XLMWithLMHead fixed - standardize conversion

pytorch_transformers/modeling_tf_bert.py

@@ -57,7 +57,7 @@ def load_bert_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
     inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
     tf_inputs = tf.constant(inputs_list)
     tfo = tf_model(tf_inputs, training=False)
-    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
+    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
 
 
 def gelu(x):

pytorch_transformers/modeling_tf_gpt2.py

@@ -46,7 +46,7 @@ def load_gpt2_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
     inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
     tf_inputs = tf.constant(inputs_list)
     tfo = tf_model(tf_inputs, training=False)
-    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
+    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
 
 
 def gelu(x):

pytorch_transformers/modeling_tf_pytorch_utils.py

@@ -19,34 +19,34 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 import logging
-
-from pytorch_transformers import is_tf_available, is_torch_available
+import os
 
 logger = logging.getLogger(__name__)
 
 
-def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path):
+def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None):
     """ Load pytorch checkpoints in a TF 2.0 model
         Conventions for TF2.0 scopes -> PyTorch attribute names conversions:
             - '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
             - '_._' is replaced by a new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)
     """
-    if not is_tf_available() or not is_torch_available():
+    try:
+        import tensorflow as tf
+        import torch
+    except ImportError as e:
         logger.error("Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see "
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.")
-        raise ImportError
-
-    import torch
+        raise e
 
     pt_path = os.path.abspath(pytorch_checkpoint_path)
     logger.info("Loading PyTorch weights from {}".format(pt_path))
 
     pt_state_dict = torch.load(pt_path, map_location='cpu')
 
-    return load_pytorch_state_dict_in_tf2_model(tf_model, pt_state_dict)
+    return load_pytorch_state_dict_in_tf2_model(tf_model, pt_state_dict, tf_inputs=tf_inputs)
 
 
-def load_pytorch_state_dict_in_tf2_model(tf_model, pt_state_dict):
+def load_pytorch_state_dict_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None):
     """ Load pytorch state_dict in a TF 2.0 model.
         Conventions for TF2.0 scopes -> PyTorch attribute names conversions:
             - '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
@@ -102,7 +102,8 @@ def load_pytorch_state_dict_in_tf2_model(tf_model, pt_state_dict):
 
     K.batch_set_value(weight_value_tuples)
 
-    tfo = tf_model(tf_inputs, training=False)  # Make sure restore ops are run
+    if tf_inputs is not None:
+        tfo = tf_model(tf_inputs, training=False)  # Make sure restore ops are run
 
     logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights))
 

pytorch_transformers/modeling_tf_xlm.py

@@ -50,11 +50,9 @@ def load_xlm_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
     inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
     attns_list = [[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]
     langs_list = [[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]
-    tf_inputs = tf.constant(inputs_list)
-    tf_attns = tf.constant(attns_list)
-    tf_langs = tf.constant(langs_list)
-    tfo = tf_model([tf_inputs, tf_attns, tf_langs], training=False)
-    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
+    tf_inputs = [tf.constant(inputs_list), tf.constant(attns_list), tf.constant(langs_list)]
+    tfo = tf_model(tf_inputs, training=False)
+    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
 
 
 def create_sinusoidal_embeddings(n_pos, dim, out):
@@ -614,7 +612,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
     """
     def __init__(self, config, *inputs, **kwargs):
         super(TFXLMWithLMHeadModel, self).__init__(config, *inputs, **kwargs)
-        self.transformer = TFXLMMainLayer(config, name='transformer___')
+        self.transformer = TFXLMMainLayer(config, name='transformer')
         self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name='pred_layer_._proj')
 
 

pytorch_transformers/modeling_tf_xlnet.py

@@ -45,7 +45,7 @@ def load_xlnet_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
     inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
     tf_inputs = tf.constant(inputs_list)
     tfo = tf_model(tf_inputs, training=False)  # build the network
-    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
+    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
 
 
 def gelu(x):

pytorch_transformers/modeling_xlm.py

@@ -563,10 +563,10 @@ class XLMPredLayer(nn.Module):
         """
         outputs = ()
         if self.asm is False:
-            scores = self.proj(x).view(-1, self.n_words)
+            scores = self.proj(x)
             outputs = (scores,) + outputs
             if y is not None:
-                loss = F.cross_entropy(scores, y, reduction='elementwise_mean')
+                loss = F.cross_entropy(scores.view(-1, self.n_words), y, reduction='elementwise_mean')
                 outputs = (loss,) + outputs
         else:
             scores = self.proj.log_prob(x)