#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import FieldTypes, KeyField, ListField
from ..operands import TensorHasInput, TensorOperandMixin
from ..datasource import tensor as astensor
from ..array_utils import as_same_device, device
from ..utils import reverse_order
from ..core import TensorOrder
def _reorder(x, axes):
if x is None:
return
return type(x)(np.array(x)[list(axes)].tolist())
class TensorTranspose(TensorHasInput, TensorOperandMixin):
_op_type_ = OperandDef.TRANSPOSE
_input = KeyField("input")
_axes = ListField("axes", FieldTypes.int32)
def __init__(self, axes=None, **kw):
super().__init__(
_axes=axes,
# transpose will create a view
create_view=True,
**kw
)
@property
def axes(self):
return getattr(self, "_axes", None)
def __call__(self, a):
shape = tuple(
s if np.isnan(s) else int(s) for s in _reorder(a.shape, self._axes)
)
if self._axes == list(reversed(range(a.ndim))):
# order reversed
tensor_order = reverse_order(a.order)
else:
tensor_order = TensorOrder.C_ORDER
return self.new_tensor([a], shape, order=tensor_order)
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._input = self._inputs[0]
def on_output_modify(self, new_output):
op = self.copy().reset_key()
return op(new_output)
def on_input_modify(self, new_input):
op = self.copy().reset_key()
return op(new_input)
@classmethod
def tile(cls, op):
tensor = op.outputs[0]
out_chunks = []
for c in op.inputs[0].chunks:
chunk_op = op.copy().reset_key()
chunk_shape = tuple(
s if np.isnan(s) else int(s) for s in _reorder(c.shape, op.axes)
)
chunk_idx = _reorder(c.index, op.axes)
out_chunk = chunk_op.new_chunk(
[c], shape=chunk_shape, index=chunk_idx, order=tensor.order
)
out_chunks.append(out_chunk)
new_op = op.copy()
nsplits = _reorder(op.inputs[0].nsplits, op.axes)
return new_op.new_tensors(
op.inputs,
op.outputs[0].shape,
order=tensor.order,
chunks=out_chunks,
nsplits=nsplits,
)
@classmethod
def execute(cls, ctx, op):
(x,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
axes = op.axes
with device(device_id):
ctx[op.outputs[0].key] = xp.transpose(x, axes or None)
[docs]def transpose(a, axes=None):
"""
Permute the dimensions of a tensor.
Parameters
----------
a : array_like
Input tensor.
axes : list of ints, optional
By default, reverse the dimensions, otherwise permute the axes
according to the values given.
Returns
-------
p : Tensor
`a` with its axes permuted. A view is returned whenever
possible.
See Also
--------
moveaxis
argsort
Notes
-----
Use `transpose(a, argsort(axes))` to invert the transposition of tensors
when using the `axes` keyword argument.
Transposing a 1-D array returns an unchanged view of the original tensor.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.arange(4).reshape((2,2))
>>> x.execute()
array([[0, 1],
[2, 3]])
>>> mt.transpose(x).execute()
array([[0, 2],
[1, 3]])
>>> x = mt.ones((1, 2, 3))
>>> mt.transpose(x, (1, 0, 2)).shape
(2, 1, 3)
"""
a = astensor(a)
if axes:
if len(axes) != a.ndim:
raise ValueError("axes don't match tensor")
if not axes:
axes = list(range(a.ndim))[::-1]
else:
axes = list(axes)
op = TensorTranspose(axes, dtype=a.dtype, sparse=a.issparse())
return op(a)