Figure 5 shows validation perplexity as a function of number of training iterations for different model sizes. We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced. 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. The loose json is then processed into a binary format for training. 50% GROWTH OF NVIDIA DEVELOPERS 50% GROWTH IN TOP500 2018 2019+60% 1.2M DEVELOPERS +50% 800K 2018 2019 13M CUDA DOWNLOADS 8M 2010 2012 2014 2016 2018 NVIDIA in World’s Most Energy Efficient Supercomputers NVIDIA in World’s Top Most Powerful Supercomputers We leverage NVIDIA's Selene supercomputer to perform scaling studies and use up to 3072 A100 GPUs for the largest model. Other than these minor changes, the distributed training is identical to the training on a single GPU. This repository is for ongoing research on training large transformer language models at scale. 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. This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with --seed). Further command line arguments are described in the source file arguments.py. Several downstream tasks are described for both GPT and BERT models below. For the NVIDIA Research team’s NLP code on Project Megatron, head over to the Megatron Language Model GitHub repository. Use Git or checkout with SVN using the web URL. al. Future research should be wary of this hyperparameter to design large transformer models that balance model performance and model efficiency. If nothing happens, download Xcode and try again. Additionally, Megatron-LM … Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. As such, multi-node training can be achieved by properly setting environment variables and using init_method='env://' in the launcher. We strongly recommend using one of NGC's recent PyTorch containers (the latest compatible version at time of publication can be pulled with docker pull nvcr.io/nvidia/pytorch:20.12-py3). We efficiently train an 8.3 billion parameter language model (24x and 5.6x larger than the size of BERT and GPT-2, respectively) on 512 NVIDIA V100 GPUs with 8-way model parallelism and achieve up to 15.1 PetaFLOPS sustained over the entire application. Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. Alternatively, you can directly download the checkpoints using: The models require vocabulary files to run. This approach allows the model to train on larger datasets, but has the constraint that all parameters must fit on a single GPU. There is also a script for GPT interactive text generation. Note that the --data-path now includes the additional _text_document suffix added in preprocessing, but does not include the file extensions. To train our GPT-2 model we created an aggregate 174GB dataset (~4.5x larger than WebText) consisting of Wikipedia, OpenWebText, RealNews, and CC-Stories. As expected, Torch distributed data parallelism is more efficient at larger model sizes. 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. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content. This approach is simple to implement, because it requires only a few extra all-reduce operations be added to the forward and backward pass. Throughout this section, we will showcase weak scaling with respect to the model parameters for both model parallel and model+data parallel cases. We make note of the detailed methods we use to compute perplexity for the sake of reproducibility. Two recent papers, BERT and GPT-2, demonstrate the benefits of large scale language modeling. 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. For example: The name of the text field of the json can be changed by using the --json-key flag in preprocess_data.py The other metadata are optional and are not used in training. These scripts use the PyTorch distributed launcher for distributed training. Our code is written in native Python, leverages mixed precision training, and utilizes the NCCL library for communication between GPUs. However, we only make a few targeted modifications to existing PyTorch transformer implementations to employ model parallelism for training large transformers. Larger language models are dramatically more useful for NLP tasks such as article completion, question answering, and dialog systems. Since we study up to 8-way model parallelism, we pad the vocabulary such that it is divisible by 128x8=1024, resulting in a padded vocabulary size of 51,200. We developed efficient, model-parallel, and multinode training of … To test the computational performance of our implementation, we consider GPT-2 models with four sets of parameters detailed in Table 1. and evaluation pipelines at https://github:com/ NVIDIA/Megatron-LM 2. 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). Figure 4 shows scaling values for both model and model+data parallelism. We have tested Megatron with NGC's PyTorch container version 20.12, which uses python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3. Data preprocessing requires NLTK, though this is not required for training, evaluation, or downstream tasks. In this work, we implement a simple and efficient model parallel approach by making only a few targeted modifications to existing PyTorch transformer implementations. We take advantage of the structure of transformer networks to create a simple model parallel implementation by adding a few synchronization primitives. Note that the --data-path now includes the additional _text_sentence suffix added in preprocessing, but does not include the file extensions. 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). The value of $T_o$ is calculated before this preprocessing. If this option is present, then instead of providing --lr-decay-iters, one will need to provide --lr-decay-samples. Published: May 15, 2020. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training. 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 demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. Most of the arguments are fairly self-explanatory. With full global batch size of 1536 on 1024 A100 GPUs, each iteration takes around 32 seconds resulting in 138 teraFLOPs per GPU which is 44% of the theoretical peak FLOPs. The training dataset can be either a single set or a multiple datasets combined with a set of weights. The configuration with 1 billion parameters fits on a single GPU whereas the 8 billion parameter models requires 8-way model parallelism (8 GPUs). We introduce model parallelism in both of these blocks separately. 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. We consider training 3 model sizes: 355 million, 2.5 billion, and 8.3 billion. Second, we developed a simple and efficient two-dimensional model-parallel approach. 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 generate text samples using largely the GPT pretraining script. Further documentation for downloading models can be found in the NGC documentation. The second GEMM in the block is parallelized along its rows and takes the output of the GeLU layer directly without requiring any communication. Large models offer significant accuracy gains, but training billions to trillions of parameters frequently runs up against fundamental hardware limitations. The subsequent GEMM from the output linear layer (after self attention) is parallelized along its rows and takes the output of the parallel attention layer directly, without requiring communication between the GPUs. 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. We recommend further preprocessing this json dataset by nltk punctuation standardization. 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. Our resulting dataset has a WikiText 8-gram overlap of 10% which is similar to the 9% 8-gram overlap between the WikiText-103 train and test sets. 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. We have provided an example of how to configure Megatron to run GPT-3 with 175 billion parameters on 1024 GPUs. However, very large models can be quite difficult to train due to memory constraints. This script runs single GPU 345M parameter GPT pretraining. We have examples of how to use these two different forms of model parallelism in these scripts: bash examples/pretrain_bert_distributed_with_mp.sh, bash examples/pretrain_gpt_distributed_with_mp.sh. 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. This script runs single GPU 345M parameter BERT pretraining. We utilize the publicly available OpenWebText library from jcpeterson and eukaryote31's work to download urls. GLUE model is comprised of the pretrained BERT model followed by a Sequence Regression module (for STS-B task) or Sequence classifier module (for the rest of the tasks).. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. NVIDIA has made the software optimizations used to accomplish these breakthroughs in conversational AI available to developers: NVIDIA GitHub BERT training code with PyTorch * NGC model scripts and check-points for TensorFlow It doesn’t require a compiler, and is orthogonal to the kind of pipeline model parallelism advocated by approaches such as gPipe. We observe that initially as the model parallel size increases, utilization slightly decreases; as hidden size increases for larger models, utilization starts increasing and reaches 49% for the largest model. 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. To do so, simply add the --finetune flag and adjust the input files and training parameters within the original training script. Our experiments are conducted on NVIDIA’s DGX SuperPOD. Work fast with our official CLI. Without model parallelism, we can fit a baseline model of 1.2B parameters on a single V100 32GB GPU, and sustain 39 TeraFLOPS during the overall training process, which is 30% of the theoretical peak FLOPS for a single GPU in a DGX2-H server. For BERT training, use the --split-sentences flag to preprocess_data.py as described above to include sentence breaks in the produced index. 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. These sections assume that the user has already installed NeMo using the Getting Started instructions in the User Guide. We partition the first GEMM in a column parallel fashion. NVIDIA just took that innovation to a new level with a turnkey data center called the DGX SuperPOD that ranks as number 22 on the list of global supercomputers.” –Jim McGregor, Forbes “In a clear demonstration of why artificial intelligence leadership demands the best compute capabilities, NVIDIA has unveiled ‘the This block consists of two GEMMs with a GeLU nonlinearity in between followed by a dropout layer. Earlier we made sure to remove WikiText test set content to avoid leakage. We observe excellent scaling numbers in both settings. 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. Model+data parallelism requires further communication of gradients after the back-propagation step and as a result the scaling numbers drop slightly. We study both model and model+data parallel scalings. We showcase this approach by training an 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism on 512 GPUs, making it the largest transformer based language model ever trained at 24x the size of BERT and 5.6x the size of GPT-2. Research on training transformer language models at scale, including BERT: https://github.com/NVIDIA/Megatron-LM al., 2019, Language Models are Unsupervised Multitask Learners, Radford et. Dynamic Evaluation of Transformer Language Models, Krause et. We describe our evaluation methodologies below; however, more details are available in our github repository. To convert the json into mmap, cached index file, or the lazy loader format use preprocess_data.py. Follow me on Twitter or … We also note that achieved aggregate petaFLOPs per second across all GPUs increases almost linearly with number of GPUs, demonstrating good weak scaling. Downstream tasks with Megatron and BioMegatron Language Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism is a … 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. Now, let's take a closer look at the model's configuration and learn to train the model. • Model parallelism does not scale efficiently beyond … To access these checkpoints, first sign up for and setup the NVIDIA GPU Cloud (NGC) Registry CLI. This results in a slight decrease in scaling. This repository is for ongoing research on training large transformer language models at scale. Because the matching tasks are quite similar, the script can be quickly tweaked to work with the Quora Question Pairs (QQP) dataset as well. In all cases, a dropout of 0.1 is used. 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. We open source our work so that the community may reproduce our efforts and extend them. Checkpointing the activations facilitates the training of larger models and/or batches. Training the largest neural language model has recently been the best way to advance the state of the art in NLP applications. We have published the code that implements this approach at our GitHub repository. To switch between these two options use --DDP-impl local or --DDP-impl torch, respectively. 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. We use two types of parallelism: data and model parallelism. As before, in GPT training, use the longer name without the extension as --data-path. NVIDIA’s custom model, with 8.3 billion parameters, is 24 times the size of BERT-Large. Both papers leverage advances in compute and available text corpora to significantly surpass state of the art performance in natural language understanding, modeling, and generation. First, place your training data in a loose json format, with one json containing a text sample per line. 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). python -m pip install virtualenvvirtualenv bert_envsource bert_env/bin/activa… The training data requires preprocessing. After installation, there are several possible workflows. 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. In addition, DeepSpeed conveniently supports flexible combination of ZeRO-powered data parallelism with custom model parallelisms, such as tensor slicing of NVIDIA's Megatron-LM. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag -m torch.distributed.launch, detailed below, are the only additional requirements to adopt distributed training. An example script to prepare data for BERT training is: The output will be two files named, in this case, my-bert_text_sentence.bin and my-bert_text_sentence.idx. We use train-iters as the training iterations requested. 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. If you'd like to use Wikipedia data for GPT training you should still clean it with nltk/spacy/ftfy, but do not use the --split-sentences flag. We facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. 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). We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our openwebtext directory. Note that for RACE, the batch size is the number of RACE query's to evaluate. Make that lambada is part of the file path. 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. 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. Projects. 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). Alternatively, one can provide --train-samples which is total number of samples to train on. Model configuration. We use the following command to run WikiText-103 evaluation on a 345M parameter model. At a respective WikiText perplexity of 12.68 and 10.81 both our 2.5B and 8.3B models surpass the previous state of the art perplexity of 16.43 set by Krause et. Project description Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. 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. Our approach is conceptually similar to Mesh-TensorFlow, we focus on intra-layer parallelism and fuse GEMMs to reduce synchronization. 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. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. Facilitates the training scripts and VALID_DATA directory contain the RACE dataset as separate.txt files different processors while employs. % of linear scaling -4 } $ the value of $ T_o $ is before. 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