User Guide


torchgpipe is available on PyPI. Install by pip:

$ pip install torchgpipe

Python 3.6+ (CPython) is required.

PyTorch 1.1+ will be installed automatically if you don’t have a satisfied one. However, we highly recommend you to use the latest version of PyTorch.

Applying GPipe

To train a module with GPipe, simply wrap it with torchgpipe.GPipe. Your module must be a nn.Sequential as GPipe will automatically split the module into partitions. A partition is a group of consecutive layers that run on a single device together. balance argument determines the number of layers in each partition. chunks argument specifies the number of micro-batches. Input, output, and intermediate tensors must be Tensor or Tuple[Tensor, ...]. See also Restrictions for more details.

The below example code shows how to split a module with four layers into two partitions each having two layers. This code also splits a mini-batch into 8 micro-batches:

from torchgpipe import GPipe

model = nn.Sequential(a, b, c, d)
model = GPipe(model, balance=[2, 2], chunks=8)

# 1st partition: nn.Sequential(a, b) on cuda:0
# 2nd partition: nn.Sequential(c, d) on cuda:1

for input in data_loader:
    output = model(input)

GPipe optimizes training using CUDA. You should not move the module to a GPU yourself, because GPipe automatically moves each partition over different devices. By default, available GPUs starting from cuda:0 are selected in order for each partition. You can also specify GPUs to use with devices parameter:

model = GPipe(model,
              balance=[2, 2],
              devices=[4, 2],  # Specify GPUs.

Input and Output Device

Unlike a typical module, with GPipe, the input device is different from the output device except for when there is only one partition. This is because the first partition and last partition are placed in different devices.

Therefore, you have to move the input and target to the corresponding devices. It can be done with GPipe.devices, which holds the list of devices for each partition:

in_device = model.devices[0]
out_device = model.devices[-1]

for input, target in data_loader:
    # input on in_device
    input =, non_blocking=True)

    # target on out_device
    target =, non_blocking=True)

    # output on out_device
    output = model(input)
    loss = F.cross_entropy(output, target)

Nested Sequentials

When GPipe splits a nn.Sequential module, it regards every child of the module as a single, non-divisible layer. However, it may be the case that some child is another sequential module and one may want to split them further.

This kind of recursive split of a nested sequential module is not intended nor supported by GPipe. It’s your responsibility to flatten the module. Fortunately, this is not hard in PyTorch. Follow this code snippet which shows how a nested sequential module can be flattened:

_3_layers = nn.Sequential(...)  # len(_3_layers) == 3
_4_layers = nn.Sequential(...)  # len(_4_layers) == 4
model = nn.Sequential(_3_layers, _4_layers)  # len(model) == 2

def flatten_sequential(module):
    def _flatten(module):
        for name, child in module.named_children():
            if isinstance(child, nn.Sequential):
                for sub_name, sub_child in _flatten(child):
                    yield (f'{name}_{sub_name}', sub_child)
                yield (name, child)
    return nn.Sequential(OrderedDict(_flatten(module)))

model = flatten_sequential(model)  # len(model) == 7
model = GPipe(model, balance=[2, 3, 2], chunks=4)

Typical Model Parallelism

The typical model parallelism is a special case of GPipe. Model parallelism is equivalent to GPipe if micro-batching and checkpointing are disabled. Set chunks=1 and checkpoint='never' for this:

model = GPipe(model, balance=[2, 2], chunks=1, checkpoint='never')

Automatic Balancing

It could be hard to determine the optimal balance of a model. In particular, if you are still designing a model, the model architecture may change over time. In this case, we highly recommend torchgpipe.balance for automatic balancing. This won’t give you the optimal balance, but a good-enough balance. Note that this is provided by torchgpipe, and is not from the GPipe paper by Huang et al.

There are two balancing tools, balance_by_time() and balance_by_size(). Both are based on per-layer profiling. Just like PyTorch JIT, you need to feed a sample input into the model. balance_by_time() traces elapsed time of each layer, while balance_by_size() detects the CUDA memory usage of each layer. Choose the balancing tool for your needs:

from torchgpipe import GPipe
from torchgpipe.balance import balance_by_time

partitions = torch.cuda.device_count()
sample = torch.rand(128, 3, 224, 224)
balance = balance_by_time(partitions, model, sample)

model = GPipe(model, balance, chunks=8)


Number of Micro-batches

Number of micro-batches has a trade-off between GPU utilization per micro-batch and total area of bubble. You need to find the best number of micro-batches for your model.

GPU may slow down when processing many small micro-batches compared to larger micro-batches. GPU will not be fully utilized if each CUDA kernel is too cheap to compute, hence too small micro-batches cause underutilization. On the other hand, the area of bubble is minimized when the size of each micro-batch is minimal. Ideally, you should choose the largest number of micro-batches that doesn’t underutilize GPUs.

