mirror of
https://github.com/ROCm/jax.git
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2aa810fe60
Before, these values could only be specified via jax.config or flags. This PR makes them proper configs, so they also work as env vars.
2428 lines
83 KiB
Python
2428 lines
83 KiB
Python
# Copyright 2018 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# pyformat: disable
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from __future__ import annotations
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import collections
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from collections.abc import Callable, Generator, Iterable, Sequence
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from concurrent.futures import ThreadPoolExecutor
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from contextlib import ExitStack, contextmanager
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import datetime
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import functools
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from functools import partial
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import inspect
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import logging
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import math
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import os
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import platform
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import re
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import sys
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import tempfile
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import textwrap
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import threading
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import time
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from typing import Any, TextIO
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import unittest
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import zlib
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from absl.testing import absltest
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from absl.testing import parameterized
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import jax
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from jax import lax
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from jax._src import api
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from jax._src import config
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from jax._src import core
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from jax._src import deprecations
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from jax._src import dispatch
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from jax._src import dtypes as _dtypes
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from jax._src import lib as _jaxlib
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from jax._src import monitoring
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from jax._src import test_warning_util
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from jax._src import xla_bridge
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from jax._src import util
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from jax._src import mesh as mesh_lib
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from jax._src.cloud_tpu_init import running_in_cloud_tpu_vm
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from jax._src.interpreters import mlir
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from jax._src.numpy.util import promote_dtypes, promote_dtypes_inexact
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from jax._src.public_test_util import ( # noqa: F401
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_assert_numpy_allclose, _check_dtypes_match, _default_tolerance, _dtype, check_close, check_grads,
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check_jvp, check_vjp, default_gradient_tolerance, default_tolerance, rand_like, tolerance)
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from jax._src.util import unzip2
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from jax.experimental.compilation_cache import compilation_cache
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from jax.tree_util import tree_all, tree_flatten, tree_map, tree_unflatten
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import numpy as np
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import numpy.random as npr
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# This submodule includes private test utilities that are not exported to
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# jax.test_util. Functionality appearing here is for internal use only, and
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# may be changed or removed at any time and without any deprecation cycle.
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_TEST_DUT = config.string_flag(
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'jax_test_dut', '',
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help=
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'Describes the device under test in case special consideration is required.'
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)
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NUM_GENERATED_CASES = config.int_flag(
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'jax_num_generated_cases',
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int(os.getenv('JAX_NUM_GENERATED_CASES', '10')),
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help='Number of generated cases to test')
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_MAX_CASES_SAMPLING_RETRIES = config.int_flag(
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'max_cases_sampling_retries',
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int(os.getenv('JAX_MAX_CASES_SAMPLING_RETRIES', '100')),
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'Number of times a failed test sample should be retried. '
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'When an unseen case cannot be generated in this many trials, the '
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'sampling process is terminated.'
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)
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_SKIP_SLOW_TESTS = config.bool_flag(
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'jax_skip_slow_tests',
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config.bool_env('JAX_SKIP_SLOW_TESTS', False),
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help='Skip tests marked as slow (> 5 sec).'
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)
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_TEST_TARGETS = config.string_flag(
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'test_targets', os.getenv('JAX_TEST_TARGETS', ''),
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'Regular expression specifying which tests to run, called via re.search on '
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'the test name. If empty or unspecified, run all tests.'
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)
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_EXCLUDE_TEST_TARGETS = config.string_flag(
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'exclude_test_targets', os.getenv('JAX_EXCLUDE_TEST_TARGETS', ''),
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'Regular expression specifying which tests NOT to run, called via re.search '
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'on the test name. If empty or unspecified, run all tests.'
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)
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TEST_WITH_PERSISTENT_COMPILATION_CACHE = config.bool_flag(
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'jax_test_with_persistent_compilation_cache',
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config.bool_env('JAX_TEST_WITH_PERSISTENT_COMPILATION_CACHE', False),
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help='If enabled, the persistent compilation cache will be enabled for all '
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'test cases. This can be used to increase compilation cache coverage.')
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HYPOTHESIS_PROFILE = config.string_flag(
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'hypothesis_profile',
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os.getenv('JAX_HYPOTHESIS_PROFILE', 'deterministic'),
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help=('Select the hypothesis profile to use for testing. Available values: '
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'deterministic, interactive'),
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)
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TEST_NUM_THREADS = config.int_flag(
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'jax_test_num_threads', int(os.getenv('JAX_TEST_NUM_THREADS', '0')),
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help='Number of threads to use for running tests. 0 means run everything '
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'in the main thread. Using > 1 thread is experimental.'
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)
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# We sanitize test names to ensure they work with "unitttest -k" and
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# "pytest -k" test filtering. pytest accepts '[' and ']' but unittest -k
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# does not. We replace sequences of problematic characters with a single '_'.
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kSanitizeNameRE = re.compile(r"[ \"'\[\](){}<>=,._]+")
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def sanitize_test_name(s: str) -> str:
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return kSanitizeNameRE.sub("_", s)
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def num_float_bits(dtype):
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return _dtypes.finfo(_dtypes.canonicalize_dtype(dtype)).bits
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def to_default_dtype(arr):
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"""Convert a value to an array with JAX's default dtype.
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This is generally used for type conversions of values returned by numpy functions,
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to make their dtypes take into account the state of the ``jax_enable_x64`` and
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``jax_default_dtype_bits`` flags.
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"""
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arr = np.asarray(arr)
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dtype = _dtypes._default_types.get(arr.dtype.kind)
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return arr.astype(_dtypes.canonicalize_dtype(dtype)) if dtype else arr
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def with_jax_dtype_defaults(func, use_defaults=True):
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"""Return a version of a function with outputs that match JAX's default dtypes.
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This is generally used to wrap numpy functions within tests, in order to make
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their default output dtypes match those of corresponding JAX functions, taking
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into account the state of the ``jax_enable_x64`` and ``jax_default_dtype_bits``
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flags.
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Args:
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use_defaults : whether to convert any given output to the default dtype. May be
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a single boolean, in which case it specifies the conversion for all outputs,
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or may be a pytree with the same structure as the function output.
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"""
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@functools.wraps(func)
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def wrapped(*args, **kwargs):
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result = func(*args, **kwargs)
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if isinstance(use_defaults, bool):
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return tree_map(to_default_dtype, result) if use_defaults else result
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else:
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f = lambda arr, use_default: to_default_dtype(arr) if use_default else arr
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return tree_map(f, result, use_defaults)
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return wrapped
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def is_sequence(x):
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try:
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iter(x)
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except TypeError:
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return False
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else:
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return True
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def _normalize_tolerance(tol):
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tol = tol or 0
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if isinstance(tol, dict):
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return {np.dtype(k): v for k, v in tol.items()}
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else:
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return dict.fromkeys(_default_tolerance, tol)
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def join_tolerance(tol1, tol2):
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tol1 = _normalize_tolerance(tol1)
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tol2 = _normalize_tolerance(tol2)
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out = tol1
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for k, v in tol2.items():
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out[k] = max(v, tol1.get(k, 0))
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return out
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def check_eq(xs, ys, err_msg=''):
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assert_close = partial(_assert_numpy_allclose, err_msg=err_msg)
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tree_all(tree_map(assert_close, xs, ys))
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@contextmanager
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def _capture_output(fp: TextIO) -> Generator[Callable[[], str], None, None]:
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"""Context manager to capture all output written to a given file object.
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Unlike ``contextlib.redirect_stdout``, this context manager works for
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any file object and also for both pure Python and native code.
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Example::
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with capture_output(sys.stdout) as get_output:
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print(42)
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print("Captured": get_output())
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Yields:
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A function returning the captured output. The function must be called
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*after* the context is no longer active.
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"""
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# ``None`` means nothing has not been captured yet.
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captured = None
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def get_output() -> str:
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if captured is None:
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raise ValueError("get_output() called while the context is active.")
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return captured
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with tempfile.NamedTemporaryFile(mode="w+", encoding='utf-8') as f:
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original_fd = os.dup(fp.fileno())
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os.dup2(f.fileno(), fp.fileno())
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try:
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yield get_output
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finally:
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# Python also has its own buffers, make sure everything is flushed.
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fp.flush()
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os.fsync(fp.fileno())
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f.seek(0)
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captured = f.read()
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os.dup2(original_fd, fp.fileno())
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capture_stdout = partial(_capture_output, sys.stdout)
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capture_stderr = partial(_capture_output, sys.stderr)
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class EventThreadLocalState(threading.local):
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def __init__(self):
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self.counts = {} # Mapping from string name to count.
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self.nested_device_put_count = 0 # Number of recursive calls to device_put
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# Per-function counts
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self.infer_params_fun_counts = None
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self.lower_jaxpr_to_fun_counts = None
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thread_local_state = EventThreadLocalState()
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def event_listener(name, *args):
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counts = thread_local_state.counts
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counts[name] = counts.get(name, 0) + 1
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# device_put handlers might call `dispatch.device_put` (e.g. on an
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# underlying payload or several). We only want to count these
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# recursive puts once, so we skip counting more than the outermost
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# one in such a call stack.
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if name == "batched_device_put_start":
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if thread_local_state.nested_device_put_count == 0:
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counts["batched_device_put"] = counts.get("batched_device_put", 0) + 1
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thread_local_state.nested_device_put_count += 1
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elif name == "batched_device_put_end":
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thread_local_state.nested_device_put_count -= 1
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elif name == "pjit._infer_params_impl":
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# For infer_params, we collect per-function data, but only while a context
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# manager is active.
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infer_counts = thread_local_state.infer_params_fun_counts
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if infer_counts is not None:
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(fun,) = args
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infer_counts[fun] += 1
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elif name == "lower_jaxpr_to_fun":
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# For infer_params, we collect per-function data, but only while a context
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# manager is active.
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lower_counts = thread_local_state.lower_jaxpr_to_fun_counts
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if lower_counts is not None:
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(fun,) = args
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lower_counts[fun] += 1
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util.test_event_listener = event_listener
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def count_events(event):
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"Returns a context-manager that yields a function that counts a test event."
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@contextmanager
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def count_event():
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before = thread_local_state.counts.get(event, 0)
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yield lambda: thread_local_state.counts.get(event, 0) - before
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return count_event
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count_device_put = count_events("batched_device_put")
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count_device_put_fast_path_hit = count_events("batched_copy_array")
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count_pjit_cpp_cache_miss = count_events("pjit_lower")
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count_jit_tracing_cache_miss = count_events("create_pjit_jaxpr")
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count_aot_jit_cpp_cache_miss = count_events("stages_compiled_call")
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count_jit_and_pmap_lowerings = count_events("lower_jaxpr_to_module")
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count_jit_compilation_cache_miss = count_events("pxla_cached_compilation")
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count_jax_array_shard_arg_calls = count_events("_array_shard_arg")
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@contextmanager
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def count_primitive_compiles():
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dispatch.xla_primitive_callable.cache_clear()
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count = [-1]
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try:
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yield lambda: count[0]
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finally:
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count[0] = dispatch.xla_primitive_callable.cache_info().misses
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@contextmanager
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def count_jit_infer_params_cache_miss():
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assert thread_local_state.infer_params_fun_counts is None
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counts = collections.Counter()
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thread_local_state.infer_params_fun_counts = counts
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try:
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yield counts
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finally:
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thread_local_state.infer_params_fun_counts = None
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@contextmanager
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def count_subjaxpr_to_hlo_conversion(fun_name):
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assert thread_local_state.lower_jaxpr_to_fun_counts is None
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counts = collections.Counter()
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thread_local_state.lower_jaxpr_to_fun_counts = counts
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try:
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yield lambda: counts[fun_name]
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finally:
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thread_local_state.lower_jaxpr_to_fun_counts = None
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@contextmanager
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def assert_num_jit_and_pmap_compilations(times):
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with count_jit_and_pmap_lowerings() as count:
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yield
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if count() != times:
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raise AssertionError(f"Expected exactly {times} XLA compilations, "
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f"but executed {count()}")
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def jaxlib_version() -> tuple[int, ...]:
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return _jaxlib.version
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def device_under_test():
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return _TEST_DUT.value or xla_bridge.get_backend().platform
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def supported_dtypes():
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if device_under_test() == "tpu":
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types = {np.bool_, np.int8, np.int16, np.int32, np.uint8, np.uint16,
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np.uint32, _dtypes.bfloat16, np.float16, np.float32, np.complex64,
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_dtypes.float8_e4m3fn, _dtypes.float8_e4m3b11fnuz,
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_dtypes.float8_e5m2}
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elif device_under_test() == "gpu":
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types = {np.bool_, np.int8, np.int16, np.int32, np.int64,
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np.uint8, np.uint16, np.uint32, np.uint64,
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_dtypes.bfloat16, np.float16, np.float32, np.float64,
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np.complex64, np.complex128, _dtypes.float8_e4m3fn,
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_dtypes.float8_e5m2}
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elif device_under_test() == "METAL":
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types = {np.int32, np.uint32, np.float32}
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else:
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types = {np.bool_, np.int8, np.int16, np.int32, np.int64,
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np.uint8, np.uint16, np.uint32, np.uint64,
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_dtypes.bfloat16, np.float16, np.float32, np.float64,
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np.complex64, np.complex128}
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if not config.enable_x64.value:
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types -= {np.uint64, np.int64, np.float64, np.complex128}
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return types
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def is_device_rocm():
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return 'rocm' in xla_bridge.get_backend().platform_version
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def is_device_cuda():
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return 'cuda' in xla_bridge.get_backend().platform_version
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def is_cloud_tpu():
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return running_in_cloud_tpu_vm
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# Returns True if it is not cloud TPU. If it is cloud TPU, returns True if it is
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# built at least `date``.
