spaCy/spacy/training/pretrain.py
Daniël de Kok e2b70df012
Configure isort to use the Black profile, recursively isort the spacy module (#12721)
* Use isort with Black profile

* isort all the things

* Fix import cycles as a result of import sorting

* Add DOCBIN_ALL_ATTRS type definition

* Add isort to requirements

* Remove isort from build dependencies check

* Typo
2023-06-14 17:48:41 +02:00

261 lines
9.5 KiB
Python

import re
import time
from collections import Counter
from pathlib import Path
from typing import Callable, Iterable, List, Optional, Union
import srsly
from thinc.api import (
Config,
Model,
Optimizer,
fix_random_seed,
set_dropout_rate,
set_gpu_allocator,
)
from thinc.config import ConfigValidationError
from wasabi import Printer
from ..errors import Errors
from ..schemas import ConfigSchemaPretrain
from ..tokens import Doc
from ..util import dot_to_object, load_model_from_config, registry
from .example import Example
def pretrain(
config: Config,
output_dir: Path,
resume_path: Optional[Path] = None,
epoch_resume: Optional[int] = None,
use_gpu: int = -1,
silent: bool = True,
skip_last: bool = False,
):
msg = Printer(no_print=silent)
if config["training"]["seed"] is not None:
fix_random_seed(config["training"]["seed"])
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
# ignore in pretraining because we're creating it now
config["initialize"]["init_tok2vec"] = None
nlp = load_model_from_config(config)
_config = nlp.config.interpolate()
P = registry.resolve(_config["pretraining"], schema=ConfigSchemaPretrain)
corpus = dot_to_object(_config, P["corpus"])
corpus = registry.resolve({"corpus": corpus})["corpus"]
batcher = P["batcher"]
model = create_pretraining_model(nlp, P)
optimizer = P["optimizer"]
# Load in pretrained weights to resume from
if resume_path is not None:
epoch_resume = _resume_model(model, resume_path, epoch_resume, silent=silent)
else:
# Without '--resume-path' the '--epoch-resume' argument is ignored
epoch_resume = 0
objective = model.attrs["loss"]
# TODO: move this to logger function?
tracker = ProgressTracker(frequency=10000)
if P["n_save_epoch"]:
msg.divider(
f"Pre-training tok2vec layer - starting at epoch {epoch_resume} - saving every {P['n_save_epoch']} epoch"
)
else:
msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
def _save_model(epoch, is_temp=False, is_last=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
if is_last:
save_path = output_dir / f"model-last.bin"
else:
save_path = output_dir / f"model{epoch}{is_temp_str}.bin"
with (save_path).open("wb") as file_:
file_.write(model.get_ref("tok2vec").to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
# TODO: I think we probably want this to look more like the
# 'create_train_batches' function?
try:
for epoch in range(epoch_resume, P["max_epochs"]):
for batch_id, batch in enumerate(batcher(corpus(nlp))):
docs = ensure_docs(batch)
loss = make_update(model, docs, optimizer, objective)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if P["n_save_every"] and (batch_id % P["n_save_every"] == 0):
_save_model(epoch, is_temp=True)
if P["n_save_epoch"]:
if epoch % P["n_save_epoch"] == 0 or epoch == P["max_epochs"] - 1:
_save_model(epoch)
else:
_save_model(epoch)
tracker.epoch_loss = 0.0
finally:
if not skip_last:
_save_model(P["max_epochs"], is_last=True)
def ensure_docs(examples_or_docs: Iterable[Union[Doc, Example]]) -> List[Doc]:
docs = []
for eg_or_doc in examples_or_docs:
if isinstance(eg_or_doc, Doc):
docs.append(eg_or_doc)
else:
docs.append(eg_or_doc.reference)
return docs
def _resume_model(
model: Model, resume_path: Path, epoch_resume: Optional[int], silent: bool = True
) -> int:
msg = Printer(no_print=silent)
msg.info(f"Resume training tok2vec from: {resume_path}")
with resume_path.open("rb") as file_:
weights_data = file_.read()
model.get_ref("tok2vec").from_bytes(weights_data)
if epoch_resume is None:
# Parse the epoch number from the given weight file
model_name = re.search(r"model\d+\.bin", str(resume_path))
if model_name:
# Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
else:
# No epoch given and couldn't infer it
raise ValueError(Errors.E1020)
msg.info(f"Resuming from epoch: {epoch_resume}")
return epoch_resume
def make_update(
model: Model, docs: Iterable[Doc], optimizer: Optimizer, objective_func: Callable
) -> float:
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
predictions, backprop = model.begin_update(docs)
loss, gradients = objective_func(model.ops, docs, predictions)
backprop(gradients)
model.finish_update(optimizer)
# Don't want to return a cupy object here
# The gradients are modified in-place by the BERT MLM,
# so we get an accurate loss
return float(loss)
def create_pretraining_model(nlp, pretrain_config):
"""Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc.
The actual tok2vec layer is stored as a reference, and only this bit will be
serialized to file and read back in when calling the 'train' command.
"""
with nlp.select_pipes(enable=[]):
nlp.initialize()
tok2vec = get_tok2vec_ref(nlp, pretrain_config)
# If the config referred to a Tok2VecListener, grab the original model instead
if type(tok2vec).__name__ == "Tok2VecListener":
original_tok2vec = (
tok2vec.upstream_name if tok2vec.upstream_name != "*" else "tok2vec"
)
tok2vec = nlp.get_pipe(original_tok2vec).model
try:
tok2vec.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
except ValueError:
component = pretrain_config["component"]
layer = pretrain_config["layer"]
raise ValueError(Errors.E874.format(component=component, layer=layer))
create_function = pretrain_config["objective"]
model = create_function(nlp.vocab, tok2vec)
model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
set_dropout_rate(model, pretrain_config["dropout"])
return model
def get_tok2vec_ref(nlp, pretrain_config):
tok2vec_component = pretrain_config["component"]
if tok2vec_component is None:
desc = (
f"To use pretrained tok2vec weights, [pretraining.component] "
f"needs to specify the component that should load them."
)
err = "component can't be null"
errors = [{"loc": ["pretraining", "component"], "msg": err}]
raise ConfigValidationError(
config=nlp.config["pretraining"], errors=errors, desc=desc
)
layer = nlp.get_pipe(tok2vec_component).model
if pretrain_config["layer"]:
layer = layer.get_ref(pretrain_config["layer"])
return layer
class ProgressTracker:
def __init__(self, frequency=1000000):
self.loss = 0.0
self.prev_loss = 0.0
self.nr_word = 0
self.words_per_epoch = Counter()
self.frequency = frequency
self.last_time = time.time()
self.last_update = 0
self.epoch_loss = 0.0
def update(self, epoch, loss, docs):
self.loss += loss
self.epoch_loss += loss
words_in_batch = sum(len(doc) for doc in docs)
self.words_per_epoch[epoch] += words_in_batch
self.nr_word += words_in_batch
words_since_update = self.nr_word - self.last_update
if words_since_update >= self.frequency:
wps = words_since_update / (time.time() - self.last_time)
self.last_update = self.nr_word
self.last_time = time.time()
loss_per_word = self.loss - self.prev_loss
status = (
epoch,
self.nr_word,
_smart_round(self.loss, width=10),
_smart_round(loss_per_word, width=6),
int(wps),
)
self.prev_loss = float(self.loss)
return status
else:
return None
def _smart_round(
figure: Union[float, int], width: int = 10, max_decimal: int = 4
) -> str:
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
format_str = "%." + str(n_decimal) + "f"
return format_str % figure