228 Repositories nvidia-docker 12407 FastPhotoStyle 10450 vid2vid 7595 DeepLearningExamples 5258 pix2pixHD 5018 apex 4926 DIGITS 4020 TensorRT 3177 thrust 3119 DALI 3019 tacotron2 2391 NeMo 2295 flownet2-pytorch 2191 Megatron-LM 1698 waveglow 1694 libcudacxx 1565 DeepRecommender 1545 nccl 1423 OpenSeq2Seq 1347 cutlass 1027 sentiment-discovery 1017 MinkowskiEngine 1003 … Set the --dataset-impl flag to mmap, cached, or lazy, respectively (default is mmap). The table below shows the model configurations along with the achieved FLOPs per second (both per GPU and aggregate over all GPUs). The model is defined in a config file which declares multiple important sections. These sections assume that the user has already installed NeMo using the Getting Started instructions in the User Guide. We developed efficient, model-parallel (tensor and pipeline), and multi-node pre-training of GPT and BERT using mixed precision. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. We partition the first GEMM in a column parallel fashion. NVIDIA's AI platform is the first to train one of the most advanced AI language models — BERT — in less than an hour and complete AI inference in just over 2 milliseconds. Research on training transformer language models at scale, including BERT: https://github.com/NVIDIA/Megatron-LM To this end, for the model+data parallel cases we fix the global batch size to 512 for all experiments which corresponds to 64-way data parallelism. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. Multi-head Self Attention •Each attention head will have unique learned weight matrices for the query (Q), key (K), and value (V), where iis the index of each attention head We leverage NVIDIA's Selene supercomputer to perform scaling studies and use up to 3072 A100 GPUs for the largest model. The baseline for all the scaling numbers is the first configuration in Table 1 running on a single GPU. Training these models requires hundreds of exaflops of compute and clever memory management to trade recomputation for a reduced memory footprint. The loose json is then processed into a binary format for training. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). and evaluation pipelines at https://github:com/ NVIDIA/Megatron-LM 2. LAMBADA uses cloze-style reading comprehension to test generative left-to-right language models by constructing examples of 4-5 sentences where the last word in the context $x_t$ is masked. A gaussian distribution with zero mean and standard deviation of 0.02 is used for weight initialization and we scale the weights of layers preceding residual connections by $\frac{1}{\sqrt{2N}}$ where $N$ is the number of layers. See the official PyTorch documentation for further description of these environment variables. This approach allows the model to train on larger datasets, but has the constraint that all parameters must fit on a single GPU. Follow me on Twitter or … To use this repository, please install the latest supported versions of PyTorch with GPU support (python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3 and above) and NVIDIA APEX. Figure 1 shows more detailed scaling results. If nothing happens, download Xcode and try again. The most comprehensive is: However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above. Badges are live and will be dynamically updated with the latest ranking of this paper. However, even for the largest configuration (8.3 billion parameters) running on 512 GPUs, we still achieve 74% scaling relative to the strong baseline single gpu configuration (1.2 billion parameters). $$PPL= \exp\left(-\frac{1}{T_o}\sum_{t}^{T} \text{log}\;P(t\vert 0:t-1)\right)$$ The WikiText-103 test corpus already comes pre-tokenized with word-level tokens that prior work has used to compute perplexity. Large models offer significant accuracy gains, but training billions to trillions of parameters frequently runs up against fundamental hardware limitations. top-k, top-p, or greedy (set top-k and top-p to 0) sampling.. We include example scripts for GPT evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy. Cloze-style datasets like LAMBADA are designed to measure a model's ability to operate in and reason about long-term contexts. al. As the number of attention heads increases, some of the GEMMS inside the self-attention layer become smaller and also the number of elements in the self attention softmax increases. The --data-path specified in later BERT training is the full path and new filename, but without the file extension. To this end, we consider the 8.3 billion parameter configuration with 8-way model parallelism and vary the number of heads from 16 to 32. Alternatively, one can provide --train-samples which is total number of samples to train on. This allows us to split per attention head parameters and workload across the GPUs, and doesn’t require any immediate communication to complete the self-attention. In this work, we built the world's largest transformer based language model on top of existing deep learning hardware, software, and models. From github: Megatron is a large, powerful transformer. We have published the code that implements this approach at our GitHub repository. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. Follow. In all cases, a dropout of 0.1 is used. To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceeding tokens) we utilize a detokenized, processed version of the LAMBADA dataset. In this webinar, we’re joined by Eri Rubin the VP of research and development at DeepCube a cnvrg.io customer and NVIDIA Deep Learning Solutions Architect Adam Tetelman to discuss how to optimize distributed training for multi-node and multi-GPU training to maximize performance. We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced. Published: May 15, 2020. NVIDIA has made the software optimizations and tools it used for accelerating these models available to developers via GitHub and the NVIDIA GPU Cloud (NGC). Model configuration. Other than these minor changes, the distributed training is identical to the training on a single GPU. Some minor modifications are required for GPT data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type: Here the output files are named my-gpt2_text_document.bin and my-gpt2_text_document.idx. For WikiText-103's test set we arrive at $T_o=245566$ and $T=270329$. Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. Downstream tasks with Megatron and BioMegatron Language Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism is a … We performed additional filtering to remove malformed, short or duplicate documents less than 128 tokens. We use the following command to run LAMBADA evaluation on a 345M parameter model. If the fine-tuning is interrupted for any reason, be sure to remove the --finetune flag before continuing, otherwise the training will start again from the beginning. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a json vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the --lr-decay-style has been set to cosine decay. In doing so, we successfully surpassed the limitations posed by traditional single GPU training by implementing a simple and efficient model parallel approach with only a few targeted modifications to the existing PyTorch transformer implementations. We have provided pretrained BERT-345M and GPT-345M checkpoints for use to evaluate or finetuning downstream tasks. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. We use two types of parallelism: data and model parallelism. All of our experiments are conducted on NVIDIA’s DGX SuperPOD and we use up to 32 DGX-2H servers (a total of 512 Tesla V100 SXM3 32GB GPUs). We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our openwebtext directory. The following figures show achieved percentage of theoretical peak FLOPs and achieved aggregate petaFLOPs per second as a function of number of GPUs. Our 8.3B model exemplifies this by setting new state of the art results for both WikiText-103 (10.81 perplexity) and LAMBADA (66.51% accuracy). Data parallel scaling is necessary for training many state of the art models which typically use a much larger global batch size. We open source our work so that the community may reproduce our efforts and extend them. Megatron is a 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism trained on 512 GPUs (NVIDIA Tesla V100), making it the largest transformer model ever trained. To use pipeline model parallelism (sharding the transformer modules into stages with an equal number of transformer modules on each stage, and then pipelining execution by breaking the batch into smaller microbatches), use the --pipeline-model-parallel-size flag to specify the number of stages to split the model into (e.g., splitting a model with 24 transformer layers across 4 stages would mean each stage gets 6 transformer layers each). Our experiments are conducted on NVIDIA’s DGX SuperPOD. Ongoing research training transformer language models at scale, including: BERT & GPT-2. Model parallel training can overcome this limitation by partitioning the model across multiple GPUs. For example, the 8.3 billion parameters case with 8-way (8 GPU) model parallel achieves 77% of linear scaling. download the GitHub extension for Visual Studio, BioMegatron: Larger Biomedical Domain Language Model, End-to-End Training of Neural Retrievers for Open-Domain Question Answering, Large Scale Multi-Actor Generative Dialog Modeling, Local Knowledge Powered Conversational Agents, MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models, RACE Reading Comprehension Dataset Leaderboard, Training Question Answering Models From Synthetic Data, Finetuning (Optional for zero-shot tasks), Downstream task evaluation or text generation. Second, we developed a simple and efficient two-dimensional model-parallel approach. While this is single GPU training, the batch size specified by --micro-batch-size is a single forward-backward path batch-size and the code will perform gradient accumulation steps until it reaches global-batch-size whcih is the batch size per iteration. bash examples/pretrain_bert_distributed.sh, bash examples/pretrain_gpt_distributed.sh. Additionally, Megatron-LM … python -m pip install virtualenvvirtualenv bert_envsource bert_env/bin/activa… Furthermore, when we look at the numbers it’s 24x the size of BERT and 5.6x the size of GPT-2. As expected, Torch distributed data parallelism is more efficient at larger model sizes. In this tutorial, we are going to describe how to finetune BioMegatron - a BERT-like Megatron-LM model pre-trained on large biomedical text corpus (PubMed abstracts and full-text commercial use collection) - on RE: Text mining chemical-protein interactions (CHEMPROT).. Lastly, to accommodate the transformer’s fixed window input size and inability to attend to all tokens [0, t-1], we