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Add DoubleSliceSpan (#1982)
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* initial commit

* tests

* release

* Rename todo.feat.rst to 1982.feat.rst

* remove math

* Update qiskit_ibm_runtime/execution_span/double_slice_span.py

Co-authored-by: Ian Hincks <[email protected]>

* CR

* Update qiskit_ibm_runtime/execution_span/double_slice_span.py

---------

Co-authored-by: Ian Hincks <[email protected]>
Co-authored-by: Ian Hincks <[email protected]>
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3 people authored Oct 22, 2024
1 parent 5a4d9dd commit 1b6a3fc
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2 changes: 2 additions & 0 deletions qiskit_ibm_runtime/execution_span/__init__.py
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Expand Up @@ -30,12 +30,14 @@
.. autosummary::
:toctree: ../stubs/
DoubleSliceSpan
ExecutionSpan
ExecutionSpans
ShapeType
SliceSpan
"""

from .double_slice_span import DoubleSliceSpan
from .execution_span import ExecutionSpan, ShapeType
from .execution_spans import ExecutionSpans
from .slice_span import SliceSpan
79 changes: 79 additions & 0 deletions qiskit_ibm_runtime/execution_span/double_slice_span.py
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# This code is part of Qiskit.
#
# (C) Copyright IBM 2024.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""DoubleSliceSpan"""

from __future__ import annotations

from datetime import datetime
from typing import Iterable

import math
import numpy as np
import numpy.typing as npt

from .execution_span import ExecutionSpan, ShapeType


class DoubleSliceSpan(ExecutionSpan):
"""An :class:`~.ExecutionSpan` for data stored in a sliceable format.
This type of execution span references pub result data by assuming that it is a sliceable
portion of the data where the shots are the outermost slice and the rest of the data is flattened.
Therefore, for each pub dependent on this span, the constructor accepts two :class:`slice` objects,
along with the corresponding shape of the data to be sliced; in contrast to
:class:`~.SliceSpan`, this class does not assume that *all* shots for a particular set of parameter
values are contiguous in the array of data.
Args:
start: The start time of the span, in UTC.
stop: The stop time of the span, in UTC.
data_slices: A map from pub indices to ``(shape_tuple, slice, slice)``.
"""

def __init__(
self,
start: datetime,
stop: datetime,
data_slices: dict[int, tuple[ShapeType, slice, slice]],
):
super().__init__(start, stop)
self._data_slices = data_slices

def __eq__(self, other: object) -> bool:
return isinstance(other, DoubleSliceSpan) and (
self.start == other.start
and self.stop == other.stop
and self._data_slices == other._data_slices
)

@property
def pub_idxs(self) -> list[int]:
return sorted(self._data_slices)

@property
def size(self) -> int:
size = 0
for shape, args_sl, shots_sl in self._data_slices.values():
size += len(range(math.prod(shape[:-1]))[args_sl]) * len(range(shape[-1])[shots_sl])
return size

def mask(self, pub_idx: int) -> npt.NDArray[np.bool_]:
shape, args_sl, shots_sl = self._data_slices[pub_idx]
mask = np.zeros(shape, dtype=np.bool_)
mask.reshape(np.prod(shape[:-1]), shape[-1])[(args_sl, shots_sl)] = True
return mask

def filter_by_pub(self, pub_idx: int | Iterable[int]) -> "DoubleSliceSpan":
pub_idx = {pub_idx} if isinstance(pub_idx, int) else set(pub_idx)
slices = {idx: val for idx, val in self._data_slices.items() if idx in pub_idx}
return DoubleSliceSpan(self.start, self.stop, slices)
1 change: 1 addition & 0 deletions release-notes/unreleased/1982.feat.rst
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Added :class:`.DoubleSliceSpan`, an :class:`ExecutionSpan` for batching with two slices.
98 changes: 97 additions & 1 deletion test/unit/test_execution_span.py
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Expand Up @@ -18,7 +18,7 @@

import numpy as np
import numpy.testing as npt
from qiskit_ibm_runtime.execution_span import SliceSpan, ExecutionSpans
from qiskit_ibm_runtime.execution_span import SliceSpan, DoubleSliceSpan, ExecutionSpans

from ..ibm_test_case import IBMTestCase

Expand Down Expand Up @@ -126,6 +126,102 @@ def test_filter_by_pub(self):
self.assertEqual(self.span2.filter_by_pub(1), SliceSpan(self.start2, self.stop2, {}))


