MusicgenModel

s: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  )   Parameters   input_ids (torch.LongTensor of shape (batch_size * num_codebooks, sequence_length)) —
Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
such as with the EncodecModel. See EncodecModel.encode() for details.
What are input IDs?
The input_ids will automatically be converted from shape (batch_size * num_codebooks, target_sequence_length) to (batch_size, num_codebooks, target_sequence_length) in the forward pass. If
you obtain audio codes from an audio encoding model, such as EncodecModel, ensure that the number of
frames is equal to 1, and that you reshape the audio codes from (frames, batch_size, num_codebooks, target_


MusicgenModel

sequence_length) to (batch_size * num_codebooks, target_sequence_length) prior to passing them as
input_ids.
   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   encoder_hidden_states (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
the decoder.   encoder_attention_mask (torch.LongTensor of shape (batch_size, encoder_sequence_length), optional) —
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in [0, 1]:
1 for tokens that are not masked,


MusicgenModel

0 for tokens that are masked.
What are attention masks?   head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of


MusicgenModel

 shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length). inputs_embeds (torch.FloatTensor of shape
(batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you
can choose to directly pass an embedded representation. This is useful if you want more control


MusicgenModel

 over how to
convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.    The MusicgenModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.


MusicgenForCausalLM

class transformers.MusicgenForCausalLM  < source > ( config: MusicgenDecoderConfig  )   Parameters   config (MusicgenConfig) — 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 from_pretrained() method to load the model weights.    The MusicGen decoder model with a language modelling head on top. The Musicgen model was proposed in Simple and Controllable Music Generation by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. It is an
encoder decoder transformer trained on the task of conditional music generation This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use


MusicgenForCausalLM

it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  )   Parameters   input_ids (torch.LongTensor of shape (batch_size * num_codebooks, sequence_length)) —
Indices of input sequence tokens in the vocabulary


MusicgenForCausalLM

, corresponding to the sequence of audio codes.
Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
such as with the EncodecModel. See EncodecModel.encode() for details.
What are input IDs?
The input_ids will automatically be converted from shape (batch_size * num_codebooks, target_sequence_length) to (batch_size, num_codebooks, target_sequence_length) in the forward pass. If
you obtain audio codes from an audio encoding model, such as EncodecModel, ensure that the number of
frames is equal to 1, and that you reshape the audio codes from (frames, batch_size, num_codebooks, target_sequence_length) to (batch_size * num_codebooks, target_sequence_length) prior to passing them as
input_ids.
   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What


MusicgenForCausalLM

are attention masks?   encoder_hidden_states (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
the decoder.   encoder_attention_mask (torch.LongTensor of shape (batch_size, encoder_sequence_length), optional) —
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder


MusicgenForCausalLM

 to avoid performing
cross-attention on hidden heads. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_siz


MusicgenForCausalLM

e, sequence_length). inputs_embeds (torch.FloatTensor of shape
(batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing 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.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
labels = input_ids Ind


MusicgenForCausalLM

ices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100
are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]
Returns:
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor): A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (MusicgenConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple havi


MusicgenForCausalLM

ng 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (


MusicgenForCausalLM

one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torc


MusicgenForCausalLM

h.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
    The MusicgenForCausalLM forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores


MusicgenForConditionalGeneration

class transformers.MusicgenForConditionalGeneration  < source > ( config: typing.Optional[transformers.models.musicgen.configuration_musicgen.MusicgenConfig] = None text_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None audio_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None decoder: typing.Optional[transformers.models.musicgen.modeling_musicgen.MusicgenForCausalLM] = None  )   Parameters   config (MusicgenConfig) — 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 from_pretrained() method to load the model weights.    The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder,for music generation tasks with one or both of text and audio prompts. The Musicgen model was proposed in Simple and Controllable Music Generation by
Jade Copet,


MusicgenForConditionalGeneration

 Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. It is an
encoder decoder transformer trained on the task of conditional music generation This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.BoolTensor] = None input_values: typing.Optional[torch.FloatTensor] = None padding_mask: typing.Optional[torch.BoolTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None e