utilize a sliding window approach with a size of 1024 tokens, and a stride of 32 tokens to compute perplexity. As such, multi-node training can be achieved by properly setting environment variables and using init_method='env://' in the launcher. Our approach is simple, does not require any new compiler or code re-wiring for model parallelism, and can be fully implemented with insertion of few simple primitives (f and g operators in Figure 2) as described in the remainder of this section. The training dataset can be either a single set or a multiple datasets combined with a set of weights. We release version 1.0 of Megatron which makes the training of large NLP models even faster and sustains 62.4 teraFLOPs in the end-to-end training that is 48% of the theoretical peak FLOPS for a single GPU in a DGX2-H server. With options global-batch-size 1536 and rampup-batch-size 16 16 5859375, the training will start with global batch size 16 and linearly increase the global batch size to 1536 over 5,859,375 samples with incrmeental steps 16. Because the matching tasks are quite similar, the script can be quickly tweaked to work with the Quora Question Pairs (QQP) dataset as well. Note that the --data-path now includes the additional _text_document suffix added in preprocessing, but does not include the file extensions. However, very large models can be quite difficult to train due to memory constraints. Our models utilize subword units, so for our version of LAMBADA evaluation we utilize the raw, unprocessed LAMBADA dataset, and require that our model predict the multiple $x_t$ subwords that make up the last word token. If nothing happens, download the GitHub extension for Visual Studio and try again. To switch between these two options use --DDP-impl local or --DDP-impl torch, respectively. Finally, we study the effect of attention heads on model parallel scaling. There are few optional parameters to play, e.g. Further command line arguments are described in the source file preprocess_data.py. This block consists of two GEMMs with a GeLU nonlinearity in between followed by a dropout layer. We have provided an example of how to configure Megatron to run GPT-3 with 175 billion parameters on 1024 GPUs. Below are some of the projects where we have directly used Megatron: Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. After installation, there are several possible workflows. To analyze the performance of training large language models, we compute perplexity on the WikiText-103 dataset and cloze-style prediction accuracy on the LAMBADA dataset. This approach splits both GEMMs in the MLP block across GPUs and requires only a single all-reduce operation in the forward pass (g operator) and a single all-reduce in the backward pass (f operator). The training data requires preprocessing. Scaling the model to 8.3 billion parameters on 512 GPUs with 8-way model parallelism, we achieved up to 15.1 PetaFLOPS sustained performance over the entire application and reached 76% scaling efficiency compared to the single GPU case. To access these checkpoints, first sign up for and setup the NVIDIA GPU Cloud (NGC) Registry CLI. Throughout this section, we will showcase weak scaling with respect to the model parameters for both model parallel and model+data parallel cases. To use tensor model parallelism (splitting execution of a single transformer module over multiple GPUs), add the --tensor-model-parallel-size flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with --seed). Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This makes it a natural evaluation metric for language models which represent a probability distribution over entire sentences or texts. Cloze-style reading comprehension uses a context of word tokens $x=x_{1:t}$ with one token $x_j$ masked; the model's objective is to correctly predict the value $y$ of the missing $j^{\text{th}}$ token based on its understanding of the surrounding context. It doesn’t require a compiler, and is orthogonal to the kind of pipeline model parallelism advocated by approaches such as gPipe. The GPT vocab file and merge table can be downloaded directly. We developed efficient, model-parallel, and multinode training of GPT-2 and BERT using mixed precision. For the NVIDIA Research team’s NLP code on Project Megatron, head over to the Megatron Language Model GitHub repository. We have published the code that implements this approach at our GitHub repository. To clean the datasets we used the ftfy library and then removed non-english content using the langdetect library. HuggingFace and Megatron tokenizers (which uses HuggingFace underneath) can be automatically instantiated by only tokenizer_name, which downloads the corresponding vocab_file from the internet. Weak scaling is typically done with scaling the batch-size, however, this approach does not address training large models that do not fit on a single GPU and also convergence performance degrades for large batch sizes. For training, we randomly split the WebText dataset into a 29:1 ratio to obtain training and validation sets, respectively. Features Below we provide a brief feature list, see our detailed featureoverview for descriptions and usage. This results in a slight decrease in scaling. development platforms. The NLP code on Project Megatron is also openly available in Megatron Language Model GitHub repository. The results are presented in Table 2. Several downstream tasks are described for both GPT and BERT models below. The TRAIN_DATA and VALID_DATA directory contain the RACE dataset as separate .txt files. As shown in Figure 2b, for the self attention block, we exploit inherent parallelism in the multihead attention operation, partitioning the GEMMs associated with key (K), query (Q), and value (V) in a column parallel fashion such that the matrix multiply corresponding to one attention head is done locally on one GPU. To convert the json into mmap, cached index file, or the lazy loader format use preprocess_data.py. Few changes need to make, such as we need to provide the path to the pretrained checkpoint, the length of the output samples, whether to generate texts unconditionally (--num-samples to denote how many samples to generate) or conditional (need to pass --sample-input-file where each line of the file will be used as the conditional texts). MegatronLM’s Supercharged V1.0 . This system is optimized for multi-node deep learning applications, with 300 GB/sec bandwidth between GPUs inside a server and 100 GB/sec of interconnect bandwidth between servers. Note that the --data-path now includes the additional _text_sentence suffix added in preprocessing, but does not include the file extensions. Note that the --strict-lambada flag should be used to require whole word matching. This Best Practices guide is intended for researchers and model developers to learn how to efficiently develop and train speech and language models using NVIDIA NeMo Toolkit. To know more in detail, check out the official announcement by NVIDIA. We describe our evaluation methodologies below; however, more details are available in our github repository. We clamp our learning rate to a minimum value of $1\times 10^{-5}$. Most of the arguments are fairly self-explanatory. We recommend either utilizing the provided Dockerfile in ./docker/ or creating a virtual environment (to avoid breaking existing tf installations) and install our requirements.txt. We've provided several scripts for pretraining both BERT and GPT in examples directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training. Background and Challenges 2.1. NVIDIA today announced breakthroughs in language understanding that allow businesses to engage more naturally with customers using real-time conversational AI. Perplexity is the exponentiation of the average cross entropy of a corpus. To do so, simply add the --finetune flag and adjust the input files and training parameters within the original training script. Several general purpose model parallel frameworks such as GPipe and Mesh-TensorFlow have been proposed recently. Future research should be wary of this hyperparameter to design large transformer models that balance model performance and model efficiency. Project description Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. For even comparison with prior works, we evaluate perplexity on the word-level WikiText-103 test dataset, and appropriately compute perplexity given the change in tokens when using our subword tokenizer. NVIDIA ADLR. All the cases from 1 billion to 1 trillion achieve more than 41% half precision utilization, which is high for an end-to-end application. The dataset ended up with approximately 40 million documents. As the model size increases, we also modestly increase the batch size. As expected, the WikiText perplexity decreases and LAMBADA accuracy increases with the growth of the model size (Table 3). We empirically found that using a smaller model in those cases improves the training time. The following script finetunes the BERT model for evaluation with the MultiNLI sentence pair corpus. We take advantage of the structure of transformer networks to create a simple model parallel implementation by adding a few synchronization primitives. We use train-iters as the training iterations requested. Our approach is conceptually similar to Mesh-TensorFlow, we focus on intra-layer parallelism and fuse GEMMs to reduce synchronization. We use the following command to run WikiText-103 evaluation on a 345M parameter model. Additionally, part of this codebase leverages tensorflow-cpu to (optionally) perform dataloading of TFRecords for BERT training. This script runs single GPU 345M parameter BERT pretraining. If nothing happens, download GitHub Desktop and try again. We consider training 3 model sizes: 355 million, 2.5 billion, and 8.3 billion. By default, the learning rate decays linearly over the training iterations starting at --lr to a minimum set by --min-lr over --lr-decay-iters iterations. The script is designed for slurm with pyxis plugin but can be easily adopted to any other scheduler. The original vocabulary size was 50,257, however, to have efficient GEMMs for the logit layer, it is critical for the vocabulary size to be a multiple of 128 as well as the number of model parallel GPUs. Further command line arguments are described in the source file arguments.py. This repository is for ongoing research on training large transformer language models at scale. We use teacher forcing in our predictions, and consider an answer correct only when all output subword predictions are correct. Update 9/5/2019: Larger dataset and stricter lambada formulation. Are Unsupervised Multitask Learners, Radford et GPT training, i.e., includes all operations including data loading,,. 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