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# TODO(b/327203806): after libtpu adds a XLA version and the oldest support
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# libtpu contains the XLA version, remove using built time to skip tests.
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def if_cloud_tpu_at_least(year: int, month: int, day: int):
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date = datetime.date(year, month, day)
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if not is_cloud_tpu():
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return True
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# The format of Cloud TPU platform_version is like:
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# PJRT C API
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# TFRT TPU v2
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# Built on Oct 30 2023 03:04:42 (1698660263) cl/577737722
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platform_version = xla_bridge.get_backend().platform_version.split('\n')[-1]
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results = re.findall(r'\(.*?\)', platform_version)
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if len(results) != 1:
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return True
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build_date = date.fromtimestamp(int(results[0][1:-1]))
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return build_date >= date
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def pjrt_c_api_version_at_least(major_version: int, minor_version: int):
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pjrt_c_api_versions = xla_bridge.backend_pjrt_c_api_version()
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if pjrt_c_api_versions is None:
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return True
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return pjrt_c_api_versions >= (major_version, minor_version)
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def get_tpu_version() -> int:
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if device_under_test() != "tpu":
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raise ValueError("Device is not TPU")
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kind = jax.devices()[0].device_kind
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if kind.endswith(' lite'):
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kind = kind[:-len(' lite')]
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assert kind[:-1] == "TPU v", kind
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return int(kind[-1])
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def is_device_tpu_at_least(version: int) -> bool:
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if device_under_test() != "tpu":
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return False
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return get_tpu_version() >= version
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def is_device_tpu(version: int | None = None, variant: str = "") -> bool:
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if device_under_test() != "tpu":
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return False
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if version is None:
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return True
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device_kind = jax.devices()[0].device_kind
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expected_version = f"v{version}{variant}"
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# Special case v5e until the name is updated in device_kind
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if expected_version == "v5e":
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return "v5 lite" in device_kind
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elif expected_version == "v6e":
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return "v6 lite" in device_kind
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return expected_version in device_kind
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def is_cuda_compute_capability_at_least(capability: str) -> bool:
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if not is_device_cuda():
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return False
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d, *_ = jax.local_devices(backend="gpu")
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target = tuple(int(x) for x in capability.split("."))
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current = tuple(int(x) for x in d.compute_capability.split("."))
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return current >= target
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def is_cuda_compute_capability_equal(capability: str) -> bool:
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if not is_device_cuda():
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return False
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d, *_ = jax.local_devices(backend="gpu")
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target = tuple(int(x) for x in capability.split("."))
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current = tuple(int(x) for x in d.compute_capability.split("."))
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return current == target
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def _get_device_tags():
|
|
"""returns a set of tags defined for the device under test"""
|
|
if is_device_rocm():
|
|
device_tags = {device_under_test(), "rocm"}
|
|
elif is_device_cuda():
|
|
device_tags = {device_under_test(), "cuda"}
|
|
elif device_under_test() == "METAL":
|
|
device_tags = {device_under_test(), "gpu"}
|
|
else:
|
|
device_tags = {device_under_test()}
|
|
return device_tags
|
|
|
|
def test_device_matches(device_types: Iterable[str]) -> bool:
|
|
assert not isinstance(
|
|
device_types, str
|
|
), 'device_types should be a list of strings'
|
|
tags = _get_device_tags()
|
|
for device_type in device_types:
|
|
assert isinstance(device_type, str), device_type
|
|
if device_type in tags:
|
|
return True
|
|
return False
|
|
|
|
test_device_matches.__test__ = False # This isn't a test case, pytest.
|
|
|
|
def _device_filter(predicate):
|
|
def skip(test_method):
|
|
@functools.wraps(test_method)
|
|
def test_method_wrapper(self, *args, **kwargs):
|
|
device_tags = _get_device_tags()
|
|
if not predicate():
|
|
test_name = getattr(test_method, '__name__', '[unknown test]')
|
|
raise unittest.SkipTest(
|
|
f"{test_name} not supported on device with tags {device_tags}.")
|
|
return test_method(self, *args, **kwargs)
|
|
return test_method_wrapper
|
|
return skip
|
|
|
|
def skip_on_devices(*disabled_devices):
|
|
"""A decorator for test methods to skip the test on certain devices."""
|
|
return _device_filter(lambda: not test_device_matches(disabled_devices))
|
|
|
|
def run_on_devices(*enabled_devices):
|
|
"""A decorator for test methods to run the test only on certain devices."""
|
|
return _device_filter(lambda: test_device_matches(enabled_devices))
|
|
|
|
def device_supports_buffer_donation():
|
|
"""A decorator for test methods to run the test only on devices that support
|
|
buffer donation."""
|
|
return _device_filter(
|
|
lambda: test_device_matches(mlir._platforms_with_donation)
|
|
)
|
|
|
|
|
|
def request_cpu_devices(nr_devices: int):
|
|
"""Requests at least `nr_devices` CPU devices.
|
|
|
|
request_cpu_devices should be called at the top-level of a test module before
|
|
main() runs.
|
|
|
|
It is not guaranteed that the number of CPU devices will be exactly
|
|
`nr_devices`: it may be more or less, depending on how exactly the test is
|
|
invoked. Test cases that require a specific number of devices should skip
|
|
themselves if that number is not met.
|
|
"""
|
|
if config.num_cpu_devices.value < nr_devices:
|
|
xla_bridge.get_backend.cache_clear()
|
|
config.update("jax_num_cpu_devices", nr_devices)
|
|
|
|
|
|
def skip_on_flag(flag_name, skip_value):
|
|
"""A decorator for test methods to skip the test when flags are set."""
|
|
def skip(test_method): # pylint: disable=missing-docstring
|
|
@functools.wraps(test_method)
|
|
def test_method_wrapper(self, *args, **kwargs):
|
|
flag_value = config._read(flag_name)
|
|
if flag_value == skip_value:
|
|
test_name = getattr(test_method, '__name__', '[unknown test]')
|
|
raise unittest.SkipTest(
|
|
f"{test_name} not supported when FLAGS.{flag_name} is {flag_value}")
|
|
return test_method(self, *args, **kwargs)
|
|
return test_method_wrapper
|
|
return skip
|
|
|
|
|
|
def pytest_mark_if_available(marker: str):
|
|
"""A decorator for test classes or methods to pytest.mark if installed."""
|
|
def wrap(func_or_class):
|
|
try:
|
|
import pytest
|
|
except ImportError:
|
|
return func_or_class
|
|
return getattr(pytest.mark, marker)(func_or_class)
|
|
return wrap
|
|
|
|
|
|
def is_running_under_pytest():
|
|
return "pytest" in sys.modules
|
|
|
|
|
|
def skip_under_pytest(reason: str):
|
|
"""A decorator for test methods to skip the test when run under pytest."""
|
|
reason = "Running under pytest: " + reason
|
|
def skip(test_method):
|
|
return unittest.skipIf(is_running_under_pytest(), reason)(test_method)
|
|
return skip
|
|
|
|
|
|
def format_test_name_suffix(opname, shapes, dtypes):
|
|
arg_descriptions = (format_shape_dtype_string(shape, dtype)
|
|
for shape, dtype in zip(shapes, dtypes))
|
|
return '{}_{}'.format(opname.capitalize(), '_'.join(arg_descriptions))
|
|
|
|
|
|
# We use special symbols, represented as singleton objects, to distinguish
|
|
# between NumPy scalars, Python scalars, and 0-D arrays.
|
|
class ScalarShape:
|
|
def __len__(self): return 0
|
|
def __getitem__(self, i): raise IndexError(f"index {i} out of range.")
|
|
class _NumpyScalar(ScalarShape): pass
|
|
class _PythonScalar(ScalarShape): pass
|
|
NUMPY_SCALAR_SHAPE = _NumpyScalar()
|
|
PYTHON_SCALAR_SHAPE = _PythonScalar()
|
|
|
|
|
|
# Some shape combinations don't make sense.
|
|
def is_valid_shape(shape, dtype):
|
|
if shape == PYTHON_SCALAR_SHAPE:
|
|
return dtype == np.dtype(type(np.array(0, dtype=dtype).item()))
|
|
return True
|
|
|
|
|
|
def _dims_of_shape(shape):
|
|
"""Converts `shape` to a tuple of dimensions."""
|
|
if type(shape) in (list, tuple):
|
|
return shape
|
|
elif isinstance(shape, ScalarShape):
|
|
return ()
|
|
elif np.ndim(shape) == 0:
|
|
return (shape,)
|
|
else:
|
|
raise TypeError(type(shape))
|
|
|
|
|
|
def _cast_to_shape(value, shape, dtype):
|
|
"""Casts `value` to the correct Python type for `shape` and `dtype`."""
|
|
if shape is NUMPY_SCALAR_SHAPE:
|
|
# explicitly cast to NumPy scalar in case `value` is a Python scalar.
|
|
return np.dtype(dtype).type(value)
|
|
elif shape is PYTHON_SCALAR_SHAPE:
|
|
# explicitly cast to Python scalar via https://stackoverflow.com/a/11389998
|
|
return np.asarray(value).item()
|
|
elif type(shape) in (list, tuple):
|
|
assert np.shape(value) == tuple(shape)
|
|
return value
|
|
elif np.ndim(shape) == 0:
|
|
assert np.shape(value) == (shape,)
|
|
return value
|
|
else:
|
|
raise TypeError(type(shape))
|
|
|
|
|
|
def dtype_str(dtype):
|
|
return np.dtype(dtype).name
|
|
|
|
|
|
def format_shape_dtype_string(shape, dtype):
|
|
if isinstance(shape, np.ndarray):
|
|
return f'{dtype_str(dtype)}[{shape}]'
|
|
elif isinstance(shape, list):
|
|
shape = tuple(shape)
|
|
return _format_shape_dtype_string(shape, dtype)
|
|
|
|
@functools.lru_cache(maxsize=64)
|
|
def _format_shape_dtype_string(shape, dtype):
|
|
if shape is NUMPY_SCALAR_SHAPE:
|
|
return dtype_str(dtype)
|
|
elif shape is PYTHON_SCALAR_SHAPE:
|
|
return 'py' + dtype_str(dtype)
|
|
elif type(shape) is tuple:
|
|
shapestr = ','.join(str(dim) for dim in shape)
|
|
return f'{dtype_str(dtype)}[{shapestr}]'
|
|
elif type(shape) is int:
|
|
return f'{dtype_str(dtype)}[{shape},]'
|
|
else:
|
|
raise TypeError(type(shape))
|
|
|
|
|
|
def _rand_dtype(rand, shape, dtype, scale=1., post=lambda x: x):
|
|
"""Produce random values given shape, dtype, scale, and post-processor.
|
|
|
|
Args:
|
|
rand: a function for producing random values of a given shape, e.g. a
|
|
bound version of either np.RandomState.randn or np.RandomState.rand.
|
|
shape: a shape value as a tuple of positive integers.
|
|
dtype: a numpy dtype.
|
|
scale: optional, a multiplicative scale for the random values (default 1).
|
|
post: optional, a callable for post-processing the random values (default
|
|
identity).
|
|
|
|
Returns:
|
|
An ndarray of the given shape and dtype using random values based on a call
|
|
to rand but scaled, converted to the appropriate dtype, and post-processed.
|
|
"""
|
|
if _dtypes.issubdtype(dtype, np.unsignedinteger):
|
|
r = lambda: np.asarray(scale * abs(rand(*_dims_of_shape(shape)))).astype(dtype)
|
|
else:
|
|
r = lambda: np.asarray(scale * rand(*_dims_of_shape(shape))).astype(dtype)
|
|
if _dtypes.issubdtype(dtype, np.complexfloating):
|
|
vals = r() + 1.0j * r()
|
|
else:
|
|
vals = r()
|
|
return _cast_to_shape(np.asarray(post(vals), dtype), shape, dtype)
|
|
|
|
|
|
def rand_fullrange(rng, standardize_nans=False):
|
|
"""Random numbers that span the full range of available bits."""
