💫 Improve error message when model.from_bytes() dies (#4014)

* Improve error message when model.from_bytes() dies

When Thinc's model.from_bytes() is called with a mismatched model, often
we get a particularly ungraceful error,

e.g. "AttributeError: FunctionLayer has no attribute G"

This is because we're trying to load the parameters for something like
a LayerNorm layer, and the model architecture has some other layer there
instead. This is obviously terrible, especially since the error *type*
is wrong.

I've changed it to raise a ValueError. The error message is still
probably a bit terse, but it's hard to be sure exactly what's gone
wrong.

* Update spacy/pipeline/pipes.pyx

* Update spacy/pipeline/pipes.pyx

* Update spacy/pipeline/pipes.pyx

* Update spacy/syntax/nn_parser.pyx

* Update spacy/syntax/nn_parser.pyx

* Update spacy/pipeline/pipes.pyx

Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>

* Update spacy/pipeline/pipes.pyx

Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>


Co-authored-by: Ines Montani <ines@ines.io>

spacy/errors.py

@@ -413,7 +413,8 @@ class Errors(object):
             "This is likely a bug in spaCy, so feel free to open an issue.")
     E148 = ("Expected {ents} KB identifiers but got {ids}. Make sure that each entity in `doc.ents` "
             "is assigned to a KB identifier.")
-
+    E149 = ("Error deserializing model. Check that the config used to create the "
+            "component matches the model being loaded.")
 
 @add_codes
 class TempErrors(object):

spacy/pipeline/pipes.pyx

@@ -167,7 +167,10 @@ class Pipe(object):
                 self.cfg["pretrained_vectors"] = self.vocab.vectors.name
             if self.model is True:
                 self.model = self.Model(**self.cfg)
-            self.model.from_bytes(b)
+            try:
+                self.model.from_bytes(b)
+            except AttributeError:
+                raise ValueError(Errors.E149)
 
         deserialize = OrderedDict()
         deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
@@ -196,7 +199,10 @@ class Pipe(object):
                 self.cfg["pretrained_vectors"] = self.vocab.vectors.name
             if self.model is True:
                 self.model = self.Model(**self.cfg)
-            self.model.from_bytes(p.open("rb").read())
+            try:
+                self.model.from_bytes(p.open("rb").read())
+            except AttributeError:
+                raise ValueError(Errors.E149)
 
         deserialize = OrderedDict()
         deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
@@ -562,7 +568,10 @@ class Tagger(Pipe):
                     "token_vector_width",
                     self.cfg.get("token_vector_width", 96))
                 self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
-            self.model.from_bytes(b)
+            try:
+                self.model.from_bytes(b)
+            except AttributeError:
+                raise ValueError(Errors.E149)
 
         def load_tag_map(b):
             tag_map = srsly.msgpack_loads(b)
@@ -600,7 +609,10 @@ class Tagger(Pipe):
             if self.model is True:
                 self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
             with p.open("rb") as file_:
-                self.model.from_bytes(file_.read())
+                try:
+                    self.model.from_bytes(file_.read())
+                except AttributeError:
+                    raise ValueError(Errors.E149)
 
         def load_tag_map(p):
             tag_map = srsly.read_msgpack(p)
@@ -1315,9 +1327,12 @@ class EntityLinker(Pipe):
 
     def from_disk(self, path, exclude=tuple(), **kwargs):
         def load_model(p):
-             if self.model is True:
+            if self.model is True:
                 self.model = self.Model(**self.cfg)
-             self.model.from_bytes(p.open("rb").read())
+            try: 
+                self.model.from_bytes(p.open("rb").read())
+            except AttributeError:
+                raise ValueError(Errors.E149)
 
         def load_kb(p):
             kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])

spacy/syntax/nn_parser.pyx

@@ -631,7 +631,10 @@ cdef class Parser:
                 cfg = {}
             with (path / 'model').open('rb') as file_:
                 bytes_data = file_.read()
-            self.model.from_bytes(bytes_data)
+            try:
+                self.model.from_bytes(bytes_data)
+            except AttributeError:
+                raise ValueError(Errors.E149)
             self.cfg.update(cfg)
         return self
 
@@ -663,6 +666,9 @@ cdef class Parser:
             else:
                 cfg = {}
             if 'model' in msg:
-                self.model.from_bytes(msg['model'])
+                try:
+                    self.model.from_bytes(msg['model'])
+                except AttributeError:
+                    raise ValueError(Errors.E149)
             self.cfg.update(cfg)
         return self