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# DeepSpeed
[![Build Status](https://msdlserving.visualstudio.com/DeepScale/_apis/build/status/DeepSpeed%20GPU%20CI?branchName=master)](https://msdlserving.visualstudio.com/DeepScale/_build/latest?definitionId=36&branchName=master)
[![License MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/Microsoft/DeepSpeed/blob/master/LICENSE)
## Installation
## Testing
DeepSpeed tracks two types of tests: unit tests and more costly model convergence tests.
The model convergence tests train
[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) and measure
end-to-end convergence and related metrics. Unit tests are found in `tests/unit/` and
model convergence tests are found in `tests/model/`.
### Unit Tests
[PyTest](https://docs.pytest.org/en/latest/) is used to execute tests. PyTest can be
installed from PyPI via `pip install pytest`. Simply invoke `pytest --forked` to run the
unit tests:
pytest --forked tests/unit/
You can also provide the `-v` flag to `pytest` to see additional information about the
tests. Note that [pytest-forked](https://github.com/pytest-dev/pytest-forked) and the
`--forked` flag are required to test CUDA functionality in distributed tests.
### Model Tests
To execute model tests, first [install DeepSpeed](#installation). The
[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) repository is cloned
as part of this process. Next, execute the model test driver:
cd tests/model/
pytest run_sanity_check.py
## Contributing
DeepSpeed welcomes your contributions!
### Prerequisites
DeepSpeed uses [pre-commit](https://pre-commit.com/) to ensure that formatting is
consistent across DeepSpeed. First, ensure that `pre-commit` is installed from either
installing DeepSpeed or `pip install pre-commit`. Next, the pre-commit hooks must be
installed once before commits can be made:
pre-commit install
Afterwards, our suite of formatting tests run automatically before each `git commit`. You
can also run these manually:
pre-commit run --all-files
### Contributor License Agreement
This project welcomes contributions and suggestions. Most contributions require you to
agree to a Contributor License Agreement (CLA) declaring that you have the right to, and
actually do, grant us the rights to use your contribution. For details, visit
https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need
to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply
follow the instructions provided by the bot. You will only need to do this once across
all repos using our CLA.
### Code of Conduct
This project has adopted the [Microsoft Open Source Code of
Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the
[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
[opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or
comments.
[![Build Status](https://msdlserving.visualstudio.com/DeepScale/_apis/build/status/DeepSpeed%20GPU%20CI?branchName=master)](https://msdlserving.visualstudio.com/DeepScale/_build/latest?definitionId=36&branchName=master)
[![License MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/Microsoft/DeepSpeed/blob/master/LICENSE)
DeepSpeed is a deep learning optimization library that makes distributed training easy,
efficient, and effective.
<p align="center"><i><b>10x Larger Models</b></i></p>
<p align="center"><i><b>5x Faster Training</b></i></p>
<p align="center"><i><b>Minimal Code Change</b></i></p>
DeepSpeed can train DL models with over a hundred billion parameters on current generation of GPU clusters, while achieving over 5x in system performance compared to the state-of-art. Early adopters of DeepSpeed have already produced language model (LM) with over 17B parameters establishing new SOTA in the LM category.
Below we provide a brief feature list, see our detailed [feature
overview](#deepspeed-feature-overview) for descriptions and usage guide.
* [Distributed Training with Mixed Precision](#distributed-training-with-mixed-precision)
* 16-bit mixed precision
* Single-GPU/Multi-GPU/Multi-Node
* [Model Parallelism](#model-parallelism)
* Support for Custom Model Parallelism
* Integration with Megatron-LM
* [Memory and Bandwidth Optimizations](#memory-and-bandwidth-optimizations)
* The Zero Redundancy Optimizer (ZeRO)
* Constant Buffer Optimization (CBO)
* Smart Gradient Accumulation
* [Training Features](#training-features)
* Simplified training API
* Gradient Clipping
* Automatic loss scaling with mixed precision
* [Training Optimizers](#training-optimizers)
* Fused Adam optimizer and arbitrary `torch.optim.Optimizer`
* Memory bandwidth optimized FP16 Optimizer
* Large Batch Training with LAMB Optimizer
* Memory efficient Training with ZeRO Optimizer
* [Training Agnostic Checkpointing](#training-agnostic-checkpointing)
* [Advanced Parameter Search](#advanced-parameter-search)
* Learning Rate Range Test
* 1Cycle Learning Rate Schedule
* [Simplified Data Loader](#simplified-data-loader)
* [Performance Analysis and Debugging](#performance-analysis-and-debugging)
## Table of Contents
| Section | Description |
| --------------------------------------- | ------------------------------------------- |
