() context manager.
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DOCS: https://spacy.io/api/doc#retokenize
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USAGE: https://spacy.io/usage/linguistic-features#retokenization
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"""
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cdef Doc doc
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cdef list merges
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cdef list splits
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cdef set tokens_to_merge
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cdef list _spans_to_merge
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def __init__(self, doc):
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self.doc = doc
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self.merges = []
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self.splits = []
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self.tokens_to_merge = set()
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self._spans_to_merge = [] # keep a record to filter out duplicates
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def merge(self, Span span, attrs=SimpleFrozenDict()):
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"""Mark a span for merging. The attrs will be applied to the resulting
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token.
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span (Span): The span to merge.
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attrs (dict): Attributes to set on the merged token.
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DOCS: https://spacy.io/api/doc#retokenizer.merge
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"""
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if (span.start, span.end) in self._spans_to_merge:
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return
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if span.end - span.start <= 0:
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raise ValueError(Errors.E199.format(start=span.start, end=span.end))
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for token in span:
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if token.i in self.tokens_to_merge:
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raise ValueError(Errors.E102.format(token=repr(token)))
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self.tokens_to_merge.add(token.i)
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self._spans_to_merge.append((span.start, span.end))
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attrs = normalize_token_attrs(self.doc.vocab, attrs)
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self.merges.append((span, attrs))
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def split(self, Token token, orths, heads, attrs=SimpleFrozenDict()):
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"""Mark a Token for splitting, into the specified orths. The attrs
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will be applied to each subtoken.
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token (Token): The token to split.
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orths (list): The verbatim text of the split tokens. Needs to match the
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text of the original token.
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heads (list): List of token or `(token, subtoken)` tuples specifying the
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tokens to attach the newly split subtokens to.
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attrs (dict): Attributes to set on all split tokens. Attribute names
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mapped to list of per-token attribute values.
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DOCS: https://spacy.io/api/doc#retokenizer.split
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"""
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if ''.join(orths) != token.text:
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raise ValueError(Errors.E117.format(new=''.join(orths), old=token.text))
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if "_" in attrs: # Extension attributes
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extensions = attrs["_"]
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for extension in extensions:
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_validate_extensions(extension)
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attrs = {key: value for key, value in attrs.items() if key != "_"}
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# NB: Since we support {"KEY": [value, value]} syntax here, this
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# will only "intify" the keys, not the values
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attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
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attrs["_"] = extensions
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else:
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# NB: Since we support {"KEY": [value, value]} syntax here, this
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# will only "intify" the keys, not the values
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attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
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if MORPH in attrs:
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for i, morph in enumerate(attrs[MORPH]):
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# add and set to normalized value
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morph = self.doc.vocab.morphology.add(self.doc.vocab.strings.as_string(morph))
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attrs[MORPH][i] = morph
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head_offsets = []
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for head in heads:
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if isinstance(head, Token):
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head_offsets.append((head.idx, 0))
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else:
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head_offsets.append((head[0].idx, head[1]))
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self.splits.append((token.idx, orths, head_offsets, attrs))
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def __enter__(self):
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self.merges = []
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self.splits = []
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return self
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def __exit__(self, *args):
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# Do the actual merging here
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if len(self.merges) >= 1:
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_merge(self.doc, self.merges)
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# Iterate in order, to keep things simple.
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for start_char, orths, heads, attrs in sorted(self.splits):
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# Resolve token index
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token_index = token_by_start(self.doc.c, self.doc.length, start_char)
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# Check we're still able to find tokens starting at the character offsets
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# referred to in the splits. If we merged these tokens previously, we
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# have to raise an error
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if token_index == -1:
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raise IndexError(Errors.E122)
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head_indices = []
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for head_char, subtoken in heads:
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head_index = token_by_start(self.doc.c, self.doc.length, head_char)
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if head_index == -1:
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raise IndexError(Errors.E123)
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# We want to refer to the token index of the head *after* the
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# mergery. We need to account for the extra tokens introduced.
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# e.g., let's say we have [ab, c] and we want a and b to depend
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# on c. The correct index for c will be 2, not 1.
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if head_index > token_index:
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head_index += len(orths)-1
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head_indices.append(head_index+subtoken)
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_split(self.doc, token_index, orths, head_indices, attrs)
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def _merge(Doc doc, merges):
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"""Retokenize the document, such that the spans described in 'merges'
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are merged into a single token. This method assumes that the merges
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are in the same order at which they appear in the doc, and that merges
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do not intersect each other in any way.
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merges: Tokens to merge, and corresponding attributes to assign to the
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merged token. By default, attributes are inherited from the
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syntactic root of the span.
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RETURNS (Token): The first newly merged token.
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"""
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cdef int i, merge_index, start, end, token_index, current_span_index, current_offset, offset, span_index
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cdef Span span
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cdef const LexemeC* lex
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cdef TokenC* token
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cdef Pool mem = Pool()
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cdef int merged_iob = 0
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# merges should not be empty, but make sure to avoid zero-length mem alloc
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assert len(merges) > 0
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tokens = <TokenC**>mem.alloc(len(merges), sizeof(TokenC))
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spans = []
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def _get_start(merge):
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return merge[0].start
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merges.sort(key=_get_start)
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for merge_index, (span, attributes) in enumerate(merges):
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start = span.start
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end = span.end
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spans.append(span)
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# House the new merged token where it starts
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token = &doc.c[start]
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start_ent_iob = doc.c[start].ent_iob
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start_ent_type = doc.c[start].ent_type
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# Initially set attributes to attributes of span root
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token.tag = doc.c[span.root.i].tag
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token.pos = doc.c[span.root.i].pos
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token.morph = doc.c[span.root.i].morph
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token.ent_iob = doc.c[span.root.i].ent_iob
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token.ent_type = doc.c[span.root.i].ent_type
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merged_iob = token.ent_iob
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# If span root is part of an entity, merged token is B-ENT
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if token.ent_iob in (1, 3):
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merged_iob = 3
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# If start token is I-ENT and previous token is of the same
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# type, then I-ENT (could check I-ENT from start to span root)
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if start_ent_iob == 1 and start > 0 \
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and start_ent_type == token.ent_type \
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and doc.c[start - 1].ent_type == token.ent_type:
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merged_iob = 1
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token.ent_iob = merged_iob
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# Set lemma to concatenated lemmas
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merged_lemma = ""
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for span_token in span:
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merged_lemma += span_token.lemma_
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if doc.c[span_token.i].spacy:
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merged_lemma += " "
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merged_lemma = merged_lemma.strip()
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token.lemma = doc.vocab.strings.add(merged_lemma)
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# Unset attributes that don't match new token
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token.norm = 0
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tokens[merge_index] = token
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# Resize the doc.tensor, if it's set. Let the last row for each token stand
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# for the merged region. To do this, we create a boolean array indicating
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# whether the row is to be deleted, then use numpy.delete
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if doc.tensor is not None and doc.tensor.size != 0:
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doc.tensor = _resize_tensor(doc.tensor,
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[(m[0].start, m[0].end) for m in merges])
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# Memorize span roots and sets dependencies of the newly merged
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# tokens to the dependencies of their roots.
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span_roots = []
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for i, span in enumerate(spans):
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span_roots.append(span.root.i)
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tokens[i].dep = span.root.dep
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# We update token.lex after keeping span root and dep, since
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# setting token.lex will change span.start and span.end properties
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# as it modifies the character offsets in the doc
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for token_index, (span, attributes) in enumerate(merges):
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new_orth = ''.join([t.text_with_ws for t in spans[token_index]])
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if spans[token_index][-1].whitespace_:
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new_orth = new_orth[:-len(spans[token_index][-1].whitespace_)]
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# add the vector of the (merged) entity to the vocab
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if not doc.vocab.get_vector(new_orth).any():
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if doc.vocab.vectors_length > 0:
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doc.vocab.set_vector(new_orth, span.vector)
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token = tokens[token_index]
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lex = doc.vocab.get(doc.mem, new_orth)
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token.lex = lex
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# We set trailing space here too
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token.spacy = doc.c[spans[token_index].end-1].spacy
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set_token_attrs(span[0], attributes)
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# Begin by setting all the head indices to absolute token positions
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# This is easier to work with for now than the offsets
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# Before thinking of something simpler, beware the case where a
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# dependency bridges over the entity. Here the alignment of the
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# tokens changes.
