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.github/dependabot.yml 0 → 100644
View file @ 21a89f1b
version: 2
updates:
# Set update schedule for GitHub Actions
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "weekly"
# vim: sw=4
.github/workflows/autopush.yml
View file @ 21a89f1b
......@@ -7,14 +7,15 @@ on:
jobs:
autopush:
name: Automatic push to gitlab.tiker.net
if: startsWith(github.repository, 'inducer/')
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- run: |
mkdir ~/.ssh && echo -e "Host gitlab.tiker.net\n\tStrictHostKeyChecking no\n" >> ~/.ssh/config
eval $(ssh-agent) && echo "$GITLAB_AUTOPUSH_KEY" | ssh-add -
git fetch --unshallow
git push "git@gitlab.tiker.net:inducer/$(basename $GITHUB_REPOSITORY).git" main
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
mirror_github_to_gitlab
env:
GITLAB_AUTOPUSH_KEY: ${{ secrets.GITLAB_AUTOPUSH_KEY }}
......
.github/workflows/ci.yml
View file @ 21a89f1b
......@@ -7,27 +7,35 @@ on:
schedule:
- cron: '17 3 * * 0'
concurrency:
group: ${{ github.head_ref || github.ref_name }}
cancel-in-progress: true
jobs:
flake8:
name: Flake8
typos:
name: Typos
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: crate-ci/typos@master
ruff:
name: Ruff
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
-
uses: actions/setup-python@v1
with:
# matches compat target in setup.py
python-version: '3.6'
uses: actions/setup-python@v5
- name: "Main Script"
run: |
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/prepare-and-run-flake8.sh
. ./prepare-and-run-flake8.sh "$(basename $GITHUB_REPOSITORY)" test examples
pip install ruff
ruff check
pylint:
name: Pylint
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: "Main Script"
run: |
USE_CONDA_BUILD=1
......@@ -38,28 +46,25 @@ jobs:
name: Mypy
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
-
uses: actions/setup-python@v1
with:
python-version: '3.x'
- uses: actions/checkout@v4
- name: "Main Script"
run: |
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
build_py_project_in_conda_env
python -m pip install mypy
python -m pip install mypy pytest
./run-mypy.sh
pytest3_pocl:
name: Pytest Conda Py3 POCL
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: "Main Script"
run: |
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/ci-support.sh
. ./ci-support.sh
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
build_py_project_in_conda_env
test_py_project
......@@ -67,7 +72,7 @@ jobs:
name: Pytest Conda Py3 Intel
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: "Main Script"
run: |
curl -L -O https://raw.githubusercontent.com/illinois-scicomp/machine-shop-maintenance/main/install-intel-icd.sh
......@@ -88,7 +93,7 @@ jobs:
name: Examples Conda Py3
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: "Main Script"
run: |
export MPLBACKEND=Agg
......@@ -100,15 +105,15 @@ jobs:
name: Documentation
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
-
uses: actions/setup-python@v1
uses: actions/setup-python@v5
with:
python-version: '3.x'
- name: "Main Script"
run: |
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/ci-support.sh
. ci-support.sh
curl -L -O https://tiker.net/ci-support-v0
. ci-support-v0
build_py_project_in_conda_env
conda install graphviz
......@@ -118,46 +123,21 @@ jobs:
downstream_tests:
strategy:
matrix:
#downstream_project: [meshmode, grudge, pytential, mirgecom]
downstream_project: [meshmode, grudge, mirgecom]
downstream_project: [meshmode, grudge, mirgecom, mirgecom_examples]
fail-fast: false
name: Tests for downstream project ${{ matrix.downstream_project }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: "Main Script"
env:
DOWNSTREAM_PROJECT: ${{ matrix.downstream_project }}
run: |
if test "$DOWNSTREAM_PROJECT" = "mirgecom"; then
git clone "https://github.com/illinois-ceesd/$DOWNSTREAM_PROJECT.git"
else
git clone "https://github.com/inducer/$DOWNSTREAM_PROJECT.git"
fi
cd "$DOWNSTREAM_PROJECT"
echo "*** $DOWNSTREAM_PROJECT version: $(git rev-parse --short HEAD)"
# Use this version of arraycontext instead of what downstream would install
sed -i "/egg=arraycontext/ c git+file://$(readlink -f ..)#egg=arraycontext" requirements.txt
# Avoid slow or complicated tests in downstream projects
export PYTEST_ADDOPTS="-k 'not (slowtest or octave or mpi)'"
if test "$DOWNSTREAM_PROJECT" = "mirgecom"; then
# can't turn off MPI in mirgecom
sudo apt-get update
sudo apt-get install openmpi-bin libopenmpi-dev
export CONDA_ENVIRONMENT=conda-env.yml
export CISUPPORT_PARALLEL_PYTEST=no
else
sed -i "/mpi4py/ d" requirements.txt
fi
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
build_py_project_in_conda_env
test_py_project
test_downstream "$DOWNSTREAM_PROJECT"
if [[ "$DOWNSTREAM_PROJECT" = "meshmode" ]]; then
python ../examples/simple-dg.py --lazy
fi
......
.gitlab-ci.yml
View file @ 21a89f1b
Python 3 POCL:
script: |
export PY_EXE=python3
export PYOPENCL_TEST=portable:pthread
# cython is here because pytential (for now, for TS) depends on it
export EXTRA_INSTALL="pybind11 cython numpy mako mpi4py oct2py"
export PYOPENCL_TEST=portable:cpu
export EXTRA_INSTALL="jax[cpu]"
export JAX_PLATFORMS=cpu
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/build-and-test-py-project.sh
. ./build-and-test-py-project.sh
tags:
......@@ -18,12 +17,30 @@ Python 3 POCL:
Python 3 Nvidia Titan V:
script: |
export PY_EXE=python3
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
export PYOPENCL_TEST=nvi:titan
export EXTRA_INSTALL="pybind11 cython numpy mako oct2py"
# cython is here because pytential (for now, for TS) depends on it
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/build-and-test-py-project.sh
. ./build-and-test-py-project.sh
build_py_project_in_venv
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
test_py_project
tags:
- python3
- nvidia-titan-v
except:
- tags
artifacts:
reports:
junit: test/pytest.xml
Python 3 POCL Nvidia Titan V:
script: |
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
export PYOPENCL_TEST=port:titan
build_py_project_in_venv
test_py_project
tags:
- python3
- nvidia-titan-v
......@@ -36,9 +53,7 @@ Python 3 Nvidia Titan V:
Python 3 POCL Examples:
script:
- test -n "$SKIP_EXAMPLES" && exit
- export PY_EXE=python3
- export PYOPENCL_TEST=portable:pthread
- export EXTRA_INSTALL="pybind11 numpy mako"
- export PYOPENCL_TEST=portable:cpu
- curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/build-py-project-and-run-examples.sh
- ". ./build-py-project-and-run-examples.sh"
tags:
......@@ -50,6 +65,11 @@ Python 3 POCL Examples:
Python 3 Conda:
script: |
export PYOPENCL_TEST=portable:cpu
# Avoid crashes like https://gitlab.tiker.net/inducer/arraycontext/-/jobs/536021
sed -i 's/jax/jax !=0.4.6/' .test-conda-env-py3.yml
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/build-and-test-py-project-within-miniconda.sh
. ./build-and-test-py-project-within-miniconda.sh
tags:
......@@ -61,26 +81,24 @@ Python 3 Conda:
Documentation:
script: |
EXTRA_INSTALL="pybind11 cython numpy"
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/build-docs.sh
CI_SUPPORT_SPHINX_VERSION_SPECIFIER=">=4.0"
. ./build-docs.sh
tags:
- python3
Flake8:
Ruff:
script:
- curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/main/prepare-and-run-flake8.sh
- . ./prepare-and-run-flake8.sh "$CI_PROJECT_NAME" test examples
- pipx install ruff
- ruff check
tags:
- python3
- docker-runner
except:
- tags
Pylint:
script: |
export PY_EXE=python3
EXTRA_INSTALL="pybind11 numpy mako"
EXTRA_INSTALL="jax[cpu]"
curl -L -O https://gitlab.tiker.net/inducer/ci-support/raw/master/prepare-and-run-pylint.sh
. ./prepare-and-run-pylint.sh "$CI_PROJECT_NAME" examples/*.py test/test_*.py
tags:
......@@ -90,12 +108,30 @@ Pylint:
Mypy:
script: |
EXTRA_INSTALL="mypy pytest"
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
build_py_project_in_venv
python -m pip install mypy
./run-mypy.sh
tags:
- python3
except:
- tags
Downstream:
parallel:
matrix:
- DOWNSTREAM_PROJECT: [meshmode, grudge, mirgecom, mirgecom_examples]
tags:
- large-node
- "docker-runner"
script: |
curl -L -O https://tiker.net/ci-support-v0
. ./ci-support-v0
test_downstream "$DOWNSTREAM_PROJECT"
if [[ "$DOWNSTREAM_PROJECT" = "meshmode" ]]; then
python ../examples/simple-dg.py --lazy
fi
.test-conda-env-py3.yml
View file @ 21a89f1b
......@@ -8,8 +8,10 @@ dependencies:
- git
- libhwloc=2
- numpy
- pocl
# pocl 3.1 required for full SVM functionality
- pocl>=3.1
- mako
- pyopencl
- islpy
- pip
- jax
MANIFEST.in deleted 100644 → 0
View file @ 3df0533e
include doc/*.rst
include doc/conf.py
include doc/make.bat
include doc/Makefile
include examples/*.py
README.rst
View file @ 21a89f1b
......@@ -7,7 +7,7 @@ arraycontext: Choose your favorite ``numpy``-workalike
.. image:: https://github.com/inducer/arraycontext/workflows/CI/badge.svg
:alt: Github Build Status
:target: https://github.com/inducer/arraycontext/actions?query=branch%3Amain+workflow%3ACI
.. image:: https://badge.fury.io/py/arraycontext.png
.. image:: https://badge.fury.io/py/arraycontext.svg
:alt: Python Package Index Release Page
:target: https://pypi.org/project/arraycontext/
......@@ -17,7 +17,9 @@ implementations for:
- numpy
- `PyOpenCL <https://documen.tician.de/pyopencl/array.html>`__
- `JAX <https://jax.readthedocs.io/en/latest/>`__
- `Pytato <https://documen.tician.de/pytato>`__ (for lazy/deferred evaluation)
with backends for ``pyopencl`` and ``jax``.
- Debugging
- Profiling
......
arraycontext/__init__.py
View file @ 21a89f1b
......@@ -2,6 +2,7 @@
An array context is an abstraction that helps you dispatch between multiple
implementations of :mod:`numpy`-like :math:`n`-dimensional arrays.
"""
from __future__ import annotations
__copyright__ = """
......@@ -28,75 +29,140 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import sys
from .context import ArrayContext
from .transform_metadata import (CommonSubexpressionTag,
ElementwiseMapKernelTag)
# deprecated, remove in 2022.
from .metadata import _FirstAxisIsElementsTag
from .container import (
ArrayContainer,
is_array_container, is_array_container_type,
get_container_context, get_container_context_recursively,
serialize_container, deserialize_container)
from .container.arithmetic import with_container_arithmetic
ArithArrayContainer,
ArrayContainer,
ArrayContainerT,
NotAnArrayContainerError,
SerializationKey,
SerializedContainer,
deserialize_container,
get_container_context_opt,
get_container_context_recursively,
get_container_context_recursively_opt,
is_array_container,
is_array_container_type,
register_multivector_as_array_container,
serialize_container,
)
from .container.arithmetic import (
with_container_arithmetic,
)
from .container.dataclass import dataclass_array_container
from .container.traversal import (
map_array_container,
multimap_array_container,
rec_map_array_container,
rec_multimap_array_container,
mapped_over_array_containers,
multimapped_over_array_containers,
rec_map_reduce_array_container,
rec_multimap_reduce_array_container,
thaw, freeze,
from_numpy, to_numpy)
flat_size_and_dtype,
flatten,
freeze,
from_numpy,
map_array_container,
map_reduce_array_container,
mapped_over_array_containers,
multimap_array_container,
multimap_reduce_array_container,
multimapped_over_array_containers,
outer,
rec_map_array_container,
rec_map_reduce_array_container,
rec_multimap_array_container,
rec_multimap_reduce_array_container,
stringify_array_container_tree,
thaw,
to_numpy,
unflatten,
with_array_context,
)
from .context import (
Array,
ArrayContext,
ArrayOrArithContainer,
ArrayOrArithContainerOrScalar,
ArrayOrArithContainerOrScalarT,
ArrayOrArithContainerT,
ArrayOrContainer,
ArrayOrContainerOrScalar,
ArrayOrContainerOrScalarT,
ArrayOrContainerT,
ArrayT,
Scalar,
ScalarLike,
tag_axes,
)
from .impl.jax import EagerJAXArrayContext
from .impl.numpy import NumpyArrayContext
from .impl.pyopencl import PyOpenCLArrayContext
from .impl.pytato import PytatoPyOpenCLArrayContext
from .pytest import (
PytestPyOpenCLArrayContextFactory,
pytest_generate_tests_for_array_contexts,
pytest_generate_tests_for_pyopencl_array_context)
from .impl.pytato import PytatoJAXArrayContext, PytatoPyOpenCLArrayContext
from .loopy import make_loopy_program
from .pytest import (
PytestArrayContextFactory,
PytestPyOpenCLArrayContextFactory,
pytest_generate_tests_for_array_contexts,
)
from .transform_metadata import CommonSubexpressionTag, ElementwiseMapKernelTag
__all__ = (
"ArrayContext",
"CommonSubexpressionTag",
"ElementwiseMapKernelTag",
"ArrayContainer",
"is_array_container", "is_array_container_type",
"get_container_context", "get_container_context_recursively",
"serialize_container", "deserialize_container",
"with_container_arithmetic",
"dataclass_array_container",
"map_array_container", "multimap_array_container",
"rec_map_array_container", "rec_multimap_array_container",
"mapped_over_array_containers",
"multimapped_over_array_containers",
"rec_map_reduce_array_container", "rec_multimap_reduce_array_container",
"thaw", "freeze",
"from_numpy", "to_numpy",
"PyOpenCLArrayContext", "PytatoPyOpenCLArrayContext",
"make_loopy_program",
"PytestPyOpenCLArrayContextFactory",
"pytest_generate_tests_for_array_contexts",
"pytest_generate_tests_for_pyopencl_array_context"
)
"ArithArrayContainer",
"Array",
"ArrayContainer",
"ArrayContainerT",
"ArrayContext",
"ArrayOrArithContainer",
"ArrayOrArithContainerOrScalar",
"ArrayOrArithContainerOrScalarT",
"ArrayOrArithContainerT",
"ArrayOrContainer",
"ArrayOrContainerOrScalar",
"ArrayOrContainerOrScalarT",
"ArrayOrContainerT",
"ArrayT",
"CommonSubexpressionTag",
"EagerJAXArrayContext",
"ElementwiseMapKernelTag",
"NotAnArrayContainerError",
"NumpyArrayContext",
"PyOpenCLArrayContext",
"PytatoJAXArrayContext",
"PytatoPyOpenCLArrayContext",
"PytestArrayContextFactory",
"PytestPyOpenCLArrayContextFactory",
"Scalar",
"ScalarLike",
"SerializationKey",
"SerializedContainer",
"dataclass_array_container",
"deserialize_container",
"flat_size_and_dtype",
"flatten",
"freeze",
"from_numpy",
"get_container_context_opt",
"get_container_context_recursively",
"get_container_context_recursively_opt",
"is_array_container",
"is_array_container_type",
"make_loopy_program",
"map_array_container",
"map_reduce_array_container",
"mapped_over_array_containers",
"multimap_array_container",
"multimap_reduce_array_container",
"multimapped_over_array_containers",
"outer",
"pytest_generate_tests_for_array_contexts",
"rec_map_array_container",
"rec_map_reduce_array_container",
"rec_multimap_array_container",
"rec_multimap_reduce_array_container",
"register_multivector_as_array_container",
"serialize_container",
"stringify_array_container_tree",
"tag_axes",
"thaw",
"to_numpy",
"unflatten",
"with_array_context",
"with_container_arithmetic",
)
# {{{ deprecation handling
......@@ -113,29 +179,24 @@ def _deprecated_acf():
_depr_name_to_replacement_and_obj = {
"FirstAxisIsElementsTag":
("meshmode.transform_metadata.FirstAxisIsElementsTag",
_FirstAxisIsElementsTag),
"_acf":
("<no replacement yet>", _deprecated_acf),
"get_container_context": (
"get_container_context_opt",
get_container_context_opt, 2022),
}
if sys.version_info >= (3, 7):
def __getattr__(name):
replacement_and_obj = _depr_name_to_replacement_and_obj.get(name, None)
if replacement_and_obj is not None:
replacement, obj = replacement_and_obj
from warnings import warn
warn(f"'arraycontext.{name}' is deprecated. "
f"Use '{replacement}' instead. "
f"'arraycontext.{name}' will continue to work until 2022.",
DeprecationWarning, stacklevel=2)
return obj
else:
raise AttributeError(name)
else:
FirstAxisIsElementsTag = _FirstAxisIsElementsTag
_acf = _deprecated_acf
def __getattr__(name):
replacement_and_obj = _depr_name_to_replacement_and_obj.get(name)
if replacement_and_obj is not None:
replacement, obj, year = replacement_and_obj
from warnings import warn
warn(f"'arraycontext.{name}' is deprecated. "
f"Use '{replacement}' instead. "
f"'arraycontext.{name}' will continue to work until {year}.",
DeprecationWarning, stacklevel=2)
return obj
else:
raise AttributeError(name)
# }}}
......
arraycontext/container/__init__.py
View file @ 21a89f1b
......@@ -3,25 +3,58 @@
"""
.. currentmodule:: arraycontext
.. autoclass:: ArrayContainer
.. autoclass:: ArithArrayContainer
.. class:: ArrayContainerT
:canonical: arraycontext.container.ArrayContainerT
:class:`~typing.TypeVar` for array container-like objects.
A type variable with a lower bound of :class:`ArrayContainer`.
.. autoclass:: ArrayContainer
.. autoexception:: NotAnArrayContainerError
Serialization/deserialization
-----------------------------
.. autofunction:: is_array_container
.. autoclass:: SerializationKey
.. autoclass:: SerializedContainer
.. autofunction:: is_array_container_type
.. autofunction:: serialize_container
.. autofunction:: deserialize_container
Context retrieval
-----------------
.. autofunction:: get_container_context
.. autofunction:: get_container_context_opt
.. autofunction:: get_container_context_recursively
.. autofunction:: get_container_context_recursively_opt
:class:`~pymbolic.geometric_algebra.MultiVector` support
---------------------------------------------------------
.. autofunction:: register_multivector_as_array_container
.. currentmodule:: arraycontext.container
Canonical locations for type annotations
----------------------------------------
.. class:: ArrayContainerT
:canonical: arraycontext.ArrayContainerT
.. class:: ArrayOrContainerT
:canonical: arraycontext.ArrayOrContainerT
.. class:: SerializationKey
:canonical: arraycontext.SerializationKey
.. class:: SerializedContainer
:canonical: arraycontext.SerializedContainer
"""
from __future__ import annotations
__copyright__ = """
Copyright (C) 2020-1 University of Illinois Board of Trustees
......@@ -47,19 +80,30 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from collections.abc import Hashable, Sequence
from functools import singledispatch
from arraycontext.context import ArrayContext
from typing import Any, Iterable, Tuple, TypeVar, Optional
from typing import TYPE_CHECKING, Protocol, TypeAlias, TypeVar
# For use in singledispatch type annotations, because sphinx can't figure out
# what 'np' is.
import numpy
import numpy as np
from typing_extensions import Self
from arraycontext.context import ArrayContext, ArrayOrScalar
ArrayContainerT = TypeVar("ArrayContainerT")
if TYPE_CHECKING:
from pymbolic.geometric_algebra import MultiVector
from arraycontext import ArrayOrContainer
# {{{ ArrayContainer
class ArrayContainer:
r"""
A generic container for the array type supported by the
class ArrayContainer(Protocol):
"""
A protocol for generic containers of the array type supported by the
:class:`ArrayContext`.
The functionality required for the container to operated is supplied via
......@@ -70,13 +114,11 @@ class ArrayContainer:
of the array.
* :func:`deserialize_container` for deserialization, which constructs a
container from a set of components.
* :func:`get_container_context` retrieves the :class:`ArrayContext` from
* :func:`get_container_context_opt` retrieves the :class:`ArrayContext` from
a container, if it has one.
This allows enumeration of the component arrays in a container and the
construction of modified containers from an iterable of those component arrays.
:func:`is_array_container` will return *True* for types that have
a container serialization function registered.
Packages may register their own types as array containers. They must not
register other types (e.g. :class:`list`) as array containers.
......@@ -92,67 +134,138 @@ class ArrayContainer:
.. note::
This class is used in type annotation. Inheriting from it confers no
special meaning or behavior.
This class is used in type annotation and as a marker of array container
attributes for :func:`~arraycontext.dataclass_array_container`.
As a protocol, it is not intended as a superclass.
"""
# Array containers do not need to have any particular features, so this
# protocol is deliberately empty.
# This *is* used as a type annotation in dataclasses that are processed
# by dataclass_array_container, where it's used to recognize attributes
# that are container-typed.
class ArithArrayContainer(ArrayContainer, Protocol):
"""
A sub-protocol of :class:`ArrayContainer` that supports basic arithmetic.
"""
# This is loose and permissive, assuming that any array can be added
# to any container. The alternative would be to plaster type-ignores
# on all those uses. Achieving typing precision on what broadcasting is
# allowable seems like a huge endeavor and is likely not feasible without
# a mypy plugin. Maybe some day? -AK, November 2024
def __neg__(self) -> Self: ...
def __abs__(self) -> Self: ...
def __add__(self, other: ArrayOrScalar | Self) -> Self: ...
def __radd__(self, other: ArrayOrScalar | Self) -> Self: ...
def __sub__(self, other: ArrayOrScalar | Self) -> Self: ...
def __rsub__(self, other: ArrayOrScalar | Self) -> Self: ...
def __mul__(self, other: ArrayOrScalar | Self) -> Self: ...
def __rmul__(self, other: ArrayOrScalar | Self) -> Self: ...
def __truediv__(self, other: ArrayOrScalar | Self) -> Self: ...
def __rtruediv__(self, other: ArrayOrScalar | Self) -> Self: ...
ArrayContainerT = TypeVar("ArrayContainerT", bound=ArrayContainer)
class NotAnArrayContainerError(TypeError):
""":class:`TypeError` subclass raised when an array container is expected."""
SerializationKey: TypeAlias = Hashable
SerializedContainer: TypeAlias = Sequence[tuple[SerializationKey, "ArrayOrContainer"]]
@singledispatch
def serialize_container(ary: ArrayContainer) -> Iterable[Tuple[Any, Any]]:
r"""Serialize the array container into an iterable over its components.
def serialize_container(
ary: ArrayContainer) -> SerializedContainer:
r"""Serialize the array container into a sequence over its components.
The order of the components and their identifiers are entirely under
the control of the container class.
the control of the container class. However, the order is required to be
deterministic, i.e. two calls to :func:`serialize_container` on
array containers of the same types with the same number of
sub-arrays must result in a sequence with the keys in the same
order.
If *ary* is mutable, the serialization function is not required to ensure
that the serialization result reflects the array state at the time of the
call to :func:`serialize_container`.
:returns: an :class:`Iterable` of 2-tuples where the first
:returns: a :class:`Sequence` of 2-tuples where the first
entry is an identifier for the component and the second entry
is an array-like component of the :class:`ArrayContainer`.
Components can themselves be :class:`ArrayContainer`\ s, allowing
for arbitrarily nested structures. The identifiers need to be hashable
but are otherwise treated as opaque.
"""
raise NotImplementedError(type(ary).__name__)
raise NotAnArrayContainerError(
f"'{type(ary).__name__}' cannot be serialized as a container")
@singledispatch
def deserialize_container(template: Any, iterable: Iterable[Tuple[Any, Any]]) -> Any:
"""Deserialize an iterable into an array container.
def deserialize_container(
template: ArrayContainerT,
serialized: SerializedContainer) -> ArrayContainerT:
"""Deserialize a sequence into an array container following a *template*.
:param template: an instance of an existing object that
can be used to aid in the deserialization. For a similar choice
see :attr:`~numpy.class.__array_finalize__`.
:param iterable: an iterable that mirrors the output of
:param serialized: a sequence that mirrors the output of
:meth:`serialize_container`.
"""
raise NotImplementedError(type(template).__name__)
raise NotAnArrayContainerError(
f"'{type(template).__name__}' cannot be deserialized as a container")
def is_array_container_type(cls: type) -> bool:
"""
:returns: *True* if the type *cls* has a registered implementation of
:func:`serialize_container`, or if it is an :class:`ArrayContainer`.
.. warning::
Not all instances of a type that this function labels an array container
must automatically be array containers. For example, while this
function will say that :class:`numpy.ndarray` is an array container
type, only object arrays *actually are* array containers.
"""
assert isinstance(cls, type), f"must pass a {type!r}, not a '{cls!r}'"
return (
cls is ArrayContainer
or (serialize_container.dispatch(cls)
is not serialize_container.__wrapped__)) # type:ignore[attr-defined]
def is_array_container(ary: Any) -> bool:
def is_array_container(ary: object) -> bool:
"""
:returns: *True* if the instance *ary* has a registered implementation of
:func:`serialize_container`.
"""
from warnings import warn
warn("is_array_container is deprecated and will be removed in 2022. "
"If you must know precisely whether something is an array container, "
"try serializing it and catch NotAnArrayContainerError. For a "
"cheaper option, see is_array_container_type.",
DeprecationWarning, stacklevel=2)
return (serialize_container.dispatch(ary.__class__)
is not serialize_container.__wrapped__) # type:ignore[attr-defined]
is not serialize_container.__wrapped__ # type:ignore[attr-defined]
# numpy values with scalar elements aren't array containers
and not (isinstance(ary, np.ndarray)
and ary.dtype.kind != "O")
)
@singledispatch
def get_container_context(ary: ArrayContainer) -> Optional[ArrayContext]:
def get_container_context_opt(ary: ArrayContainer) -> ArrayContext | None:
"""Retrieves the :class:`ArrayContext` from the container, if any.
This function is not recursive, so it will only search at the root level
......@@ -167,10 +280,11 @@ def get_container_context(ary: ArrayContainer) -> Optional[ArrayContext]:
# {{{ object arrays as array containers
@serialize_container.register(np.ndarray)
def _serialize_ndarray_container(ary: np.ndarray) -> Iterable[Tuple[Any, Any]]:
def _serialize_ndarray_container(
ary: numpy.ndarray) -> SerializedContainer:
if ary.dtype.char != "O":
raise ValueError(
f"only object arrays are supported, given dtype '{ary.dtype}'")
raise NotAnArrayContainerError(
f"cannot serialize '{type(ary).__name__}' with dtype '{ary.dtype}'")
# special-cased for speed
if ary.ndim == 1:
......@@ -181,20 +295,22 @@ def _serialize_ndarray_container(ary: np.ndarray) -> Iterable[Tuple[Any, Any]]:
for j in range(ary.shape[1])
]
else:
return np.ndenumerate(ary)
return list(np.ndenumerate(ary))
@deserialize_container.register(np.ndarray)
def _deserialize_ndarray_container(
template: np.ndarray,
iterable: Iterable[Tuple[Any, Any]]) -> np.ndarray:
# https://github.com/python/mypy/issues/13040
def _deserialize_ndarray_container( # type: ignore[misc]
template: numpy.ndarray,
serialized: SerializedContainer) -> numpy.ndarray:
# disallow subclasses
assert type(template) is np.ndarray
assert template.dtype.char == "O"
result = type(template)(template.shape, dtype=object)
for i, subary in iterable:
result[i] = subary
for i, subary in serialized:
# FIXME: numpy annotations don't seem to handle object arrays very well
result[i] = subary # type: ignore[call-overload]
return result
......@@ -203,37 +319,101 @@ def _deserialize_ndarray_container(
# {{{ get_container_context_recursively
def get_container_context_recursively(ary: Any) -> Optional[ArrayContext]:
def get_container_context_recursively_opt(
ary: ArrayContainer) -> ArrayContext | None:
"""Walks the :class:`ArrayContainer` hierarchy to find an
:class:`ArrayContext` associated with it.
If different components that have different array contexts are found at
any level, an assertion error is raised.
"""
actx = None
if not is_array_container(ary):
return actx
Returns *None* if no array context was found.
"""
# try getting the array context directly
actx = get_container_context(ary)
actx = get_container_context_opt(ary)
if actx is not None:
return actx
for _, subary in serialize_container(ary):
context = get_container_context_recursively(subary)
if context is None:
continue
try:
iterable = serialize_container(ary)
except NotAnArrayContainerError:
return actx
else:
for _, subary in iterable:
context = get_container_context_recursively_opt(subary)
if context is None:
continue
if not __debug__:
return context
elif actx is None:
actx = context
else:
assert actx is context
return actx
def get_container_context_recursively(ary: ArrayContainer) -> ArrayContext | None:
"""Walks the :class:`ArrayContainer` hierarchy to find an
:class:`ArrayContext` associated with it.
If different components that have different array contexts are found at
any level, an assertion error is raised.
if not __debug__:
return context
elif actx is None:
actx = context
else:
assert actx is context
Raises an error if no array container is found.
"""
actx = get_container_context_recursively_opt(ary)
if actx is None:
# raise ValueError("no array context was found")
from warnings import warn
warn("No array context was found. This will be an error starting in "
"July of 2022. If you would like the function to return "
"None if no array context was found, use "
"get_container_context_recursively_opt.",
DeprecationWarning, stacklevel=2)
return actx
# }}}
# {{{ MultiVector support, see pymbolic.geometric_algebra
# FYI: This doesn't, and never should, make arraycontext directly depend on pymbolic.
# (Though clearly there exists a dependency via loopy.)
def _serialize_multivec_as_container(mv: MultiVector) -> SerializedContainer:
return list(mv.data.items())
# FIXME: Ignored due to https://github.com/python/mypy/issues/13040
def _deserialize_multivec_as_container( # type: ignore[misc]
template: MultiVector,
serialized: SerializedContainer) -> MultiVector:
from pymbolic.geometric_algebra import MultiVector
return MultiVector(dict(serialized), space=template.space)
def _get_container_context_opt_from_multivec(mv: MultiVector) -> None:
return None
def register_multivector_as_array_container() -> None:
"""Registers :class:`~pymbolic.geometric_algebra.MultiVector` as an
:class:`ArrayContainer`. This function may be called multiple times. The
second and subsequent calls have no effect.
"""
from pymbolic.geometric_algebra import MultiVector
if MultiVector not in serialize_container.registry:
serialize_container.register(MultiVector)(_serialize_multivec_as_container)
deserialize_container.register(MultiVector)(
_deserialize_multivec_as_container)
get_container_context_opt.register(MultiVector)(
_get_container_context_opt_from_multivec)
assert MultiVector in serialize_container.registry
# }}}
# vim: foldmethod=marker
arraycontext/container/arithmetic.py
View file @ 21a89f1b
This diff is collapsed.
arraycontext/container/dataclass.py
View file @ 21a89f1b
......@@ -4,6 +4,7 @@
.. currentmodule:: arraycontext
.. autofunction:: dataclass_array_container
"""
from __future__ import annotations
__copyright__ = """
......@@ -30,34 +31,170 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from dataclasses import fields
from collections.abc import Mapping, Sequence
from dataclasses import fields, is_dataclass
from typing import NamedTuple, Union, get_args, get_origin
from arraycontext.container import is_array_container_type
# {{{ dataclass containers
class _Field(NamedTuple):
"""Small lookalike for :class:`dataclasses.Field`."""
init: bool
name: str
type: type
def is_array_type(tp: type) -> bool:
from arraycontext import Array
return tp is Array or is_array_container_type(tp)
def dataclass_array_container(cls: type) -> type:
"""A class decorator that makes the class to which it is applied a
"""A class decorator that makes the class to which it is applied an
:class:`ArrayContainer` by registering appropriate implementations of
:func:`serialize_container` and :func:`deserialize_container`.
*cls* must be a :func:`~dataclasses.dataclass`.
Attributes that are not array containers are allowed. In order to decide
whether an attribute is an array container, the declared attribute type
is checked by the criteria from :func:`is_array_container`.
is checked by the criteria from :func:`is_array_container_type`. This
includes some support for type annotations:
* a :class:`typing.Union` of array containers is considered an array container.
* other type annotations, e.g. :class:`typing.Optional`, are not considered
array containers, even if they wrap one.
.. note::
When type annotations are strings (e.g. because of
``from __future__ import annotations``),
this function relies on :func:`inspect.get_annotations`
(with ``eval_str=True``) to obtain type annotations. This
means that *cls* must live in a module that is importable.
"""
from dataclasses import is_dataclass
from types import GenericAlias, UnionType
assert is_dataclass(cls)
array_fields = [
f for f in fields(cls) if is_array_container_type(f.type)]
non_array_fields = [
f for f in fields(cls) if not is_array_container_type(f.type)]
def is_array_field(f: _Field) -> bool:
field_type = f.type
# NOTE: unions of array containers are treated separately to handle
# unions of only array containers, e.g. `Union[np.ndarray, Array]`, as
# they can work seamlessly with arithmetic and traversal.
#
# `Optional[ArrayContainer]` is not allowed, since `None` is not
# handled by `with_container_arithmetic`, which is the common case
# for current container usage. Other type annotations, e.g.
# `Tuple[Container, Container]`, are also not allowed, as they do not
# work with `with_container_arithmetic`.
#
# This is not set in stone, but mostly driven by current usage!
origin = get_origin(field_type)
# NOTE: `UnionType` is returned when using `Type1 | Type2`
if origin in (Union, UnionType):
if all(is_array_type(arg) for arg in get_args(field_type)):
return True
else:
raise TypeError(
f"Field '{f.name}' union contains non-array container "
"arguments. All arguments must be array containers.")
# NOTE: this should never happen due to using `inspect.get_annotations`
assert not isinstance(field_type, str)
if __debug__:
if not f.init:
raise ValueError(
f"Field with 'init=False' not allowed: '{f.name}'")
# NOTE:
# * `GenericAlias` catches typed `list`, `tuple`, etc.
# * `_BaseGenericAlias` catches `List`, `Tuple`, etc.
# * `_SpecialForm` catches `Any`, `Literal`, etc.
from typing import ( # type: ignore[attr-defined]
_BaseGenericAlias,
_SpecialForm,
)
if isinstance(field_type, GenericAlias | _BaseGenericAlias | _SpecialForm):
# NOTE: anything except a Union is not allowed
raise TypeError(
f"Typing annotation not supported on field '{f.name}': "
f"'{field_type!r}'")
if not isinstance(field_type, type):
raise TypeError(
f"Field '{f.name}' not an instance of 'type': "
f"'{field_type!r}'")
return is_array_type(field_type)
from pytools import partition
array_fields = _get_annotated_fields(cls)
array_fields, non_array_fields = partition(is_array_field, array_fields)
if not array_fields:
raise ValueError(f"'{cls}' must have fields with array container type "
"in order to use the 'dataclass_array_container' decorator")
return _inject_dataclass_serialization(cls, array_fields, non_array_fields)
def _get_annotated_fields(cls: type) -> Sequence[_Field]:
"""Get a list of fields in the class *cls* with evaluated types.
