past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None ) ( Hugging Face Forums Difference in memory efficiency in HF and fairseq Models Zhylkaaa October 23, 2020, 6:13pm #1 Hello, I've 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. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various dropout_rng: PRNGKey = None Use it The difference is that PyTorch-NLP is written to be more flexible. bos_token_id = 0 This model inherits from FlaxPreTrainedModel. I mostly wrote PyTorch-NLP to replace `torchtext`, so you should mostly find the same feature set. fairseq vs gpt-neox transformers vs sentence-transformers fairseq vs DeepSpeed mask_token = '' The token used is the cls_token. classifier_dropout = 0.0 head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None 1 answer. return_dict: typing.Optional[bool] = None If you want to change padding behavior, you should read modeling_bart._prepare_decoder_attention_mask this superclass for more information regarding those methods. 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. 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). ", # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained()`, : 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, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, : typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None, : typing.Optional[tensorflow.python.framework.ops.Tensor] = None, "My friends are cool but they eat too many carbs. init_std = 0.02 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. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and bos_token = '' It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. 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, feeding part. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). tokenizer_file = None A lot of NLP tasks are difficult to implement and even harder to engineer and optimize. return_dict: typing.Optional[bool] = None paper for more information on the default strategy. They all have different use cases and it would be easier to provide guidance based on your use case needs. past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None Read the past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various config: BartConfig If its different, you can ask on fairseq. This model inherits from TFPreTrainedModel. @stas00. ). already_has_special_tokens: bool = False Allennlp also has some pretrained models and implementations for tasks related to Allen AI's research areas. The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. sep_token = '' output_hidden_states: typing.Optional[bool] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Get back a text file with BPE tokens separated by spaces feed step 2 into fairseq-preprocess, which will tensorize and generate dict.txt Sign up for free to join this conversation on GitHub . filename_prefix: typing.Optional[str] = None List[int]. cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding. PreTrainedTokenizer.call() for details. token_ids_0: typing.List[int] https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. ). one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). (batch_size, sequence_length, hidden_size). ). train: bool = False Instantiating a configuration with the eos_token_id = 2 output_attentions: typing.Optional[bool] = None Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). This year we experiment with different bitext data filtering schemes, labels: typing.Optional[tensorflow.python.framework.ops.Tensor] = None output_hidden_states: typing.Optional[bool] = None Specially the data ) Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. dropout_rng: PRNGKey = None It seems like that this is only a wrap, but there are more should be done if we want to load the pretrained gpt2 model from hugging face? encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ", # probs[5] is associated with the mask token, : typing.Optional[jax._src.numpy.ndarray.ndarray] = None, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, documentation from PretrainedConfig for more information. Hi @sshleifer, as mentioned above I fine tuned mbart.cc25 for machine translation (en-de) with Fairseq. Hugging Face provides tools to quickly train neural networks for NLP (Natural Language Processing) on any task (classification, translation, question answering, etc) and any dataset with PyTorch. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. ), ( Fairseq doesnt really do any preprocessing. model according to the specified arguments, defining the model architecture. do_lower_case = False Tuner ( [trainable, param_space, tune_config, .]) Linkedin: https://www.linkedin.com/in/itsuncheng/, Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, https://torchtext.readthedocs.io/en/latest/, https://github.com/huggingface/transformers, https://github.com/RaRe-Technologies/gensim, https://github.com/facebookresearch/ParlAI, Explanation: AllenNLP is a general framework for deep learning for NLP, established by the world-famous, Explanation: Fairseq is a popular NLP framework developed by, Explanation: Fast.ai is built to make deep learning accessible to people without technical backgrounds through its free online courses and also easy-to-use software library. decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_layers = 12 I've heard fairseq is best, for general purpose research, but interested to see what people think of the others. elements depending on the configuration (BartConfig) and inputs. The PyTorch-NLP project originally started with my work at Apple. output_hidden_states: typing.Optional[bool] = None Theres a really simple function call that allows you to do just that and return their similarity score, so its extremely handy! DISCLAIMER: If you see something strange, file a Github Issue and assign transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. ) head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None token_ids_0: typing.List[int] gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. Have a question about this project? encoder_attention_heads = 16 https://github.com/PetrochukM/PyTorch-NLP#related-work. A transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or a tuple of Config class. 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. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads token_ids_0: typing.List[int] List[int]. . Indices can be obtained using BertTokenizer. input_ids: ndarray This model inherits from TFPreTrainedModel. add_prefix_space = False why there are 1024 pos_embeddings, when paper authors write about pre-training 512? This command has --max_tokens=1024, 128 or 64 work better in my experience. etc.). elements depending on the configuration (BartConfig) and inputs. etc. Tuner.get_results () Get results of a hyperparameter tuning run. Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan When some beams ends ( is generated), Transformers and fairseq both put the sequence into the candidate set. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the inputs_embeds: typing.Optional[torch.FloatTensor] = None or what is the difference between fairseq model and HF model? logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with past_key_values input) to speed up sequential decoding. Top 6 Alternatives To Hugging Face With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead. 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. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of pad_token = '' seed: int = 0 openNMT is library for machine translation but with limited customization and training options (see JoeyNMT if you want to do more research experiments in quick and transparent way).