|
|
def gen(shape, dtype, post=lambda x: x):
|
|
dtype = np.dtype(dtype)
|
|
size = dtype.itemsize * math.prod(_dims_of_shape(shape))
|
|
vals = rng.randint(0, np.iinfo(np.uint8).max, size=size, dtype=np.uint8)
|
|
vals = post(vals).view(dtype)
|
|
if shape is PYTHON_SCALAR_SHAPE:
|
|
# Sampling from the full range of the largest available uint type
|
|
# leads to overflows in this case; sample from signed ints instead.
|
|
if dtype == np.uint64:
|
|
vals = vals.astype(np.int64)
|
|
elif dtype == np.uint32 and not config.enable_x64.value:
|
|
vals = vals.astype(np.int32)
|
|
vals = vals.reshape(shape)
|
|
# Non-standard NaNs cause errors in numpy equality assertions.
|
|
if standardize_nans and np.issubdtype(dtype, np.floating):
|
|
vals[np.isnan(vals)] = np.nan
|
|
return _cast_to_shape(vals, shape, dtype)
|
|
return gen
|
|
|
|
|
|
def rand_default(rng, scale=3):
|
|
return partial(_rand_dtype, rng.randn, scale=scale)
|
|
|
|
|
|
def rand_nonzero(rng):
|
|
post = lambda x: np.where(x == 0, np.array(1, dtype=x.dtype), x)
|
|
return partial(_rand_dtype, rng.randn, scale=3, post=post)
|
|
|
|
|
|
def rand_positive(rng):
|
|
post = lambda x: x + 1
|
|
return partial(_rand_dtype, rng.rand, scale=2, post=post)
|
|
|
|
|
|
def rand_small(rng):
|
|
return partial(_rand_dtype, rng.randn, scale=1e-3)
|
|
|
|
|
|
def rand_not_small(rng, offset=10.):
|
|
post = lambda x: x + np.where(x > 0, offset, -offset)
|
|
return partial(_rand_dtype, rng.randn, scale=3., post=post)
|
|
|
|
|
|
def rand_small_positive(rng):
|
|
return partial(_rand_dtype, rng.rand, scale=2e-5)
|
|
|
|
def rand_uniform(rng, low=0.0, high=1.0):
|
|
assert low < high
|
|
post = lambda x: x * (high - low) + low
|
|
return partial(_rand_dtype, rng.rand, post=post)
|
|
|
|
|
|
def rand_some_equal(rng):
|
|
|
|
def post(x):
|
|
x_ravel = x.ravel()
|
|
if len(x_ravel) == 0:
|
|
return x
|
|
flips = rng.rand(*np.shape(x)) < 0.5
|
|
return np.where(flips, x_ravel[0], x)
|
|
|
|
return partial(_rand_dtype, rng.randn, scale=100., post=post)
|
|
|
|
|
|
def rand_some_inf(rng):
|
|
"""Return a random sampler that produces infinities in floating types."""
|
|
base_rand = rand_default(rng)
|
|
|
|
# TODO: Complex numbers are not correctly tested
|
|
# If blocks should be switched in order, and relevant tests should be fixed
|
|
def rand(shape, dtype):
|
|
"""The random sampler function."""
|
|
if not _dtypes.issubdtype(dtype, np.floating):
|
|
# only float types have inf
|
|
return base_rand(shape, dtype)
|
|
|
|
if _dtypes.issubdtype(dtype, np.complexfloating):
|
|
base_dtype = np.real(np.array(0, dtype=dtype)).dtype
|
|
out = (rand(shape, base_dtype) +
|
|
np.array(1j, dtype) * rand(shape, base_dtype))
|
|
return _cast_to_shape(out, shape, dtype)
|
|
|
|
dims = _dims_of_shape(shape)
|
|
posinf_flips = rng.rand(*dims) < 0.1
|
|
neginf_flips = rng.rand(*dims) < 0.1
|
|
|
|
vals = base_rand(shape, dtype)
|
|
vals = np.where(posinf_flips, np.array(np.inf, dtype=dtype), vals)
|
|
vals = np.where(neginf_flips, np.array(-np.inf, dtype=dtype), vals)
|
|
|
|
return _cast_to_shape(np.asarray(vals, dtype=dtype), shape, dtype)
|
|
|
|
return rand
|
|
|
|
def rand_some_nan(rng):
|
|
"""Return a random sampler that produces nans in floating types."""
|
|
base_rand = rand_default(rng)
|
|
|
|
def rand(shape, dtype):
|
|
"""The random sampler function."""
|
|
if _dtypes.issubdtype(dtype, np.complexfloating):
|
|
base_dtype = np.real(np.array(0, dtype=dtype)).dtype
|
|
out = (rand(shape, base_dtype) +
|
|
np.array(1j, dtype) * rand(shape, base_dtype))
|
|
return _cast_to_shape(out, shape, dtype)
|
|
|
|
if not _dtypes.issubdtype(dtype, np.floating):
|
|
# only float types have inf
|
|
return base_rand(shape, dtype)
|
|
|
|
dims = _dims_of_shape(shape)
|
|
r = rng.rand(*dims)
|
|
nan_flips = r < 0.1
|
|
neg_nan_flips = r < 0.05
|
|
|
|
vals = base_rand(shape, dtype)
|
|
vals = np.where(nan_flips, np.array(np.nan, dtype=dtype), vals)
|
|
vals = np.where(neg_nan_flips, np.array(-np.nan, dtype=dtype), vals)
|
|
|
|
return _cast_to_shape(np.asarray(vals, dtype=dtype), shape, dtype)
|
|
|
|
return rand
|
|
|
|
def rand_some_inf_and_nan(rng):
|
|
"""Return a random sampler that produces infinities in floating types."""
|
|
base_rand = rand_default(rng)
|
|
|
|
# TODO: Complex numbers are not correctly tested
|
|
# If blocks should be switched in order, and relevant tests should be fixed
|
|
def rand(shape, dtype):
|
|
"""The random sampler function."""
|
|
if not _dtypes.issubdtype(dtype, np.floating):
|
|
# only float types have inf
|
|
return base_rand(shape, dtype)
|
|
|
|
if _dtypes.issubdtype(dtype, np.complexfloating):
|
|
base_dtype = np.real(np.array(0, dtype=dtype)).dtype
|
|
out = (rand(shape, base_dtype) +
|
|
np.array(1j, dtype) * rand(shape, base_dtype))
|
|
return _cast_to_shape(out, shape, dtype)
|
|
|
|
dims = _dims_of_shape(shape)
|
|
posinf_flips = rng.rand(*dims) < 0.1
|
|
neginf_flips = rng.rand(*dims) < 0.1
|
|
nan_flips = rng.rand(*dims) < 0.1
|
|
|
|
vals = base_rand(shape, dtype)
|
|
vals = np.where(posinf_flips, np.array(np.inf, dtype=dtype), vals)
|
|
vals = np.where(neginf_flips, np.array(-np.inf, dtype=dtype), vals)
|
|
vals = np.where(nan_flips, np.array(np.nan, dtype=dtype), vals)
|
|
|
|
return _cast_to_shape(np.asarray(vals, dtype=dtype), shape, dtype)
|
|
|
|
return rand
|
|
|
|
# TODO(mattjj): doesn't handle complex types
|
|
def rand_some_zero(rng):
|
|
"""Return a random sampler that produces some zeros."""
|
|
base_rand = rand_default(rng)
|
|
|
|
def rand(shape, dtype):
|
|
"""The random sampler function."""
|
|
dims = _dims_of_shape(shape)
|
|
zeros = rng.rand(*dims) < 0.5
|
|
|
|
vals = base_rand(shape, dtype)
|
|
vals = np.where(zeros, np.array(0, dtype=dtype), vals)
|
|
|
|
return _cast_to_shape(np.asarray(vals, dtype=dtype), shape, dtype)
|
|
|
|
return rand
|
|
|
|
|
|
def rand_int(rng, low=0, high=None):
|
|
def fn(shape, dtype):
|
|
nonlocal high
|
|
gen_dtype = dtype if np.issubdtype(dtype, np.integer) else np.int64
|
|
if low == 0 and high is None:
|
|
if np.issubdtype(dtype, np.integer):
|
|
high = np.iinfo(dtype).max
|
|
else:
|
|
raise ValueError("rand_int requires an explicit `high` value for "
|
|
"non-integer types.")
|
|
return rng.randint(low, high=high, size=shape,
|
|
dtype=gen_dtype).astype(dtype)
|
|
return fn
|
|
|
|
def rand_unique_int(rng, high=None):
|
|
def fn(shape, dtype):
|
|
return rng.choice(np.arange(high or math.prod(shape), dtype=dtype),
|
|
size=shape, replace=False)
|
|
return fn
|
|
|
|
def rand_indices_unique_along_axis(rng):
|
|
"""Sample an array of given shape containing indices up to dim (exclusive),
|
|
such that the indices are unique along the given axis.
|
|
Optionally, convert some of the resulting indices to negative indices."""
|
|
def fn(dim, shape, axis, allow_negative=True):
|
|
batch_size = math.prod(shape[:axis] + shape[axis:][1:])
|
|
idx = [
|
|
rng.choice(dim, size=shape[axis], replace=False)
|
|
for _ in range(batch_size)
|
|
]
|
|
idx = np.array(idx).reshape(batch_size, shape[axis])
|
|
idx = idx.reshape(shape[:axis] + shape[axis:][1:] + (shape[axis],))
|
|
idx = np.moveaxis(idx, -1, axis)
|
|
|
|
# assert that indices are unique along the given axis
|
|
count = partial(np.bincount, minlength=dim)
|
|
assert (np.apply_along_axis(count, axis, idx) <= 1).all()
|
|
|
|
if allow_negative:
|
|
mask = rng.choice([False, True], idx.shape)
|
|
idx[mask] -= dim
|
|
return idx
|
|
|
|
return fn
|
|
|
|
def rand_bool(rng):
|
|
def generator(shape, dtype):
|
|
return _cast_to_shape(
|
|
np.asarray(rng.rand(*_dims_of_shape(shape)) < 0.5, dtype=dtype),
|
|
shape, dtype)
|
|
return generator
|
|
|
|
def check_raises(thunk, err_type, msg):
|
|
try:
|
|
thunk()
|
|
assert False
|
|
except err_type as e:
|
|
assert str(e).startswith(msg), f"\n{e}\n\n{msg}\n"
|
|
|
|
def check_raises_regexp(thunk, err_type, pattern):
|
|
try:
|
|
thunk()
|
|
assert False
|
|
except err_type as e:
|
|
assert re.match(pattern, str(e)), f"{e}\n\n{pattern}\n"
|
|
|
|
|
|
def iter_eqns(jaxpr):
|
|
# TODO(necula): why doesn't this search in params?
|
|
yield from jaxpr.eqns
|
|
for subjaxpr in core.subjaxprs(jaxpr):
|
|
yield from iter_eqns(subjaxpr)
|
|
|
|
def assert_dot_precision(expected_precision, fun, *args):
|
|
jaxpr = api.make_jaxpr(fun)(*args)
|
|
precisions = [eqn.params['precision'] for eqn in iter_eqns(jaxpr.jaxpr)
|
|
if eqn.primitive == lax.dot_general_p]
|
|
for precision in precisions:
|
|
msg = f"Unexpected precision: {expected_precision} != {precision}"
|
|
if isinstance(precision, tuple):
|
|
assert precision[0] == expected_precision, msg
|
|
assert precision[1] == expected_precision, msg
|
|
else:
|
|
assert precision == expected_precision, msg
|
|
|
|
def assert_dot_preferred_element_type(expected, fun, *args, **kwargs):
|
|
jaxpr = api.make_jaxpr(partial(fun, **kwargs))(*args)
|
|
pref_eltypes = [eqn.params['preferred_element_type'] for eqn in iter_eqns(jaxpr.jaxpr)
|
|
if eqn.primitive == lax.dot_general_p]
|
|
for pref_eltype in pref_eltypes:
|
|
msg = f"Unexpected preferred_element_type: {expected} != {pref_eltype}"
|
|
assert expected == pref_eltype, msg
|
|
|
|
def cases_from_gens(*gens):
|
|
sizes = [1, 3, 10]
|
|
cases_per_size = int(NUM_GENERATED_CASES.value / len(sizes)) + 1
|
|
for size in sizes:
|
|
for i in range(cases_per_size):
|
|
yield (f'_{size}_{i}',) + tuple(gen(size) for gen in gens)
|
|
|
|
def named_cases_from_sampler(gen):
|
|
seen = set()
|
|
retries = 0
|
|
rng = npr.RandomState(42)
|
|
def choose_one(x):
|
|
if not isinstance(x, (list, tuple)):
|
|
x = list(x)
|
|
return [x[rng.randint(len(x))]]
|
|
while (len(seen) < NUM_GENERATED_CASES.value and
|
|
retries < _MAX_CASES_SAMPLING_RETRIES.value):
|
|
retries += 1
|
|
cases = list(gen(choose_one))
|
|
if not cases:
|
|
continue
|
|
if len(cases) > 1:
|
|
raise RuntimeError("Generator is expected to only return a single case when sampling")
|
|
case = cases[0]
|
|
if case["testcase_name"] in seen:
|
|
continue
|
|
retries = 0
|
|
seen.add(case["testcase_name"])
|
|
yield case
|
|
|
|
|
|
# Random sampling for every parameterized test is expensive. Do it once and
|
|
# cache the result.
|
|
@functools.cache
|
|
def _choice(n, m):
|
|
rng = np.random.RandomState(42)
|
|
return rng.choice(n, size=m, replace=False)
|
|
|
|
def sample_product_testcases(*args, **kw):
|
|
"""Non-decorator form of sample_product."""
