dmx.compressor.numerical.smoothquant.SmoothQuant

class dmx.compressor.numerical.smoothquant.SmoothQuant(a_ch_axis: int, b_ch_axis: int, a_dynamic: bool = False, b_dynamic: bool = False, migration_strength: float = 0.5, scale_format: str | Format = 'SAME', scale_min: float = 1e-05, **kwargs)

SmoothQuant is a quantization technique that reduces MatMul quantization error by migrating the quantization difficulty from the first input of the MatMul (input A) to the second input (input B).

https://arxiv.org/pdf/2211.10438.pdf

Parameters:

• a_ch_axis (int) – channel axis for input A of the MatMul

• b_ch_axis (int) – channel axis for input B of the MatMul

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

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

• migration_strength (float) – controls how much quantization difficulty we want to migrate from input A to input B, 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”.

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

`a_ch_axis`

channel axis for input A of the MatMul

Type:

int

`b_ch_axis`

channel axis for input B of the MatMul

Type:

int

`a_dynamic`

If set to True, the maximum value of input A will be calculated dynamically, default is False.

Type:

bool

`b_dynamic`

If set to True, the maximum value of input B will be calculated dynamically, default is False.

Type:

bool

`migration_strength`

controls how much quantization difficulty we want to migrate from input A to input B, should be between 0 and 1, default is 0.5.

Type:

float

`scale_format`

the numerical format to store and compute the scaler, default is “SAME”.

Type:

str or dmx.Format

`scale_min`

minimum epsilon value used to prevent division by zero calculating the scaling factors, default is 1e-5.

Type:

float

`enabled`

If set to True, smoothQuant will be enabled for both input A and input B

Type:

bool

`scale`

scaling factors used to scale input A and input B to (input A / scale) and (input B * scale), respectively.

Type:

Tensor

`a_maxabs`

the maximum value of absolute of input A

Type:

Tensor

`b_maxabs`

the maximum value of absolute of input B

Type:

Tensor

__init__(a_ch_axis: int, b_ch_axis: int, a_dynamic: bool = False, b_dynamic: bool = False, migration_strength: float = 0.5, scale_format: str | Format = 'SAME', scale_min: float = 1e-05, **kwargs) → None

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

Methods

__init__(a_ch_axis, b_ch_axis[, a_dynamic, ...])	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(a_maxabs, b_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 smoothQuant
float()	Casts all floating point parameters and buffers to float datatype.
forward(a, b)	Computes the smoothQuant scaling tensor and scales inputs A and B
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.
scale_a(a)	If smoothQuant is enabled, scales input A.
scale_b(b)	If smoothQuant is enabled, scales input B.
set_dynamic([a_dynamic, b_dynamic])	Sets/resets the dynamic flag for inputs A and B.
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
training