mirror of
https://github.com/explosion/spaCy.git
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305 lines
12 KiB
Python
305 lines
12 KiB
Python
from typing import List, Callable, Tuple, Dict, Iterable, Iterator, Union, Any, IO
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from typing import Optional, TYPE_CHECKING
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from pathlib import Path
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from timeit import default_timer as timer
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from thinc.api import Optimizer, Config, constant, fix_random_seed, set_gpu_allocator
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import random
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import wasabi
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import sys
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from .example import Example
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from ..schemas import ConfigSchemaTraining
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from ..errors import Errors
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from ..util import resolve_dot_names, registry
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if TYPE_CHECKING:
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from ..language import Language # noqa: F401
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def train(
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nlp: "Language",
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output_path: Optional[Path] = None,
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*,
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use_gpu: int = -1,
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stdout: IO = sys.stdout,
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stderr: IO = sys.stderr,
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) -> None:
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"""Train a pipeline.
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nlp (Language): The initialized nlp object with the full config.
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output_path (Path): Optional output path to save trained model to.
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use_gpu (int): Whether to train on GPU. Make sure to call require_gpu
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before calling this function.
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stdout (file): A file-like object to write output messages. To disable
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printing, set to io.StringIO.
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stderr (file): A second file-like object to write output messages. To disable
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printing, set to io.StringIO.
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RETURNS (Path / None): The path to the final exported model.
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"""
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# We use no_print here so we can respect the stdout/stderr options.
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msg = wasabi.Printer(no_print=True)
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# Create iterator, which yields out info after each optimization step.
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config = nlp.config.interpolate()
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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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T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
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dot_names = [T["train_corpus"], T["dev_corpus"]]
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train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
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optimizer = T["optimizer"]
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score_weights = T["score_weights"]
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batcher = T["batcher"]
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train_logger = T["logger"]
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before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
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# Components that shouldn't be updated during training
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frozen_components = T["frozen_components"]
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# Create iterator, which yields out info after each optimization step.
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training_step_iterator = train_while_improving(
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nlp,
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optimizer,
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create_train_batches(train_corpus(nlp), batcher, T["max_epochs"]),
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create_evaluation_callback(nlp, dev_corpus, score_weights),
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dropout=T["dropout"],
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accumulate_gradient=T["accumulate_gradient"],
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patience=T["patience"],
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max_steps=T["max_steps"],
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eval_frequency=T["eval_frequency"],
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exclude=frozen_components,
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)
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stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}"))
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if frozen_components:
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stdout.write(msg.info(f"Frozen components: {frozen_components}"))
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stdout.write(msg.info(f"Initial learn rate: {optimizer.learn_rate}"))
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with nlp.select_pipes(disable=frozen_components):
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log_step, finalize_logger = train_logger(nlp, stdout, stderr)
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try:
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for batch, info, is_best_checkpoint in training_step_iterator:
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log_step(info if is_best_checkpoint else None)
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if is_best_checkpoint is not None and output_path is not None:
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with nlp.select_pipes(disable=frozen_components):
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update_meta(T, nlp, info)
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with nlp.use_params(optimizer.averages):
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nlp = before_to_disk(nlp)
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nlp.to_disk(output_path / "model-best")
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except Exception as e:
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if output_path is not None:
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# We don't want to swallow the traceback if we don't have a
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# specific error, but we do want to warn that we're trying
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# to do something here.
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stdout.write(
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msg.warn(
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f"Aborting and saving the final best model. "
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f"Encountered exception: {str(e)}"
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)
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)
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raise e
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finally:
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finalize_logger()
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if output_path is not None:
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final_model_path = output_path / "model-last"
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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nlp.to_disk(final_model_path)
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else:
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nlp.to_disk(final_model_path)
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# This will only run if we don't hit an error
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stdout.write(msg.good("Saved pipeline to output directory", final_model_path))
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def train_while_improving(
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nlp: "Language",
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optimizer: Optimizer,
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train_data,
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evaluate,
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*,
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dropout: float,
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eval_frequency: int,
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accumulate_gradient: int,
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patience: int,
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max_steps: int,
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exclude: List[str],
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):
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"""Train until an evaluation stops improving. Works as a generator,
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with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
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where info is a dict, and is_best_checkpoint is in [True, False, None] --
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None indicating that the iteration was not evaluated as a checkpoint.
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The evaluation is conducted by calling the evaluate callback.
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Positional arguments:
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nlp: The spaCy pipeline to evaluate.
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optimizer: The optimizer callable.
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train_data (Iterable[Batch]): A generator of batches, with the training
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data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
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data iterable needs to take care of iterating over the epochs and
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shuffling.
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evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation.
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The callback should take no arguments and return a tuple
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`(main_score, other_scores)`. The main_score should be a float where
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higher is better. other_scores can be any object.
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Every iteration, the function yields out a tuple with:
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* batch: A list of Example objects.
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* info: A dict with various information about the last update (see below).