@ddt.ddt
class TestDoubleSliceSpan(IBMTestCase):
"""Class for testing DoubleSliceSpan."""

def setUp(self) -> None:
super().setUp()
self.start1 = datetime(2024, 10, 11, 4, 31, 30)
self.stop1 = datetime(2024, 10, 11, 4, 31, 34)
self.slices1 = {
2: ((1, 100), slice(1), slice(4, 9)),
0: ((3, 5, 10), slice(10, 13), slice(2, 5)),
}
self.span1 = DoubleSliceSpan(self.start1, self.stop1, self.slices1)

self.start2 = datetime(2024, 10, 16, 11, 9, 20)
self.stop2 = datetime(2024, 10, 16, 11, 9, 30)
self.slices2 = {
0: ((5, 100), slice(3, 5), slice(20, 40)),
1: ((1, 5, 3), slice(2, 5), slice(3)),
}
self.span2 = DoubleSliceSpan(self.start2, self.stop2, self.slices2)

def test_limits(self):
"""Test the start and stop properties"""
self.assertEqual(self.span1.start, self.start1)
self.assertEqual(self.span1.stop, self.stop1)
self.assertEqual(self.span2.start, self.start2)
self.assertEqual(self.span2.stop, self.stop2)

def test_equality(self):
"""Test the equality method."""
self.assertEqual(self.span1, self.span1)
self.assertEqual(self.span1, DoubleSliceSpan(self.start1, self.stop1, self.slices1))
self.assertNotEqual(self.span1, "aoeu")
self.assertNotEqual(self.span1, self.span2)

def test_duration(self):
"""Test the duration property"""
self.assertEqual(self.span1.duration, 4)
self.assertEqual(self.span2.duration, 10)

def test_repr(self):
"""Test the repr method"""
expect = "start='2024-10-11 04:31:30', stop='2024-10-11 04:31:34', size=14"
self.assertEqual(repr(self.span1), f"DoubleSliceSpan(<{expect}>)")

def test_size(self):
"""Test the size property"""
self.assertEqual(self.span1.size, 1 * 5 + 3 * 3)
self.assertEqual(self.span2.size, 2 * 20 + 3 * 3)

def test_pub_idxs(self):
"""Test the pub_idxs property"""
self.assertEqual(self.span1.pub_idxs, [0, 2])
self.assertEqual(self.span2.pub_idxs, [0, 1])

def test_mask(self):
"""Test the mask() method"""
mask1 = np.zeros((1, 100), dtype=bool)
mask1[0][4:9] = True
npt.assert_array_equal(self.span1.mask(2), mask1)

mask2 = [[[0, 0, 0], [0, 0, 0], [1, 1, 1], [1, 1, 1], [1, 1, 1]]]
npt.assert_array_equal(self.span2.mask(1), mask2)

@ddt.data(
(0, True, True),
([0, 1], True, True),
([0, 1, 2], True, True),
([1, 2], True, True),
([1], False, True),
(2, True, False),
([0, 2], True, True),
)
@ddt.unpack
def test_contains_pub(self, idx, span1_expected_res, span2_expected_res):
"""The the contains_pub method"""
self.assertEqual(self.span1.contains_pub(idx), span1_expected_res)
self.assertEqual(self.span2.contains_pub(idx), span2_expected_res)

def test_filter_by_pub(self):
"""The the filter_by_pub method"""
self.assertEqual(self.span1.filter_by_pub([]), DoubleSliceSpan(self.start1, self.stop1, {}))
self.assertEqual(self.span2.filter_by_pub([]), DoubleSliceSpan(self.start2, self.stop2, {}))

self.assertEqual(
self.span1.filter_by_pub([1, 0]),
DoubleSliceSpan(self.start1, self.stop1, {0: self.slices1[0]}),
)

self.assertEqual(
self.span1.filter_by_pub(2),
DoubleSliceSpan(self.start1, self.stop1, {2: self.slices1[2]}),
)


@ddt.ddt
class TestExecutionSpans(IBMTestCase):
"""Class for testing ExecutionSpans."""
Expand Down

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