mirror of
https://github.com/explosion/spaCy.git
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267 lines
10 KiB
Python
267 lines
10 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, to_categorical, CosineDistance, L2Distance
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from pathlib import Path
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from functools import partial
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from collections import Counter
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import srsly
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import numpy
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import time
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import re
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from wasabi import Printer
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from .example import Example
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from ..tokens import Doc
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from ..attrs import ID
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from ..ml.models.multi_task import build_cloze_multi_task_model
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from ..ml.models.multi_task import build_cloze_characters_multi_task_model
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from ..schemas import ConfigSchemaTraining, 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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):
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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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nlp = load_model_from_config(config)
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_config = nlp.config.interpolate()
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T = registry.resolve(_config["training"], schema=ConfigSchemaTraining)
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P = registry.resolve(_config["pretraining"], schema=ConfigSchemaPretrain)
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corpus = dot_to_object(T, P["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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_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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# TODO: move this to logger function?
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tracker = ProgressTracker(frequency=10000)
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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):
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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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with (output_dir / f"model{epoch}{is_temp_str}.bin").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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objective = create_objective(P["objective"])
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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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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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_save_model(epoch)
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tracker.epoch_loss = 0.0
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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: int, silent: bool = True,
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) -> None:
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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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# 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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msg.info(f"Resuming from epoch: {epoch_resume}")
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else:
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msg.info(f"Resuming from epoch: {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_objective(config: Config):
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"""Create the objective for pretraining.
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We'd like to replace this with a registry function but it's tricky because
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we're also making a model choice based on this. For now we hard-code support
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for two types (characters, vectors). For characters you can specify
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n_characters, for vectors you can specify the loss.
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Bleh.
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"""
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objective_type = config["type"]
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if objective_type == "characters":
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return partial(get_characters_loss, nr_char=config["n_characters"])
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elif objective_type == "vectors":
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if config["loss"] == "cosine":
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distance = CosineDistance(normalize=True, ignore_zeros=True)
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return partial(get_vectors_loss, distance=distance)
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elif config["loss"] == "L2":
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distance = L2Distance(normalize=True, ignore_zeros=True)
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return partial(get_vectors_loss, distance=distance)
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else:
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raise ValueError("Unexpected loss type", config["loss"])
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else:
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raise ValueError("Unexpected objective_type", objective_type)
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def get_vectors_loss(ops, docs, prediction, distance):
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"""Compute a loss based on a distance between the documents' vectors and
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the prediction.
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"""
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# The simplest way to implement this would be to vstack the
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# token.vector values, but that's a bit inefficient, especially on GPU.
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# Instead we fetch the index into the vectors table for each of our tokens,
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# and look them up all at once. This prevents data copying.
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ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
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target = docs[0].vocab.vectors.data[ids]
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d_target, loss = distance(prediction, target)
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return loss, d_target
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def get_characters_loss(ops, docs, prediction, nr_char):
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"""Compute a loss based on a number of characters predicted from the docs."""
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target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
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target_ids = target_ids.reshape((-1,))
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target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
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target = target.reshape((-1, 256 * nr_char))
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diff = prediction - target
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loss = (diff ** 2).sum()
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d_target = diff / float(prediction.shape[0])
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return loss, d_target
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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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component = nlp.get_pipe(pretrain_config["component"])
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if pretrain_config.get("layer"):
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tok2vec = component.model.get_ref(pretrain_config["layer"])
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else:
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tok2vec = component.model
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# TODO
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maxout_pieces = 3
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hidden_size = 300
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if pretrain_config["objective"]["type"] == "vectors":
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model = build_cloze_multi_task_model(
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nlp.vocab, tok2vec, hidden_size=hidden_size, maxout_pieces=maxout_pieces
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)
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elif pretrain_config["objective"]["type"] == "characters":
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model = build_cloze_characters_multi_task_model(
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nlp.vocab,
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tok2vec,
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hidden_size=hidden_size,
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maxout_pieces=maxout_pieces,
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nr_char=pretrain_config["objective"]["n_characters"],
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)
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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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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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