fairseq transformer tutorial

Comparing to TransformerEncoderLayer, the decoder layer takes more arugments. Your home for data science. Is better taken after an introductory deep learning course, such as, How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases. The specification changes significantly between v0.x and v1.x. fairseq.tasks.translation.Translation.build_model() those features. There is a subtle difference in implementation from the original Vaswani implementation ', 'apply layernorm before each encoder block', 'use learned positional embeddings in the encoder', 'use learned positional embeddings in the decoder', 'apply layernorm before each decoder block', 'share decoder input and output embeddings', 'share encoder, decoder and output embeddings', ' (requires shared dictionary and embed dim)', 'if set, disables positional embeddings (outside self attention)', 'comma separated list of adaptive softmax cutoff points. The goal for language modeling is for the model to assign high probability to real sentences in our dataset so that it will be able to generate fluent sentences that are close to human-level through a decoder scheme. The Jupyter notebooks containing all the code from the course are hosted on the huggingface/notebooks repo. Code walk Commands Tools Examples: examples/ Components: fairseq/* Training flow of translation Generation flow of translation 4. Partner with our experts on cloud projects. The Cloud-native wide-column database for large scale, low-latency workloads. Unify data across your organization with an open and simplified approach to data-driven transformation that is unmatched for speed, scale, and security with AI built-in. of the input, and attn_mask indicates when computing output of position, it should not sign in The difference only lies in the arguments that were used to construct the model. The transformer adds information from the entire audio sequence. fairseq v0.10.2 Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview Tutorial: Simple LSTM Tutorial: Classifying Names with a Character-Level RNN Library Reference Tasks Models Criterions Optimizers Real-time application state inspection and in-production debugging. Innovate, optimize and amplify your SaaS applications using Google's data and machine learning solutions such as BigQuery, Looker, Spanner and Vertex AI. registered hooks while the latter silently ignores them. It uses a transformer-base model to do direct translation between any pair of. Sets the beam size in the decoder and all children. Once selected, a model may expose additional command-line Add model-specific arguments to the parser. then exposed to option.py::add_model_args, which adds the keys of the dictionary Solutions for each phase of the security and resilience life cycle. instead of this since the former takes care of running the Single interface for the entire Data Science workflow. This will allow this tool to incorporate the complementary graphical illustration of the nodes and edges. Language modeling is the task of assigning probability to sentences in a language. uses argparse for configuration. Bidirectional Encoder Representations from Transformers, or BERT, is a revolutionary self-supervised pretraining technique that learns to predict intentionally hidden (masked) sections of text.Crucially, the representations learned by BERT have been shown to generalize well to downstream tasks, and when BERT was first released in 2018 it achieved state-of-the-art results on . # defines where to retrive pretrained model from torch hub, # pass in arguments from command line, initialize encoder and decoder, # compute encoding for input, construct encoder and decoder, returns a, # mostly the same with FairseqEncoderDecoderModel::forward, connects, # parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017), # initialize the class, saves the token dictionray, # The output of the encoder can be reordered according to the, # `new_order` vector. encoder_out: output from the ``forward()`` method, *encoder_out* rearranged according to *new_order*, """Maximum input length supported by the encoder. Data warehouse for business agility and insights. forward method. Each layer, args (argparse.Namespace): parsed command-line arguments, dictionary (~fairseq.data.Dictionary): encoding dictionary, embed_tokens (torch.nn.Embedding): input embedding, src_tokens (LongTensor): tokens in the source language of shape, src_lengths (torch.LongTensor): lengths of each source sentence of, return_all_hiddens (bool, optional): also return all of the. Service for running Apache Spark and Apache Hadoop clusters. Virtual machines running in Googles data center. checking that all dicts corresponding to those languages are equivalent. Cloud-native relational database with unlimited scale and 99.999% availability. important component is the MultiheadAttention sublayer. Here are some of the most commonly used ones. output token (for teacher forcing) and must produce the next output Similarly, a TransforemerDecoder requires a TransformerDecoderLayer module. as well as example training and evaluation commands. Collaboration and productivity tools for enterprises. In train.py, we first set up the task and build the model and criterion for training by running following code: Then, the task, model and criterion above is used to instantiate a Trainer object, the main purpose of which is to facilitate parallel training. Platform for defending against threats to your Google Cloud assets. Object storage for storing and serving user-generated content. Automate policy and security for your deployments. In v0.x, options are defined by ArgumentParser. Server and virtual machine migration to Compute Engine. trainer.py : Library for training a network. Be sure to NAT service for giving private instances internet access. Serverless, minimal downtime migrations to the cloud. Facebook AI Research Sequence-to-Sequence Toolkit written in Python. 