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Minor NEL type fixes (#10860)

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* Fix TODO about typing

Fix was simple: just request an array2f.

* Add type ignore

Maxout has a more restrictive type than the residual layer expects (only
Floats2d vs any Floats).

* Various cleanup

This moves a lot of lines around but doesn't change any functionality.
Details:

1. use `continue` to reduce indentation
2. move sentence doc building inside conditional since it's otherwise
   unused
3. reduces some temporary assignments
This commit is contained in:
Paul O'Leary McCann 2022-06-01 07:41:28 +09:00 committed by GitHub
parent 56d4055d96
commit dca2e8c644
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 56 additions and 56 deletions
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2
spacy/ml/models/entity_linker.py
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@ -23,7 +23,7 @@ def build_nel_encoder(
((tok2vec >> list2ragged()) & build_span_maker())
>> extract_spans()
>> reduce_mean()
>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0)) # type: ignore
>> output_layer
)
model.set_ref("output_layer", output_layer)

18
spacy/pipeline/entity_linker.py
View File

@ -355,7 +355,7 @@ class EntityLinker(TrainablePipe):
keep_ents.append(eidx)
eidx += 1
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32")
selected_encodings = sentence_encodings[keep_ents]
# if there are no matches, short circuit
@ -368,13 +368,12 @@ class EntityLinker(TrainablePipe):
method="get_loss", msg="gold entities do not match up"
)
raise RuntimeError(err)
# TODO: fix typing issue here
gradients = self.distance.get_grad(selected_encodings, entity_encodings) # type: ignore
gradients = self.distance.get_grad(selected_encodings, entity_encodings)
# to match the input size, we need to give a zero gradient for items not in the kb
out = self.model.ops.alloc2f(*sentence_encodings.shape)
out[keep_ents] = gradients
loss = self.distance.get_loss(selected_encodings, entity_encodings) # type: ignore
loss = self.distance.get_loss(selected_encodings, entity_encodings)
loss = loss / len(entity_encodings)
return float(loss), out
@ -391,18 +390,21 @@ class EntityLinker(TrainablePipe):
self.validate_kb()
entity_count = 0
final_kb_ids: List[str] = []
xp = self.model.ops.xp
if not docs:
return final_kb_ids
if isinstance(docs, Doc):
docs = [docs]
for i, doc in enumerate(docs):
if len(doc) == 0:
continue
sentences = [s for s in doc.sents]
if len(doc) > 0:
# Looping through each entity (TODO: rewrite)
for ent in doc.ents:
sent = ent.sent
sent_index = sentences.index(sent)
sent_index = sentences.index(ent.sent)
assert sent_index >= 0
if self.incl_context:
# get n_neighbour sentences, clipped to the length of the document
start_sentence = max(0, sent_index - self.n_sents)
end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
@ -410,8 +412,6 @@ class EntityLinker(TrainablePipe):
end_token = sentences[end_sentence].end
sent_doc = doc[start_token:end_token].as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
xp = self.model.ops.xp
if self.incl_context:
sentence_encoding = self.model.predict([sent_doc])[0]
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)