megatron nvidia github

In all cases, a dropout of 0.1 is used. Alternatively, you can directly download the checkpoints using: The models require vocabulary files to run. The script is designed for slurm with pyxis plugin but can be easily adopted to any other scheduler. Neural Language Model Pretraining Pretrained language models have become an indispensable part of NLP researchers’ toolkits. We introduce model parallelism in both of these blocks separately. Work fast with our official CLI. To convert the json into mmap, cached index file, or the lazy loader format use preprocess_data.py. As expected, the WikiText perplexity decreases and LAMBADA accuracy increases with the growth of the model size (Table 3). 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 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). 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. Set the --dataset-impl flag to mmap, cached, or lazy, respectively (default is mmap). 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. All of the scripts and recipes to reproduce our work using TensorFlow, including data preparation, have been published on GitHub. Note that the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging. We have published the code that implements this approach at our GitHub repository. 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. These sections assume that the user has already installed NeMo using the Getting Started instructions in the User Guide. To test the computational performance of our implementation, we consider GPT-2 models with four sets of parameters detailed in Table 1. It uses 8-way and 16-way tensor and pipeline parallelism, respectively. 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). The value of $T_o$ is calculated before this preprocessing. 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. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. Now, let's take a closer look at the model's configuration and learn to train the model. al. The following figures show achieved percentage of theoretical peak FLOPs and achieved aggregate petaFLOPs per second as a function of number of GPUs. Our experiments are conducted on NVIDIA’s DGX SuperPOD . We partition the first GEMM in a column parallel fashion. The TRAIN_DATA and VALID_DATA directory contain the RACE dataset as separate .txt files. 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. An epoch is defined as 68,507 iterations to train over the entire 174GB corpus. 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. Furthermore, when we look at the numbers it’s 24x the size of BERT and 5.6x the size of GPT-2. About the Authors About Shar Narasimhan Shar Narasimhan is a senior product marketing manager for AI specializing in deep learning training and OEM engagements on the Tesla data center team at NVIDIA. As expected, Torch distributed data parallelism is more efficient at larger model sizes. This enables us to perform all GEMMs in a simple transformer layer using only two all reduces in the forward path and two in the backward path (see Figure 3). This formulation is equivalent to the original task of word prediction. 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. Our 8.3B model achievs a validation perplexity of 9.27. We describe our evaluation methodologies below; however, more details are available in our github repository. 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. For WikiText-103's test set we arrive at $T_o=245566$ and $T=270329$. We consider training 3 model sizes: 355 million, 2.5 billion, and 8.3 billion. The output of the second GEMM is then reduced across the GPUs before passing the output to the dropout layer. Finally, we study the effect of attention heads on model parallel scaling. We generate text samples using largely the GPT pretraining script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with --seed). Model parallel training can overcome this limitation by partitioning the model across multiple GPUs. 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. We use the following command to run WikiText-103 evaluation on a 345M parameter model. Other than these minor changes, the distributed training is identical to the training on a single GPU. development platforms. 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). Project description Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. 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. 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). To fit these models into memory, existing solutions make trade-offs between computation, communication, and development efficiency: • Data parallelism does not help reduce memory footprint per device: a model with more than 1 billion parameters runs out of memory even on GPUs with 32GB of memory. 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. 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. 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. 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. Join this webinar to learn how NVIDIA researchers created Megatron, the largest Transformer language model ever trained with 8.3 billion parameters at 24x the size of BERT and 5.6x the size of GPT-2. Learning rates in all cases are set to $1.5\times 10^{-4}$ with single cycle cosine annealing and 3000 iteration warmup. We take advantage of the structure of transformer networks to create a simple model parallel implementation by adding a few synchronization primitives. al., 2019. This script runs single GPU 345M parameter BERT pretraining. python -m pip install virtualenvvirtualenv bert_envsource bert_env/bin/activa… To use this repo please install the latest supported versions of PyTorch with GPU support. See the official PyTorch documentation for further description of these environment variables. We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced. We have provided pretrained BERT-345M and GPT-345M checkpoints for use to evaluate or finetuning downstream tasks. 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. To have consistent GEMM sizes in the self attention layer, the hidden size per attention head is kept constant at 96 while the number of heads and layers are varied to obtain configurations ranging from 1 billion to 8 billion parameters. 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. As the model size increases, we also modestly increase the batch size. 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. 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. Since each RACE query has four samples, the effective batch size passed through the model will be four times the batch size specified on the command line. There are few optional parameters to play, e.g. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. 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. Our experiments are conducted on NVIDIA’s DGX SuperPOD. This block consists of two GEMMs with a GeLU nonlinearity in between followed by a dropout layer. This repository is for ongoing research on training large transformer language models at scale. The training dataset can be either a single set or a multiple datasets combined with a set of weights. This approach is simple to implement, because it requires only a few extra all-reduce operations be added to the forward and backward pass. 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. Several approaches to model parallelism exist, but they are difficult to use, either because they rely on custom compilers, or because they scale poorly or require changes to the optimizer. After installation, there are several possible workflows. The typical paradigm for training models has made use of weak scaling approaches and distributed data parallelism to scale training batch size with number of GPUs. We observe excellent scaling numbers in both settings. Features Below we provide a brief feature list, see our detailed featureoverview for descriptions and usage. This repository is for ongoing research on training large transformer language models at scale. Our models achieve 61.52% and 66.51% accuracy on LAMBADA even without any stopword filtration, surpassing Radford et. Data preprocessing requires NLTK, though this is not required for training, evaluation, or downstream tasks. We have provided an example of how to configure Megatron to run GPT-3 with 175 billion parameters on 1024 GPUs. To know more in detail, check out the official announcement by NVIDIA. Training the largest neural language model has recently been the best way to advance the state of the art in NLP applications. Lastly, the output logit layer is also partitioned along the vocabulary dimension, and to avoid a massive all-gather across the vocabulary dimension, we fuse this layer with the cross entropy loss. 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. Make that lambada is part of the file path. Note that the --data-path now includes the additional _text_sentence suffix added in preprocessing, but does not include the file extensions. It doesn’t require a compiler, and is orthogonal to the kind of pipeline model parallelism advocated by approaches such as gPipe. Most of the arguments are fairly self-explanatory. With weak scaling, we found that increasingly large transformer models can be trained in a similar amount of time compared to their smaller counterparts and can demonstrably improve performance. Leveraging large corpus pretraining to learn robust neural representations of … The table below shows the model configurations along with the achieved FLOPs per second (both per GPU and aggregate over all GPUs). Update 9/5/2019: Larger dataset and stricter lambada formulation. 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. Dynamic Evaluation of Transformer Language Models, Krause et. 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. 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