Expand initialize/training config validation

Validate both `[initialize]` and `[training]` in `debug data` and
`nlp.initialize()` with separate config validation error blocks that
indicate which block of the config is being validated.

spacy/cli/debug_config.py

@@ -7,7 +7,7 @@ import typer
 
 from ._util import Arg, Opt, show_validation_error, parse_config_overrides
 from ._util import import_code, debug_cli
-from ..schemas import ConfigSchemaTraining
+from ..schemas import ConfigSchemaInit, ConfigSchemaTraining
 from ..util import registry
 from .. import util
 
@@ -55,6 +55,11 @@ def debug_config(
         config = util.load_config(config_path, overrides=overrides)
         nlp = util.load_model_from_config(config)
         config = nlp.config.interpolate()
+    msg.divider("Config validation for [initialize]")
+    with show_validation_error(config_path):
+        T = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
+    msg.divider("Config validation for [training]")
+    with show_validation_error(config_path):
         T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
         dot_names = [T["train_corpus"], T["dev_corpus"]]
         util.resolve_dot_names(config, dot_names)

spacy/training/initialize.py

@@ -1,4 +1,5 @@
 from typing import Union, Dict, Optional, Any, List, IO, TYPE_CHECKING
+from pydantic import BaseModel
 from thinc.api import Config, fix_random_seed, set_gpu_allocator
 from thinc.api import ConfigValidationError
 from pathlib import Path
@@ -12,7 +13,7 @@ import tqdm
 from ..lookups import Lookups
 from ..vectors import Vectors
 from ..errors import Errors
-from ..schemas import ConfigSchemaTraining
+from ..schemas import ConfigSchemaInit, ConfigSchemaTraining
 from ..util import registry, load_model_from_config, resolve_dot_names, logger
 from ..util import load_model, ensure_path, OOV_RANK, DEFAULT_OOV_PROB
 
@@ -23,6 +24,9 @@ if TYPE_CHECKING:
 def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
     raw_config = config
     config = raw_config.interpolate()
+    # Validate config before accessing values
+    _validate_config_block(config, "initialize", ConfigSchemaInit)
+    _validate_config_block(config, "training", ConfigSchemaTraining)
     if config["training"]["seed"] is not None:
         fix_random_seed(config["training"]["seed"])
     allocator = config["training"]["gpu_allocator"]
@@ -269,3 +273,13 @@ def ensure_shape(lines):
         length = len(captured)
         yield f"{length} {width}"
         yield from captured
+
+
+def _validate_config_block(config: Config, block: str, schema: BaseModel):
+    try:
+        registry.resolve(config[block], validate=True, schema=schema)
+    except ConfigValidationError as e:
+        title = f"Config validation error for [{block}]"
+        desc = "For more information run: python -m spacy debug config config.cfg"
+        err = ConfigValidationError.from_error(e, title=title, desc=desc)
+        raise err from None