Adjust to new Schedule class and pass scores to Optimizer (#12008)

* Adjust to new `Schedule` class and pass scores to `Optimizer`

Requires https://github.com/explosion/thinc/pull/804

* Bump minimum Thinc requirement to 9.0.0.dev1

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=9.0.0.dev0,<9.1.0",
+    "thinc>=9.0.0.dev1,<9.1.0",
     "numpy>=1.15.0",
 ]
 build-backend = "setuptools.build_meta"

requirements.txt

@@ -3,7 +3,7 @@ spacy-legacy>=3.0.10,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=9.0.0.dev0,<9.1.0
+thinc>=9.0.0.dev1,<9.1.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0

setup.cfg

@@ -38,7 +38,7 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=9.0.0.dev0,<9.1.0
+    thinc>=9.0.0.dev1,<9.1.0
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0

spacy/training/batchers.py

@@ -2,11 +2,12 @@ from typing import Union, Iterable, Sequence, TypeVar, List, Callable, Iterator
 from typing import Optional, Any
 from functools import partial
 import itertools
+from thinc.schedules import Schedule, constant as constant_schedule
 
 from ..util import registry, minibatch
 
 
-Sizing = Union[Sequence[int], int]
+Sizing = Union[Sequence[int], int, Schedule[int]]
 ItemT = TypeVar("ItemT")
 BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
 
@@ -111,12 +112,13 @@ def minibatch_by_padded_size(
         The `len` function is used by default.
     """
     if isinstance(size, int):
-        size_ = itertools.repeat(size)  # type: Iterator[int]
+        size_ = constant_schedule(size)
     else:
-        size_ = iter(size)
-    for outer_batch in minibatch(seqs, size=buffer):
+        assert isinstance(size, Schedule)
+        size_ = size
+    for step, outer_batch in enumerate(minibatch(seqs, size=buffer)):
         outer_batch = list(outer_batch)
-        target_size = next(size_)
+        target_size = size_(step)
         for indices in _batch_by_length(outer_batch, target_size, get_length):
             subbatch = [outer_batch[i] for i in indices]
             padded_size = max(len(seq) for seq in subbatch) * len(subbatch)
@@ -147,10 +149,12 @@ def minibatch_by_words(
         item. The `len` function is used by default.
     """
     if isinstance(size, int):
-        size_ = itertools.repeat(size)  # type: Iterator[int]
+        size_ = constant_schedule(size)
     else:
-        size_ = iter(size)
-    target_size = next(size_)
+        assert isinstance(size, Schedule)
+        size_ = size
+    step = 0
+    target_size = size_(step)
     tol_size = target_size * tolerance
     batch = []
     overflow = []
@@ -175,7 +179,8 @@ def minibatch_by_words(
         else:
             if batch:
                 yield batch
-            target_size = next(size_)
+            step += 1
+            target_size = size_(step)
             tol_size = target_size * tolerance
             batch = overflow
             batch_size = overflow_size
@@ -193,7 +198,8 @@ def minibatch_by_words(
             else:
                 if batch:
                     yield batch
-                target_size = next(size_)
+                step += 1
+                target_size = size_(step)
                 tol_size = target_size * tolerance
                 batch = [seq]
                 batch_size = n_words

spacy/training/loop.py

@@ -204,7 +204,7 @@ def train_while_improving(
         if before_update:
             before_update_args = {"step": step, "epoch": epoch}
             before_update(nlp, before_update_args)
-        dropout = next(dropouts)  # type: ignore
+        dropout = dropouts(optimizer.step)  # type: ignore
         for subbatch in subdivide_batch(batch, accumulate_gradient):
             nlp.update(
                 subbatch,
@@ -230,6 +230,7 @@ def train_while_improving(
                     score, other_scores = evaluate()
             else:
                 score, other_scores = evaluate()
+            optimizer.last_score = score
             results.append((score, step))
             is_best_checkpoint = score == max(results)[0]
         else:

spacy/util.py

@@ -9,7 +9,7 @@ import re
 from pathlib import Path
 import thinc
 from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer
-from thinc.api import ConfigValidationError, Model
+from thinc.api import ConfigValidationError, Model, constant as constant_schedule
 import functools
 import itertools
 import numpy
@@ -1582,12 +1582,12 @@ def minibatch(items, size):
     so that batch-size can vary on each step.
     """
     if isinstance(size, int):
-        size_ = itertools.repeat(size)
+        size_ = constant_schedule(size)
     else:
         size_ = size
     items = iter(items)
-    while True:
-        batch_size = next(size_)
+    for step in itertools.count():
+        batch_size = size_(step)
         batch = list(itertools.islice(items, int(batch_size)))
         if len(batch) == 0:
             break