dmx.compressor.modeling.nn.torch_modules.MaxPool2d

class dmx.compressor.modeling.nn.torch_modules.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

An extension of PyTorch’s MaxPool2d layer to support DmxModule configurations. This module applies a 2D max pooling over an input signal composed of several input planes.

Parameters:

• kernel_size (int or tuple) – Size of the window to take a max over.

• stride (int or tuple, optional) – Stride of the window. Defaults to None.

• padding (int or tuple, optional) – Zero-padding added to both sides of the input. Defaults to 0.

• dilation (int or tuple, optional) – Spacing between kernel elements. Defaults to 1.

• return_indices (bool, optional) – If True, will return the max indices in a second tensor. Defaults to False.

• ceil_mode (bool, optional) – If True, will use ceil instead of floor to compute the output shape. Defaults to False.

None specific to this subclass. Inherits attributes from parent classes.

_forward (_input

Tensor) -> Tensor: Computes the forward pass of the 2D max pooling.

__init__(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) → None

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(kernel_size[, stride, padding, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
align_device(_input, args, kwargs, _device)
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
approx_forward(inputs, *args, **kwargs)
approximator_wrapper(inputs, approx_args, ...)	Override this in the DMX modules to enable pre-processing of the inputs, and the SIMD approximator arguments before calling the SIMD reference kernels.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
calibrating_quantizers(hyperparams)
calibrating_smoothquant(hyperparams)
check_format_dim_consistency()
check_input_format_dim_consistency()
check_residual_format_dim_consistency()
check_sparseness_dim_consistency()
check_weight_format_dim_consistency()
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
configure(config)	A function that changes the format of the ops and loading state dicts according to the config file.
count_flops(_input, _output)
counting_flops([zero])
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
dmx_config([freeze])	A function that the DmxModuleConfig object for the module
double()	Casts all floating point parameters and buffers to double datatype.
enable_approximation_function_tuning(state, ...)
enable_flop_counter([state])
enable_optimal_brain_compression(state, ...)
enable_quantizer_calib(state, hyperparams)
enable_smoothquant_calib(state, hyperparams)
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
fold_weight_and_bias()	A function that applies the ops the weights and biases using the corresponding formats.
forward(input, *args, **kwargs)	Forward pass of the module with quantization ops applied.
from_raw(raw)	Creates a new MaxPool2d object (DmxModule) from a given PyTorch MaxPool2d layer.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
infer_ch_axis()
init_casts()
init_smoothquant([migration_strength, ...])
init_sparsifier()
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict, *[, strict, assign])
load_state_dict_and_register_url(url)	A function that loads state dict from a url and sets url to self.state_dict_url
measuring_runtime(_records, device)	Context manager for monitoring runtime of DmxModule
modules()	Return an iterator over all modules in the network.
monitoring(_records)	Context manager for monitoring input/output to/from the DmxModule
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
optimal_brain_compressing(hyperparams)
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
save_state_dict_and_register_url(parent_dir)	A function that saves the current state dict of the module to a url under a specified parent directory
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
slanc_tuning(hyperparams)
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_compiler_graph()	Returns a compiler friendly graph
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
transform(config)	A function that changes the format of the ops and loading state dicts according to the config file.
tuning_approximation_function(hyperparams)
type(dst_type)	Casts all parameters and buffers to dst_type.
update_params_with_raw(raw)	Update parameters of a DmxModule from a torch.nn.Module.
update_smoothquant_scale(input)
xpu([device])	Move all model parameters and buffers to the XPU.
zero_flop_counter()
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
accum_format
approximation_function
bias_format
bops
call_super_init
dump_patches
effective_weight
flop_counter
flop_counter_enabled
flops
functional_forward
input_formats
input_precision
is_compound
last_input_shape
last_output_shape
multiplier_format
output_formats
plugins
residual_format
weight_elem_count
weight_format
weight_hypernet	Returns a function that processes weight according to the ops format of the module
weight_precision
weight_scale
weight_size_in_bytes
weight_sparseness
weight_storage_format
weight_storage_precision
weight_storage_scale
weight_storage_zero_point
weight_zero_point
kernel_size
stride
padding
dilation
return_indices
ceil_mode
training