Fix vectors check for sourced components (#8559)

* Fix vectors check for sourced components

Since vectors are not loaded when components are sourced, store a hash
for the vectors of each sourced component and compare it to the loaded
vectors after the vectors are loaded from the `[initialize]` block.

* Pop temporary info

* Remove stored hash in remove_pipe

* Add default for pop

* Add additional convert/debug/assemble CLI tests

.github/azure-steps.yml

@@ -62,6 +62,30 @@ steps:
 
   - script: |
       python -m spacy download ca_core_news_sm
+      python -m spacy download ca_core_news_md
       python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
     displayName: 'Test download CLI'
     condition: eq(variables['python_version'], '3.8')
+
+  - script: |
+      python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
+    displayName: 'Test convert CLI'
+    condition: eq(variables['python_version'], '3.8')
+
+  - script: |
+      python -m spacy init config -p ner -l ca ner.cfg
+      python -m spacy debug config ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy
+    displayName: 'Test debug config CLI'
+    condition: eq(variables['python_version'], '3.8')
+
+  - script: |
+      python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
+      PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
+    displayName: 'Test assemble CLI'
+    condition: eq(variables['python_version'], '3.8')
+
+  - script: |
+      python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
+      python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
+    displayName: 'Test assemble CLI vectors warning'
+    condition: eq(variables['python_version'], '3.8')

spacy/language.py

@@ -934,6 +934,7 @@ class Language:
         # because factory may be used for something else
         self._pipe_meta.pop(name)
         self._pipe_configs.pop(name)
+        self.meta.get("_sourced_vectors_hashes", {}).pop(name, None)
         # Make sure name is removed from the [initialize] config
         if name in self._config["initialize"]["components"]:
             self._config["initialize"]["components"].pop(name)
@@ -1680,6 +1681,8 @@ class Language:
         # If components are loaded from a source (existing models), we cache
         # them here so they're only loaded once
         source_nlps = {}
+        source_nlp_vectors_hashes = {}
+        nlp.meta["_sourced_vectors_hashes"] = {}
         for pipe_name in config["nlp"]["pipeline"]:
             if pipe_name not in pipeline:
                 opts = ", ".join(pipeline.keys())
@@ -1719,7 +1722,12 @@ class Language:
                                     name, source_name, pipe_cfg["replace_listeners"]
                                 )
                                 listeners_replaced = True
+                    with warnings.catch_warnings():
+                        warnings.filterwarnings("ignore", message="\\[W113\\]")
                         nlp.add_pipe(source_name, source=source_nlps[model], name=pipe_name)
+                    if model not in source_nlp_vectors_hashes:
+                        source_nlp_vectors_hashes[model] = hash(source_nlps[model].vocab.vectors.to_bytes())
+                    nlp.meta["_sourced_vectors_hashes"][pipe_name] = source_nlp_vectors_hashes[model]
                     # Delete from cache if listeners were replaced
                     if listeners_replaced:
                         del source_nlps[model]

spacy/training/initialize.py

@@ -9,6 +9,7 @@ import gzip
 import zipfile
 import tqdm
 from itertools import islice
+import warnings
 
 from .pretrain import get_tok2vec_ref
 from ..lookups import Lookups
@@ -124,6 +125,12 @@ def init_vocab(
     if vectors is not None:
         load_vectors_into_model(nlp, vectors)
         logger.info(f"Added vectors: {vectors}")
+    # warn if source model vectors are not identical
+    sourced_vectors_hashes = nlp.meta.pop("_sourced_vectors_hashes", {})
+    vectors_hash = hash(nlp.vocab.vectors.to_bytes())
+    for sourced_component, sourced_vectors_hash in sourced_vectors_hashes.items():
+        if vectors_hash != sourced_vectors_hash:
+            warnings.warn(Warnings.W113.format(name=sourced_component))
     logger.info("Finished initializing nlp object")