As a side note, BatchNorm tends to perform worse with smaller batch size. Large number of micro-batches may affect the final performance of model using BatchNorm negatively just like in nn.DataParallel.


Checkpointing drastically helps to reduce memory usage, but the overall training would slow down by about 25%. You can handle how to apply checkpointing on your model. There are three options:

  • 'always' – Apply checkpointing over all micro-batches.

  • 'except_last' (default) – Apply checkpointing except the last micro-batch.

  • 'never' – Checkpointing is never applied.

Usually, checkpointing at the last micro-batch may not be useful because the saved memory will be reconstructed immediately. That’s why we choose 'except_last' as the default option.

If you decide not to use checkpointing at all, nn.DataParallel might be more efficient than GPipe.

Referential Transparency

Checkpointing executes forward propagation again at backpropagation, which is called recomputation. We assume that both the executions are identical. Hence, all layers should be referentially transparent in forward propagation. Here are the typical cases that break referential transparency:

In-place Operations:

We do not recommend using in-place operations with checkpointing. Especially, if an in-place operation such as add_(1) is applied to the input of a checkpointed partition, then the recomputation can’t recover the original input.

Randomness not managed by PyTorch:

The randomness managed by PyTorch, including torch.manual_seed(), torch.rand(), or nn.Dropout, is deterministically reproduced in recomputation. But other randomnesses, such as Python standard random or numpy.random, are not. We highly recommend to use PyTorch randomness for referential transparency.

Side Effects:

Some modules such as BatchNorm update their state in forward propagation. Hence, updated state in recomputation might not be identical to the original state.


If you get any errors, check the following restrictions first.


Your module must be nn.Sequential. For example, the models in torchvision are not sequential. They can’t be wrapped by GPipe directly:

>>> from torchvision.models.resnet import resnet101
>>> model = resnet101()
>>> type(model)
>>> GPipe(model, balance=..., chunks=...)
Traceback (most recent call last)
TypeError: module must be nn.Sequential to be partitioned

See the sequential ResNet example to figure out how to make a model into a nn.Sequential model.

nn.Sequential assumes that every underlying layer takes only one argument. Calling forward(x) on nn.Sequential(A(), B(), C()) is essentially the same as calling C(B(A(x))). Hence, you can’t design an underlying layer with multiple arguments:

class MyModule(nn.Module):
    def forward(self, a, b, c):
        return a + b - c

model = nn.Sequential(..., MyModule(), ...)
model(input)  # FAILS!
Tensor or Tensors:

As we discussed above, each layer must take only one argument due to nn.Sequential. There is one more restriction. Every underlying layers’ input and output must be Tensor or Tuple[Tensor, ...]:

# OK
def forward(input: Tensor) -> Tensor: ...
def forward(input: Tensor) -> Tuple[Tensor, Tensor]: ...
def forward(input: Tuple[Tensor, Tensor]) -> Tensor: ...

# Error
def forward(input1: Tensor, input2: Tensor) -> Tensor: ...
def forward(input: Tensor, label: str) -> Tensor: ...
def forward(input: Tensor) -> Dict[str, Tensor]: ...
def forward(input: Tensor) -> Tuple[Tensor, str]: ...

The reason is that GPipe can’t assume how the non-tensor inputs for a mini-batch can be split for micro-batches.

Unique Parameters:

GPipe places each partition on the corresponding device. When placing a partition, the parameters of the partition are also moved to the destination. GPipe cannot support a module with a parameter on two or more devices:

>>> conv1 = nn.Conv2d(3, 3, 1)
>>> conv2 = nn.Conv2d(3, 3, 1)
>>> conv1.weight = conv2.weight
>>> model = nn.Sequential(conv1, conv2)
>>> model = GPipe(model, balance=[1, 1], ...)
Traceback (most recent call last)
ValueError: module with duplicate parameters in distinct children is not supported

Complex Modules

This part of the documentation discusses how to implement a complex module compatible with GPipe. First, you should understand how GPipe works. See Understanding GPipe.

Skip Connections

Many deep learning models, such as ResNet, AmoebaNet, or U-Net, contain skip connections. There are two ways to implement skip connections. Let’s assume we have to implement a skip connection like this:

latent = layer1(input)
latent = layer2(latent)
output = layer3(latent) + input  # skip connection

