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Add training.before_update callback

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This callback can be used to implement training paradigms like gradual (un)freezing of components (e.g: the Transformer) after a certain number of training steps to mitigate catastrophic forgetting during fine-tuning.
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shademe 2022-11-02 17:40:04 +01:00
parent 2fb7e4dc74
commit f64dbe6925
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3 changed files with 7 additions and 0 deletions
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1
spacy/schemas.py
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@ -329,6 +329,7 @@ class ConfigSchemaTraining(BaseModel):
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
before_update: Optional[Callable[["Language", int], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
# fmt: on
class Config:

5
spacy/training/loop.py
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@ -59,6 +59,7 @@ def train(
batcher = T["batcher"]
train_logger = T["logger"]
before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
before_update = T["before_update"]
# Helper function to save checkpoints. This is a closure for convenience,
# to avoid passing in all the args all the time.
@ -89,6 +90,7 @@ def train(
eval_frequency=T["eval_frequency"],
exclude=frozen_components,
annotating_components=annotating_components,
before_update=before_update,
)
clean_output_dir(output_path)
stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n")
@ -150,6 +152,7 @@ def train_while_improving(
max_steps: int,
exclude: List[str],
annotating_components: List[str],
before_update: Optional[Callable[[Language, int], None]],
):
"""Train until an evaluation stops improving. Works as a generator,
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
@ -198,6 +201,8 @@ def train_while_improving(
words_seen = 0
start_time = timer()
for step, (epoch, batch) in enumerate(train_data):
if before_update:
before_update(nlp, step)
dropout = next(dropouts) # type: ignore
for subbatch in subdivide_batch(batch, accumulate_gradient):
nlp.update(

1
website/docs/api/data-formats.md
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@ -186,6 +186,7 @@ process that are used when you run [`spacy train`](/api/cli#train).
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `before_to_disk` | Optional callback to modify `nlp` object right before it is saved to disk during and after training. Can be used to remove or reset config values or disable components. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `before_update` | Optional callback that is invoked at the start of each training step with the `nlp` object and the current step. Can be used to make deferred changes to components. Defaults to `null`. ~~Optional[Callable[[Language, int], None]]~~ |
| `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ |
| `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |