chore: migrate to hatch + use hatchling for the build backend, drop Python 2 support

.github/workflows/check.yml

@@ -22,12 +22,12 @@ jobs:
  #- ruff
  - package
  steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v4
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5.1.1
  with:
  python-version: '3.10'
  - name: Install build tools
  run: |
  python -m pip install tox wheel
  - name: Run ${{ matrix.env }}
- run: tox -e ${{ matrix.env }}
+ run: python -m tox -e ${{ matrix.env }}

.github/workflows/publish.yml

@@ -13,8 +13,8 @@ jobs:
  publish:
  runs-on: ubuntu-latest
  steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v4
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5.1.1
  with:
  python-version: '3.10'
  - name: Install build tools

.github/workflows/test.yml

@@ -29,18 +29,22 @@ jobs:
  - '3.12'
  - pypy-3.9
  - pypy-3.10
+ # TODO: bring this back later
+ exclude:
+ - platform: windows-latest
+ python-version: 'pypy-3.10'
  steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v4
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5.1.1
  with:
  python-version: ${{ matrix.python-version }}
  - name: Install Python build tools
  run: python -m pip install tox wheel
  - name: Run tests
- run: tox run -e py
+ run: python -m tox run -e py
  - name: Install Scipy prerequisites for Ubuntu
  if: startsWith(matrix.platform, 'ubuntu')
  run: sudo apt-get install libopenblas-dev
  - name: Run tests with scipy
  if: startsWith(matrix.platform, 'ubuntu') || startsWith(matrix.python-version, 'pypy') != true
- run: tox run -e py-scipy
+ run: python -m tox run -e py-scipy

autograd/builtins.py

@@ -1,4 +1,3 @@
-from six import with_metaclass
 from .util import subvals
 from .extend import (Box, primitive, notrace_primitive, VSpace, vspace,
  SparseObject, defvjp, defvjp_argnum, defjvp, defjvp_argnum)
@@ -86,24 +85,25 @@ def fwd_grad_make_sequence(argnum, g, ans, seq_type, *args, **kwargs):
 defjvp_argnum(make_sequence, fwd_grad_make_sequence)
 
 
-class TupleMeta(type_):
+class TupleMeta(type(tuple_)):
  def __instancecheck__(self, instance):
  return isinstance(instance, tuple_)
-class tuple(with_metaclass(TupleMeta, tuple_)):
+
+class tuple(tuple_, metaclass=TupleMeta):
  def __new__(cls, xs):
  return make_sequence(tuple_, *xs)
 
 class ListMeta(type_):
  def __instancecheck__(self, instance):
  return isinstance(instance, list_)
-class list(with_metaclass(ListMeta, list_)):
+class list(list_, metaclass=ListMeta):
  def __new__(cls, xs):
- return make_sequence(list_,  *xs)
+ return make_sequence(list_, *xs)
 
 class DictMeta(type_):
  def __instancecheck__(self, instance):
  return isinstance(instance, dict_)
-class dict(with_metaclass(DictMeta, dict_)):
+class dict(dict_, metaclass=DictMeta):
  def __new__(cls, *args, **kwargs):
  result = dict_(*args, **kwargs)
  if result:

autograd/numpy/numpy_vjps.py

@@ -1,5 +1,4 @@
 from __future__ import absolute_import
-from six import string_types
 from functools import partial
 import numpy as onp
 from ..util import func
@@ -588,7 +587,7 @@ def grad_einsum(argnum, ans, operands_, kwargs):
  result_meta = anp.metadata(operands_[argnum])
  def vjp(g):
  operands = operands_
- if isinstance(operands[0], string_types): # using "ijk" convention.
+ if isinstance(operands[0], str): # using "ijk" convention.
  in_subs, out_subs, _ = anp.parse_einsum_input(*operands)
  string, operands = operands[0], operands[1:]
 

