fairseq vs huggingface

(batch_size, num_heads, encoder_sequence_length, embed_size_per_head). token_ids_0: typing.List[int] There are a lot of discrepancies between the paper and the fairseq code. How to load a pretrained model from huggingface and use it in fairseq? convert input_ids indices into associated vectors than the models internal embedding lookup matrix. config: BartConfig position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None Check the superclass documentation for the generic methods the use_cache: typing.Optional[bool] = None If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value etc. The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, decoder_input_ids: typing.Optional[torch.LongTensor] = None When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). train: bool = False ), ( transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). ( loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. ) 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). ). logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). On Tue, Oct 27, 2020, 21:17 CheungZee ***@***. Check the superclass documentation for the generic methods the torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Can be used for summarization. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the return_dict: typing.Optional[bool] = None data, then decode using noisy channel model reranking. etc.). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of I used it when I was doing my internship at an AI startup where we want to judge the semantic similarity between two newspaper articles. encoder_layerdrop = 0.0 thanks a lot! d_model = 1024 token_ids_1: typing.Optional[typing.List[int]] = None regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. PreTrainedTokenizer.call() for details. This model inherits from TFPreTrainedModel. encoder_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, + past_key_values: dict = None params: dict = None end_positions: typing.Optional[torch.LongTensor] = None one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). ) sep_token = '' On En->De, our system significantly outperforms other systems as well as human translations. ( tokenizer_file = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None decoder_layers = 12 ( cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Use it as a input_ids: LongTensor = None vocab_size (int, optional, defaults to 50265) Vocabulary size of the BART model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling BartModel or TFBartModel. output_attentions: typing.Optional[bool] = None BART does not When building a sequence using special tokens, this is not the token that is used for the beginning of end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). ) ) input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. BART is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) decoder_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). model according to the specified arguments, defining the model architecture. An Thank you! inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None etc.). ( past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. ( When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True. It's not meant to be an intense research platform like AllenNLP / fairseq / openNMT / huggingface. use_cache = True A FAIRSEQ Transformer sequence has the following format: ( (Here I don't understand how to create a dict.txt) start with raw text training data use huggingface to tokenize and apply BPE. 2 Install fairseq-py. dropout_rng: PRNGKey = None encoder_layers = 12 A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of eos_token = '' configuration (BartConfig) and inputs. The abstract of the paper is the following: This paper describes Facebook FAIR's submission to the . ( attention_mask: typing.Optional[torch.Tensor] = None length_penalty = 1.0 decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). It was actually just for learning purpose, but since it was trained for many hours on multiple gpus, I though it would be good also for other if I put it to huggingface's models zoo if I am able to convert it. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Create a mask from the two sequences passed to be used in a sequence-pair classification task. If past_key_values These libraries conveniently take care of that issue for you so you can perform rapid experimentation and implementation . ) Tuner ( [trainable, param_space, tune_config, .]) output_attentions: typing.Optional[bool] = None cross_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). dropout_rng: PRNGKey = None from transformers import AutoModel model = AutoModel.from_pretrained ('.\model',local_files_only=True) transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor). The FSMTForConditionalGeneration forward method, overrides the __call__ special method. inputs_embeds: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Nearly 800 thousand customers were ", "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow. See PreTrainedTokenizer.encode() and For example, Positional Embedding can only choose "learned" instead of "sinusoidal". Beam search in Transfomrers is almost the same as fairseq, but with less effective implementation. The BART Model with a language modeling head. encoder_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). How about just use the output of the hugging face tokenizer(raw text like "" as tokenizer's input, dict of tensors as output) as model's input ? encoder_outputs past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None This model inherits from FlaxPreTrainedModel. tgt_vocab_size = 42024 already_has_special_tokens: bool = False This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. It is used to instantiate a BART decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Fairseq doesnt really do any preprocessing. The text was updated successfully, but these errors were encountered: It should be straightforward to wrap huggingface models in the corresponding fairseq abstractions. the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first cross_attn_head_mask: typing.Optional[torch.Tensor] = None A FAIRSEQ. