Support registered vectors

spacy/default_config.cfg

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

spacy/errors.py

@@ -549,6 +549,7 @@ class Errors(metaclass=ErrorsWithCodes):
             "during training, make sure to include it in 'annotating components'")
 
     # New errors added in v3.x
+    E849 = ("Unable to {action} vectors for vectors of type {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, "

spacy/language.py

@@ -20,6 +20,8 @@ import traceback
 
 from . import ty
 from .tokens.underscore import Underscore
+from .strings import StringStore
+from .vectors import BaseVectors
 from .vocab import Vocab, create_vocab
 from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
 from .training import Example, validate_examples
@@ -134,6 +136,7 @@ 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:
@@ -174,6 +177,10 @@ 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))
@@ -1750,6 +1757,7 @@ 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"]
@@ -1770,7 +1778,7 @@ 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):

spacy/ml/staticvectors.py

@@ -6,7 +6,7 @@ from thinc.api import Model, Ops, registry
 
 from ..tokens import Doc
 from ..errors import Errors
-from ..vectors import Mode
+from ..vectors import Vectors, Mode
 from ..vocab import Vocab
 
 
@@ -43,11 +43,14 @@ def forward(
     keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
     vocab: Vocab = docs[0].vocab
     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:
@@ -56,7 +59,7 @@ 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

spacy/schemas.py

@@ -375,6 +375,7 @@ 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:

spacy/vectors.pyx

@@ -1,3 +1,5 @@
+# cython: infer_types=True, profile=True, binding=True
+from typing import Callable
 cimport numpy as np
 from libc.stdint cimport uint32_t, uint64_t
 from cython.operator cimport dereference as deref
@@ -6,7 +8,8 @@ from murmurhash.mrmr cimport hash128_x64
 
 import functools
 import numpy
-from typing import cast
+from pathlib import Path
+from typing import cast, TYPE_CHECKING, Union
 import warnings
 from enum import Enum
 import srsly
@@ -21,6 +24,10 @@ from .errors import Errors, Warnings
 from . import util
 
 
+if TYPE_CHECKING:
+    from .vocab import Vocab  # noqa: F401
+
+
 def unpickle_vectors(bytes_data):
     return Vectors().from_bytes(bytes_data)
 
@@ -34,7 +41,51 @@ 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 get_batch(self, keys):
+        raise NotImplementedError
+
+    @property
+    def vectors_length(self):
+        raise NotImplementedError
+
+    def add(self, key, *, vector=None):
+        raise NotImplementedError
+
+    # 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.misc("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

spacy/vocab.pyx

@@ -88,8 +88,9 @@ cdef class Vocab:
             return self._vectors
 
         def __set__(self, vectors):
-            for s in vectors.strings:
-                self.strings.add(s)
+            if hasattr(vectors, "strings"):
+                for s in vectors.strings:
+                    self.strings.add(s)
             self._vectors = vectors
             self._vectors.strings = self.strings
 
@@ -188,7 +189,7 @@ 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
@@ -284,12 +285,17 @@ cdef class Vocab:
 
     @property
     def vectors_length(self):
-        return self.vectors.shape[1]
+        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("reset", 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:
@@ -299,6 +305,8 @@ 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(action="deduplicate", vectors_type=type(self.vectors)))
         if self.vectors.mode != VectorsMode.default:
             raise ValueError(Errors.E858.format(
                 mode=self.vectors.mode,
@@ -352,6 +360,8 @@ cdef class Vocab:
 
         DOCS: https://spacy.io/api/vocab#prune_vectors
         """
+        if not isinstance(self.vectors, Vectors):
+            raise ValueError(Errors.E849.format(action="prune", vectors_type=type(self.vectors)))
         if self.vectors.mode != VectorsMode.default:
             raise ValueError(Errors.E858.format(
                 mode=self.vectors.mode,
@@ -400,7 +410,10 @@ cdef class Vocab:
             orth = self.strings.add(orth)
         if self.has_vector(orth):
             return self.vectors[orth]
-        xp = get_array_module(self.vectors.data)
+        if isinstance(self.vectors, Vectors):
+            xp = get_array_module(self.vectors.data)
+        else:
+            xp = get_current_ops().xp
         vectors = xp.zeros((self.vectors_length,), dtype="f")
         return vectors