autograd/scipy/signal.py

@@ -6,7 +6,7 @@
 from autograd.extend import primitive, defvjp
 
 from numpy.lib.stride_tricks import as_strided
-from six import iteritems
+
 
 @primitive
 def convolve(A, B, axes=None, dot_axes=[(),()], mode='full'):
@@ -79,8 +79,8 @@ def parse_axes(A_shape, B_shape, conv_axes, dot_axes, mode):
  'conv' : tuple(range(i2, i3))}
  conv_shape = (compute_conv_size(A_shape[i], B_shape[j], mode)
  for i, j in zip(axes['A']['conv'], axes['B']['conv']))
- shapes = {'A' : {s : (A_shape[i] for i in ax) for s, ax in iteritems(axes['A'])},
- 'B' : {s : (B_shape[i] for i in ax) for s, ax in iteritems(axes['B'])}}
+ shapes = {'A': {s: tuple(A_shape[i] for i in ax) for s, ax in axes['A'].items()},
+ 'B': {s: tuple(B_shape[i] for i in ax) for s, ax in axes['B'].items()}}
  shapes['out'] = {'ignore_A' : shapes['A']['ignore'],
  'ignore_B' : shapes['B']['ignore'],
  'conv' : conv_shape}

conda_recipe/conda.yaml

@@ -14,7 +14,8 @@ source:
 requirements:
  build:
  - python
- - setuptools
+ - hatch
+ - hatchling
  - future
  - numpy >=1.9
 
@@ -24,7 +25,7 @@ requirements:
  - numpy >=1.9
 
 build:
- script: python setup.py build && python setup.py install
+ script: pip install . --no-deps
 
 test:
  # Python imports

examples/data_mnist.py

@@ -1,9 +1,6 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import sys
-if sys.version < "3":
- from future.standard_library import install_aliases
- install_aliases()
 
 import os
 import gzip

pyproject.toml

@@ -1,11 +1,12 @@
 [build-system]
-requires = ["setuptools>=44"]
-build-backend = "setuptools.build_meta"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
 [project]
 name = "autograd"
 version = "1.6.3"
-description = "Efficiently computes derivatives of numpy code."
+requires-python = ">=3.8"
+description = "Efficiently computes derivatives of NumPy code."
 readme = "README.md"
 license = {file = "license.txt"}
 authors = [
@@ -16,22 +17,20 @@ authors = [
 ]
 maintainers = [
  {name = "Jamie Townsend", email = "j.h.n.townsend@uva.nl"},
+ {name = "Fabian Joswig", email = "fabian.joswig@uni-muenster.de"},
+ {name = "Agriya Khetarpal", email = "agriyakhetarpal@outlook.com"},
 ]
 classifiers = [
  "Development Status :: 4 - Beta",
  "Intended Audience :: Information Technology",
  "Intended Audience :: Science/Research",
  "License :: OSI Approved :: MIT License",
- "Programming Language :: Python :: 2",
- "Programming Language :: Python :: 2.7",
- "Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.5",
- "Programming Language :: Python :: 3.6",
- "Programming Language :: Python :: 3.7",
  "Programming Language :: Python :: 3.8",
  "Programming Language :: Python :: 3.9",
  "Programming Language :: Python :: 3.10",
  "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Topic :: Scientific/Engineering",
 ]
 keywords = [
  "Automatic differentiation",
@@ -41,14 +40,16 @@ keywords = [
  "optimization",
  "neural networks",
  "Python",
- "Numpy",
- "Scipy",
+ "NumPy",
+ "SciPy",
 ]
 dependencies = [
- "numpy>=1.12",
- "six",
- "future>=0.15.2; python_version < '3'",
+ "numpy<2",
 ]
+# dynamic = ["version"]
+
+[project.urls]
+Source = "https://github.com/HIPS/autograd"
 
 [project.optional-dependencies]
 scipy = [
@@ -69,12 +70,3 @@ extend-exclude = []
 extend-ignore = ["E731"]
 extend-select = ["I", "W"]
 line-length = 109
-
-[tool.setuptools]
-packages=[
- "autograd",
- "autograd.numpy",
- "autograd.scipy",
- "autograd.scipy.stats",
- "autograd.misc",
-]

tox.ini

@@ -1,6 +1,8 @@
 # Tox (https://tox.wiki/) - run tests in isolation using virtualenv.
 # Also contains config settings for tools that don't look into pyproject.toml.
 
+# TODO: Migrate to tool.hatch.run or noxfile.py
+
 [tox]
 envlist =
  ruff
@@ -33,10 +35,10 @@ deps = pyclean
 commands = pyclean {posargs:. --debris --erase junit-report.xml --yes}
 
 [testenv:ruff]
-description = Lightening-fast linting for Python
+description = Lightning-fast linting for Python
 skip_install = true
 deps = ruff
-commands = ruff {posargs:.}
+commands = ruff check {posargs:.} # TODO: Fix style failures
 
 [testenv:package]
 description = Build package and check metadata (or upload package)