If any of the fields in *cls* have type annotations that are strings, e.g.
from using ``from __future__ import annotations``, this function evaluates
them using :func:`inspect.get_annotations`. Note that this requires the class
to live in a module that is importable.
:return: a list of fields.
"""
from inspect import get_annotations
result = []
cls_ann: Mapping[str, type] | None = None
for field in fields(cls):
field_type_or_str = field.type
if isinstance(field_type_or_str, str):
if cls_ann is None:
cls_ann = get_annotations(cls, eval_str=True)
field_type = cls_ann[field.name]
else:
field_type = field_type_or_str
result.append(_Field(init=field.init, name=field.name, type=field_type))
return result
def _inject_dataclass_serialization(
cls: type,
array_fields: Sequence[_Field],
non_array_fields: Sequence[_Field]) -> type:
"""Implements :func:`~arraycontext.serialize_container` and
:func:`~arraycontext.deserialize_container` for the given dataclass *cls*.
This function modifies *cls* in place, so the returned value is the same
object with additional functionality.
:arg array_fields: fields of the given dataclass *cls* which are considered
array containers and should be serialized.
:arg non_array_fields: remaining fields of the dataclass *cls* which are
copied over from the template array in deserialization.
"""
assert is_dataclass(cls)
serialize_expr = ", ".join(
f"({f.name!r}, ary.{f.name})" for f in array_fields)
template_kwargs = ", ".join(
......
arraycontext/container/traversal.py
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This diff is collapsed.
arraycontext/context.py
View file @ 21a89f1b
# mypy: disallow-untyped-defs
"""
.. _freeze-thaw:
......@@ -39,7 +41,7 @@ Here are some rules of thumb to use when dealing with thawing and freezing:
- Note that array contexts need not necessarily be passed as a separate
argument. Passing thawed data as an argument to a function suffices
to supply an array context. The array context can be extracted from
a thawed argument using, e.g., :func:`~arraycontext.get_container_context`
a thawed argument using, e.g., :func:`~arraycontext.get_container_context_opt`
or :func:`~arraycontext.get_container_context_recursively`.
What does this mean concretely?
......@@ -70,13 +72,75 @@ actual array contexts:
an array expression that has been built up by the user
(using, e.g. :func:`pytato.generate_loopy`).
The interface of an array context
---------------------------------
.. currentmodule:: arraycontext
The :class:`ArrayContext` Interface
-----------------------------------
.. autoclass:: ArrayContext
.. autofunction:: tag_axes
Types and Type Variables for Arrays and Containers
--------------------------------------------------
.. autoclass:: Array
.. autodata:: ArrayT
A type variable with a lower bound of :class:`Array`.
.. autodata:: ScalarLike
A type annotation for scalar types commonly usable with arrays.
See also :class:`ArrayContainer` and :class:`ArrayOrContainerT`.
.. autodata:: ArrayOrContainer
.. autodata:: ArrayOrContainerT
A type variable with a bound of :class:`ArrayOrContainer`.
.. autodata:: ArrayOrArithContainer
.. autodata:: ArrayOrArithContainerT
A type variable with a bound of :class:`ArrayOrArithContainer`.
.. autodata:: ArrayOrArithContainerOrScalar
.. autodata:: ArrayOrArithContainerOrScalarT
A type variable with a bound of :class:`ArrayOrContainerOrScalar`.
.. autodata:: ArrayOrContainerOrScalar
.. autodata:: ArrayOrContainerOrScalarT
A type variable with a bound of :class:`ArrayOrContainerOrScalar`.
.. currentmodule:: arraycontext.context
Canonical locations for type annotations
----------------------------------------
.. class:: ArrayT
:canonical: arraycontext.ArrayT
.. class:: ArrayOrContainerT
:canonical: arraycontext.ArrayOrContainerT
.. class:: ArrayOrContainerOrScalarT
:canonical: arraycontext.ArrayOrContainerOrScalarT
"""
from __future__ import annotations
__copyright__ = """
Copyright (C) 2020-1 University of Illinois Board of Trustees
......@@ -102,12 +166,119 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from typing import Sequence, Union, Callable, Any, Tuple
from abc import ABC, abstractmethod, abstractproperty
from abc import ABC, abstractmethod
from collections.abc import Callable, Mapping
from typing import TYPE_CHECKING, Any, Protocol, TypeAlias, TypeVar, Union, overload
from warnings import warn
import numpy as np
from typing_extensions import Self
from pytools import memoize_method
from pytools.tag import Tag
from pytools.tag import ToTagSetConvertible
if TYPE_CHECKING:
import loopy
from arraycontext.container import ArithArrayContainer, ArrayContainer
# {{{ typing
class Array(Protocol):
"""A :class:`~typing.Protocol` for the array type supported by
:class:`ArrayContext`.
This is meant to aid in typing annotations. For a explicit list of
supported types see :attr:`ArrayContext.array_types`.
.. attribute:: shape
.. attribute:: size
.. attribute:: dtype
.. attribute:: __getitem__
In addition, arrays are expected to support basic arithmetic.
"""
@property
def shape(self) -> tuple[int, ...]:
...
@property
def size(self) -> int:
...
@property
def dtype(self) -> np.dtype[Any]:
...
# Covering all the possible index variations is hard and (kind of) futile.
# If you'd like to see how, try changing the Any to
# AxisIndex = slice | int | "Array"
# Index = AxisIndex |tuple[AxisIndex]
def __getitem__(self, index: Any) -> Array:
...
# some basic arithmetic that's supposed to work
def __neg__(self) -> Self: ...
def __abs__(self) -> Self: ...
def __add__(self, other: Self | ScalarLike) -> Self: ...
def __radd__(self, other: Self | ScalarLike) -> Self: ...
def __sub__(self, other: Self | ScalarLike) -> Self: ...
def __rsub__(self, other: Self | ScalarLike) -> Self: ...
def __mul__(self, other: Self | ScalarLike) -> Self: ...
def __rmul__(self, other: Self | ScalarLike) -> Self: ...
def __truediv__(self, other: Self | ScalarLike) -> Self: ...
def __rtruediv__(self, other: Self | ScalarLike) -> Self: ...
# deprecated, use ScalarLike instead
ScalarLike: TypeAlias = int | float | complex | np.generic
Scalar = ScalarLike
ScalarLikeT = TypeVar("ScalarLikeT", bound=ScalarLike)
# NOTE: I'm kind of not sure about the *Tc versions of these type variables.
# mypy seems better at understanding arithmetic performed on the *Tc versions
# than the *T, versions, whereas pyright doesn't seem to care.
#
# This issue seems to be part of it:
# https://github.com/python/mypy/issues/18203
# but there is likely other stuff lurking.
#
# For now, they're purposefully not in the main arraycontext.* name space.
ArrayT = TypeVar("ArrayT", bound=Array)
ArrayOrScalar: TypeAlias = "Array | ScalarLike"
ArrayOrContainer: TypeAlias = "Array | ArrayContainer"
ArrayOrArithContainer: TypeAlias = "Array | ArithArrayContainer"
ArrayOrContainerT = TypeVar("ArrayOrContainerT", bound=ArrayOrContainer)
ArrayOrContainerTc = TypeVar("ArrayOrContainerTc",
Array, "ArrayContainer", "ArithArrayContainer")
ArrayOrArithContainerT = TypeVar("ArrayOrArithContainerT", bound=ArrayOrArithContainer)
ArrayOrArithContainerTc = TypeVar("ArrayOrArithContainerTc",
Array, "ArithArrayContainer")
ArrayOrContainerOrScalar: TypeAlias = "Array | ArrayContainer | ScalarLike"
ArrayOrArithContainerOrScalar: TypeAlias = "Array | ArithArrayContainer | ScalarLike"
ArrayOrContainerOrScalarT = TypeVar(
"ArrayOrContainerOrScalarT",
bound=ArrayOrContainerOrScalar)
ArrayOrArithContainerOrScalarT = TypeVar(
"ArrayOrArithContainerOrScalarT",
bound=ArrayOrArithContainerOrScalar)
ArrayOrContainerOrScalarTc = TypeVar(
"ArrayOrContainerOrScalarTc",
ScalarLike, Array, "ArrayContainer", "ArithArrayContainer")
ArrayOrArithContainerOrScalarTc = TypeVar(
"ArrayOrArithContainerOrScalarTc",
ScalarLike, Array, "ArithArrayContainer")
ContainerOrScalarT = TypeVar("ContainerOrScalarT", bound="ArrayContainer | ScalarLike")
NumpyOrContainerOrScalar = Union[np.ndarray, "ArrayContainer", ScalarLike]
# }}}
# {{{ ArrayContext
......@@ -122,10 +293,6 @@ class ArrayContext(ABC):
.. versionadded:: 2020.2
.. automethod:: empty
.. automethod:: zeros
.. automethod:: empty_like
.. automethod:: zeros_like
.. automethod:: from_numpy
.. automethod:: to_numpy
.. automethod:: call_loopy
......@@ -133,71 +300,102 @@ class ArrayContext(ABC):
.. attribute:: np
Provides access to a namespace that serves as a work-alike to
:mod:`numpy`. The actual level of functionality provided is up to the
:mod:`numpy`. The actual level of functionality provided is up to the
individual array context implementation, however the functions and
objects available under this namespace must not behave differently
from :mod:`numpy`.
As a baseline, special functions available through :mod:`loopy`
(e.g. ``sin``, ``exp``) are accessible through this interface.
A full list of implemented functionality is given in
:ref:`numpy-coverage`.
Callables accessible through this namespace vectorize over object
arrays, including :class:`arraycontext.ArrayContainer`\ s.
.. attribute:: array_types
A :class:`tuple` of types that are the valid base array classes
the context can operate on.
A :class:`tuple` of types that are the valid array classes the
context can operate on. However, it is not necessary that *all* the
:class:`ArrayContext`\ 's operations are legal for the types in
*array_types*. Note that this tuple is *only* intended for use
with :func:`isinstance`. Other uses are not allowed. This allows
for 'types' with overridden :meth:`type.__instancecheck__`.
.. automethod:: freeze
.. automethod:: thaw
.. automethod:: freeze_thaw
.. automethod:: tag
.. automethod:: tag_axis
.. automethod:: compile
"""
array_types: Tuple[type, ...] = ()
array_types: tuple[type, ...] = ()
def __init__(self):
def __init__(self) -> None:
self.np = self._get_fake_numpy_namespace()
def _get_fake_numpy_namespace(self):
from .fake_numpy import BaseFakeNumpyNamespace
return BaseFakeNumpyNamespace(self)
@abstractmethod
def empty(self, shape, dtype):
pass
def _get_fake_numpy_namespace(self) -> Any:
...
@abstractmethod
def zeros(self, shape, dtype):
pass
def __hash__(self) -> int:
raise TypeError(f"unhashable type: '{type(self).__name__}'")
def empty_like(self, ary):
return self.empty(shape=ary.shape, dtype=ary.dtype)
def zeros(self,
shape: int | tuple[int, ...],
dtype: np.dtype[Any]) -> Array:
warn(f"{type(self).__name__}.zeros is deprecated and will stop "
"working in 2025. Use actx.np.zeros instead.",
DeprecationWarning, stacklevel=2)
def zeros_like(self, ary):
return self.zeros(shape=ary.shape, dtype=ary.dtype)
return self.np.zeros(shape, dtype)
@overload
def from_numpy(self, array: np.ndarray) -> Array:
...
@overload
def from_numpy(self, array: ContainerOrScalarT) -> ContainerOrScalarT:
...
@abstractmethod
def from_numpy(self, array: np.ndarray):
def from_numpy(self,
array: NumpyOrContainerOrScalar
) -> ArrayOrContainerOrScalar:
r"""
:returns: the :class:`numpy.ndarray` *array* converted to the
array context's array type. The returned array will be
:meth:`thaw`\ ed.
:meth:`thaw`\ ed. When working with array containers each leaf
must be an :class:`~numpy.ndarray` or scalar, which is then converted
to the context's array type leaving the container structure
intact.
