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Merge pull request #5916 from explosion/feature/new-thinc-config

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Ines Montani 2020-08-16 15:24:12 +02:00 committed by GitHub
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3
MANIFEST.in
View File

@ -1,8 +1,9 @@
recursive-include include *.h
recursive-include spacy *.pyx *.pxd *.txt *.cfg
recursive-include spacy *.pyx *.pxd *.txt *.cfg *.jinja
include LICENSE
include README.md
include pyproject.toml
recursive-exclude spacy/lang *.json
recursive-include spacy/lang *.json.gz
recursive-include spacy/cli *.json
recursive-include licenses *

2
pyproject.toml
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@ -6,7 +6,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.0.0a23,<8.0.0a30",
"thinc>=8.0.0a27,<8.0.0a30",
"blis>=0.4.0,<0.5.0",
"pytokenizations",
"smart_open>=2.0.0,<3.0.0"

3
requirements.txt
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@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a23,<8.0.0a30
thinc>=8.0.0a27,<8.0.0a30
blis>=0.4.0,<0.5.0
ml_datasets>=0.1.1
murmurhash>=0.28.0,<1.1.0
@ -26,3 +26,4 @@ pytest>=4.6.5
pytest-timeout>=1.3.0,<2.0.0
mock>=2.0.0,<3.0.0
flake8>=3.5.0,<3.6.0
jinja2

4
setup.cfg
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@ -34,13 +34,13 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.0.0a23,<8.0.0a30
thinc>=8.0.0a27,<8.0.0a30
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a23,<8.0.0a30
thinc>=8.0.0a27,<8.0.0a30
blis>=0.4.0,<0.5.0
wasabi>=0.7.1,<1.1.0
srsly>=2.1.0,<3.0.0

2
spacy/cli/__init__.py
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@ -15,7 +15,7 @@ from .debug_model import debug_model # noqa: F401
from .evaluate import evaluate # noqa: F401
from .convert import convert # noqa: F401
from .init_model import init_model # noqa: F401
from .init_config import init_config # noqa: F401
from .init_config import init_config, fill_config # noqa: F401
from .validate import validate # noqa: F401
from .project.clone import project_clone # noqa: F401
from .project.assets import project_assets # noqa: F401

10
spacy/cli/_util.py
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@ -179,13 +179,13 @@ def show_validation_error(
file_path: Optional[Union[str, Path]] = None,
*,
title: str = "Config validation error",
hint_init: bool = True,
hint_fill: bool = True,
):
"""Helper to show custom config validation errors on the CLI.
file_path (str / Path): Optional file path of config file, used in hints.
title (str): Title of the custom formatted error.
hint_init (bool): Show hint about filling config.
hint_fill (bool): Show hint about filling config.
"""
try:
yield
@ -195,14 +195,14 @@ def show_validation_error(
# helper for this in Thinc
err_text = str(e).replace("Config validation error", "").strip()
print(err_text)
if hint_init and "field required" in err_text:
if hint_fill and "field required" in err_text:
config_path = file_path if file_path is not None else "config.cfg"
msg.text(
"If your config contains missing values, you can run the 'init "
"config' command to fill in all the defaults, if possible:",
"fill-config' command to fill in all the defaults, if possible:",
spaced=True,
)
print(f"{COMMAND} init config {config_path} --base {config_path}\n")
print(f"{COMMAND} init fill-config {config_path} --base {config_path}\n")
sys.exit(1)

5
spacy/cli/debug_data.py
View File

@ -5,7 +5,6 @@ import sys
import srsly
from wasabi import Printer, MESSAGES, msg, diff_strings
import typer
from thinc.api import Config
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli, get_sourced_components
@ -49,7 +48,7 @@ def debug_config_cli(
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
with show_validation_error(config_path):
config = Config().from_disk(config_path, overrides=overrides)
config = util.load_config(config_path, overrides=overrides)
try:
nlp, _ = util.load_model_from_config(config, auto_fill=auto_fill)
except ValueError as e:
@ -134,7 +133,7 @@ def debug_data(
if not config_path.exists():
msg.fail("Config file not found", config_path, exists=1)
with show_validation_error(config_path):
cfg = Config().from_disk(config_path, overrides=config_overrides)
cfg = util.load_config(config_path, overrides=config_overrides)
nlp, config = util.load_model_from_config(cfg)
# Use original config here, not resolved version
sourced_components = get_sourced_components(cfg)

7
spacy/cli/debug_model.py
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@ -1,7 +1,7 @@
from typing import Dict, Any, Optional
from pathlib import Path
from wasabi import msg
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam, Config
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
from thinc.api import Model, data_validation
import typer
@ -49,16 +49,15 @@ def debug_model_cli(
}
config_overrides = parse_config_overrides(ctx.args)
with show_validation_error(config_path):
cfg = Config().from_disk(config_path, overrides=config_overrides)
config = util.load_config(config_path, overrides=config_overrides)
try:
nlp, config = util.load_model_from_config(cfg)
nlp, config = util.load_model_from_config(config_path)
except ValueError as e:
msg.fail(str(e), exits=1)
seed = config["pretraining"]["seed"]
if seed is not None:
msg.info(f"Fixing random seed: {seed}")
fix_random_seed(seed)
pipe = nlp.get_pipe(component)
if hasattr(pipe, "model"):
model = pipe.model

