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Add training option to set annotations on update (#7767)

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* Add training option to set annotations on update

Add a `[training]` option called `set_annotations_on_update` to specify
a list of components for which the predicted annotations should be set
on `example.predicted` immediately after that component has been
updated. The predicted annotations can be accessed by later components
in the pipeline during the processing of the batch in the same `update`
call.

* Rename to annotates / annotating_components

* Add test for `annotating_components` when training from config

* Add documentation
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Adriane Boyd 2021-04-26 16:53:53 +02:00 committed by GitHub
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9 changed files with 289 additions and 38 deletions
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2
spacy/default_config.cfg
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@ -80,6 +80,8 @@ eval_frequency = 200
score_weights = {} score_weights = {}
# Names of pipeline components that shouldn't be updated during training # Names of pipeline components that shouldn't be updated during training
frozen_components = [] frozen_components = []
# Names of pipeline components that should set annotations during training
annotating_components = []
# Location in the config where the dev corpus is defined # Location in the config where the dev corpus is defined
dev_corpus = "corpora.dev" dev_corpus = "corpora.dev"
# Location in the config where the train corpus is defined # Location in the config where the train corpus is defined

29
spacy/language.py
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@ -1074,6 +1074,7 @@ class Language:
losses: Optional[Dict[str, float]] = None, losses: Optional[Dict[str, float]] = None,
component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
exclude: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(),
annotates: Iterable[str] = SimpleFrozenList(),
): ):
"""Update the models in the pipeline. """Update the models in the pipeline.
@ -1081,10 +1082,13 @@ class Language:
_: Should not be set - serves to catch backwards-incompatible scripts. _: Should not be set - serves to catch backwards-incompatible scripts.
drop (float): The dropout rate. drop (float): The dropout rate.
sgd (Optimizer): An optimizer. sgd (Optimizer): An optimizer.
losses (Dict[str, float]): Dictionary to update with the loss, keyed by component. losses (Dict[str, float]): Dictionary to update with the loss, keyed by
component.
component_cfg (Dict[str, Dict]): Config parameters for specific pipeline component_cfg (Dict[str, Dict]): Config parameters for specific pipeline
components, keyed by component name. components, keyed by component name.
exclude (Iterable[str]): Names of components that shouldn't be updated. exclude (Iterable[str]): Names of components that shouldn't be updated.
annotates (Iterable[str]): Names of components that should set
annotations on the predicted examples after updating.
RETURNS (Dict[str, float]): The updated losses dictionary RETURNS (Dict[str, float]): The updated losses dictionary
DOCS: https://spacy.io/api/language#update DOCS: https://spacy.io/api/language#update
@ -1103,15 +1107,16 @@ class Language:
sgd = self._optimizer sgd = self._optimizer
if component_cfg is None: if component_cfg is None:
component_cfg = {} component_cfg = {}
pipe_kwargs = {}
for i, (name, proc) in enumerate(self.pipeline): for i, (name, proc) in enumerate(self.pipeline):
component_cfg.setdefault(name, {}) component_cfg.setdefault(name, {})
pipe_kwargs[name] = deepcopy(component_cfg[name])
component_cfg[name].setdefault("drop", drop) component_cfg[name].setdefault("drop", drop)
pipe_kwargs[name].setdefault("batch_size", self.batch_size)
for name, proc in self.pipeline: for name, proc in self.pipeline:
if name in exclude or not hasattr(proc, "update"): if name not in exclude and hasattr(proc, "update"):
continue proc.update(examples, sgd=None, losses=losses, **component_cfg[name])
proc.update(examples, sgd=None, losses=losses, **component_cfg[name]) if sgd not in (None, False):
if sgd not in (None, False):
for name, proc in self.pipeline:
if ( if (
name not in exclude name not in exclude
and hasattr(proc, "is_trainable") and hasattr(proc, "is_trainable")
@ -1119,6 +1124,18 @@ class Language:
and proc.model not in (True, False, None) and proc.model not in (True, False, None)
): ):
proc.finish_update(sgd) proc.finish_update(sgd)
if name in annotates:
for doc, eg in zip(
_pipe(
(eg.predicted for eg in examples),
proc=proc,
name=name,
default_error_handler=self.default_error_handler,
kwargs=pipe_kwargs[name],
),
examples,
):
eg.predicted = doc
return losses return losses
def rehearse( def rehearse(

1
spacy/schemas.py
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@ -313,6 +313,7 @@ class ConfigSchemaTraining(BaseModel):
optimizer: Optimizer = Field(..., title="The optimizer to use") optimizer: Optimizer = Field(..., title="The optimizer to use")
logger: Logger = Field(..., title="The logger to track training progress") logger: Logger = Field(..., title="The logger to track training progress")
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training") 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_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
# fmt: on # fmt: on

113
spacy/tests/pipeline/test_annotates_on_update.py Normal file
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@ -0,0 +1,113 @@
from typing import Callable, Iterable, Iterator
import pytest
import io
from thinc.api import Config
from spacy.language import Language
from spacy.training import Example
from spacy.training.loop import train
from spacy.lang.en import English
from spacy.util import registry, load_model_from_config
@pytest.fixture
def config_str():
return """
[nlp]
lang = "en"
pipeline = ["sentencizer","assert_sents"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 1000
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
[components]
[components.assert_sents]
factory = "assert_sents"
[components.sentencizer]
factory = "sentencizer"
punct_chars = null
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
annotating_components = ["sentencizer"]
max_steps = 2
[corpora]
[corpora.dev]
@readers = "unannotated_corpus"
[corpora.train]
@readers = "unannotated_corpus"
"""
def test_annotates_on_update():
# The custom component checks for sentence annotation
@Language.factory("assert_sents", default_config={})
def assert_sents(nlp, name):
return AssertSents(name)
class AssertSents:
def __init__(self, name, **cfg):
self.name = name
pass
def __call__(self, doc):
if not doc.has_annotation("SENT_START"):
raise ValueError("No sents")
return doc
def update(self, examples, *, drop=0.0, sgd=None, losses=None):
for example in examples:
if not example.predicted.has_annotation("SENT_START"):
raise ValueError("No sents")
return {}
nlp = English()
nlp.add_pipe("sentencizer")
nlp.add_pipe("assert_sents")
# When the pipeline runs, annotations are set
doc = nlp("This is a sentence.")
examples = []
for text in ["a a", "b b", "c c"]:
examples.append(Example(nlp.make_doc(text), nlp(text)))
for example in examples:
assert not example.predicted.has_annotation("SENT_START")
# If updating without setting annotations, assert_sents will raise an error
with pytest.raises(ValueError):
nlp.update(examples)
# Updating while setting annotations for the sentencizer succeeds
nlp.update(examples, annotates=["sentencizer"])
def test_annotating_components_from_config(config_str):
@registry.readers("unannotated_corpus")
def create_unannotated_corpus() -> Callable[[Language], Iterable[Example]]:
return UnannotatedCorpus()
class UnannotatedCorpus:
def __call__(self, nlp: Language) -> Iterator[Example]:
for text in ["a a", "b b", "c c"]:
doc = nlp.make_doc(text)
yield Example(doc, doc)
orig_config = Config().from_str(config_str)
nlp = load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp.config["training"]["annotating_components"] == ["sentencizer"]
train(nlp)
nlp.config["training"]["annotating_components"] = []
with pytest.raises(ValueError):
train(nlp)

40
spacy/tests/pipeline/test_pipe_methods.py
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@ -1,7 +1,9 @@
import pytest import pytest
from spacy.language import Language from spacy.language import Language
from spacy.pipeline import TrainablePipe from spacy.pipeline import TrainablePipe
from spacy.training import Example
from spacy.util import SimpleFrozenList, get_arg_names from spacy.util import SimpleFrozenList, get_arg_names
from spacy.lang.en import English
@pytest.fixture @pytest.fixture
@ -417,3 +419,41 @@ def test_pipe_methods_initialize():
assert "test" in nlp.config["initialize"]["components"] assert "test" in nlp.config["initialize"]["components"]
nlp.remove_pipe("test") nlp.remove_pipe("test")
assert "test" not in nlp.config["initialize"]["components"] assert "test" not in nlp.config["initialize"]["components"]
def test_update_with_annotates():
name = "test_with_annotates"
results = {}
def make_component(name):
results[name] = ""
def component(doc):
nonlocal results
results[name] += doc.text
return doc
return component
c1 = Language.component(f"{name}1", func=make_component(f"{name}1"))
c2 = Language.component(f"{name}2", func=make_component(f"{name}2"))
components = set([f"{name}1", f"{name}2"])
nlp = English()
texts = ["a", "bb", "ccc"]
examples = []
for text in texts:
examples.append(Example(nlp.make_doc(text), nlp.make_doc(text)))
for components_to_annotate in [[], [f"{name}1"], [f"{name}1", f"{name}2"], [f"{name}2", f"{name}1"]]:
for key in results:
results[key] = ""
nlp = English(vocab=nlp.vocab)
nlp.add_pipe(f"{name}1")
nlp.add_pipe(f"{name}2")
nlp.update(examples, annotates=components_to_annotate)
for component in components_to_annotate:
assert results[component] == "".join(eg.predicted.text for eg in examples)
for component in components - set(components_to_annotate):
assert results[component] == ""

