---
title: "BERT Pre-training"
excerpt: ""
---

In this tutorial we will apply DeepSpeed to pre-train the BERT
(**B**idirectional **E**ncoder **R**epresentations from **T**ransformers),
which is widely used for many Natural Language Processing (NLP) tasks. The
details of BERT can be found here: [BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).


We will go through how to setup the data pipeline and how to run the original
BERT model. Then we will show step-by-step how to modify the model to
leverage DeepSpeed. Finally, we demonstrate the performance evaluation and
memory usage reduction from using DeepSpeed.

## Pre-training Bing BERT without DeepSpeed

We work from adaptations of
[huggingface/transformers](https://github.com/huggingface/transformers) and
[NVIDIA/DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples).
We have forked this repo under
[DeepSpeedExamples/bing_bert](https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert)
and made several modifications in their script:

  * We adopted the modeling code from NVIDIA's BERT under `bing_bert/nvidia/`.
  * We extended the data pipeline from [Project Turing](https://msturing.org/)
    under `bing_bert/turing/`.


### Training Data Setup

**Note:** *Downloading and pre-processing instructions are coming soon.*

Download the Wikipedia and BookCorpus datasets and specify their paths in the
model config file `DeepSpeedExamples/bing_bert/bert_large_adam_seq128.json`:

```json
{
  ...
  "datasets": {
      "wiki_pretrain_dataset": "/data/bert/bnorick_format/128/wiki_pretrain",
      "bc_pretrain_dataset": "/data/bert/bnorick_format/128/bookcorpus_pretrain"
  },
  ...
}
```


### Running the Bing BERT model

From `DeepSpeedExamples/bing_bert`, run:

```bash
python train.py  \
    --cf bert_large_adam_seq128.json \
    --train_batch_size 64 \
    --max_seq_length 128 \
    --gradient_accumulation_steps 1  \
    --max_grad_norm 1.0 \
    --fp16 \
    --loss_scale 0 \
    --delay_allreduce \
    --max_steps 10 \
    --output_dir <path-to-model-output>
```


## Enabling DeepSpeed

To use DeepSpeed we need to edit two files :

* `train.py`: Main entry point for training
* `utils.py`: Training parameters and checkpoints saving/loading utilities


### Argument Parsing

We first need to add DeepSpeed's argument parsing to `train.py`
using `deepspeed.add_config_arguments()`. This step allows the application to
recognize DeepSpeed specific configurations.

```python
def get_arguments():
    parser = get_argument_parser()
    # Include DeepSpeed configuration arguments
    parser = deepspeed.add_config_arguments(parser)

    args = parser.parse_args()

    return args
```


### Initialization and Training

We modify the `train.py` to enable training with DeepSpeed.

#### Initialization

We use `deepspeed.initialize()` to create the model, optimizer, and learning
rate scheduler. For the Bing BERT model, we initialize DeepSpeed in its
`prepare_model_optimizer()` function as below, to pass the raw model and
optimizer (specified from the command option).
```python
def prepare_model_optimizer(args):
    # Loading Model
    model = BertMultiTask(args)

    # Optimizer parameters
    optimizer_parameters = prepare_optimizer_parameters(args, model)
    model.network, optimizer, _, _ = deepspeed.initialize(args=args,
                                         model=model.network,
                                         model_parameters=optimizer_parameters,
                                         dist_init_required=False)
    return model, optimizer
```
Note that for Bing BERT, the raw model is kept in `model.network`, so we pass
`model.network` as a parameter instead of just model.

#### Training

The `model` returned by `deepspeed.initialize` is the DeepSpeed _model
engine_ that we will use to train the model using the forward, backward and
step API. Since the model engine exposes the same forward pass API as
`nn.Module` objects, there is no change in the forward pass.
Thus, we only modify the the backward pass and optimizer/scheduler steps.