230
spacy/cli/init_config.py
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@ -1,81 +1,185 @@
from typing import Optional, List
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
from wasabi import msg
from pydantic import BaseModel
import srsly
import re
from ..util import load_model_from_config, get_lang_class, load_model
from ._util import init_cli, Arg, Opt, show_validation_error
from .. import util
from ._util import init_cli, Arg, Opt, show_validation_error, COMMAND
TEMPLATE_ROOT = Path(__file__).parent / "templates"
TEMPLATE_PATH = TEMPLATE_ROOT / "quickstart_training.jinja"
RECOMMENDATIONS_PATH = TEMPLATE_ROOT / "quickstart_training_recommendations.json"
class Optimizations(str, Enum):
efficiency = "efficiency"
accuracy = "accuracy"
class RecommendationsTrfItem(BaseModel):
name: str
size_factor: int
class RecommendationsTrf(BaseModel):
efficiency: RecommendationsTrfItem
accuracy: RecommendationsTrfItem
class RecommendationSchema(BaseModel):
word_vectors: Optional[str] = None
transformer: Optional[RecommendationsTrf] = None
@init_cli.command("config")
def init_config_cli(
# fmt: off
output_path: Path = Arg("-", help="Output path or - for stdout", allow_dash=True),
base_path: Optional[Path] = Opt(None, "--base", "-b", help="Optional base config to fill", exists=True, dir_okay=False),
model: Optional[str] = Opt(None, "--model", "-m", help="Optional model to copy config from"),
lang: Optional[str] = Opt(None, "--lang", "-l", help="Optional language code for blank config"),
pipeline: Optional[str] = Opt(None, "--pipeline", "-p", help="Optional pipeline components to use")
output_file: Path = Arg("-", help="File to save config.cfg to (or - for stdout)", 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."""
validate_cli_args(base_path, model, lang)
is_stdout = str(output_path) == "-"
pipeline = [p.strip() for p in pipeline.split(",")] if pipeline else []
cfg = init_config(output_path, base_path, model, lang, pipeline, silent=is_stdout)
if is_stdout:
print(cfg.to_str())
"""
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),
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, diff=diff)
def fill_config(
output_file: Path, base_path: Path, *, diff: bool = False
) -> Tuple[Config, Config]:
is_stdout = str(output_file) == "-"
msg = Printer(no_print=is_stdout)
with show_validation_error(hint_fill=False):
with msg.loading("Auto-filling config..."):
config = util.load_config(base_path)
try:
nlp, _ = util.load_model_from_config(config, auto_fill=True)
except ValueError as e:
msg.fail(str(e), exits=1)
before = config.to_str()
after = nlp.config.to_str()
if before == after:
msg.warn("Nothing to auto-fill: base config is already complete")
else:
cfg.to_disk(output_path)
msg.good("Saved config", output_path)
msg.good("Auto-filled config with all values")
if diff and not is_stdout:
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(nlp.config, output_file, is_stdout=is_stdout)
def init_config(
output_path: Path,
config_path: Optional[Path],
model: Optional[str],
lang: Optional[str],
pipeline: Optional[List[str]],
silent: bool = False,
) -> Config:
if config_path is not None:
msg.info("Generating config from base config", show=not silent)
with show_validation_error(config_path, hint_init=False):
config = Config().from_disk(config_path)
try:
nlp, _ = load_model_from_config(config, auto_fill=True)
except ValueError as e:
msg.fail(str(e), exits=1)
return nlp.config
if model is not None:
ext = f" with pipeline {pipeline}" if pipeline else ""
msg.info(f"Generating config from model {model}{ext}", show=not silent)
nlp = load_model(model)
for existing_pipe_name in nlp.pipe_names:
if existing_pipe_name not in pipeline:
nlp.remove_pipe(existing_pipe_name)
for pipe_name in pipeline:
if pipe_name not in nlp.pipe_names:
nlp.add_pipe(pipe_name)
return nlp.config
if lang is not None:
ext = f" with pipeline {pipeline}" if pipeline else ""
msg.info(f"Generating config for language '{lang}'{ext}", show=not silent)
nlp = get_lang_class(lang)()
for pipe_name in pipeline:
nlp.add_pipe(pipe_name)
return nlp.config
def validate_cli_args(
config_path: Optional[Path], model: Optional[str], lang: Optional[str]
output_file: Path, *, lang: str, pipeline: List[str], optimize: str, cpu: bool
) -> None:
args = {"--base": config_path, "--model": model, "--lang": lang}
if sum(arg is not None for arg in args.values()) != 1:
existing = " ".join(f"{a} {v}" for a, v in args.items() if v is not 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)
recommendations = srsly.read_json(RECOMMENDATIONS_PATH)
lang_defaults = util.get_lang_class(lang).Defaults
has_letters = lang_defaults.writing_system.get("has_letters", True)
# 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()
with TEMPLATE_PATH.open("r") as f:
template = Template(f.read())
variables = {
"lang": lang,
"components": pipeline,
"optimize": optimize,
"hardware": "cpu" if cpu else "gpu",
"transformer_data": reco["transformer"],
"word_vectors": reco["word_vectors"],
"has_letters": has_letters,
}
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}")
use_transformer = bool(template_vars.use_transformer)
if use_transformer:
require_spacy_transformers(msg)
with show_validation_error(hint_fill=False):
config = util.load_config_from_str(base_template)
try:
nlp, _ = util.load_model_from_config(config, auto_fill=True)
except ValueError as e:
msg.fail(str(e), exits=1)
if use_transformer:
nlp.config.pop("pretraining", {}) # TODO: solve this better
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) -> None:
msg = Printer(no_print=is_stdout)
if is_stdout:
print(config.to_str())
else:
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"]
print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}")
def require_spacy_transformers(msg: Printer) -> None:
try:
import spacy_transformers # noqa: F401
except ImportError:
msg.fail(
"The init config command expects only one of the following arguments: "
"--base (base config to fill and update), --lang (language code to "
"use for blank config) or --model (base model to copy config from).",
f"Got: {existing if existing else 'no arguments'}",
"Using a transformer-based pipeline requires spacy-transformers "
"to be installed.",
exits=1,
)

4
spacy/cli/pretrain.py
View File

@ -5,7 +5,7 @@ import time
import re
from collections import Counter
from pathlib import Path
from thinc.api import use_pytorch_for_gpu_memory, require_gpu, Config
from thinc.api import use_pytorch_for_gpu_memory, require_gpu
from thinc.api import set_dropout_rate, to_categorical, fix_random_seed
from thinc.api import CosineDistance, L2Distance
from wasabi import msg
@ -88,7 +88,7 @@ def pretrain(
msg.info("Using CPU")
msg.info(f"Loading config from: {config_path}")
with show_validation_error(config_path):
config = Config().from_disk(config_path, overrides=config_overrides)
config = util.load_config(config_path, overrides=config_overrides)
nlp, config = util.load_model_from_config(config)
# TODO: validate that [pretraining] block exists
if not output_dir.exists():