| [Why DeepSpeed?](#why-deepspeed) | DeepSpeed overview |
| [Installation](#installation) | Installation instructions |
| [Feature Overview](#feature-overview) | Preview of DeepSpeed's features |
| [Testing](#testing) | Instructions for testing DeepSpeed |
| [Contributing](#contributing) | Instructions for contributing to DeepSpeed |
## Why DeepSpeed?
Training advanced deep learning models is challenging. Beyond model design,
model scientists also need to set up the state-of-the-art training techniques
such as distributed training, mixed precision, gradient accumulation, and
checkpointing. Yet still, scientists may not achieve the desired system
performance and convergence rate. Large model sizes are even more challenging:
a large model easily runs out of memory with pure data paralelism and it is
difficult to use model parallelism. DeepSpeed addresses these challenges to
accelerate model development *and* training.
### Distributed, Effective, and Efficient Training with Ease
The DeepSpeed API is a lightweight wrapper on [PyTorch](https://pytorch.org/). This
means that you can use everything you love in PyTorch and without learning a new
platform. In addition, DeepSpeed manages all of the boilerplate state-of-the-art
training techniques, such as distributed training, mixed precision, gradient
accumulation, and checkpoints so that you can focus on your model development. Most
importantly, you can leverage the distinctive efficiency and effectiveness benefit of
DeepSpeed to boost speed and scale with just a few lines of code changes to your PyTorch
models.
### Speed
DeepSpeed achieves high performance and fast convergence through a combination of
efficiency optimizations on compute/communication/memory/IO and effectiveness
optimizations on advanced hyperparameter tuning and optimizers. For example:
* DeepSpeed trains BERT-large to parity in 14 hours using 64 GPUs (4 DGX-2 boxes) and in
3.7 hours using 256 GPUs (16 DGX-2 boxes).
**BERT-large Training Times**
| Devices | Source | Training Time (hours) |
| ------------- | --------- | ---------------------:|
| 64 TPUs | Google | 96 |
| 64 V100 GPUs | DeepSpeed | **14** |
| 256 V100 GPUs | NVIDIA | 3.9 |
| 256 V100 GPUs | DeepSpeed | **3.7** |
<!---*Read more*: [BERT tutorial](../../Tutorials/bert_pretraining/deepspeed_bert_training.md)-->
*BERT Tutorial*: Coming Soon
* DeepSpeed trains GPT2 (1.5 billion parameters) 3.75x faster than state-of-art, NVIDIA
Megatron on Azure GPUs.
*Read more*: [GPT tutorial](../../Tutorials/Megatron_GPT2/MegatronGPT2Tutorial.md)
### Memory efficiency
DeepSpeed provides memory-efficient data parallelism and enables training models without
model parallelism. For example, DeepSpeed can train models with up to 6 billion parameters on
NVIDIA V100 GPUs with 32GB of device memory. In comparison, existing frameworks (e.g.,
PyTorch's Distributed Data Parallel) run out of memory with 1.5 billion parameter models.
DeepSpeed reduces the training memory footprint through a novel solution called Zero
Redundancy Optimizer (ZeRO). Unlike basic data parallelism where memory states are
replicated across data-parallel processes, ZeRO partitions model states to save
significant memory. The current implementation (stage 1 of ZeRO) reduces memory by up to
4x relative to the state-of-art. You can read more about ZeRO in our [technical
report](https://arxiv.org/abs/1910.02054).
With this impressive memory reduction, early adopters of DeepSpeed have already produced language model (LM) with over 17B parameters called [Turing-NLG](link-to-turing-blog) establishing new SOTA in the LM category.
### Scalability
DeepSpeed supports efficient data parallelism, model parallelism, and their
combination. ZeRO boosts the scaling capability and efficiency further.
* DeepSpeed provides system support to run models up to 100 billion parameters,
10x larger than the state-of-art (8 billion NVIDIA GPT, 11 billion Google T5).
