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de32011e4c
* Adjust pretrain command * chane naming and add finally block * Add unit test * Add unit test assertions * Update spacy/training/pretrain.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * change finally block * Add to docs * Update website/docs/usage/embeddings-transformers.mdx * Add flag to skip saving model-last --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
255 lines
9.5 KiB
Python
255 lines
9.5 KiB
Python
from typing import Optional, Callable, Iterable, Union, List
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from thinc.api import Config, fix_random_seed, set_gpu_allocator, Model, Optimizer
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from thinc.api import set_dropout_rate
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from pathlib import Path
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from collections import Counter
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import srsly
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import time
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import re
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from thinc.config import ConfigValidationError
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from wasabi import Printer
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from .example import Example
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from ..errors import Errors
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from ..tokens import Doc
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from ..schemas import ConfigSchemaPretrain
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from ..util import registry, load_model_from_config, dot_to_object
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def pretrain(
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config: Config,
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output_dir: Path,
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resume_path: Optional[Path] = None,
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epoch_resume: Optional[int] = None,
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use_gpu: int = -1,
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silent: bool = True,
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skip_last: bool = False,
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):
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msg = Printer(no_print=silent)
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if config["training"]["seed"] is not None:
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fix_random_seed(config["training"]["seed"])
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allocator = config["training"]["gpu_allocator"]
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if use_gpu >= 0 and allocator:
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set_gpu_allocator(allocator)
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# ignore in pretraining because we're creating it now
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config["initialize"]["init_tok2vec"] = None
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nlp = load_model_from_config(config)
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_config = nlp.config.interpolate()
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P = registry.resolve(_config["pretraining"], schema=ConfigSchemaPretrain)
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corpus = dot_to_object(_config, P["corpus"])
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corpus = registry.resolve({"corpus": corpus})["corpus"]
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batcher = P["batcher"]
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model = create_pretraining_model(nlp, P)
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optimizer = P["optimizer"]
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# Load in pretrained weights to resume from
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if resume_path is not None:
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epoch_resume = _resume_model(model, resume_path, epoch_resume, silent=silent)
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else:
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# Without '--resume-path' the '--epoch-resume' argument is ignored
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epoch_resume = 0
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objective = model.attrs["loss"]
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# TODO: move this to logger function?
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tracker = ProgressTracker(frequency=10000)
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if P["n_save_epoch"]:
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msg.divider(
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f"Pre-training tok2vec layer - starting at epoch {epoch_resume} - saving every {P['n_save_epoch']} epoch"
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)
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else:
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msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
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row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
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msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
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def _save_model(epoch, is_temp=False, is_last=False):
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is_temp_str = ".temp" if is_temp else ""
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with model.use_params(optimizer.averages):
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if is_last:
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save_path = output_dir / f"model-last.bin"
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else:
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save_path = output_dir / f"model{epoch}{is_temp_str}.bin"
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with (save_path).open("wb") as file_:
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file_.write(model.get_ref("tok2vec").to_bytes())
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log = {
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"nr_word": tracker.nr_word,
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"loss": tracker.loss,
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"epoch_loss": tracker.epoch_loss,
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"epoch": epoch,
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}
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with (output_dir / "log.jsonl").open("a") as file_:
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file_.write(srsly.json_dumps(log) + "\n")
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# TODO: I think we probably want this to look more like the
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# 'create_train_batches' function?
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try:
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for epoch in range(epoch_resume, P["max_epochs"]):
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for batch_id, batch in enumerate(batcher(corpus(nlp))):
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docs = ensure_docs(batch)
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loss = make_update(model, docs, optimizer, objective)
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progress = tracker.update(epoch, loss, docs)
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if progress:
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msg.row(progress, **row_settings)
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if P["n_save_every"] and (batch_id % P["n_save_every"] == 0):
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_save_model(epoch, is_temp=True)
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if P["n_save_epoch"]:
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if epoch % P["n_save_epoch"] == 0 or epoch == P["max_epochs"] - 1:
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_save_model(epoch)
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else:
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_save_model(epoch)
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tracker.epoch_loss = 0.0
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finally:
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if not skip_last:
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_save_model(P["max_epochs"], is_last=True)
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def ensure_docs(examples_or_docs: Iterable[Union[Doc, Example]]) -> List[Doc]:
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docs = []
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for eg_or_doc in examples_or_docs:
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if isinstance(eg_or_doc, Doc):
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docs.append(eg_or_doc)
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else:
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docs.append(eg_or_doc.reference)
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return docs
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def _resume_model(
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model: Model, resume_path: Path, epoch_resume: Optional[int], silent: bool = True
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) -> int:
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msg = Printer(no_print=silent)
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msg.info(f"Resume training tok2vec from: {resume_path}")
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with resume_path.open("rb") as file_:
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weights_data = file_.read()
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model.get_ref("tok2vec").from_bytes(weights_data)
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if epoch_resume is None:
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# Parse the epoch number from the given weight file
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model_name = re.search(r"model\d+\.bin", str(resume_path))
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if model_name:
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# Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
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epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
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else:
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# No epoch given and couldn't infer it
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raise ValueError(Errors.E1020)
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msg.info(f"Resuming from epoch: {epoch_resume}")
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return epoch_resume
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def make_update(
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model: Model, docs: Iterable[Doc], optimizer: Optimizer, objective_func: Callable
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) -> float:
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"""Perform an update over a single batch of documents.
