dmx.compressor.numerical.smoothquant.ActivationWeightSmoothQuant

class dmx.compressor.numerical.smoothquant.ActivationWeightSmoothQuant(ch_axis: int, win_ch_axis: int, migration_strength: float = 0.5, scale_format: str | Format = 'SAME', dynamic: bool = False, scale_min: float = 1e-05, **kwargs)

This is the derived class for Activation x Weight smoothQuant.

Parameters:

• ch_axis (int) – channel axis for the input activation tensor

• win_ch_axis (int) – channel axis for the weight tensor

• migration_strength (float) – controls how much quantization difficulty we want to migrate from activations to weights, should be between 0 and 1, default is 0.5.

• scale_format (str or dmx.Format) – the numerical format to store and compute the scaler, default is “SAME”.

• dynamic (bool) – If set to True, the maximum value of activations will be calculated dynamically, default is False.

• scale_min (float) – minimum epsilon value used to prevent division by zero calculating the scaling factors, default is 1e-5.

`ch_axis`

channel axis for the input activation tensor

Type:

int

`win_ch_axis`

channel axis for the weight tensor

Type:

int

`fused_to_weight`

If set to True, the scaling factors will be fused to the weights, cannot be enabled when dynamic is set.

Type:

bool

__init__(ch_axis: int, win_ch_axis: int, migration_strength: float = 0.5, scale_format: str | Format = 'SAME', dynamic: bool = False, scale_min: float = 1e-05, **kwargs) → None

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

Methods

__init__(ch_axis, win_ch_axis[, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
compute_scale(inp_maxabs)	Computes the scaling tensor.
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
disable()	Disables smoothQuant.
double()	Casts all floating point parameters and buffers to double datatype.
enable([enabled])	Sets/resets the enabled flag.
eval()	Set the module in evaluation mode.
extra_repr()	Returns the extra representation of Activation x Weight smoothQuant
float()	Casts all floating point parameters and buffers to float datatype.
forward(inp, wgt)	Computes the smoothQuant scaling tensor and scales input activation and weight
fuse_to_weight(wgt)	Fuses the scaling factor to the weight tensor.
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.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict, *[, strict, assign])
modules()	Return an iterator over all modules in the network.
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.
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.
reset_a_maxabs()	Resets a_maxabs to an empty tensor.
reset_b_maxabs()	Resets b_maxabs to an empty tensor.
reset_scale()	Resets the scaling tensor to an empty tensor.
reset_weight_maxabs()	Resets weight maxabs.
scale_a(a)	If smoothQuant is enabled, scales input A.
scale_b(b)	If smoothQuant is enabled, scales input B.
scale_input(inp)	Scales the input activation.
scale_weight(wgt)	Scales weight.
set_dynamic([dynamic])	Sets/resets the dynamic flag for the input activation
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_migration_strength(migration_strength)	Sets the migration_strength factor.
set_scale_format([format])	Sets/resets the scale_format.
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_().
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_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
a_maxabs_exists	Checks if a_maxabs is already calculated.
b_maxabs_exists	Checks if b_maxabs is already calculated.
calibrating
call_super_init
dump_patches
dynamic	Checks if the dynamic flag is set for the input activation.
input_maxabs_exists	Checks if input_maxabs is already calculated.
weight_maxabs_computed	Checks if weight_maxabs is already calculated.
training