mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-01 00:17:44 +03:00
228 lines
8.6 KiB
Python
228 lines
8.6 KiB
Python
from typing import Optional, Callable, Iterable, Union, List
|
|
from thinc.api import Config, fix_random_seed, set_gpu_allocator, Model, Optimizer
|
|
from thinc.api import set_dropout_rate
|
|
from pathlib import Path
|
|
from collections import Counter
|
|
import srsly
|
|
import time
|
|
import re
|
|
|
|
from thinc.config import ConfigValidationError
|
|
from wasabi import Printer
|
|
|
|
from .example import Example
|
|
from ..errors import Errors
|
|
from ..tokens import Doc
|
|
from ..schemas import ConfigSchemaPretrain
|
|
from ..util import registry, load_model_from_config, dot_to_object
|
|
|
|
|
|
def pretrain(
|
|
config: Config,
|
|
output_dir: Path,
|
|
resume_path: Optional[Path] = None,
|
|
epoch_resume: Optional[int] = None,
|
|
use_gpu: int = -1,
|
|
silent: bool = True,
|
|
):
|
|
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)
|
|
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:
|
|
_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)
|
|
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_temp_str = ".temp" if is_temp else ""
|
|
with model.use_params(optimizer.averages):
|
|
with (output_dir / f"model{epoch}{is_temp_str}.bin").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?
|
|
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)
|
|
_save_model(epoch)
|
|
tracker.epoch_loss = 0.0
|
|
|
|
|
|
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: int, silent: bool = True
|
|
) -> None:
|
|
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)
|
|
# 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
|
|
msg.info(f"Resuming from epoch: {epoch_resume}")
|
|
else:
|
|
msg.info(f"Resuming from epoch: {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
|