237
spacy/cli/templates/quickstart_training.jinja Normal file
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@ -0,0 +1,237 @@
{# This is a template for training configs used for the quickstart widget in
the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = (transformer_data and hardware != "cpu") -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
[paths]
train = ""
dev = ""
[system]
use_pytorch_for_gpu_memory = {{ "true" if use_transformer else "false" }}
[nlp]
lang = "{{ lang }}"
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components %}
pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
[components]
{# TRANSFORMER PIPELINE #}
{%- if use_transformer -%}
[components.transformer]
factory = "transformer"
[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v1"
name = "{{ transformer["name"] }}"
tokenizer_config = {"use_fast": true}
[components.transformer.model.get_spans]
@span_getters = "strided_spans.v1"
window = 128
stride = 96
{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy-transformers.Tok2VecListener.v1"
grad_factor = 1.0
[components.tagger.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy-transformers.Tok2VecListener.v1"
grad_factor = 1.0
[components.parser.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "ner" in components -%}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy-transformers.Tok2VecListener.v1"
grad_factor = 1.0
[components.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{# NON-TRANSFORMER PIPELINE #}
{% else -%}
{%- if hardware == "gpu" -%}
# There are no recommended transformer weights available for language '{{ lang }}'
# yet, so the pipeline described here is not transformer-based.
{%- endif %}
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode:width}
rows = {{ 2000 if optimize == "efficiency" else 7000 }}
also_embed_subwords = {{ true if has_letters else false }}
also_use_static_vectors = {{ true if optimize == "accuracy" else false }}
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = {{ 96 if optimize == "efficiency" else 256 }}
depth = {{ 4 if optimize == "efficiency" else 8 }}
window_size = 1
maxout_pieces = 3
{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{%- endif %}
{% if "ner" in components %}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{% endif %}
{% endif %}
{% for pipe in components %}
{% if pipe not in ["tagger", "parser", "ner"] %}
{# Other components defined by the user: we just assume they're factories #}
[components.{{ pipe }}]
factory = "{{ pipe }}"
{% endif %}
{% endfor %}
[training]
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}
{% if use_transformer -%}
accumulate_gradient = {{ transformer["size_factor"] }}
{% endif %}
[training.optimizer]
@optimizers = "Adam.v1"
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 5e-5
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
max_length = {{ 500 if hardware == "gpu" else 0 }}
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
max_length = 0
{% if use_transformer %}
[training.batcher]
@batchers = "batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
{%- else %}
[training.batcher]
@batchers = "batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
{% endif %}
[training.score_weights]
{%- if "tagger" in components %}
tag_acc = {{ (1.0 / components|length)|round(2) }}
{%- endif -%}
{%- if "parser" in components %}
dep_uas = 0.0
dep_las = {{ (1.0 / components|length)|round(2) }}
sents_f = 0.0
{%- endif %}
{%- if "ner" in components %}
ents_f = {{ (1.0 / components|length)|round(2) }}
ents_p = 0.0
ents_r = 0.0
{%- endif -%}

13
spacy/cli/templates/quickstart_training_recommendations.json Normal file
View File

@ -0,0 +1,13 @@
{
"en": {
"word_vectors": "en_vectors_web_lg",
"transformer": {
"efficiency": { "name": "roberta-base", "size_factor": 3 },
"accuracy": { "name": "roberta-base", "size_factor": 3 }
}
},
"de": {
"word_vectors": null,
"transformer": null
}
}

2
spacy/cli/train.py
View File

@ -75,7 +75,7 @@ def train(
msg.info("Using CPU")
msg.info(f"Loading config and nlp from: {config_path}")
with show_validation_error(config_path):
config = Config().from_disk(config_path, overrides=config_overrides)
config = util.load_config(config_path, overrides=config_overrides)
if config.get("training", {}).get("seed") is not None:
fix_random_seed(config["training"]["seed"])
# Use original config here before it's resolved to functions

