bert-pretraining.md

title	BERT Pre-training
excerpt

In this tutorial we will apply DeepSpeed to pre-train the BERT (Bidirectional Encoder Representations from Transformers), 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.

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 and NVIDIA/DeepLearningExamples. We have forked this repo under DeepSpeedExamples/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 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:

{
  ...
  "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:

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.

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).

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.

# 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.

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.

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():

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:

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.

{
  "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.

Start Training

An example of launching deepspeed_train.py on four nodes with four GPUs each would be:

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 \
    --output_dir <output_directory>

See the Getting Started guide for more information on launching DeepSpeed.

---

Reproducing BERT Training Results with DeepSpeed

Our BERT training result is competitive across the industry in terms of achieving F1 score of 90.5 or better on the SQUAD 1.1 dev set:

• Comparing with the original BERT training time from Google, it took them about 96 hours to reach parity on 64 TPU2 chips, while it took us 14 hours on 4 DGX-2 nodes of 64 V100 GPUs.
• On 256 GPUs, it took us 3.7 hours, faster than state-of-art result (3.9 hours) from Nvidia using their superpod on the same number of GPUs (link).

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Our configuration for the BERT training result above can be reproduced with the scripts/json configs in our DeepSpeed repo. Below is a table containing a summary of the configurations. Specifically see the ds_train_bert_bsz16k_seq128.sh and ds_train_bert_bsz16k_seq512.sh scripts for more details in DeepSpeedExamples.

Parameters	128 Sequence	512 Sequence
Total batch size	16K	16K
Train micro batch size per gpu	64	8
Optimizer	Lamb	Lamb
Learning rate	4e-3	1e-3
Min Lamb coefficient	0.08	0.08
Max Lamb coefficient	0.5	0.5
Learning rate scheduler	warmup_linear_decay_exp	warmup_linear_decay_exp
Warmup proportion	0.02	0.01
Decay rate	0.90	0.70
Decay step	1000	1000
Max Training steps	187000	18700
Rewarm LR	N/A	True
Output checkpoint number	150	162
Sample count	402679081	34464170
Iteration count	24430	2089

DeepSpeed Throughput Results

We have measured the throughput results of DeepSpeed using both the Adam optimizer and LAMB optimizer. We measure the throughput by measuring the wall clock time to process one mini-batch and dividing the mini-batch size with the elapsed wall clock time. The table below shows that for sequence length 128, DeepSpeed achieves 200 samples/sec throughput on a single V100 GPU, and it obtains 53X and 57.4X speedups over the single GPU run for Adam and LAMB respectively:

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