[WIP] Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, Mul…

…tipleC… (#5614)

* Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleChoice} models and tests

* AutoModels


Tiny tweaks

* Style

* Final changes before merge

* Re-order for simpler review

* Final fixes

* Addressing @sgugger's comments

* Test MultipleChoice

src/transformers/__init__.py

@@ -278,6 +278,7 @@
         XLMForTokenClassification,
         XLMForQuestionAnswering,
         XLMForQuestionAnsweringSimple,
+        XLMForMultipleChoice,
         XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     )
     from .modeling_bart import (
@@ -356,6 +357,8 @@
         FlaubertForTokenClassification,
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
+        FlaubertForTokenClassification,
+        FlaubertForMultipleChoice,
         FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     )
 

src/transformers/modeling_auto.py

@@ -98,6 +98,7 @@
 )
 from .modeling_encoder_decoder import EncoderDecoderModel
 from .modeling_flaubert import (
+    FlaubertForMultipleChoice,
     FlaubertForQuestionAnsweringSimple,
     FlaubertForSequenceClassification,
     FlaubertForTokenClassification,
@@ -142,6 +143,7 @@
 from .modeling_t5 import T5ForConditionalGeneration, T5Model
 from .modeling_transfo_xl import TransfoXLLMHeadModel, TransfoXLModel
 from .modeling_xlm import (
+    XLMForMultipleChoice,
     XLMForQuestionAnsweringSimple,
     XLMForSequenceClassification,
     XLMForTokenClassification,
@@ -338,6 +340,7 @@
         (XLNetConfig, XLNetForTokenClassification),
         (AlbertConfig, AlbertForTokenClassification),
         (ElectraConfig, ElectraForTokenClassification),
+        (FlaubertConfig, FlaubertForTokenClassification),
     ]
 )
 
@@ -353,6 +356,8 @@
         (MobileBertConfig, MobileBertForMultipleChoice),
         (XLNetConfig, XLNetForMultipleChoice),
         (AlbertConfig, AlbertForMultipleChoice),
+        (XLMConfig, XLMForMultipleChoice),
+        (FlaubertConfig, FlaubertForMultipleChoice),
     ]
 )
 

src/transformers/modeling_flaubert.py

@@ -25,6 +25,7 @@
 from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_outputs import BaseModelOutput
 from .modeling_xlm import (
+    XLMForMultipleChoice,
     XLMForQuestionAnswering,
     XLMForQuestionAnsweringSimple,
     XLMForSequenceClassification,
@@ -382,3 +383,22 @@ def __init__(self, config):
         super().__init__(config)
         self.transformer = FlaubertModel(config)
         self.init_weights()
+
+
+@add_start_docstrings(
+    """Flaubert Model with a multiple choice classification head on top (a linear layer on top of
+    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertForMultipleChoice(XLMForMultipleChoice):
+    """
+    This class overrides :class:`~transformers.XLMForMultipleChoice`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = FlaubertConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = FlaubertModel(config)
+        self.init_weights()

src/transformers/modeling_tf_flaubert.py

@@ -22,14 +22,15 @@
 
 from .configuration_flaubert import FlaubertConfig
 from .file_utils import add_start_docstrings
-from .modeling_tf_utils import keras_serializable, shape_list
+from .modeling_tf_utils import cast_bool_to_primitive, keras_serializable, shape_list
 from .modeling_tf_xlm import (
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
     TFXLMForSequenceClassification,
     TFXLMForTokenClassification,
     TFXLMMainLayer,
     TFXLMModel,
+    TFXLMPredLayer,
     TFXLMWithLMHeadModel,
     get_masks,
 )
@@ -123,6 +124,8 @@ def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
         self.layerdrop = getattr(config, "layerdrop", 0.0)
         self.pre_norm = getattr(config, "pre_norm", False)
+        self.output_attentions = config.output_attentions
+        self.output_hidden_states = config.output_hidden_states
 
     def call(
         self,
@@ -135,9 +138,9 @@ def call(
         cache=None,
         head_mask=None,
         inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
         training=False,
-        output_attentions=False,
-        output_hidden_states=False,
     ):
         # removed: src_enc=None, src_len=None
         if isinstance(inputs, (tuple, list)):
@@ -150,7 +153,9 @@ def call(
             cache = inputs[6] if len(inputs) > 6 else cache
             head_mask = inputs[7] if len(inputs) > 7 else head_mask
             inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
-            assert len(inputs) <= 9, "Too many inputs."
+            output_attentions = inputs[9] if len(inputs) > 9 else output_attentions
+            output_hidden_states = inputs[10] if len(inputs) > 10 else output_hidden_states
+            assert len(inputs) <= 11, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
@@ -161,10 +166,15 @@ def call(
             cache = inputs.get("cache", cache)
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
-            assert len(inputs) <= 9, "Too many inputs."
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            assert len(inputs) <= 11, "Too many inputs."
         else:
             input_ids = inputs
 
