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Multi-GPU training

Lightning supports multiple ways of doing distributed training.

Preparing your code

To train on CPU/GPU/TPU without changing your code, we need to build a few good habits :)

Delete .cuda() or .to() calls

Delete any calls to .cuda() or .to(device).

# before lightning
def forward(self, x):
    x = x.cuda(0)
    layer_1.cuda(0)
    x_hat = layer_1(x)

# after lightning
def forward(self, x):
    x_hat = layer_1(x)

Init using type_as

When you need to create a new tensor, use type_as. This will make your code scale to any arbitrary number of GPUs or TPUs with Lightning

# before lightning
def forward(self, x):
    z = torch.Tensor(2, 3)
    z = z.cuda(0)

# with lightning
def forward(self, x):
    z = torch.Tensor(2, 3)
    z = z.type_as(x, device=self.device)

Every LightningModule knows what device it is on. You can access that reference via self.device.

Remove samplers

For multi-node or TPU training, in PyTorch we must use torch.nn.DistributedSampler. The sampler makes sure each GPU sees the appropriate part of your data.

# without lightning
def train_dataloader(self):
    dataset = MNIST(...)
    sampler = None

    if self.on_tpu:
        sampler = DistributedSampler(dataset)

    return DataLoader(dataset, sampler=sampler)

With Lightning, you don’t need to do this because it takes care of adding the correct samplers when needed.

# with lightning
def train_dataloader(self):
    dataset = MNIST(...)
    return DataLoader(dataset)

Note

If you don’t want this behavior, disable it with Trainer(replace_sampler_ddp=False)

Note

For iterable datasets, we don’t do this automatically.

Make Model Picklable

It’s very likely your code is already picklable, so you don’t have to do anything to make this change. However, if you run distributed and see an error like this:

self._launch(process_obj)
File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47,
in _launch reduction.dump(process_obj, fp)
File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function <lambda> at 0x2b599e088ae8>:
attribute lookup <lambda> on __main__ failed

This means you have something in your model definition, transforms, optimizer, dataloader or callbacks that is cannot be pickled. By pickled we mean the following would fail.

import pickle
pickle.dump(some_object)

This is a limitation of using multiple processes for distributed training within PyTorch. To fix this issue, find your piece of code that cannot be pickled. The end of the stacktrace is usually helpful.

self._launch(process_obj)
File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47,
in _launch reduction.dump(process_obj, fp)
File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle [THIS IS THE THING TO FIND AND DELETE]:
attribute lookup <lambda> on __main__ failed

ie: in the stacktrace example here, there seems to be a lambda function somewhere in the user code which cannot be pickled.

Distributed modes

Lightning allows multiple ways of training

  • Data Parallel (distributed_backend=’dp’) (multiple-gpus, 1 machine)

  • DistributedDataParallel (distributed_backend=’ddp’) (multiple-gpus across many machines).

  • DistributedDataParallel2 (distributed_backend=’ddp2’) (dp in a machine, ddp across machines).

  • Horovod (distributed_backend=’horovod’) (multi-machine, multi-gpu, configured at runtime)

  • TPUs (num_tpu_cores=8|x) (tpu or TPU pod)

Note

If you request multiple GPUs without setting a mode, ddp will be automatically used.

Data Parallel (dp)

DataParallel splits a batch across k GPUs. That is, if you have a batch of 32 and use dp with 2 gpus, each GPU will process 16 samples, after which the root node will aggregate the results.

Warning

DP use is discouraged by PyTorch and Lightning. Use ddp which is more stable and at least 3x faster

# train on 2 GPUs (using dp mode)
trainer = Trainer(gpus=2, distributed_backend='dp')

Distributed Data Parallel

DistributedDataParallel works as follows.

  1. Each GPU across every node gets its own process.

  2. Each GPU gets visibility into a subset of the overall dataset. It will only ever see that subset.

  3. Each process inits the model.

Note

Make sure to set the random seed so that each model inits with the same weights

  1. Each process performs a full forward and backward pass in parallel.

  2. The gradients are synced and averaged across all processes.

  3. Each process updates its optimizer.

# train on 8 GPUs (same machine (ie: node))
trainer = Trainer(gpus=8, distributed_backend='ddp')

# train on 32 GPUs (4 nodes)
trainer = Trainer(gpus=8, distributed_backend='ddp', num_nodes=4)

Distributed Data Parallel 2

In certain cases, it’s advantageous to use all batches on the same machine instead of a subset. For instance you might want to compute a NCE loss where it pays to have more negative samples.

In this case, we can use ddp2 which behaves like dp in a machine and ddp across nodes. DDP2 does the following:

  1. Copies a subset of the data to each node.

  2. Inits a model on each node.

  3. Runs a forward and backward pass using DP.

  4. Syncs gradients across nodes.

  5. Applies the optimizer updates.

# train on 32 GPUs (4 nodes)
trainer = Trainer(gpus=8, distributed_backend='ddp2', num_nodes=4)

Horovod

Horovod allows the same training script to be used for single-GPU, multi-GPU, and multi-node training.

Like Distributed Data Parallel, every process in Horovod operates on a single GPU with a fixed subset of the data. Gradients are averaged across all GPUs in parallel during the backward pass, then synchronously applied before beginning the next step.

The number of worker processes is configured by a driver application (horovodrun or mpirun). In the training script, Horovod will detect the number of workers from the environment, and automatically scale the learning rate to compensate for the increased total batch size.