|
|
args = [list(arg) for arg in args]
|
|
kw = [(k, list(v)) for k, v in kw.items()]
|
|
n = math.prod(len(a) for a in args) * math.prod(len(v) for _, v in kw)
|
|
testcases = []
|
|
for i in _choice(n, min(n, NUM_GENERATED_CASES.value)):
|
|
testcase = {}
|
|
for a in args:
|
|
testcase.update(a[i % len(a)])
|
|
i //= len(a)
|
|
for k, v in kw:
|
|
testcase[k] = v[i % len(v)]
|
|
i //= len(v)
|
|
testcases.append(testcase)
|
|
return testcases
|
|
|
|
def sample_product(*args, **kw):
|
|
"""Decorator that samples from a cartesian product of test cases.
|
|
|
|
Similar to absltest.parameterized.product(), except that it samples from the
|
|
cartesian product rather than returning the whole thing.
|
|
|
|
Arguments:
|
|
*args: each positional argument is a list of dictionaries. The entries
|
|
in a dictionary correspond to name=value argument pairs; one dictionary
|
|
will be chosen for each test case. This allows multiple parameters to be
|
|
correlated.
|
|
**kw: each keyword argument is a list of values. One value will be chosen
|
|
for each test case.
|
|
"""
|
|
return parameterized.parameters(*sample_product_testcases(*args, **kw))
|
|
|
|
# We use a reader-writer lock to protect test execution. Tests that may run in
|
|
# parallel acquire a read lock; tests that are not thread-safe acquire a write
|
|
# lock.
|
|
if hasattr(util, 'Mutex'):
|
|
_test_rwlock = util.Mutex()
|
|
|
|
def _run_one_test(test: unittest.TestCase, result: ThreadSafeTestResult):
|
|
if getattr(test.__class__, "thread_hostile", False):
|
|
_test_rwlock.writer_lock()
|
|
try:
|
|
test(result) # type: ignore
|
|
finally:
|
|
_test_rwlock.writer_unlock()
|
|
else:
|
|
_test_rwlock.reader_lock()
|
|
try:
|
|
test(result) # type: ignore
|
|
finally:
|
|
_test_rwlock.reader_unlock()
|
|
|
|
|
|
@contextmanager
|
|
def thread_unsafe_test():
|
|
"""Decorator for tests that are not thread-safe.
|
|
|
|
Note: this decorator (naturally) only applies to what it wraps, not to, say,
|
|
code in separate setUp() or tearDown() methods.
|
|
"""
|
|
if TEST_NUM_THREADS.value <= 0:
|
|
yield
|
|
return
|
|
|
|
_test_rwlock.assert_reader_held()
|
|
_test_rwlock.reader_unlock()
|
|
_test_rwlock.writer_lock()
|
|
try:
|
|
yield
|
|
finally:
|
|
_test_rwlock.writer_unlock()
|
|
_test_rwlock.reader_lock()
|
|
else:
|
|
# TODO(phawkins): remove this branch when jaxlib 0.5.0 is the minimum.
|
|
_test_rwlock = threading.Lock()
|
|
|
|
def _run_one_test(test: unittest.TestCase, result: ThreadSafeTestResult):
|
|
_test_rwlock.acquire()
|
|
try:
|
|
test(result) # type: ignore
|
|
finally:
|
|
_test_rwlock.release()
|
|
|
|
|
|
@contextmanager
|
|
def thread_unsafe_test():
|
|
yield # No reader-writer lock, so we get no parallelism.
|
|
|
|
|
|
def thread_unsafe_test_class():
|
|
"Decorator that marks a TestCase class as thread-hostile."
|
|
def f(klass):
|
|
assert issubclass(klass, unittest.TestCase), type(klass)
|
|
klass.thread_hostile = True
|
|
return klass
|
|
return f
|
|
|
|
|
|
class ThreadSafeTestResult:
|
|
"""
|
|
Wraps a TestResult to make it thread safe.
|
|
|
|
We do this by accumulating API calls and applying them in a batch under a
|
|
lock at the conclusion of each test case.
|
|
|
|
We duck type instead of inheriting from TestResult because we aren't actually
|
|
a perfect implementation of TestResult, and would rather get a loud error
|
|
for things we haven't implemented.
|
|
"""
|
|
def __init__(self, lock: threading.Lock, result: unittest.TestResult):
|
|
self.lock = lock
|
|
self.test_result = result
|
|
self.actions: list[Callable] = []
|
|
|
|
def startTest(self, test: unittest.TestCase):
|
|
del test
|
|
self.start_time = time.time()
|
|
|
|
def stopTest(self, test: unittest.TestCase):
|
|
stop_time = time.time()
|
|
with self.lock:
|
|
# If test_result is an ABSL _TextAndXMLTestResult we override how it gets
|
|
# the time. This affects the timing that shows up in the XML output
|
|
# consumed by CI.
|
|
time_getter = getattr(self.test_result, "time_getter", None)
|
|
try:
|
|
self.test_result.time_getter = lambda: self.start_time
|
|
self.test_result.startTest(test)
|
|
for callback in self.actions:
|
|
callback()
|
|
self.test_result.time_getter = lambda: stop_time
|
|
self.test_result.stopTest(test)
|
|
finally:
|
|
if time_getter is not None:
|
|
self.test_result.time_getter = time_getter
|
|
|
|
def addSuccess(self, test: unittest.TestCase):
|
|
self.actions.append(lambda: self.test_result.addSuccess(test))
|
|
|
|
def addSkip(self, test: unittest.TestCase, reason: str):
|
|
self.actions.append(lambda: self.test_result.addSkip(test, reason))
|
|
|
|
def addError(self, test: unittest.TestCase, err):
|
|
self.actions.append(lambda: self.test_result.addError(test, err))
|
|
|
|
def addFailure(self, test: unittest.TestCase, err):
|
|
self.actions.append(lambda: self.test_result.addFailure(test, err))
|
|
|
|
def addExpectedFailure(self, test: unittest.TestCase, err):
|
|
self.actions.append(lambda: self.test_result.addExpectedFailure(test, err))
|
|
|
|
def addDuration(self, test: unittest.TestCase, elapsed):
|
|
self.actions.append(lambda: self.test_result.addDuration(test, elapsed))
|
|
|
|
|
|
class JaxTestSuite(unittest.TestSuite):
|
|
"""Runs tests in parallel using threads if TEST_NUM_THREADS is > 1.
|
|
|
|
Caution: this test suite does not run setUpClass or setUpModule methods if
|
|
thread parallelism is enabled.
|
|
"""
|
|
|
|
def __init__(self, suite: unittest.TestSuite):
|
|
super().__init__(list(suite))
|
|
|
|
def run(self, result: unittest.TestResult, debug: bool = False) -> unittest.TestResult:
|
|
if TEST_NUM_THREADS.value <= 0:
|
|
return super().run(result)
|
|
|
|
test_warning_util.install_threadsafe_warning_handlers()
|
|
|
|
executor = ThreadPoolExecutor(TEST_NUM_THREADS.value)
|
|
lock = threading.Lock()
|
|
futures = []
|
|
|
|
def run_test(test):
|
|
"Recursively runs tests in a test suite or test case."
|
|
if isinstance(test, unittest.TestSuite):
|
|
for subtest in test:
|
|
run_test(subtest)
|
|
else:
|
|
test_result = ThreadSafeTestResult(lock, result)
|
|
futures.append(executor.submit(_run_one_test, test, test_result))
|
|
|
|
with executor:
|
|
run_test(self)
|
|
for future in futures:
|
|
future.result()
|
|
|
|
return result
|
|
|
|
|
|
class JaxTestLoader(absltest.TestLoader):
|
|
suiteClass = JaxTestSuite
|
|
|
|
def getTestCaseNames(self, testCaseClass):
|
|
names = super().getTestCaseNames(testCaseClass)
|
|
if _TEST_TARGETS.value:
|
|
pattern = re.compile(_TEST_TARGETS.value)
|
|
names = [name for name in names
|
|
if pattern.search(f"{testCaseClass.__name__}.{name}")]
|
|
if _EXCLUDE_TEST_TARGETS.value:
|
|
pattern = re.compile(_EXCLUDE_TEST_TARGETS.value)
|
|
names = [name for name in names
|
|
if not pattern.search(f"{testCaseClass.__name__}.{name}")]
|
|
return names
|
|
|
|
|
|
def with_config(**kwds):
|
|
"""Test case decorator for subclasses of JaxTestCase"""
|
|
def decorator(cls):
|
|
assert inspect.isclass(cls) and issubclass(cls, JaxTestCase), "@with_config can only wrap JaxTestCase class definitions."
|
|
cls._default_thread_local_config = {}
|
|
for b in cls.__bases__:
|
|
cls._default_thread_local_config.update(b._default_thread_local_config)
|
|
cls._default_thread_local_config.update(kwds)
|
|
return cls
|
|
return decorator
|
|
|
|
def with_global_config(**kwds):
|
|
"""Test case decorator for subclasses of JaxTestCase"""
|
|
def decorator(cls):
|
|
assert inspect.isclass(cls) and issubclass(cls, JaxTestCase), "@with_config can only wrap JaxTestCase class definitions."
|
|
cls._default_global_config = {}
|
|
for b in cls.__bases__:
|
|
cls._default_global_config.update(b._default_global_config)
|
|
cls._default_global_config.update(kwds)
|
|
return cls
|
|
return decorator
|
|
|
|
|
|
def promote_like_jnp(fun, inexact=False):
|
|
"""Decorator that promotes the arguments of `fun` to `jnp.result_type(*args)`.
|
|
|
|
jnp and np have different type promotion semantics; this decorator allows
|
|
tests make an np reference implementation act more like a jnp
|
|
implementation.
|
|
"""
|
|
_promote = promote_dtypes_inexact if inexact else promote_dtypes
|
|
def wrapper(*args, **kw):
|
|
flat_args, tree = tree_flatten(args)
|
|
args = tree_unflatten(tree, _promote(*flat_args))
|
|
return fun(*args, **kw)
|
|
return wrapper
|
|
|
|
@contextmanager
|
|
def global_config_context(**kwds):
|
|
original_config = {}
|
|
try:
|
|
for key, value in kwds.items():
|
|
original_config[key] = config._read(key)
|
|
config.update(key, value)
|
|
yield
|
|
finally:
|
|
for key, value in original_config.items():
|
|
config.update(key, value)
|
|
|
|
@contextmanager
|
|
def thread_local_config_context(**kwds):
|
|
stack = ExitStack()
|
|
for config_name, value in kwds.items():
|
|
stack.enter_context(config.config_states[config_name](value))
|
|
try:
|
|
yield
|
|
finally:
|
|
stack.close()
|
|
|
|
class NotPresent:
|
|
def __repr__(self):
|
|
return "<not present>"
|
|
|
|
|
|
@contextmanager
|
|
def assert_global_configs_unchanged():
|
|
starting_config = jax.config.values.copy()
|
|
yield
|
|
ending_config = jax.config.values
|
|
|
|
if starting_config == ending_config:
|
|
return
|
|
differing = {k: (starting_config.get(k, NotPresent()), ending_config.get(k, NotPresent()))
|
|
for k in (starting_config.keys() | ending_config.keys())
|
|
if (k not in starting_config or k not in ending_config
|
|
or starting_config[k] != ending_config[k])}
|
|
raise AssertionError(f"Test changed global config values. Differing values are: {differing}")
|
|
|
|
|
|
class JaxTestCase(parameterized.TestCase):
|
|
"""Base class for JAX tests including numerical checks and boilerplate."""
|
|
_default_global_config: dict[str, Any] = {}
|
|
_default_thread_local_config = {
|
|
'jax_enable_checks': True,
|
|
'jax_numpy_dtype_promotion': 'strict',
|
|
'jax_numpy_rank_promotion': 'raise',
|
|
'jax_traceback_filtering': 'off',
|
|
'jax_legacy_prng_key': 'error',
|
|
}
|
|
|
|
_context_stack: ExitStack | None = None
|
|
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.enter_context(assert_global_configs_unchanged())
|
|
|
|
# We use the adler32 hash for two reasons.
|
|
# a) it is deterministic run to run, unlike hash() which is randomized.
|
|
# b) it returns values in int32 range, which RandomState requires.
|
|
self._rng = npr.RandomState(zlib.adler32(self._testMethodName.encode()))
|
|
|
|
# TODO(phawkins): use TestCase.enterContext once Python 3.11 is the minimum
|
|
# version.
|
|
self._context_stack = ExitStack()
|
|
self.addCleanup(self._context_stack.close)
|
|
stack = self._context_stack
|
|
stack.enter_context(global_config_context(**self._default_global_config))
|
|
for config_name, value in self._default_thread_local_config.items():
|
|
stack.enter_context(jax._src.config.config_states[config_name](value))
|
|
|
|
if TEST_WITH_PERSISTENT_COMPILATION_CACHE.value:
|
|
assert TEST_NUM_THREADS.value <= 1, "Persistent compilation cache is not thread-safe."