+        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -257,9 +267,12 @@ def call(
 
             # self attention
             if not self.pre_norm:
-                attn_outputs = self.attentions[i]([tensor, attn_mask, None, cache, head_mask[i]], training=training)
+                attn_outputs = self.attentions[i](
+                    [tensor, attn_mask, None, cache, head_mask[i], output_attentions], training=training
+                )
                 attn = attn_outputs[0]
-                attentions = attentions + (attn_outputs[1],)
+                if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
+                    attentions = attentions + (attn_outputs[1],)
                 attn = self.dropout(attn, training=training)
                 tensor = tensor + attn
                 tensor = self.layer_norm1[i](tensor)
@@ -269,7 +282,7 @@ def call(
                     [tensor_normalized, attn_mask, None, cache, head_mask[i]], training=training
                 )
                 attn = attn_outputs[0]
-                if output_attentions:
+                if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
                     attentions = attentions + (attn_outputs[1],)
                 attn = self.dropout(attn, training=training)
                 tensor = tensor + attn
@@ -292,7 +305,7 @@ def call(
             tensor = tensor * mask[..., tf.newaxis]
 
         # Add last hidden state
-        if output_hidden_states:
+        if cast_bool_to_primitive(output_hidden_states, self.output_hidden_states) is True:
             hidden_states = hidden_states + (tensor,)
 
         # update cache length
@@ -303,9 +316,9 @@ def call(
         # tensor = tensor.transpose(0, 1)
 
         outputs = (tensor,)
-        if output_hidden_states:
+        if cast_bool_to_primitive(output_hidden_states, self.output_hidden_states) is True:
             outputs = outputs + (hidden_states,)
-        if output_attentions:
+        if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
             outputs = outputs + (attentions,)
         return outputs  # outputs, (hidden_states), (attentions)
 
@@ -321,6 +334,7 @@ class TFFlaubertWithLMHeadModel(TFXLMWithLMHeadModel):
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
         self.transformer = TFFlaubertMainLayer(config, name="transformer")
+        self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name="pred_layer_._proj")
 
 
 @add_start_docstrings(

src/transformers/modeling_tf_xlm.py

@@ -19,6 +19,7 @@
 import itertools
 import logging
 import math
+import warnings
 
 import numpy as np
 import tensorflow as tf
@@ -827,6 +828,9 @@ def __init__(self, config, *inputs, **kwargs):
 
         self.transformer = TFXLMMainLayer(config, name="transformer")
         self.sequence_summary = TFSequenceSummary(config, initializer_range=config.init_std, name="sequence_summary")
+        self.logits_proj = tf.keras.layers.Dense(
+            1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
+        )
 
     @property
     def dummy_inputs(self):
@@ -835,7 +839,10 @@ def dummy_inputs(self):
         Returns:
             tf.Tensor with dummy inputs
         """
-        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
+        return {
+            "input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS),
+            "langs": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS),
+        }
 
     @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="xlm-mlm-en-2048")
@@ -892,7 +899,7 @@ def call(
             output_attentions = inputs[9] if len(inputs) > 9 else output_attentions
             output_hidden_states = inputs[10] if len(inputs) > 10 else output_hidden_states
             labels = inputs[11] if len(inputs) > 11 else labels
-            assert len(inputs) <= 11, "Too many inputs."
+            assert len(inputs) <= 12, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
@@ -921,24 +928,39 @@ def call(
         flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
         flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
         flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+        flat_langs = tf.reshape(langs, (-1, seq_length)) if langs is not None else None
+        flat_inputs_embeds = (
+            tf.reshape(inputs_embeds, (-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]))
+            if inputs_embeds is not None
+            else None
+        )
+
+        if lengths is not None:
+            warnings.warn(
+                "The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
+                "attention mask instead.",
+                FutureWarning,
+            )
+            lengths = None
 
         flat_inputs = [
             flat_input_ids,
             flat_attention_mask,
-            langs,
+            flat_langs,
             flat_token_type_ids,
             flat_position_ids,
             lengths,
             cache,
             head_mask,
-            inputs_embeds,
+            flat_inputs_embeds,
             output_attentions,
             output_hidden_states,
         ]
 
         transformer_outputs = self.transformer(flat_inputs, training=training)
         output = transformer_outputs[0]
         logits = self.sequence_summary(output)
+        logits = self.logits_proj(logits)
         reshaped_logits = tf.reshape(logits, (-1, num_choices))
 
         outputs = (reshaped_logits,) + transformer_outputs[1:]  # add hidden states and attention if they are here