mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
6c2a8b6b38
Applies a bunch of new ruff lint rules that are now stable. Some of these improve efficiency or readability. Since I already did passes on the codebase for these when they were in preview, there should be relatively few changes to the codebase. This is just more for future hardening of it. Pull Request resolved: https://github.com/pytorch/pytorch/pull/129825 Approved by: https://github.com/XuehaiPan, https://github.com/jansel, https://github.com/malfet
2392 lines
75 KiB
Python
2392 lines
75 KiB
Python
# mypy: ignore-errors
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"""
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Utility function to facilitate testing.
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"""
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import contextlib
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import gc
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import operator
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import os
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import platform
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import pprint
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import re
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import shutil
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import sys
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import warnings
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from functools import wraps
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from io import StringIO
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from tempfile import mkdtemp, mkstemp
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from warnings import WarningMessage
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import torch._numpy as np
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from torch._numpy import arange, asarray as asanyarray, empty, float32, intp, ndarray
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__all__ = [
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"assert_equal",
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"assert_almost_equal",
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"assert_approx_equal",
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"assert_array_equal",
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"assert_array_less",
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"assert_string_equal",
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"assert_",
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"assert_array_almost_equal",
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"build_err_msg",
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"decorate_methods",
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"print_assert_equal",
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"verbose",
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"assert_",
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"assert_array_almost_equal_nulp",
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"assert_raises_regex",
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"assert_array_max_ulp",
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"assert_warns",
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"assert_no_warnings",
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"assert_allclose",
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"IgnoreException",
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"clear_and_catch_warnings",
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"temppath",
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"tempdir",
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"IS_PYPY",
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"HAS_REFCOUNT",
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"IS_WASM",
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"suppress_warnings",
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"assert_array_compare",
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"assert_no_gc_cycles",
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"break_cycles",
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"IS_PYSTON",
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]
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verbose = 0
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IS_WASM = platform.machine() in ["wasm32", "wasm64"]
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IS_PYPY = sys.implementation.name == "pypy"
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IS_PYSTON = hasattr(sys, "pyston_version_info")
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HAS_REFCOUNT = getattr(sys, "getrefcount", None) is not None and not IS_PYSTON
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def assert_(val, msg=""):
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"""
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Assert that works in release mode.
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Accepts callable msg to allow deferring evaluation until failure.
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The Python built-in ``assert`` does not work when executing code in
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optimized mode (the ``-O`` flag) - no byte-code is generated for it.
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For documentation on usage, refer to the Python documentation.
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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if not val:
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try:
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smsg = msg()
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except TypeError:
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smsg = msg
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raise AssertionError(smsg)
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def gisnan(x):
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return np.isnan(x)
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def gisfinite(x):
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return np.isfinite(x)
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def gisinf(x):
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return np.isinf(x)
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def build_err_msg(
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arrays,
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err_msg,
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header="Items are not equal:",
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verbose=True,
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names=("ACTUAL", "DESIRED"),
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precision=8,
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):
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msg = ["\n" + header]
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if err_msg:
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if err_msg.find("\n") == -1 and len(err_msg) < 79 - len(header):
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msg = [msg[0] + " " + err_msg]
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else:
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msg.append(err_msg)
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if verbose:
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for i, a in enumerate(arrays):
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if isinstance(a, ndarray):
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# precision argument is only needed if the objects are ndarrays
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# r_func = partial(array_repr, precision=precision)
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r_func = ndarray.__repr__
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else:
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r_func = repr
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try:
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r = r_func(a)
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except Exception as exc:
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r = f"[repr failed for <{type(a).__name__}>: {exc}]"
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if r.count("\n") > 3:
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r = "\n".join(r.splitlines()[:3])
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r += "..."
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msg.append(f" {names[i]}: {r}")
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return "\n".join(msg)
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def assert_equal(actual, desired, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two objects are not equal.
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Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
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check that all elements of these objects are equal. An exception is raised
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at the first conflicting values.
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When one of `actual` and `desired` is a scalar and the other is array_like,
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the function checks that each element of the array_like object is equal to
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the scalar.
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This function handles NaN comparisons as if NaN was a "normal" number.
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That is, AssertionError is not raised if both objects have NaNs in the same
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positions. This is in contrast to the IEEE standard on NaNs, which says
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that NaN compared to anything must return False.
