[Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a bidirectional transformer in the style of BERT with text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. This model contains 345 million parameters. It is made up of 24 layers, 16 attention heads with a hidden size of 1024. Find more information at [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM) # How to run Megatron BERT using Transformers ## Prerequisites In that guide, we run all the commands from a folder called `$MYDIR` and defined as (in `bash`): ``` export MYDIR=$HOME ``` Feel free to change the location at your convenience. To run some of the commands below, you'll have to clone `Transformers`. ``` git clone https://github.com/huggingface/transformers.git $MYDIR/transformers ``` ## Get the checkpoint from the NVIDIA GPU Cloud You must create a directory called `nvidia/megatron-bert-cased-345m`. ``` mkdir -p $MYDIR/nvidia/megatron-bert-cased-345m ``` You can download the checkpoint from the [NVIDIA GPU Cloud (NGC)](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m). For that you have to [sign up](https://ngc.nvidia.com/signup) for and setup the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1). Alternatively, you can directly download the checkpoint using: ``` wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O $MYDIR/nvidia/megatron-bert-cased-345m/checkpoint.zip ``` ## Converting the checkpoint In order to be loaded into `Transformers`, the checkpoint has to be converted. You should run the following commands for that purpose. Those commands will create `config.json` and `pytorch_model.bin` in `$MYDIR/nvidia/megatron-bert-cased-345m`. You can move those files to different directories if needed. ``` python3 $MYDIR/transformers/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py $MYDIR/nvidia/megatron-bert-cased-345m/checkpoint.zip ``` As explained in [PR #14956](https://github.com/huggingface/transformers/pull/14956), if when running this conversion script and you're getting an exception: ``` ModuleNotFoundError: No module named 'megatron.model.enums' ``` you need to tell python where to find the clone of Megatron-LM, e.g.: ``` cd /tmp git clone https://github.com/NVIDIA/Megatron-LM PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ... ``` Or, if you already have it cloned elsewhere, simply adjust the path to the existing path. If the training was done using a Megatron-LM fork, e.g. [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/) then you may need to have that one in your path, i.e., /path/to/Megatron-DeepSpeed. ## Masked LM The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform a `Masked LM` task. ``` import os import torch from transformers import BertTokenizer, MegatronBertForMaskedLM # The tokenizer. Megatron was trained with standard tokenizer(s). tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m') # The path to the config/checkpoint (see the conversion step above). directory = os.path.join(os.environ['MYDIR'], 'nvidia/megatron-bert-cased-345m') # Load the model from $MYDIR/nvidia/megatron-bert-cased-345m. model = MegatronBertForMaskedLM.from_pretrained(directory) # Copy to the device and use FP16. assert torch.cuda.is_available() device = torch.device("cuda") model.to(device) model.eval() model.half() # Create inputs (from the BERT example page). input = tokenizer("The capital of France is [MASK]", return_tensors="pt").to(device) label = tokenizer("The capital of France is Paris", return_tensors="pt")["input_ids"].to(device) # Run the model. with torch.no_grad(): output = model(**input, labels=label) print(output) ``` ## Next sentence prediction The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform next sentence prediction. ``` import os import torch from transformers import BertTokenizer, MegatronBertForNextSentencePrediction # The tokenizer. Megatron was trained with standard tokenizer(s). tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m') # The path to the config/checkpoint (see the conversion step above). directory = os.path.join(os.environ['MYDIR'], 'nvidia/megatron-bert-cased-345m') # Load the model from $MYDIR/nvidia/megatron-bert-cased-345m. model = MegatronBertForNextSentencePrediction.from_pretrained(directory) # Copy to the device and use FP16. assert torch.cuda.is_available() device = torch.device("cuda") model.to(device) model.eval() model.half() # Create inputs (from the BERT example page). input = tokenizer('In Italy, pizza served in formal settings is presented unsliced.', 'The sky is blue due to the shorter wavelength of blue light.', return_tensors='pt').to(device) label = torch.LongTensor([1]).to(device) # Run the model. with torch.no_grad(): output = model(**input, labels=label) print(output) ``` # Original code The original code for Megatron can be found here: [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM).