Fix config resolution and interpolation

TODO: auto-interpolate in Thinc if config is dict (i.e. likely subsection)

spacy/cli/debug_data.py

@@ -97,7 +97,9 @@ def debug_data(
     with show_validation_error(config_path):
         cfg = util.load_config(config_path, overrides=config_overrides)
         nlp = util.load_model_from_config(cfg)
-        T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
+        T = registry.resolve(
+            nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
+        )
     # Use original config here, not resolved version
     sourced_components = get_sourced_components(cfg)
     frozen_components = T["frozen_components"]

spacy/cli/debug_model.py

@@ -63,7 +63,9 @@ def debug_model_cli(
         set_gpu_allocator(allocator)
     with show_validation_error(config_path):
         nlp = util.load_model_from_config(raw_config)
-        T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
+        T = registry.resolve(
+            nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
+        )
     seed = T["seed"]
     if seed is not None:
         msg.info(f"Fixing random seed: {seed}")

spacy/tests/training/test_readers.py

@@ -42,7 +42,9 @@ def test_readers():
     dot_names = ["training.train_corpus", "training.dev_corpus"]
     train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
     assert isinstance(train_corpus, Callable)
-    T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
+    T = registry.resolve(
+        nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
+    )
     optimizer = T["optimizer"]
     # simulate a training loop
     nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
@@ -53,7 +55,8 @@ def test_readers():
     # ensure the pipeline runs
     doc = nlp("Quick test")
     assert doc.cats
-    extra_corpus = registry.resolve(nlp.config["corpora"])["extra"]
+    corpora = {"corpora": nlp.config.interpolate()["corpora"]}
+    extra_corpus = registry.resolve(corpora)["corpora"]["extra"]
     assert isinstance(extra_corpus, Callable)
 
 
@@ -91,7 +94,9 @@ def test_cat_readers(reader, additional_config):
     nlp = load_model_from_config(config, auto_fill=True)
     dot_names = ["training.train_corpus", "training.dev_corpus"]
     train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
-    T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
+    T = registry.resolve(
+        nlp.config["training"].interpolate(), schema=ConfigSchemaTraining
+    )
     optimizer = T["optimizer"]
     # simulate a training loop
     nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)

spacy/training/pretrain.py

@@ -33,8 +33,9 @@ def pretrain(
     if use_gpu >= 0 and allocator:
         set_gpu_allocator(allocator)
     nlp = load_model_from_config(config)
-    T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
-    P = registry.resolve(nlp.config["pretraining"], schema=ConfigSchemaPretrain)
+    _config = nlp.config.interpolate()
+    T = registry.resolve(_config["training"], schema=ConfigSchemaTraining)
+    P = registry.resolve(_config["pretraining"], schema=ConfigSchemaPretrain)
     corpus = dot_to_object(T, P["corpus"])
     batcher = P["batcher"]
     model = create_pretraining_model(nlp, P)

spacy/util.py

@@ -413,7 +413,12 @@ def resolve_dot_names(config: Config, dot_names: List[Optional[str]]) -> Tuple[A
         section = ref.split(".")[0]
         # We want to avoid resolving the same thing twice
         if section not in resolved:
-            resolved[section] = registry.resolve(config[section])
+            if registry.is_promise(config[section]):
+                # Otherwise we can't resolve [corpus] if it's a promise
+                result = registry.resolve({"config": config[section]})["config"]
+            else:
+                result = registry.resolve(config[section])
+            resolved[section] = result
         try:
             objects.append(dot_to_object(resolved, ref))
         except KeyError: