explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-25 01:16:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

Support registered vectors (#12492)

Browse Source 
* Support registered vectors

* Format

* Auto-fill [nlp] on load from config and from bytes/disk

* Only auto-fill [nlp]

* Undo all changes to Language.from_disk

* Expand BaseVectors

These methods are needed in various places for training and vector
similarity.

* isort

* More linting

* Only fill [nlp.vectors]

* Update spacy/vocab.pyx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Revert changes to test related to auto-filling [nlp]

* Add vectors registry

* Rephrase error about vocab methods for vectors

* Switch to dummy implementation for BaseVectors.to_ops

* Add initial draft of docs

* Remove example from BaseVectors docs

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update website/docs/api/basevectors.mdx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Fix type and lint bpemb example

* Update website/docs/api/basevectors.mdx

---------

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
This commit is contained in:
Adriane Boyd 2023-08-01 15:46:08 +02:00 committed by GitHub
parent 9ffa5d8a15
commit 0fe43f40f1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
12 changed files with 425 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
spacy/default_config.cfg
View File

@ -26,6 +26,9 @@ batch_size = 1000
[nlp.tokenizer]
@tokenizers = "spacy.Tokenizer.v1"
[nlp.vectors]
@vectors = "spacy.Vectors.v1"
# The pipeline components and their models
[components]

2
spacy/errors.py
View File

@ -553,6 +553,8 @@ class Errors(metaclass=ErrorsWithCodes):
"during training, make sure to include it in 'annotating components'")
# New errors added in v3.x
E849 = ("The vocab only supports {method} for vectors of type "
"spacy.vectors.Vectors, not {vectors_type}.")
E850 = ("The PretrainVectors objective currently only supports default or "
"floret vectors, not {mode} vectors.")
E851 = ("The 'textcat' component labels should only have values of 0 or 1, "

18
spacy/language.py
View File

@ -65,6 +65,7 @@ from .util import (
registry,
warn_if_jupyter_cupy,
)
from .vectors import BaseVectors
from .vocab import Vocab, create_vocab
PipeCallable = Callable[[Doc], Doc]
@ -158,6 +159,7 @@ class Language:
max_length: int = 10**6,
meta: Dict[str, Any] = {},
create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
create_vectors: Optional[Callable[["Vocab"], BaseVectors]] = None,
batch_size: int = 1000,
**kwargs,
) -> None:
@ -198,6 +200,10 @@ class Language:
if vocab is True:
vectors_name = meta.get("vectors", {}).get("name")
vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name)
if not create_vectors:
vectors_cfg = {"vectors": self._config["nlp"]["vectors"]}
create_vectors = registry.resolve(vectors_cfg)["vectors"]
vocab.vectors = create_vectors(vocab)
else:
if (self.lang and vocab.lang) and (self.lang != vocab.lang):
raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
@ -1765,6 +1771,10 @@ class Language:
).merge(config)
if "nlp" not in config:
raise ValueError(Errors.E985.format(config=config))
# fill in [nlp.vectors] if not present (as a narrower alternative to
# auto-filling [nlp] from the default config)
if "vectors" not in config["nlp"]:
config["nlp"]["vectors"] = {"@vectors": "spacy.Vectors.v1"}
config_lang = config["nlp"].get("lang")
if config_lang is not None and config_lang != cls.lang:
raise ValueError(
@ -1796,6 +1806,7 @@ class Language:
filled["nlp"], validate=validate, schema=ConfigSchemaNlp
)
create_tokenizer = resolved_nlp["tokenizer"]
create_vectors = resolved_nlp["vectors"]
before_creation = resolved_nlp["before_creation"]
after_creation = resolved_nlp["after_creation"]
after_pipeline_creation = resolved_nlp["after_pipeline_creation"]
@ -1816,7 +1827,12 @@ class Language:
# 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, meta=meta)
nlp = lang_cls(
vocab=vocab,
create_tokenizer=create_tokenizer,
create_vectors=create_vectors,
meta=meta,
)
if after_creation is not None:
nlp = after_creation(nlp)
if not isinstance(nlp, cls):