"""
pass
@overload
def to_numpy(self, array: Array) -> np.ndarray:
...
@overload
def to_numpy(self, array: ContainerOrScalarT) -> ContainerOrScalarT:
...
@abstractmethod
def to_numpy(self, array):
def to_numpy(self,
array: ArrayOrContainerOrScalar
) -> NumpyOrContainerOrScalar:
r"""
:returns: *array*, an array recognized by the context, converted
to a :class:`numpy.ndarray`. *array* must be
:meth:`thaw`\ ed.
:returns: an :class:`numpy.ndarray` for each array recognized by the
context. The input *array* must be :meth:`thaw`\ ed.
When working with array containers each leaf must be one of
the context's array types or a scalar, which is then converted to
an :class:`~numpy.ndarray` leaving the container structure intact.
"""
pass
def call_loopy(self, program, **kwargs):
@abstractmethod
def call_loopy(self,
t_unit: loopy.TranslationUnit,
**kwargs: Any) -> dict[str, Array]:
"""Execute the :mod:`loopy` program *program* on the arguments
*kwargs*.
......@@ -210,7 +408,7 @@ class ArrayContext(ABC):
"""
@abstractmethod
def freeze(self, array):
def freeze(self, array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
"""Return a version of the context-defined array *array* that is
'frozen', i.e. suitable for long-term storage and reuse. Frozen arrays
do not support arithmetic. For example, in the context of
......@@ -221,12 +419,10 @@ class ArrayContext(ABC):
Freezing makes the array independent of this :class:`ArrayContext`;
it is permitted to :meth:`thaw` it in a different one, as long as that
context understands the array format.
See also :func:`arraycontext.freeze`.
"""
@abstractmethod
def thaw(self, array):
def thaw(self, array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
"""Take a 'frozen' array and return a new array representing the data in
*array* that is able to perform arithmetic and other operations, using
the execution resources of this context. In the context of
......@@ -236,39 +432,66 @@ class ArrayContext(ABC):
the data in *array*.
The returned array may not be used with other contexts while thawed.
"""
See also :func:`arraycontext.thaw`.
def freeze_thaw(
self, array: ArrayOrContainerOrScalarT
) -> ArrayOrContainerOrScalarT:
r"""Evaluate an input array or container to "frozen" data return a new
"thawed" array or container representing the evaluation result that is
ready for use. This is a shortcut for calling :meth:`freeze` and
:meth:`thaw`.
This method can be useful in array contexts backed by, e.g.
:mod:`pytato`, to force the evaluation of a built-up array expression
(and thereby avoid reevaluations for expressions built on the array).
"""
return self.thaw(self.freeze(array))
@abstractmethod
def tag(self, tags: Union[Sequence[Tag], Tag], array):
def tag(self,
tags: ToTagSetConvertible,
array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
"""If the array type used by the array context is capable of capturing
metadata, return a version of *array* with the *tags* applied. *array*
itself is not modified.
itself is not modified. When working with array containers, the
tags are applied to each leaf of the container.
See :ref:`metadata` as well as application-specific metadata types.
.. versionadded:: 2021.2
"""
@abstractmethod
def tag_axis(self, iaxis, tags: Union[Sequence[Tag], Tag], array):
def tag_axis(self,
iaxis: int, tags: ToTagSetConvertible,
array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
"""If the array type used by the array context is capable of capturing
metadata, return a version of *array* in which axis number *iaxis* has
the *tags* applied. *array* itself is not modified.
the *tags* applied. *array* itself is not modified. When working with
array containers, the tags are applied to each leaf of the container.
See :ref:`metadata` as well as application-specific metadata types.
.. versionadded:: 2021.2
"""
@memoize_method
def _get_einsum_prg(self, spec, arg_names, tagged):
def _get_einsum_prg(self,
spec: str, arg_names: tuple[str, ...],
tagged: ToTagSetConvertible) -> loopy.TranslationUnit:
import loopy as lp
from .loopy import _DEFAULT_LOOPY_OPTIONS
from loopy.version import MOST_RECENT_LANGUAGE_VERSION
from .loopy import _DEFAULT_LOOPY_OPTIONS
return lp.make_einsum(
spec,
arg_names,
options=_DEFAULT_LOOPY_OPTIONS,
lang_version=MOST_RECENT_LANGUAGE_VERSION,
tags=tagged,
default_order=lp.auto,
default_offset=lp.auto,
)
# This lives here rather than in .np because the interface does not
......@@ -284,7 +507,10 @@ class ArrayContext(ABC):
# That's why einsum's interface here needs to be cluttered with
# metadata, and that's why it can't live under .np.
# [1] https://github.com/inducer/meshmode/issues/177
def einsum(self, spec, *args, arg_names=None, tagged=()):
def einsum(self,
spec: str, *args: Array,
arg_names: tuple[str, ...] | None = None,
tagged: ToTagSetConvertible = ()) -> Array:
"""Computes the result of Einstein summation following the
convention in :func:`numpy.einsum`.
......@@ -304,15 +530,16 @@ class ArrayContext(ABC):
:return: the output of the einsum :mod:`loopy` program
"""
if arg_names is None:
arg_names = tuple("arg%d" % i for i in range(len(args)))
arg_names = tuple(f"arg{i}" for i in range(len(args)))
prg = self._get_einsum_prg(spec, arg_names, tagged)
return self.call_loopy(
out_ary = self.call_loopy(
prg, **{arg_names[i]: arg for i, arg in enumerate(args)}
)["out"]
return self.tag(tagged, out_ary)
@abstractmethod
def clone(self):
def clone(self) -> Self:
"""If possible, return a version of *self* that is semantically
equivalent (i.e. implements all array operations in the same way)
but is a separate object. May return *self* if that is not possible.
......@@ -351,10 +578,52 @@ class ArrayContext(ABC):
return f
# undocumented for now
@abstractproperty
def permits_inplace_modification(self):
pass
@property
@abstractmethod
def permits_inplace_modification(self) -> bool:
"""
*True* if the arrays allow in-place modifications.
"""
# undocumented for now
@property
@abstractmethod
def supports_nonscalar_broadcasting(self) -> bool:
"""
*True* if the arrays support non-scalar broadcasting.
"""
# undocumented for now
@property
@abstractmethod
def permits_advanced_indexing(self) -> bool:
"""
*True* if the arrays support :mod:`numpy`'s advanced indexing semantics.
"""
# }}}
# {{{ tagging helpers
def tag_axes(
actx: ArrayContext,
dim_to_tags: Mapping[int, ToTagSetConvertible],
ary: ArrayT) -> ArrayT:
"""
Return a copy of *ary* with the axes in *dim_to_tags* tagged with their
corresponding tags. Equivalent to repeated application of
:meth:`ArrayContext.tag_axis`.
"""
for iaxis, tags in dim_to_tags.items():
ary = actx.tag_axis(iaxis, tags, ary)
return ary
# }}}
class UntransformedCodeWarning(UserWarning):
pass
# vim: foldmethod=marker
arraycontext/fake_numpy.py
View file @ 21a89f1b
from __future__ import annotations
__copyright__ = """
Copyright (C) 2020-1 University of Illinois Board of Trustees
"""
......@@ -23,59 +26,25 @@ THE SOFTWARE.
"""
import operator
from abc import ABC, abstractmethod
from typing import Any
import numpy as np
from arraycontext.container import is_array_container, serialize_container
from arraycontext.container.traversal import (
rec_map_array_container, multimapped_over_array_containers)
from pytools import memoize_in
# {{{ _get_scalar_func_loopy_program
def _get_scalar_func_loopy_program(actx, c_name, nargs, naxes):
@memoize_in(actx, _get_scalar_func_loopy_program)
def get(c_name, nargs, naxes):
from pymbolic import var
var_names = ["i%d" % i for i in range(naxes)]
size_names = ["n%d" % i for i in range(naxes)]
subscript = tuple(var(vname) for vname in var_names)
from islpy import make_zero_and_vars
v = make_zero_and_vars(var_names, params=size_names)
domain = v[0].domain()
for vname, sname in zip(var_names, size_names):
domain = domain & v[0].le_set(v[vname]) & v[vname].lt_set(v[sname])
domain_bset, = domain.get_basic_sets()
import loopy as lp
from .loopy import make_loopy_program
from arraycontext.transform_metadata import ElementwiseMapKernelTag
return make_loopy_program(
[domain_bset],
[
lp.Assignment(
var("out")[subscript],
var(c_name)(*[
var("inp%d" % i)[subscript] for i in range(nargs)]))
],
name="actx_special_%s" % c_name,
tags=(ElementwiseMapKernelTag(),))
return get(c_name, nargs, naxes)
# }}}
from arraycontext.container import NotAnArrayContainerError, serialize_container
from arraycontext.container.traversal import rec_map_array_container
# {{{ BaseFakeNumpyNamespace
class BaseFakeNumpyNamespace:
class BaseFakeNumpyNamespace(ABC):
def __init__(self, array_context):
self._array_context = array_context
self.linalg = self._get_fake_numpy_linalg_namespace()
def _get_fake_numpy_linalg_namespace(self):
return BaseFakeNumpyLinalgNamespace(self.array_context)
return BaseFakeNumpyLinalgNamespace(self._array_context)
_numpy_math_functions = frozenset({
# https://numpy.org/doc/stable/reference/routines.math.html
......@@ -124,74 +93,20 @@ class BaseFakeNumpyNamespace:
# Miscellaneous
"convolve", "clip", "sqrt", "cbrt", "square", "absolute", "abs", "fabs",
"sign", "heaviside", "maximum", "fmax", "nan_to_num",
"sign", "heaviside", "maximum", "fmax", "nan_to_num", "isnan", "minimum",
"fmin",
# FIXME:
# "interp",
})
_numpy_to_c_arc_functions = {
"arcsin": "asin",
"arccos": "acos",
"arctan": "atan",
"arctan2": "atan2",
"arcsinh": "asinh",
"arccosh": "acosh",
"arctanh": "atanh",
}
_c_to_numpy_arc_functions = {c_name: numpy_name
for numpy_name, c_name in _numpy_to_c_arc_functions.items()}
def __getattr__(self, name):
def loopy_implemented_elwise_func(*args):
actx = self._array_context
prg = _get_scalar_func_loopy_program(actx,
c_name, nargs=len(args), naxes=len(args[0].shape))
outputs = actx.call_loopy(prg,
**{"inp%d" % i: arg for i, arg in enumerate(args)})
return outputs["out"]
if name in self._c_to_numpy_arc_functions:
from warnings import warn
warn(f"'{name}' in ArrayContext.np is deprecated. "
"Use '{c_to_numpy_arc_functions[name]}' as in numpy. "
"The old name will stop working in 2021.",
DeprecationWarning, stacklevel=3)
# normalize to C names anyway
c_name = self._numpy_to_c_arc_functions.get(name, name)
# limit which functions we try to hand off to loopy
if name in self._numpy_math_functions:
return multimapped_over_array_containers(loopy_implemented_elwise_func)
else:
raise AttributeError(name)
def _new_like(self, ary, alloc_like):
from numbers import Number
if isinstance(ary, np.ndarray) and ary.dtype.char == "O":
# NOTE: we don't want to match numpy semantics on object arrays,
# e.g. `np.zeros_like(x)` returns `array([0, 0, ...], dtype=object)`
# FIXME: what about object arrays nested in an ArrayContainer?
raise NotImplementedError("operation not implemented for object arrays")
elif is_array_container(ary):
return rec_map_array_container(alloc_like, ary)
elif isinstance(ary, Number):
# NOTE: `np.zeros_like(x)` returns `array(x, shape=())`, which
# is best implemented by concrete array contexts, if at all
raise NotImplementedError("operation not implemented for scalars")
else:
return alloc_like(ary)
def empty_like(self, ary):
return self._new_like(ary, self._array_context.empty_like)
@abstractmethod
def zeros(self, shape, dtype):
...