* DeepSpeed can run large models more efficiently, up to 6x faster for models with
various sizes spanning 1.5B to 100B.
*Read more*: [technical report](https://arxiv.org/abs/1910.02054),
[GPT tutorial](../../Tutorials/Megatron_GPT2/MegatronGPT2Tutorial.md),
and [QANet tutorial](../../Tutorials/QANet/QANetTutorial.md).
![DeepSpeed-vs-Megatron](./docs/figures/DeepSpeed-vs-Megatron.png)
### Fast convergence for effectiveness
DeepSpeed supports advanced hyperparameter tuning and large batch size
optimizers such as [LAMB](https://arxiv.org/abs/1904.00962). These improve the
effectiveness of model training and reduce the number of samples required to
convergence to desired accuracy.
*Read more*: [Tuning tutorial](../../Tutorials/1cycle/1Cycle.md), [QANet
tutorial](../../Tutorials/QANet/QANetTutorial.md) and *BERT Tutorial*: Coming Soon
<!---[BERT
tutorial](../../Tutorials/BingBertSquad/BingBertSquadTutorial.md),
-->
## Installation
**TODO**
## Feature Overview
### Distributed Training with Mixed Precision
#### Mixed Precision Training
Enable 16-bit (FP16) training by in the `deepspeed_config` JSON.
```json
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
}
```
#### Single-GPU, Multi-GPU, and Multi-Node Training
Easily switch between single-GPU, single-node multi-GPU, or multi-node multi-GPU
execution by specifying resources with a hostfile.
```bash
deepspeed --hostfile=<hostfile> \
<client_entry.py> <client args> \
--deepspeed --deepspeed_config ds_config.json
```
The script `<client_entry.py>` will execute on the resources specified in `<hostfile>`.
**TODO: explain hostfile formatting**
### Model Parallelism
#### Support for Custom Model Parallelism
DeepSpeed is supports all forms of model parallelism including tensor slicing based
approaches such as the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), or a
pipelined parallelism approach such as
[PipeDream](https://github.com/msr-fiddle/pipedream) or
[GPipe](https://github.com/kakaobrain/torchgpipe). It does so by only requiring the model
parallelism framework to provide a *model parallelism unit* (`mpu`) that implements a few
bookkeeping functionalities:
```python
mpu.get_model_parallel_rank()
mpu.get_model_parallel_group()
mpu.get_model_parallel_world_size()
mpu.get_data_parallel_rank/group/world_size()
mpu.get_data_parallel_group()
mpu.get_data_parallel_world_size()
```
#### Integration with Megatron-LM
**TODO: port tutorial to its own page**
DeepSpeed is fully compatible with [Megatron](https://github.com/NVIDIA/Megatron-LM).
Please see the [Megatron-LM tutorial](docs/tutorials/MegatronGPT2Tutorial.md) for details.
### Memory and Bandwidth Optimizations
#### The Zero Redundancy Optimizer (ZeRO)
[ZeRO](https://arxiv.org/abs/1910.02054) is at the heart of DeepSpeed and
enables large model training at a scale that is simply not possible with model
parallelism alone. When enabled, ZeRO allows training models with
over 6 billion parameters without any model parallelism, and up to 100 billion
parameter models with model parallelism on current generation hardware.
For more details see the [ZeRO paper](https://arxiv.org/abs/1910.02054), [GPT
tutorial](../../Tutorials/Megatron_GPT2/MegatronGPT2Tutorial.md) on integration with
DeepSpeed. Additional tutorals including *BERT Tutorial*: Coming Soon.
<!---[BERT
tutorial](../../Tutorials/BingBertSquad/BingBertSquadTutorial.md),
-->
#### Constant Buffer Optimization (CBO)
CBO enables high network and memory throughput while restricting memory usage to a
constant size. For memory- and network-bound operations such as normalization or
allreduce collectives, the performance depends on the size of the operand. Simply fusing
all operands into a single large operand can enable great throughput at the expense of
unnecessary memory overhead. CBO in DeepSpeed fuses smaller operands into approximately a
pre-defined sized buffer large enough to achieve great performance without the
unnecessary memory overhead.