62
spacy/language.py
View File

@ -21,7 +21,7 @@ from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
from .gold import Example, validate_examples
from .scorer import Scorer
from .util import create_default_optimizer, registry
from .util import SimpleFrozenDict, combine_score_weights
from .util import SimpleFrozenDict, combine_score_weights, CONFIG_SECTION_ORDER
from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .lang.punctuation import TOKENIZER_INFIXES
@ -36,7 +36,7 @@ from . import about
# This is the base config will all settings (training etc.)
DEFAULT_CONFIG_PATH = Path(__file__).parent / "default_config.cfg"
DEFAULT_CONFIG = Config().from_disk(DEFAULT_CONFIG_PATH)
DEFAULT_CONFIG = util.load_config(DEFAULT_CONFIG_PATH)
class BaseDefaults:
@ -45,7 +45,7 @@ class BaseDefaults:
Language.Defaults.
"""
config: Config = Config()
config: Config = Config(section_order=CONFIG_SECTION_ORDER)
tokenizer_exceptions: Dict[str, List[dict]] = BASE_EXCEPTIONS
prefixes: Optional[List[Union[str, Pattern]]] = TOKENIZER_PREFIXES
suffixes: Optional[List[Union[str, Pattern]]] = TOKENIZER_SUFFIXES
@ -134,7 +134,7 @@ class Language:
# of the rest.
util.registry._entry_point_factories.get_all()
self._config = util.deep_merge_configs(self.default_config, DEFAULT_CONFIG)
self._config = DEFAULT_CONFIG.merge(self.default_config)
self._meta = dict(meta)
self._path = None
self._optimizer = None
@ -167,9 +167,7 @@ class Language:
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_config = util.deep_merge_configs(
cls.Defaults.config, DEFAULT_CONFIG
)
cls.default_config = DEFAULT_CONFIG.merge(cls.Defaults.config)
cls.default_config["nlp"]["lang"] = cls.lang
@property
@ -532,6 +530,7 @@ class Language:
name: Optional[str] = None,
*,
config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> Callable[[Doc], Doc]:
"""Create a pipeline component. Mostly used internally. To create and
@ -542,6 +541,7 @@ class Language:
Defaults to factory name if not set.
config (Optional[Dict[str, Any]]): Config parameters to use for this
component. Will be merged with default config, if available.
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
@ -568,7 +568,7 @@ class Language:
# This is unideal, but the alternative would mean you always need to
# specify the full config settings, which is not really viable.
if pipe_meta.default_config:
config = util.deep_merge_configs(config, pipe_meta.default_config)
config = Config(pipe_meta.default_config).merge(config)
# We need to create a top-level key because Thinc doesn't allow resolving
# top-level references to registered functions. Also gives nicer errors.
# The name allows components to know their pipe name and use it in the
@ -582,12 +582,14 @@ class Language:
cfg = {factory_name: config}
# We're calling the internal _fill here to avoid constructing the
# registered functions twice
# TODO: customize validation to make it more readable / relate it to
# pipeline component and why it failed, explain default config
resolved, filled = registry.resolve(cfg, validate=validate)
filled = filled[factory_name]
filled = Config(filled[factory_name])
filled["factory"] = factory_name
filled.pop("@factories", None)
# Merge the final filled config with the raw config (including non-
# interpolated variables)
if raw_config:
filled = filled.merge(raw_config)
self._pipe_configs[name] = filled
return resolved[factory_name]
@ -613,7 +615,10 @@ class Language:
)
)
pipe = source.get_pipe(source_name)
pipe_config = util.copy_config(source.config["components"][source_name])
# Make sure the source config is interpolated so we don't end up with
# orphaned variables in our final config
source_config = source.config.interpolate()
pipe_config = util.copy_config(source_config["components"][source_name])
self._pipe_configs[name] = pipe_config
return pipe, pipe_config["factory"]
@ -628,6 +633,7 @@ class Language:
last: Optional[bool] = None,
source: Optional["Language"] = None,
config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> Callable[[Doc], Doc]:
"""Add a component to the processing pipeline. Valid components are
@ -649,6 +655,7 @@ class Language:
component from.
config (Optional[Dict[str, Any]]): Config parameters to use for this
component. Will be merged with default config, if available.
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
@ -678,7 +685,11 @@ class Language:
lang_code=self.lang,
)
pipe_component = self.create_pipe(
factory_name, name=name, config=config, validate=validate,
factory_name,
name=name,
config=config,
raw_config=raw_config,
validate=validate,
)
pipe_index = self._get_pipe_index(before, after, first, last)
self._pipe_meta[name] = self.get_factory_meta(factory_name)
@ -1379,7 +1390,9 @@ class Language:
DOCS: https://spacy.io/api/language#from_config
"""
if auto_fill:
config = util.deep_merge_configs(config, cls.default_config)
config = Config(
cls.default_config, section_order=CONFIG_SECTION_ORDER
).merge(config)
if "nlp" not in config:
raise ValueError(Errors.E985.format(config=config))
config_lang = config["nlp"]["lang"]
@ -1417,16 +1430,20 @@ class Language:
or lang_cls is not cls
):
raise ValueError(Errors.E943.format(value=type(lang_cls)))
# Note that we don't load vectors here, instead they get loaded explicitly
# inside stuff like the spacy train function. If we loaded them here,
# then we would load them twice at runtime: once when we make from config,
# and then again when we load from disk.
nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer)
if after_creation is not None:
nlp = after_creation(nlp)
if not isinstance(nlp, cls):
raise ValueError(Errors.E942.format(name="creation", value=type(nlp)))
# Note that we don't load vectors here, instead they get loaded explicitly
# inside stuff like the spacy train function. If we loaded them here,
# then we would load them twice at runtime: once when we make from config,
# and then again when we load from disk.
pipeline = config.get("components", {})
# To create the components we need to use the final interpolated config
# so all values are available (if component configs use variables).
# Later we replace the component config with the raw config again.
interpolated = filled.interpolate() if not filled.is_interpolated else filled
pipeline = interpolated.get("components", {})
# If components are loaded from a source (existing models), we cache
# them here so they're only loaded once
source_nlps = {}
@ -1435,6 +1452,7 @@ class Language:
opts = ", ".join(pipeline.keys())
raise ValueError(Errors.E956.format(name=pipe_name, opts=opts))
pipe_cfg = util.copy_config(pipeline[pipe_name])
raw_config = Config(filled["components"][pipe_name])
if pipe_name not in disable:
if "factory" not in pipe_cfg and "source" not in pipe_cfg:
err = Errors.E984.format(name=pipe_name, config=pipe_cfg)
@ -1444,7 +1462,11 @@ class Language:
# The pipe name (key in the config) here is the unique name
# of the component, not necessarily the factory
nlp.add_pipe(
factory, name=pipe_name, config=pipe_cfg, validate=validate,
factory,
name=pipe_name,
config=pipe_cfg,
validate=validate,
raw_config=raw_config,
)
else:
model = pipe_cfg["source"]

52
spacy/tests/serialize/test_serialize_config.py
View File

@ -4,7 +4,7 @@ import spacy
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.language import Language
from spacy.util import registry, deep_merge_configs, load_model_from_config
from spacy.util import registry, load_model_from_config
from spacy.ml.models import build_Tok2Vec_model, build_tb_parser_model
from spacy.ml.models import MultiHashEmbed, MaxoutWindowEncoder
@ -194,37 +194,6 @@ def test_serialize_parser():
assert upper.get_dim("nI") == 66
def test_deep_merge_configs():
config = {"a": "hello", "b": {"c": "d"}}
defaults = {"a": "world", "b": {"c": "e", "f": "g"}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 2
assert merged["a"] == "hello"
assert merged["b"] == {"c": "d", "f": "g"}
config = {"a": "hello", "b": {"@test": "x", "foo": 1}}
defaults = {"a": "world", "b": {"@test": "x", "foo": 100, "bar": 2}, "c": 100}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "x", "foo": 1, "bar": 2}
assert merged["c"] == 100
config = {"a": "hello", "b": {"@test": "x", "foo": 1}, "c": 100}
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "x", "foo": 1}
assert merged["c"] == 100
# Test that leaving out the factory just adds to existing
config = {"a": "hello", "b": {"foo": 1}, "c": 100}
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
merged = deep_merge_configs(config, defaults)
assert len(merged) == 3
assert merged["a"] == "hello"
assert merged["b"] == {"@test": "y", "foo": 1, "bar": 2}
assert merged["c"] == 100
def test_config_nlp_roundtrip():
"""Test that a config prduced by the nlp object passes training config
validation."""
@ -311,3 +280,22 @@ def test_config_overrides():
nlp = spacy.load(d)
assert isinstance(nlp, English)
assert nlp.pipe_names == ["tok2vec", "tagger"]
def test_config_interpolation():
config = Config().from_str(nlp_config_string, interpolate=False)
assert config["training"]["train_corpus"]["path"] == "${paths:train}"
interpolated = config.interpolate()
assert interpolated["training"]["train_corpus"]["path"] == ""
nlp = English.from_config(config)
assert nlp.config["training"]["train_corpus"]["path"] == "${paths:train}"
# Ensure that variables are preserved in nlp config
width = "${components.tok2vec.model:width}"
assert config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
assert nlp.config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
interpolated2 = nlp.config.interpolate()
assert interpolated2["training"]["train_corpus"]["path"] == ""
assert interpolated2["components"]["tagger"]["model"]["tok2vec"]["width"] == 342
nlp2 = English.from_config(interpolated)
assert nlp2.config["training"]["train_corpus"]["path"] == ""
assert nlp2.config["components"]["tagger"]["model"]["tok2vec"]["width"] == 342