@abstractmethod
def zeros_like(self, ary):
return self._new_like(ary, self._array_context.zeros_like)
...
def conjugate(self, x):
# NOTE: conjugate distributes over object arrays, but it looks for a
......@@ -201,22 +116,110 @@ class BaseFakeNumpyNamespace:
conj = conjugate
# {{{ linspace
# based on
# https://github.com/numpy/numpy/blob/v1.25.0/numpy/core/function_base.py#L24-L182
def linspace(self, start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis=0):
num = operator.index(num)
if num < 0:
raise ValueError(f"Number of samples, {num}, must be non-negative.")
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an __array_interface__,
# gh-6634
if isinstance(start, self._array_context.array_types):
raise NotImplementedError("start as an actx array")
if isinstance(stop, self._array_context.array_types):
raise NotImplementedError("stop as an actx array")
start = np.array(start) * 1.0
stop = np.array(stop) * 1.0
dt = np.result_type(start, stop, float(num))
if dtype is None:
dtype = dt
integer_dtype = False
else:
integer_dtype = np.issubdtype(dtype, np.integer)
delta = stop - start
y = self.arange(0, num, dtype=dt).reshape((-1,) + (1,) * delta.ndim)
if div > 0:
step = delta / div
# any_step_zero = _nx.asanyarray(step == 0).any()
any_step_zero = self._array_context.to_numpy(step == 0).any()
if any_step_zero:
delta_actx = self._array_context.from_numpy(delta)
# Special handling for denormal numbers, gh-5437
y = y / div
y = y * delta_actx
else:
step_actx = self._array_context.from_numpy(step)
y = y * step_actx
else:
delta_actx = self._array_context.from_numpy(delta)
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = np.nan
# Multiply with delta to allow possible override of output class.
y = y * delta_actx
y += start
# FIXME reenable, without in-place ops
# if endpoint and num > 1:
# y[-1, ...] = stop
if axis != 0:
# y = _nx.moveaxis(y, 0, axis)
raise NotImplementedError("axis != 0")
if integer_dtype:
y = self.floor(y) # pylint: disable=no-member
# FIXME: Use astype
# https://github.com/inducer/pytato/issues/456
if retstep:
return y, step
# return y.astype(dtype), step
else:
return y
# return y.astype(dtype)
# }}}
def arange(self, *args: Any, **kwargs: Any):
raise NotImplementedError
# }}}
# {{{ BaseFakeNumpyLinalgNamespace
def _scalar_list_norm(ary, ord):
def _reduce_norm(actx, arys, ord):
from functools import reduce
from numbers import Number
if ord is None:
ord = 2
from numbers import Number
if ord == np.inf:
return max(ary)
# NOTE: these are ordered by an expected usage frequency
if ord == 2:
return actx.np.sqrt(sum(subary*subary for subary in arys))
elif ord == np.inf:
return reduce(actx.np.maximum, arys)
elif ord == -np.inf:
return min(ary)
return reduce(actx.np.minimum, arys)
elif isinstance(ord, Number) and ord > 0:
return sum(iary**ord for iary in ary)**(1/ord)
return sum(subary**ord for subary in arys)**(1/ord)
else:
raise NotImplementedError(f"unsupported value of 'ord': {ord}")
......@@ -226,9 +229,7 @@ class BaseFakeNumpyLinalgNamespace:
self._array_context = array_context
def norm(self, ary, ord=None):
from numbers import Number
if isinstance(ary, Number):
if np.isscalar(ary):
return abs(ary)
actx = self._array_context
......@@ -249,10 +250,13 @@ class BaseFakeNumpyLinalgNamespace:
return flat_norm(ary, ord=ord)
if is_array_container(ary):
return _scalar_list_norm([
self.norm(subary, ord=ord)
for _, subary in serialize_container(ary)
try:
iterable = serialize_container(ary)
except NotAnArrayContainerError:
pass
else:
return _reduce_norm(actx, [
self.norm(subary, ord=ord) for _, subary in iterable
], ord=ord)
if ord is None:
......@@ -262,16 +266,20 @@ class BaseFakeNumpyLinalgNamespace:
raise NotImplementedError("only vector norms are implemented")
if ary.size == 0:
return 0
return ary.dtype.type(0)
from numbers import Number
if ord == 2:
return actx.np.sqrt(actx.np.sum(abs(ary)**2))
if ord == np.inf:
return self._array_context.np.max(abs(ary))
return actx.np.max(abs(ary))
elif ord == -np.inf:
return self._array_context.np.min(abs(ary))
return actx.np.min(abs(ary))
elif isinstance(ord, Number) and ord > 0:
return self._array_context.np.sum(abs(ary)**ord)**(1/ord)
return actx.np.sum(abs(ary)**ord)**(1/ord)
else:
raise NotImplementedError(f"unsupported value of 'ord': {ord}")
# }}}
......
arraycontext/impl/__init__.py
View file @ 21a89f1b
from __future__ import annotations
__copyright__ = """
Copyright (C) 2020-1 University of Illinois Board of Trustees
"""
......
arraycontext/impl/jax/__init__.py 0 → 100644
View file @ 21a89f1b
"""
.. currentmodule:: arraycontext
.. autoclass:: EagerJAXArrayContext
"""
from __future__ import annotations
__copyright__ = """
Copyright (C) 2021 University of Illinois Board of Trustees
"""
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from collections.abc import Callable
import numpy as np
from pytools.tag import ToTagSetConvertible
from arraycontext.container.traversal import rec_map_array_container, with_array_context
from arraycontext.context import Array, ArrayContext, ArrayOrContainer, ScalarLike
class EagerJAXArrayContext(ArrayContext):
"""
A :class:`ArrayContext` that uses
:class:`jax.Array` instances for its base array
class and performs all array operations eagerly. See
:class:`~arraycontext.PytatoJAXArrayContext` for a lazier version.
.. note::
JAX stores a global configuration state in :data:`jax.config`. Callers
are expected to maintain those. Most important for scientific computing
workloads being ``jax_enable_x64``.
"""
def __init__(self) -> None:
super().__init__()
import jax.numpy as jnp
self.array_types = (jnp.ndarray, )
def _get_fake_numpy_namespace(self):
from .fake_numpy import EagerJAXFakeNumpyNamespace
return EagerJAXFakeNumpyNamespace(self)
def _rec_map_container(
self, func: Callable[[Array], Array], array: ArrayOrContainer,
allowed_types: tuple[type, ...] | None = None, *,
default_scalar: ScalarLike | None = None,
strict: bool = False) -> ArrayOrContainer:
if allowed_types is None:
allowed_types = self.array_types
def _wrapper(ary):
if isinstance(ary, allowed_types):
return func(ary)
elif np.isscalar(ary):
if default_scalar is None:
return ary
else:
return np.array(ary).dtype.type(default_scalar)
else:
raise TypeError(
f"{type(self).__name__}.{func.__name__[1:]} invoked with "
f"an unsupported array type: got '{type(ary).__name__}', "
f"but expected one of {allowed_types}")
return rec_map_array_container(_wrapper, array)
# {{{ ArrayContext interface
def from_numpy(self, array):
def _from_numpy(ary):
import jax
return jax.device_put(ary)
return with_array_context(
self._rec_map_container(_from_numpy, array, allowed_types=(np.ndarray,)),
actx=self)
def to_numpy(self, array):
def _to_numpy(ary):
import jax
return jax.device_get(ary)
return with_array_context(
self._rec_map_container(_to_numpy, array),
actx=None)
def freeze(self, array):
def _freeze(ary):
return ary.block_until_ready()
return with_array_context(self._rec_map_container(_freeze, array), actx=None)
def thaw(self, array):
return with_array_context(array, actx=self)
def tag(self, tags: ToTagSetConvertible, array):
# Sorry, not capable.
return array
def tag_axis(self, iaxis, tags: ToTagSetConvertible, array):
# TODO: See `jax.experimental.maps.xmap`, probably that should be useful?
return array
def call_loopy(self, t_unit, **kwargs):
raise NotImplementedError(
"Calling loopy on JAX arrays is not supported. Maybe rewrite"
" the loopy kernel as numpy-flavored array operations using"
" ArrayContext.np.")
def einsum(self, spec, *args, arg_names=None, tagged=()):
import jax.numpy as jnp
if arg_names is not None:
from warnings import warn
warn("'arg_names' don't bear any significance in "
f"{type(self).__name__}.", stacklevel=2)
return jnp.einsum(spec, *args)
def clone(self):
return type(self)()
# }}}
# {{{ properties
@property
def permits_inplace_modification(self):
return False
@property
def supports_nonscalar_broadcasting(self):
return True
@property
def permits_advanced_indexing(self):
return True
# }}}
# vim: foldmethod=marker
arraycontext/impl/jax/fake_numpy.py 0 → 100644
View file @ 21a89f1b
from __future__ import annotations
__copyright__ = """
Copyright (C) 2021 University of Illinois Board of Trustees
"""
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from functools import partial, reduce
import numpy as np
import jax.numpy as jnp
from arraycontext.container import (
NotAnArrayContainerError,
serialize_container,
)
from arraycontext.container.traversal import (
rec_map_array_container,
rec_map_reduce_array_container,
rec_multimap_array_container,
)
from arraycontext.context import Array, ArrayOrContainer
from arraycontext.fake_numpy import BaseFakeNumpyLinalgNamespace, BaseFakeNumpyNamespace
class EagerJAXFakeNumpyLinalgNamespace(BaseFakeNumpyLinalgNamespace):
# Everything is implemented in the base class for now.
pass
class EagerJAXFakeNumpyNamespace(BaseFakeNumpyNamespace):
"""
A :mod:`numpy` mimic for :class:`~arraycontext.EagerJAXArrayContext`.
"""
def _get_fake_numpy_linalg_namespace(self):
return EagerJAXFakeNumpyLinalgNamespace(self._array_context)
def __getattr__(self, name):
return partial(rec_multimap_array_container, getattr(jnp, name))
# NOTE: the order of these follows the order in numpy docs
# NOTE: when adding a function here, also add it to `array_context.rst` docs!
# {{{ array creation routines
def zeros(self, shape, dtype):
return jnp.zeros(shape=shape, dtype=dtype)
def empty_like(self, ary):
from warnings import warn
warn(f"{type(self._array_context).__name__}.np.empty_like is "
"deprecated and will stop working in 2023. Prefer actx.np.zeros_like "
"instead.",
DeprecationWarning, stacklevel=2)
def _empty_like(array):
return self._array_context.empty(array.shape, array.dtype)
return self._array_context._rec_map_container(_empty_like, ary)
def zeros_like(self, ary):
def _zeros_like(array):
return self._array_context.zeros(array.shape, array.dtype)
return self._array_context._rec_map_container(
_zeros_like, ary, default_scalar=0)
def ones_like(self, ary):
return self.full_like(ary, 1)
def full_like(self, ary, fill_value):
def _full_like(subary):
return jnp.full_like(subary, fill_value)
return self._array_context._rec_map_container(
_full_like, ary, default_scalar=fill_value)
# }}}
# {{{ array manipulation routies
def reshape(self, a, newshape, order="C"):
return rec_map_array_container(
lambda ary: jnp.reshape(ary, newshape, order=order),
a)
def ravel(self, a, order="C"):
"""
.. warning::
Since :func:`jax.numpy.reshape` does not support orders `A`` and
``K``, in such cases we fallback to using ``order = C``.
