spaCy/spacy/training/pretrain.py
Paul O'Leary McCann f3981bd0c8
Clarify how to fill in init_tok2vec after pretraining (#9639)
* Clarify how to fill in init_tok2vec after pretraining

* Ignore init_tok2vec arg in pretraining

* Update docs, config setting

* Remove obsolete note about not filling init_tok2vec early

This seems to have also caught some lines that needed cleanup.
2021-11-18 15:38:30 +01:00

246 lines
9.1 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)
# 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_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)
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
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