MusicgenForConditionalGeneration

ncoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None past_key_values: typing.Tuple[typing.Tuple[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs  ) → transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (bat


MusicgenForConditionalGeneration

ch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size * num_codebooks, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
such as with the EncodecModel. See EncodecModel.encode() for details.
What are decoder input IDs?
The decoder_input_ids will automatically be converted from shape (batch_size * num_codebooks, target_sequence_length) to (batch_size, num_codebooks, target_sequence_length) in the forward pass. If
you obtain audio codes from an audio encoding model, such as EncodecModel, ensure that the number of
frames is equal to 1,


MusicgenForConditionalGeneration

 and that you reshape the audio codes from (frames, batch_size, num_codebooks, target_sequence_length) to (batch_size * num_codebooks, target_sequence_length) prior to passing them as
decoder_input_ids.
   decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.


MusicgenForConditionalGeneration

   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of


MusicgenForConditionalGeneration

shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length). inputs_embeds (torch.FloatTensor of shape
(batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing 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.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequenc


MusicgenForConditionalGeneration

e_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. Se


MusicgenForConditionalGeneration

e hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (MusicgenConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers


MusicgenForConditionalGeneration

, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.


MusicgenForConditionalGeneration

output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_


MusicgenForConditionalGeneration

hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The MusicgenForConditionalGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance after


MusicgenForConditionalGeneration

wards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import AutoProcessor, MusicgenForConditionalGeneration
>>> import torch
>>> processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
>>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
>>> inputs = processor(
...     text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
...     padding=True,
...     return_tensors="pt",
... )
>>> pad_token_id = model.generation_config.pad_token_id
>>> decoder_input_ids = (
...     torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long)
...     * pad_token_id
... )
>>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
>>> logits.shape  # (bsz * num_codebooks, tgt_len, vocab_size)
torch.Size([8,


Overview

OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It’s a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
The abstract from the paper is the following:
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2


Overview

 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.
Tips:
GPT-2 is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left. GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
observed in the run_generation.py example script. The model can take the past_key_values (for PyTorch) or past (for TF) as input, which is the previously computed
key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computin


Overview

g
pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the
GPT2Model.forward() method, or for TF the past argument of the
TFGPT2Model.call() method for more information on its usage. Enabling the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn flags will apply the training stability
improvements from Mistral (for PyTorch only).
Write With Transformer is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
This model was contributed by thomwolf. The original code can be found her


Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 Text Generation
A blog on how to Finetune a non-English GPT-2 Model with Hugging Face. A blog on How to generate text: using different decoding methods for language generation with Transformers with GPT-2. A blog on Training CodeParrot 🦜 from Scratch, a large GPT-2 model. A blog on Faster Text Generation with TensorFlow and XLA with GPT-2. A blog on How to train a Language


Resources

Model with Megatron-LM with a GPT-2 model. A notebook on how to finetune GPT2 to generate lyrics in the style of your favorite artist. 🌎 A notebook on how to finetune GPT2 to generate tweets in the style of your favorite Twitter user. 🌎 Causal language modeling chapter of the 🤗 Hugging Face Course. GPT2LMHeadModel is supported by this causal language modeling example script, text generation example script, and notebook. TFGPT2LMHeadModel is supported by this causal language modeling example script and notebook. FlaxGPT2LMHeadModel is supported by this causal language modeling example script and notebook. Text classification task guide Token classification task guide Causal language modeling task guide


GPT2Config

class transformers.GPT2Config  < source > ( vocab_size = 50257 n_positions = 1024 n_embd = 768 n_layer = 12 n_head = 12 n_inner = None activation_function = 'gelu_new' resid_pdrop = 0.1 embd_pdrop = 0.1 attn_pdrop = 0.1 layer_norm_epsilon = 1e-05 initializer_range = 0.02 summary_type = 'cls_index' summary_use_proj = True summary_activation = None summary_proj_to_labels = True summary_first_dropout = 0.1 scale_attn_weights = True use_cache = True bos_token_id = 50256 eos_token_id = 50256 scale_attn_by_inverse_layer_idx = False reorder_and_upcast_attn = False **kwargs  )   Parameters   vocab_size (int, optional, defaults to 50257) —
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling GPT2Model or TFGPT2Model.   n_positions (int, optional, defaults to 1024) —
T