|
|
stack.enter_context(config.enable_compilation_cache(True))
|
|
stack.enter_context(config.raise_persistent_cache_errors(True))
|
|
stack.enter_context(config.persistent_cache_min_compile_time_secs(0))
|
|
stack.enter_context(config.persistent_cache_min_entry_size_bytes(0))
|
|
tmp_dir = stack.enter_context(tempfile.TemporaryDirectory())
|
|
stack.enter_context(config.compilation_cache_dir(tmp_dir))
|
|
stack.callback(compilation_cache.reset_cache)
|
|
|
|
def tearDown(self) -> None:
|
|
assert core.reset_trace_state()
|
|
super().tearDown()
|
|
|
|
def rng(self):
|
|
return self._rng
|
|
|
|
def assertDeprecationWarnsOrRaises(self, deprecation_id: str, message: str):
|
|
"""Assert warning or error, depending on deprecation state.
|
|
|
|
For use with functions that call :func:`jax._src.deprecations.warn`.
|
|
"""
|
|
if deprecations.is_accelerated(deprecation_id):
|
|
return self.assertRaisesRegex(ValueError, message)
|
|
else:
|
|
return self.assertWarnsRegex(DeprecationWarning, message)
|
|
|
|
def assertArraysEqual(self, x, y, *, check_dtypes=True, err_msg='',
|
|
allow_object_dtype=False, verbose=True):
|
|
"""Assert that x and y arrays are exactly equal."""
|
|
if check_dtypes:
|
|
self.assertDtypesMatch(x, y)
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
|
|
if (not allow_object_dtype) and (x.dtype == object or y.dtype == object):
|
|
# See https://github.com/jax-ml/jax/issues/17867
|
|
raise TypeError(
|
|
"assertArraysEqual may be poorly behaved when np.asarray casts to dtype=object. "
|
|
"If comparing PRNG keys, consider random_test.KeyArrayTest.assertKeysEqual. "
|
|
"If comparing collections of arrays, consider using assertAllClose. "
|
|
"To let this test proceed anyway, pass allow_object_dtype=True.")
|
|
|
|
# Work around https://github.com/numpy/numpy/issues/18992
|
|
with np.errstate(over='ignore'):
|
|
np.testing.assert_array_equal(x, y, err_msg=err_msg,
|
|
verbose=verbose)
|
|
|
|
def assertArraysAllClose(self, x, y, *, check_dtypes=True, atol=None,
|
|
rtol=None, err_msg=''):
|
|
"""Assert that x and y are close (up to numerical tolerances)."""
|
|
self.assertEqual(x.shape, y.shape)
|
|
atol = max(tolerance(_dtype(x), atol), tolerance(_dtype(y), atol))
|
|
rtol = max(tolerance(_dtype(x), rtol), tolerance(_dtype(y), rtol))
|
|
|
|
_assert_numpy_allclose(x, y, atol=atol, rtol=rtol, err_msg=err_msg)
|
|
|
|
if check_dtypes:
|
|
self.assertDtypesMatch(x, y)
|
|
|
|
def assertDtypesMatch(self, x, y, *, canonicalize_dtypes=True):
|
|
if not config.enable_x64.value and canonicalize_dtypes:
|
|
self.assertEqual(_dtypes.canonicalize_dtype(_dtype(x), allow_extended_dtype=True),
|
|
_dtypes.canonicalize_dtype(_dtype(y), allow_extended_dtype=True))
|
|
else:
|
|
self.assertEqual(_dtype(x), _dtype(y))
|
|
|
|
def assertAllClose(self, x, y, *, check_dtypes=True, atol=None, rtol=None,
|
|
canonicalize_dtypes=True, err_msg=''):
|
|
"""Assert that x and y, either arrays or nested tuples/lists, are close."""
|
|
if isinstance(x, dict):
|
|
self.assertIsInstance(y, dict)
|
|
self.assertEqual(set(x.keys()), set(y.keys()))
|
|
for k in x.keys():
|
|
self.assertAllClose(x[k], y[k], check_dtypes=check_dtypes, atol=atol,
|
|
rtol=rtol, canonicalize_dtypes=canonicalize_dtypes,
|
|
err_msg=err_msg)
|
|
elif is_sequence(x) and not hasattr(x, '__array__'):
|
|
self.assertTrue(is_sequence(y) and not hasattr(y, '__array__'))
|
|
self.assertEqual(len(x), len(y))
|
|
for x_elt, y_elt in zip(x, y):
|
|
self.assertAllClose(x_elt, y_elt, check_dtypes=check_dtypes, atol=atol,
|
|
rtol=rtol, canonicalize_dtypes=canonicalize_dtypes,
|
|
err_msg=err_msg)
|
|
elif hasattr(x, '__array__') or np.isscalar(x):
|
|
self.assertTrue(hasattr(y, '__array__') or np.isscalar(y))
|
|
if check_dtypes:
|
|
self.assertDtypesMatch(x, y, canonicalize_dtypes=canonicalize_dtypes)
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
self.assertArraysAllClose(x, y, check_dtypes=False, atol=atol, rtol=rtol,
|
|
err_msg=err_msg)
|
|
elif x == y:
|
|
return
|
|
else:
|
|
raise TypeError((type(x), type(y)))
|
|
|
|
def assertMultiLineStrippedEqual(self, expected, what):
|
|
"""Asserts two strings are equal, after dedenting and stripping each line."""
|
|
expected = textwrap.dedent(expected)
|
|
what = textwrap.dedent(what)
|
|
ignore_space_re = re.compile(r'\s*\n\s*')
|
|
expected_clean = re.sub(ignore_space_re, '\n', expected.strip())
|
|
what_clean = re.sub(ignore_space_re, '\n', what.strip())
|
|
if what_clean != expected_clean:
|
|
# Print it so we can copy-and-paste it into the test
|
|
print(f"Found\n{what}\n")
|
|
self.assertMultiLineEqual(expected_clean, what_clean,
|
|
msg=f"Found\n{what}\nExpecting\n{expected}")
|
|
|
|
|
|
@contextmanager
|
|
def assertNoWarnings(self):
|
|
with test_warning_util.raise_on_warnings():
|
|
yield
|
|
|
|
# We replace assertWarns and assertWarnsRegex with functions that use the
|
|
# thread-safe warning utilities. Unlike the unittest versions these only
|
|
# function as context managers.
|
|
@contextmanager
|
|
def assertWarns(self, warning, *, msg=None):
|
|
with test_warning_util.record_warnings() as ws:
|
|
yield
|
|
for w in ws:
|
|
if not isinstance(w.message, warning):
|
|
continue
|
|
if msg is not None and msg not in str(w.message):
|
|
continue
|
|
return
|
|
self.fail(f"Expected warning not found {warning}:'{msg}', got "
|
|
f"{ws}")
|
|
|
|
@contextmanager
|
|
def assertWarnsRegex(self, warning, regex):
|
|
if regex is not None:
|
|
regex = re.compile(regex)
|
|
|
|
with test_warning_util.record_warnings() as ws:
|
|
yield
|
|
for w in ws:
|
|
if not isinstance(w.message, warning):
|
|
continue
|
|
if regex is not None and not regex.search(str(w.message)):
|
|
continue
|
|
return
|
|
self.fail(f"Expected warning not found {warning}:'{regex}', got "
|
|
f"{ws}")
|
|
|
|
|
|
def _CompileAndCheck(self, fun, args_maker, *, check_dtypes=True, tol=None,
|
|
rtol=None, atol=None, check_cache_misses=True):
|
|
"""Helper method for running JAX compilation and allclose assertions."""
|
|
args = args_maker()
|
|
|
|
def wrapped_fun(*args):
|
|
self.assertTrue(python_should_be_executing)
|
|
return fun(*args)
|
|
|
|
python_should_be_executing = True
|
|
python_ans = fun(*args)
|
|
|
|
python_shapes = tree_map(lambda x: np.shape(x), python_ans)
|
|
np_shapes = tree_map(lambda x: np.shape(np.asarray(x)), python_ans)
|
|
self.assertEqual(python_shapes, np_shapes)
|
|
|
|
cache_misses = dispatch.xla_primitive_callable.cache_info().misses
|
|
python_ans = fun(*args)
|
|
if check_cache_misses and TEST_NUM_THREADS.value <= 1:
|
|
self.assertEqual(
|
|
cache_misses, dispatch.xla_primitive_callable.cache_info().misses,
|
|
"Compilation detected during second call of {} in op-by-op "
|
|
"mode.".format(fun))
|
|
|
|
cfun = api.jit(wrapped_fun)
|
|
python_should_be_executing = True
|
|
monitored_ans = cfun(*args)
|
|
|
|
python_should_be_executing = False
|
|
compiled_ans = cfun(*args)
|
|
|
|
self.assertAllClose(python_ans, monitored_ans, check_dtypes=check_dtypes,
|
|
atol=atol or tol, rtol=rtol or tol)
|
|
self.assertAllClose(python_ans, compiled_ans, check_dtypes=check_dtypes,
|
|
atol=atol or tol, rtol=rtol or tol)
|
|
|
|
args = args_maker()
|
|
|
|
python_should_be_executing = True
|
|
python_ans = fun(*args)
|
|
|
|
python_should_be_executing = False
|
|
compiled_ans = cfun(*args)
|
|
|
|
self.assertAllClose(python_ans, compiled_ans, check_dtypes=check_dtypes,
|
|
atol=atol or tol, rtol=rtol or tol)
|
|
|
|
def _CheckAgainstNumpy(self, numpy_reference_op, lax_op, args_maker,
|
|
check_dtypes=True, tol=None, atol=None, rtol=None,
|
|
canonicalize_dtypes=True):
|
|
args = args_maker()
|
|
lax_ans = lax_op(*args)
|
|
numpy_ans = numpy_reference_op(*args)
|
|
self.assertAllClose(numpy_ans, lax_ans, check_dtypes=check_dtypes,
|
|
atol=atol or tol, rtol=rtol or tol,
|
|
canonicalize_dtypes=canonicalize_dtypes)
|
|
|
|
_PJIT_IMPLEMENTATION = jax.jit
|
|
_PJIT_IMPLEMENTATION._name = "jit"
|
|
_NOOP_JIT_IMPLEMENTATION = lambda x, *args, **kwargs: x
|
|
_NOOP_JIT_IMPLEMENTATION._name = "noop"
|
|
|
|
JIT_IMPLEMENTATION = (
|
|
_PJIT_IMPLEMENTATION,
|
|
_NOOP_JIT_IMPLEMENTATION,
|
|
)
|
|
|
|
class BufferDonationTestCase(JaxTestCase):
|
|
def assertDeleted(self, x):
|
|
self.assertTrue(x.is_deleted())
|
|
|
|
def assertNotDeleted(self, x):
|
|
self.assertFalse(x.is_deleted())
|
|
|
|
|
|
ignore_warning = test_warning_util.ignore_warning
|
|
|
|
# -------------------- Mesh parametrization helpers --------------------
|
|
|
|
MeshSpec = list[tuple[str, int]]
|
|
|
|
@contextmanager
|
|
def with_mesh(named_shape: MeshSpec) -> Generator[None, None, None]:
|
|
"""Test utility for setting up meshes given mesh data from `schedules`."""
|
|
# This is similar to the `with_mesh` function above, but isn't a decorator.
|
|
axis_names, shape = unzip2(named_shape)
|
|
size = math.prod(shape)
|
|
local_devices = list(jax.local_devices())
|
|
if len(local_devices) < size:
|
|
raise unittest.SkipTest(f"Test requires {size} local devices")
|
|
mesh_devices = np.array(local_devices[:size]).reshape(shape) # type: ignore
|
|
with jax.sharding.Mesh(mesh_devices, axis_names):
|
|
yield
|
|
|
|
def with_mesh_from_kwargs(f):
|
|
return lambda *args, **kwargs: with_mesh(kwargs['mesh'])(f)(*args, **kwargs)
|
|
|
|
def with_and_without_mesh(f):
|
|
return parameterized.named_parameters(
|
|
{"testcase_name": name, "mesh": mesh, "axis_resources": axis_resources}
|
|
for name, mesh, axis_resources in (
|
|
('', (), ()),
|
|
('Mesh', (('x', 2),), (('i', 'x'),))
|
|
))(with_mesh_from_kwargs(f))
|
|
|
|
def with_user_mesh(sizes, names, axis_types=None):
|
|
axis_types = ({mesh_lib.AxisTypes.Explicit: names}
|
|
if axis_types is None else axis_types)
|
|
def decorator(fn):
|
|
def mesh_fn(*args, **kwargs):
|
|
mesh = create_mesh(sizes, names, axis_types=axis_types)
|
|
with mesh_lib.use_mesh(mesh):
|
|
return fn(*args, **kwargs, mesh=mesh)
|
|
return mesh_fn
|
|
return decorator
|
|
|
|
|
|
def create_mesh(mesh_shape, axis_names, iota_order=False, axis_types=None):
|
|
size = math.prod(mesh_shape)
|
|
if len(jax.devices()) < size:
|
|
raise unittest.SkipTest(f"Test requires {size} global devices.")