To make this module sequential, we define modules for each layer. Simply, a skip connection can be implemented by making underlying layers with Tuple[Tensor, Tensor] parameter and return type:

class Layer1(nn.Module):
    #         ┌────────────────┐
    # input --│-+-> layer1 ----│--> output
    #         │ '--------------│--> skip
    #         └────────────────┘
    def forward(self, input):
        skip = input
        return layer1(input), skip

class Layer2(nn.Module):
    #         ┌────────────────┐
    # input --│---> layer2 ----│--> output
    #  skip --│----------------│--> skip
    #         └────────────────┘
    def forward(self, input_and_skip):
        input, skip = input_and_skip
        return layer2(input), skip

class Layer3(nn.Module):
    #         ┌────────────────┐
    # input --│---> layer3 --+-│--> output
    #  skip --│--------------' │
    #         └────────────────┘
    def forward(self, input_and_skip):
        input, skip = input_and_skip
        return layer3(input) + skip

model = nn.Sequential(Layer1(), Layer2(), Layer3())

Because of the skip connection being represented as a normal parameter, GPipe can move the tensors from partition to partition:

model = GPipe(model, balance=[1, 1, 1], chunks=8)

This seems a fairly straightforward way to implement skip connections. But there is a disadvantage. In the above example, the skip tensor is copied to the second device, but it is never used at the device. Unnecessary copies of skip tensors may waste time and memory. The following section introduces an alternative approach for skip connection.

Long Skip Connections

The disadvantage mentioned above might be catastrophic if it involves unnecessary copies of a large tensor, and/or over many devices. The second case often occurs when implementing long skip connections.

Let’s assume now we have 8 layers between input and output:

latent = layer1(input)
latent = layer2(latent)
latent = layer3(latent)
latent = layer4(latent)
latent = layer5(latent)
latent = layer6(latent)
latent = layer7(latent)
output = layer8(latent) + input  # skip connection

With the prior approach, the skip tensor will be copied to every device, but six devices do not need it. The alternative approach is to expose in which layer the skip tensor is produced and consumed. We introduce the @skippable class decorator to toss the tensor directly, without needing to pass it to irrelevant layers in between. A module can stash a tensor into the storage or pop. This functionality works perfectly fine even when the module is not wrapped by GPipe.

The decorator declares which skip tensors would be stashed or popped in the decorated module. Let us explain how to implement the 8-layer example above using torchgpipe.skip. Here we use the name “skip” for the skip connection between Layer1 and Layer8:

# Layer1 stashes 'skip'.
class Layer1(nn.Module):

# Layer8 pops 'skip'.
class Layer8(nn.Module):

When Layer1 prepares a skip tensor, it can stash the tensor into the hidden storage by yield stash(). As you may have noticed, we define forward() as a generator instead of a normal function:

class Layer1(nn.Module):
    def forward(self, input):
        skip = input
        yield stash('skip', skip)
        return layer1(input)

Similarly, Layer8 also can pop the stashed skip tensor by yield pop():

class Layer8(nn.Module):
    def forward(self, input):
        skip = yield pop('skip')
        return layer8(input) + skip

Now the intermediate layers do not interact with the skip tensor at all:

class Layer2(nn.Module):
    def forward(self, input):
        return layer2(input)
class Layer7(nn.Module):
    def forward(self, input):
        return layer7(input)

You can design any complex skip connections with @skippable since a skippable module could stash and/or pop multiple skip tensors. However, there are restrictions:

  • Every skip name must be unique within a sequential module.

  • Every skip tensor must be stashed and popped exactly once.

Then, how can we instantiate multiple skippable modules from the same class in a sequential module? You can isolate some skip names into a Namespace. For example, a conceptual U-Net can be designed like this. There are 3 pairs of Encoder and Decoder:

# 1F. Encoder -------- Decoder -- Segment
#        \                /
# 2F.  Encoder ------ Decoder
#          \            /
# 3F.   Encoder ---- Decoder
#            \        /
# 4F.        Bottleneck

class Encoder(nn.Module):

class Decoder(nn.Module):

ns_1f = Namespace()
ns_2f = Namespace()
ns_3f = Namespace()

model = nn.Sequential(

Some skip connection may be conditional on input. However, @skippable doesn’t allow stash() or pop() missing. Instead, it allows None in place of skip tensor:

class MaybeStash(nn.Module):
    def forward(self, input):
        skip = input if test(input) else None
        yield stash('skip', skip)
        return f(input)

class MaybePop(nn.Module):
    def forward(self, input):
        output = f(input)
        skip = yield pop('skip')
        if skip is not None:
            output += skip
        return output

Detecting Recomputation

Checkpointing in GPipe performs forward propagations twice. The second forward propagation is called recomputation. This may cause a problem when a module such as nn.BatchNorm2d updates its running estimates of batch statistics on each forward propagation. It should not update the running estimates again during the recomputation. To avoid updating the running estimates twice, modules’ forward method needs be able to detect that this is the recomputation.

It can be done by is_recomputing(). This function returns True if called during the recomputation:

class Counter(nn.Module):
    def __init__(self):
        self.counter = 0

    def forward(self, input):
        if not is_recomputing():
            self.counter += 1
        return input


deferred_batch_norm=True on GPipe will prevent updating the running statistics twice.