oneflow.nn.QatConv1d

class oneflow.nn.QatConv1d(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: Union[int, Tuple[int]] = 1, padding: Union[str, int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', quantization_formula: str = 'google', quantization_bit: int = 8, quantization_scheme: str = 'symmetric', weight_quant_per_layer: bool = True, input_quant_momentum: float = 0.95)

A Conv1d module attached with nn.MinMaxObserver, nn.MovingAverageMinMaxObserver and nn.FakeQuantization modules for weight and input, used for quantization aware training.

The parameters of QatConv1d are the same as Conv1d with some extra parameters for fake quantization, see MinMaxObserver, MovingAverageMinMaxObserver and FakeQuantization for more details.

Parameters

  • in_channels (int) – Number of channels in the input image

  • out_channels (int) – Number of channels produced by the convolution

  • kernel_size (int or tuple) – Size of the convolving kernel

  • stride (int or tuple, optional) – Stride of the convolution. Default: 1

  • padding (int, tuple or str, optional) – Padding added to both sides of the input. Default: 0

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

  • groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1

  • bias (bool, optional) – If True, adds a learnable bias to the output. Default: True

  • padding_mode (string, optional) – 'zeros'. Default: 'zeros'

  • quantization_formula (str) – Support “google” or “cambricon”.

  • quantization_bit (int) – Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8.

  • quantization_scheme (str) – “symmetric” or “affine”, quantize to signed / unsigned integer. Defaults to “symmetric”.

  • weight_quant_per_layer (bool) – True or False, means per-layer / per-channel for weight quantization. Defaults to True.

  • input_quant_momentum (float) – Smoothing parameter for exponential moving average operation for input quantization. Defaults to 0.95.

Shape:

  • Input: \((N, C_{in}, L_{in})\)

  • Output: \((N, C_{out}, L_{out})\) where

    \[\begin{split}L_{out} = \\left\\lfloor\\frac{L_{in} + 2 \\times \\text{padding} - \\text{dilation} \\times (\\text{kernel\\_size} - 1) - 1}{\\text{stride}} + 1\\right\\rfloor\end{split}\]
weight

the learnable weights of the module of shape \((\\text{out\\_channels}, \\frac{\\text{in\\_channels}}{\\text{groups}}, \\text{kernel\\_size})\). The values of these weights are sampled from \(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\) where \(k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}\)

Type

Tensor

bias

the learnable bias of the module of shape (out_channels). If bias is True, then the values of these weights are sampled from \(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\) where \(k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}\)

Type

Tensor

For example:

>>> import numpy as np
>>> import oneflow as flow
>>> import oneflow.nn as nn

>>> arr = np.random.randn(20, 16, 50)
>>> input = flow.Tensor(arr)
>>> m = nn.QatConv1d(16, 33, 3, stride=2, quantization_formula="google", quantization_bit=8, quantization_scheme="symmetric")
>>> output = m(input)
__init__(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: Union[int, Tuple[int]] = 1, padding: Union[str, int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', quantization_formula: str = 'google', quantization_bit: int = 8, quantization_scheme: str = 'symmetric', weight_quant_per_layer: bool = True, input_quant_momentum: float = 0.95)

Calls super().__setattr__(‘a’, a) instead of the typical self.a = a to avoid Module.__setattr__ overhead. Module’s __setattr__ has special handling for parameters, submodules, and buffers but simply calls into super().__setattr__ for all other attributes.

Methods

__call__(*args, **kwargs)

Call self as a function.

__delattr__(name)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattr__(name)

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(in_channels, out_channels, kernel_size)

Calls super().__setattr__(‘a’, a) instead of the typical self.a = a to avoid Module.__setattr__ overhead.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

Create and return a new object.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value)

Implement setattr(self, name, value).

__setstate__(state)

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_apply(fn)

_conv_forward(x, weight, bias)

_get_backward_hooks()

Returns the backward hooks for use in the call function.

_get_name()

_load_from_state_dict(state_dict, prefix, …)

_maybe_warn_non_full_backward_hook(args, …)

_named_members(get_members_fn[, prefix, recurse])

_register_load_state_dict_pre_hook(hook[, …])

These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self.

_register_state_dict_hook(hook)

These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set.

_save_to_state_dict(destination, prefix, …)

_shallow_repr()

_to_memory_format(memory_format)

Casts the parameters and buffers in this module to another memory format.

add_module(name, module)

Adds a child module to the current module.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

buffers([recurse])

Returns an iterator over module buffers.

children()

Returns an iterator over immediate children modules.

cpu()

Moves all model parameters and buffers to the CPU.

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Sets the module in evaluation mode.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(x)

get_parameter(target)

Return the parameter refenreced by target.

get_submodule(target)

Get submodule accroding to the name of submodule.

half()

Casts all floating point parameters and buffers to half datatype.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

make_contiguous_params_group()

Get contiguous parameters group after creating the whole module.

modules()

Returns an iterator over all modules in the network.

named_buffers([prefix, recurse])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix])

Returns 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])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Returns an iterator over module parameters.

register_backward_hook(hook)

Registers a backward hook on the module.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_forward_hook(hook)

Registers a forward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_full_backward_hook(hook)

Registers a backward hook on the module.

register_parameter(name, param)

Adds a parameter to the module.

register_state_dict_pre_hook(hook)

These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

reset_parameters()

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

to_consistent(*args, **kwargs)

This interface is no longer available, please use oneflow.nn.Module.to_global() instead.

to_empty(*, device)

Moves the parameters and buffers to the specified device without copying storage.

to_global([placement, sbp])

Convert the parameters and buffers to global.

to_local()

to_memory_format(memory_format)

train([mode])

Sets the module in training mode.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.

Attributes

_grad_t

alias of Union[Tuple[oneflow.Tensor, …], oneflow.Tensor]