22
spacy/tests/test_cli.py
View File

@ -1,11 +1,14 @@
import pytest
from spacy.gold import docs_to_json, biluo_tags_from_offsets
from spacy.gold.converters import iob2docs, conll_ner2docs, conllu2docs
from spacy.lang.en import English
from spacy.schemas import ProjectConfigSchema, validate
from spacy.cli.pretrain import make_docs
from spacy.cli.init_config import init_config, RECOMMENDATIONS_PATH
from spacy.cli.init_config import RecommendationSchema
from spacy.cli._util import validate_project_commands, parse_config_overrides
from spacy.util import get_lang_class
import srsly
def test_cli_converters_conllu2json():
@ -319,3 +322,20 @@ def test_parse_config_overrides(args, expected):
def test_parse_config_overrides_invalid(args):
with pytest.raises(SystemExit):
parse_config_overrides(args)
@pytest.mark.parametrize("lang", ["en", "nl"])
@pytest.mark.parametrize(
"pipeline", [["tagger", "parser", "ner"], [], ["ner", "textcat", "sentencizer"]]
)
@pytest.mark.parametrize("optimize", ["efficiency", "accuracy"])
def test_init_config(lang, pipeline, optimize):
# TODO: add more tests and also check for GPU with transformers
init_config("-", lang=lang, pipeline=pipeline, optimize=optimize, cpu=True)
def test_model_recommendations():
recommendations = srsly.read_json(RECOMMENDATIONS_PATH)
for lang, data in recommendations.items():
assert get_lang_class(lang)
assert RecommendationSchema(**data)

72
spacy/util.py
View File

@ -58,6 +58,12 @@ if TYPE_CHECKING:
OOV_RANK = numpy.iinfo(numpy.uint64).max
LEXEME_NORM_LANGS = ["da", "de", "el", "en", "id", "lb", "pt", "ru", "sr", "ta", "th"]
# Default order of sections in the config.cfg. Not all sections needs to exist,
# and additional sections are added at the end, in alphabetical order.
# fmt: off
CONFIG_SECTION_ORDER = ["paths", "variables", "system", "nlp", "components", "training", "pretraining"]
# fmt: on
logging.basicConfig()
logger = logging.getLogger("spacy")
@ -263,9 +269,7 @@ def load_model_from_path(
if not meta:
meta = get_model_meta(model_path)
config_path = model_path / "config.cfg"
if not config_path.exists() or not config_path.is_file():
raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
config = Config().from_disk(config_path, overrides=dict_to_dot(config))
config = load_config(config_path, overrides=dict_to_dot(config))
nlp, _ = load_model_from_config(config, vocab=vocab, disable=disable)
return nlp.from_disk(model_path, exclude=disable)
@ -316,6 +320,29 @@ def load_model_from_init_py(
)
def load_config(
path: Union[str, Path],
overrides: Dict[str, Any] = SimpleFrozenDict(),
interpolate: bool = False,
) -> Config:
"""Load a config file. Takes care of path validation and section order."""
config_path = ensure_path(path)
if not config_path.exists() or not config_path.is_file():
raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
return Config(section_order=CONFIG_SECTION_ORDER).from_disk(
config_path, overrides=overrides, interpolate=interpolate
)
def load_config_from_str(
text: str, overrides: Dict[str, Any] = SimpleFrozenDict(), interpolate: bool = False
):
"""Load a full config from a string."""
return Config(section_order=CONFIG_SECTION_ORDER).from_str(
text, overrides=overrides, interpolate=interpolate,
)
def get_installed_models() -> List[str]:
"""List all model packages currently installed in the environment.
@ -901,45 +928,6 @@ def copy_config(config: Union[Dict[str, Any], Config]) -> Config:
raise ValueError(Errors.E961.format(config=config)) from None
def deep_merge_configs(
config: Union[Dict[str, Any], Config], defaults: Union[Dict[str, Any], Config]
) -> Config:
"""Deep merge two configs, a base config and its defaults. Ignores
references to registered functions to avoid filling in
config (Dict[str, Any]): The config.
destination (Dict[str, Any]): The config defaults.
RETURNS (Dict[str, Any]): The merged config.
"""
config = copy_config(config)
merged = _deep_merge_configs(config, defaults)
return Config(merged)
def _deep_merge_configs(
config: Union[Dict[str, Any], Config], defaults: Union[Dict[str, Any], Config]
) -> Union[Dict[str, Any], Config]:
for key, value in defaults.items():
if isinstance(value, dict):
node = config.setdefault(key, {})
if not isinstance(node, dict):
continue
promises = [key for key in value if key.startswith("@")]
promise = promises[0] if promises else None
# We only update the block from defaults if it refers to the same
# registered function
if (
promise
and any(k.startswith("@") for k in node)
and (promise in node and node[promise] != value[promise])
):
continue
defaults = _deep_merge_configs(node, value)
elif key not in config:
config[key] = value
return config
def dot_to_dict(values: Dict[str, Any]) -> Dict[str, dict]:
"""Convert dot notation to a dict. For example: {"token.pos": True,
"token._.xyz": True} becomes {"token": {"pos": True, "_": {"xyz": True }}}.

