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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-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).

{}
arxiv:1909.08053 has_space region:us
TAGS
#arxiv-1909.08053 #has_space #region-us 



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. 

Find more information at URL


# 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'):



Feel free to change the location at your convenience.

To run some of the commands below, you'll have to clone 'Transformers'. 



## Get the checkpoint from the NVIDIA GPU Cloud 

You must create a directory called '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.

Alternatively, you can directly download the checkpoint using:



## 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 'URL' and 'pytorch_model.bin' in '$MYDIR/nvidia/megatron-bert-cased-345m'. 
You can move those files to different directories if needed.



As explained in PR #14956, if when running this conversion 
script and you're getting an exception:

you need to tell python where to find the clone of Megatron-LM, e.g.:

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 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.



## Next sentence prediction

The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform next
sentence prediction.



# Original code

The original code for Megatron can be found here: URL

# 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'):



Feel free to change the location at your convenience.

To run some of the commands below, you'll have to clone 'Transformers'. ## Get the checkpoint from the NVIDIA GPU Cloud 

You must create a directory called '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.

Alternatively, you can directly download the checkpoint using: ## 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 'URL' and 'pytorch_model.bin' in '$MYDIR/nvidia/megatron-bert-cased-345m'. 
You can move those files to different directories if needed.



As explained in PR #14956, if when running this conversion 
script and you're getting an exception:

you need to tell python where to find the clone of Megatron-LM, e.g.:

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 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. ## Next sentence prediction

The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform next
sentence prediction. # Original code

The original code for Megatron can be found here: URL
TAGS
#arxiv-1909.08053 #has_space #region-us 
 # 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'):



Feel free to change the location at your convenience.

To run some of the commands below, you'll have to clone 'Transformers'. ## Get the checkpoint from the NVIDIA GPU Cloud 

You must create a directory called '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.

Alternatively, you can directly download the checkpoint using: ## 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 'URL' and 'pytorch_model.bin' in '$MYDIR/nvidia/megatron-bert-cased-345m'. 
You can move those files to different directories if needed.



As explained in PR #14956, if when running this conversion 
script and you're getting an exception:

you need to tell python where to find the clone of Megatron-LM, e.g.:

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 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. ## Next sentence prediction

The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform next
sentence prediction. # Original code

The original code for Megatron can be found here: URL
passage: TAGS
#arxiv-1909.08053 #has_space #region-us 
# 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'):



Feel free to change the location at your convenience.

To run some of the commands below, you'll have to clone 'Transformers'.## Get the checkpoint from the NVIDIA GPU Cloud 

You must create a directory called '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.

Alternatively, you can directly download the checkpoint using:## 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 'URL' and 'pytorch_model.bin' in '$MYDIR/nvidia/megatron-bert-cased-345m'. 
You can move those files to different directories if needed.



As explained in PR #14956, if when running this conversion 
script and you're getting an exception:

you need to tell python where to find the clone of Megatron-LM, e.g.:

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 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.## Next sentence prediction

The following code shows how to use the Megatron BERT checkpoint and the Transformers API to perform next
sentence prediction.# Original code

The original code for Megatron can be found here: URL