Source code for aitemplate.frontend.nn.linear

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"""
Linear module.
"""
from aitemplate.compiler import ops
from aitemplate.frontend.nn.module import Module
from aitemplate.frontend.nn.parameter import Parameter
from aitemplate.testing import detect_target


[docs]class Linear(Module): r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` Args: in_channels: size of each input sample out_channels: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` specialization: elementwise operation to add after the linear operation, Default: ``None`` dtype: data type, default: ``float16`` Shape: - Input: :math:`(*, H_{in})` where :math:`*` means any number of dimensions including none and :math:`H_{in} = \text{in_channels}`. - Output: :math:`(*, H_{out})` where all but the last dimension are the same shape as the input and :math:`H_{out} = \text{out_channels}`. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out_channels}, \text{in_channels})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_channels}}` bias: the learnable bias of the module of shape :math:`(\text{out_channels})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in_channels}}` Examples:: >>> m = nn.Linear(20, 30) >>> input = Tensor(shape=[128, 20]) >>> output = m(input) Tensor(shape=[128, 30]) """ USE_CUDA = None def __init__( self, in_channels, out_channels, bias=True, specialization=None, dtype="float16", **kwargs, ): super().__init__() if Linear.USE_CUDA is None: Linear.USE_CUDA = detect_target().name() == "cuda" self.weight = Parameter(shape=[out_channels, in_channels], dtype=dtype) op_name = "gemm_rcr_bias" if bias else "gemm_rcr" if specialization is not None: op_name += "_" + specialization if bias: self.bias = Parameter(shape=[out_channels], dtype=dtype) op_func = getattr(ops, op_name) self._op_name = op_name self.op = op_func(**kwargs) self.use_bias = bias self.in_channels = in_channels
[docs] def forward(self, *args): assert len(args) >= 1 x = args[0] if not self.USE_CUDA and len(x._attrs["shape"]) != 2: x = ops.reshape()(x, [-1, self.in_channels]) inputs = [x, self.weight.tensor()] if self.use_bias: inputs.append(self.bias.tensor()) if len(args) == 2: inputs.append(args[1]) output = self.op(*inputs) return output