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Move functionality to config training setting

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This commit is contained in:
thomashacker 2023-01-19 14:54:28 +01:00
parent 2bb8db88f8
commit 58502caa57
5 changed files with 27 additions and 27 deletions
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12
spacy/cli/train.py
View File

@ -22,8 +22,7 @@ def train_cli(
output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
use_rehearse: bool = Opt(False, "--use_rehearse", "-r", help="Perform 'rehearsal updates' on a pre-trained model")
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
"""
@ -43,13 +42,7 @@ def train_cli(
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
train(
config_path,
output_path,
use_gpu=use_gpu,
overrides=overrides,
use_rehearse=use_rehearse,
)
train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)
def train(
@ -88,5 +81,4 @@ def train(
use_gpu=use_gpu,
stdout=sys.stdout,
stderr=sys.stderr,
use_rehearse=use_rehearse,
)

8
spacy/language.py
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@ -1183,6 +1183,7 @@ class Language:
losses: Optional[Dict[str, float]] = None,
component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
exclude: Iterable[str] = SimpleFrozenList(),
rehearse_components: List[str] = [],
) -> Dict[str, float]:
"""Make a "rehearsal" update to the models in the pipeline, to prevent
forgetting. Rehearsal updates run an initial copy of the model over some
@ -1195,6 +1196,7 @@ class Language:
component_cfg (Dict[str, Dict]): Config parameters for specific pipeline
components, keyed by component name.
exclude (Iterable[str]): Names of components that shouldn't be updated.
rehearse_components (List[str]): Names of components that should be rehearsed
RETURNS (dict): Results from the update.
EXAMPLE:
@ -1216,7 +1218,11 @@ class Language:
component_cfg = {}
for name, proc in pipes:
if name in exclude or not hasattr(proc, "rehearse"):
if (
name in exclude
or not hasattr(proc, "rehearse")
or name not in rehearse_components
):
continue
proc.rehearse( # type: ignore[attr-defined]
examples, sgd=None, losses=losses, **component_cfg.get(name, {})

1
spacy/schemas.py
View File

@ -356,6 +356,7 @@ class ConfigSchemaTraining(BaseModel):
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")
annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
rehearse_components: List[str] = Field(..., title="Pipeline components that should be rehearsed 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", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
# fmt: on

4
spacy/training/initialize.py
View File

@ -60,6 +60,8 @@ def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
optimizer = T["optimizer"]
# Components that shouldn't be updated during training
frozen_components = T["frozen_components"]
# Components that shouldn't be updated during training
rehearse_components = T["rehearse_components"]
# Sourced components that require resume_training
resume_components = [p for p in sourced if p not in frozen_components]
logger.info(f"Pipeline: {nlp.pipe_names}")
@ -67,6 +69,8 @@ def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
with nlp.select_pipes(enable=resume_components):
logger.info(f"Resuming training for: {resume_components}")
nlp.resume_training(sgd=optimizer)
if rehearse_components:
logger.info(f"Rehearsing components: {rehearse_components}")
# Make sure that listeners are defined before initializing further
nlp._link_components()
with nlp.select_pipes(disable=[*frozen_components, *resume_components]):

29
spacy/training/loop.py
View File

@ -26,7 +26,6 @@ def train(
output_path: Optional[Path] = None,
*,
use_gpu: int = -1,
use_rehearse: bool = False,
stdout: IO = sys.stdout,
stderr: IO = sys.stderr,
) -> Tuple["Language", Optional[Path]]:
@ -36,7 +35,6 @@ def train(
output_path (Optional[Path]): Optional output path to save trained model to.
use_gpu (int): Whether to train on GPU. Make sure to call require_gpu
before calling this function.
use_rehearse (bool): Use nlp.rehearse after nlp.update
stdout (file): A file-like object to write output messages. To disable
printing, set to io.StringIO.
stderr (file): A second file-like object to write output messages. To disable
@ -56,10 +54,7 @@ def train(
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
dot_names = [T["train_corpus"], T["dev_corpus"]]
train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
if use_rehearse:
optimizer = nlp.resume_training()
else:
optimizer = T["optimizer"]
optimizer = T["optimizer"]
score_weights = T["score_weights"]
batcher = T["batcher"]
train_logger = T["logger"]
@ -82,6 +77,8 @@ def train(
frozen_components = T["frozen_components"]
# Components that should set annotations on update
annotating_components = T["annotating_components"]
# Components that should be rehearsed after update
rehearse_components = T["rehearse_components"]
# Create iterator, which yields out info after each optimization step.
training_step_iterator = train_while_improving(
nlp,
@ -93,9 +90,9 @@ def train(
patience=T["patience"],
max_steps=T["max_steps"],
eval_frequency=T["eval_frequency"],
use_rehearse=use_rehearse,
exclude=frozen_components,
annotating_components=annotating_components,
rehearse_components=rehearse_components,
before_update=before_update,
)
clean_output_dir(output_path)
@ -156,9 +153,9 @@ def train_while_improving(
accumulate_gradient: int,
patience: int,
max_steps: int,
use_rehearse: bool = False,
exclude: List[str],
annotating_components: List[str],
rehearse_components: List[str],
before_update: Optional[Callable[["Language", Dict[str, Any]], None]],
):
"""Train until an evaluation stops improving. Works as a generator,
@ -217,17 +214,17 @@ def train_while_improving(
subbatch,
drop=dropout,
losses=losses,
sgd=False, # type: ignore[arg-type]
sgd=None, # type: ignore[arg-type]
exclude=exclude,
annotates=annotating_components,
)
if use_rehearse:
nlp.rehearse(
subbatch,
losses=losses,
sgd=False, # type: ignore[arg-type]
exclude=exclude,
)
nlp.rehearse(
subbatch,
losses=losses,
sgd=None, # type: ignore[arg-type]
exclude=exclude,
rehearse_components=rehearse_components,
)
# TODO: refactor this so we don't have to run it separately in here
for name, proc in nlp.pipeline:
if (