Backward propagation is performed by calling `backward(loss)` directly with
the model engine.
```python
# Compute loss
if args.deepspeed:
    model.network.backward(loss)
else:
    if args.fp16:
        optimizer.backward(loss)
    else:
        loss.backward()
```

The `step()` function in DeepSpeed engine updates the model parameters as
well as the learning rate. Zeroing the gradients is handled automatically by
DeepSpeed after the weights have been updated after each step.
```python
if args.deepspeed:
    model.network.step()
else:
    optimizer.step()
    optimizer.zero_grad()
```

### Checkpoints Saving & Loading
DeepSpeed's model engine has flexible APIs for checkpoint saving and loading
in order to handle the both the client model state and its own internal
state.

```python
def save_checkpoint(self, save_dir, tag, client_state={})
def load_checkpoint(self, load_dir, tag)
```

In `train.py`, we use DeepSpeed's checkpointing API in the
`checkpoint_model()` function as below, where we collect the client model
states and pass them to the model engine by calling `save_checkpoint()`:
```python
def checkpoint_model(PATH, ckpt_id, model, epoch, last_global_step, last_global_data_samples, **kwargs):
    """Utility function for checkpointing model + optimizer dictionaries
       The main purpose for this is to be able to resume training from that instant again
    """
    checkpoint_state_dict = {'epoch': epoch,
                             'last_global_step': last_global_step,
                             'last_global_data_samples': last_global_data_samples}
    # Add extra kwargs too
    checkpoint_state_dict.update(kwargs)

    success = model.network.save_checkpoint(PATH, ckpt_id, checkpoint_state_dict)

    return
```

In the `load_training_checkpoint()` function, we use DeepSpeed's loading
checkpoint API and return the states for the client model:
```python
def load_training_checkpoint(args, model, PATH, ckpt_id):
    """Utility function for checkpointing model + optimizer dictionaries
       The main purpose for this is to be able to resume training from that instant again
    """

    _, checkpoint_state_dict = model.network.load_checkpoint(PATH, ckpt_id)

    epoch = checkpoint_state_dict['epoch']
    last_global_step = checkpoint_state_dict['last_global_step']
    last_global_data_samples = checkpoint_state_dict['last_global_data_samples']
    del checkpoint_state_dict
    return (epoch, last_global_step, last_global_data_samples)
```



### DeepSpeed JSON Config File

The last step to use DeepSpeed is to create a configuration JSON file (e.g.,
`deepspeed_bsz4096_adam_config.json`). This file provides DeepSpeed specific
parameters defined by the user, e.g., batch size per GPU, optimizer and its
parameters, and whether enabling training with FP16.

```json
{
  "train_batch_size": 4096,
  "train_micro_batch_size_per_gpu": 64,
  "steps_per_print": 1000,
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": 2e-4,
      "max_grad_norm": 1.0,
      "weight_decay": 0.01,
      "bias_correction": false
    }
  },
  "fp16": {
    "enabled": true,
    "loss_scale": 0,
    "initial_scale_power": 16
  }
}
```

In particular, this sample json is specifying the following configuration parameters to DeepSpeed:

1. `train_batch_size`: use effective batch size of 4096
2. `train_micro_batch_size_per_gpu`: each GPU has enough memory to fit batch size of 64 instantaneously
3. `optimizer`: use Adam training optimizer
4. `fp16`: enable FP16 mixed precision training with an initial loss scale factor 2^16.

That's it! That's all you need do in order to use DeepSpeed in terms of
modifications. We have included a modified `train.py` file called
`DeepSpeedExamples/bing_bert/deepspeed_train.py` with all of the changes
applied.