75
website/docs/api/cli.md
View File

@ -101,39 +101,62 @@ files and model directories.
### init config {#init-config new="3"}
Initialize and export a [`config.cfg` file](/usage/training#config) for training
and update it with all default values, if possible. Config files used for
training should always be complete and not contain any hidden defaults or
missing values, so this command helps you create your final config. It takes
**one** of the following options:
- `--base`: Base **config** to auto-fill, e.g. created using the
[training quickstart](/usage/training#quickstart) widget.
- `--lang`: Base **language** code to use for blank config.
- `--model`: Base **model** to copy config from.
Initialize and save a [`config.cfg` file](/usage/training#config) using the
**recommended settings** for your use case. It works just like the
[quickstart widget](/usage/training#quickstart), only that it also auto-fills
all default values and exports a [training](/usage/training#config)-ready
config. The settings you specify will impact the suggested model architectures
and pipeline setup, as well as the hyperparameters. You can also adjust and
customize those settings in your config file later.
> ```bash
> ### with base config {wrap="true"}
> $ python -m spacy init config config.cfg --base base.cfg
> ```
>
> ```bash
> ### blank language {wrap="true"}
> $ python -m spacy init config config.cfg --lang en --pipeline tagger,parser
> ### Example {wrap="true"}
> $ python -m spacy init config config.cfg --lang en --pipeline ner,textcat --optimize accuracy
> ```
```bash
$ python -m spacy init config [output] [--base] [--lang] [--model] [--pipeline]
$ python -m spacy init config [output_file] [--lang] [--pipeline]
[--optimize] [--cpu]
```
| Argument | Type | Description |
| ------------------ | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `output` | positional | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. |
| `--base`, `-b` | option | Optional base config file to auto-fill with defaults. |
| `--lang`, `-l` | option | Optional language code to use for blank config. If a `--pipeline` is specified, the components will be added in order. |
| `--model`, `-m` | option | Optional base model to copy config from. If a `--pipeline` is specified, only those components will be kept, and all other components not in the model will be added. |
| `--pipeline`, `-p` | option | Optional comma-separated pipeline of components to add to blank language or model. |
| **CREATES** | config | Complete and auto-filled config file for training. |
| Argument | Type | Description |
| ------------------ | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `output_file` | positional | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. |
| `--lang`, `-l` | option | Optional code of the [language](/usage/models#languages) to use. Defaults to `"en"`. |
| `--pipeline`, `-p` | option | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include in the model. Defaults to `"tagger,parser,ner"`. |
| `--optimize`, `-o` | option | `"efficiency"` or `"accuracy"`. 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. Defaults to `"efficiency"`. |
| `--cpu`, `-C` | flag | Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | file | The config file for training. |
### init fill-config {#init-fill-config new="3"}
Auto-fill a partial [`config.cfg` file](/usage/training#config) file with **all
default values**, e.g. a config generated with the
[quickstart widget](/usage/training#quickstart). Config files used for training
should always be complete and not contain any hidden defaults or missing values,
so this command helps you create your final training config. In order to find
the available settings and defaults, all functions referenced in the config will
be created, and their signatures are used to find the defaults. If your config
contains a problem that can't be resolved automatically, spaCy will show you a
validation error with more details.
> ```bash
> ### Example {wrap="true"}
> $ python -m spacy init fill-config base.cfg config.cfg
> ```
```bash
$ python -m spacy init fill-config [base_path] [output_file] [--diff]
```
| Argument | Type | Description |
| -------------- | ---------- | ------------------------------------------------------------------------------------------------------------- |
| `base_path` | positional | Path to base config to fill, e.g. generated by the [quickstart widget](/usage/training#quickstart). |
| `output_file` | positional | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. |
| `--diff`, `-D` | flag | Print a visual diff highlighting the changes. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | file | Complete and auto-filled config file for training. |
### init model {#init-model new="2"}

5
website/docs/api/data-formats.md
View File

@ -20,8 +20,9 @@ Config files define the training process and model pipeline and can be passed to
[`spacy train`](/api/cli#train). They use
[Thinc's configuration system](https://thinc.ai/docs/usage-config) under the
hood. For details on how to use training configs, see the
[usage documentation](/usage/training#config). To get started with a blank
config or fill a partial config with all defaults, you can use the
[usage documentation](/usage/training#config). To get started with the
recommended settings for your use case, check out the
[quickstart widget](/usage/training#quickstart) or run the
[`init config`](/api/cli#init-config) command.
> #### What does the @ mean?

37
website/docs/usage/training.md
View File

@ -37,27 +37,37 @@ The recommended way to train your spaCy models is via the
single [`config.cfg`](#config) **configuration file** that includes all settings
and hyperparameters. You can optionally [overwritten](#config-overrides)
settings on the command line, and load in a Python file to register
[custom functions](#custom-code) and architectures.
[custom functions](#custom-code) and architectures. This quickstart widget helps
you generate a starter config with the **recommended settings** for your
specific use case. It's also available in spaCy as the
[`init config`](/api/cli#init-config) command.
> #### Instructions
> #### Instructions: widget
>
> 1. Select your requirements and settings.
> 2. Use the buttons at the bottom to save the result to your clipboard or a
> file `base_config.cfg`.
> 3. Run [`init config`](/api/cli#init-config) to create a full training config.
> 3. Run [`init fill-config`](/api/cli#init-fill-config) to create a full
> config.
> 4. Run [`train`](/api/cli#train) with your config and data.
>
> #### Instructions: CLI
>
> 1. Run the [`init config`](/api/cli#init-config) command and specify your
> requirements and settings as CLI arguments.
> 2. Run [`train`](/api/cli#train) with the exported config and data.
import QuickstartTraining from 'widgets/quickstart-training.js'
<QuickstartTraining download="base_config.cfg" />
After you've saved the starter config to a file `base_config.cfg`, you can use
the [`init config`](/api/cli#init-config) command to fill in the remaining
defaults. Training configs should always be **complete and without hidden
defaults**, to keep your experiments reproducible.
the [`init fill-config`](/api/cli#init-fill-config) command to fill in the
remaining defaults. Training configs should always be **complete and without
hidden defaults**, to keep your experiments reproducible.
```bash
$ python -m spacy init config config.cfg --base base_config.cfg
$ python -m spacy init fill-config base_config.cfg config.cfg
```
> #### Tip: Debug your data
@ -70,10 +80,13 @@ $ python -m spacy init config config.cfg --base base_config.cfg
> $ python -m spacy debug data config.cfg --verbose
> ```
You can now add your data and run [`train`](/api/cli#train) with your config.
See the [`convert`](/api/cli#convert) command for details on how to convert your
data to spaCy's binary `.spacy` format. You can either include the data paths in
the `[paths]` section of your config, or pass them in via the command line.
Instead of exporting your starter config from the quickstart widget and
auto-filling it, you can also use the [`init config`](/api/cli#init-config)
command and specify your requirement and settings and CLI arguments. You can now
add your data and run [`train`](/api/cli#train) with your config. See the
[`convert`](/api/cli#convert) command for details on how to convert your data to
spaCy's binary `.spacy` format. You can either include the data paths in the
`[paths]` section of your config, or pass them in via the command line.
```bash
$ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy
@ -601,7 +614,7 @@ settings in the block will be passed to the function as keyword arguments. Keep
in mind that the config shouldn't have any hidden defaults and all arguments on
the functions need to be represented in the config. If your function defines
**default argument values**, spaCy is able to auto-fill your config when you run
[`init config`](/api/cli#init-config).
[`init fill-config`](/api/cli#init-fill-config).
```ini
### config.cfg (excerpt)