|
|
if iota_order:
|
|
devices = sorted(jax.devices(), key=lambda d: d.id)
|
|
mesh_devices = np.array(devices[:size]).reshape(mesh_shape)
|
|
return jax.sharding.Mesh(mesh_devices, axis_names, axis_types=axis_types)
|
|
else:
|
|
if axis_types is None:
|
|
explicit_axes = auto_axes = manual_axes = None
|
|
else:
|
|
explicit_axes = axis_types.get(mesh_lib.AxisTypes.Explicit, None)
|
|
auto_axes = axis_types.get(mesh_lib.AxisTypes.Auto, None)
|
|
manual_axes = axis_types.get(mesh_lib.AxisTypes.Manual, None)
|
|
return jax.make_mesh(mesh_shape, axis_names, explicit_axes=explicit_axes,
|
|
auto_axes=auto_axes,
|
|
manual_axes=manual_axes)
|
|
|
|
class _cached_property:
|
|
null = object()
|
|
|
|
def __init__(self, method):
|
|
self._method = method
|
|
self._value = self.null
|
|
|
|
def __get__(self, obj, cls):
|
|
if self._value is self.null:
|
|
self._value = self._method(obj)
|
|
return self._value
|
|
|
|
|
|
class _LazyDtypes:
|
|
"""A class that unifies lists of supported dtypes.
|
|
|
|
These could be module-level constants, but device_under_test() is not always
|
|
known at import time, so we need to define these lists lazily.
|
|
"""
|
|
def supported(self, dtypes):
|
|
supported = supported_dtypes()
|
|
return type(dtypes)(d for d in dtypes if d in supported)
|
|
|
|
@_cached_property
|
|
def custom_floats(self):
|
|
float_dtypes = [
|
|
_dtypes.bfloat16,
|
|
_dtypes.float8_e4m3b11fnuz,
|
|
_dtypes.float8_e4m3fn,
|
|
_dtypes.float8_e4m3fnuz,
|
|
_dtypes.float8_e5m2,
|
|
_dtypes.float8_e5m2fnuz,
|
|
]
|
|
if _dtypes.float8_e3m4 is not None:
|
|
float_dtypes += [_dtypes.float8_e3m4]
|
|
if _dtypes.float8_e4m3 is not None:
|
|
float_dtypes += [_dtypes.float8_e4m3]
|
|
if _dtypes.float8_e8m0fnu is not None:
|
|
float_dtypes += [_dtypes.float8_e8m0fnu]
|
|
return self.supported(float_dtypes)
|
|
|
|
@_cached_property
|
|
def floating(self):
|
|
return self.supported([np.float32, np.float64])
|
|
|
|
@_cached_property
|
|
def all_floating(self):
|
|
return self.supported([_dtypes.bfloat16, np.float16, np.float32, np.float64])
|
|
|
|
@_cached_property
|
|
def integer(self):
|
|
return self.supported([np.int32, np.int64])
|
|
|
|
@_cached_property
|
|
def all_integer(self):
|
|
return self.supported([np.int8, np.int16, np.int32, np.int64])
|
|
|
|
@_cached_property
|
|
def unsigned(self):
|
|
return self.supported([np.uint32, np.uint64])
|
|
|
|
@_cached_property
|
|
def all_unsigned(self):
|
|
return self.supported([np.uint8, np.uint16, np.uint32, np.uint64])
|
|
|
|
@_cached_property
|
|
def complex(self):
|
|
return self.supported([np.complex64, np.complex128])
|
|
|
|
@_cached_property
|
|
def boolean(self):
|
|
return self.supported([np.bool_])
|
|
|
|
@_cached_property
|
|
def inexact(self):
|
|
return self.floating + self.complex
|
|
|
|
@_cached_property
|
|
def all_inexact(self):
|
|
return self.all_floating + self.complex
|
|
|
|
@_cached_property
|
|
def numeric(self):
|
|
return self.floating + self.integer + self.unsigned + self.complex
|
|
|
|
@_cached_property
|
|
def all(self):
|
|
return (self.all_floating + self.all_integer + self.all_unsigned +
|
|
self.complex + self.boolean)
|
|
|
|
|
|
dtypes = _LazyDtypes()
|
|
|
|
|
|
def strict_promotion_if_dtypes_match(dtypes):
|
|
"""
|
|
Context manager to enable strict promotion if all dtypes match,
|
|
and enable standard dtype promotion otherwise.
|
|
"""
|
|
if all(dtype == dtypes[0] for dtype in dtypes):
|
|
return jax.numpy_dtype_promotion('strict')
|
|
return jax.numpy_dtype_promotion('standard')
|
|
|
|
_version_regex = re.compile(r"([0-9]+(?:\.[0-9]+)*)(?:(rc|dev).*)?")
|
|
def parse_version(v: str) -> tuple[int, ...]:
|
|
m = _version_regex.match(v)
|
|
if m is None:
|
|
raise ValueError(f"Unable to parse version '{v}'")
|
|
return tuple(int(x) for x in m.group(1).split('.'))
|
|
|
|
def numpy_version():
|
|
return parse_version(np.__version__)
|
|
|
|
def parameterized_filterable(*,
|
|
kwargs: Sequence[dict[str, Any]],
|
|
testcase_name: Callable[[dict[str, Any]], str] | None = None,
|
|
one_containing: str | None = None,
|
|
):
|
|
"""Decorator for named parameterized tests, with filtering support.
|
|
|
|
Works like ``parameterized.named_parameters``, except that it sanitizes the test
|
|
names so that we can use ``pytest -k`` and ``python test.py -k`` test filtering.
|
|
This means, e.g., that many special characters are replaced with `_`.
|
|
It also supports the ``one_containing`` arg to select one of the tests, while
|
|
leaving the name unchanged, which is useful for IDEs to be able to easily
|
|
pick up the enclosing test name.
|
|
|
|
Usage:
|
|
@jtu.parameterized_filterable(
|
|
# one_containing="a_4",
|
|
[dict(a=4, b=5),
|
|
dict(a=5, b=4)])
|
|
def test_my_test(self, *, a, b): ...
|
|
|
|
Args:
|
|
kwargs: Each entry is a set of kwargs to be passed to the test function.
|
|
testcase_name: Optionally, a function to construct the testcase_name from
|
|
one kwargs dict. If not given then ``kwargs`` may contain ``testcase_name`` and
|
|
otherwise the test case name is constructed as ``str(kwarg)``.
|
|
We sanitize the test names to work with -k test filters. See
|
|
``sanitize_test_name``.
|
|
one_containing: If given, then leaves the test name unchanged, and use
|
|
only one of the ``kwargs`` whose `testcase_name` includes ``one_containing``.
|
|
"""
|
|
# Ensure that all kwargs contain a testcase_name
|
|
kwargs_with_testcase_name: Sequence[dict[str, Any]]
|
|
if testcase_name is not None:
|
|
kwargs_with_testcase_name = [
|
|
dict(testcase_name=sanitize_test_name(str(testcase_name(kw))), **kw)
|
|
for kw in kwargs]
|
|
else:
|
|
for kw in kwargs:
|
|
testcase_name = kw.get("testcase_name")
|
|
if testcase_name is None:
|
|
testcase_name = "_".join(f"{k}={kw[k]}" # type: ignore
|
|
for k in sorted(kw.keys()))
|
|
kw["testcase_name"] = sanitize_test_name(testcase_name) # type: ignore
|
|
|
|
kwargs_with_testcase_name = kwargs
|
|
if one_containing is not None:
|
|
filtered = tuple(kw for kw in kwargs_with_testcase_name
|
|
if one_containing in kw["testcase_name"])
|
|
assert filtered, (
|
|
f"No testcase_name contains '{one_containing}'. "
|
|
"The testcase_name values are\n " +
|
|
"\n ".join(kw["testcase_name"] for kw in kwargs_with_testcase_name))
|
|
kw = filtered[0]
|
|
kw["testcase_name"] = ""
|
|
return parameterized.named_parameters([kw])
|
|
else:
|
|
return parameterized.named_parameters(*kwargs_with_testcase_name)
|
|
|
|
@contextmanager
|
|
def register_event_duration_listener(callback):
|
|
"""Manages registering/unregistering an event duration listener callback."""
|
|
try:
|
|
monitoring.register_event_duration_secs_listener(callback)
|
|
yield
|
|
finally:
|
|
monitoring._unregister_event_duration_listener_by_callback(callback)
|
|
|
|
|
|
@contextmanager
|
|
def set_env(**kwargs):
|
|
"""Context manager to temporarily set/unset one or more environment variables.
|
|
|
|
Caution: setting environment variables is not thread-safe. If you use this
|
|
utility, you must annotate your test using, e.g., @thread_unsafe_test() or
|
|
@thread_unsafe_test_class().
|
|
|
|
Examples:
|
|
|
|
>>> import os
|
|
>>> os.environ['my_var'] = 'original'
|
|
|
|
>>> with set_env(my_var=None, other_var='some_value'):
|
|
... print("my_var is set:", 'my_var' in os.environ)
|
|
... print("other_var =", os.environ['other_var'])
|
|
...
|
|
my_var is set: False
|
|
other_var = some_value
|
|
|
|
>>> os.environ['my_var']
|
|
'original'
|
|
>>> 'other_var' in os.environ
|
|
False
|
|
"""
|
|
original = {key: os.environ.pop(key, None) for key in kwargs}
|
|
os.environ.update({k: v for k, v in kwargs.items() if v is not None})
|
|
try:
|
|
yield
|
|
finally:
|
|
_ = [os.environ.pop(key, None) for key in kwargs]
|
|
os.environ.update({k: v for k, v in original.items() if v is not None})
|
|
|
|
def fwd_bwd_jaxprs(f, *example_args):
|
|
fwd_jaxpr, (y_shape, res_shape) = jax.make_jaxpr(
|
|
lambda *args: jax.vjp(f, *args), return_shape=True)(*example_args)
|
|
bwd_jaxpr = jax.make_jaxpr(lambda res, outs: res(outs))(res_shape, y_shape)
|
|
return fwd_jaxpr, bwd_jaxpr
|
|
|
|
|
|
def numpy_vecdot(x, y, axis):
|
|
"""Implementation of numpy.vecdot for testing on numpy < 2.0.0"""
|
|
if numpy_version() >= (2, 0, 0):
|
|
raise ValueError("should be calling vecdot directly on numpy 2.0.0")
|
|
x = np.moveaxis(x, axis, -1)
|
|
y = np.moveaxis(y, axis, -1)
|
|
x, y = np.broadcast_arrays(x, y)
|
|
return np.matmul(np.conj(x[..., None, :]), y[..., None])[..., 0, 0]
|
|
|
|
|
|
def complex_plane_sample(dtype, size_re=10, size_im=None):
|
|
"""Return a 2-D array of complex numbers that covers the complex plane
|
|
with a grid of samples.
|
|
|
|
The size of the grid is (3 + 2 * size_im) x (3 + 2 * size_re)
|
|
that includes infinity points, extreme finite points, and the
|
|
specified number of points from real and imaginary axis.
|
|
|
|
For example:
|
|
|
|
>>> print(complex_plane_sample(np.complex64, 0, 3))
|
|
[[-inf -infj 0. -infj inf -infj]
|
|
[-inf-3.4028235e+38j 0.-3.4028235e+38j inf-3.4028235e+38j]
|
|
[-inf-2.0000000e+00j 0.-2.0000000e+00j inf-2.0000000e+00j]
|
|
[-inf-1.1754944e-38j 0.-1.1754944e-38j inf-1.1754944e-38j]
|
|
[-inf+0.0000000e+00j 0.+0.0000000e+00j inf+0.0000000e+00j]
|
|
[-inf+1.1754944e-38j 0.+1.1754944e-38j inf+1.1754944e-38j]
|
|
[-inf+2.0000000e+00j 0.+2.0000000e+00j inf+2.0000000e+00j]
|
|
[-inf+3.4028235e+38j 0.+3.4028235e+38j inf+3.4028235e+38j]
|
|
[-inf +infj 0. +infj inf +infj]]
|
|
|
|
"""
|
|
if size_im is None:
|
|
size_im = size_re
|
|
finfo = np.finfo(dtype)
|
|
|
|
machine = platform.machine()
|
|
is_arm_cpu = machine.startswith('aarch') or machine.startswith('arm')
|
|
smallest = np.nextafter(finfo.tiny, finfo.max) if is_arm_cpu and platform.system() == 'Darwin' else finfo.tiny
|
|
|
|
def make_axis_points(size):
|
|
prec_dps_ratio = 3.3219280948873626
|
|
logmin = logmax = finfo.maxexp / prec_dps_ratio
|
|
logtiny = finfo.minexp / prec_dps_ratio
|
|
axis_points = np.zeros(3 + 2 * size, dtype=finfo.dtype)
|
|
|
|
with ignore_warning(category=RuntimeWarning):
|
|
# Silence RuntimeWarning: overflow encountered in cast
|
|
half_neg_line = -np.logspace(logmin, logtiny, size, dtype=finfo.dtype)
|
|
half_line = -half_neg_line[::-1]
|
|
axis_points[-size - 1:-1] = half_line
|
|
axis_points[1:size + 1] = half_neg_line
|
|
|
|
if size > 1:
|
|
axis_points[1] = finfo.min
|
|
axis_points[-2] = finfo.max
|
|
if size > 0:
|
|
axis_points[size] = -smallest
|
|
axis_points[-size - 1] = smallest
|
|
axis_points[0] = -np.inf
|
|
axis_points[-1] = np.inf
|
|
return axis_points
|
|
|
|
real_axis_points = make_axis_points(size_re)
|
|
imag_axis_points = make_axis_points(size_im)
|
|
|
|
real_part = real_axis_points.reshape((-1, 3 + 2 * size_re)).repeat(3 + 2 * size_im, 0).astype(dtype)
|
|
|
|
imag_part = imag_axis_points.repeat(2).view(dtype)
|
|
imag_part.real[:] = 0
|
|
imag_part = imag_part.reshape((3 + 2 * size_im, -1)).repeat(3 + 2 * size_re, 1)
|
|
|
|
return real_part + imag_part
|
|
|
|
|
|
class vectorize_with_mpmath(np.vectorize):
|
|
"""Same as numpy.vectorize but using mpmath backend for function evaluation.