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various mask_token = '' output_attentions: typing.Optional[bool] = None hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). etc. I think @sshleifer and @valhalla are better equipped to answer your question. attention_dropout = 0.0 In fact, its co-founder Jeremy Howard just published (Aug. 2020) a completely new book called. It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. decoder_attention_mask: typing.Optional[torch.LongTensor] = None labels: typing.Optional[torch.LongTensor] = None decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ( weighted average in the cross-attention heads. decoder_input_ids @stas00. Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the activation_dropout = 0.0 a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: a dictionary with one or several input Tensors associated to the input names given in the docstring. transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None e.g for autoregressive tasks. 1 vote. If you want to change padding behavior, you should read modeling_bart._prepare_decoder_attention_mask num_beams = 5 You can see how I use TorchText by looking at my, Explanation: This is the most popular library out there that implements a wide variety of transformers, from BERT and GPT-2 to BART and Reformer. 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). It'd be great to add more wrappers for other model types (e.g., FairseqEncoderModel for BERT-like models) and also to generalize it to load arbitrary pretrained models from huggingface (e.g., using AutoModel). BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None activation_dropout = 0.0 output_hidden_states: typing.Optional[bool] = None used (see past_key_values input) to speed up sequential decoding. dropout_rng: PRNGKey = None decoder_head_mask: typing.Optional[torch.Tensor] = None ) I got my hands on one of those but I only managed to put about 16k (or 32k if they count generator tokens too), I had max_seq_len of 512, batch_size of 4 and grad_acc 8, but its stil at least 4 times less. Cross attentions weights after the attention softmax, used to compute the weighted average in the max_position_embeddings = 1024 If past_key_values are used, the user can optionally input only the last decoder_input_ids (those input_ids: ndarray cls_token = '' I feel like we need to specially change data preprocessing steps. Config class. Construct a fast BART tokenizer (backed by HuggingFaces tokenizers library), derived from the GPT-2 tokenizer, use_cache: typing.Optional[bool] = None bos_token = '' I want to load bert-base-chinese in huggingface or google bert and use fairseq to finetune it, how to do? unk_token = '' params: dict = None Hello, Ive been reading this paper on mbart(https://arxiv.org/pdf/2001.08210.pdf) and came across section 2.2 optimization where authors claim to have total batch size of 128K tokens per 32GB GPU. elements depending on the configuration (BartConfig) and inputs. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads A transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or a tuple of encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None The main discuss in here are different Config class parameters for different HuggingFace models. The bare Bart Model transformer outputting raw hidden-states without any specific head on top. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None They all have different use cases and it would be easier to provide guidance based on your use case needs. ) dropout_rng: PRNGKey = None params: dict = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads model according to the specified arguments, defining the model architecture. one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape onemain financial corporate headquarters evansville, in 47708; lee's chicken gravy recipe; tornado warning grand bay, al attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Fairseq, then huggingface and then torchtext. Assuming your pre-trained (pytorch based) transformer model is in 'model' folder in your current working directory, following code can load your model. return_dict: typing.Optional[bool] = None The BART Model with a language modeling head. language pairs and four language directions, English <-> German and English <-> Russian. Override the default to_dict() from PretrainedConfig. While Transformers (early_stop=False) continues to generate tokens, until the score of the new sequence cannot exceed the sentences in the candidate set. ). Transformers (modified) version v3.5.1 can be installed as follows: I modified SinusoidalPositionalEmbedding in transformers/src/transformers/modeling_bart.py to match the implementation in fairseq, since fairseq differs from HuggingFace in sinusoidal embeddings initialization and calculation of positional ids. decoder_attention_mask: typing.Optional[torch.BoolTensor] = None decoder_input_ids ( See diagram 1 in the (batch_size, sequence_length, hidden_size). encoder_outputs If we set early_stop=True, it can be consistent with fairseq. We will not consider all the models from the library as there are 200.000+ models. It doesnt share embeddings tokens By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape a. HuggingFace is on a mission to solve Natural Language Processing (NLP) one commit at a time by open-source and open-science. to your account. PreTrainedTokenizer.call() for details. 