User Guide¶
Installation¶
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.
chunks=8)
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 = input.to(in_device, non_blocking=True)
# target on out_device
target = target.to(out_device, non_blocking=True)
# output on out_device
output = model(input)
loss = F.cross_entropy(output, target)
loss.backward()
...
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)
else:
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)
Trade-offs¶
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¶
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()
, ornn.Dropout
, is deterministically reproduced in recomputation. But other randomnesses, such as Python standardrandom
ornumpy.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.
Restrictions¶
If you get any errors, check the following restrictions first.
- Sequential:
Your module must be
nn.Sequential
. For example, the models intorchvision
are not sequential. They can’t be wrapped byGPipe
directly:>>> from torchvision.models.resnet import resnet101 >>> model = resnet101() >>> type(model) torchvision.models.resnet.ResNet >>> 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. Callingforward(x)
onnn.Sequential(A(), B(), C())
is essentially the same as callingC(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 beTensor
orTuple[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'.
@skippable(stash=['skip'])
class Layer1(nn.Module):
...
# Layer8 pops 'skip'.
@skippable(pop=['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:
@skippable(stash=['skip'])
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()
:
@skippable(pop=['skip'])
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
@skippable(stash=['skip'])
class Encoder(nn.Module):
...
@skippable(pop=['skip'])
class Decoder(nn.Module):
...
ns_1f = Namespace()
ns_2f = Namespace()
ns_3f = Namespace()
model = nn.Sequential(
Encoder().isolate(ns_1f),
Encoder().isolate(ns_2f),
Encoder().isolate(ns_3f),
Bottleneck(),
Decoder().isolate(ns_3f),
Decoder().isolate(ns_2f),
Decoder().isolate(ns_1f),
Segment(),
)
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:
@skippable(stash=['skip'])
class MaybeStash(nn.Module):
def forward(self, input):
skip = input if test(input) else None
yield stash('skip', skip)
return f(input)
@skippable(pop=['skip'])
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):
super().__init__()
self.counter = 0
def forward(self, input):
if not is_recomputing():
self.counter += 1
return input
Note
deferred_batch_norm=True
on GPipe
will prevent
updating the running statistics twice.