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Fix speed problem with top_k>1 on CPU in edit tree lemmatizer (#12017)

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* Refactor _scores2guesses

* Handle arrays on GPU

* Convert argmax result to raw integer

Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>

* Use NumpyOps() to copy data to CPU

Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>

* Changes based on review comments

* Use different _scores2guesses depending on tree_k

* Add tests for corner cases

* Add empty line for consistency

* Improve naming

Co-authored-by: Daniël de Kok <me@github.danieldk.eu>

* Improve naming

Co-authored-by: Daniël de Kok <me@github.danieldk.eu>

Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
This commit is contained in:
Richard Hudson 2023-01-20 19:34:11 +01:00 committed by GitHub
parent 8a3ca77d9e
commit f9e020dd67
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2 changed files with 69 additions and 17 deletions
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69
spacy/pipeline/edit_tree_lemmatizer.py
View File

@ -5,8 +5,8 @@ from itertools import islice
import numpy as np
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d, Ints2d
from thinc.api import Config, Model, SequenceCategoricalCrossentropy, NumpyOps
from thinc.types import Floats2d, Ints2d
from ._edit_tree_internals.edit_trees import EditTrees
from ._edit_tree_internals.schemas import validate_edit_tree
@ -20,6 +20,10 @@ from ..vocab import Vocab
from .. import util
# The cutoff value of *top_k* above which an alternative method is used to process guesses.
TOP_K_GUARDRAIL = 20
default_model_config = """
[model]
@architectures = "spacy.Tagger.v2"
@ -115,6 +119,7 @@ class EditTreeLemmatizer(TrainablePipe):
self.cfg: Dict[str, Any] = {"labels": []}
self.scorer = scorer
self.numpy_ops = NumpyOps()
def get_loss(
self, examples: Iterable[Example], scores: List[Floats2d]
@ -144,6 +149,18 @@ class EditTreeLemmatizer(TrainablePipe):
return float(loss), d_scores
def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
if self.top_k == 1:
scores2guesses = self._scores2guesses_top_k_equals_1
elif self.top_k <= TOP_K_GUARDRAIL:
scores2guesses = self._scores2guesses_top_k_greater_1
else:
scores2guesses = self._scores2guesses_top_k_guardrail
# The behaviour of *_scores2guesses_top_k_greater_1()* is efficient for values
# of *top_k>1* that are likely to be useful when the edit tree lemmatizer is used
# for its principal purpose of lemmatizing tokens. However, the code could also
# be used for other purposes, and with very large values of *top_k* the method
# becomes inefficient. In such cases, *_scores2guesses_top_k_guardrail()* is used
# instead.
n_docs = len(list(docs))
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
@ -153,20 +170,52 @@ class EditTreeLemmatizer(TrainablePipe):
return guesses
scores = self.model.predict(docs)
assert len(scores) == n_docs
guesses = self._scores2guesses(docs, scores)
guesses = scores2guesses(docs, scores)
assert len(guesses) == n_docs
return guesses
def _scores2guesses(self, docs, scores):
def _scores2guesses_top_k_equals_1(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
if self.top_k == 1:
doc_guesses = doc_scores.argmax(axis=1).reshape(-1, 1)
else:
doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
doc_guesses = doc_scores.argmax(axis=1)
doc_guesses = self.numpy_ops.asarray(doc_guesses)
if not isinstance(doc_guesses, np.ndarray):
doc_guesses = doc_guesses.get()
doc_compat_guesses = []
for i, token in enumerate(doc):
tree_id = self.cfg["labels"][doc_guesses[i]]
if self.trees.apply(tree_id, token.text) is not None:
doc_compat_guesses.append(tree_id)
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_greater_1(self, docs, scores):
guesses = []
top_k = min(self.top_k, len(self.labels))
for doc, doc_scores in zip(docs, scores):
doc_scores = self.numpy_ops.asarray(doc_scores)
doc_compat_guesses = []
for i, token in enumerate(doc):
for _ in range(top_k):
candidate = int(doc_scores[i].argmax())
candidate_tree_id = self.cfg["labels"][candidate]
if self.trees.apply(candidate_tree_id, token.text) is not None:
doc_compat_guesses.append(candidate_tree_id)
break
doc_scores[i, candidate] = np.finfo(np.float32).min
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_guardrail(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
doc_guesses = self.numpy_ops.asarray(doc_guesses)
doc_compat_guesses = []
for token, candidates in zip(doc, doc_guesses):

17
spacy/tests/pipeline/test_edit_tree_lemmatizer.py
View File

@ -101,14 +101,15 @@ def test_initialize_from_labels():
}
def test_no_data():
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_no_data(top_k):
# Test that the lemmatizer provides a nice error when there's no tagging data / labels
TEXTCAT_DATA = [
("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
]
nlp = English()
nlp.add_pipe("trainable_lemmatizer")
nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp.add_pipe("textcat")
train_examples = []
@ -119,10 +120,11 @@ def test_no_data():
nlp.initialize(get_examples=lambda: train_examples)
def test_incomplete_data():
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_incomplete_data(top_k):
# Test that the lemmatizer works with incomplete information
nlp = English()
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in PARTIAL_DATA:
@ -154,9 +156,10 @@ def test_incomplete_data():
assert xp.count_nonzero(dX[1][1]) == 0
def test_overfitting_IO():
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_overfitting_IO(top_k):
nlp = English()
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in TRAIN_DATA:
@ -189,7 +192,7 @@ def test_overfitting_IO():
# Check model after a {to,from}_bytes roundtrip
nlp_bytes = nlp.to_bytes()
nlp3 = English()
nlp3.add_pipe("trainable_lemmatizer")
nlp3.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp3.from_bytes(nlp_bytes)
doc3 = nlp3(test_text)
assert doc3[0].lemma_ == "she"