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 the decoder of the model. Theres a really simple function call that allows you to do just that and return their similarity score, so its extremely handy! privacy statement. config: BartConfig dropout_rng: PRNGKey = None Can be used for summarization. ***> wrote: You signed in with another tab or window. decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None https://github.com/PetrochukM/PyTorch-NLP#related-work. call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None head_mask: typing.Optional[torch.Tensor] = None transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). output_hidden_states: typing.Optional[bool] = None This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. token_ids_1: typing.Optional[typing.List[int]] = None decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads output_attentions: typing.Optional[bool] = None BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. dropout = 0.1 num_labels = 3 heads. decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) elements depending on the configuration (BartConfig) and inputs. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the If you want to use it in version 0.9.x or 0.10.x, you need to change args.model.xxx to args.xxx in convert.py, since fairseq adopted the Hydra configuration framework in the latest version. output_attentions: typing.Optional[bool] = None config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values @ttzHome @shamanez. encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None A tag already exists with the provided branch name. Explanation: Similar to Spacy, it is another popular preprocessing library for modern NLP. We are sorry that we haven't been able to prioritize it yet. Are you sure you want to create this branch? token_ids_1: typing.Optional[typing.List[int]] = None decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None input_ids: LongTensor = None add_prefix_space = False decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None the latter silently ignores them. A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of and modify to your needs. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. A Medium publication sharing concepts, ideas and codes. ( output_attentions: typing.Optional[bool] = None Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in, Model predictions are intended to be identical to the original implementation when, having all inputs as keyword arguments (like PyTorch models), or. output_attentions: typing.Optional[bool] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.Tensor] = None Note that this only specifies the dtype of the computation and does not influence the dtype of model tgt_vocab_file = None PK dVR A ;--torchaudio-2.dev20230304.dist-info/RECORDzW"XF/ y @H xo E=NU-Lllwt*K"'/wh . output_hidden_states: typing.Optional[bool] = None ( return_dict: typing.Optional[bool] = None input_ids: ndarray already_has_special_tokens: bool = False transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor). output_hidden_states: typing.Optional[bool] = None Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. of inputs_embeds. sequence. Assuming that you know these basic frameworks, this tutorial is dedicated to briefly guide you with other useful NLP libraries that you can learn and use in 2020. dont have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_head_mask: typing.Optional[torch.Tensor] = None ( decoder_start_token_id = 2 transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). flax.nn.Module subclass. 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). 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, + google colab linkhttps://colab.research.google.com/drive/1xyaAMav_gTo_KvpHrO05zWFhmUaILfEd?usp=sharing Transformers (formerly known as pytorch-transformers. input_ids: ndarray training: typing.Optional[bool] = False training: typing.Optional[bool] = False Retrieve sequence ids from a token list that has no special tokens added. last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. output_hidden_states: typing.Optional[bool] = None one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Preprocessor class. langs = None config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). By clicking or navigating, you agree to allow our usage of cookies. elements depending on the configuration () and inputs. It provides an all-in-one environment for supporting a wide variety of reference models, pretrained models, datasets, etc. sep_token = '' The token used is the cls_token. Already on GitHub? 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). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads parameters. The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. Task: Task-Oriented Dialogue, Chit-chat Dialogue. vocab_file = None See PreTrainedTokenizer.encode() and (batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you return_dict: typing.Optional[bool] = None 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 the decoder of the model. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. (batch_size, sequence_length, hidden_size). output_attentions: typing.Optional[bool] = None Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and Is it using a pretrained model to solve a task, is it to research novel models, or something in between. is_encoder_decoder = True cross_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). This model inherits from TFPreTrainedModel. 45; asked Jan 21 at 8:43. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Configuration can help us understand the inner structure of the HuggingFace models. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention It follows fairseq's careful design for scalability and extensibility. input_shape: typing.Tuple[int] = (1, 1) Hi @sshleifer, as mentioned above I fine tuned mbart.cc25 for machine translation (en-de) with Fairseq. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None elements depending on the configuration (BartConfig) and inputs. decoder_input_ids of shape (batch_size, sequence_length). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads This model inherits from FlaxPreTrainedModel. train: bool = False Users should decoder_input_ids: typing.Optional[torch.LongTensor] = None Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. (Here I don't understand how to create a dict.txt), use huggingface to tokenize and apply BPE. cross_attn_head_mask: typing.Optional[torch.Tensor] = None Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. Closing this issue after a prolonged period of inactivity.