11
spacy/ml/staticvectors.py
View File

@ -9,7 +9,7 @@ from thinc.util import partial
from ..attrs import ORTH
from ..errors import Errors, Warnings
from ..tokens import Doc
from ..vectors import Mode
from ..vectors import Mode, Vectors
from ..vocab import Vocab
@ -48,11 +48,14 @@ def forward(
key_attr: int = getattr(vocab.vectors, "attr", ORTH)
keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
if vocab.vectors.mode == Mode.default:
if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
V = model.ops.asarray(vocab.vectors.data)
rows = vocab.vectors.find(keys=keys)
V = model.ops.as_contig(V[rows])
elif vocab.vectors.mode == Mode.floret:
elif isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret:
V = vocab.vectors.get_batch(keys)
V = model.ops.as_contig(V)
elif hasattr(vocab.vectors, "get_batch"):
V = vocab.vectors.get_batch(keys)
V = model.ops.as_contig(V)
else:
@ -61,7 +64,7 @@ def forward(
vectors_data = model.ops.gemm(V, W, trans2=True)
except ValueError:
raise RuntimeError(Errors.E896)
if vocab.vectors.mode == Mode.default:
if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
# Convert negative indices to 0-vectors
# TODO: more options for UNK tokens
vectors_data[rows < 0] = 0

1
spacy/schemas.py
View File

@ -397,6 +397,7 @@ class ConfigSchemaNlp(BaseModel):
after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
batch_size: Optional[int] = Field(..., title="Default batch size")
vectors: Callable = Field(..., title="Vectors implementation")
# fmt: on
class Config:

1
spacy/util.py
View File

@ -118,6 +118,7 @@ class registry(thinc.registry):
augmenters = catalogue.create("spacy", "augmenters", entry_points=True)
loggers = catalogue.create("spacy", "loggers", entry_points=True)
scorers = catalogue.create("spacy", "scorers", entry_points=True)
vectors = catalogue.create("spacy", "vectors", entry_points=True)
# These are factories registered via third-party packages and the
# spacy_factories entry point. This registry only exists so we can easily
# load them via the entry points. The "true" factories are added via the

75
spacy/vectors.pyx
View File

@ -1,3 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
from typing import Callable
from cython.operator cimport dereference as deref
from libc.stdint cimport uint32_t, uint64_t
from libcpp.set cimport set as cppset
@ -5,7 +8,8 @@ from murmurhash.mrmr cimport hash128_x64
import warnings
from enum import Enum
from typing import cast
from pathlib import Path
from typing import TYPE_CHECKING, Union, cast
import numpy
import srsly
@ -21,6 +25,9 @@ from .attrs import IDS
from .errors import Errors, Warnings
from .strings import get_string_id
if TYPE_CHECKING:
from .vocab import Vocab # noqa: F401 # no-cython-lint
def unpickle_vectors(bytes_data):
return Vectors().from_bytes(bytes_data)
@ -35,7 +42,71 @@ class Mode(str, Enum):
return list(cls.__members__.keys())
cdef class Vectors:
cdef class BaseVectors:
def __init__(self, *, strings=None):
# Make sure abstract BaseVectors is not instantiated.
if self.__class__ == BaseVectors:
raise TypeError(
Errors.E1046.format(cls_name=self.__class__.__name__)
)
def __getitem__(self, key):
raise NotImplementedError
def __contains__(self, key):
raise NotImplementedError
def is_full(self):
raise NotImplementedError
def get_batch(self, keys):
raise NotImplementedError
@property
def shape(self):
raise NotImplementedError
def __len__(self):
raise NotImplementedError
@property
def vectors_length(self):
raise NotImplementedError
@property
def size(self):
raise NotImplementedError
def add(self, key, *, vector=None):
raise NotImplementedError
def to_ops(self, ops: Ops):
pass
# add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
# allow serialization
def to_bytes(self, **kwargs):
return b""
def from_bytes(self, data: bytes, **kwargs):
return self
def to_disk(self, path: Union[str, Path], **kwargs):
return None
def from_disk(self, path: Union[str, Path], **kwargs):
return self
@util.registry.vectors("spacy.Vectors.v1")
def create_mode_vectors() -> Callable[["Vocab"], BaseVectors]:
def vectors_factory(vocab: "Vocab") -> BaseVectors:
return Vectors(strings=vocab.strings)
return vectors_factory
cdef class Vectors(BaseVectors):
"""Store, save and load word vectors.
Vectors data is kept in the vectors.data attribute, which should be an