|
|
"""
|
|
|
|
map_float_to_complex = dict(float16='complex32', float32='complex64', float64='complex128', float128='complex256', longdouble='clongdouble')
|
|
map_complex_to_float = {v: k for k, v in map_float_to_complex.items()}
|
|
|
|
float_prec = dict(
|
|
# float16=11,
|
|
float32=24,
|
|
float64=53,
|
|
# float128=113,
|
|
# longdouble=113
|
|
)
|
|
|
|
float_minexp = dict(
|
|
float16=-14,
|
|
float32=-126,
|
|
float64=-1022,
|
|
float128=-16382
|
|
)
|
|
|
|
float_maxexp = dict(
|
|
float16=16,
|
|
float32=128,
|
|
float64=1024,
|
|
float128=16384,
|
|
)
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
mpmath = kwargs.pop('mpmath', None)
|
|
if mpmath is None:
|
|
raise ValueError('vectorize_with_mpmath: no mpmath argument specified')
|
|
self.extra_prec_multiplier = kwargs.pop('extra_prec_multiplier', 0)
|
|
self.extra_prec = kwargs.pop('extra_prec', 0)
|
|
self.mpmath = mpmath
|
|
self.contexts = dict()
|
|
self.contexts_inv = dict()
|
|
for fp_format, prec in self.float_prec.items():
|
|
ctx = self.mpmath.mp.clone()
|
|
ctx.prec = prec
|
|
self.contexts[fp_format] = ctx
|
|
self.contexts_inv[ctx] = fp_format
|
|
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def get_context(self, x):
|
|
if isinstance(x, (np.ndarray, np.floating, np.complexfloating)):
|
|
fp_format = str(x.dtype)
|
|
fp_format = self.map_complex_to_float.get(fp_format, fp_format)
|
|
return self.contexts[fp_format]
|
|
raise NotImplementedError(f'get mpmath context from {type(x).__name__} instance')
|
|
|
|
def nptomp(self, x):
|
|
"""Convert numpy array/scalar to an array/instance of mpmath number type.
|
|
"""
|
|
if isinstance(x, np.ndarray):
|
|
return np.fromiter(map(self.nptomp, x.flatten()), dtype=object).reshape(x.shape)
|
|
elif isinstance(x, np.floating):
|
|
mpmath = self.mpmath
|
|
ctx = self.get_context(x)
|
|
prec, rounding = ctx._prec_rounding
|
|
if np.isposinf(x):
|
|
return ctx.make_mpf(mpmath.libmp.finf)
|
|
elif np.isneginf(x):
|
|
return ctx.make_mpf(mpmath.libmp.fninf)
|
|
elif np.isnan(x):
|
|
return ctx.make_mpf(mpmath.libmp.fnan)
|
|
elif np.isfinite(x):
|
|
mantissa, exponent = np.frexp(x)
|
|
man = int(np.ldexp(mantissa, prec))
|
|
exp = int(exponent - prec)
|
|
r = ctx.make_mpf(mpmath.libmp.from_man_exp(man, exp, prec, rounding))
|
|
assert ctx.isfinite(r), r._mpf_
|
|
return r
|
|
elif isinstance(x, np.complexfloating):
|
|
re, im = self.nptomp(x.real), self.nptomp(x.imag)
|
|
return re.context.make_mpc((re._mpf_, im._mpf_))
|
|
raise NotImplementedError(f'convert {type(x).__name__} instance to mpmath number type')
|
|
|
|
def mptonp(self, x):
|
|
"""Convert mpmath instance to numpy array/scalar type.
|
|
"""
|
|
if isinstance(x, np.ndarray) and x.dtype.kind == 'O':
|
|
x_flat = x.flatten()
|
|
item = x_flat[0]
|
|
ctx = item.context
|
|
fp_format = self.contexts_inv[ctx]
|
|
if isinstance(item, ctx.mpc):
|
|
dtype = getattr(np, self.map_float_to_complex[fp_format])
|
|
elif isinstance(item, ctx.mpf):
|
|
dtype = getattr(np, fp_format)
|
|
else:
|
|
dtype = None
|
|
if dtype is not None:
|
|
return np.fromiter(map(self.mptonp, x_flat), dtype=dtype).reshape(x.shape)
|
|
elif isinstance(x, self.mpmath.ctx_mp.mpnumeric):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
fp_format = self.contexts_inv[ctx]
|
|
dtype = getattr(np, self.map_float_to_complex[fp_format])
|
|
r = dtype().reshape(1).view(getattr(np, fp_format))
|
|
r[0] = self.mptonp(x.real)
|
|
r[1] = self.mptonp(x.imag)
|
|
return r.view(dtype)[0]
|
|
elif isinstance(x, ctx.mpf):
|
|
fp_format = self.contexts_inv[ctx]
|
|
dtype = getattr(np, fp_format)
|
|
if ctx.isfinite(x):
|
|
sign, man, exp, bc = self.mpmath.libmp.normalize(*x._mpf_, *ctx._prec_rounding)
|
|
assert bc >= 0, (sign, man, exp, bc, x._mpf_)
|
|
if exp + bc < self.float_minexp[fp_format]:
|
|
return -ctx.zero if sign else ctx.zero
|
|
if exp + bc > self.float_maxexp[fp_format]:
|
|
return ctx.ninf if sign else ctx.inf
|
|
man = dtype(-man if sign else man)
|
|
r = np.ldexp(man, exp)
|
|
assert np.isfinite(r), (x, r, x._mpf_, man)
|
|
return r
|
|
elif ctx.isnan(x):
|
|
return dtype(np.nan)
|
|
elif ctx.isinf(x):
|
|
return dtype(-np.inf if x._mpf_[0] else np.inf)
|
|
raise NotImplementedError(f'convert {type(x)} instance to numpy floating point type')
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
mp_args = []
|
|
context = None
|
|
for a in args:
|
|
if isinstance(a, (np.ndarray, np.floating, np.complexfloating)):
|
|
mp_args.append(self.nptomp(a))
|
|
if context is None:
|
|
context = self.get_context(a)
|
|
else:
|
|
assert context is self.get_context(a)
|
|
else:
|
|
mp_args.append(a)
|
|
|
|
extra_prec = int(context.prec * self.extra_prec_multiplier) + self.extra_prec
|
|
with context.extraprec(extra_prec):
|
|
result = super().__call__(*mp_args, **kwargs)
|
|
|
|
if isinstance(result, tuple):
|
|
lst = []
|
|
for r in result:
|
|
if ((isinstance(r, np.ndarray) and r.dtype.kind == 'O')
|
|
or isinstance(r, self.mpmath.ctx_mp.mpnumeric)):
|
|
r = self.mptonp(r)
|
|
lst.append(r)
|
|
return tuple(lst)
|
|
|
|
if ((isinstance(result, np.ndarray) and result.dtype.kind == 'O')
|
|
or isinstance(result, self.mpmath.ctx_mp.mpnumeric)):
|
|
return self.mptonp(result)
|
|
|
|
return result
|
|
|
|
|
|
class numpy_with_mpmath:
|
|
"""Namespace of universal functions on numpy arrays that use mpmath
|
|
backend for evaluation and return numpy arrays as outputs.
|
|
"""
|
|
|
|
_provides = [
|
|
'abs', 'absolute', 'sqrt', 'exp', 'expm1', 'exp2',
|
|
'log', 'log1p', 'log10', 'log2',
|
|
'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan',
|
|
'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh',
|
|
'square', 'positive', 'negative', 'conjugate', 'sign', 'sinc',
|
|
'normalize',
|
|
]
|
|
|
|
_mp_names = dict(
|
|
abs='absmin', absolute='absmin',
|
|
log='ln',
|
|
arcsin='asin', arccos='acos', arctan='atan',
|
|
arcsinh='asinh', arccosh='acosh', arctanh='atanh',
|
|
)
|
|
|
|
def __init__(self, mpmath, extra_prec_multiplier=0, extra_prec=0):
|
|
self.mpmath = mpmath
|
|
|
|
for name in self._provides:
|
|
mp_name = self._mp_names.get(name, name)
|
|
|
|
if hasattr(self, name):
|
|
op = getattr(self, name)
|
|
else:
|
|
|
|
def op(x, mp_name=mp_name):
|
|
return getattr(x.context, mp_name)(x)
|
|
|
|
setattr(self, name, vectorize_with_mpmath(op, mpmath=mpmath, extra_prec_multiplier=extra_prec_multiplier, extra_prec=extra_prec))
|
|
|
|
# The following function methods operate on mpmath number instances.
|
|
# The corresponding function names must be listed in
|
|
# numpy_with_mpmath._provides list.
|
|
|
|
def square(self, x):
|
|
return x * x
|
|
|
|
def positive(self, x):
|
|
return x
|
|
|
|
def negative(self, x):
|
|
return -x
|
|
|
|
def sqrt(self, x):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in sqrt(+-inf+-infj) evaluation (see mpmath/mpmath#776).
|
|
# TODO(pearu): remove this function when mpmath 1.4 or newer
|
|
# will be the required test dependency.
|
|
if ctx.isinf(x.imag):
|
|
return ctx.make_mpc((ctx.inf._mpf_, x.imag._mpf_))
|
|
return ctx.sqrt(x)
|
|
|
|
def expm1(self, x):
|
|
return x.context.expm1(x)
|
|
|
|
def log1p(self, x):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in log(+-inf+-infj) evaluation (see mpmath/mpmath#774).
|
|
# TODO(pearu): remove this function when mpmath 1.4 or newer
|
|
# will be the required test dependency.
|
|
if ctx.isinf(x.real) and ctx.isinf(x.imag):
|
|
pi = ctx.pi
|
|
if x.real > 0 and x.imag > 0:
|
|
return ctx.make_mpc((x.real._mpf_, (pi / 4)._mpf_))
|
|
if x.real > 0 and x.imag < 0:
|
|
return ctx.make_mpc((x.real._mpf_, (-pi / 4)._mpf_))
|
|
if x.real < 0 and x.imag < 0:
|
|
return ctx.make_mpc(((-x.real)._mpf_, (-3 * pi / 4)._mpf_))
|
|
if x.real < 0 and x.imag > 0:
|
|
return ctx.make_mpc(((-x.real)._mpf_, (3 * pi / 4)._mpf_))
|
|
return ctx.log1p(x)
|
|
|
|
def tan(self, x):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in tan(+-inf+-infj) evaluation (see mpmath/mpmath#781).
|
|
# TODO(pearu): remove this function when mpmath 1.4 or newer
|
|
# will be the required test dependency.
|
|
if ctx.isinf(x.imag) and (ctx.isinf(x.real) or ctx.isfinite(x.real)):
|
|
if x.imag > 0:
|
|
return ctx.make_mpc((ctx.zero._mpf_, ctx.one._mpf_))
|
|
return ctx.make_mpc((ctx.zero._mpf_, (-ctx.one)._mpf_))
|
|
if ctx.isinf(x.real) and ctx.isfinite(x.imag):
|
|
return ctx.make_mpc((ctx.nan._mpf_, ctx.nan._mpf_))
|
|
return ctx.tan(x)
|
|
|
|
def tanh(self, x):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in tanh(+-inf+-infj) evaluation (see mpmath/mpmath#781).