Horovod can be configured in the training script to run with any number of GPUs / processes as follows:

# train Horovod on GPU (number of GPUs / machines provided on command-line)
trainer = Trainer(distributed_backend='horovod', gpus=1)

# train Horovod on CPU (number of processes / machines provided on command-line)
trainer = Trainer(distributed_backend='horovod')

When starting the training job, the driver application will then be used to specify the total number of worker processes:

# run training with 4 GPUs on a single machine
horovodrun -np 4 python train.py

# run training with 8 GPUs on two machines (4 GPUs each)
horovodrun -np 8 -H hostname1:4,hostname2:4 python train.py

See the official Horovod documentation for details on installation and performance tuning.

DP/DDP2 caveats

In DP and DDP2 each GPU within a machine sees a portion of a batch. DP and ddp2 roughly do the following:

def distributed_forward(batch, model):
    batch = torch.Tensor(32, 8)
    gpu_0_batch = batch[:8]
    gpu_1_batch = batch[8:16]
    gpu_2_batch = batch[16:24]
    gpu_3_batch = batch[24:]

    y_0 = model_copy_gpu_0(gpu_0_batch)
    y_1 = model_copy_gpu_1(gpu_1_batch)
    y_2 = model_copy_gpu_2(gpu_2_batch)
    y_3 = model_copy_gpu_3(gpu_3_batch)

    return [y_0, y_1, y_2, y_3]

So, when Lightning calls any of the training_step, validation_step, test_step you will only be operating on one of those pieces.

# the batch here is a portion of the FULL batch
def training_step(self, batch, batch_idx):
    y_0 = batch

For most metrics, this doesn’t really matter. However, if you want to add something to your computational graph (like softmax) using all batch parts you can use the training_step_end step.

def training_step_end(self, outputs):
    # only use when  on dp
    outputs = torch.cat(outputs, dim=1)
    softmax = softmax(outputs, dim=1)
    out = softmax.mean()
    return out

In pseudocode, the full sequence is:

# get data
batch = next(dataloader)

# copy model and data to each gpu
batch_splits = split_batch(batch, num_gpus)
models = copy_model_to_gpus(model)

# in parallel, operate on each batch chunk
all_results = []
for gpu_num in gpus:
    batch_split = batch_splits[gpu_num]
    gpu_model = models[gpu_num]
    out = gpu_model(batch_split)
    all_results.append(out)

# use the full batch for something like softmax
full out = model.training_step_end(all_results)

to illustrate why this is needed, let’s look at dataparallel

def training_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self(batch)

    # on dp or ddp2 if we did softmax now it would be wrong
    # because batch is actually a piece of the full batch
    return y_hat

def training_step_end(self, batch_parts_outputs):
    # batch_parts_outputs has outputs of each part of the batch

    # do softmax here
    outputs = torch.cat(outputs, dim=1)
    softmax = softmax(outputs, dim=1)
    out = softmax.mean()

    return out

If training_step_end is defined it will be called regardless of tpu, dp, ddp, etc… which means it will behave the same no matter the backend.

Validation and test step also have the same option when using dp

def validation_step_end(self, batch_parts_outputs):
    ...

def test_step_end(self, batch_parts_outputs):
    ...

Implement Your Own Distributed (DDP) training

If you need your own way to init PyTorch DDP you can override pytorch_lightning.core.LightningModule.().

If you also need to use your own DDP implementation, override: pytorch_lightning.core.LightningModule.configure_ddp().

Batch size

When using distributed training make sure to modify your learning rate according to your effective batch size.

Let’s say you have a batch size of 7 in your dataloader.

class LitModel(LightningModule):

    def train_dataloader(self):
        return Dataset(..., batch_size=7)

In (DDP, Horovod) your effective batch size will be 7 * gpus * num_nodes.

# effective batch size = 7 * 8
Trainer(gpus=8, distributed_backend='ddp|horovod')

# effective batch size = 7 * 8 * 10
Trainer(gpus=8, num_nodes=10, distributed_backend='ddp|horovod')

In DDP2, your effective batch size will be 7 * num_nodes. The reason is that the full batch is visible to all GPUs on the node when using DDP2.

# effective batch size = 7
Trainer(gpus=8, distributed_backend='ddp2')

# effective batch size = 7 * 10
Trainer(gpus=8, num_nodes=10, distributed_backend='ddp2')

Note

Huge batch sizes are actually really bad for convergence. Check out: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

PytorchElastic

Lightning supports the use of PytorchElastic to enable fault-tolerent and elastic distributed job scheduling. To use it, specify the ‘ddp’ or ‘ddp2’ backend and the number of gpus you want to use in the trainer.

Trainer(gpus=8, distributed_backend='ddp')

Following the PytorchElastic Quickstart documentation, you then need to start a single-node etcd server on one of the hosts:

etcd --enable-v2
     --listen-client-urls http://0.0.0.0:2379,http://127.0.0.1:4001
     --advertise-client-urls PUBLIC_HOSTNAME:2379

And then launch the elastic job with:

python -m torchelastic.distributed.launch
        --nnodes=MIN_SIZE:MAX_SIZE
        --nproc_per_node=TRAINERS_PER_NODE
        --rdzv_id=JOB_ID
        --rdzv_backend=etcd
        --rdzv_endpoint=ETCD_HOST:ETCD_PORT
        YOUR_LIGHTNING_TRAINING_SCRIPT.py (--arg1 ... train script args...)

See the official PytorchElastic documentation for details on installation and more use cases.

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