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Source code for torch.nn.modules.linear

# mypy: allow-untyped-defs
import math
from typing import Any

import torch
from torch import Tensor
from torch.nn.parameter import Parameter, UninitializedParameter
from .. import functional as F
from .. import init
from .module import Module
from .lazy import LazyModuleMixin


__all__ = [
    'Bilinear',
    'Identity',
    'LazyLinear',
    'Linear',
]


[docs]class Identity(Module): r"""A placeholder identity operator that is argument-insensitive. Args: args: any argument (unused) kwargs: any keyword argument (unused) Shape: - Input: :math:`(*)`, where :math:`*` means any number of dimensions. - Output: :math:`(*)`, same shape as the input. Examples:: >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 20]) """ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__() def forward(self, input: Tensor) -> Tensor: return input
[docs]class Linear(Module): r"""Applies an affine linear transformation to the incoming data: :math:`y = xA^T + b`. This module supports :ref:`TensorFloat32<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward. Args: in_features: size of each input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input: :math:`(*, H_{in})` where :math:`*` means any number of dimensions including none and :math:`H_{in} = \text{in\_features}`. - Output: :math:`(*, H_{out})` where all but the last dimension are the same shape as the input and :math:`H_{out} = \text{out\_features}`. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` Examples:: >>> m = nn.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) """ __constants__ = ['in_features', 'out_features'] in_features: int out_features: int weight: Tensor def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs)) if bias: self.bias = Parameter(torch.empty(out_features, **factory_kwargs)) else: self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self) -> None: # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with # uniform(-1/sqrt(in_features), 1/sqrt(in_features)). For details, see # https://github.com/pytorch/pytorch/issues/57109 init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 init.uniform_(self.bias, -bound, bound) def forward(self, input: Tensor) -> Tensor: return F.linear(input, self.weight, self.bias) def extra_repr(self) -> str: return f'in_features={self.in_features}, out_features={self.out_features}, bias={self.bias is not None}'
# This class exists solely to avoid triggering an obscure error when scripting # an improperly quantized attention layer. See this issue for details: # https://github.com/pytorch/pytorch/issues/58969 # TODO: fail fast on quantization API usage error, then remove this class # and replace uses of it with plain Linear class NonDynamicallyQuantizableLinear(Linear): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: super().__init__(in_features, out_features, bias=bias, device=device, dtype=dtype)
[docs]class Bilinear(Module): r"""Applies a bilinear transformation to the incoming data: :math:`y = x_1^T A x_2 + b`. Args: in1_features: size of each first input sample in2_features: size of each second input sample out_features: size of each output sample bias: If set to False, the layer will not learn an additive bias. Default: ``True`` Shape: - Input1: :math:`(*, H_{in1})` where :math:`H_{in1}=\text{in1\_features}` and :math:`*` means any number of additional dimensions including none. All but the last dimension of the inputs should be the same. - Input2: :math:`(*, H_{in2})` where :math:`H_{in2}=\text{in2\_features}`. - Output: :math:`(*, H_{out})` where :math:`H_{out}=\text{out\_features}` and all but the last dimension are the same shape as the input. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` Examples:: >>> m = nn.Bilinear(20, 30, 40) >>> input1 = torch.randn(128, 20) >>> input2 = torch.randn(128, 30) >>> output = m(input1, input2) >>> print(output.size()) torch.Size([128, 40]) """ __constants__ = ['in1_features', 'in2_features', 'out_features'] in1_features: int in2_features: int out_features: int weight: Tensor def __init__(self, in1_features: int, in2_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() self.in1_features = in1_features self.in2_features = in2_features self.out_features = out_features self.weight = Parameter(torch.empty((out_features, in1_features, in2_features), **factory_kwargs)) if bias: self.bias = Parameter(torch.empty(out_features, **factory_kwargs)) else: self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self) -> None: bound = 1 / math.sqrt(self.weight.size(1)) init.uniform_(self.weight, -bound, bound) if self.bias is not None: init.uniform_(self.bias, -bound, bound) def forward(self, input1: Tensor, input2: Tensor) -> Tensor: return F.bilinear(input1, input2, self.weight, self.bias) def extra_repr(self) -> str: return (f'in1_features={self.in1_features}, in2_features={self.in2_features}, ' f'out_features={self.out_features}, bias={self.bias is not None}')
[docs]class LazyLinear(LazyModuleMixin, Linear): r"""A :class:`torch.nn.Linear` module where `in_features` is inferred. In this module, the `weight` and `bias` are of :class:`torch.nn.UninitializedParameter` class. They will be initialized after the first call to ``forward`` is done and the module will become a regular :class:`torch.nn.Linear` module. The ``in_features`` argument of the :class:`Linear` is inferred from the ``input.shape[-1]``. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` """ cls_to_become = Linear # type: ignore[assignment] weight: UninitializedParameter bias: UninitializedParameter # type: ignore[assignment] def __init__(self, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} # bias is hardcoded to False to avoid creating tensor # that will soon be overwritten. super().__init__(0, 0, False) self.weight = UninitializedParameter(**factory_kwargs) self.out_features = out_features if bias: self.bias = UninitializedParameter(**factory_kwargs) def reset_parameters(self) -> None: if not self.has_uninitialized_params() and self.in_features != 0: super().reset_parameters() def initialize_parameters(self, input) -> None: # type: ignore[override] if self.has_uninitialized_params(): with torch.no_grad(): self.in_features = input.shape[-1] self.weight.materialize((self.out_features, self.in_features)) if self.bias is not None: self.bias.materialize((self.out_features,)) self.reset_parameters()
# TODO: PartialLinear - maybe in sparse?

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