Make registry.resolve return resolve config only

examples/00_intro_to_thinc.ipynb

@@ -256,7 +256,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_config1, _ = registry.resolve(config1)\n",
+    "loaded_config1 = registry.resolve(config1)\n",
     "loaded_config1"
    ]
   },
@@ -301,7 +301,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_config2, _ = registry.resolve(config2)\n",
+    "loaded_config2 = registry.resolve(config2)\n",
     "loaded_config2"
    ]
   },
@@ -368,7 +368,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_config, filled_config = registry.resolve(config)\n",
+    "loaded_config = registry.resolve(config)\n",
     "loaded_config"
    ]
   },
@@ -469,7 +469,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_config, _ = registry.resolve(config)\n",
+    "loaded_config = registry.resolve(config)\n",
     "loaded_config"
    ]
   },
@@ -516,7 +516,7 @@
     "\"\"\"\n",
     "\n",
     "config = Config().from_str(CONFIG3)\n",
-    "loaded_config, _ = registry.resolve(config)\n",
+    "loaded_config = registry.resolve(config)\n",
     "loaded_config"
    ]
   },

examples/02_transformers_tagger_bert.ipynb

@@ -294,7 +294,7 @@
    "source": [
     "from thinc.api import Config, registry\n",
     "\n",
-    "C, _ = registry.resolve(Config().from_str(CONFIG))\n",
+    "C = registry.resolve(Config().from_str(CONFIG))\n",
     "C"
    ]
   },

examples/03_pos_tagger_basic_cnn.ipynb

@@ -251,7 +251,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "C, _ = registry.resolve(config)\n",
+    "C = registry.resolve(config)\n",
     "C"
    ]
   },
@@ -351,7 +351,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "C, _ = registry.resolve(Config().from_str(CONFIG))\n",
+    "C = registry.resolve(Config().from_str(CONFIG))\n",
     "C"
    ]
   },

examples/03_textcat_basic_neural_bow.ipynb

@@ -170,7 +170,7 @@
    "source": [
     "from thinc.api import registry, Config\n",
     "\n",
-    "C, _ = registry.resolve(Config().from_str(CONFIG))\n",
+    "C = registry.resolve(Config().from_str(CONFIG))\n",
     "C"
    ]
   },

examples/04_parallel_training_ray.ipynb

@@ -253,7 +253,7 @@
    "source": [
     "### Setting up the model\n",
     "\n",
-    "Using the `CONFIG` defined above, we can load the settings and set up the model and optimizer. Thinc's `registry.resolve` will parse the config, resolve all references to registered functions and return a dict of the resolved objects and a filled config with all defaults."
+    "Using the `CONFIG` defined above, we can load the settings and set up the model and optimizer. Thinc's `registry.resolve` will parse the config, resolve all references to registered functions and return a dict of the resolved objects."
    ]
   },
   {
@@ -263,7 +263,7 @@
    "outputs": [],
    "source": [
     "from thinc.api import registry, Config\n",
-    "C, _ = registry.resolve(Config().from_str(CONFIG))\n",
+    "C = registry.resolve(Config().from_str(CONFIG))\n",
     "C"
    ]
   },

examples/benchmarks/lstm_tagger.py

@@ -113,7 +113,7 @@ def main(pytorch: bool = False, gpu_id: int = -1):
             print(f"Skipping {name}")
             continue
         set_backend(name, gpu_id)
-        C, _ = registry.resolve(Config().from_str(CONFIG))
+        C = registry.resolve(Config().from_str(CONFIG))
         model = C["model"]
         X, Y = get_dummy_data(**C["data"])
         print("Copy to device")

examples/transformers_tagger.py

@@ -51,7 +51,7 @@ def main(path: Optional[Path] = None, out_dir: Optional[Path] = None):
     # In the optimizer block we write @optimizers = "Adam.v1". This tells Thinc
     # to use registry.optimizers to fetch the "Adam.v1" function. You can
     # register your own functions as well and build up trees of objects.
-    C, _ = thinc.registry.resolve(config)
+    C = thinc.registry.resolve(config)
     words_per_subbatch = C["training"]["words_per_subbatch"]
     n_epoch = C["training"]["n_epoch"]
     batch_size = C["training"]["batch_size"]

thinc/config.py

@@ -704,10 +704,11 @@ def resolve(
         schema: Type[BaseModel] = EmptySchema,
         overrides: Dict[str, Any] = {},
         validate: bool = True,
-    ) -> Tuple[Dict[str, Any], Config]:
-        return cls._make(
+    ) -> Dict[str, Any]:
+        resolved, _ = cls._make(
             config, schema=schema, overrides=overrides, validate=validate, resolve=True
         )
+        return resolved
 
     @classmethod
     def fill(

thinc/tests/layers/test_layers_api.py

@@ -121,8 +121,7 @@ def assert_data_match(Y, out_data):
 @pytest.mark.parametrize("name,kwargs,in_data,out_data", TEST_CASES)
 def test_layers_from_config(name, kwargs, in_data, out_data):
     cfg = {"@layers": name, **kwargs}
-    resolved, _ = registry.resolve({"config": cfg})
-    model = resolved["config"]
+    model = registry.resolve({"config": cfg})["config"]
     if "LSTM" in name:
         model = with_padded(model)
     valid = True
@@ -139,8 +138,7 @@ def test_layers_from_config(name, kwargs, in_data, out_data):
 @pytest.mark.parametrize("name,kwargs,in_data,out_data", TEST_CASES_SUMMABLE)
 def test_layers_with_residual(name, kwargs, in_data, out_data):
     cfg = {"@layers": "residual.v1", "layer": {"@layers": name, **kwargs}}
-    resolved, _ = registry.resolve({"config": cfg})
-    model = resolved["config"]
+    model = registry.resolve({"config": cfg})["config"]
     if "LSTM" in name:
         model = with_padded(model)
     model.initialize(in_data, out_data)

thinc/tests/layers/test_transforms.py

@@ -40,8 +40,7 @@ def array_data(ragged_data):
 
 
 def check_transform(transform, in_data, out_data):
-    resolved, _ = registry.resolve({"config": {"@layers": transform}})
-    model = resolved["config"]
+    model = registry.resolve({"config": {"@layers": transform}})["config"]
     input_checker = get_data_checker(in_data)
     output_checker = get_data_checker(out_data)
     model.initialize(in_data, out_data)