GPT2Config

he maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).   n_embd (int, optional, defaults to 768) —
Dimensionality of the embeddings and hidden states.   n_layer (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.   n_head (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.   n_inner (int, optional, defaults to None) —
Dimensionality of the inner feed-forward layers. None will set it to 4 times n_embd   activation_function (str, optional, defaults to "gelu_new") —
Activation function, to be selected in the list ["relu", "silu", "gelu", "tanh", "gelu_new"].   resid_pdrop (float, optional, defaults to 0.1) —
The dropout probability for all fully connected laye


GPT2Config

rs in the embeddings, encoder, and pooler.   embd_pdrop (float, optional, defaults to 0.1) —
The dropout ratio for the embeddings.   attn_pdrop (float, optional, defaults to 0.1) —
The dropout ratio for the attention.   layer_norm_epsilon (float, optional, defaults to 1e-5) —
The epsilon to use in the layer normalization layers.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   summary_type (string, optional, defaults to "cls_index") —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Has to be one of the following options:
"last": Take the last token hidden state (like XLNet).
"first": Take the first token hidden state (like BERT).
"mean": Take the mean of all tokens hidden s


GPT2Config

tates.
"cls_index": Supply a Tensor of classification token position (like GPT/GPT-2).
"attn": Not implemented now, use multi-head attention.
   summary_use_proj (bool, optional, defaults to True) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Whether or not to add a projection after the vector extraction.   summary_activation (str, optional) —
Argument used when doing sequence summary. Used in for the multiple choice head in
GPT2DoubleHeadsModel.
Pass "tanh" for a tanh activation to the output, any other value will result in no activation.   summary_proj_to_labels (bool, optional, defaults to True) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Whether the projection outputs should have config.num_labels or con


GPT2Config

fig.hidden_size classes.   summary_first_dropout (float, optional, defaults to 0.1) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
The dropout ratio to be used after the projection and activation.   scale_attn_weights (bool, optional, defaults to True) —
Scale attention weights by dividing by sqrt(hidden_size)..   use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).   scale_attn_by_inverse_layer_idx (bool, optional, defaults to False) —
Whether to additionally scale attention weights by 1 / layer_idx + 1.   reorder_and_upcast_attn (bool, optional, defaults to False) —
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() w


GPT2Config

hen training with mixed precision.    This is the configuration class to store the configuration of a GPT2Model or a TFGPT2Model. It is used to
instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the GPT-2
gpt2 architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Example:   Copied >>> from transformers import GPT2Config, GPT2Model
>>> # Initializing a GPT2 configuration
>>> configuration = GPT2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = GPT2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config


GPT2Tokenizer

class transformers.GPT2Tokenizer  < source > ( vocab_file merges_file errors = 'replace' unk_token = '<|endoftext|>' bos_token = '<|endoftext|>' eos_token = '<|endoftext|>' pad_token = None add_prefix_space = False add_bos_token = False **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   unk_token (str, optional, defaults to <|endoftext|>) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   bos_token (str, optional, defaults to <|endoftext|>)


GPT2Tokenizer

—
The beginning of sequence token.   eos_token (str, optional, defaults to <|endoftext|>) —
The end of sequence token.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPT2 tokenizer detect beginning of words by the preceding space).    Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will  be encoded differently whether it is at the beginning of the sentence (without space) or not:   Copied >>> from transformers import GPT2Tokenizer
>>> tokenizer = GPT2Tokenizer.from_pretr


GPT2Tokenizer

ained("gpt2")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]
>>> tokenizer(" Hello world")["input_ids"]
[18435, 995] You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str] = None


GPT2TokenizerFast

class transformers.GPT2TokenizerFast  < source > ( vocab_file = None merges_file = None tokenizer_file = None unk_token = '<|endoftext|>' bos_token = '<|endoftext|>' eos_token = '<|endoftext|>' add_prefix_space = False **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   unk_token (str, optional, defaults to <|endoftext|>) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   bos_token (str, optional, defaults to <|endoftext|>) —
The beginning of sequence token.


