Add nlp.batch_size setting

Add a default `batch_size` setting for `Language.pipe` and
`Language.evaluate` as `nlp.batch_size`.

spacy/cli/templates/quickstart_training.jinja

@@ -19,6 +19,7 @@ lang = "{{ lang }}"
 {%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components %}
 pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
 tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
+batch_size = {{ 128 if hardware == "gpu" else 1000 }}
 
 [components]
 

spacy/default_config.cfg

@@ -20,6 +20,8 @@ disabled = []
 before_creation = null
 after_creation = null
 after_pipeline_creation = null
+# Default batch size to use with nlp.pipe and nlp.evaluate
+batch_size = 1000
 
 [nlp.tokenizer]
 @tokenizers = "spacy.Tokenizer.v1"

spacy/language.py

@@ -121,6 +121,7 @@ class Language:
         max_length: int = 10 ** 6,
         meta: Dict[str, Any] = {},
         create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
+        batch_size: int = 1000,
         **kwargs,
     ) -> None:
         """Initialise a Language object.
@@ -138,6 +139,7 @@ class Language:
             100,000 characters in one text.
         create_tokenizer (Callable): Function that takes the nlp object and
             returns a tokenizer.
+        batch_size (int): Default batch size for pipe and evaluate.
 
         DOCS: https://nightly.spacy.io/api/language#init
         """
@@ -173,6 +175,7 @@ class Language:
             tokenizer_cfg = {"tokenizer": self._config["nlp"]["tokenizer"]}
             create_tokenizer = registry.resolve(tokenizer_cfg)["tokenizer"]
         self.tokenizer = create_tokenizer(self)
+        self.batch_size = batch_size
 
     def __init_subclass__(cls, **kwargs):
         super().__init_subclass__(**kwargs)
@@ -1268,7 +1271,7 @@ class Language:
         self,
         examples: Iterable[Example],
         *,
-        batch_size: int = 256,
+        batch_size: Optional[int] = None,
         scorer: Optional[Scorer] = None,
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
         scorer_cfg: Optional[Dict[str, Any]] = None,
@@ -1288,6 +1291,8 @@ class Language:
         DOCS: https://nightly.spacy.io/api/language#evaluate
         """
         validate_examples(examples, "Language.evaluate")
+        if batch_size is None:
+            batch_size = self.batch_size
         if component_cfg is None:
             component_cfg = {}
         if scorer_cfg is None:
@@ -1366,7 +1371,7 @@ class Language:
         texts: Iterable[str],
         *,
         as_tuples: bool = False,
-        batch_size: int = 1000,
+        batch_size: Optional[int] = None,
         disable: Iterable[str] = SimpleFrozenList(),
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
         n_process: int = 1,
@@ -1377,7 +1382,7 @@ class Language:
         as_tuples (bool): If set to True, inputs should be a sequence of
             (text, context) tuples. Output will then be a sequence of
             (doc, context) tuples. Defaults to False.
-        batch_size (int): The number of texts to buffer.
+        batch_size (Optional[int]): The number of texts to buffer.
         disable (List[str]): Names of the pipeline components to disable.
         component_cfg (Dict[str, Dict]): An optional dictionary with extra keyword
             arguments for specific components.
@@ -1404,6 +1409,8 @@ class Language:
             return
         if component_cfg is None:
             component_cfg = {}
+        if batch_size is None:
+            batch_size = self.batch_size
 
         pipes = (
             []
@@ -1618,6 +1625,7 @@ class Language:
                     nlp.add_pipe(source_name, source=source_nlps[model], name=pipe_name)
         disabled_pipes = [*config["nlp"]["disabled"], *disable]
         nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
+        nlp.batch_size = config["nlp"]["batch_size"]
         nlp.config = filled if auto_fill else config
         if after_pipeline_creation is not None:
             nlp = after_pipeline_creation(nlp)

spacy/schemas.py

@@ -329,6 +329,7 @@ class ConfigSchemaNlp(BaseModel):
     before_creation: Optional[Callable[[Type["Language"]], Type["Language"]]] = Field(..., title="Optional callback to modify Language class before initialization")
     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")
     # fmt: on
 
     class Config:

website/docs/api/language.md

@@ -42,6 +42,7 @@ information in [`Language.meta`](/api/language#meta) and not to configure the
 | `max_length`       | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. ~~int~~                                    |
 | `meta`             | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~                                                        |
 | `create_tokenizer` | Optional function that receives the `nlp` object and returns a tokenizer. ~~Callable[[Language], Callable[[str], Doc]]~~ |
+| `batch_size`       | Default batch size for `pipe` and `evaluate`. Defaults to `1000`. ~~int~~                                                |
 
 ## Language.from_config {#from_config tag="classmethod" new="3"}
 
@@ -195,7 +196,7 @@ more efficient than processing texts one-by-one.
 | `texts`                                    | A sequence of strings. ~~Iterable[str]~~                                                                                                                            |
 | _keyword-only_                             |                                                                                                                                                                     |
 | `as_tuples`                                | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ |
-| `batch_size`                               | The number of texts to buffer. ~~int~~                                                                                                                              |
+| `batch_size`                               | The number of texts to buffer. ~~Optional[int]~~                                                                                                                    |
 | `disable`                                  | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~                                                                     |
 | `cleanup`                                  | If `True`, unneeded strings are freed to control memory use. Experimental. ~~bool~~                                                                                 |
 | `component_cfg`                            | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~                      |
@@ -357,7 +358,7 @@ objects instead of tuples of `Doc` and `GoldParse` objects.
 | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
 | `examples`      | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~                                                              |
 | _keyword-only_  |                                                                                                                                                |
-| `batch_size`    | The batch size to use. ~~int~~                                                                                                                 |
+| `batch_size`    | The batch size to use. ~~Optional[int]~~                                                                                                       |
 | `scorer`        | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~                                     |
 | `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
 | `scorer_cfg`    | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~                                    |