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This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: :obj:`model(inputs)`. If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument : - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})` Parameters: config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. a Args: input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. If `past` is used, optionally only the last `input_ids` have to be input (see `past`). Indices can be obtained using :class:`transformers.GPT2Tokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.encode_plus` for details. `What are input IDs? <../glossary.html#input-ids>`__ past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `past` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. `What are attention masks? <../glossary.html#attention-mask>`__ token_type_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` corresponds to a `sentence B` token If `past` is used, optionally only the last `token_type_ids` have to be input (see `past`). `What are token type IDs? <../glossary.html#token-type-ids>`_ position_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1]``. `What are position IDs? <../glossary.html#position-ids>`_ head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**. input_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `past` is used, optionally only the last `input_embeds` have to be input (see `past`). training (:obj:`boolean`, `optional`, defaults to :obj:`False`): Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them (if set to :obj:`False`) for evaluation. z]The bare GPT2 Model transformer outputing raw hidden-states without any specific head on top.cs,eZdZfddZeeddZZS) TFGPT2Modelcs&tj|f||t|dd|_dSNrr|r!r"rrrr4rrr"szTFGPT2Model.__init__cKs|j|f|}|S)a$ Return: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs: last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the last layer of the model. past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: import tensorflow as tf from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1 outputs = model(input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple )r)r/rLr2rUrrrris#zTFGPT2Model.call)rjrkrlr"rGPT2_INPUTS_DOCSTRINGrirnrrr4rrs rz~The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). cs<eZdZfddZddZddZeeddZZ S) TFGPT2LMHeadModelcs&tj|f||t|dd|_dSrrrr4rrr"szTFGPT2LMHeadModel.__init__cCs|jjSr6rrrrrrget_output_embeddingssz'TFGPT2LMHeadModel.get_output_embeddingscKs,|rt|dddfd}|||ddS)NrArc)rLrrc)rZ expand_dims)r/rLrr2rrrprepare_inputs_for_generationsz/TFGPT2LMHeadModel.prepare_inputs_for_generationcKs<|j|f|}|d}|jj|dd}|f|dd}|S)a Return: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs: prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: import tensorflow as tf from transformers import GPT2Tokenizer, TFGPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2LMHeadModel.from_pretrained('gpt2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1 outputs = model(input_ids) logits = outputs[0] rlinearrrNr)r/rLr2transformer_outputsr lm_logitsrUrrrri$s $zTFGPT2LMHeadModel.call) rjrkrlr"rrrrrirnrrr4rrs  rarThe GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the input embeddings, the classification head takes as input the input of a specified classification token index in the input sequence). c s6eZdZfddZddZeed dd ZZS) TFGPT2DoubleHeadsModelcs>tj|f||d|_t|dd|_t||jdd|_dS)Nrrr|multiple_choice_headr)r!r"Z num_labelsrrrrrrr4rrr"[szTFGPT2DoubleHeadsModel.__init__cCs|jjSr6rrrrrrcsz,TFGPT2DoubleHeadsModel.get_output_embeddingsNTFc Cst|ttfr|d} t|dkr*|dn|}t|dkrB|dn|}t|dkrZ|dn|}t|dkrr|dn|}t|dkr|dn|}t|dkr|dn|}t|dkr|dn|}t|d kr|d n| } t|d kstd nt|trz|d } |d |}|d|}|d|}|d|}|d|}|d|}|d|}|d| } t|d ks~td n|} | dk rt| } nt|dd} | d} | dk rt | d| fnd}|dk rt |d| fnd}|dk rt |d| fnd}|dk rt |d| fnd}|||||||| g}|j || d}|d}t || t|dd}|j j |dd}|j ||g| d}tj |dd}||f|dd}|S)a| mc_token_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input) Index of the classification token in each input sequence. Selected in the range ``[0, input_ids.size(-1) - 1[``. Return: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs: lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`): Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: # For example purposes. Not runnable. import tensorflow as tf from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2DoubleHeadsModel.from_pretrained('gpt2') # Add a [CLS] to the vocabulary (we should train it also!) # This option is currently not implemented in TF 2.0 raise NotImplementedError tokenizer.add_special_tokens({'cls_token': '[CLS]'}) model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] encoded_choices = [tokenizer.encode(s) for s in choices] cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices] input_ids = tf.constant(encoded_choices)[None, :] # Batch size: 1, number of choices: 2 mc_token_ids = tf.constant([cls_token_location]) # Batch size: 1 outputs = model(input_ids, mc_token_ids=mc_token_ids) lm_prediction_scores, mc_prediction_scores = outputs[:2] rrrrr~rrrr rrrrPrrrQr mc_token_idsrcNrArDrrrC)rrrrr%rrr rrIrrrZsqueeze)r/rLrrPrrrQrrrcrErZ input_shapesZ seq_lengthZflat_input_idsZflat_attention_maskZflat_token_type_idsZflat_position_idsZ flat_inputsrrrZ mc_logitsrUrrrrifs`E             zTFGPT2DoubleHeadsModel.call) NNNNNNNTF) rjrkrlr"rrrrirnrrr4rrRs r)'rloggingr\rZ tensorflowrZconfiguration_gpt2rZ file_utilsrrZmodeling_tf_utilsrrrr r r r Ztokenization_utilsr getLoggerrjloggerrrr(r)ZLayerrrorwrrZGPT2_START_DOCSTRINGrrrrrrrrsN $    p5 0,=