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docs (iterable): A batch of `Doc` objects.
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optimizer (callable): An optimizer.
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RETURNS loss: A float for the loss.
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"""
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predictions, backprop = model.begin_update(docs)
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loss, gradients = objective_func(model.ops, docs, predictions)
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backprop(gradients)
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model.finish_update(optimizer)
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# Don't want to return a cupy object here
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# The gradients are modified in-place by the BERT MLM,
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# so we get an accurate loss
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return float(loss)
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def create_pretraining_model(nlp, pretrain_config):
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"""Define a network for the pretraining. We simply add an output layer onto
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the tok2vec input model. The tok2vec input model needs to be a model that
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takes a batch of Doc objects (as a list), and returns a list of arrays.
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Each array in the output needs to have one row per token in the doc.
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The actual tok2vec layer is stored as a reference, and only this bit will be
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serialized to file and read back in when calling the 'train' command.
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"""
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with nlp.select_pipes(enable=[]):
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nlp.initialize()
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tok2vec = get_tok2vec_ref(nlp, pretrain_config)
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# If the config referred to a Tok2VecListener, grab the original model instead
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if type(tok2vec).__name__ == "Tok2VecListener":
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original_tok2vec = (
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tok2vec.upstream_name if tok2vec.upstream_name != "*" else "tok2vec"
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)
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tok2vec = nlp.get_pipe(original_tok2vec).model
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try:
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tok2vec.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
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except ValueError:
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component = pretrain_config["component"]
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layer = pretrain_config["layer"]
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raise ValueError(Errors.E874.format(component=component, layer=layer))
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create_function = pretrain_config["objective"]
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model = create_function(nlp.vocab, tok2vec)
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model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
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set_dropout_rate(model, pretrain_config["dropout"])
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return model
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def get_tok2vec_ref(nlp, pretrain_config):
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tok2vec_component = pretrain_config["component"]
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if tok2vec_component is None:
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desc = (
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f"To use pretrained tok2vec weights, [pretraining.component] "
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f"needs to specify the component that should load them."
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)
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err = "component can't be null"
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errors = [{"loc": ["pretraining", "component"], "msg": err}]
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raise ConfigValidationError(
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config=nlp.config["pretraining"], errors=errors, desc=desc
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)
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layer = nlp.get_pipe(tok2vec_component).model
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if pretrain_config["layer"]:
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layer = layer.get_ref(pretrain_config["layer"])
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return layer
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class ProgressTracker:
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def __init__(self, frequency=1000000):
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self.loss = 0.0
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self.prev_loss = 0.0
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self.nr_word = 0
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self.words_per_epoch = Counter()
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self.frequency = frequency
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self.last_time = time.time()
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self.last_update = 0
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self.epoch_loss = 0.0
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def update(self, epoch, loss, docs):
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self.loss += loss
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self.epoch_loss += loss
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words_in_batch = sum(len(doc) for doc in docs)
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self.words_per_epoch[epoch] += words_in_batch
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self.nr_word += words_in_batch
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words_since_update = self.nr_word - self.last_update
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if words_since_update >= self.frequency:
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wps = words_since_update / (time.time() - self.last_time)
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self.last_update = self.nr_word
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self.last_time = time.time()
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loss_per_word = self.loss - self.prev_loss
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status = (
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epoch,
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self.nr_word,
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_smart_round(self.loss, width=10),
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_smart_round(loss_per_word, width=6),
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int(wps),
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)
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self.prev_loss = float(self.loss)
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return status
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else:
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return None
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def _smart_round(
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figure: Union[float, int], width: int = 10, max_decimal: int = 4
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) -> str:
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"""Round large numbers as integers, smaller numbers as decimals."""
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n_digits = len(str(int(figure)))
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n_decimal = width - (n_digits + 1)
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if n_decimal <= 1:
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return str(int(figure))
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else:
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n_decimal = min(n_decimal, max_decimal)
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format_str = "%." + str(n_decimal) + "f"
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return format_str % figure
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