add init_config to nlp.create_pipe

spacy/language.py

@@ -600,6 +600,7 @@ class Language:
         *,
         config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
         raw_config: Optional[Config] = None,
+        init_config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
         validate: bool = True,
     ) -> Callable[[Doc], Doc]:
         """Create a pipeline component. Mostly used internally. To create and
@@ -611,6 +612,9 @@ class Language:
         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.
+        init_config (Optional[Dict[str, Any]]): Config parameters to use to
+            initialize this component. Will be used to update the internal
+            'initialize' 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.
@@ -621,8 +625,13 @@ class Language:
         if not isinstance(config, dict):
             err = Errors.E962.format(style="config", name=name, cfg_type=type(config))
             raise ValueError(err)
+        if not isinstance(init_config, dict):
+            err = Errors.E962.format(style="init_config", name=name, cfg_type=type(init_config))
+            raise ValueError(err)
         if not srsly.is_json_serializable(config):
             raise ValueError(Errors.E961.format(config=config))
+        if not srsly.is_json_serializable(init_config):
+            raise ValueError(Errors.E961.format(config=init_config))
         if not self.has_factory(factory_name):
             err = Errors.E002.format(
                 name=factory_name,
@@ -634,6 +643,8 @@ class Language:
             raise ValueError(err)
         pipe_meta = self.get_factory_meta(factory_name)
         config = config or {}
+        if init_config:
+            self._config["initialize"]["components"][name] = init_config
         # 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:
@@ -708,6 +719,7 @@ class Language:
         source: Optional["Language"] = None,
         config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
         raw_config: Optional[Config] = None,
+        init_config: Optional[Dict[str, Any]] = SimpleFrozenDict(),
         validate: bool = True,
     ) -> Callable[[Doc], Doc]:
         """Add a component to the processing pipeline. Valid components are
@@ -730,6 +742,9 @@ class Language:
         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.
+        init_config (Optional[Dict[str, Any]]): Config parameters to use to
+            initialize this component. Will be used to update the internal
+            'initialize' 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.
@@ -763,6 +778,7 @@ class Language:
                 name=name,
                 config=config,
                 raw_config=raw_config,
+                init_config=init_config,
                 validate=validate,
             )
         pipe_index = self._get_pipe_index(before, after, first, last)
@@ -842,6 +858,7 @@ class Language:
         factory_name: str,
         *,
         config: Dict[str, Any] = SimpleFrozenDict(),
+        init_config: Dict[str, Any] = SimpleFrozenDict(),
         validate: bool = True,
     ) -> None:
         """Replace a component in the pipeline.
@@ -850,6 +867,9 @@ class Language:
         factory_name (str): Factory name of replacement component.
         config (Optional[Dict[str, Any]]): Config parameters to use for this
             component. Will be merged with default config, if available.
+        init_config (Optional[Dict[str, Any]]): Config parameters to use to
+            initialize this component. Will be used to update the internal
+            'initialize' config.
         validate (bool): Whether to validate the component config against the
             arguments and types expected by the factory.
 
@@ -866,13 +886,14 @@ class Language:
         self.remove_pipe(name)
         if not len(self._components) or pipe_index == len(self._components):
             # we have no components to insert before/after, or we're replacing the last component
-            self.add_pipe(factory_name, name=name, config=config, validate=validate)
+            self.add_pipe(factory_name, name=name, config=config, init_config=init_config, validate=validate)
         else:
             self.add_pipe(
                 factory_name,
                 name=name,
                 before=pipe_index,
                 config=config,
+                init_config=init_config,
                 validate=validate,
             )
 

spacy/tests/pipeline/test_entity_linker.py

@@ -110,7 +110,7 @@ def test_kb_invalid_entity_vector(nlp):
 
 
 def test_kb_default(nlp):
-    """Test that the default (empty) KB is loaded when not providing a config"""
+    """Test that the default (empty) KB is loaded upon construction"""
     entity_linker = nlp.add_pipe("entity_linker", config={})
     assert len(entity_linker.kb) == 0
     assert entity_linker.kb.get_size_entities() == 0
@@ -122,7 +122,7 @@ def test_kb_default(nlp):
 def test_kb_custom_length(nlp):
     """Test that the default (empty) KB can be configured with a custom entity length"""
     entity_linker = nlp.add_pipe(
-        "entity_linker", config={"kb_loader": {"entity_vector_length": 35}}
+        "entity_linker", config={"entity_vector_length": 35}
     )
     assert len(entity_linker.kb) == 0
     assert entity_linker.kb.get_size_entities() == 0
@@ -140,7 +140,7 @@ def test_kb_undefined(nlp):
 def test_kb_empty(nlp):
     """Test that the EL can't train with an empty KB"""
     config = {"kb_loader": {"@misc": "spacy.EmptyKB.v1", "entity_vector_length": 342}}
-    entity_linker = nlp.add_pipe("entity_linker", config=config)
+    entity_linker = nlp.add_pipe("entity_linker", init_config=config)
     assert len(entity_linker.kb) == 0
     with pytest.raises(ValueError):
         entity_linker.initialize(lambda: [])
@@ -217,8 +217,10 @@ def test_el_pipe_configuration(nlp):
     # run an EL pipe without a trained context encoder, to check the candidate generation step only
     nlp.add_pipe(
         "entity_linker",
-        config={"kb_loader": {"@misc": "myAdamKB.v1"}, "incl_context": False},
+        config={"incl_context": False},
+        init_config={"kb_loader": {"@misc": "myAdamKB.v1"}},
     )
+    nlp.initialize()
     # With the default get_candidates function, matching is case-sensitive
     text = "Douglas and douglas are not the same."
     doc = nlp(text)
@@ -238,11 +240,14 @@ def test_el_pipe_configuration(nlp):
         "entity_linker",
         "entity_linker",
         config={
-            "kb_loader": {"@misc": "myAdamKB.v1"},
             "incl_context": False,
             "get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
         },
+        init_config={
+            "kb_loader": {"@misc": "myAdamKB.v1"},
+        },
     )
+    nlp.initialize()
     doc = nlp(text)
     assert doc[0].ent_kb_id_ == "Q2"
     assert doc[1].ent_kb_id_ == ""
@@ -356,8 +361,9 @@ def test_preserving_links_asdoc(nlp):
     ]
     ruler = nlp.add_pipe("entity_ruler")
     ruler.add_patterns(patterns)
-    el_config = {"kb_loader": {"@misc": "myLocationsKB.v1"}, "incl_prior": False}
+    config = {"incl_prior": False}
-    entity_linker = nlp.add_pipe("entity_linker", config=el_config, last=True)
+    init_config = {"kb_loader": {"@misc": "myLocationsKB.v1"}}
+    entity_linker = nlp.add_pipe("entity_linker", config=config, init_config=init_config, last=True)
     nlp.initialize()
     assert entity_linker.model.get_dim("nO") == vector_length
 
@@ -456,7 +462,7 @@ def test_overfitting_IO():
     # Create the Entity Linker component and add it to the pipeline
     entity_linker = nlp.add_pipe(
         "entity_linker",
-        config={"kb_loader": {"@misc": "myOverfittingKB.v1"}},
+        init_config={"kb_loader": {"@misc": "myOverfittingKB.v1"}},
         last=True,
     )