torchfunc.performance.layers

Check any performance caveats related to PyTorch and it’s layers.

Using functionalities below you can check whether your architecture follows current good practices related to performance of torch.nn.Module concrete layers.

class torchfunc.performance.layers.Depthwise(checkers: Tuple[Callable[[torch.nn.modules.module.Module], bool]] = None)

Check whether any convolution layer is a so-called depthwise convolution.

Depthwise convolution is faster for images with input data in format (batch, height, width, channel) as specialized kernels are available.

Currently PyTorch does not support this functionality, so using those may actually slow down your neural network.

Depthwise convolution might still be useful in order to save memory, not so performance-wise.

For easy to follow guidelines, use tips method of this class.

Example:

model = torch.nn.Sequential(
    torch.nn.Conv1d(64, 64, kernel_size=3, groups=64),
    torch.nn.Conv2d(3, 32, kernel_size=3, groups=1),
    torch.nn.Conv2d(32, 32, kernel_size=3, groups=32),
)
for index in torchfunc.performance.layers.Depthwise().children(model):
    print(index) # Should print 0 and 2

checkers

Functions checking whether given module is depthwise convolution. Should return True in such case, False otherwise. Default: Depthwise.default_checker; if module’s groups count is equal to module’s in_channels True is returned. Works for PyTorch’s ConvNd layers.

Type

Tuple[Callable], optional

children(module: torch.nn.modules.module.Module)

Look for Depthwise convolution using module’s children() method (shallow scanning).

Parameters

module (torch.nn.Module) – Module to be scanned

Yields

int – Indices where module is considered depthwise convolution.

classmethod default_checker(module)

Default checking method suitable for PyTorch’s built-in convolution layers.

Checks whether count of groups is equal to count of in_channels.

Important:

If you want to provide custom checker, you should return True (module being depthwise convolution) or False for any module that is passed to this function.

Parameters

module (torch.nn.Module) – Module (or submodule) for which True means it’s depthwise.

Returns

Submodule’s indices where depthwise convolution was located.

Return type

List[int]

modules(module: torch.nn.modules.module.Module)

Look for Depthwise convolution using modules() method (recursive scanning).

Parameters

module (torch.nn.Module) – Module to be scanned

Yields

int – Indices where module is considered depthwise convolution.

tips(module: torch.nn.modules.module.Module) → str

Return str representation of modules() method.

It is advised to use this function to get tips in order to easily fix performance issues related to depthwise convolution.

Parameters

module (torch.nn.Module) – Module to be scanned

Returns

String representing tips related to depthwise convolution.

Return type

str

class torchfunc.performance.layers.Inplace(inplace: Tuple[str] = 'inplace')

Check whether any submodule/child of module is set to inplace mode.

Inplace operations may interfere with traced module (kernel fusion) and cause slowdowns. See this issue for more information.

Example:

model = torch.nn.Sequential(
    torch.nn.Conv2d(3, 64, kernel_size=3, groups=64),
    torch.nn.ReLU(inplace=True),
    torch.nn.Conv2d(64, 64, kernel_size=3),
    torch.nn.ReLU6(inplace=True),
    torch.nn.Conv2d(64, 128, kernel_size=3, groups=32),
)

for index in torchfunc.performance.layers.Inplace().children(model):
    print(index) # Should print 1 and 3

For easy to follow guidelines, use tips method of this class.

attribute

Attributes names indicating whether current op is inplace. Do not specify if you are not using custom modules not following pytorch’s conventions. Default: ("inplace",). Existence of all those attributes will be checked in module. If any of them exists and is True, it will be considered as inplace operation.

Type

Tuple[str], optional

children(module: torch.nn.modules.module.Module)

Look for inplace operation using children() method (shallow scanning).

Yields

int – Indices where module is probably inplace.

modules(module: torch.nn.modules.module.Module)

Look for inplace operation using modules() method (recursive scanning).

Yields

int – Indices where module is probably inplace.

tips(module: torch.nn.modules.module.Module) → str

Return str representation of modules() method.

It is advised to use this function to get tips in order to easily fix performance issues related to inplace operations.

Parameters

module (torch.nn.Module) – Module to be scanned

Returns

String representing tips related to inplace operations.

Return type

str