revise share all embedding bugs and inference bugs

fairseq/utils.py

@@ -131,36 +131,36 @@ def _upgrade_state_dict(state):
     return state
     return state
 
 
 
 
-def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
-    """Load an ensemble of models for inference.
-
-    model_arg_overrides allows you to pass a dictionary model_arg_overrides --
-    {'arg_name': arg} -- to override model args that were used during model
-    training
-    """
-    # load model architectures and weights
-    states = []
-    for filename in filenames:
-        if not os.path.exists(filename):
-            raise IOError('Model file not found: {}'.format(filename))
-        state = torch.load(filename, map_location=lambda s, l: default_restore_location(s, 'cpu'))
-        #state = _upgrade_state_dict(state)
-        states.append(state)
-    args = states[0]['args']
-    if model_arg_overrides is not None:
-        args = _override_model_args(args, model_arg_overrides)
-
-    # build ensemble
-    ensemble = []
-    for state in states:
-        model = task.build_model(args)
-        print(model)
-        model.upgrade_state_dict(state['model'])
-        model.load_state_dict(state['model'], strict=True)
-        ensemble.append(model)
-    return ensemble, args
+# def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
+#     """Load an ensemble of models for inference.
+#
+#     model_arg_overrides allows you to pass a dictionary model_arg_overrides --
+#     {'arg_name': arg} -- to override model args that were used during model
+#     training
+#     """
+#     # load model architectures and weights
+#     states = []
+#     for filename in filenames:
+#         if not os.path.exists(filename):
+#             raise IOError('Model file not found: {}'.format(filename))
+#         state = torch.load(filename, map_location=lambda s, l: default_restore_location(s, 'cpu'))
+#         #state = _upgrade_state_dict(state)
+#         states.append(state)
+#     args = states[0]['args']
+#     if model_arg_overrides is not None:
+#         args = _override_model_args(args, model_arg_overrides)
+#
+#     # build ensemble
+#     ensemble = []
+#     for state in states:
+#         model = task.build_model(args)
+#         print(model)
+#         model.upgrade_state_dict(state['model'])
+#         model.load_state_dict(state['model'], strict=True)
+#         ensemble.append(model)
+#     return ensemble, args
 
 
-def load_ensemble_for_inference_2(filenames, task, model_arg_overrides=None):
+def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
     """Load an ensemble of models for inference.
     """Load an ensemble of models for inference.
 
 
     model_arg_overrides allows you to pass a dictionary model_arg_overrides --
     model_arg_overrides allows you to pass a dictionary model_arg_overrides --

scripts/convert_t2t_to_fairseq.py

@@ -91,10 +91,12 @@ def find_useful_param(model_file):
           share_decoder_input_and_softmax = True
           share_decoder_input_and_softmax = True
           tgt_vocab_size = int(match.group(1))
           tgt_vocab_size = int(match.group(1))
 
 
-        match = re.match('symbol_modality_(\d+)_(\d+)/shared/$', key)
+        match = re.match('symbol_modality_(\d+)_(\d+)/shared/', key)
         if match and share_all_embedding is False:
         if match and share_all_embedding is False:
           share_all_embedding = True
           share_all_embedding = True
+          src_vocab_size = int(match.group(1))
           tgt_vocab_size = int(match.group(1))
           tgt_vocab_size = int(match.group(1))
+          emb_size = int(match.group(2))
 
 
   except Exception as e:  # pylint: disable=broad-except
   except Exception as e:  # pylint: disable=broad-except
     print(str(e))
     print(str(e))