README & Tokenizer

README.md

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+<!---
+# ##############################################################################################
+# 
+# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# 
+# ##############################################################################################
+-->
+
+# 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](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:
+
+### BERT 345M cased
+
+```
+wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O $MYDIR/nvidia/megatron-bert-cased-345m/checkpoint.zip
+```
+
+## Converting the checkpoint
+
+In order to be loaded into `Transformers`, the checkpoint have to be converted. You should run the following commands for that purpose. 
+Those commands will create `config.json` and `pytorch_model.bin` in `$MYDIR/nvidia/megatron-bert-cased-345m`. 
+You can move those files to different directories if needed.
+
+### BERT 345M cased
+
+```
+python3 $MYDIR/transformers/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py $MYDIR/nvidia/megatron-bert-cased-345m/checkpoint.zip
+```
+
+## 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).

tokenizer_config.json

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+{
+  "do_lower_case": false
+}