mirror of https://github.com/explosion/spaCy.git
332 lines
13 KiB
Python
332 lines
13 KiB
Python
import random
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import numpy
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import time
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import re
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from collections import Counter
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import plac
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from pathlib import Path
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from thinc.api import Linear, Maxout, chain, list2array, use_pytorch_for_gpu_memory
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from wasabi import msg
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import srsly
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from ..errors import Errors
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from ..ml.models.multi_task import build_masked_language_model
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from ..tokens import Doc
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from ..attrs import ID, HEAD
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from .. import util
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from ..gold import Example
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@plac.annotations(
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# fmt: off
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texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", "positional", None, str),
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vectors_model=("Name or path to spaCy model with vectors to learn from", "positional", None, str),
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output_dir=("Directory to write models to on each epoch", "positional", None, Path),
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config_path=("Path to config file", "positional", None, Path),
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use_gpu=("Use GPU", "option", "g", int),
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resume_path=("Path to pretrained weights from which to resume pretraining", "option", "r", Path),
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epoch_resume=("The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files.", "option", "er", int),
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# fmt: on
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)
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def pretrain(
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texts_loc,
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vectors_model,
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config_path,
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output_dir,
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use_gpu=-1,
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resume_path=None,
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epoch_resume=None,
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):
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"""
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Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
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using an approximate language-modelling objective. Specifically, we load
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pretrained vectors, and train a component like a CNN, BiLSTM, etc to predict
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vectors which match the pretrained ones. The weights are saved to a directory
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after each epoch. You can then pass a path to one of these pretrained weights
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files to the 'spacy train' command.
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This technique may be especially helpful if you have little labelled data.
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However, it's still quite experimental, so your mileage may vary.
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To load the weights back in during 'spacy train', you need to ensure
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all settings are the same between pretraining and training. Ideally,
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this is done by using the same config file for both commands.
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"""
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if not config_path or not config_path.exists():
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msg.fail("Config file not found", config_path, exits=1)
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if use_gpu >= 0:
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msg.info("Using GPU")
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util.use_gpu(use_gpu)
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else:
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msg.info("Using CPU")
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msg.info(f"Loading config from: {config_path}")
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config = util.load_config(config_path, create_objects=False)
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util.fix_random_seed(config["pretraining"]["seed"])
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if config["pretraining"]["use_pytorch_for_gpu_memory"]:
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use_pytorch_for_gpu_memory()
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if output_dir.exists() and [p for p in output_dir.iterdir()]:
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if resume_path:
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msg.warn(
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"Output directory is not empty. ",
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"If you're resuming a run from a previous model in this directory, "
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"the old models for the consecutive epochs will be overwritten "
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"with the new ones.",
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)
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else:
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msg.warn(
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"Output directory is not empty. ",
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"It is better to use an empty directory or refer to a new output path, "
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"then the new directory will be created for you.",
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)
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if not output_dir.exists():
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output_dir.mkdir()
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msg.good(f"Created output directory: {output_dir}")
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srsly.write_json(output_dir / "config.json", config)
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msg.good("Saved config file in the output directory")
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config = util.load_config(config_path, create_objects=True)
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pretrain_config = config["pretraining"]
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# Load texts from file or stdin
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if texts_loc != "-": # reading from a file
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texts_loc = Path(texts_loc)
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if not texts_loc.exists():
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msg.fail("Input text file doesn't exist", texts_loc, exits=1)
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with msg.loading("Loading input texts..."):
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texts = list(srsly.read_jsonl(texts_loc))
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if not texts:
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msg.fail("Input file is empty", texts_loc, exits=1)
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msg.good("Loaded input texts")
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random.shuffle(texts)
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else: # reading from stdin
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msg.info("Reading input text from stdin...")
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texts = srsly.read_jsonl("-")
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with msg.loading(f"Loading model '{vectors_model}'..."):
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nlp = util.load_model(vectors_model)
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msg.good(f"Loaded model '{vectors_model}'")
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tok2vec_path = pretrain_config["tok2vec_model"]
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tok2vec = config
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for subpath in tok2vec_path.split("."):
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tok2vec = tok2vec.get(subpath)
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model = create_pretraining_model(nlp, tok2vec)
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optimizer = pretrain_config["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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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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if not epoch_resume:
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msg.fail(
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"You have to use the --epoch-resume setting when using a renamed weight file for --resume-path",
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exits=True,
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)
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elif epoch_resume < 0:
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msg.fail(
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f"The argument --epoch-resume has to be greater or equal to 0. {epoch_resume} is invalid",
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exits=True,
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)
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else:
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msg.info(f"Resuming from epoch: {epoch_resume}")
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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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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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skip_counter = 0
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loss_func = pretrain_config["loss_func"]
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for epoch in range(epoch_resume, pretrain_config["max_epochs"]):
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examples = [Example(doc=text) for text in texts]
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batches = util.minibatch_by_words(examples, size=pretrain_config["batch_size"])
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for batch_id, batch in enumerate(batches):
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docs, count = make_docs(
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nlp,
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[ex.doc for ex in batch],
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max_length=pretrain_config["max_length"],
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min_length=pretrain_config["min_length"],
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)
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skip_counter += count
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loss = make_update(model, docs, optimizer, distance=loss_func)
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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 texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
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break
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if pretrain_config["n_save_every"] and (
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batch_id % pretrain_config["n_save_every"] == 0
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):
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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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if texts_loc != "-":
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# Reshuffle the texts if texts were loaded from a file
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random.shuffle(texts)
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if skip_counter > 0:
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msg.warn(f"Skipped {skip_counter} empty values")
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msg.good("Successfully finished pretrain")
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def make_update(model, docs, optimizer, distance):
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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 = get_vectors_loss(model.ops, docs, predictions, distance)
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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 make_docs(nlp, batch, min_length, max_length):
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docs = []
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skip_count = 0
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for record in batch:
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if not isinstance(record, dict):
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raise TypeError(Errors.E137.format(type=type(record), line=record))
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if "tokens" in record:
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words = record["tokens"]
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if not words:
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skip_count += 1
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continue
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doc = Doc(nlp.vocab, words=words)
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elif "text" in record:
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text = record["text"]
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if not text:
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skip_count += 1
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continue
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doc = nlp.make_doc(text)
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else:
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raise ValueError(Errors.E138.format(text=record))
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if "heads" in record:
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heads = record["heads"]
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heads = numpy.asarray(heads, dtype="uint64")
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heads = heads.reshape((len(doc), 1))
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doc = doc.from_array([HEAD], heads)
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if min_length <= len(doc) < max_length:
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docs.append(doc)
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return docs, skip_count
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def get_vectors_loss(ops, docs, prediction, distance):
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"""Compute a mean-squared error loss between the documents' vectors and
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the prediction.
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Note that this is ripe for customization! We could compute the vectors
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in some other word, e.g. with an LSTM language model, or use some other
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type of objective.
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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 create_pretraining_model(nlp, tok2vec):
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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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output_size = nlp.vocab.vectors.data.shape[1]
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output_layer = chain(
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Maxout(nO=300, nP=3, normalize=True, dropout=0.0), Linear(output_size)
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)
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model = chain(tok2vec, list2array())
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model = chain(model, output_layer)
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model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
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mlm_model = build_masked_language_model(nlp.vocab, model)
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mlm_model.set_ref("tok2vec", tok2vec)
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mlm_model.set_ref("output_layer", output_layer)
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mlm_model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
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return mlm_model
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class ProgressTracker(object):
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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(figure, width=10, max_decimal=4):
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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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