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Parameters
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----------
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actual : array_like
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The object to check.
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desired : array_like
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The expected object.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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Raises
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------
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AssertionError
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If actual and desired are not equal.
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Examples
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--------
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>>> np.testing.assert_equal([4,5], [4,6])
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Traceback (most recent call last):
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...
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AssertionError:
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Items are not equal:
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item=1
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ACTUAL: 5
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DESIRED: 6
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The following comparison does not raise an exception. There are NaNs
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in the inputs, but they are in the same positions.
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>>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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num_nones = sum([actual is None, desired is None])
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if num_nones == 1:
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raise AssertionError(f"Not equal: {actual} != {desired}")
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elif num_nones == 2:
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return True
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# else, carry on
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if isinstance(actual, np.DType) or isinstance(desired, np.DType):
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result = actual == desired
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if not result:
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raise AssertionError(f"Not equal: {actual} != {desired}")
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else:
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return True
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if isinstance(desired, str) and isinstance(actual, str):
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assert actual == desired
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return
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if isinstance(desired, dict):
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if not isinstance(actual, dict):
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raise AssertionError(repr(type(actual)))
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assert_equal(len(actual), len(desired), err_msg, verbose)
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for k in desired.keys():
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if k not in actual:
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raise AssertionError(repr(k))
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assert_equal(actual[k], desired[k], f"key={k!r}\n{err_msg}", verbose)
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return
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if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
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assert_equal(len(actual), len(desired), err_msg, verbose)
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for k in range(len(desired)):
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assert_equal(actual[k], desired[k], f"item={k!r}\n{err_msg}", verbose)
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return
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from torch._numpy import imag, iscomplexobj, isscalar, ndarray, real, signbit
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if isinstance(actual, ndarray) or isinstance(desired, ndarray):
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return assert_array_equal(actual, desired, err_msg, verbose)
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msg = build_err_msg([actual, desired], err_msg, verbose=verbose)
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# Handle complex numbers: separate into real/imag to handle
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# nan/inf/negative zero correctly
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# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
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try:
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usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
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except (ValueError, TypeError):
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usecomplex = False
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if usecomplex:
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if iscomplexobj(actual):
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actualr = real(actual)
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actuali = imag(actual)
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else:
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actualr = actual
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actuali = 0
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if iscomplexobj(desired):
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desiredr = real(desired)
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desiredi = imag(desired)
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else:
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desiredr = desired
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desiredi = 0
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try:
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assert_equal(actualr, desiredr)
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assert_equal(actuali, desiredi)
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except AssertionError:
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raise AssertionError(msg) # noqa: B904
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# isscalar test to check cases such as [np.nan] != np.nan
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if isscalar(desired) != isscalar(actual):
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raise AssertionError(msg)
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# Inf/nan/negative zero handling
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try:
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isdesnan = gisnan(desired)
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isactnan = gisnan(actual)
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if isdesnan and isactnan:
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return # both nan, so equal
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# handle signed zero specially for floats
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array_actual = np.asarray(actual)
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array_desired = np.asarray(desired)
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if desired == 0 and actual == 0:
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if not signbit(desired) == signbit(actual):
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raise AssertionError(msg)
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except (TypeError, ValueError, NotImplementedError):
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pass
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try:
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# Explicitly use __eq__ for comparison, gh-2552
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if not (desired == actual):
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raise AssertionError(msg)
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except (DeprecationWarning, FutureWarning) as e:
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# this handles the case when the two types are not even comparable
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if "elementwise == comparison" in e.args[0]:
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raise AssertionError(msg) # noqa: B904
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else:
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raise
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def print_assert_equal(test_string, actual, desired):
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"""
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Test if two objects are equal, and print an error message if test fails.
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The test is performed with ``actual == desired``.
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Parameters
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----------
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test_string : str
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The message supplied to AssertionError.
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actual : object
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The object to test for equality against `desired`.
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desired : object
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The expected result.
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Examples
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--------
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>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]) # doctest: +SKIP
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>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2]) # doctest: +SKIP
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Traceback (most recent call last):
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...
