mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
393 lines
13 KiB
Python
393 lines
13 KiB
Python
from typing import Optional, Dict, List, Union, Sequence
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from timeit import default_timer as timer
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from pydantic import BaseModel, FilePath
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import plac
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import tqdm
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from pathlib import Path
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from wasabi import msg
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import thinc
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import thinc.schedules
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from thinc.api import Model
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import random
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from ..gold import GoldCorpus
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from .. import util
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from ..errors import Errors
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registry = util.registry
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CONFIG_STR = """
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[training]
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patience = 10
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eval_frequency = 10
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dropout = 0.2
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init_tok2vec = null
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vectors = null
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max_epochs = 100
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orth_variant_level = 0.0
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gold_preproc = false
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max_length = 0
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use_gpu = 0
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scores = ["ents_p", "ents_r", "ents_f"]
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score_weights = {"ents_f": 1.0}
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limit = 0
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[training.batch_size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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compound = 1.001
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[optimizer]
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@optimizers = "Adam.v1"
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learn_rate = 0.001
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beta1 = 0.9
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beta2 = 0.999
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[nlp]
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lang = "en"
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vectors = ${training:vectors}
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[nlp.pipeline.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.ner]
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factory = "ner"
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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hidden_width = 64
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maxout_pieces = 3
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = ${nlp:vectors}
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width = 128
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depth = 4
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window_size = 1
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embed_size = 10000
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maxout_pieces = 3
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subword_features = true
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"""
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class PipelineComponent(BaseModel):
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factory: str
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model: Model
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class Config:
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arbitrary_types_allowed = True
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class ConfigSchema(BaseModel):
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optimizer: Optional["Optimizer"]
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class training(BaseModel):
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patience: int = 10
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eval_frequency: int = 100
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dropout: float = 0.2
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init_tok2vec: Optional[FilePath] = None
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vectors: Optional[str] = None
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max_epochs: int = 100
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orth_variant_level: float = 0.0
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gold_preproc: bool = False
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max_length: int = 0
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use_gpu: int = 0
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scores: List[str] = ["ents_p", "ents_r", "ents_f"]
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score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
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limit: int = 0
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batch_size: Union[Sequence[int], int]
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class nlp(BaseModel):
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lang: str
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vectors: Optional[str]
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pipeline: Optional[Dict[str, PipelineComponent]]
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class Config:
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extra = "allow"
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@plac.annotations(
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# fmt: off
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train_path=("Location of JSON-formatted training data", "positional", None, Path),
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dev_path=("Location of JSON-formatted development data", "positional", None, Path),
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config_path=("Path to config file", "positional", None, Path),
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output_path=("Output directory to store model in", "option", "o", Path),
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meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
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raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
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use_gpu=("Use GPU", "option", "g", int),
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# fmt: on
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)
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def train_from_config_cli(
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train_path,
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dev_path,
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config_path,
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output_path=None,
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meta_path=None,
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raw_text=None,
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debug=False,
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verbose=False,
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use_gpu=-1
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):
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"""
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Train or update a spaCy model. Requires data to be formatted in spaCy's
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JSON format. To convert data from other formats, use the `spacy convert`
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command.
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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 not train_path or not train_path.exists():
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msg.fail("Training data not found", train_path, exits=1)
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if not dev_path or not dev_path.exists():
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msg.fail("Development data not found", dev_path, exits=1)
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if meta_path is not None and not meta_path.exists():
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msg.fail("Can't find model meta.json", meta_path, exits=1)
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if output_path is not None and not output_path.exists():
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output_path.mkdir()
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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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train_from_config(
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config_path,
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{"train": train_path, "dev": dev_path},
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output_path=output_path,
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meta_path=meta_path,
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raw_text=raw_text,
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)
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def train_from_config(
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config_path, data_paths, raw_text=None, meta_path=None, output_path=None,
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):
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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["training"]["seed"])
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nlp_config = config["nlp"]
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config = util.load_config(config_path, create_objects=True)
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msg.info("Creating nlp from config")
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nlp = util.load_model_from_config(nlp_config)
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optimizer = config["optimizer"]
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training = config["training"]
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limit = training["limit"]
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msg.info("Loading training corpus")
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corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit)
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msg.info("Initializing the nlp pipeline")
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nlp.begin_training(lambda: corpus.train_examples)
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train_batches = create_train_batches(nlp, corpus, training)
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evaluate = create_evaluation_callback(nlp, optimizer, corpus, training)
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# Create iterator, which yields out info after each optimization step.