#### Smart Gradient Accumulation
Gradient accumulation allows running larger batch size with limited memory by breaking an
effective batch into several sequential micro-batches, and averaging the parameter
gradients across these micro-batches. Furthermore, instead of averaging the gradients of
each micro-batch across all GPUs, the gradients are averaged locally during each step of
the sequence, and a single `allreduce` is done at the end of the sequence to produce the
averaged gradients for the effective batch across all GPUs. This strategy significantly
reduces the communication involved over the approach of averaging globally for each
micro-batch, specially when the number of micro-batches per effective batch is large.
### Training Features
#### Simplified training API
The DeepSpeed core API consists of just a handful of methods:
* initialization: `initialize`
* training: `backward` and `step`
* argument parsing: `add_config_arguments`
* checkpointing : `load_checkpoint` and `store_checkpoint`
DeepSpeed supports all the features described in this document, via the use of these API,
along with a `deepspeed_config` JSON file for enabling and disabling the features. Please
see [core API doc](../../API/core_api/core_api.md) for more details.
#### Gradient Clipping
DeepSpeed handles gradient clipping under the hood based on the max gradient norm
specified by the user. See [core API doc](../../API/core_api/core_api.md) for more
details.
#### Automatic loss scaling with mixed precision
DeepSpeed internally handles loss scaling for mixed precision training. The parameters
for loss scaling can be specified in the `deepspeed_config` JSON file. See [core API
doc](../../API/core_api/core_api.md) for more details.
### Training Optimizers
#### Fused Adam optimizer and arbitrary torch.optim.Optimizer
With DeepSpeed, the user can choose to use a high performance implementation of ADAM from
NVIDIA, or any training optimizer that extends torch's `torch.optim.Optimizer` class.
#### Memory bandwidth optimized FP16 Optimizer
Mixed precision training is handled by the DeepSpeed FP16 Optimizer. This optimizer not
only handles FP16 training but is also highly efficient. The performance of weight update
is primarily dominated by the memory bandwidth, and the achieved memory bandwidth is
dependent on the size of the input operands. The FP16 Optimizer is designed to maximize
the achievable memory bandwidth by merging all the parameters of the model into a single
large buffer, and applying the weight updates in a single kernel, allowing it to achieve
high memory bandwidth.
#### Large Batch Training with LAMB Optimizer
**TODO: port tutorial**
DeepSpeed makes it easy to train with large batch sizes by enabling the LAMB Optimizer.
For more details on LAMB, see the [BERT
tutorial](../../Tutorials/BingBertSquad/BingBertSquadTutorial.md) and the [LAMB
paper](https://arxiv.org/pdf/1904.00962.pdf).
#### Memory-Efficient Training with ZeRO Optimizer
DeepSpeed can train models up with up to 6 billion parameters without parallelism, and
models with up to 100 billion parameters with 16-way model parallelism. This leap in
model size is possible though the memory efficiency achieved via the ZeRO Optimizer. For
more details see [ZeRO paper](https://arxiv.org/abs/1910.02054) .
### Training Agnostic Checkpointing
**TODO: API documentation**
DeepSpeed can simplify checkpointing for you regardless of whether you are using data
parallel training, model parallel training, mixed-precision training, a mix of these
three, or using the zero optimizer to enable larger model sizes. See the [getting
started](../../Onboard/onboard/onboard.md) or [core API
doc](../../API/core_api/core_api.md) for details.
### Advanced parameter search
DeepSpeed supports multiple Learning Rate Schedules to enable faster convergence for
large batch scaling.
#### Learning Rate Range Test
Please refer to [Learning Rate Range Test](../../Tutorials/lrrt/lrrt.md).
#### 1Cycle Learning Rate Schedule
Please refer to [1Cycle Learning Rate Schedule](../../Tutorials/1cycle/1Cycle.md).
### Simplified Data Loader
DeepSpeed abstracts away data parallelism and model parallelism from the user when it
comes to data loading. Users simply provide a PyTorch dataset, and DeepSpeed data loader
can automatically handle batch creation appropriately.
### Performance Analysis and Debugging
For performance debugging, DeepSpeed can give you a detailed breakdown of the time spent
in different parts of the training with by simply enabling it in the `deepspeed_config`
file. See [core API doc](../../API/core_api/core_api.md).
```json
{
"wallclock_breakdwon": true
}
```
## Testing
DeepSpeed tracks two types of tests: unit tests and more costly model convergence tests.
