![]() We strongly recommend using the latest release of NGC's PyTorch container with DGX nodes. Activation Checkpointing and Recomputation.However, for large transformer models, this overhead is not large and can almost entirely eliminted by overlapping the gradient all-reduce with backpropagation. Data parallelism introduces some overhead due to the gradient all-reduce required between the data parallel groups. Note that these numbers are also measured on benchmark runs and in this case are measured using a data parallel size of one. As the model size increases, we achieve better GPU utilization and for the one trillion parameter model, we reach a MFU and HFU of 56.3% and 57.0%, respectively. The following table shows both model (MFU) and hardware (HFU) FLOPs utilization for select configurations up to 1T parameters (see our paper for a description of how these are calculated). Note that these results are from benchmark runs and these models were not trained to convergence however, the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging. The graph below shows that we scale nearly linear up to 1 trillion parameter models running on 3072 GPUs. Each cluster node has 8 NVIDIA 80GB A100 GPUs. We leverage NVIDIA's Selene supercomputer to perform scaling studies and use up to 3072 A100 GPUs for the largest model. As the model size increases, we also modestly increase the batch size. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. All models use a vocabulary size of 51,200 and a sequence length of 2048. 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. Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. Multi-Stage Prompting for Knowledgeable Dialogue Generation.Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models.Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases.Training Question Answering Models From Synthetic Data.RACE Reading Comprehension Dataset Leaderboard.MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.Local Knowledge Powered Conversational Agents. ![]()
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