16
spacy/training/loop.py
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@ -74,6 +74,8 @@ def train(
# Components that shouldn't be updated during training # Components that shouldn't be updated during training
frozen_components = T["frozen_components"] frozen_components = T["frozen_components"]
# Components that should set annotations on update
annotating_components = T["annotating_components"]
# Create iterator, which yields out info after each optimization step. # Create iterator, which yields out info after each optimization step.
training_step_iterator = train_while_improving( training_step_iterator = train_while_improving(
nlp, nlp,
@ -86,11 +88,17 @@ def train(
max_steps=T["max_steps"], max_steps=T["max_steps"],
eval_frequency=T["eval_frequency"], eval_frequency=T["eval_frequency"],
exclude=frozen_components, exclude=frozen_components,
annotating_components=annotating_components,
) )
clean_output_dir(output_path) clean_output_dir(output_path)
stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n") stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n")
if frozen_components: if frozen_components:
stdout.write(msg.info(f"Frozen components: {frozen_components}") + "\n") stdout.write(msg.info(f"Frozen components: {frozen_components}") + "\n")
if annotating_components:
stdout.write(
msg.info(f"Set annotations on update for: {annotating_components}")
+ "\n"
)
stdout.write(msg.info(f"Initial learn rate: {optimizer.learn_rate}") + "\n") stdout.write(msg.info(f"Initial learn rate: {optimizer.learn_rate}") + "\n")
with nlp.select_pipes(disable=frozen_components): with nlp.select_pipes(disable=frozen_components):
log_step, finalize_logger = train_logger(nlp, stdout, stderr) log_step, finalize_logger = train_logger(nlp, stdout, stderr)
@ -142,6 +150,7 @@ def train_while_improving(
patience: int, patience: int,
max_steps: int, max_steps: int,
exclude: List[str], exclude: List[str],
annotating_components: List[str],
): ):
"""Train until an evaluation stops improving. Works as a generator, """Train until an evaluation stops improving. Works as a generator,
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`, with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
@ -193,7 +202,12 @@ def train_while_improving(
dropout = next(dropouts) dropout = next(dropouts)
for subbatch in subdivide_batch(batch, accumulate_gradient): for subbatch in subdivide_batch(batch, accumulate_gradient):
nlp.update( nlp.update(
subbatch, drop=dropout, losses=losses, sgd=False, exclude=exclude subbatch,
drop=dropout,
losses=losses,
sgd=False,
exclude=exclude,
annotates=annotating_components,
) )
# TODO: refactor this so we don't have to run it separately in here # TODO: refactor this so we don't have to run it separately in here
for name, proc in nlp.pipeline: for name, proc in nlp.pipeline:

37
website/docs/api/data-formats.md
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@ -182,24 +182,25 @@ single corpus once and then divide it up into `train` and `dev` partitions.
This section defines settings and controls for the training and evaluation This section defines settings and controls for the training and evaluation
process that are used when you run [`spacy train`](/api/cli#train). process that are used when you run [`spacy train`](/api/cli#train).
| Name | Description | | Name | Description |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ | | `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]]]]~~ | | `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_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]]~~ |
| `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ | | `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~~ | | `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ | | `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be initialized or updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ | | `frozen_components` | Pipeline component names that are "frozen" and shouldn't be initialized or updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ | | `annotating_components` | Pipeline component names that should set annotations on the predicted docs during training. See [here](/usage/training#annotating-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `logger` | Callable that takes the `nlp` and stdout and stderr `IO` objects, sets up the logger, and returns two new callables to log a training step and to finalize the logger. Defaults to [`ConsoleLogger`](/api/top-level#ConsoleLogger). ~~Callable[[Language, IO, IO], [Tuple[Callable[[Dict[str, Any]], None], Callable[[], None]]]]~~ | | `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ |
| `max_epochs` | Maximum number of epochs to train for. `0` means an unlimited number of epochs. `-1` means that the train corpus should be streamed rather than loaded into memory with no shuffling within the training loop. Defaults to `0`. ~~int~~ | | `logger` | Callable that takes the `nlp` and stdout and stderr `IO` objects, sets up the logger, and returns two new callables to log a training step and to finalize the logger. Defaults to [`ConsoleLogger`](/api/top-level#ConsoleLogger). ~~Callable[[Language, IO, IO], [Tuple[Callable[[Dict[str, Any]], None], Callable[[], None]]]]~~ |
| `max_steps` | Maximum number of update steps to train for. `0` means an unlimited number of steps. Defaults to `20000`. ~~int~~ | | `max_epochs` | Maximum number of epochs to train for. `0` means an unlimited number of epochs. `-1` means that the train corpus should be streamed rather than loaded into memory with no shuffling within the training loop. Defaults to `0`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ | | `max_steps` | Maximum number of update steps to train for. `0` means an unlimited number of steps. Defaults to `20000`. ~~int~~ |
| `patience` | How many steps to continue without improvement in evaluation score. `0` disables early stopping. Defaults to `1600`. ~~int~~ | | `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ | | `patience` | How many steps to continue without improvement in evaluation score. `0` disables early stopping. Defaults to `1600`. ~~int~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ | | `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ | | `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ |
### pretraining {#config-pretraining tag="section,optional"} ### pretraining {#config-pretraining tag="section,optional"}

