mirror of https://github.com/explosion/spaCy.git
107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
from typing import Optional, Dict, Any, Union, List
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from pathlib import Path
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from wasabi import msg, table
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from thinc.api import Config
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from thinc.config import VARIABLE_RE
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import typer
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from ._util import Arg, Opt, show_validation_error, parse_config_overrides
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from ._util import import_code, debug_cli
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from ..schemas import ConfigSchemaInit, ConfigSchemaTraining
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from ..util import registry
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from .. import util
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@debug_cli.command(
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"config",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def debug_config_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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show_funcs: bool = Opt(False, "--show-functions", "-F", help="Show an overview of all registered functions used in the config and where they come from (modules, files etc.)"),
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show_vars: bool = Opt(False, "--show-variables", "-V", help="Show an overview of all variables referenced in the config and their values. This will also reflect variables overwritten on the CLI.")
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# fmt: on
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):
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"""Debug a config file and show validation errors. The command will
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create all objects in the tree and validate them. Note that some config
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validation errors are blocking and will prevent the rest of the config from
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being resolved. This means that you may not see all validation errors at
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once and some issues are only shown once previous errors have been fixed.
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Similar as with the 'train' command, you can override settings from the config
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as command line options. For instance, --training.batch_size 128 overrides
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the value of "batch_size" in the block "[training]".
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DOCS: https://spacy.io/api/cli#debug-config
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"""
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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debug_config(
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config_path, overrides=overrides, show_funcs=show_funcs, show_vars=show_vars
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)
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def debug_config(
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config_path: Path,
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*,
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overrides: Dict[str, Any] = {},
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show_funcs: bool = False,
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show_vars: bool = False,
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):
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msg.divider("Config validation")
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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nlp = util.load_model_from_config(config)
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config = nlp.config.interpolate()
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msg.divider("Config validation for [initialize]")
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with show_validation_error(config_path):
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T = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
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msg.divider("Config validation for [training]")
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with show_validation_error(config_path):
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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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util.resolve_dot_names(config, dot_names)
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msg.good("Config is valid")
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if show_vars:
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variables = get_variables(config)
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msg.divider(f"Variables ({len(variables)})")
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head = ("Variable", "Value")
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msg.table(variables, header=head, divider=True, widths=(41, 34), spacing=2)
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if show_funcs:
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funcs = get_registered_funcs(config)
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msg.divider(f"Registered functions ({len(funcs)})")
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for func in funcs:
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func_data = {
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"Registry": f"@{func['registry']}",
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"Name": func["name"],
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"Module": func["module"],
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"File": f"{func['file']} (line {func['line_no']})",
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}
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msg.info(f"[{func['path']}]")
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print(table(func_data).strip())
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def get_registered_funcs(config: Config) -> List[Dict[str, Optional[Union[str, int]]]]:
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result = []
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for key, value in util.walk_dict(config):
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if not key[-1].startswith("@"):
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continue
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# We have a reference to a registered function
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reg_name = key[-1][1:]
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registry = getattr(util.registry, reg_name)
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path = ".".join(key[:-1])
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info = registry.find(value)
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result.append({"name": value, "registry": reg_name, "path": path, **info})
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return result
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def get_variables(config: Config) -> Dict[str, Any]:
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result = {}
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for variable in sorted(set(VARIABLE_RE.findall(config.to_str()))):
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path = variable[2:-1].replace(":", ".")
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value = util.dot_to_object(config, path)
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result[variable] = repr(value)
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return result
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