"""
if order in "AK":
from warnings import warn
warn(f"ravel with order='{order}' not supported by JAX,"
" using order=C.", stacklevel=1)
order = "C"
return rec_map_array_container(
lambda subary: jnp.ravel(subary, order=order), a)
def transpose(self, a, axes=None):
return rec_multimap_array_container(jnp.transpose, a, axes)
def broadcast_to(self, array, shape):
return rec_map_array_container(partial(jnp.broadcast_to, shape=shape), array)
def concatenate(self, arrays, axis=0):
return rec_multimap_array_container(jnp.concatenate, arrays, axis)
def stack(self, arrays, axis=0):
return rec_multimap_array_container(
lambda *args: jnp.stack(arrays=args, axis=axis),
*arrays)
# }}}
# {{{ linear algebra
def vdot(self, x, y, dtype=None):
from arraycontext import rec_multimap_reduce_array_container
def _rec_vdot(ary1, ary2):
common_dtype = np.result_type(ary1, ary2)
if dtype not in (None, common_dtype):
raise NotImplementedError(
f"{type(self).__name__} cannot take dtype in vdot.")
return jnp.vdot(ary1, ary2)
return rec_multimap_reduce_array_container(sum, _rec_vdot, x, y)
# }}}
# {{{ logic functions
def all(self, a):
return rec_map_reduce_array_container(
partial(reduce, jnp.logical_and), jnp.all, a)
def any(self, a):
return rec_map_reduce_array_container(
partial(reduce, jnp.logical_or), jnp.any, a)
def array_equal(self, a: ArrayOrContainer, b: ArrayOrContainer) -> Array:
actx = self._array_context
# NOTE: not all backends support `bool` properly, so use `int8` instead
true_ary = actx.from_numpy(np.int8(True))
false_ary = actx.from_numpy(np.int8(False))
def rec_equal(x, y):
if type(x) is not type(y):
return false_ary
try:
serialized_x = serialize_container(x)
serialized_y = serialize_container(y)
except NotAnArrayContainerError:
if x.shape != y.shape:
return false_ary
else:
return jnp.all(jnp.equal(x, y))
else:
if len(serialized_x) != len(serialized_y):
return false_ary
return reduce(
jnp.logical_and,
[(true_ary if kx_i == ky_i else false_ary)
and rec_equal(x_i, y_i)
for (kx_i, x_i), (ky_i, y_i)
in zip(serialized_x, serialized_y, strict=True)],
true_ary)
return rec_equal(a, b)
# }}}
# {{{ mathematical functions
def sum(self, a, axis=None, dtype=None):
return rec_map_reduce_array_container(
sum,
partial(jnp.sum, axis=axis, dtype=dtype),
a)
def amin(self, a, axis=None):
return rec_map_reduce_array_container(
partial(reduce, jnp.minimum), partial(jnp.amin, axis=axis), a)
min = amin
def amax(self, a, axis=None):
return rec_map_reduce_array_container(
partial(reduce, jnp.maximum), partial(jnp.amax, axis=axis), a)
max = amax
# }}}
# {{{ sorting, searching and counting
def where(self, criterion, then, else_):
return rec_multimap_array_container(jnp.where, criterion, then, else_)
# }}}
arraycontext/impl/numpy/__init__.py 0 → 100644
View file @ 21a89f1b
"""
.. currentmodule:: arraycontext
A :mod:`numpy`-based array context.
.. autoclass:: NumpyArrayContext
"""
from __future__ import annotations
__copyright__ = """
Copyright (C) 2021 University of Illinois Board of Trustees
"""
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from typing import Any, overload
import numpy as np
import loopy as lp
from pytools.tag import ToTagSetConvertible
from arraycontext.container.traversal import rec_map_array_container, with_array_context
from arraycontext.context import (
Array,
ArrayContext,
ArrayOrContainerOrScalar,
ArrayOrContainerOrScalarT,
ContainerOrScalarT,
NumpyOrContainerOrScalar,
UntransformedCodeWarning,
)
class NumpyNonObjectArrayMetaclass(type):
def __instancecheck__(cls, instance: Any) -> bool:
return isinstance(instance, np.ndarray) and instance.dtype != object
class NumpyNonObjectArray(metaclass=NumpyNonObjectArrayMetaclass):
pass
class NumpyArrayContext(ArrayContext):
"""
A :class:`ArrayContext` that uses :class:`numpy.ndarray` to represent arrays.
.. automethod:: __init__
"""
_loopy_transform_cache: dict[lp.TranslationUnit, lp.ExecutorBase]
def __init__(self) -> None:
super().__init__()
self._loopy_transform_cache = {}
array_types = (NumpyNonObjectArray,)
def _get_fake_numpy_namespace(self):
from .fake_numpy import NumpyFakeNumpyNamespace
return NumpyFakeNumpyNamespace(self)
# {{{ ArrayContext interface
def clone(self):
return type(self)()
@overload
def from_numpy(self, array: np.ndarray) -> Array:
...
@overload
def from_numpy(self, array: ContainerOrScalarT) -> ContainerOrScalarT:
...
def from_numpy(self,
array: NumpyOrContainerOrScalar
) -> ArrayOrContainerOrScalar:
return array
@overload
def to_numpy(self, array: Array) -> np.ndarray:
...
@overload
def to_numpy(self, array: ContainerOrScalarT) -> ContainerOrScalarT:
...
def to_numpy(self,
array: ArrayOrContainerOrScalar
) -> NumpyOrContainerOrScalar:
return array
def call_loopy(
self,
t_unit: lp.TranslationUnit, **kwargs: Any
) -> dict[str, Array]:
t_unit = t_unit.copy(target=lp.ExecutableCTarget())
try:
executor = self._loopy_transform_cache[t_unit]
except KeyError:
executor = self.transform_loopy_program(t_unit).executor()
self._loopy_transform_cache[t_unit] = executor
_, result = executor(**kwargs)
return result
def freeze(self, array):
def _freeze(ary):
return ary
return with_array_context(rec_map_array_container(_freeze, array), actx=None)
def thaw(self, array):
def _thaw(ary):
return ary
return with_array_context(rec_map_array_container(_thaw, array), actx=self)
# }}}
def transform_loopy_program(self, t_unit):
from warnings import warn
warn("Using the base "
f"{type(self).__name__}.transform_loopy_program "
"to transform a translation unit. "
"This is a no-op and will result in unoptimized C code for"
"the requested optimization, all in a single statement."
"This will work, but is unlikely to be performant."
f"Instead, subclass {type(self).__name__} and implement "
"the specific transform logic required to transform the program "
"for your package or application. Check higher-level packages "
"(e.g. meshmode), which may already have subclasses you may want "
"to build on.",
UntransformedCodeWarning, stacklevel=2)
return t_unit
def tag(self,
tags: ToTagSetConvertible,
array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
# Numpy doesn't support tagging
return array
def tag_axis(self,
iaxis: int, tags: ToTagSetConvertible,
array: ArrayOrContainerOrScalarT) -> ArrayOrContainerOrScalarT:
# Numpy doesn't support tagging
return array
def einsum(self, spec, *args, arg_names=None, tagged=()):
return np.einsum(spec, *args)
@property
def permits_inplace_modification(self):
return True
@property
def supports_nonscalar_broadcasting(self):
return True
@property
def permits_advanced_indexing(self):
return True
arraycontext/impl/numpy/fake_numpy.py 0 → 100644
View file @ 21a89f1b
from __future__ import annotations
__copyright__ = """
Copyright (C) 2021 University of Illinois Board of Trustees
"""
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from functools import partial, reduce
from typing import cast
import numpy as np
from arraycontext.container import NotAnArrayContainerError, serialize_container
from arraycontext.container.traversal import (
rec_map_array_container,
rec_map_reduce_array_container,
rec_multimap_array_container,
rec_multimap_reduce_array_container,
)
from arraycontext.context import Array, ArrayOrContainer
from arraycontext.fake_numpy import (
BaseFakeNumpyLinalgNamespace,
BaseFakeNumpyNamespace,
)
class NumpyFakeNumpyLinalgNamespace(BaseFakeNumpyLinalgNamespace):
# Everything is implemented in the base class for now.
pass
_NUMPY_UFUNCS = frozenset({"concatenate", "reshape", "transpose",
"ones_like", "where",
*BaseFakeNumpyNamespace._numpy_math_functions
})
class NumpyFakeNumpyNamespace(BaseFakeNumpyNamespace):
"""
A :mod:`numpy` mimic for :class:`NumpyArrayContext`.
"""
def _get_fake_numpy_linalg_namespace(self):
return NumpyFakeNumpyLinalgNamespace(self._array_context)
def zeros(self, shape, dtype):
return np.zeros(shape, dtype)
def __getattr__(self, name):
if name in _NUMPY_UFUNCS:
from functools import partial
return partial(rec_multimap_array_container,
getattr(np, name))
raise AttributeError(name)
def sum(self, a, axis=None, dtype=None):
return rec_map_reduce_array_container(sum, partial(np.sum,
axis=axis,
dtype=dtype),
a)
def min(self, a, axis=None):
return rec_map_reduce_array_container(
partial(reduce, np.minimum), partial(np.amin, axis=axis), a)
def max(self, a, axis=None):
return rec_map_reduce_array_container(
partial(reduce, np.maximum), partial(np.amax, axis=axis), a)
def stack(self, arrays, axis=0):
return rec_multimap_array_container(
lambda *args: np.stack(arrays=args, axis=axis),
*arrays)
def broadcast_to(self, array, shape):
return rec_map_array_container(partial(np.broadcast_to, shape=shape), array)
# {{{ relational operators
def equal(self, x, y):
return rec_multimap_array_container(np.equal, x, y)
def not_equal(self, x, y):
return rec_multimap_array_container(np.not_equal, x, y)
def greater(self, x, y):
return rec_multimap_array_container(np.greater, x, y)
def greater_equal(self, x, y):
return rec_multimap_array_container(np.greater_equal, x, y)
def less(self, x, y):
return rec_multimap_array_container(np.less, x, y)
def less_equal(self, x, y):
return rec_multimap_array_container(np.less_equal, x, y)
# }}}
def ravel(self, a, order="C"):
return rec_map_array_container(partial(np.ravel, order=order), a)
def vdot(self, x, y):
return rec_multimap_reduce_array_container(sum, np.vdot, x, y)
def any(self, a):
return rec_map_reduce_array_container(partial(reduce, np.logical_or),
lambda subary: np.any(subary), a)
def all(self, a):
return rec_map_reduce_array_container(partial(reduce, np.logical_and),
lambda subary: np.all(subary), a)
def array_equal(self, a: ArrayOrContainer, b: ArrayOrContainer) -> Array:
false_ary = np.array(False)
true_ary = np.array(True)
if type(a) is not type(b):
return false_ary
try:
serialized_x = serialize_container(a)
serialized_y = serialize_container(b)
except NotAnArrayContainerError:
assert isinstance(a, np.ndarray)
assert isinstance(b, np.ndarray)
return np.array(np.array_equal(a, b))
else:
if len(serialized_x) != len(serialized_y):
return false_ary
return np.logical_and.reduce(
[(true_ary if kx_i == ky_i else false_ary)
and cast(np.ndarray, self.array_equal(x_i, y_i))
for (kx_i, x_i), (ky_i, y_i)
in zip(serialized_x, serialized_y, strict=True)],
initial=true_ary)
def arange(self, *args, **kwargs):
return np.arange(*args, **kwargs)
def linspace(self, *args, **kwargs):
return np.linspace(*args, **kwargs)
def zeros_like(self, ary):
return rec_map_array_container(np.zeros_like, ary)
def reshape(self, a, newshape, order="C"):
return rec_map_array_container(
lambda ary: ary.reshape(newshape, order=order),
a)
# vim: fdm=marker
arraycontext/impl/pyopencl/__init__.py
View file @ 21a89f1b
"""
from __future__ import annotations
__doc__ = """
.. currentmodule:: arraycontext
.. autoclass:: PyOpenCLArrayContext
.. automodule:: arraycontext.impl.pyopencl.taggable_cl_array
"""
__copyright__ = """
......@@ -27,19 +31,27 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from collections.abc import Callable
from typing import TYPE_CHECKING
from warnings import warn
from typing import Dict, List, Sequence, Optional, Union, TYPE_CHECKING
import numpy as np
from pytools.tag import Tag
from pytools.tag import ToTagSetConvertible
from arraycontext.context import ArrayContext
from arraycontext.container.traversal import rec_map_array_container, with_array_context
from arraycontext.context import (
Array,
ArrayContext,
ArrayOrContainer,
ScalarLike,
UntransformedCodeWarning,
)
if TYPE_CHECKING:
import pyopencl
import loopy as lp
import pyopencl
# {{{ PyOpenCLArrayContext
......@@ -65,13 +77,15 @@ class PyOpenCLArrayContext(ArrayContext):
of arrays are created (e.g. as results of computation), the associated cost
may become significant. Using e.g. :class:`pyopencl.tools.MemoryPool`
as the allocator can help avoid this cost.
.. automethod:: transform_loopy_program
"""
def __init__(self,
queue: "pyopencl.CommandQueue",
allocator: Optional["pyopencl.tools.AllocatorInterface"] = None,
wait_event_queue_length: Optional[int] = None,
force_device_scalars: bool = False) -> None:
queue: pyopencl.CommandQueue,
allocator: pyopencl.tools.AllocatorBase | None = None,
wait_event_queue_length: int | None = None,
force_device_scalars: bool | None = None) -> None:
r"""
:arg wait_event_queue_length: The length of a queue of
:class:`~pyopencl.Event` objects that are maintained by the
......@@ -92,24 +106,18 @@ class PyOpenCLArrayContext(ArrayContext):
For now, *wait_event_queue_length* should be regarded as an
experimental feature that may change or disappear at any minute.
:arg force_device_scalars: if *True*, scalar results returned from
reductions in :attr:`ArrayContext.np` will be kept on the device.
If *False*, the equivalent of :meth:`~ArrayContext.freeze` and
:meth:`~ArrayContext.to_numpy` is applied to transfer the results
to the host.
"""
if not force_device_scalars:
warn("Configuring the PyOpenCLArrayContext to return host scalars "
"from reductions is deprecated. "
"To configure the PyOpenCLArrayContext to return "
"device scalars, pass 'force_device_scalars=True' to the "
"constructor. "
"Support for returning host scalars will be removed in 2022.",
DeprecationWarning, stacklevel=2)
if force_device_scalars is not None:
warn(
"`force_device_scalars` is deprecated and will be removed in 2025.",
DeprecationWarning, stacklevel=2)
if not force_device_scalars:
raise ValueError(
"Passing force_device_scalars=False is not allowed.")
import pyopencl as cl
import pyopencl.array as cla
import pyopencl.array as cl_array
super().__init__()
self.context = queue.context
......@@ -118,67 +126,134 @@ class PyOpenCLArrayContext(ArrayContext):
if wait_event_queue_length is None:
wait_event_queue_length = 10
self._force_device_scalars = force_device_scalars
self._force_device_scalars = True
# Subclasses might still be using the old
# "force_devices_scalars: bool = False" interface, in which case we need
# to explicitly pass force_device_scalars=True in clone()
self._passed_force_device_scalars = force_device_scalars is not None
self._wait_event_queue_length = wait_event_queue_length
self._kernel_name_to_wait_event_queue: Dict[str, List[cl.Event]] = {}
self._kernel_name_to_wait_event_queue: dict[str, list[cl.Event]] = {}
if queue.device.type & cl.device_type.GPU:
if allocator is None:
warn("PyOpenCLArrayContext created without an allocator on a GPU. "
"This can lead to high numbers of memory allocations. "
"Please consider using a pyopencl.tools.MemoryPool. "
"Run with allocator=False to disable this warning.")
"Run with allocator=False to disable this warning.",
stacklevel=2)
if __debug__:
# Use "running on GPU" as a proxy for "they care about speed".
warn("You are using the PyOpenCLArrayContext on a GPU, but you "
"are running Python in debug mode. Use 'python -O' for "
"a noticeable speed improvement.")
"a noticeable speed improvement.",
stacklevel=2)
self._loopy_transform_cache: \
Dict["lp.TranslationUnit", "lp.TranslationUnit"] = {}
dict[lp.TranslationUnit, lp.TranslationUnit] = {}
self.array_types = (cla.Array,)
# TODO: Ideally this should only be `(TaggableCLArray,)`, but
# that would break the logic in the downstream users.
self.array_types = (cl_array.Array,)
def _get_fake_numpy_namespace(self):
from arraycontext.impl.pyopencl.fake_numpy import PyOpenCLFakeNumpyNamespace
return PyOpenCLFakeNumpyNamespace(self)
def _rec_map_container(
self, func: Callable[[Array], Array], array: ArrayOrContainer,
allowed_types: tuple[type, ...] | None = None, *,
default_scalar: ScalarLike | None = None,
strict: bool = False) -> ArrayOrContainer:
import arraycontext.impl.pyopencl.taggable_cl_array as tga
if allowed_types is None:
# TODO: replace with 'self.array_types' once `cla.Array` support
# is completely removed
allowed_types = (tga.TaggableCLArray,)
def _wrapper(ary):
if isinstance(ary, allowed_types):
return func(ary)
elif not strict and isinstance(ary, self.array_types):
from warnings import warn
warn(f"Invoking {type(self).__name__}.{func.__name__[1:]} with "
f"{type(ary).__name__} will be unsupported in 2023. Use "
"'to_tagged_cl_array' to convert instances to TaggableCLArray.",
DeprecationWarning, stacklevel=2)
return func(tga.to_tagged_cl_array(ary))
elif np.isscalar(ary):
if default_scalar is None:
return ary
else:
return np.array(ary).dtype.type(default_scalar)
else:
raise TypeError(
f"{type(self).__name__}.{func.__name__[1:]} invoked with "
f"an unsupported array type: got '{type(ary).__name__}', "
f"but expected one of {allowed_types}")
return rec_map_array_container(_wrapper, array)
# {{{ ArrayContext interface
def empty(self, shape, dtype):
import pyopencl.array as cl_array
return cl_array.empty(self.queue, shape=shape, dtype=dtype,
allocator=self.allocator)
def from_numpy(self, array):
import arraycontext.impl.pyopencl.taggable_cl_array as tga
def zeros(self, shape, dtype):
import pyopencl.array as cl_array
return cl_array.zeros(self.queue, shape=shape, dtype=dtype,
allocator=self.allocator)
def _from_numpy(ary):
return tga.to_device(self.queue, ary, allocator=self.allocator)
def from_numpy(self, array: np.ndarray):
import pyopencl.array as cl_array
return cl_array.to_device(self.queue, array, allocator=self.allocator)
return with_array_context(
self._rec_map_container(_from_numpy, array, (np.ndarray,), strict=True),
actx=self)
def to_numpy(self, array):
if not self._force_device_scalars and np.isscalar(array):
return array
def _to_numpy(ary):
return ary.get(queue=self.queue)
return with_array_context(
self._rec_map_container(_to_numpy, array),
actx=None)
def freeze(self, array):
def _freeze(ary):
ary.finish()
return ary.with_queue(None)
return with_array_context(self._rec_map_container(_freeze, array), actx=None)
def thaw(self, array):
def _thaw(ary):
return ary.with_queue(self.queue)
return with_array_context(self._rec_map_container(_thaw, array), actx=self)
return array.get(queue=self.queue)
def tag(self, tags: ToTagSetConvertible, array):
def _tag(ary):
return ary.tagged(tags)
return self._rec_map_container(_tag, array)
def tag_axis(self, iaxis: int, tags: ToTagSetConvertible, array):
def _tag_axis(ary):
return ary.with_tagged_axis(iaxis, tags)
return self._rec_map_container(_tag_axis, array)
def call_loopy(self, t_unit, **kwargs):
try:
t_unit = self._loopy_transform_cache[t_unit]
executor = self._loopy_transform_cache[t_unit]
except KeyError:
orig_t_unit = t_unit
t_unit = self.transform_loopy_program(t_unit)
self._loopy_transform_cache[orig_t_unit] = t_unit
executor = self.transform_loopy_program(t_unit).executor(self.context)
self._loopy_transform_cache[orig_t_unit] = executor
del orig_t_unit
evt, result = t_unit(self.queue, **kwargs, allocator=self.allocator)
evt, result = executor(self.queue, **kwargs, allocator=self.allocator)
if self._wait_event_queue_length is not False:
prg_name = t_unit.default_entrypoint.name
prg_name = executor.t_unit.default_entrypoint.name
wait_event_queue = self._kernel_name_to_wait_event_queue.setdefault(
prg_name, [])
......@@ -186,27 +261,37 @@ class PyOpenCLArrayContext(ArrayContext):
if len(wait_event_queue) > self._wait_event_queue_length:
wait_event_queue.pop(0).wait()
return result
import arraycontext.impl.pyopencl.taggable_cl_array as tga
def freeze(self, array):
array.finish()
return array.with_queue(None)
# FIXME: Inherit loopy tags for these arrays
return {name: tga.to_tagged_cl_array(ary) for name, ary in result.items()}
def thaw(self, array):
return array.with_queue(self.queue)
def clone(self):
if self._passed_force_device_scalars:
return type(self)(self.queue, self.allocator,
wait_event_queue_length=self._wait_event_queue_length,
force_device_scalars=True)
else:
return type(self)(self.queue, self.allocator,
wait_event_queue_length=self._wait_event_queue_length)
# }}}
def transform_loopy_program(self, t_unit):
# {{{ transform_loopy_program
def transform_loopy_program(self, t_unit: lp.TranslationUnit) -> lp.TranslationUnit:
from warnings import warn
warn("Using arraycontext.PyOpenCLArrayContext.transform_loopy_program "
"to transform a program. This is deprecated and will stop working "
"in 2022. Instead, subclass PyOpenCLArrayContext and implement "
"the specific logic required to transform the program for your "
"package or application. Check higher-level packages "
warn("Using the base "
f"{type(self).__name__}.transform_loopy_program "
"to transform a translation unit. "
"This is largely a no-op and unlikely to result in fast generated "
"code."
f"Instead, subclass {type(self).__name__} and implement "
"the specific transform logic required to transform the program "
"for your package or application. Check higher-level packages "
"(e.g. meshmode), which may already have subclasses you may want "
"to build on.",
DeprecationWarning, stacklevel=2)
UntransformedCodeWarning, stacklevel=2)
# accommodate loopy with and without kernel callables
......@@ -220,40 +305,17 @@ class PyOpenCLArrayContext(ArrayContext):
"to create this kernel?")
all_inames = default_entrypoint.all_inames()
# FIXME: This could be much smarter.
inner_iname = None
# import with underscore to avoid DeprecationWarning
from arraycontext.metadata import _FirstAxisIsElementsTag
if (len(default_entrypoint.instructions) == 1
and isinstance(default_entrypoint.instructions[0], lp.Assignment)
and any(isinstance(tag, _FirstAxisIsElementsTag)
# FIXME: Firedrake branch lacks kernel tags
for tag in getattr(default_entrypoint, "tags", ()))):
stmt, = default_entrypoint.instructions
out_inames = [v.name for v in stmt.assignee.index_tuple]
assert out_inames
outer_iname = out_inames[0]
if len(out_inames) >= 2:
inner_iname = out_inames[1]
elif "iel" in all_inames:
outer_iname = "iel"
inner_iname = None
if "idof" in all_inames:
inner_iname = "idof"
elif "i0" in all_inames:
if "i0" in all_inames:
outer_iname = "i0"
if "i1" in all_inames:
inner_iname = "i1"
else:
raise RuntimeError(
"Unable to reason what outer_iname and inner_iname "
f"needs to be; all_inames is given as: {all_inames}"
)
return t_unit
if inner_iname is not None:
t_unit = lp.split_iname(t_unit, inner_iname, 16, inner_tag="l.0")
......@@ -261,23 +323,24 @@ class PyOpenCLArrayContext(ArrayContext):
return t_unit
def tag(self, tags: Union[Sequence[Tag], Tag], array):
# Sorry, not capable.
return array
def tag_axis(self, iaxis, tags: Union[Sequence[Tag], Tag], array):
# Sorry, not capable.
return array
# }}}
def clone(self):
return type(self)(self.queue, self.allocator,
wait_event_queue_length=self._wait_event_queue_length,
force_device_scalars=self._force_device_scalars)
# {{{ properties
@property
def permits_inplace_modification(self):
return True
@property
def supports_nonscalar_broadcasting(self):
return False
@property
def permits_advanced_indexing(self):
return False
# }}}
# }}}
# vim: foldmethod=marker