5
website/docs/usage/transformers.md
View File

@ -163,8 +163,9 @@ resolved, the function is created and passed into the model as an argument.
Remember that the `config.cfg` used for training should contain **no missing
values** and requires all settings to be defined. You don't want any hidden
defaults creeping in and changing your results! spaCy will tell you if settings
are missing, and you can run [`spacy init config`](/api/cli#init-config) with to
automatically fill in all defaults.
are missing, and you can run
[`spacy init fill-config`](/api/cli#init-fill-config) to automatically fill in
all defaults.
</Infobox>

3
website/docs/usage/v3.md
View File

@ -152,7 +152,8 @@ The following methods, attributes and commands are new in spaCy v3.0.
| [`Language.config`](/api/language#config) | The [config](/usage/training#config) used to create the current `nlp` object. An instance of [`Config`](https://thinc.ai/docs/api-config#config) and can be saved to disk and used for training. |
| [`Pipe.score`](/api/pipe#score) | Method on trainable pipeline components that returns a dictionary of evaluation scores. |
| [`registry`](/api/top-level#registry) | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config). |
| [`init config`](/api/cli#init-config) | CLI command for initializing a [training config](/usage/training) file for a blank language or auto-filling a partial config. |
| [`init config`](/api/cli#init-config) | CLI command for initializing a [training config](/usage/training) file with the recommended settings. |
| [`init fill-config`](/api/cli#init-fill-config) | CLI command for auto-filling a partial config with all defaults and missing values. |
| [`debug config`](/api/cli#debug-config) | CLI command for debugging a [training config](/usage/training) file and showing validation errors. |
| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |

68
website/setup/jinja_to_js.py
View File

@ -1,4 +1,6 @@
# Forked from: https://github.com/jonbretman/jinja-to-js
# With additional functionality: in/not in, replace, pprint, round, + for lists,
# rendering empty dicts
# This script is mostly used to generate the JavaScript function for the
# training quicktart widget.
import contextlib
@ -315,7 +317,7 @@ class JinjaToJS(object):
if callable(handler):
handler(node, **kwargs)
else:
raise Exception("Unknown node %s" % node)
raise Exception(f"Unknown node {node} ({node_name})")
def _process_extends(self, node, **kwargs):
"""
@ -431,6 +433,13 @@ class JinjaToJS(object):
self.output.write(node.name)
def _process_dict(self, node, **kwargs):
with self._interpolation():
with self._python_bool_wrapper(**kwargs):
if node.items:
raise ValueError(f"Can't process non-empty dict in epxression: {node}")
self.output.write("{}")
def _process_getattr(self, node, **kwargs):
"""
Processes a `GetAttr` node. e.g. {{ foo.bar }}
@ -697,6 +706,27 @@ class JinjaToJS(object):
self._process_node(node.node, **new_kwargs)
self.output.write(")")
def _process_filter_replace(self, node, **kwargs):
# We're getting a quoted string from Python/Jinja as the pattern to
# replace, but to replace all occurrences in JS, we typically need a
# regex, which would be annoying to convert. So we're using split/join
# instead here.
with self._interpolation():
with self._python_bool_wrapper(**kwargs) as new_kwargs:
self._process_node(node.node, **new_kwargs)
self.output.write(".split(")
self._process_node(node.args[0], **new_kwargs)
self.output.write(").join(")
self._process_node(node.args[1], **new_kwargs)
self.output.write(")")
def _process_filter_pprint(self, node, **kwargs):
with self._interpolation():
with self._python_bool_wrapper(**kwargs) as new_kwargs:
self.output.write("JSON.stringify(")
self._process_node(node.node, **new_kwargs)
self.output.write(")")
def _process_filter_attr(self, node, **kwargs):
with self._interpolation():
with self._python_bool_wrapper(**kwargs) as new_kwargs:
@ -746,7 +776,10 @@ class JinjaToJS(object):
with self._python_bool_wrapper(**kwargs) as new_kwargs:
self.output.write("Math.round((")
self._process_node(node.node, **new_kwargs)
self.output.write("+ Number.EPSILON) * 100) / 100")
self.output.write("+ Number.EPSILON) * 10**")
self._process_node(node.args[0], **new_kwargs)
self.output.write(") / 10**")
self._process_node(node.args[0], **new_kwargs)
def _process_filter_last(self, node, **kwargs):
with self._interpolation():
@ -867,8 +900,10 @@ class JinjaToJS(object):
)
with option(kwargs, use_python_bool_wrapper=False):
if operand.op == "in":
if operand.op == "in" or operand.op == "notin":
# Special case for "in" operator
if operand.op == "notin":
self.output.write("!")
self._process_node(operand.expr, **kwargs)
self.output.write(".includes(")
self._process_node(node.expr, **kwargs)
@ -1027,7 +1062,18 @@ class JinjaToJS(object):
self.output.write(")")
def _process_add(self, node, **kwargs):
self._process_math(node, math_operator=" + ", **kwargs)
# Handle + operator for lists, which behaves differently in JS. Currently
# only works if we have an explicit list node on either side (in which
# case we assume both are lists).
if isinstance(node.left, nodes.List) or isinstance(node.right, nodes.List):
with self._interpolation():
with self._python_bool_wrapper(**kwargs) as new_kwargs:
self._process_node(node.left, **new_kwargs)
self.output.write(".concat(")
self._process_node(node.right, **new_kwargs)
self.output.write(")")
else:
self._process_math(node, math_operator=" + ", **kwargs)
def _process_sub(self, node, **kwargs):
self._process_math(node, math_operator=" - ", **kwargs)
@ -1190,16 +1236,22 @@ def main(
# fmt: off
template_path: Path = typer.Argument(..., exists=True, dir_okay=False, help="Path to .jinja file"),
output: Path = typer.Argument(None, help="Path to output module (stdout if unset)"),
data_path: Path = typer.Option(None, "--data", help="Optional JSON file with additional data to be included as DATA")
# fmt: on
):
"""Convert a jinja2 template to a JavaScript module."""
compiler = JinjaToJS(
template_path.parent, template_path.parts[-1], js_module_format="es6"
)
data = "{}"
if data_path is not None:
with data_path.open("r", encoding="utf8") as f:
data = json.dumps(json.loads(f.read())) # dump and load for compactness
tpl_file = template_path.parts[-1]
compiler = JinjaToJS(template_path.parent, tpl_file, js_module_format="es6")
header = f"// This file was auto-generated by {__file__} based on {tpl_file}"
data_str = f"export const DATA = {data}"
result = compiler.get_output()
if output is not None:
with output.open("w") as f:
f.write(result)
f.write(f"{header}\n{result}\n{data_str}")
print(f"Updated {output.parts[-1]}")
else:
print(result)

