Update doc to new model outputs (#5946)

* Update doc to new model outputs

* Fix outputs in quicktour

docs/source/quicktour.rst

@@ -230,13 +230,18 @@ final activations of the model.
 
     >>> ## PYTORCH CODE
     >>> print(pt_outputs)
-    (tensor([[-4.0833,  4.3364],
-            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>),)
+    SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
+            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
     >>> ## TENSORFLOW CODE
     >>> print(tf_outputs)
     (<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0832963 ,  4.3364134 ],
-           [ 0.08181238, -0.04178794]], dtype=float32)>,)
+    array([[-4.0832963 ,  4.336414  ],
+           [ 0.08181786, -0.04179301]], dtype=float32)>,)
+
+The model can return more than just the final activations, which is why the PyTorch output is a special class and the
+TensorFlow output is a tuple. Here we only asked for the final activations, so we get a tuple with one element on the
+TensorFlow side and a :class:`~transformers.modeling_outputs.SequenceClassifierOutput` with just the ``logits`` field
+filled on the PyTorch side.
 
 .. note::
 
@@ -249,7 +254,7 @@ Let's apply the SoftMax activation to get predictions.
 
     >>> ## PYTORCH CODE
     >>> import torch.nn.functional as F
-    >>> pt_predictions = F.softmax(pt_outputs[0], dim=-1)
+    >>> pt_predictions = F.softmax(pt_outputs.logits, dim=-1)
     >>> ## TENSORFLOW CODE
     >>> import tensorflow as tf
     >>> tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)
@@ -262,7 +267,7 @@ We can see we get the numbers from before:
     >>> print(tf_predictions)
     tf.Tensor(
     [[2.2042994e-04 9.9977952e-01]
-     [5.3086078e-01 4.6913919e-01]], shape=(2, 2), dtype=float32)
+     [5.3086340e-01 4.6913657e-01]], shape=(2, 2), dtype=float32)
     >>> ## PYTORCH CODE
     >>> print(pt_predictions)
     tensor([[2.2043e-04, 9.9978e-01],
@@ -285,6 +290,12 @@ training loop. 🤗 Transformers also provides a :class:`~transformers.Trainer`
 you are using TensorFlow) class to help with your training (taking care of things such as distributed training, mixed
 precision, etc.). See the :doc:`training tutorial <training>` for more details.
 
+.. note::
+
+    Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
+    They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
+    case the attributes not set (that have :obj:`None` values) are ignored.
+
 Once your model is fine-tuned, you can save it with its tokenizer in the following way:
 
 ::

docs/source/task_summary.rst

@@ -98,8 +98,8 @@ of each other. The process is the following:
     >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
     >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")
 
-    >>> paraphrase_classification_logits = model(**paraphrase)[0]
-    >>> not_paraphrase_classification_logits = model(**not_paraphrase)[0]
+    >>> paraphrase_classification_logits = model(**paraphrase).logits
+    >>> not_paraphrase_classification_logits = model(**not_paraphrase).logits
 
     >>> paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
     >>> not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
@@ -375,7 +375,7 @@ Here is an example doing masked language modeling using a model and a tokenizer.
     >>> input = tokenizer.encode(sequence, return_tensors="pt")
     >>> mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]
 
-    >>> token_logits = model(input)[0]
+    >>> token_logits = model(input).logits
     >>> mask_token_logits = token_logits[0, mask_token_index, :]
 
     >>> top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
@@ -436,7 +436,7 @@ Here is an example using the tokenizer and model and leveraging the :func:`~tran
     >>> input_ids = tokenizer.encode(sequence, return_tensors="pt")
 
     >>> # get logits of last hidden state
-    >>> next_token_logits = model(input_ids)[0][:, -1, :]
+    >>> next_token_logits = model(input_ids).logits[:, -1, :]
 
     >>> # filter
     >>> filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
@@ -666,7 +666,7 @@ Here is an example doing named entity recognition using a model and a tokenizer.
     >>> tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
     >>> inputs = tokenizer.encode(sequence, return_tensors="pt")
 
-    >>> outputs = model(inputs)[0]
+    >>> outputs = model(inputs).logits
     >>> predictions = torch.argmax(outputs, dim=2)
     >>> ## TENSORFLOW CODE
     >>> from transformers import TFAutoModelForTokenClassification, AutoTokenizer

docs/source/training.rst

@@ -99,7 +99,7 @@ backwards pass and update the weights:
 
     labels = torch.tensor([1,0]).unsqueeze(0)
     outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
-    loss = outputs[0]
+    loss = outputs.loss
     loss.backward()
     optimizer.step()
 