The model convergence tests train
[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) and measure
end-to-end convergence and related metrics. Unit tests are found in `tests/unit/` and
the model convergence tests are found in `tests/model/`.
### Unit Tests
[PyTest](https://docs.pytest.org/en/latest/) is used to execute tests. PyTest can be
installed from PyPI via `pip install pytest`. Simply invoke `pytest --forked` to run the
unit tests:
```bash
pytest --forked tests/unit/
```
You can also provide the `-v` flag to `pytest` to see additional information about the
tests. Note that [pytest-forked](https://github.com/pytest-dev/pytest-forked) and the
`--forked` flag are required to test CUDA functionality in distributed tests.
### Model Tests
To execute model tests, first [install DeepSpeed](#installation). The
[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) repository is cloned
as part of this process. Next, execute the model test driver:
```bash
cd tests/model/
pytest run_sanity_check.py
```
Note that the `--forked` flag is not necessary for the model tests.
## Contributing
DeepSpeed welcomes your contributions!
### Prerequisites
DeepSpeed uses [pre-commit](https://pre-commit.com/) to ensure that formatting is
consistent across DeepSpeed. First, ensure that `pre-commit` is installed from either
installing DeepSpeed or `pip install pre-commit`. Next, the pre-commit hooks must be
installed once before commits can be made:
```bash
pre-commit install
```
Afterwards, our suite of formatting tests run automatically before each `git commit`. You
can also run these manually:
```bash
pre-commit run --all-files
```
### Contributor License Agreement
This project welcomes contributions and suggestions. Most contributions require you to
agree to a Contributor License Agreement (CLA) declaring that you have the right to, and
actually do, grant us the rights to use your contribution. For details, visit
https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need
to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply
follow the instructions provided by the bot. You will only need to do this once across
all repos using our CLA.
### Code of Conduct
This project has adopted the [Microsoft Open Source Code of
Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the
[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
[opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or
comments.
docs/tutorials/MegatronGPT2Tutorial.md 0 → 100755
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# Tutorial: Megatron-LM GPT2 with DeepSpeed
**TODO: these two links are broken (not yet implemented).**
We advise you to first read through the guides for [Setup and
Onboarding](../../Onboard/onboard/onboard.md) and [Model
Training](../../Onboard/model_training/deepspeed_model_training.md).
In this tutorial we will be adding DeepSpeed to Megatron-LM GPT2 model, which
is a large, powerful transformer. Megatron-LM supports model-parallel and multi-node
training. Please see the corresponding paper for more details: [Megatron-LM:
Training Multi-Billion Parameter Language Models Using Model
Parallelism](https://arxiv.org/abs/1909.08053).
First, we discuss data and environment setup and how to train the GPT-2 model with the
original Megatron-LM. Next, we proceed step-by-step in enabling this model to run with
DeepSpeed. Finally, we demonstrate the **_performance gains_**, and **_memory footprint
reduction_** from using DeepSpeed.
## 1 Training GPT-2 with the Original Megatron-LM
The original model code from
[Megatron-LM](https://github.com/NVIDIA/Megatron-LM). We've copied this repo
under
[DeepSpeedExamples/Megatron-LM/](https://github.com/microsoft/DeepSpeedExamples/tree/master/Megatron-LM)
and made it available as a submodule. To download, execute:
```bash
git submodule update --init --recursive
```
### 1.1 Training Data Setup
* Follow Megatron's [instructions](https://github.com/NVIDIA/Megatron-LM#collecting-gpt2-webtext-data)
to download the webtext data and place a symbolic link under `DeepSpeedExamples/Megatron-LM/data`:
* (*Microsoft*:) Raw and pre-processed data has already been downloaded on
all DLTS clusters: `/data/Megatron-LM/data/`. You can simply execute
`ln -s /data/Megatron-LM/data DeepSpeedExamples/Megatron-LM/`.
### 1.2 Running Unmodified Megatron-LM GPT2 model
* For a single GPU run:
- change `scripts/pretrain_gpt2.sh`, set its `--train-data` argument as `"webtext"`.