12
spacy/vocab.pyx
View File

@ -94,6 +94,7 @@ cdef class Vocab:
return self._vectors
def __set__(self, vectors):
if hasattr(vectors, "strings"):
for s in vectors.strings:
self.strings.add(s)
self._vectors = vectors
@ -193,7 +194,7 @@ cdef class Vocab:
lex = <LexemeC*>mem.alloc(1, sizeof(LexemeC))
lex.orth = self.strings.add(string)
lex.length = len(string)
if self.vectors is not None:
if self.vectors is not None and hasattr(self.vectors, "key2row"):
lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK)
else:
lex.id = OOV_RANK
@ -289,12 +290,17 @@ cdef class Vocab:
@property
def vectors_length(self):
if hasattr(self.vectors, "shape"):
return self.vectors.shape[1]
else:
return -1
def reset_vectors(self, *, width=None, shape=None):
"""Drop the current vector table. Because all vectors must be the same
width, you have to call this to change the size of the vectors.
"""
if not isinstance(self.vectors, Vectors):
raise ValueError(Errors.E849.format(method="reset_vectors", vectors_type=type(self.vectors)))
if width is not None and shape is not None:
raise ValueError(Errors.E065.format(width=width, shape=shape))
elif shape is not None:
@ -304,6 +310,8 @@ cdef class Vocab:
self.vectors = Vectors(strings=self.strings, shape=(self.vectors.shape[0], width))
def deduplicate_vectors(self):
if not isinstance(self.vectors, Vectors):
raise ValueError(Errors.E849.format(method="deduplicate_vectors", vectors_type=type(self.vectors)))
if self.vectors.mode != VectorsMode.default:
raise ValueError(Errors.E858.format(
mode=self.vectors.mode,
@ -357,6 +365,8 @@ cdef class Vocab:
DOCS: https://spacy.io/api/vocab#prune_vectors
"""
if not isinstance(self.vectors, Vectors):
raise ValueError(Errors.E849.format(method="prune_vectors", vectors_type=type(self.vectors)))
if self.vectors.mode != VectorsMode.default:
raise ValueError(Errors.E858.format(
mode=self.vectors.mode,

