pytorch/docs/source/ddp_comm_hooks.md

# DDP Communication Hooks

DDP communication hook is a generic interface to control how to communicate
gradients across workers by overriding the vanilla allreduce in
[DistributedDataParallel](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel.).
A few built-in communication hooks are provided,
and users can easily apply any of these hooks to optimize communication.
Besides, the hook interface can also support user-defined communication
strategies for more advanced use cases.

## How to Use a Communication Hook?

To use a communication hook, the user just needs to let the DDP model register
the hook before the training loop as below.

{func}`torch.nn.parallel.DistributedDataParallel.register_comm_hook`

## What Does a Communication Hook Operate On?

A communication hook provides a flexible way to allreduce gradients.
Therefore, it mainly operates on the gradients on each replica before allreduce,
which are bucketized to increase the overlap between communication and computation.
Particularly, {class}`torch.distributed.GradBucket` represents a bucket of gradient tensors to be allreduced.

```{eval-rst}
.. autoclass:: torch.distributed.GradBucket

.. autofunction:: torch.distributed.GradBucket.index
.. autofunction:: torch.distributed.GradBucket.buffer
.. autofunction:: torch.distributed.GradBucket.gradients
.. autofunction:: torch.distributed.GradBucket.is_last
.. autofunction:: torch.distributed.GradBucket.set_buffer
.. autofunction:: torch.distributed.GradBucket.parameters
```

## Default Communication Hooks

Default communication hooks are simple **stateless** hooks, so the input state
in `register_comm_hook` is either a process group or `None`.
The input `bucket` is a {class}`torch.distributed.GradBucket` object.

```{eval-rst}
.. currentmodule:: torch.distributed.algorithms.ddp_comm_hooks.default_hooks
.. autofunction:: allreduce_hook
.. autofunction:: fp16_compress_hook
.. autofunction:: bf16_compress_hook
```

Additionally, a communication hook wrapper is provided to support {meth}`~fp16_compress_hook` or {meth}`~bf16_compress_hook` as a wrapper,
which can be combined with other communication hooks.

```{eval-rst}
.. autofunction:: fp16_compress_wrapper
.. autofunction:: bf16_compress_wrapper
```
## PowerSGD Communication Hook

PowerSGD ([Vogels et al., NeurIPS 2019](https://arxiv.org/abs/1905.13727))
is a gradient compression algorithm, which can provide very high compression
rates and accelerate bandwidth-bound distributed training.
This algorithm needs to maintain both some hyperparameters and the internal
state. Therefore, PowerSGD communication hook is a **stateful** hook,
and the user needs to provide a state object defined as below.

### PowerSGD State

```{eval-rst}
.. currentmodule:: torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook
.. autoclass:: PowerSGDState
```
### PowerSGD Hooks

```{warning}
PowerSGD typically requires extra memory of the same size as the model's
gradients to enable error feedback, which can compensate for biased
compressed communication and improve accuracy.
```

```{warning}
PowerSGD hooks may conflict with [Apex automatic mixed precision package](https://github.com/NVIDIA/apex).
Please use PyTorch [native automatic mixed precision package](https://pytorch.org/docs/stable/amp.html)
instead.
```

```{eval-rst}
.. autofunction:: powerSGD_hook
.. autofunction:: batched_powerSGD_hook
```
## Debugging Communication Hooks

As the name implies, debugging communication hooks are **only** used for debugging and performance optimization purpose.
```{eval-rst}
.. currentmodule:: torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks
```
```{warning}
Debugging communication hooks do not necessarily output the correct results.
```
```{eval-rst}
.. autofunction:: noop_hook
```
## Checkpointing of Communication Hooks

```{eval-rst}
.. currentmodule:: torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook
```
A stateful communication hook can be saved as a part of model checkpointing to enable trainer restarts.
To make a hook serializable, ``__setstate__`` and ``__getstate__`` should be defined.

```{warning}
`__getstate__` should exclude non-serializable attributes from a returned dictionary.
```
```{warning}
`__setstate__` should properly initialize non-serializable attributes, excluded from a provided `state`.
```
{class}`PowerSGDState` has `__setstate__` and `__getstate__` implemented and can be used as a reference.

```{eval-rst}
.. class:: PowerSGDState
    :noindex:

    .. automethod:: PowerSGDState.__getstate__
    .. automethod:: PowerSGDState.__setstate__
```
Here is a simple, end-to-end example of saving and reloading PowerSGD state and hook.

```python

import os
import sys
import tempfile
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as mp

from torch.nn.parallel import DistributedDataParallel
from torch.distributed.algorithms.ddp_comm_hooks import powerSGD_hook as powerSGD

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(24,24)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(24,12)

    def forward(self, x):
        return self.fc2(self.relu(self.fc1(x)))

def setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'

    # initialize the process group
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

def cleanup():
    dist.destroy_process_group()

def run_demo(demo_fn, world_size):
    mp.spawn(
        demo_fn,
        args=(world_size,),
        nprocs=world_size,
        join=True)

def demo_serialization(rank, world_size):
    setup(rank, world_size)

    CHECKPOINT = tempfile.gettempdir() + "/checkpoint.pt"

    model = SimpleModel().to(rank)
    ddp_model = DistributedDataParallel(model, device_ids=[rank])

    powersgd_hook = powerSGD.powerSGD_hook
    powersgd_state = powerSGD.PowerSGDState(process_group=None)

    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
    ddp_model.register_comm_hook(powersgd_state, powersgd_hook)

    state = {
        'state_dict': ddp_model.state_dict(),
        'comm_hook': powersgd_hook,
        'comm_hook_state': powersgd_state}

    if rank == 0:
        torch.save(state, CHECKPOINT)

    dist.barrier()
    map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
    checkpoint = torch.load(CHECKPOINT, map_location=map_location)

    new_ddp_model = DistributedDataParallel(SimpleModel().to(rank), device_ids=[rank])
    new_ddp_model.load_state_dict(checkpoint['state_dict'])
    powersgd_hook = checkpoint['comm_hook']
    powersgd_state = checkpoint['comm_hook_state']

    new_ddp_model.register_comm_hook(powersgd_state, powersgd_hook)

    if rank == 0:
        os.remove(CHECKPOINT)

    cleanup()

if __name__ == "__main__":
    n_gpus = torch.cuda.device_count()
    assert n_gpus >= 2, f"Requires at least 2 GPUs to run, but got {n_gpus}"
    world_size = n_gpus
    run_demo(demo_serialization, world_size)
```

## Acknowledgements

Many thanks to PowerSGD paper author **Thijs Vogels** for the code review on
PowerSGD communication hook, as well as the
[comparison experiments](https://observablehq.com/@tvogels/powersgd-benchmark),
which show that the performance of PowerSGD communication hook is on par with
the implementation in the original [paper](https://arxiv.org/abs/1905.13727).