21
website/docs/api/transformer.md
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@ -245,14 +245,14 @@ and call the optimizer, while the others simply increment the gradients.
> losses = trf.update(examples, sgd=optimizer) > losses = trf.update(examples, sgd=optimizer)
> ``` > ```
| Name | Description | | Name | Description |
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects. Only the [`Example.predicted`](/api/example#predicted) `Doc` object is used, the reference `Doc` is ignored. ~~Iterable[Example]~~ | | `examples` | A batch of [`Example`](/api/example) objects. Only the [`Example.predicted`](/api/example#predicted) `Doc` object is used, the reference `Doc` is ignored. ~~Iterable[Example]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ | | `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ | | `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ | | `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ | | **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Transformer.create_optimizer {#create_optimizer tag="method"} ## Transformer.create_optimizer {#create_optimizer tag="method"}
@ -493,6 +493,11 @@ This requires sentence boundaries to be set (e.g. by the
depending on the sentence lengths. However, it does provide the transformer with depending on the sentence lengths. However, it does provide the transformer with
more meaningful windows to attend over. more meaningful windows to attend over.
To set sentence boundaries with the `sentencizer` during training, add a
`sentencizer` to the beginning of the pipeline and include it in
[`[training.annotating_components]`](/usage/training#annotating-components) to
have it set the sentence boundaries before the `transformer` component runs.
### strided_spans.v1 {#strided_spans tag="registered function"} ### strided_spans.v1 {#strided_spans tag="registered function"}
> #### Example config > #### Example config

68
website/docs/usage/training.md
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@ -414,11 +414,11 @@ as-is. They are also excluded when calling
> #### Note on frozen components > #### Note on frozen components
> >
> Even though frozen components are not **updated** during training, they will > Even though frozen components are not **updated** during training, they will
> still **run** during training and evaluation. This is very important, because > still **run** during evaluation. This is very important, because they may
> they may still impact your model's performance – for instance, a sentence > still impact your model's performance – for instance, a sentence boundary
> boundary detector can impact what the parser or entity recognizer considers a > detector can impact what the parser or entity recognizer considers a valid
> valid parse. So the evaluation results should always reflect what your > parse. So the evaluation results should always reflect what your pipeline will
> pipeline will produce at runtime. > produce at runtime.
```ini ```ini
[nlp] [nlp]
@ -455,6 +455,64 @@ replace_listeners = ["model.tok2vec"]
</Infobox> </Infobox>
### Using predictions from preceding components {#annotating-components new="3.1"}
By default, components are updated in isolation during training, which means
that they don't see the predictions of any earlier components in the pipeline. A
component receives [`Example.predicted`](/api/example) as input and compares its
predictions to [`Example.reference`](/api/example) without saving its
annotations in the `predicted` doc.
Instead, if certain components should **set their annotations** during training,
use the setting `annotating_components` in the `[training]` block to specify a
list of components. For example, the feature `DEP` from the parser could be used
as a tagger feature by including `DEP` in the tok2vec `attrs` and including
`parser` in `annotating_components`:
```ini
### config.cfg (excerpt) {highlight="7,12"}
[nlp]
pipeline = ["parser", "tagger"]
[components.tagger.model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tagger.model.tok2vec.encode.width}
attrs = ["NORM","DEP"]
rows = [5000,2500]
include_static_vectors = false
[training]
annotating_components = ["parser"]
```
Any component in the pipeline can be included as an annotating component,
including frozen components. Frozen components can set annotations during
training just as they would set annotations during evaluation or when the final
pipeline is run. The config excerpt below shows how a frozen `ner` component and
a `sentencizer` can provide the required `doc.sents` and `doc.ents` for the
entity linker during training:
```ini
### config.cfg (excerpt)
[nlp]
pipeline = ["sentencizer", "ner", "entity_linker"]
[components.ner]
source = "en_core_web_sm"
[training]
frozen_components = ["ner"]
annotating_components = ["sentencizer", "ner"]
```
<Infobox variant="warning" title="Training speed with annotating components" id="annotating-components-speed">
Be aware that non-frozen annotating components with statistical models will
**run twice** on each batch, once to update the model and once to apply the
now-updated model to the predicted docs.
</Infobox>
### Using registered functions {#config-functions} ### Using registered functions {#config-functions}
The training configuration defined in the config file doesn't have to only The training configuration defined in the config file doesn't have to only