Merge branch 'patch-1' of https://github.com/stas00/Megatron-LM into github-pr

README.md

@@ -13,7 +13,9 @@ Below are some of the projects where we have directly used Megatron:
 
 Megatron is also used in [NeMo Megatron](https://developer.nvidia.com/nvidia-nemo#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.
 
-Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. 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. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. The table below shows the model configurations along with the achieved FLOPs (both per GPU and aggregate over all GPUs). 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.
+Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. 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. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. Each cluster node has 8 NVIDIA 80GB A100 GPUs. The table below shows the model configurations along with the achieved FLOPs (both per GPU and aggregate over all GPUs). 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.
+
+Additionally, the model parallel size column reports a combined tensor and pipeline parallelism degrees. For numbers larger than 8, typically tensor parallel of size 8 was used. So, for example, the 145B model reports the total model parallel size of 64, which means that this setup used TP=8 and PP=8.
 
 ![Cases](images/cases_april2021.png)