- run `bash scripts/pretrain_gpt2.sh`
* For multiple GPUs and/or nodes run:
- change `scripts/pretrain_gpt2_model_parallel.sh`
- set its `--train-data` argument as `"webtext"`
- `GPUS_PER_NODE` indicates how many GPUs per node involved in the testing
- `NNODES` indicates how many nodes involved in the testing
- run `bash scripts/pretrain_gpt2_model_parallel.sh`
## 2 Enabling DeepSpeed
To use DeepSpeed we will modify three files :
* `arguments.py` : Arguments configurations
* `pretrain_gpt2.py` : Main entry point for training
* `utils.py` : Checkpoints saving and loading utilities
### 2.1 Argument Parsing
The first step is to apply DeepSpeed is adding DeepSpeed arguments to
Megatron-LM GPT2 model, using `deepspeed.add_config_arguments()` in
`arguments.py`.
```python
def get_args():
"""Parse all the args."""
parser = argparse.ArgumentParser(description='PyTorch BERT Model')
parser = add_model_config_args(parser)
parser = add_fp16_config_args(parser)
parser = add_training_args(parser)
parser = add_evaluation_args(parser)
parser = add_text_generate_args(parser)
parser = add_data_args(parser)
# Include DeepSpeed configuration arguments
parser = deepspeed.add_config_arguments(parser)
```
### 2.2 Initialization and Training
We modify `pretrain.py` to enable training with DeepSpeed.
#### 2.2.1 Initialization
We use `deepspeed.initialize` to create `model_engine`, `optimizer` and LR
`scheduler`. Below is its definition:
```python
def initialize(args,
model,
optimizer=None,
model_parameters=None,
training_data=None,
lr_scheduler=None,
mpu=None,
dist_init_required=True,
collate_fn=None):
```
For the Megatron-LM GPT2 model, we initialize DeepSpeed in its
`setup_model_and_optimizer()` function as below, to pass the raw `model`,
`optimizer`, `args`, `lr_scheduler` and `mpu`.
```python
def setup_model_and_optimizer(args):
"""Setup model and optimizer."""
model = get_model(args)
optimizer = get_optimizer(model, args)
lr_scheduler = get_learning_rate_scheduler(optimizer, args)
if args.deepspeed:
import deepspeed
print_rank_0("DeepSpeed is enabled.")
model, optimizer, _, lr_scheduler = deepspeed.initialize(
model=model,
optimizer=optimizer,
args=args,
lr_scheduler=lr_scheduler,
mpu=mpu,
dist_init_required=False
)
```
Note that when FP16 is enabled, Megatron-LM GPT2 adds a wrapper to the `Adam`
optimizer. DeepSpeed has its own FP16 Optimizer, so we need to pass the `Adam`
optimizer to DeepSpeed directly without any wrapper. We return the unwrapped
Adam optimizer from `get_optimizer()` when DeepSpeed is enabled.
```python
def get_optimizer(model, args):
"""Setup the optimizer."""
......
# Use Adam.
optimizer = Adam(param_groups,
lr=args.lr, weight_decay=args.weight_decay)
if args.deepspeed:
# fp16 wrapper is not required for DeepSpeed.
return optimizer
```
#### 2.2.2 Using the Training API
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.
##### Forward Propagation
The forward propagation API is compatible to PyTorch and no change is required.
##### Backward Propagation
Backward propagation is done by calling `backward(loss)` directly on the model engine.
```python
def backward_step(optimizer, model, lm_loss, args, timers):
"""Backward step."""
# Total loss.
loss = lm_loss
# Backward pass.
if args.deepspeed:
model.backward(loss)
else:
optimizer.zero_grad()
if args.fp16:
optimizer.backward(loss, update_master_grads=False)
else:
loss.backward()
```
Zeroing the gradients is handled automatically by DeepSpeed after the weights
have been updated using a mini-batch.
Furthermore, DeepSpeed addresses distributed data parallel and FP16 under the
hood, simplifying code in multiple places.
(A) DeepSpeed also performs gradient averaging automatically at the gradient
accumulation boundaries. So we skip the allreduce communication.
```python
if args.deepspeed:
# DeepSpeed backward propagation already addressed all reduce communication.
# Reset the timer to avoid breaking timer logs below.
timers('allreduce').reset()
else:
torch.distributed.all_reduce(reduced_losses.data)
reduced_losses.data = reduced_losses.data / args.world_size
if not USE_TORCH_DDP:
timers('allreduce').start()
model.allreduce_params(reduce_after=False,
fp32_allreduce=args.fp32_allreduce)
timers('allreduce').stop()
```
(B) We also skip updating master gradients, since DeepSpeed addresses it internally.