GPT2TokenizerFast

eos_token (str, optional, defaults to <|endoftext|>) —
The end of sequence token.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPT2 tokenizer detect beginning of words by the preceding space).   trim_offsets (bool, optional, defaults to True) —
Whether or not the post-processing step should trim offsets to avoid including whitespaces.    Construct a “fast” GPT-2 tokenizer (backed by HuggingFace’s tokenizers library). Based on byte-level
Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will  be encoded differently whether it is at the be


GPT2TokenizerFast

ginning of the sentence (without space) or not:   Copied >>> from transformers import GPT2TokenizerFast
>>> tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]
>>> tokenizer(" Hello world")["input_ids"]
[18435, 995] You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance. When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.


GPT2 specific outputs

class transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None mc_loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None mc_logits: FloatTensor = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Language modeling loss.   mc_loss (torch.FloatTensor of shape (1,), optional, returned when mc_labels is provided) —
Multiple choice classification loss.   logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) —
Prediction scores of t


GPT2 specific outputs

he language modeling head (scores for each vocabulary token before SoftMax).   mc_logits (torch.FloatTensor of shape (batch_size, num_choices)) —
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).   past_key_values (Tuple[Tuple[torch.Tensor]], optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of length config.n_layers, containing tuples of tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.   hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for


GPT2 specific outputs

 the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.   attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.    Base class for outputs of models predicting if two sentences are consecutive or not.
 class transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput  < source > ( logits: tf.Tensor = None mc_logits: tf.Tensor = None past_key_values: List[tf.Tensor] |


GPT2 specific outputs

 None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None  )   Parameters   logits (tf.Tensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   mc_logits (tf.Tensor of shape (batch_size, num_choices)) —
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).   past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values inpu


GPT2 specific outputs

t) to speed up sequential decoding.   hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.   attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (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.    Base class for outputs of models predicting if two sentences are consecutive or no


GPT2Model

class transformers.GPT2Model  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None


GPT2Model

past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_valu


GPT2Model

es is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input_ids as they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention


GPT2Model

 on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
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 ID


GPT2Model

s?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional)


GPT2Model

—
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of t


GPT2Model

he model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is


GPT2Model

 passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (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.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTe


GPT2Model

nsor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
  The GPT2Model forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, GPT2Model
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = GPT2Model.from_pretrained("gpt2")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_s


GPT2LMHeadModel

class transformers.GPT2LMHeadModel  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings). This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None


GPT2LMHeadModel

past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_


GPT2LMHeadModel

key_values is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input_ids as they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices


GPT2LMHeadModel

. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
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?   head_mask (torch.FloatTensor of shape (num_heads,) or


GPT2LMHeadModel

 (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions u


GPT2LMHeadModel

nder returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
labels = input_ids Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100
are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]   Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising


GPT2LMHeadModel

various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.


GPT2LMHeadModel

output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (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.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if mod


GPT2LMHeadModel

el is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
  The GPT2LMHeadModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> import torch
>>> from transformers import AutoTokenizer, GPT2LMHeadModel
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = GPT2LMHeadModel.from_pretrained("gpt2")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs, labels=inputs["input_ids"])
>>> loss = outputs.loss
>>> logits = outputs.


class transformers.GPT2DoubleHeadsModel  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    The 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). This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch


.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None mc_token_ids: typing.Optional[torch.LongTensor] = None labels: typing.Optional[torch.LongTensor] = None mc_labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs  ) → transformers.models.gpt


2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input_ids a


s they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in t


he position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (se


e
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   mc_token_ids (torch.LongTensor of shape (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].   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
labels = input_ids. Indices are selected in [-100, 0, ..., config.vocab_size - 1]. All labels set to