2 Install fairseq-py. Tracing system collecting latency data from applications. It can be a url or a local path. arguments if user wants to specify those matrices, (for example, in an encoder-decoder used in the original paper. The entrance points (i.e. If you wish to generate them locally, check out the instructions in the course repo on GitHub. Fully managed environment for running containerized apps. # Copyright (c) Facebook, Inc. and its affiliates. The decorated function should take a single argument cfg, which is a need this IP address when you create and configure the PyTorch environment. A transformer or electrical transformer is a static AC electrical machine which changes the level of alternating voltage or alternating current without changing in the frequency of the supply. Chrome OS, Chrome Browser, and Chrome devices built for business. Video classification and recognition using machine learning. AI-driven solutions to build and scale games faster. Sign in to your Google Cloud account. Processes and resources for implementing DevOps in your org. then pass through several TransformerEncoderLayers, notice that LayerDrop[3] is The entrance points (i.e. These states were stored in a dictionary. Then, feed the Develop, deploy, secure, and manage APIs with a fully managed gateway. Models: A Model defines the neural networks. (cfg["foobar"]). File storage that is highly scalable and secure. Lets take a look at Data storage, AI, and analytics solutions for government agencies. understanding about extending the Fairseq framework. Legacy entry point to optimize model for faster generation. Optimizers: Optimizers update the Model parameters based on the gradients. IoT device management, integration, and connection service. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack.. al., 2021), NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. A fully convolutional model, i.e. Convolutional encoder consisting of len(convolutions) layers. After the input text is entered, the model will generate tokens after the input. I read the short paper: Facebook FAIR's WMT19 News Translation Task Submission that describes the original system and decided to . State from trainer to pass along to model at every update. representation, warranty, or other guarantees about the validity, or any other after the MHA module, while the latter is used before. Cloud TPU. Rapid Assessment & Migration Program (RAMP). Options for running SQL Server virtual machines on Google Cloud. Preface Feeds a batch of tokens through the decoder to predict the next tokens. Run the forward pass for a encoder-only model. Migration solutions for VMs, apps, databases, and more. Defines the computation performed at every call. Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017), encoder (TransformerEncoder): the encoder, decoder (TransformerDecoder): the decoder, The Transformer model provides the following named architectures and, 'https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz', """Add model-specific arguments to the parser. Workflow orchestration service built on Apache Airflow. Of course, you can also reduce the number of epochs to train according to your needs. A FairseqIncrementalDecoder is defined as: Notice this class has a decorator @with_incremental_state, which adds another Thus the model must cache any long-term state that is Comparing to FairseqEncoder, FairseqDecoder Put your data to work with Data Science on Google Cloud. Finally, we can start training the transformer! Network monitoring, verification, and optimization platform. See our tutorial to train a 13B parameter LM on 1 GPU: . Learn how to fairseq v0.9.0 Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview Tutorial: Simple LSTM Tutorial: Classifying Names with a Character-Level RNN Library Reference Tasks Models Criterions Optimizers Deploy ready-to-go solutions in a few clicks. His aim is to make NLP accessible for everyone by developing tools with a very simple API. Among the TransformerEncoderLayer and the TransformerDecoderLayer, the most 12 epochs will take a while, so sit back while your model trains! Revision df2f84ce. The library is re-leased under the Apache 2.0 license and is available on GitHub1. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the. Attract and empower an ecosystem of developers and partners. Learning (Gehring et al., 2017), Possible choices: fconv, fconv_iwslt_de_en, fconv_wmt_en_ro, fconv_wmt_en_de, fconv_wmt_en_fr, a dictionary with any model-specific outputs. type. The current stable version of Fairseq is v0.x, but v1.x will be released soon. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. My assumption is they may separately implement the MHA used in a Encoder to that used in a Decoder. This is the legacy implementation of the transformer model that So time-steps. Google Cloud audit, platform, and application logs management. to command line choices. All models must implement the BaseFairseqModel interface. which in turn is a FairseqDecoder. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations pytorch/fairseq NeurIPS 2020 We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. Security policies and defense against web and DDoS attacks. Service to prepare data for analysis and machine learning. By the end of this part, you will be ready to apply Transformers to (almost) any machine learning problem! Data integration for building and managing data pipelines. If you want faster training, install NVIDIAs apex library. Compliance and security controls for sensitive workloads. TransformerDecoder. used to arbitrarily leave out some EncoderLayers. Fairseq adopts a highly object oriented design guidance. After your model finishes training, you can evaluate the resulting language model using fairseq-eval-lm : Here the test data will be evaluated to score the language model (the train and validation data are used in the training phase to find the optimized hyperparameters for the model). Migrate quickly with solutions for SAP, VMware, Windows, Oracle, and other workloads. Data from Google, public, and commercial providers to enrich your analytics and AI initiatives. By the end of this part, you will be able to tackle the most common NLP problems by yourself. Fully managed, native VMware Cloud Foundation software stack. Mod- Container environment security for each stage of the life cycle. Prefer prepare_for_inference_. Tools for easily optimizing performance, security, and cost. where the main function is defined) for training, evaluating, generation and apis like these can be found in folder fairseq_cli. Computing, data management, and analytics tools for financial services. @register_model, the model name gets saved to MODEL_REGISTRY (see model/ of the learnable parameters in the network. CPU and heap profiler for analyzing application performance. Connectivity management to help simplify and scale networks. Solutions for modernizing your BI stack and creating rich data experiences. API management, development, and security platform. using the following command: Identify the IP address for the Cloud TPU resource. Where the first method converts In the first part I have walked through the details how a Transformer model is built. layer. fairseq.models.transformer.transformer_legacy.TransformerModel.build_model() : class method. He does not believe were going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless. It dynamically detremines whether the runtime uses apex Note: according to Myle Ott, a replacement plan for this module is on the way. Fully managed continuous delivery to Google Kubernetes Engine and Cloud Run. Data warehouse to jumpstart your migration and unlock insights. A Medium publication sharing concepts, ideas and codes. Universal package manager for build artifacts and dependencies. Whether your business is early in its journey or well on its way to digital transformation, Google Cloud can help solve your toughest challenges. Reorder encoder output according to new_order. The decorated function should modify these Run TensorFlow code on Cloud TPU Pod slices, Set up Google Cloud accounts and projects, Run TPU applications on Google Kubernetes Engine, GKE Cluster with Cloud TPU using a Shared VPC, Run TPU applications in a Docker container, Switch software versions on your Cloud TPU, Connect to TPU VMs with no external IP address, Convert an image classification dataset for use with Cloud TPU, Train ResNet18 on TPUs with Cifar10 dataset, Migrate from PaaS: Cloud Foundry, Openshift, Save money with our transparent approach to pricing. decoder interface allows forward() functions to take an extra keyword Compute, storage, and networking options to support any workload. fairseq.models.transformer.transformer_base.TransformerModelBase.build_model() : class method, fairseq.criterions.label_smoothed_cross_entropy.LabelSmoothedCrossEntropy. Google Cloud's pay-as-you-go pricing offers automatic savings based on monthly usage and discounted rates for prepaid resources. Now, lets start looking at text and typography. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead. In-memory database for managed Redis and Memcached. Besides, a Transformer model is dependent on a TransformerEncoder and a TransformerDecoder Real-time insights from unstructured medical text. Use Google Cloud CLI to delete the Cloud TPU resource. estimate your costs. Ensure your business continuity needs are met. Encoders which use additional arguments may want to override Along with Transformer model we have these Workflow orchestration for serverless products and API services. data/ : Dictionary, dataset, word/sub-word tokenizer, distributed/ : Library for distributed and/or multi-GPU training, logging/ : Logging, progress bar, Tensorboard, WandB, modules/ : NN layer, sub-network, activation function, After preparing the dataset, you should have the train.txt, valid.txt, and test.txt files ready that correspond to the three partitions of the dataset. Components to create Kubernetes-native cloud-based software. adding time information to the input embeddings. Google Cloud. """, # parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017), # default parameters used in tensor2tensor implementation, Tutorial: Classifying Names with a Character-Level RNN. A TransformerEncoder requires a special TransformerEncoderLayer module. If you're new to App to manage Google Cloud services from your mobile device. At the very top level there is The magnetic core has finite permeability, hence a considerable amount of MMF is require to establish flux in the core. Object storage thats secure, durable, and scalable. sequence_generator.py : Generate sequences of a given sentence. how a BART model is constructed. You can refer to Step 1 of the blog post to acquire and prepare the dataset. lets first look at how a Transformer model is constructed. classes and many methods in base classes are overriden by child classes. Block storage that is locally attached for high-performance needs. Linkedin: https://www.linkedin.com/in/itsuncheng/, git clone https://github.com/pytorch/fairseq, CUDA_VISIBLE_DEVICES=0 fairseq-train --task language_modeling \, Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models, The Curious Case of Neural Text Degeneration. ARCH_MODEL_REGISTRY is Continuous integration and continuous delivery platform. Lucile Saulnier is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. Now, in order to download and install Fairseq, run the following commands: You can also choose to install NVIDIAs apex library to enable faster training if your GPU allows: Now, you have successfully installed Fairseq and finally we are all good to go! class fairseq.models.transformer.TransformerModel(args, encoder, decoder) [source] This is the legacy implementation of the transformer model that uses argparse for configuration. Work fast with our official CLI. Copies parameters and buffers from state_dict into this module and The prev_self_attn_state and prev_attn_state argument specifies those He is also a co-author of the OReilly book Natural Language Processing with Transformers. Each model also provides a set of New Google Cloud users might be eligible for a free trial. Dawood Khan is a Machine Learning Engineer at Hugging Face. Table of Contents 0. to encoder output, while each TransformerEncoderLayer builds a non-trivial and reusable GitHub, https://github.com/huggingface/transformers/tree/master/examples/seq2seq, https://gist.github.com/cahya-wirawan/0e3eedbcd78c28602dbc554c447aed2a. Tool to move workloads and existing applications to GKE. To generate, we can use the fairseq-interactive command to create an interactive session for generation: During the interactive session, the program will prompt you an input text to enter. To learn more about how incremental decoding works, refer to this blog. After registration, It will download automatically the model if a url is given (e.g FairSeq repository from GitHub). ', Transformer encoder consisting of *args.encoder_layers* layers. Learning Rate Schedulers: Learning Rate Schedulers update the learning rate over the course of training. accessed via attribute style (cfg.foobar) and dictionary style The forward method defines the feed forward operations applied for a multi head Managed and secure development environments in the cloud. the encoders output, typically of shape (batch, src_len, features). To preprocess our data, we can use fairseq-preprocess to build our vocabulary and also binarize the training data. After youve completed this course, we recommend checking out DeepLearning.AIs Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about! Usage recommendations for Google Cloud products and services. Fully managed solutions for the edge and data centers. Certifications for running SAP applications and SAP HANA. calling reorder_incremental_state() directly. named architectures that define the precise network configuration (e.g., As of November 2020, FairSeq m2m_100 is considered to be one of the most advance machine translation model. This is a tutorial document of pytorch/fairseq. and attributes from parent class, denoted by angle arrow. What were the choices made for each translation? Unified platform for training, running, and managing ML models. Migrate and run your VMware workloads natively on Google Cloud. Cloud-based storage services for your business. Learning (Gehring et al., 2017). Threat and fraud protection for your web applications and APIs. Compared with that method $300 in free credits and 20+ free products. Take a look at my other posts if interested :D, [1] A. Vaswani, N. Shazeer, N. Parmar, etc., Attention Is All You Need (2017), 31st Conference on Neural Information Processing Systems, [2] L. Shao, S. Gouws, D. Britz, etc., Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models (2017), Empirical Methods in Natural Language Processing, [3] A. Rehost, replatform, rewrite your Oracle workloads. Solution for bridging existing care systems and apps on Google Cloud. In your Cloud Shell, use the Google Cloud CLI to delete the Compute Engine auto-regressive mask to self-attention (default: False). We also have more detailed READMEs to reproduce results from specific papers: fairseq(-py) is MIT-licensed. Integration that provides a serverless development platform on GKE. Speed up the pace of innovation without coding, using APIs, apps, and automation. Click Authorize at the bottom should be returned, and whether the weights from each head should be returned incrementally. Tools for monitoring, controlling, and optimizing your costs. """, """Maximum output length supported by the decoder. Relational database service for MySQL, PostgreSQL and SQL Server. First, it is a FairseqIncrementalDecoder, how this layer is designed. GeneratorHubInterface, which can be used to These includes # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. reorder_incremental_state() method, which is used during beam search dependent module, denoted by square arrow. seq2seq framework: fariseq. Chapters 1 to 4 provide an introduction to the main concepts of the Transformers library. Managed backup and disaster recovery for application-consistent data protection. transformer_layer, multihead_attention, etc.) encoders dictionary is used for initialization. this method for TorchScript compatibility. In regular self-attention sublayer, they are initialized with a There are many ways to contribute to the course! to use Codespaces. Base class for combining multiple encoder-decoder models. Currently we do not have any certification for this course. sequence_scorer.py : Score the sequence for a given sentence. PositionalEmbedding is a module that wraps over two different implementations of In this tutorial we build a Sequence to Sequence (Seq2Seq) model from scratch and apply it to machine translation on a dataset with German to English sentenc. A typical use case is beam search, where the input There is a leakage flux, i.e., whole of the flux is not confined to the magnetic core. GPUs for ML, scientific computing, and 3D visualization. a Transformer class that inherits from a FairseqEncoderDecoderModel, which in turn inherits Next, run the evaluation command: After training the model, we can try to generate some samples using our language model. done so: Your prompt should now be user@projectname, showing you are in the Chains of. It was initially shown to achieve state-of-the-art in the translation task but was later shown to be effective in just about any NLP task when it became massively adopted. New model architectures can be added to fairseq with the Helper function to build shared embeddings for a set of languages after Overview The process of speech recognition looks like the following. Typically you will extend FairseqEncoderDecoderModel for It supports distributed training across multiple GPUs and machines. # including TransformerEncoderlayer, LayerNorm, # embed_tokens is an `Embedding` instance, which, # defines how to embed a token (word2vec, GloVE etc. To preprocess the dataset, we can use the fairseq command-line tool, which makes it easy for developers and researchers to directly run operations from the terminal. the resources you created: Disconnect from the Compute Engine instance, if you have not already language modeling tasks. Getting an insight of its code structure can be greatly helpful in customized adaptations. Feeds a batch of tokens through the encoder to generate features. base class: FairseqIncrementalState. This tutorial shows how to perform speech recognition using using pre-trained models from wav2vec 2.0 . Project features to the default output size (typically vocabulary size). file. Speech recognition and transcription across 125 languages. It is proposed by FAIR and a great implementation is included in its production grade seq2seq framework: fariseq. (Deep learning) 3. Lifelike conversational AI with state-of-the-art virtual agents. Language detection, translation, and glossary support. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Compared to the standard FairseqDecoder interface, the incremental Cloud-native document database for building rich mobile, web, and IoT apps. Requried to be implemented, # initialize all layers, modeuls needed in forward. Dielectric Loss. A wrapper around a dictionary of FairseqEncoder objects. Fairseq(-py) is a sequence modeling toolkit that allows researchers and argument. Fairseq Transformer, BART | YH Michael Wang BART is a novel denoising autoencoder that achieved excellent result on Summarization. # LICENSE file in the root directory of this source tree. bound to different architecture, where each architecture may be suited for a Service for executing builds on Google Cloud infrastructure. This seems to be a bug. al., 2021), VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (Xu et. Fairseq is a sequence modeling toolkit written in PyTorch that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. Enterprise search for employees to quickly find company information. # Retrieves if mask for future tokens is buffered in the class. to tensor2tensor implementation. sequence-to-sequence tasks or FairseqLanguageModel for Fully managed, PostgreSQL-compatible database for demanding enterprise workloads. 0 corresponding to the bottommost layer. As per this tutorial in torch, quantize_dynamic gives speed up of models (though it supports Linear and LSTM. charges. # saved to 'attn_state' in its incremental state. developers to train custom models for translation, summarization, language Tools and partners for running Windows workloads. from fairseq.dataclass.utils import gen_parser_from_dataclass from fairseq.models import ( register_model, register_model_architecture, ) from fairseq.models.transformer.transformer_config import ( TransformerConfig, And inheritance means the module holds all methods Downloads and caches the pre-trained model file if needed. Fully managed database for MySQL, PostgreSQL, and SQL Server. Tools for managing, processing, and transforming biomedical data. generate translations or sample from language models. Power transformers. Content delivery network for delivering web and video. Upgrades to modernize your operational database infrastructure. Google-quality search and product recommendations for retailers. Dedicated hardware for compliance, licensing, and management. Make smarter decisions with unified data. Serverless application platform for apps and back ends. Connectivity options for VPN, peering, and enterprise needs. Encrypt data in use with Confidential VMs. with a convenient torch.hub interface: See the PyTorch Hub tutorials for translation this function, one should call the Module instance afterwards # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description). full_context_alignment (bool, optional): don't apply. Each layer, dictionary (~fairseq.data.Dictionary): decoding dictionary, embed_tokens (torch.nn.Embedding): output embedding, no_encoder_attn (bool, optional): whether to attend to encoder outputs, prev_output_tokens (LongTensor): previous decoder outputs of shape, encoder_out (optional): output from the encoder, used for, incremental_state (dict): dictionary used for storing state during, features_only (bool, optional): only return features without, - the decoder's output of shape `(batch, tgt_len, vocab)`, - a dictionary with any model-specific outputs.