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| [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-uncased-345m`. | |
| ``` | |
| mkdir -p $MYDIR/nvidia/megatron-bert-uncased-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_uncased/zip -O $MYDIR/nvidia/megatron-bert-uncased-345m/checkpoint.zip | |
| ``` | |
| ## Converting the checkpoint | |
| In order to be loaded into `Transformers`, the checkpoint has to be converted. You should run the following commands for that purpose. | |
| Those commands will create `config.json` and `pytorch_model.bin` in `$MYDIR/nvidia/megatron-bert-{cased,uncased}-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-uncased-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-uncased-345m') | |
| # The path to the config/checkpoint (see the conversion step above). | |
| directory = os.path.join(os.environ['MYDIR'], 'nvidia/megatron-bert-uncased-345m') | |
| # Load the model from $MYDIR/nvidia/megatron-bert-uncased-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-uncased-345m') | |
| # The path to the config/checkpoint (see the conversion step above). | |
| directory = os.path.join(os.environ['MYDIR'], 'nvidia/megatron-bert-uncased-345m') | |
| # Load the model from $MYDIR/nvidia/megatron-bert-uncased-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). | |