-100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size - 1]   mc_labels (torch.LongTensor of shape (batch_size), optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices]
where num_choices is the size of the second dimension of the input tensors. (see input_ids above)   Returns
transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor)

A transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
mc_loss (torch.FloatTensor of shape (1,), optional, returned when mc_labels is provided) — Multiple choice classification loss


.
logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (torch.FloatTensor of shape (batch_size, num_choices)) — Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
past_key_values (Tuple[Tuple[torch.Tensor]], optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of length config.n_layers, containing tuples of tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (


one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The GPT2DoubleHeadsModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> import torch


>>> from transformers import AutoTokenizer, GPT2DoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
>>> # Add a [CLS] to the vocabulary (we should train it also!)
>>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
>>> # Update the model embeddings with the new vocabulary size
>>> embedding_layer = model.resize_token_embeddings(len(tokenizer))
>>> 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 = torch.tensor(encoded_choices).unsqueeze(0)  # Batch size: 1, number of choices: 2
>>> mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_logits = outputs.logits
>>> mc_logits = outputs.


class transformers.GPT2ForQuestionAnswering  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    The GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits). This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to gener


al usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None start_positions: typing.Optional[torch.LongTensor] = None end_positions: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value st


ates). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input_ids as they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_ke


y_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
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?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selec


ted in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of


 all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.   end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.   Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

A transformers.mod


eling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).


Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (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.
  The GPT2ForQuestionAnswering forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them. This example uses a random model as the real ones are all very big. To get proper results, you should use
gpt2 instead of gpt2. If you get out-o


f-memory when loading that checkpoint, you can try
adding device_map="auto" in the from_pretrained call.  Example:   Copied >>> from transformers import AutoTokenizer, GPT2ForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = GPT2ForQuestionAnswering.from_pretrained("gpt2")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.lo


class transformers.GPT2ForSequenceClassification  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    The GPT2 Model transformer with a sequence classification head on top (linear layer). GPT2ForSequenceClassification uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a
pad_token_id is defined in the configuration, it finds the last token that is not a padding token in each row. If
no pad_token_id is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when inputs_embeds are passed instead of input_ids, it does the same (take the last value


 in
each row of the batch). This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[


bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decodin


g. The input_ids which have
their past given to this model should not be passed as input_ids as they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional)


—
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?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to s


peed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.SequenceClassifierOutputWithPast or a tuple of
torch.FloatTens


or (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True


 is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (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.
  The GPT2ForSequenceClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and


post processing steps while
the latter silently ignores them.  Example of single-label classification:   Copied >>> import torch
>>> from transformers import AutoTokenizer, GPT2ForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-updown")
>>> model = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss  Example of multi-label classification:   Copied >>> import torch
>>> from transformers import AutoTok


enizer, GPT2ForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-updown")
>>> model = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = GPT2ForSequenceClassification.from_pretrained(
...     "microsoft/DialogRPT-updown", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labe


class transformers.GPT2ForTokenClassification  < source > ( config  )   Parameters   config (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 from_pretrained() method to load the model weights.    GPT2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
a


nd behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_k


ey_values is None else
past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input
sequence tokens in the vocabulary.
If past_key_values is used, only input_ids that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input_ids as they have already been computed.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avo


id performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, input_ids_length), optional) —
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.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_positio


n_embeddings - 1].
What are position IDs?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing 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_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see


past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)

A transf


ormers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outp


uts.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (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.
  The GPT2ForTokenClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, GPT2ForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("brad1141/gpt2-finetuned-co


mp2")
>>> model = GPT2ForTokenClassification.from_pretrained("brad1141/gpt2-finetuned-comp2")
>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_token_class_ids = logits.argmax(-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['Lead', 'Lead', 'Lead', 'Position', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead']
>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(),


TFGPT2Model

class transformers.TFGPT2Model  < source > ( *args **kwargs  )   Parameters   config (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 from_pretrained() method to load the model weights.    The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and layers in transformers accept two formats as input: having all inputs as keyword arguments (like PyTorch