Merge branch 'develop' into feature/project-spacy-version

pyproject.toml

@@ -7,7 +7,7 @@ requires = [
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
     "thinc>=8.0.0a43,<8.0.0a50",
-    "blis>=0.4.0,<0.5.0",
+    "blis>=0.4.0,<0.8.0",
     "pytokenizations",
     "pathy"
 ]

requirements.txt

@@ -2,7 +2,7 @@
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
 thinc>=8.0.0a43,<8.0.0a50
-blis>=0.4.0,<0.5.0
+blis>=0.4.0,<0.8.0
 ml_datasets==0.2.0a0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.8.0,<1.1.0

setup.cfg

@@ -41,7 +41,7 @@ install_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     thinc>=8.0.0a43,<8.0.0a50
-    blis>=0.4.0,<0.5.0
+    blis>=0.4.0,<0.8.0
     wasabi>=0.8.0,<1.1.0
     srsly>=2.3.0,<3.0.0
     catalogue>=2.0.1,<2.1.0

spacy/about.py

@@ -1,6 +1,6 @@
 # fmt: off
 __title__ = "spacy-nightly"
-__version__ = "3.0.0a33"
+__version__ = "3.0.0a34"
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
 __projects__ = "https://github.com/explosion/projects"

spacy/errors.py

@@ -456,10 +456,14 @@ class Errors:
             "issue tracker: http://github.com/explosion/spaCy/issues")
 
     # TODO: fix numbering after merging develop into master
-    E092 = ("The sentence-per-line IOB/IOB2 file is not formatted correctly. "
+    E901 = ("Failed to remove existing output directory: {path}. If your "
+            "config and the components you train change between runs, a "
+            "non-empty output directory can lead to stale pipeline data. To "
+            "solve this, remove the existing directories in the output directory.")
+    E902 = ("The sentence-per-line IOB/IOB2 file is not formatted correctly. "
             "Try checking whitespace and delimiters. See "
             "https://nightly.spacy.io/api/cli#convert")
-    E093 = ("The token-per-line NER file is not formatted correctly. Try checking "
+    E903 = ("The token-per-line NER file is not formatted correctly. Try checking "
             "whitespace and delimiters. See https://nightly.spacy.io/api/cli#convert")
     E904 = ("Cannot initialize StaticVectors layer: nO dimension unset. This "
             "dimension refers to the output width, after the linear projection "

spacy/lookups.py

@@ -289,7 +289,6 @@ class Lookups:
 
         DOCS: https://nightly.spacy.io/api/lookups#to_disk
         """
-        if len(self._tables):
         path = ensure_path(path)
         if not path.exists():
             path.mkdir()

spacy/pipeline/morphologizer.pyx

@@ -210,7 +210,7 @@ class Morphologizer(Tagger):
 
         examples (Iterable[Examples]): The batch of examples.
         scores: Scores representing the model's predictions.
-        RETUTNRS (Tuple[float, float]): The loss and the gradient.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
 
         DOCS: https://nightly.spacy.io/api/morphologizer#get_loss
         """

spacy/pipeline/pipe.pyx

@@ -162,7 +162,7 @@ cdef class Pipe:
 
         examples (Iterable[Examples]): The batch of examples.
         scores: Scores representing the model's predictions.
-        RETUTNRS (Tuple[float, float]): The loss and the gradient.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
 
         DOCS: https://nightly.spacy.io/api/pipe#get_loss
         """

spacy/pipeline/senter.pyx

@@ -104,7 +104,7 @@ class SentenceRecognizer(Tagger):
 
         examples (Iterable[Examples]): The batch of examples.
         scores: Scores representing the model's predictions.
-        RETUTNRS (Tuple[float, float]): The loss and the gradient.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
 
         DOCS: https://nightly.spacy.io/api/sentencerecognizer#get_loss
         """

spacy/pipeline/tagger.pyx

@@ -249,7 +249,7 @@ class Tagger(Pipe):
 
         examples (Iterable[Examples]): The batch of examples.
         scores: Scores representing the model's predictions.
-        RETUTNRS (Tuple[float, float]): The loss and the gradient.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
 
         DOCS: https://nightly.spacy.io/api/tagger#get_loss
         """

spacy/pipeline/textcat.py

@@ -281,7 +281,7 @@ class TextCategorizer(Pipe):
 
         examples (Iterable[Examples]): The batch of examples.
         scores: Scores representing the model's predictions.
-        RETUTNRS (Tuple[float, float]): The loss and the gradient.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
 
         DOCS: https://nightly.spacy.io/api/textcategorizer#get_loss
         """

spacy/training/augment.py

@@ -5,7 +5,7 @@ import copy
 from functools import partial
 from pydantic import BaseModel, StrictStr
 
-from ..util import registry, logger
+from ..util import registry
 from ..tokens import Doc
 from .example import Example
 
@@ -119,9 +119,8 @@ def make_orth_variants(
     orig_token_dict = copy.deepcopy(token_dict)
     ndsv = orth_variants.get("single", [])
     ndpv = orth_variants.get("paired", [])
-    logger.debug(f"Data augmentation: {len(ndsv)} single / {len(ndpv)} paired variants")
+    words = token_dict.get("ORTH", [])
-    words = token_dict.get("words", [])
+    tags = token_dict.get("TAG", [])
-    tags = token_dict.get("tags", [])
     # keep unmodified if words or tags are not defined
     if words and tags:
         if lower:
@@ -154,8 +153,8 @@ def make_orth_variants(
                             if words[word_idx] in pair:
                                 pair_idx = pair.index(words[word_idx])
                     words[word_idx] = punct_choices[punct_idx][pair_idx]
-        token_dict["words"] = words
+        token_dict["ORTH"] = words
-        token_dict["tags"] = tags
+        token_dict["TAG"] = tags
     # modify raw
     if raw is not None:
         variants = []

spacy/training/converters/conll_ner_to_docs.py

@@ -103,7 +103,7 @@ def conll_ner_to_docs(
             lines = [line.strip() for line in conll_sent.split("\n") if line.strip()]
             cols = list(zip(*[line.split() for line in lines]))
             if len(cols) < 2:
-                raise ValueError(Errors.E093)
+                raise ValueError(Errors.E903)
             length = len(cols[0])
             words.extend(cols[0])
             sent_starts.extend([True] + [False] * (length - 1))

spacy/training/converters/iob_to_docs.py

@@ -46,7 +46,7 @@ def read_iob(raw_sents, vocab, n_sents):
                 sent_words, sent_iob = zip(*sent_tokens)
                 sent_tags = ["-"] * len(sent_words)
             else:
-                raise ValueError(Errors.E092)
+                raise ValueError(Errors.E902)
             words.extend(sent_words)
             tags.extend(sent_tags)
             iob.extend(sent_iob)

spacy/training/loop.py

@@ -3,19 +3,24 @@ from typing import Optional, TYPE_CHECKING
 from pathlib import Path
 from timeit import default_timer as timer
 from thinc.api import Optimizer, Config, constant, fix_random_seed, set_gpu_allocator
+from wasabi import Printer
 import random
-import wasabi
 import sys
+import shutil
 
 from .example import Example
 from ..schemas import ConfigSchemaTraining
 from ..errors import Errors
-from ..util import resolve_dot_names, registry
+from ..util import resolve_dot_names, registry, logger
 
 if TYPE_CHECKING:
     from ..language import Language  # noqa: F401
 
 
+DIR_MODEL_BEST = "model-best"
+DIR_MODEL_LAST = "model-last"
+
+
 def train(
     nlp: "Language",
     output_path: Optional[Path] = None,
@@ -38,7 +43,7 @@ def train(
     RETURNS (Path / None): The path to the final exported model.
     """
     # We use no_print here so we can respect the stdout/stderr options.
-    msg = wasabi.Printer(no_print=True)
+    msg = Printer(no_print=True)
     # Create iterator, which yields out info after each optimization step.
     config = nlp.config.interpolate()
     if config["training"]["seed"] is not None:
@@ -69,6 +74,7 @@ def train(
         eval_frequency=T["eval_frequency"],
         exclude=frozen_components,
     )
+    clean_output_dir(output_path)
     stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n")
     if frozen_components:
         stdout.write(msg.info(f"Frozen components: {frozen_components}") + "\n")
@@ -83,7 +89,7 @@ def train(
                     update_meta(T, nlp, info)
                 with nlp.use_params(optimizer.averages):
                     nlp = before_to_disk(nlp)
-                    nlp.to_disk(output_path / "model-best")
+                    nlp.to_disk(output_path / DIR_MODEL_BEST)
     except Exception as e:
         if output_path is not None:
             # We don't want to swallow the traceback if we don't have a
@@ -100,7 +106,7 @@ def train(
     finally:
         finalize_logger()
         if output_path is not None:
-            final_model_path = output_path / "model-last"
+            final_model_path = output_path / DIR_MODEL_LAST
             if optimizer.averages:
                 with nlp.use_params(optimizer.averages):
                     nlp.to_disk(final_model_path)
@@ -305,3 +311,19 @@ def create_before_to_disk_callback(
         return modified_nlp
 
     return before_to_disk
+
+
+def clean_output_dir(path: Union[str, Path]) -> None:
+    """Remove an existing output directory. Typically used to ensure that that
+    a directory like model-best and its contents aren't just being overwritten
+    by nlp.to_disk, which could preserve existing subdirectories (e.g.
+    components that don't exist anymore).
+    """
+    if path is not None and path.exists():
+        for subdir in [path / DIR_MODEL_BEST, path / DIR_MODEL_LAST]:
+            if subdir.exists():
+                try:
+                    shutil.rmtree(str(subdir))
+                    logger.debug(f"Removed existing output directory: {subdir}")
+                except Exception as e:
+                    raise IOError(Errors.E901.format(path=path)) from e

spacy/vocab.pyx

@@ -445,9 +445,9 @@ cdef class Vocab:
         setters = ["strings", "vectors"]
         if "strings" not in exclude:
             self.strings.to_disk(path / "strings.json")
-        if "vectors" not in "exclude" and self.vectors is not None:
+        if "vectors" not in "exclude":
             self.vectors.to_disk(path)
-        if "lookups" not in "exclude" and self.lookups is not None:
+        if "lookups" not in "exclude":
             self.lookups.to_disk(path)
 
     def from_disk(self, path, *, exclude=tuple()):

website/src/styles/quickstart.module.sass

@@ -38,7 +38,7 @@
     cursor: pointer
     display: inline-block
     padding: 0.35rem 0.5rem 0.25rem 0
-    margin: 0 1rem 0.75rem 0
+    margin: 0 1rem 0.5rem 0
     font-size: var(--font-size-xs)
     font-weight: bold
 
@@ -73,16 +73,19 @@
         background: var(--color-theme)
 
     .checkbox + &:before
+        $size: 18px
         content: ""
         display: inline-block
-        width: 20px
+        width: $size
-        height: 20px
+        height: $size
         border: 1px solid var(--color-subtle)
         vertical-align: middle
         margin-right: 0.5rem
         cursor: pointer
-        border-radius: var(--border-radius)
+        border-radius: $size / 4
         background: var(--color-back)
+        position: relative
+        top: -1px
 
     .checkbox:checked + &:before
         // Embed "check" icon here for simplicity

website/src/widgets/quickstart-install.js

@@ -4,6 +4,8 @@ import { StaticQuery, graphql } from 'gatsby'
 import { Quickstart, QS } from '../components/quickstart'
 import { repo } from '../components/util'
 
