Move weight serialization to Thinc

requirements.txt

@@ -3,8 +3,8 @@ pathlib
 numpy>=1.7
 cymem>=1.30,<1.32
 preshed>=1.0.0,<2.0.0
-thinc>=6.6.0,<6.7.0
-murmurhash>=0.26,<0.27
+thinc>=6.7.0,<6.8.0
+murmurhash>=0.28,<0.29
 plac<1.0.0,>=0.9.6
 six
 ujson>=1.35

setup.py

@@ -188,10 +188,10 @@ def setup_package():
             ext_modules=ext_modules,
             install_requires=[
                 'numpy>=1.7',
-                'murmurhash>=0.26,<0.27',
+                'murmurhash>=0.28,<0.29',
                 'cymem>=1.30,<1.32',
                 'preshed>=1.0.0,<2.0.0',
-                'thinc>=6.6.0,<6.7.0',
+                'thinc>=6.7.0,<6.8.0',
                 'plac<1.0.0,>=0.9.6',
                 'pip>=9.0.0,<10.0.0',
                 'six',

spacy/pipeline.pyx

@@ -169,7 +169,7 @@ class TokenVectorEncoder(object):
         if self.model is True:
             self.model = self.Model()
         deserialize = OrderedDict((
-            ('model', lambda b: util.model_from_bytes(self.model, b)),
+            ('model', lambda b: self.model.from_bytes(b)),
             ('vocab', lambda b: self.vocab.from_bytes(b))
         ))
         util.from_bytes(bytes_data, deserialize, exclude)
@@ -186,7 +186,7 @@ class TokenVectorEncoder(object):
         if self.model is True:
             self.model = self.Model()
         deserialize = OrderedDict((
-            ('model', lambda p: util.model_from_bytes(self.model, p.open('rb').read())),
+            ('model', lambda p: self.model.from_bytes(p.open('rb').read())),
             ('vocab', lambda p: self.vocab.from_disk(p))
         ))
         util.from_disk(path, deserialize, exclude)
@@ -307,7 +307,7 @@ class NeuralTagger(object):
             if self.model is True:
                 token_vector_width = util.env_opt('token_vector_width', 128)
                 self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
-                util.model_from_bytes(self.model, b)
+                self.model.from_bytes(b)
         deserialize = OrderedDict((
             ('vocab', lambda b: self.vocab.from_bytes(b)),
             ('model', lambda b: load_model(b)),
@@ -324,7 +324,7 @@ class NeuralTagger(object):
 
     def from_disk(self, path, **exclude):
         deserialize = {
-            'model': lambda p: util.model_from_bytes(self.model, p.open('rb').read()),
+            'model': lambda p: self.model.from_bytes(p.open('rb').read()),
             'vocab': lambda p: self.vocab.from_disk(p)
         }
         util.from_disk(path, deserialize, exclude)

spacy/syntax/nn_parser.pyx

@@ -660,10 +660,10 @@ cdef class Parser:
                 cfg = {}
             with (path / 'lower_model').open('rb') as file_:
                 bytes_data = file_.read()
-            util.model_from_bytes(self.model[0], bytes_data)
+            self.model[0].from_bytes(bytes_data)
             with (path / 'upper_model').open('rb') as file_:
                 bytes_data = file_.read()
-            util.model_from_bytes(self.model[1], bytes_data)
+            self.model[1].from_bytes(bytes_data)
             self.cfg.update(cfg)
         return self
 
@@ -691,8 +691,8 @@ cdef class Parser:
                 self.model, cfg = self.Model(self.moves.n_moves)
             else:
                 cfg = {}
-            util.model_from_bytes(self.model[0], msg['lower_model'])
-            util.model_from_bytes(self.model[1], msg['upper_model'])
+            self.model[0].from_bytes(msg['lower_model'])
+            util.model[1].from_bytes(msg['upper_model'])
             self.cfg.update(cfg)
         return self
 

spacy/util.py

@@ -452,60 +452,6 @@ def from_disk(path, readers, exclude):
     return path
 
 
-# This stuff really belongs in thinc -- but I expect
-# to refactor how all this works in thinc anyway.
-# What a mess!
-def model_to_bytes(model):
-    weights = []
-    queue = [model]
-    i = 0
-    for layer in queue:
-        if hasattr(layer, '_mem'):
-            weights.append({
-                'dims': normalize_string_keys(getattr(layer, '_dims', {})),
-                'params': []})
-            if hasattr(layer, 'seed'):
-                weights[-1]['seed'] = layer.seed
-
-            for (id_, name), (start, row, shape) in layer._mem._offsets.items():
-                if row == 1:
-                    continue
-                param = layer._mem.get((id_, name))
-                if not isinstance(layer._mem.weights, numpy.ndarray):
-                    param = param.get()
-                weights[-1]['params'].append(
-                    {
-                        'name': name,
-                        'offset': start,
-                        'shape': shape,
-                        'value': param,
-                    }
-                )
-            i += 1
-        if hasattr(layer, '_layers'):
-            queue.extend(layer._layers)
-    return msgpack.dumps({'weights': weights})
-
-
-def model_from_bytes(model, bytes_data):
-    data = msgpack.loads(bytes_data)
-    weights = data['weights']
-    queue = [model]
-    i = 0
-    for layer in queue:
-        if hasattr(layer, '_mem'):
-            if 'seed' in weights[i]:
-                layer.seed = weights[i]['seed']
-            for dim, value in weights[i]['dims'].items():
-                setattr(layer, dim, value)
-            for param in weights[i]['params']:
-                dest = getattr(layer, param['name'])
-                copy_array(dest, param['value'])
-            i += 1
-        if hasattr(layer, '_layers'):
-            queue.extend(layer._layers)
-
-
 def print_table(data, title=None):
     """Print data in table format.