fixing XLM conversion tests with dummy input

transformers/modeling_tf_pytorch_utils.py

@@ -78,6 +78,7 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i
     logger.info("Loading PyTorch weights from {}".format(pt_path))
 
     pt_state_dict = torch.load(pt_path, map_location='cpu')
+    logger.info("PyTorch checkpoint contains {:,} parameters".format(sum(t.numel() for t in pt_state_dict.values())))
 
     return load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys)
 
@@ -134,7 +135,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         start_prefix_to_remove = tf_model.base_model_prefix + '.'
 
     symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights
-
+    tf_loaded_numel = 0
     weight_value_tuples = []
     all_pytorch_weights = set(list(pt_state_dict.keys()))
     for symbolic_weight in symbolic_weights:
@@ -159,6 +160,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             e.args += (symbolic_weight.shape, array.shape)
             raise e
 
+        tf_loaded_numel += array.size
         # logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
 
         weight_value_tuples.append((symbolic_weight, array))
@@ -169,6 +171,8 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
     if tf_inputs is not None:
         tfo = tf_model(tf_inputs, training=False)  # Make sure restore ops are run
 
+    logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel))
+
     logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights))
 
     return tf_model

transformers/modeling_tf_xlm.py

@@ -460,7 +460,7 @@ class TFXLMPreTrainedModel(TFPreTrainedModel):
             langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
         else:
             langs_list = None
-        return [inputs_list, attns_list, langs_list]
+        return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
 
 
 XLM_START_DOCSTRING = r"""    The XLM model was proposed in

transformers/modeling_xlm.py

@@ -227,6 +227,16 @@ class XLMPreTrainedModel(PreTrainedModel):
     def __init__(self, *inputs, **kwargs):
         super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs)
 
+    @property
+    def dummy_inputs(self):
+        inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
+        attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
+        if self.config.use_lang_emb and self.config.n_langs > 1:
+            langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
+        else:
+            langs_list = None
+        return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
+
     def _init_weights(self, module):
         """ Initialize the weights. """
         if isinstance(module, nn.Embedding):
@@ -646,7 +656,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
                                                langs=langs,
                                                token_type_ids=token_type_ids,
                                                position_ids=position_ids,
-                                               lengths=lengths, 
+                                               lengths=lengths,
                                                cache=cache,
                                                head_mask=head_mask,
                                                inputs_embeds=inputs_embeds)