@@ -111,7 +111,7 @@ The following is equivalent to the previous example:
     from torch.nn import functional as F
     labels = torch.tensor([1,0]).unsqueeze(0)
     outputs = model(input_ids, attention_mask=attention_mask)
-    loss = F.cross_entropy(labels, outputs[0])
+    loss = F.cross_entropy(labels, outputs.logitd)
     loss.backward()
     optimizer.step()
 

src/transformers/file_utils.py

@@ -226,7 +226,8 @@ PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
         >>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1
 
         >>> outputs = model(**inputs, labels=labels)
-        >>> loss, scores = outputs[:2]
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
 """
 
 PT_QUESTION_ANSWERING_SAMPLE = r"""
@@ -243,7 +244,9 @@ PT_QUESTION_ANSWERING_SAMPLE = r"""
         >>> end_positions = torch.tensor([3])
 
         >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
-        >>> loss, start_scores, end_scores = outputs[:3]
+        >>> loss = outputs.loss
+        >>> start_scores = outputs.start_scores
+        >>> end_scores = outputs.end_scores
 """
 
 PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
@@ -258,7 +261,8 @@ PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
         >>> outputs = model(**inputs, labels=labels)
-        >>> loss, logits = outputs[:2]
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
 """
 
 PT_MASKED_LM_SAMPLE = r"""
@@ -273,7 +277,8 @@ PT_MASKED_LM_SAMPLE = r"""
         >>> input_ids = tokenizer("Hello, my dog is cute", return_tensors="pt")["input_ids"]
 
         >>> outputs = model(input_ids, labels=input_ids)
-        >>> loss, prediction_scores = outputs[:2]
+        >>> loss = outputs.loss
+        >>> prediction_logits = outputs.logits
 """
 
 PT_BASE_MODEL_SAMPLE = r"""
@@ -288,7 +293,7 @@ PT_BASE_MODEL_SAMPLE = r"""
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
 
-        >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+        >>> last_hidden_states = outputs.last_hidden_state
 """
 
 PT_MULTIPLE_CHOICE_SAMPLE = r"""
@@ -309,7 +314,8 @@ PT_MULTIPLE_CHOICE_SAMPLE = r"""
         >>> outputs = model(**{{k: v.unsqueeze(0) for k,v in encoding.items()}}, labels=labels)  # batch size is 1
 
         >>> # the linear classifier still needs to be trained
-        >>> loss, logits = outputs[:2]
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
 """
 
 PT_CAUSAL_LM_SAMPLE = r"""
@@ -323,7 +329,8 @@ PT_CAUSAL_LM_SAMPLE = r"""
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs, labels=inputs["input_ids"])
-        >>> loss, logits = outputs[:2]
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
 """
 
 TF_TOKEN_CLASSIFICATION_SAMPLE = r"""

src/transformers/modeling_albert.py

@@ -683,7 +683,8 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
         >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
         >>> outputs = model(input_ids)
 
-        >>> prediction_scores, sop_scores = outputs[:2]
+        >>> prediction_logits = outputs.prediction_logits
+        >>> sop_logits = outputs.sop_logits
 
         """
 

src/transformers/modeling_bart.py

@@ -996,7 +996,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
 
             model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
             input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
-            logits = model(input_ids)[0]
+            logits = model(input_ids).logits
 
             masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
             probs = logits[0, masked_index].softmax(dim=0)

src/transformers/modeling_bert.py

@@ -873,8 +873,8 @@ class BertForPreTraining(BertPreTrainedModel):
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
 
-        >>> prediction_scores, seq_relationship_scores = outputs[:2]
-
+        >>> prediction_logits = outptus.prediction_logits
+        >>> seq_relationship_logits = outputs.seq_relationship_logits
         """
         if "masked_lm_labels" in kwargs:
             warnings.warn(
@@ -978,7 +978,7 @@ class BertLMHeadModel(BertPreTrainedModel):
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
 
-        >>> prediction_scores = outputs.prediction_scores
+        >>> prediction_logits = outputs.logits
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 
@@ -1181,7 +1181,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
 
         >>> outputs = model(**encoding, next_sentence_label=torch.LongTensor([1]))
-        >>> logits = outputs.seq_relationship_scores
+        >>> logits = outputs.logits
         >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple

src/transformers/modeling_distilbert.py

@@ -876,8 +876,8 @@ class DistilBertForMultipleChoice(DistilBertPreTrainedModel):
         >>> outputs = model(**{k: v.unsqueeze(0) for k,v in encoding.items()}, labels=labels) # batch size is 1
 