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for i in range(doc.length):
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doc.c[i].head += i
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# Set the head of the merged token from the Span
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for i in range(len(merges)):
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tokens[i].head = doc.c[span_roots[i]].head
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# Adjust deps before shrinking tokens
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# Tokens which point into the merged token should now point to it
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# Subtract the offset from all tokens which point to >= end
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offsets = []
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current_span_index = 0
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current_offset = 0
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for i in range(doc.length):
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if current_span_index < len(spans) and i == spans[current_span_index].end:
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# Last token was the last of the span
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current_offset += (spans[current_span_index].end - spans[current_span_index].start) -1
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current_span_index += 1
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if current_span_index < len(spans) and \
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spans[current_span_index].start <= i < spans[current_span_index].end:
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offsets.append(spans[current_span_index].start - current_offset)
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else:
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offsets.append(i - current_offset)
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for i in range(doc.length):
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doc.c[i].head = offsets[doc.c[i].head]
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# Now compress the token array
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offset = 0
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in_span = False
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span_index = 0
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for i in range(doc.length):
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if in_span and i == spans[span_index].end:
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# First token after a span
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in_span = False
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span_index += 1
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if span_index < len(spans) and i == spans[span_index].start:
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# First token in a span
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doc.c[i - offset] = doc.c[i] # move token to its place
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offset += (spans[span_index].end - spans[span_index].start) - 1
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in_span = True
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if not in_span:
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doc.c[i - offset] = doc.c[i] # move token to its place
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for i in range(doc.length - offset, doc.length):
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memset(&doc.c[i], 0, sizeof(TokenC))
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doc.c[i].lex = &EMPTY_LEXEME
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doc.length -= offset
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# ...And, set heads back to a relative position
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for i in range(doc.length):
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doc.c[i].head -= i
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# Set the left/right children, left/right edges
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if doc.has_annotation("DEP"):
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set_children_from_heads(doc.c, 0, doc.length)
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# Make sure ent_iob remains consistent
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make_iob_consistent(doc.c, doc.length)
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# Return the merged Python object
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return doc[spans[0].start]
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def _resize_tensor(tensor, ranges):
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delete = []
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for start, end in ranges:
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for i in range(start, end-1):
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delete.append(i)
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xp = get_array_module(tensor)
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if xp is numpy:
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return xp.delete(tensor, delete, axis=0)
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else:
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offset = 0
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copy_start = 0
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resized_shape = (tensor.shape[0] - len(delete), tensor.shape[1])
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for start, end in ranges:
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if copy_start > 0:
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tensor[copy_start - offset:start - offset] = tensor[copy_start: start]
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offset += end - start - 1
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copy_start = end - 1
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tensor[copy_start - offset:resized_shape[0]] = tensor[copy_start:]
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return xp.asarray(tensor[:resized_shape[0]])
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def _split(Doc doc, int token_index, orths, heads, attrs):
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"""Retokenize the document, such that the token at
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`doc[token_index]` is split into tokens with the orth 'orths'
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token_index(int): token index of the token to split.
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orths: IDs of the verbatim text content of the tokens to create
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**attributes: Attributes to assign to each of the newly created tokens. By default,
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attributes are inherited from the original token.
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RETURNS (Token): The first newly created token.
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"""
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cdef int nb_subtokens = len(orths)
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cdef const LexemeC* lex
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cdef TokenC* token
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cdef TokenC orig_token = doc.c[token_index]
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cdef int orig_length = len(doc)
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if(len(heads) != nb_subtokens):
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raise ValueError(Errors.E115)
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# First, make the dependencies absolutes
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for i in range(doc.length):
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doc.c[i].head += i
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# Adjust dependencies, so they refer to post-split indexing
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offset = nb_subtokens - 1
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for i in range(doc.length):
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if doc.c[i].head > token_index:
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doc.c[i].head += offset
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# Double doc.c max_length if necessary (until big enough for all new tokens)
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while doc.length + nb_subtokens - 1 >= doc.max_length:
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doc._realloc(doc.max_length * 2)
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# Move tokens after the split to create space for the new tokens
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doc.length = len(doc) + nb_subtokens -1
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to_process_tensor = (doc.tensor is not None and doc.tensor.size != 0)
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if to_process_tensor:
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xp = get_array_module(doc.tensor)
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if xp is numpy:
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doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0)
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else:
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shape = (doc.tensor.shape[0] + nb_subtokens, doc.tensor.shape[1])
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resized_array = xp.zeros(shape, dtype="float32")
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resized_array[:doc.tensor.shape[0]] = doc.tensor[:doc.tensor.shape[0]]
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doc.tensor = resized_array
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for token_to_move in range(orig_length - 1, token_index, -1):
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doc.c[token_to_move + nb_subtokens - 1] = doc.c[token_to_move]
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if to_process_tensor:
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doc.tensor[token_to_move + nb_subtokens - 1] = doc.tensor[token_to_move]
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# Host the tokens in the newly created space
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cdef int idx_offset = 0
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for i, orth in enumerate(orths):
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token = &doc.c[token_index + i]
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lex = doc.vocab.get(doc.mem, orth)
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token.lex = lex
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# If lemma is currently set, set default lemma to orth
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if token.lemma != 0:
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token.lemma = lex.orth
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token.norm = 0 # reset norm
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if to_process_tensor:
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# setting the tensors of the split tokens to array of zeros
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doc.tensor[token_index + i:token_index + i + 1] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32")
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# Update the character offset of the subtokens
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if i != 0:
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token.idx = orig_token.idx + idx_offset
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idx_offset += len(orth)
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# Set token.spacy to False for all non-last split tokens, and
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# to origToken.spacy for the last token
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if (i < nb_subtokens - 1):
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token.spacy = False
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else:
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token.spacy = orig_token.spacy
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# Make IOB consistent
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if (orig_token.ent_iob == 3):
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if i == 0:
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token.ent_iob = 3
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else:
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token.ent_iob = 1
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else:
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# In all other cases subtokens inherit iob from origToken
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token.ent_iob = orig_token.ent_iob
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# Apply attrs to each subtoken
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for attr_name, attr_values in attrs.items():
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for i, attr_value in enumerate(attr_values):
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token = &doc.c[token_index + i]
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if attr_name == "_":
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for ext_attr_key, ext_attr_value in attr_value.items():
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doc[token_index + i]._.set(ext_attr_key, ext_attr_value)
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# NB: We need to call get_string_id here because only the keys are
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# "intified" (since we support "KEY": [value, value] syntax here).