|
|
# TODO(pearu): remove this function when mpmath 1.4 or newer
|
|
# will be the required test dependency.
|
|
if ctx.isinf(x.imag) and (ctx.isinf(x.real) or ctx.isfinite(x.real)):
|
|
if x.imag > 0:
|
|
return ctx.make_mpc((ctx.zero._mpf_, ctx.one._mpf_))
|
|
return ctx.make_mpc((ctx.zero._mpf_, (-ctx.one)._mpf_))
|
|
if ctx.isinf(x.real) and ctx.isfinite(x.imag):
|
|
return ctx.make_mpc((ctx.nan._mpf_, ctx.nan._mpf_))
|
|
return ctx.tanh(x)
|
|
|
|
def log2(self, x):
|
|
return x.context.ln(x) / x.context.ln2
|
|
|
|
def log10(self, x):
|
|
return x.context.ln(x) / x.context.ln10
|
|
|
|
def exp2(self, x):
|
|
return x.context.exp(x * x.context.ln2)
|
|
|
|
def arcsin(self, x):
|
|
ctx = x.context
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in asin(+-inf+-infj) evaluation (see
|
|
# mpmath/mpmath#793).
|
|
# TODO(pearu): remove the if-block below when mpmath 1.4 or
|
|
# newer will be the required test dependency.
|
|
pi = ctx.pi
|
|
inf = ctx.inf
|
|
zero = ctx.zero
|
|
if ctx.isinf(x.real):
|
|
sign_real = -1 if x.real < 0 else 1
|
|
real = sign_real * pi / (4 if ctx.isinf(x.imag) else 2)
|
|
imag = -inf if x.imag < 0 else inf
|
|
return ctx.make_mpc((real._mpf_, imag._mpf_))
|
|
elif ctx.isinf(x.imag):
|
|
return ctx.make_mpc((zero._mpf_, x.imag._mpf_))
|
|
|
|
# On branch cut, mpmath.mp.asin returns different value compared
|
|
# to mpmath.fp.asin and numpy.arcsin (see
|
|
# mpmath/mpmath#786). The following if-block ensures
|
|
# compatibility with numpy.arcsin.
|
|
if x.real > 1 and x.imag == 0:
|
|
return ctx.asin(x).conjugate()
|
|
|
|
return ctx.asin(x)
|
|
|
|
def arccos(self, x):
|
|
ctx = x.context
|
|
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in acos(+-inf+-infj) evaluation (see
|
|
# mpmath/mpmath#793).
|
|
# TODO(pearu): remove the if-block below when mpmath 1.4 or
|
|
# newer will be the required test dependency.
|
|
pi = ctx.pi
|
|
inf = ctx.inf
|
|
zero = ctx.zero
|
|
|
|
if ctx.isinf(x.imag):
|
|
if ctx.isinf(x.real):
|
|
real = pi / 4 if x.real > 0 else 3 * pi / 4
|
|
else:
|
|
real = pi / 2
|
|
imag = inf if x.imag < 0 else -inf
|
|
return ctx.make_mpc((real._mpf_, imag._mpf_))
|
|
elif ctx.isinf(x.real):
|
|
inf = ctx.inf
|
|
sign_imag = -1 if x.imag < 0 else 1
|
|
real = zero if x.real > 0 else pi
|
|
return ctx.make_mpc((real._mpf_, (-sign_imag * inf)._mpf_))
|
|
# On branch cut, mpmath.mp.acos returns different value
|
|
# compared to mpmath.fp.acos and numpy.arccos. The
|
|
# following if-block ensures compatibility with
|
|
# numpy.arccos.
|
|
if x.imag == 0 and x.real > 1:
|
|
return -ctx.acos(x)
|
|
|
|
return ctx.acos(x)
|
|
|
|
def arcsinh(self, x):
|
|
ctx = x.context
|
|
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in asinh(+-inf+-infj) evaluation
|
|
# (see mpmath/mpmath#749).
|
|
# TODO(pearu): remove the if-block below when mpmath 1.4 or
|
|
# newer will be the required test dependency.
|
|
pi = ctx.pi
|
|
inf = ctx.inf
|
|
zero = ctx.zero
|
|
if ctx.isinf(x.imag):
|
|
sign_imag = -1 if x.imag < 0 else 1
|
|
real = -inf if x.real < 0 else inf
|
|
imag = sign_imag * pi / (4 if ctx.isinf(x.real) else 2)
|
|
return ctx.make_mpc((real._mpf_, imag._mpf_))
|
|
elif ctx.isinf(x.real):
|
|
return ctx.make_mpc((x.real._mpf_, zero._mpf_))
|
|
|
|
# On branch cut, mpmath.mp.asinh returns different value
|
|
# compared to mpmath.fp.asinh and numpy.arcsinh (see
|
|
# mpmath/mpmath#786). The following if-block ensures
|
|
# compatibility with numpy.arcsinh.
|
|
if x.real == 0 and x.imag < -1:
|
|
return (-ctx.asinh(x)).conjugate()
|
|
return ctx.asinh(x)
|
|
|
|
def arccosh(self, x):
|
|
ctx = x.context
|
|
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in acosh(+-inf+-infj) evaluation
|
|
# (see mpmath/mpmath#749).
|
|
pi = ctx.pi
|
|
inf = ctx.inf
|
|
zero = ctx.zero
|
|
if ctx.isinf(x.real):
|
|
sign_imag = -1 if x.imag < 0 else 1
|
|
imag = (
|
|
(3 if x.real < 0 else 1) * sign_imag * pi / 4
|
|
if ctx.isinf(x.imag)
|
|
else (sign_imag * pi if x.real < 0 else zero)
|
|
)
|
|
return ctx.make_mpc((inf._mpf_, imag._mpf_))
|
|
elif ctx.isinf(x.imag):
|
|
sign_imag = -1 if x.imag < 0 else 1
|
|
imag = sign_imag * pi / 2
|
|
return ctx.make_mpc((inf._mpf_, imag._mpf_))
|
|
return ctx.acosh(x)
|
|
|
|
def arctan(self, x):
|
|
ctx = x.context
|
|
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in atan(+-inf+-infj) evaluation
|
|
# (see mpmath/mpmath#775 with the fix).
|
|
# TODO(pearu): remove the if-block below when mpmath 1.4 or
|
|
# newer will be the required test dependency.
|
|
pi = ctx.pi
|
|
zero = ctx.zero
|
|
if ctx.isinf(x.real) or ctx.isinf(x.imag):
|
|
if x.real < 0:
|
|
return ctx.make_mpc(((-pi / 2)._mpf_, zero._mpf_))
|
|
return ctx.make_mpc(((pi / 2)._mpf_, zero._mpf_))
|
|
|
|
# On branch cut, mpmath.mp.atan returns different value compared
|
|
# to mpmath.fp.atan and numpy.arctan (see mpmath/mpmath#865).
|
|
# The following if-block ensures compatibility with
|
|
# numpy.arctan.
|
|
if x.real == 0 and x.imag < -1:
|
|
return (-ctx.atan(x)).conjugate()
|
|
return ctx.atan(x)
|
|
|
|
def arctanh(self, x):
|
|
ctx = x.context
|
|
|
|
if isinstance(x, ctx.mpc):
|
|
# Workaround mpmath 1.3 bug in atanh(+-inf+-infj) evaluation
|
|
# (see mpmath/mpmath#775 with the fix).
|
|
# TODO(pearu): remove the if-block below when mpmath 1.4 or
|
|
# newer will be the required test dependency.
|
|
pi = ctx.pi
|
|
zero = ctx.zero
|
|
if ctx.isinf(x.real) or ctx.isinf(x.imag):
|
|
if x.imag < 0:
|
|
return ctx.make_mpc((zero._mpf_, (-pi / 2)._mpf_))
|
|
return ctx.make_mpc((zero._mpf_, (pi / 2)._mpf_))
|
|
|
|
# On branch cut, mpmath.mp.atanh returns different value
|
|
# compared to mpmath.fp.atanh and numpy.arctanh. The following
|
|
# if-block ensures compatibility with numpy.arctanh.
|
|
if x.imag == 0 and x.real > 1:
|
|
return ctx.atanh(x).conjugate()
|
|
return ctx.atanh(x)
|
|
|
|
def normalize(self, exact, reference, value):
|
|
"""Normalize reference and value using precision defined by the
|
|
difference of exact and reference.
|
|
"""
|
|
def worker(ctx, s, e, r, v):
|
|
ss, sm, se, sbc = s._mpf_
|
|
es, em, ee, ebc = e._mpf_
|
|
rs, rm, re, rbc = r._mpf_
|
|
vs, vm, ve, vbc = v._mpf_
|
|
|
|
if not (ctx.isfinite(e) and ctx.isfinite(r) and ctx.isfinite(v)):
|
|
return r, v
|
|
|
|
me = min(se, ee, re, ve)
|
|
|
|
# transform mantissa parts to the same exponent base
|
|
sm_e = sm << (se - me)
|
|
em_e = em << (ee - me)
|
|
rm_e = rm << (re - me)
|
|
vm_e = vm << (ve - me)
|
|
|
|
# find matching higher and non-matching lower bits of e and r
|
|
sm_b = bin(sm_e)[2:] if sm_e else ''
|
|
em_b = bin(em_e)[2:] if em_e else ''
|
|
rm_b = bin(rm_e)[2:] if rm_e else ''
|
|
vm_b = bin(vm_e)[2:] if vm_e else ''
|
|
|
|
m = max(len(sm_b), len(em_b), len(rm_b), len(vm_b))
|
|
em_b = '0' * (m - len(em_b)) + em_b
|
|
rm_b = '0' * (m - len(rm_b)) + rm_b
|
|
|
|
c1 = 0
|
|
for b0, b1 in zip(em_b, rm_b):
|
|
if b0 != b1:
|
|
break
|
|
c1 += 1
|
|
c0 = m - c1
|
|
|
|
# truncate r and v mantissa
|
|
rm_m = rm_e >> c0
|
|
vm_m = vm_e >> c0
|
|
|
|
# normalized r and v
|
|
nr = ctx.make_mpf((rs, rm_m, -c1, len(bin(rm_m)) - 2)) if rm_m else (-ctx.zero if rs else ctx.zero)
|
|
nv = ctx.make_mpf((vs, vm_m, -c1, len(bin(vm_m)) - 2)) if vm_m else (-ctx.zero if vs else ctx.zero)
|
|
|
|
return nr, nv
|
|
|
|
ctx = exact.context
|
|
scale = abs(exact)
|
|
if isinstance(exact, ctx.mpc):
|
|
rr, rv = worker(ctx, scale, exact.real, reference.real, value.real)
|
|
ir, iv = worker(ctx, scale, exact.imag, reference.imag, value.imag)
|
|
return ctx.make_mpc((rr._mpf_, ir._mpf_)), ctx.make_mpc((rv._mpf_, iv._mpf_))
|
|
elif isinstance(exact, ctx.mpf):
|
|
return worker(ctx, scale, exact, reference, value)
|
|
else:
|
|
assert 0 # unreachable
|
|
|
|
# Hypothesis testing support
|
|
def setup_hypothesis(max_examples=30) -> None:
|
|
"""Sets up the hypothesis profiles.
|
|
|
|
Sets up the hypothesis testing profiles, and selects the one specified by
|
|
the ``JAX_HYPOTHESIS_PROFILE`` environment variable (or the
|
|
``--jax_hypothesis_profile`` configuration.
|
|
|
|
Args:
|
|
max_examples: the maximum number of hypothesis examples to try, when using
|
|
the default "deterministic" profile.
|
|
"""
|
|
try:
|
|
import hypothesis as hp
|
|
except (ModuleNotFoundError, ImportError):
|
|
return
|
|
|
|
# In our tests we often use subclasses with slightly different class variables
|
|
# to generate whole suites of parameterized tests, but this approach does not
|
|
# work well with Hypothesis databases, which use some function of the method
|
|
# identity to generate keys. But, if the method is defined in a superclass,
|
|
# all subclasses share the same key. This key collision can lead to confusing
|
|
# false positives in other health checks.
|
|
#
|
|
# Still, as far as I understand, for as long as we don't use the example
|
|
# database, it should be perfectly safe to suppress this health check. This
|
|
# seems simpler than rewriting our tests that trigger this behavior. See
|
|
# the end of https://github.com/HypothesisWorks/hypothesis/issues/3446 for
|
|
# more context.
|
|
suppressed_checks = []
|
|
if hasattr(hp.HealthCheck, "differing_executors"):
|
|
suppressed_checks.append(hp.HealthCheck.differing_executors)
|
|
|
|
hp.settings.register_profile(
|
|
"deterministic",
|
|
database=None,
|
|
derandomize=True,
|
|
deadline=None,
|
|
max_examples=max_examples,
|
|
print_blob=True,
|
|
suppress_health_check=suppressed_checks,
|
|
)
|
|
hp.settings.register_profile(
|
|
"interactive",
|
|
parent=hp.settings.load_profile("deterministic"),
|
|
max_examples=1,
|
|
report_multiple_bugs=False,
|
|
verbosity=hp.Verbosity.verbose,
|
|
# Don't try and shrink
|
|
phases=(
|
|
hp.Phase.explicit,
|
|
hp.Phase.reuse,
|
|
hp.Phase.generate,
|
|
hp.Phase.target,
|
|
hp.Phase.explain,
|
|
),
|
|
)
|
|
profile = HYPOTHESIS_PROFILE.value
|
|
logging.info("Using hypothesis profile: %s", profile)
|
|
hp.settings.load_profile(profile)
|