```python
# Update master gradients.
if not args.deepspeed:
if args.fp16:
optimizer.update_master_grads()
# Clipping gradients helps prevent the exploding gradient.
if args.clip_grad > 0:
if not args.fp16:
mpu.clip_grad_norm(model.parameters(), args.clip_grad)
else:
optimizer.clip_master_grads(args.clip_grad)
return lm_loss_reduced
```
##### Updating the Model Parameters
The `step()` function in DeepSpeed engine updates the model parameters as well
as the learning rate.
```python
if args.deepspeed:
model.step()
else:
optimizer.step()
# Update learning rate.
if not (args.fp16 and optimizer.overflow):
lr_scheduler.step()
else:
skipped_iter = 1
```
##### Loss Scaling
The GPT2 training script logs the loss scaling value during training. Inside,
the DeepSpeed optimizer, this value is stored as `cur_scale` instead of
`loss_scale` in Megatron's optimizer. Therefore, we appropriately replace it in
the logging string.
```python
if args.fp16:
log_string += ' loss scale {:.1f} |'.format(
optimizer.cur_scale if args.deepspeed else optimizer.loss_scale)
```
### 2.3 Checkpoints Saving & Loading
DeepSpeed engine has flexible APIs for checkpoint saving and loading, to handle
the states from both the client model and its own internal.
```python
def save_checkpoint(self, save_dir, tag, client_state={})
def load_checkpoint(self, load_dir, tag)
```
Applying DeepSpeed needs to update utils.py in which Megatron-LM GPT2 saves and
loads its checkpoints.
A new function `save_ds_checkpoint()` is created as below for DeepSpeed, it
collects the client model states and passes to DeepSpeed engine by calling
`save_checkpoint()` of DeepSpeed.
```python
def save_ds_checkpoint(iteration, model, args):
"""Save a model checkpoint."""
sd = {}
sd['iteration'] = iteration
# rng states.
if not args.no_save_rng:
sd['random_rng_state'] = random.getstate()
sd['np_rng_state'] = np.random.get_state()
sd['torch_rng_state'] = torch.get_rng_state()
sd['cuda_rng_state'] = torch.cuda.get_rng_state()
sd['rng_tracker_states'] = mpu.get_cuda_rng_tracker().get_states()
model.save_checkpoint(args.save, iteration, client_state = sd)
```
In Megatron-LM GPT2 `save_checkpoint()` function, adds following lines to
invoke the above function for DeepSpeed.
```python
def save_checkpoint(iteration, model, optimizer,
lr_scheduler, args):
"""Save a model checkpoint."""
if args.deepspeed:
save_ds_checkpoint(iteration, model, args)
else:
......
```
In `load_checkpoint()` function, use DeepSpeed loading checkpoint API as below,
and return the states for the client model.
```python
def load_checkpoint(model, optimizer, lr_scheduler, args):
"""Load a model checkpoint."""
iteration, release = get_checkpoint_iteration(args)
if args.deepspeed:
checkpoint_name, sd = model.load_checkpoint(args.load, iteration)
if checkpoint_name is None:
if mpu.get_data_parallel_rank() == 0:
print("Unable to load checkpoint.")
return iteration
else:
......
```
### 2.4 Train scripts
Assume webtext data was prepared in previous step, to start training
Megatron-LM GPT2 model with DeepSpeed applied, execute the following command to
start training.
- Single GPU run
- run `bash scripts/ds_pretrain_gpt2.sh`
- Multiple GPUs/Nodes run
- run `bash scripts/ds_pretrain_gpt2_model_parallel.sh`
## 3 DeepSpeed Improvements over Megatron
DeepSpeed enables training very large models effectively via the advanced [ZeRO
optimizer](https://arxiv.org/abs/1910.02054v2). ZeRO significantly reduces the memory
footprint for training large models which means large models can be trained with i) less
model parallelism and ii) larger batch sizes. A lower model parallelism degree improves
training efficiency by increasing the granularity of the computation such as the matrix
multiplication where performance is directly related to the size of the matrices.
Furthermore, less model parallelism also results in less communication between model
parallel GPUs, which further boosts performance. Larger batch size has a similar effect
of increasing the computational granularity as well as reducing communication, also
resulting in better performance. Therefore, using DeepSpeed with Megatron can be
significantly faster than using Megatron without DeepSpeed.