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msg.info("Start training")
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training_step_iterator = train_while_improving(
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nlp,
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optimizer,
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train_batches,
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evaluate,
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training["dropout"],
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training["patience"],
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training["eval_frequency"],
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training["accumulate_gradient"]
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)
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msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
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print_row = setup_printer(training, nlp)
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try:
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progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
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for batch, info, is_best_checkpoint in training_step_iterator:
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progress.update(1)
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if is_best_checkpoint is not None:
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progress.close()
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print_row(info)
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if is_best_checkpoint and output_path is not None:
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nlp.to_disk(output_path)
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progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
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finally:
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if output_path is not None:
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with nlp.use_params(optimizer.averages):
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final_model_path = output_path / "model-final"
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nlp.to_disk(final_model_path)
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msg.good("Saved model to output directory", final_model_path)
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# with msg.loading("Creating best model..."):
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# best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
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# msg.good("Created best model", best_model_path)
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def create_train_batches(nlp, corpus, cfg):
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is_first = True
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while True:
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train_examples = list(corpus.train_dataset(
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nlp,
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noise_level=0.0,
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orth_variant_level=cfg["orth_variant_level"],
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gold_preproc=cfg["gold_preproc"],
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max_length=cfg["max_length"],
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ignore_misaligned=True,
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))
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if len(train_examples) == 0:
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raise ValueError(Errors.E988)
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random.shuffle(train_examples)
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batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"])
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for batch in batches:
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yield batch
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def create_evaluation_callback(nlp, optimizer, corpus, cfg):
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def evaluate():
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dev_examples = list(
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corpus.dev_dataset(
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nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True
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)
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)
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n_words = sum(len(ex.doc) for ex in dev_examples)
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start_time = timer()
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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scorer = nlp.evaluate(dev_examples, batch_size=32)
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else:
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scorer = nlp.evaluate(dev_examples, batch_size=32)
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end_time = timer()
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wps = n_words / (end_time - start_time)
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scores = scorer.scores
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# Calculate a weighted sum based on score_weights for the main score
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weights = cfg["score_weights"]
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weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
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scores["speed"] = wps
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return weighted_score, scores
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return evaluate
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def train_while_improving(
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nlp, optimizer, train_data, evaluate, dropout, patience, eval_frequency,
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accumulate_gradient
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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, which should
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Positional arguments:
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nlp: The spaCy pipeline to evaluate.
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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 zipped sequence of Tuple[Doc, GoldParse] pairs.
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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 form the last evaluation.
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other_scores: : The other scores from the last evaluation.
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loss: 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 = thinc.schedules.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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to_enable = [name for name, proc in nlp.pipeline if hasattr(proc, "model")]
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for step, batch in enumerate(train_data):
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dropout = next(dropouts)
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with nlp.select_pipes(enable=to_enable):
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for subbatch in subdivide_batch(batch, accumulate_gradient):
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nlp.update(subbatch, drop=dropout, losses=losses, sgd=False)
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for name, proc in nlp.pipeline:
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if hasattr(proc, "model"):
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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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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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info = {
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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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}
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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
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best_score, best_step = max(results)
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if (step - best_step) >= patience:
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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.doc))
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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 setup_printer(training, nlp):
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score_cols = training["scores"]
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score_widths = [max(len(col), 6) for col in score_cols]
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loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names]
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loss_widths = [max(len(col), 8) for col in loss_cols]
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table_header = ["#"] + loss_cols + score_cols + ["Score"]
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table_header = [col.upper() for col in table_header]
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table_widths = [6] + loss_widths + score_widths + [6]
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table_aligns = ["r" for _ in table_widths]
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msg.row(table_header, widths=table_widths)
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msg.row(["-" * width for width in table_widths])
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def print_row(info):
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losses = [
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"{0:.2f}".format(float(info["losses"].get(pipe_name, 0.0)))
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for pipe_name in nlp.pipe_names
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]
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scores = [
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"{0:.2f}".format(float(info["other_scores"].get(col, 0.0))) for col in score_cols
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]
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data = [info["step"]] + losses + scores + ["{0:.2f}".format(float(info["score"]))]
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msg.row(data, widths=table_widths, aligns=table_aligns)
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return print_row
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