### Enabling DeepSpeed's Transformer Kernel

To enable the transformer kernel for higher performance, first add an argument
`--deepspeed_transformer_kernel` in `utils.py`, we can set it as `False` by
default, for easily turning on/off.

```python
 parser.add_argument('--deepspeed_transformer_kernel',
                     default=False,
                     action='store_true',
                     help='Use DeepSpeed transformer kernel to accelerate.')
```
Then in the `BertEncoder` class of the modeling source file, instantiate
transformer layers using DeepSpeed transformer kernel as below.

```python
     if args.deepspeed_transformer_kernel:
         from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig, DeepSpeedConfig

         if hasattr(args, 'deepspeed_config') and args.deepspeed_config:
             ds_config = DeepSpeedConfig(args.deepspeed_config)
         else:
             raise RuntimeError('deepspeed_config is not found in args.')

         cuda_config = DeepSpeedTransformerConfig(
             batch_size = ds_config.train_micro_batch_size_per_gpu,
             max_seq_length = args.max_seq_length,
             hidden_size = config.hidden_size,
             heads = config.num_attention_heads,
             attn_dropout_ratio = config.attention_probs_dropout_prob,
             hidden_dropout_ratio = config.hidden_dropout_prob,
             num_hidden_layers = config.num_hidden_layers,
             initializer_range = config.initializer_range,
             local_rank = args.local_rank if hasattr(args, 'local_rank') else -1,
             seed = args.seed,
             fp16 = ds_config.fp16_enabled,
             pre_layer_norm=True,
             attn_dropout_checkpoint=args.attention_dropout_checkpoint,
             normalize_invertible=args.normalize_invertible,
             gelu_checkpoint=args.gelu_checkpoint,
             stochastic_mode=True)

         self.layer = nn.ModuleList([copy.deepcopy(DeepSpeedTransformerLayer(i, cuda_config)) for i in range(config.num_hidden_layers)])
     else:
         layer = BertLayer(config)
         self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
```
All configuration settings come from the DeepSpeed configuration file and
command arguments and thus we must pass the `args` variable to here in this model.

Note:

1. `batch_size` is the maximum bath size of input data, all fine-tuning training data or prediction data shouldn't exceed this threshold, otherwise it will throw an exception. In the DeepSpeed configuration file micro batch size is defined as `train_micro_batch_size_per_gpu`, e.g. if it is set as 8 and prediction uses batch size of 12, we can use 12 as transformer kernel batch size, or using "--predict_batch_size" argument to set prediction batch size to 8 or a smaller number.
2. `local_rank` in DeepSpeedTransformerConfig is used to assign the transformer kernel to the correct device. Since the model already runs set_device() before here, so does not need to be set here.
3. `stochastic_mode` has higher performance when it is enabled, we enable it in pre-training, and disable it in fine-tuning.
4. The transformer kernel has its own parameters and so the checkpoint files
  generated with transformer kernel must to be loaded by the model with
  transformer kernel enabled (such as in fine-tuning).

For more details about the transformer kernel, please see [DeepSpeed
Transformer Kernel](/tutorials/transformer_kernel/) and [DeepSpeed Fast-Bert
Training](https://www.deepspeed.ai/news/2020/05/27/fastest-bert-training.html).


### Start Training
An example of launching `deepspeed_train.py` on four nodes with four GPUs each would be:
```bash
deepspeed --num_nodes 4  \
    deepspeed_train.py \
    --deepspeed \
    --deepspeed_config  deepspeed_bsz4096_adam_config.json \
    --cf /path-to-deepspeed/examples/tests/bing_bert/bert_large_adam_seq128.json \
    --train_batch_size 4096  \
    --max_seq_length 128 \
    --gradient_accumulation_steps 4 \
    --max_grad_norm 1.0 \
    --fp16 \
    --loss_scale 0 \
    --delay_allreduce \
    --max_steps 32 \
    --print_steps 1 \
    --deepspeed_transformer_kernel \
    --output_dir <output_directory>
```
See the [Getting Started](/getting-started/) guide for more information on
launching DeepSpeed.

------

## Reproducing Fastest BERT Training Results with DeepSpeed

We achieve the fastest BERT training time while remaining competitive across the industry in terms of achieving F1 score of 90.5 or better on the SQUAD 1.1 dev set. Please follow the [BERT fine-tuning](/tutorials/bert-finetuning/) tutorial to fine-tune your model that was pre-trained by transformer kernel and reproduce the SQUAD F1 score.

