from typing import Optional, List, Tuple from enum import Enum from pathlib import Path from wasabi import Printer, diff_strings from thinc.api import Config import srsly import re from .. import util from ..language import DEFAULT_CONFIG_PRETRAIN_PATH from ..schemas import RecommendationSchema from ._util import init_cli, Arg, Opt, show_validation_error, COMMAND ROOT = Path(__file__).parent / "templates" TEMPLATE_PATH = ROOT / "quickstart_training.jinja" RECOMMENDATIONS = srsly.read_yaml(ROOT / "quickstart_training_recommendations.yml") class Optimizations(str, Enum): efficiency = "efficiency" accuracy = "accuracy" @init_cli.command("config") def init_config_cli( # fmt: off output_file: Path = Arg(..., help="File to save config.cfg to or - for stdout (will only output config and no additional logging info)", allow_dash=True), lang: Optional[str] = Opt("en", "--lang", "-l", help="Two-letter code of the language to use"), pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include in the model (without 'tok2vec' or 'transformer')"), optimize: Optimizations = Opt(Optimizations.efficiency.value, "--optimize", "-o", help="Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters."), cpu: bool = Opt(False, "--cpu", "-C", help="Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters."), # fmt: on ): """ Generate a starter config.cfg for training. Based on your requirements specified via the CLI arguments, this command generates a config with the optimal settings for you use case. This includes the choice of architecture, pretrained weights and related hyperparameters. """ if isinstance(optimize, Optimizations): # instance of enum from the CLI optimize = optimize.value pipeline = [p.strip() for p in pipeline.split(",")] init_config(output_file, lang=lang, pipeline=pipeline, optimize=optimize, cpu=cpu) @init_cli.command("fill-config") def init_fill_config_cli( # fmt: off base_path: Path = Arg(..., help="Base config to fill", exists=True, dir_okay=False), output_file: Path = Arg("-", help="File to save config.cfg to (or - for stdout)", allow_dash=True), pretraining: bool = Opt(False, "--pretraining", "-p", help="Include config for pretraining (with 'spacy pretrain')"), diff: bool = Opt(False, "--diff", "-D", help="Print a visual diff highlighting the changes") # fmt: on ): """ Fill partial config.cfg with default values. Will add all missing settings from the default config and will create all objects, check the registered functions for their default values and update the base config. This command can be used with a config generated via the training quickstart widget: https://nightly.spacy.io/usage/training#quickstart """ fill_config(output_file, base_path, pretraining=pretraining, diff=diff) def fill_config( output_file: Path, base_path: Path, *, pretraining: bool = False, diff: bool = False, silent: bool = False, ) -> Tuple[Config, Config]: is_stdout = str(output_file) == "-" no_print = is_stdout or silent msg = Printer(no_print=no_print) with show_validation_error(hint_fill=False): config = util.load_config(base_path) nlp, _ = util.load_model_from_config(config, auto_fill=True, validate=False) # Load a second time with validation to be extra sure that the produced # config result is a valid config nlp, _ = util.load_model_from_config(nlp.config) filled = nlp.config if pretraining: validate_config_for_pretrain(filled, msg) pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH) filled = pretrain_config.merge(filled) before = config.to_str() after = filled.to_str() if before == after: msg.warn("Nothing to auto-fill: base config is already complete") else: msg.good("Auto-filled config with all values") if diff and not no_print: if before == after: msg.warn("No diff to show: nothing was auto-filled") else: msg.divider("START CONFIG DIFF") print("") print(diff_strings(before, after)) msg.divider("END CONFIG DIFF") print("") save_config(filled, output_file, is_stdout=is_stdout, silent=silent) return config, filled def init_config( output_file: Path, *, lang: str, pipeline: List[str], optimize: str, cpu: bool ) -> None: is_stdout = str(output_file) == "-" msg = Printer(no_print=is_stdout) try: from jinja2 import Template except ImportError: msg.fail("This command requires jinja2", "pip install jinja2", exits=1) with TEMPLATE_PATH.open("r") as f: template = Template(f.read()) # Filter out duplicates since tok2vec and transformer are added by template pipeline = [pipe for pipe in pipeline if pipe not in ("tok2vec", "transformer")] reco = RecommendationSchema(**RECOMMENDATIONS.get(lang, {})).dict() variables = { "lang": lang, "components": pipeline, "optimize": optimize, "hardware": "cpu" if cpu else "gpu", "transformer_data": reco["transformer"], "word_vectors": reco["word_vectors"], "has_letters": reco["has_letters"], } if variables["transformer_data"] and not has_spacy_transformers(): msg.warn( "To generate a more effective transformer-based config (GPU-only), " "install the spacy-transformers package and re-run this command. " "The config generated now does not use transformers." ) variables["transformer_data"] = None base_template = template.render(variables).strip() # Giving up on getting the newlines right in jinja for now base_template = re.sub(r"\n\n\n+", "\n\n", base_template) # Access variables declared in templates template_vars = template.make_module(variables) use_case = { "Language": lang, "Pipeline": ", ".join(pipeline), "Optimize for": optimize, "Hardware": variables["hardware"].upper(), "Transformer": template_vars.transformer.get("name", False), } msg.info("Generated template specific for your use case") for label, value in use_case.items(): msg.text(f"- {label}: {value}") with show_validation_error(hint_fill=False): config = util.load_config_from_str(base_template) nlp, _ = util.load_model_from_config(config, auto_fill=True) msg.good("Auto-filled config with all values") save_config(nlp.config, output_file, is_stdout=is_stdout) def save_config( config: Config, output_file: Path, is_stdout: bool = False, silent: bool = False ) -> None: no_print = is_stdout or silent msg = Printer(no_print=no_print) if is_stdout: print(config.to_str()) else: if not output_file.parent.exists(): output_file.parent.mkdir(parents=True) config.to_disk(output_file, interpolate=False) msg.good("Saved config", output_file) msg.text("You can now add your data and train your model:") variables = ["--paths.train ./train.spacy", "--paths.dev ./dev.spacy"] if not no_print: print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}") def has_spacy_transformers() -> bool: try: import spacy_transformers # noqa: F401 return True except ImportError: return False def validate_config_for_pretrain(config: Config, msg: Printer) -> None: if "tok2vec" not in config["nlp"]["pipeline"]: msg.warn( "No tok2vec component found in the pipeline. If your tok2vec " "component has a different name, you may need to adjust the " "tok2vec_model reference in the [pretraining] block. If you don't " "have a tok2vec component, make sure to add it to your [components] " "and the pipeline specified in the [nlp] block, so you can pretrain " "weights for it." )