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* is_best_checkpoint: A value in None, False, True, indicating whether this
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was the best evaluation so far. You should use this to save the model
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checkpoints during training. If None, evaluation was not conducted on
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that iteration. False means evaluation was conducted, but a previous
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evaluation was better.
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The info dict provides the following information:
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epoch (int): How many passes over the data have been completed.
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step (int): How many steps have been completed.
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score (float): The main score from the last evaluation.
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other_scores: : The other scores from the last evaluation.
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losses: The accumulated losses throughout training.
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checkpoints: A list of previous results, where each result is a
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(score, step, epoch) tuple.
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"""
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if isinstance(dropout, float):
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dropouts = constant(dropout)
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else:
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dropouts = dropout
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results = []
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losses = {}
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words_seen = 0
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start_time = timer()
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for step, (epoch, batch) in enumerate(train_data):
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dropout = next(dropouts)
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for subbatch in subdivide_batch(batch, accumulate_gradient):
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nlp.update(
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subbatch, drop=dropout, losses=losses, sgd=False, exclude=exclude
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)
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# TODO: refactor this so we don't have to run it separately in here
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for name, proc in nlp.pipeline:
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if (
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name not in exclude
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and hasattr(proc, "model")
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and proc.model not in (True, False, None)
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):
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proc.model.finish_update(optimizer)
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optimizer.step_schedules()
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if not (step % eval_frequency):
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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score, other_scores = evaluate()
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else:
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score, other_scores = evaluate()
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results.append((score, step))
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is_best_checkpoint = score == max(results)[0]
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else:
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score, other_scores = (None, None)
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is_best_checkpoint = None
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words_seen += sum(len(eg) for eg in batch)
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info = {
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"epoch": epoch,
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"step": step,
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"score": score,
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"other_scores": other_scores,
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"losses": losses,
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"checkpoints": results,
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"seconds": int(timer() - start_time),
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"words": words_seen,
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}
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yield batch, info, is_best_checkpoint
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if is_best_checkpoint is not None:
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losses = {}
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# Stop if no improvement in `patience` updates (if specified)
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best_score, best_step = max(results)
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if patience and (step - best_step) >= patience:
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break
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# Stop if we've exhausted our max steps (if specified)
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if max_steps and step >= max_steps:
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break
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def subdivide_batch(batch, accumulate_gradient):
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batch = list(batch)
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batch.sort(key=lambda eg: len(eg.predicted))
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sub_len = len(batch) // accumulate_gradient
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start = 0
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for i in range(accumulate_gradient):
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subbatch = batch[start : start + sub_len]
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if subbatch:
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yield subbatch
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start += len(subbatch)
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subbatch = batch[start:]
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if subbatch:
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yield subbatch
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def create_evaluation_callback(
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nlp: "Language", dev_corpus: Callable, weights: Dict[str, float]
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) -> Callable[[], Tuple[float, Dict[str, float]]]:
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weights = {key: value for key, value in weights.items() if value is not None}
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def evaluate() -> Tuple[float, Dict[str, float]]:
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dev_examples = list(dev_corpus(nlp))
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scores = nlp.evaluate(dev_examples)
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# Calculate a weighted sum based on score_weights for the main score.
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# We can only consider scores that are ints/floats, not dicts like
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# entity scores per type etc.
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for key, value in scores.items():
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if key in weights and not isinstance(value, (int, float)):
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raise ValueError(Errors.E915.format(name=key, score_type=type(value)))
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try:
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weighted_score = sum(
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scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights
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)
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except KeyError as e:
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keys = list(scores.keys())
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err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
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raise KeyError(err) from None
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return weighted_score, scores
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return evaluate
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def create_train_batches(
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iterator: Iterator[Example],
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batcher: Callable[[Iterable[Example]], Iterable[Example]],
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max_epochs: int,
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):
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epoch = 0
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examples = list(iterator)
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if not examples:
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# Raise error if no data
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raise ValueError(Errors.E986)
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while max_epochs < 1 or epoch != max_epochs:
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random.shuffle(examples)
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for batch in batcher(examples):
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yield epoch, batch
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epoch += 1
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def update_meta(
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training: Union[Dict[str, Any], Config], nlp: "Language", info: Dict[str, Any]
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) -> None:
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nlp.meta["performance"] = {}
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for metric in training["score_weights"]:
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if metric is not None:
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nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0)
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for pipe_name in nlp.pipe_names:
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nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]
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def create_before_to_disk_callback(
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callback: Optional[Callable[["Language"], "Language"]]
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) -> Callable[["Language"], "Language"]:
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from ..language import Language # noqa: F811
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def before_to_disk(nlp: Language) -> Language:
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if not callback:
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return nlp
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modified_nlp = callback(nlp)
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if not isinstance(modified_nlp, Language):
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err = Errors.E914.format(name="before_to_disk", value=type(modified_nlp))
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raise ValueError(err)
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return modified_nlp
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return before_to_disk
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