# nvidia /megatron-bert-cased-345m

Megatron 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.

# 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). For that you have to sign up for and setup the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the NGC documentation.

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

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

# 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.