143
website/docs/api/basevectors.mdx Normal file
View File

@ -0,0 +1,143 @@
---
title: BaseVectors
teaser: Abstract class for word vectors
tag: class
source: spacy/vectors.pyx
version: 3.7
---
`BaseVectors` is an abstract class to support the development of custom vectors
implementations.
For use in training with [`StaticVectors`](/api/architectures#staticvectors),
`get_batch` must be implemented. For improved performance, use efficient
batching in `get_batch` and implement `to_ops` to copy the vector data to the
current device. See an example custom implementation for
[BPEmb subword embeddings](/usage/embeddings-transformers#custom-vectors).
## BaseVectors.\_\_init\_\_ {id="init",tag="method"}
Create a new vector store.
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `strings` | The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~ |
## BaseVectors.\_\_getitem\_\_ {id="getitem",tag="method"}
Get a vector by key. If the key is not found in the table, a `KeyError` should
be raised.
| Name | Description |
| ----------- | ---------------------------------------------------------------- |
| `key` | The key to get the vector for. ~~Union[int, str]~~ |
| **RETURNS** | The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## BaseVectors.\_\_len\_\_ {id="len",tag="method"}
Return the number of vectors in the table.
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | The number of vectors in the table. ~~int~~ |
## BaseVectors.\_\_contains\_\_ {id="contains",tag="method"}
Check whether there is a vector entry for the given key.
| Name | Description |
| ----------- | -------------------------------------------- |
| `key` | The key to check. ~~int~~ |
| **RETURNS** | Whether the key has a vector entry. ~~bool~~ |
## BaseVectors.add {id="add",tag="method"}
Add a key to the table, if possible. If no keys can be added, return `-1`.
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------- |
| `key` | The key to add. ~~Union[str, int]~~ |
| **RETURNS** | The row the vector was added to, or `-1` if the operation is not supported. ~~int~~ |
## BaseVectors.shape {id="shape",tag="property"}
Get `(rows, dims)` tuples of number of rows and number of dimensions in the
vector table.
| Name | Description |
| ----------- | ------------------------------------------ |
| **RETURNS** | A `(rows, dims)` pair. ~~Tuple[int, int]~~ |
## BaseVectors.size {id="size",tag="property"}
The vector size, i.e. `rows * dims`.
| Name | Description |
| ----------- | ------------------------ |
| **RETURNS** | The vector size. ~~int~~ |
## BaseVectors.is_full {id="is_full",tag="property"}
Whether the vectors table is full and no slots are available for new keys.
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | Whether the vectors table is full. ~~bool~~ |
## BaseVectors.get_batch {id="get_batch",tag="method",version="3.2"}
Get the vectors for the provided keys efficiently as a batch. Required to use
the vectors with [`StaticVectors`](/api/architectures#StaticVectors) for
training.
| Name | Description |
| ------ | --------------------------------------- |
| `keys` | The keys. ~~Iterable[Union[int, str]]~~ |
## BaseVectors.to_ops {id="to_ops",tag="method"}
Dummy method. Implement this to change the embedding matrix to use different
Thinc ops.
| Name | Description |
| ----- | -------------------------------------------------------- |
| `ops` | The Thinc ops to switch the embedding matrix to. ~~Ops~~ |
## BaseVectors.to_disk {id="to_disk",tag="method"}
Dummy method to allow serialization. Implement to save vector data with the
pipeline.
| Name | Description |
| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## BaseVectors.from_disk {id="from_disk",tag="method"}
Dummy method to allow serialization. Implement to load vector data from a saved
pipeline.
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified vectors object. ~~BaseVectors~~ |
## BaseVectors.to_bytes {id="to_bytes",tag="method"}
Dummy method to allow serialization. Implement to serialize vector data to a
binary string.
| Name | Description |
| ----------- | ---------------------------------------------------- |
| **RETURNS** | The serialized form of the vectors object. ~~bytes~~ |
## BaseVectors.from_bytes {id="from_bytes",tag="method"}
Dummy method to allow serialization. Implement to load vector data from a binary
string.
| Name | Description |
| ----------- | ----------------------------------- |
| `data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The vectors object. ~~BaseVectors~~ |

9
website/docs/api/vectors.mdx
View File

@ -297,10 +297,9 @@ The vector size, i.e. `rows * dims`.
## Vectors.is_full {id="is_full",tag="property"}
Whether the vectors table is full and has no slots are available for new keys.
If a table is full, it can be resized using
[`Vectors.resize`](/api/vectors#resize). In `floret` mode, the table is always
full and cannot be resized.
Whether the vectors table is full and no slots are available for new keys. If a
table is full, it can be resized using [`Vectors.resize`](/api/vectors#resize).
In `floret` mode, the table is always full and cannot be resized.
> #### Example
>
@ -441,7 +440,7 @@ Load state from a binary string.
> #### Example
>
> ```python
> fron spacy.vectors import Vectors
> from spacy.vectors import Vectors
> vectors_bytes = vectors.to_bytes()
> new_vectors = Vectors(StringStore())
> new_vectors.from_bytes(vectors_bytes)

