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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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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"],
}