Ported BERT pre-training tutorial

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docs/_tutorials/bert-pretraining.md

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+---
+title: "Pre-training Bing BERT"
+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.
+
+
+### 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 \
+    --output_dir <output_directory>
+```
+See the [Getting Started](/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](https://devblogs.nvidia.com/training-bert-with-gpus/)).
+
+![BERT Training Time](/assets/images/bert-large-training-time.png){: .align-center}
+
+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](https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert).
+
+
+| 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:
+
+![](/assets/images/deepspeed-throughput-seq128.png){: .align-center}
+
+![](/assets/images/deepspeed-throughput-seq512.png){: .align-center}