The observed performance improvements depend on several factors such as the memory per
GPU, the local GPU interconnect (i.e., PCI-E vs NVLINK vs NVSwitch), the model size,
inter node network interconnect, etc. Below, we show some of the performance improvements
from using DeepSpeed over Megatron on a 16 GPU Low Bandwidth (40 Gbps) cluster and a 400 GPU DGX-2 High Bandwidth (800 Gbps) cluster.
For details please see the [ZeRO Paper](https://arxiv.org/abs/1910.02054v2). We also
present performance improvement on a 64 GPU cluster along with detailed configuration
analysis to show where the improvements come from.
![DeepSpeed-vs-Megatron](../figures/DeepSpeed-vs-Megatron.png)
### 3.1 On Low Bandwidth GPU Cluster
The figure above shows that training 1.5B parameter model with DeepSpeed is
nearly 4x faster than without DeepSpeed on a cluster with 4 nodes, 4 GPU per
node, and 16 GPUs total. These GPUs have 16GB of memory each, and PCI-E
interconnects GPUs within a node, and 40 Gbps infiniband across nodes.
The performance improvement comes from lower model parallelism degree and
larger batch size as discussed earlier. Training 1.5B parameter model with
Megatron alone requires 4-way model parallelism, and can only fit an effective
batch size of 32 using all 16 GPUs. On the other hand, DeepSpeed does not
require any model-parallelism to train this model, and can support and
effective batch size of 128 without running out of memory, resulting in
significantly higher performance.
### 3.2 On High bandwidth DGX-2 GPU Cluster
Each GPU on the DGX-2 cluster has 32 GB of memory, and GPUs inside a box is connected via
the high-bandwidth NVSwitch. DGX-2 nodes are connected to each other via 800 Gbps (8 x 100Gbps) infiniband interconnect. As such, running a 1.5B model on DGX-2 requires less model
parallelism, and the performance improvement from DeepSpeed for this model size is not
significant. However, at larger model sizes, Megatron still requires significantly larger
model parallelism degree, and can only run much smaller batch sizes than DeepSpeed.
Therefore, as the model sizes get larger, DeepSpeed starts to significantly outperform
Megatron.
### 3.3 Performance Improvements with Configuration Details
The figure below compares DeepSpeed with Megatron on a 64 GPU cluster with 4
DGX-2 nodes. To give the readers a clear idea of source of the performance
improvements, we also present the configuration table for both Megatron and
DeepSpeed. It shows the smallest model parallelism degree and the largest batch
size that can be used to train these models without running out of memory. As
discussed above, the tables demonstrate that DeepSpeed can run with smaller
achieve better performance than Megatron.
![DeepSpeed Performance SpeedUp](../figures/megatron-gpt2-perf-test.png)
**a ) Megatron-LM GPT2 Baseline**
| | Model Parallelism | Data Parallelism | #gpus | batch size | layers | hidden size | attention heads | samples / sec |
| ---- | ----------------: | ---------------: | ----: | ---------: | -----: | -----------:| --------------: | ------------: |
| 1.5B | 2 | 32 | 64 | 512 | 48 | 1600 | 16 | 128.56 |
| 4B | 4 | 16 | 64 | 128 | 64 | 2304 | 16 | 49.36 |
| 8B | 4 | 16 | 64 | 128 | 72 | 3072 | 24 | 24.57 |
| 20B | 16 | 4 | 64 | 16 | 111 | 3808 | 32 | 3.42 |
**b ) Megatron-LM GPT2 with DeepSpeed**
| | Model Parallelism | Data Parallelism | #gpus | batch size | layers | hidden size | attention heads | samples / sec |
| ---- | ----------------: | ---------------: | ----: | ---------: | -----: | -----------:| --------------: | ------------: |
| 1.5B | 1 | 64 | 64 | 2048 | 48 | 1600 | 16 | 151.35 |
| 4B | 1 | 64 | 64 | 512 | 64 | 2304 | 16 | 75.13 |
| 8B | 2 | 32 | 64 | 512 | 72 | 3072 | 24 | 43.52 |
| 20B | 4 | 16 | 64 | 128 | 111 | 3808 | 32 | 12.65 |
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