         >>> # the linear classifier still needs to be trained
-        >>> loss, logits = outputs[:2]
-
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
         num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

src/transformers/modeling_dpr.py

@@ -423,8 +423,7 @@ class DPRContextEncoder(DPRPretrainedContextEncoder):
         tokenizer = DPRContextEncoderTokenizer.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
         model = DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
         input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
-        embeddings = model(input_ids)[0]  # the embeddings of the given context.
-
+        embeddings = model(input_ids).pooler_output
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -502,7 +501,7 @@ class DPRQuestionEncoder(DPRPretrainedQuestionEncoder):
         tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
         model = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
         input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
-        embeddings = model(input_ids)[0]  # the embeddings of the given question.
+        embeddings = model(input_ids).pooler_output
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -583,9 +582,9 @@ class DPRReader(DPRPretrainedReader):
                 return_tensors='pt'
             )
         outputs = model(**encoded_inputs)
-        start_logits = outputs[0]  # The logits of the start of the spans
-        end_logits = outputs[1]  # The logits of the end of the spans
-        relevance_logits = outputs[2]  # The relevance scores of the passages
+        start_logits = outputs.stat_logits
+        end_logits = outputs.end_logits
+        relevance_logits = outputs.relevance_logits
 
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/modeling_electra.py

@@ -525,8 +525,7 @@ class ElectraForPreTraining(ElectraPreTrainedModel):
         >>> model = ElectraForPreTraining.from_pretrained('google/electra-small-discriminator')
 
         >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-        >>> scores = model(input_ids)[0]
-
+        >>> logits = model(input_ids).logits
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 

src/transformers/modeling_gpt2.py

@@ -754,7 +754,8 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         >>> mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1
 
         >>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
-        >>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
+        >>> lm_logits = outputs.lm_logits
+        >>> mc_logits = outputs.mc_logits
 
         """
         if "lm_labels" in kwargs:

src/transformers/modeling_longformer.py

@@ -1090,7 +1090,9 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
 
         >>> attention_mask = None  # default is local attention everywhere, which is a good choice for MaskedLM
         ...                        # check ``LongformerModel.forward`` for more details how to set `attention_mask`
-        >>> loss, prediction_scores = model(input_ids, attention_mask=attention_mask, labels=input_ids)
+        >>> outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
+        >>> loss = outputs.loss
+        >>> prediction_logits = output.logits
         """
 
         if "masked_lm_labels" in kwargs:
@@ -1299,10 +1301,12 @@ class LongformerForQuestionAnswering(BertPreTrainedModel):
         >>> # the forward method will automatically set global attention on question tokens
         >>> attention_mask = encoding["attention_mask"]
 
-        >>> start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
+        >>> outputs = model(input_ids, attention_mask=attention_mask)
+        >>> start_logits = outputs.start_logits
+        >>> end_logits = outputs.end_logits
         >>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
 
-        >>> answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
+        >>> answer_tokens = all_tokens[torch.argmax(start_logits) :torch.argmax(end_logits)+1]
         >>> answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # remove space prepending space token
 
         """

src/transformers/modeling_mobilebert.py

@@ -979,7 +979,8 @@ class MobileBertForPreTraining(MobileBertPreTrainedModel):
         >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
         >>> outputs = model(input_ids)
 
-        >>> prediction_scores, seq_relationship_scores = outputs[:2]
+        >>> prediction_logits = outptus.prediction_logits
+        >>> seq_relationship_logits = outputs.seq_relationship_logits
 
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
@@ -1186,7 +1187,9 @@ class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
         >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
         >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
 
-        >>> loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1]))
+        >>> outputs = model(**encoding, next_sentence_label=torch.LongTensor([1]))
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 

src/transformers/modeling_openai.py

@@ -659,7 +659,8 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         mc_token_ids = torch.tensor([input_ids.size(-1)-1, input_ids.size(-1)-1]).unsqueeze(0)  # Batch size 1
 
         outputs = model(input_ids, mc_token_ids=mc_token_ids)
-        lm_prediction_scores, mc_prediction_scores = outputs[:2]
+        lm_logits = outputs.lm_logits
+        mc_logits = outputs.mc_logits
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
         if "lm_labels" in kwargs:

src/transformers/modeling_xlm.py

@@ -989,7 +989,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
         >>> end_positions = torch.tensor([3])
 
         >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
-        >>> loss = outputs[0]
+        >>> loss = outputs.loss
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
 

src/transformers/modeling_xlnet.py

@@ -1366,8 +1366,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         target_mapping[0, 0, -1] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)
 
         outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping, labels=labels)
-        loss, next_token_logits = outputs[:2]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
-
+        loss = outputs.loss
+        next_token_logits = outputs.logits  # Logits have shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
         use_cache = self.training or (use_cache if use_cache is not None else self.config.use_cache)
@@ -1876,7 +1876,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
         >>> end_positions = torch.tensor([3])
         >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
 
-        >>> loss = outputs[0]
+        >>> loss = outputs.loss
         """
         return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
         use_cache = self.training or (use_cache if use_cache is not None else self.config.use_cache)