Source code for aitemplate.compiler.ops.reduce.vector_norm

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"""
vector_norm op implementation that simulates pytorch's linalg.vector_norm.
Currently, we only support L2 norm.
"""

from aitemplate.compiler.ops.reduce.reduce_common import reduce_base

# pylint: disable=C0103


[docs]class vector_norm(reduce_base): """ Vector_norm op implementation that simulates pytorch's linalg.vector_norm. Currently, we only support L2 norm. * .attr.:`ord_kind` (int or float or str), optional specifies the vector norm to be computed. (default: 2) * .attr.:`dim` (None or int or tuple of python:ints), optional the dimension or dimensions to be normalized. (default: None, in this case the input tensor will be treated as a 1-D tensor) * .attr.:`keepdim` (bool), optional keep the normalized dimensions if True, default is False * .attr.:`dtype` (str), optional the type of the return tensor. If it is not None, the input tensor is cast to dtype before reduction. Args: input (Tensor): the input tensor. Return: Tensor. """ def __init__(self, ord_kind=2, dim=None, keepdim=False, dtype=None) -> None: """initialize the op""" if dim is None: raise NotImplementedError( "flattening input tensor before normalization is not supported yet" ) super().__init__(dim, keepdim, dtype) self._attrs["op"] = "vector_norm" self._attrs["ord_kind"] = str(ord_kind) def _get_op_attributes(self): return { "dim": self._attrs["reduction_axes"], "dtype": self._attrs["output_type"], "keepdim": self._attrs["keepdim"], "ord_kind": self._attrs["ord_kind"], }