+const DEFAULT_MODELS = ['en']
+const DEFAULT_OPT = 'efficiency'
 const DEFAULT_HARDWARE = 'cpu'
 const DEFAULT_CUDA = 'cuda100'
 const CUDA = {
@@ -68,9 +70,13 @@ const QuickstartInstall = ({ id, title }) => {
     const [train, setTrain] = useState(false)
     const [hardware, setHardware] = useState(DEFAULT_HARDWARE)
     const [cuda, setCuda] = useState(DEFAULT_CUDA)
+    const [selectedModels, setModels] = useState(DEFAULT_MODELS)
+    const [efficiency, setEfficiency] = useState(DEFAULT_OPT === 'efficiency')
     const setters = {
         hardware: v => (Array.isArray(v) ? setHardware(v[0]) : setCuda(v)),
         config: v => setTrain(v.includes('train')),
+        models: setModels,
+        optimize: v => setEfficiency(v.includes('efficiency')),
     }
     const showDropdown = {
         hardware: () => hardware === 'gpu',
@@ -89,13 +95,37 @@ const QuickstartInstall = ({ id, title }) => {
                     ...DATA,
                     {
                         id: 'models',
-                        title: 'Trained Pipelines',
+                        title: 'Trained pipelines',
                         multiple: true,
                         options: models
                             .sort((a, b) => a.name.localeCompare(b.name))
-                            .map(({ code, name }) => ({ id: code, title: name })),
+                            .map(({ code, name }) => ({
+                                id: code,
+                                title: name,
+                                checked: DEFAULT_MODELS.includes(code),
+                            })),
                     },
                 ]
+                if (selectedModels.length) {
+                    data.push({
+                        id: 'optimize',
+                        title: 'Select pipeline for',
+                        options: [
+                            {
+                                id: 'efficiency',
+                                title: 'efficiency',
+                                checked: DEFAULT_OPT === 'efficiency',
+                                help: 'Faster and smaller pipeline, but less accurate',
+                            },
+                            {
+                                id: 'accuracy',
+                                title: 'accuracy',
+                                checked: DEFAULT_OPT === 'accuracy',
+                                help: 'Larger and slower pipeline, but more accurate',
+                            },
+                        ],
+                    })
+                }
                 return (
                     <Quickstart
                         data={data}
@@ -149,11 +179,14 @@ const QuickstartInstall = ({ id, title }) => {
                             conda install -c conda-forge spacy-lookups-data
                         </QS>
 
-                        {models.map(({ code, models: modelOptions }) => (
+                        {models.map(({ code, models: modelOptions }) => {
+                            const pkg = modelOptions[efficiency ? 0 : modelOptions.length - 1]
+                            return (
                                 <QS models={code} key={code}>
-                                python -m spacy download {modelOptions[0]}
+                                    python -m spacy download {pkg}
                                 </QS>
-                        ))}
+                            )
+                        })}
                     </Quickstart>
                 )
             }}

website/src/widgets/quickstart-models.js

@@ -31,25 +31,33 @@ const data = [
     },
     {
         id: 'optimize',
-        title: 'Optimize for',
+        title: 'Select for',
-        help:
-            'Optimize for efficiency (faster & smaller model) or higher accuracy (larger & slower model)',
         options: [
-            { id: 'efficiency', title: 'efficiency', checked: DEFAULT_OPT === 'efficiency' },
+            {
-            { id: 'accuracy', title: 'accuracy', checked: DEFAULT_OPT === 'accuracy' },
+                id: 'efficiency',
+                title: 'efficiency',
+                checked: DEFAULT_OPT === 'efficiency',
+                help: 'Faster and smaller pipeline, but less accurate',
+            },
+            {
+                id: 'accuracy',
+                title: 'accuracy',
+                checked: DEFAULT_OPT === 'accuracy',
+                help: 'Larger and slower pipeline, but more accurate',
+            },
         ],
     },
     {
         id: 'config',
         title: 'Options',
         multiple: true,
-        options: [{ id: 'example', title: 'Show usage example' }],
+        options: [{ id: 'example', title: 'Show text example' }],
     },
 ]
 
 const QuickstartInstall = ({ id, title, description, children }) => {
     const [lang, setLang] = useState(DEFAULT_LANG)
-    const [efficiency, setEfficiency] = useState(DEFAULT_OPT)
+    const [efficiency, setEfficiency] = useState(DEFAULT_OPT === 'efficiency')
     const setters = {
         lang: setLang,
         optimize: v => setEfficiency(v.includes('efficiency')),