107
website/setup/quickstart_training_cpu.jinja
View File

@ -1,107 +0,0 @@
{# Template for "CPU" configs. The transformer will use a different template. #}
# This is an auto-generated partial config for training a model.
# To use it for training, auto-fill it with all default values.
# python -m spacy init config config.cfg --base base_config.cfg
[paths]
train = ""
dev = ""
[nlp]
lang = "{{ lang }}"
pipeline = {{ pipeline|safe }}
vectors = {{ ('"en_vectors_web_lg"' if optimize == "accuracy" else false)|safe }}
tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
[components]
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode:width}
rows = {{ 2000 if optimize == "efficiency" else 7000 }}
also_embed_subwords = {{ true if has_letters else false }}
also_use_static_vectors = {{ true if optimize == "accuracy" else false }}
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = {{ 96 if optimize == "efficiency" else 256 }}
depth = {{ 4 if optimize == "efficiency" else 8 }}
window_size = 1
maxout_pieces = 3
{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{%- endif %}
{% if "ner" in components -%}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
{% endif -%}
[training]
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
[training.score_weights]
{%- if "tagger" in components %}
tag_acc = {{ (1.0 / components|length)|round() }}
{%- endif -%}
{%- if "parser" in components %}
dep_uas = 0.0
dep_las = {{ (1.0 / components|length)|round() }}
sents_f = 0.0
{%- endif %}
{%- if "ner" in components %}
ents_f = {{ (1.0 / components|length)|round() }}
ents_p = 0.0
ents_r = 0.0
{%- endif -%}

139
website/setup/quickstart_training_gpu.jinja
View File

@ -1,139 +0,0 @@
{# Template for "CPU" configs. The transformer will use a different template. #}
# This is an auto-generated partial config for training a model.
# To use it for training, auto-fill it with all default values.
# python -m spacy init config config.cfg --base base_config.cfg
[paths]
train = ""
dev = ""
[nlp]
lang = "{{ lang }}"
pipeline = {{ pipeline|safe }}
vectors = null
tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
[components]
[components.transformer]
factory = "transformer"
[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v1"
{#- name = {{ transformer_info["name"] }} #}
name = "roberta-base"
tokenizer_config = {"use_fast": true}
[components.transformer.model.get_spans]
@span_getters = "strided_spans.v1"
window = 128
stride = 96
{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "ner" in components -%}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.parser.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
[training]
{#- accumulate_gradient = {{ transformer_info["size_factor"] }} #}
accumulate_gradient = 3
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 1e-8
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 5e-5
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
gold_preproc = false
max_length = 500
limit = 0
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
gold_preproc = false
max_length = 0
limit = 0
[training.batcher]
@batchers = "batch_by_padded.v1"
discard_oversize = true
batch_size = 2000
[training.score_weights]
{%- if "tagger" in components %}
tag_acc = {{ (1.0 / components|length)|round(2) }}
{%- endif -%}
{%- if "parser" in components %}
dep_uas = 0.0
dep_las = {{ (1.0 / components|length)|round(2) }}
sents_f = 0.0
{%- endif %}
{%- if "ner" in components %}
ents_f = {{ (1.0 / components|length)|round(2) }}
ents_p = 0.0
ents_r = 0.0
{%- endif -%}

2
website/setup/setup.sh
View File

@ -1 +1 @@
python jinja_to_js.py quickstart_training_cpu.jinja ../src/widgets/quickstart-training-generator.js
python jinja_to_js.py ../../spacy/cli/templates/quickstart_training.jinja ../src/widgets/quickstart-training-generator.js --data ../../spacy/cli/templates/quickstart_training_recommendations.json

4
website/src/styles/quickstart.module.sass
View File

@ -125,9 +125,9 @@
display: block
.small
font-size: var(--font-size-sm)
font-size: var(--font-size-code)
line-height: 1.65
white-space: pre
white-space: pre-wrap
max-height: 400px
overflow-y: auto

6
website/src/widgets/quickstart-training-generator.js
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File diff suppressed because one or more lines are too long

14
website/src/widgets/quickstart-training.js
View File

@ -2,14 +2,17 @@ import React, { useState } from 'react'
import { StaticQuery, graphql } from 'gatsby'
import highlightCode from 'gatsby-remark-prismjs/highlight-code.js'
import { Quickstart, QS } from '../components/quickstart'
import generator from './quickstart-training-generator'
import { Quickstart } from '../components/quickstart'
import generator, { DATA as GENERATOR_DATA } from './quickstart-training-generator'
import { isString, htmlToReact } from '../components/util'
const DEFAULT_LANG = 'en'
const DEFAULT_HARDWARE = 'gpu'
const DEFAULT_OPT = 'efficiency'
const COMPONENTS = ['tagger', 'parser', 'ner', 'textcat']
const COMMENT = `# This is an auto-generated partial config. To use it with 'spacy train'
# you can run spacy init fill-config to auto-fill all default settings:
# python -m spacy init fill-config ./base_config.cfg ./config.cfg`
const DATA = [
{
@ -61,14 +64,17 @@ export default function QuickstartTraining({ id, title, download = 'config.cfg'
hardware: setHardware,
optimize: setOptimize,
}
const reco = GENERATOR_DATA[lang] || {}
const content = generator({
lang,
pipeline: stringify(components),
components,
optimize,
hardware,
transformer_data: reco.transformer,
word_vectors: reco.word_vectors,
})
const rawContent = content.trim().replace(/\n\n\n+/g, '\n\n')
const rawStr = content.trim().replace(/\n\n\n+/g, '\n\n')
const rawContent = `${COMMENT}\n${rawStr}`
const displayContent = highlightCode('ini', rawContent)
.split('\n')
.map(line => (line.startsWith('#') ? `<span class="token comment">${line}</span>` : line))