- We complete BERT pre-training in 44 minutes using 1024 V100 GPUs (64 NVIDIA DGX-2 nodes). In comparison, the previous SOTA from NVIDIA takes 47 mins using 1472 V100 GPUs. DeepSpeed is not only faster but also uses 30% less resources. Using the same 1024 GPUS, NVIDIA BERT is 52% slower than DeepSpeed, taking 67 minutes to train.
- Comparing with the original BERT training time from Google in which it took
about 96 hours to reach parity on 64 TPU2 chips, we train in less than 9 hours on
4 DGX-2 nodes of 64 V100 GPUs.
- On 256 GPUs, it took us 2.4 hours, faster than state-of-art result (3.9
hours) from NVIDIA using their superpod on the same number of GPUs
([link](https://devblogs.nvidia.com/training-bert-with-gpus/)).

| Number of nodes | Number of V100 GPUs | Time         |
| --------------- | ------------------- | ------------ |
| 1 DGX-2         | 16                  | 33 hr 13 min |
| 4 DGX-2         | 64                  | 8 hr 41 min  |
| 16 DGX-2        | 256                 | 144 min      |
| 64 DGX-2        | 1024                | 44 min       |

Our configuration for the BERT training result above can be reproduced with
the scripts/json configs in our DeepSpeedExamples repo. Below is a table containing a
summary of the configurations. Specifically see the
`ds_train_bert_bsz64k_seq128.sh` and `ds_train_bert_bsz32k_seq512.sh` scripts
for more details in
[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert).


| Parameters               | 128 Sequence              | 512 Sequence              |
| ------------------------ | ------------------------- | ------------------------- |
| Total batch size         | 64K                       | 32K                       |
| Train micro batch size per gpu | 64                  | 8                         |
| Optimizer                | Lamb                      | Lamb                      |
| Learning rate            | 11e-3                     | 2e-3                      |
| Initial learning rate (`lr_offset`)   | 10e-4        | 0.0                       |
| Min Lamb coefficient     | 0.01                      | 0.01                      |
| Max Lamb coefficient     | 0.3                       | 0.3                       |
| Learning rate scheduler  | `warmup_exp_decay_exp`    | `warmup_exp_decay_exp`    |
| Warmup proportion        | 0.02                      | 0.02                      |
| Decay rate               | 0.90                      | 0.90                      |
| Decay step               | 250                       | 150                       |
| Max training steps       | 7500                      | 7500                      |
| Rewarm learning rate     | N/A                       | True                      |
| Output checkpoint number | 150                       | 160-162                   |
| Sample count             | 403M                      | 18-22M                    |
| Epoch count              | 150                       | 160-162                   |


## DeepSpeed Single GPU Throughput Results

![DeepSpeed Single GPU Bert Training Throughput 128](/assets/images/transformer_kernel_perf_seq128.PNG){: .align-center}

![DeepSpeed Single GPU Bert Training Throughput 512](/assets/images/transformer_kernel_perf_seq512.PNG){: .align-center}

Compared to SOTA, DeepSpeed significantly improves single GPU performance for transformer-based model like BERT. Figure above shows the single GPU throughput of training BertBERT-Large optimized through DeepSpeed, compared with two well-known Pytorch implementations, NVIDIA BERT and HuggingFace BERT. DeepSpeed reaches as high as 64 and 53 teraflops throughputs (corresponding to 272 and 52 samples/second) for sequence lengths of 128 and 512, respectively, exhibiting up to 28% throughput improvements over NVIDIA BERT and up to 62% over HuggingFace BERT.  We also support up to 1.8x larger batch size without running out of memory.

For more details on how we achieve the record breaking BERT training time please check out deep dive into DeepSpeed BERT [Fastest BERT Training](https://www.deepspeed.ai/news/2020/05/18/bert-record.html)