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else:
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# Set attributes on both token and lexeme to take care of token
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# attribute vs. lexical attribute without having to enumerate
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# them. If an attribute name is not valid, set_struct_attr will
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# ignore it. Exception: set NORM only on tokens.
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Token.set_struct_attr(token, attr_name, get_string_id(attr_value))
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if attr_name != NORM:
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Lexeme.set_struct_attr(<LexemeC*>token.lex, attr_name, get_string_id(attr_value))
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# Assign correct dependencies to the inner token
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for i, head in enumerate(heads):
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doc.c[token_index + i].head = head
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# Transform the dependencies into relative ones again
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for i in range(doc.length):
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doc.c[i].head -= i
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# set children from head
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if doc.has_annotation("DEP"):
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set_children_from_heads(doc.c, 0, doc.length)
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def _validate_extensions(extensions):
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if not isinstance(extensions, dict):
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raise ValueError(Errors.E120.format(value=repr(extensions)))
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for key, value in extensions.items():
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# Get the extension and make sure it's available and writable
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extension = Token.get_extension(key)
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if not extension: # Extension attribute doesn't exist
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raise ValueError(Errors.E118.format(attr=key))
|
|
if not is_writable_attr(extension):
|
|
raise ValueError(Errors.E119.format(attr=key))
|
|
|
|
|
|
cdef make_iob_consistent(TokenC* tokens, int length):
|
|
cdef int i
|
|
if tokens[0].ent_iob == 1:
|
|
tokens[0].ent_iob = 3
|
|
for i in range(1, length):
|
|
if tokens[i].ent_iob == 1 and tokens[i - 1].ent_type != tokens[i].ent_type:
|
|
tokens[i].ent_iob = 3
|
|
|
|
|
|
def normalize_token_attrs(Vocab vocab, attrs):
|
|
if "_" in attrs: # Extension attributes
|
|
extensions = attrs["_"]
|
|
_validate_extensions(extensions)
|
|
attrs = {key: value for key, value in attrs.items() if key != "_"}
|
|
attrs = intify_attrs(attrs, strings_map=vocab.strings)
|
|
attrs["_"] = extensions
|
|
else:
|
|
attrs = intify_attrs(attrs, strings_map=vocab.strings)
|
|
if MORPH in attrs:
|
|
# add and set to normalized value
|
|
morph = vocab.morphology.add(vocab.strings.as_string(attrs[MORPH]))
|
|
attrs[MORPH] = morph
|
|
return attrs
|
|
|
|
|
|
def set_token_attrs(Token py_token, attrs):
|
|
cdef TokenC* token = py_token.c
|
|
cdef const LexemeC* lex = token.lex
|
|
cdef Doc doc = py_token.doc
|
|
# Assign attributes
|
|
for attr_name, attr_value in attrs.items():
|
|
if attr_name == "_": # Set extension attributes
|
|
for ext_attr_key, ext_attr_value in attr_value.items():
|
|
py_token._.set(ext_attr_key, ext_attr_value)
|
|
else:
|
|
# Set attributes on both token and lexeme to take care of token
|
|
# attribute vs. lexical attribute without having to enumerate
|
|
# them. If an attribute name is not valid, set_struct_attr will
|
|
# ignore it. Exception: set NORM only on tokens.
|
|
Token.set_struct_attr(token, attr_name, attr_value)
|
|
if attr_name != NORM:
|
|
Lexeme.set_struct_attr(<LexemeC*>lex, attr_name, attr_value)
|