159
website/docs/usage/embeddings-transformers.mdx
View File

@ -632,6 +632,165 @@ def MyCustomVectors(
)
```
#### Creating a custom vectors implementation {id="custom-vectors",version="3.7"}
You can specify a custom registered vectors class under `[nlp.vectors]` in order
to use static vectors in formats other than the ones supported by
[`Vectors`](/api/vectors). Extend the abstract [`BaseVectors`](/api/basevectors)
class to implement your custom vectors.
As an example, the following `BPEmbVectors` class implements support for
[BPEmb subword embeddings](https://bpemb.h-its.org/):
```python
# requires: pip install bpemb
import warnings
from pathlib import Path
from typing import Callable, Optional, cast
from bpemb import BPEmb
from thinc.api import Ops, get_current_ops
from thinc.backends import get_array_ops
from thinc.types import Floats2d
from spacy.strings import StringStore
from spacy.util import registry
from spacy.vectors import BaseVectors
from spacy.vocab import Vocab
class BPEmbVectors(BaseVectors):
def __init__(
self,
*,
strings: Optional[StringStore] = None,
lang: Optional[str] = None,
vs: Optional[int] = None,
dim: Optional[int] = None,
cache_dir: Optional[Path] = None,
encode_extra_options: Optional[str] = None,
model_file: Optional[Path] = None,
emb_file: Optional[Path] = None,
):
kwargs = {}
if lang is not None:
kwargs["lang"] = lang
if vs is not None:
kwargs["vs"] = vs
if dim is not None:
kwargs["dim"] = dim
if cache_dir is not None:
kwargs["cache_dir"] = cache_dir
if encode_extra_options is not None:
kwargs["encode_extra_options"] = encode_extra_options
if model_file is not None:
kwargs["model_file"] = model_file
if emb_file is not None:
kwargs["emb_file"] = emb_file
self.bpemb = BPEmb(**kwargs)
self.strings = strings
self.name = repr(self.bpemb)
self.n_keys = -1
self.mode = "BPEmb"
self.to_ops(get_current_ops())
def __contains__(self, key):
return True
def is_full(self):
return True
def add(self, key, *, vector=None, row=None):
warnings.warn(
(
"Skipping BPEmbVectors.add: the bpemb vector table cannot be "
"modified. Vectors are calculated from bytepieces."
)
)
return -1
def __getitem__(self, key):
return self.get_batch([key])[0]
def get_batch(self, keys):
keys = [self.strings.as_string(key) for key in keys]
bp_ids = self.bpemb.encode_ids(keys)
ops = get_array_ops(self.bpemb.emb.vectors)
indices = ops.asarray(ops.xp.hstack(bp_ids), dtype="int32")
lengths = ops.asarray([len(x) for x in bp_ids], dtype="int32")
vecs = ops.reduce_mean(cast(Floats2d, self.bpemb.emb.vectors[indices]), lengths)
return vecs
@property
def shape(self):
return self.bpemb.vectors.shape
def __len__(self):
return self.shape[0]
@property
def vectors_length(self):
return self.shape[1]
@property
def size(self):
return self.bpemb.vectors.size
def to_ops(self, ops: Ops):
self.bpemb.emb.vectors = ops.asarray(self.bpemb.emb.vectors)
@registry.vectors("BPEmbVectors.v1")
def create_bpemb_vectors(
lang: Optional[str] = "multi",
vs: Optional[int] = None,
dim: Optional[int] = None,
cache_dir: Optional[Path] = None,
encode_extra_options: Optional[str] = None,
model_file: Optional[Path] = None,
emb_file: Optional[Path] = None,
) -> Callable[[Vocab], BPEmbVectors]:
def bpemb_vectors_factory(vocab: Vocab) -> BPEmbVectors:
return BPEmbVectors(
strings=vocab.strings,
lang=lang,
vs=vs,
dim=dim,
cache_dir=cache_dir,
encode_extra_options=encode_extra_options,
model_file=model_file,
emb_file=emb_file,
)
return bpemb_vectors_factory
```
<Infobox variant="warning">
Note that the serialization methods are not implemented, so the embeddings are
loaded from your local cache or downloaded by `BPEmb` each time the pipeline is
loaded.
</Infobox>
To use this in your pipeline, specify this registered function under
`[nlp.vectors]` in your config:
```ini
[nlp.vectors]
@vectors = "BPEmbVectors.v1"
lang = "en"
```
Or specify it when creating a blank pipeline:
```python
nlp = spacy.blank("en", config={"nlp.vectors": {"@vectors": "BPEmbVectors.v1", "lang": "en"}})
```
Remember to include this code with `--code` when using
[`spacy train`](/api/cli#train) and [`spacy package`](/api/cli#package).
## Pretraining {id="pretraining"}
The [`spacy pretrain`](/api/cli#pretrain) command lets you initialize your

1
website/meta/sidebars.json
View File

@ -131,6 +131,7 @@
"label": "Other",
"items": [
{ "text": "Attributes", "url": "/api/attributes" },
{ "text": "BaseVectors", "url": "/api/basevectors" },
{ "text": "Corpus", "url": "/api/corpus" },
{ "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" },
{ "text": "KnowledgeBase", "url": "/api/kb" },