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AssertionError: Test XYZ of func xyz failed
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ACTUAL:
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[0, 1]
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DESIRED:
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[0, 2]
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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import pprint
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if not (actual == desired):
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msg = StringIO()
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msg.write(test_string)
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msg.write(" failed\nACTUAL: \n")
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pprint.pprint(actual, msg)
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msg.write("DESIRED: \n")
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pprint.pprint(desired, msg)
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raise AssertionError(msg.getvalue())
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def assert_almost_equal(actual, desired, decimal=7, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two items are not equal up to desired
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precision.
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.. note:: It is recommended to use one of `assert_allclose`,
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`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
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instead of this function for more consistent floating point
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comparisons.
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The test verifies that the elements of `actual` and `desired` satisfy.
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``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
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That is a looser test than originally documented, but agrees with what the
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actual implementation in `assert_array_almost_equal` did up to rounding
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vagaries. An exception is raised at conflicting values. For ndarrays this
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delegates to assert_array_almost_equal
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Parameters
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----------
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actual : array_like
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The object to check.
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desired : array_like
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The expected object.
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decimal : int, optional
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Desired precision, default is 7.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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Raises
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------
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AssertionError
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If actual and desired are not equal up to specified precision.
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See Also
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--------
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assert_allclose: Compare two array_like objects for equality with desired
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relative and/or absolute precision.
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assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
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Examples
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--------
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>>> from torch._numpy.testing import assert_almost_equal
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>>> assert_almost_equal(2.3333333333333, 2.33333334)
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>>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
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Traceback (most recent call last):
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...
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AssertionError:
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Arrays are not almost equal to 10 decimals
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ACTUAL: 2.3333333333333
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DESIRED: 2.33333334
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>>> assert_almost_equal(np.array([1.0,2.3333333333333]),
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... np.array([1.0,2.33333334]), decimal=9)
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Traceback (most recent call last):
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...
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AssertionError:
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Arrays are not almost equal to 9 decimals
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<BLANKLINE>
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Mismatched elements: 1 / 2 (50%)
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Max absolute difference: 6.666699636781459e-09
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Max relative difference: 2.8571569790287484e-09
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x: torch.ndarray([1.0000, 2.3333], dtype=float64)
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y: torch.ndarray([1.0000, 2.3333], dtype=float64)
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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from torch._numpy import imag, iscomplexobj, ndarray, real
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# Handle complex numbers: separate into real/imag to handle
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# nan/inf/negative zero correctly
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# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
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try:
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usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
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except ValueError:
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usecomplex = False
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def _build_err_msg():
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header = "Arrays are not almost equal to %d decimals" % decimal
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return build_err_msg([actual, desired], err_msg, verbose=verbose, header=header)
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if usecomplex:
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if iscomplexobj(actual):
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actualr = real(actual)
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actuali = imag(actual)
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else:
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actualr = actual
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actuali = 0
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if iscomplexobj(desired):
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desiredr = real(desired)
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desiredi = imag(desired)
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else:
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desiredr = desired
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desiredi = 0
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try:
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assert_almost_equal(actualr, desiredr, decimal=decimal)
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assert_almost_equal(actuali, desiredi, decimal=decimal)
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except AssertionError:
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raise AssertionError(_build_err_msg()) # noqa: B904
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if isinstance(actual, (ndarray, tuple, list)) or isinstance(
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desired, (ndarray, tuple, list)
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):
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return assert_array_almost_equal(actual, desired, decimal, err_msg)
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try:
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# If one of desired/actual is not finite, handle it specially here:
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# check that both are nan if any is a nan, and test for equality
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# otherwise
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if not (gisfinite(desired) and gisfinite(actual)):
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if gisnan(desired) or gisnan(actual):
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if not (gisnan(desired) and gisnan(actual)):
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raise AssertionError(_build_err_msg())
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else:
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if not desired == actual:
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raise AssertionError(_build_err_msg())
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return
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except (NotImplementedError, TypeError):
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pass
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if abs(desired - actual) >= np.float64(1.5 * 10.0 ** (-decimal)):
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raise AssertionError(_build_err_msg())
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def assert_approx_equal(actual, desired, significant=7, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two items are not equal up to significant
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digits.
|
|
|
|
.. note:: It is recommended to use one of `assert_allclose`,
|
|
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
|
|
instead of this function for more consistent floating point
|
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comparisons.
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Given two numbers, check that they are approximately equal.
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Approximately equal is defined as the number of significant digits
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that agree.
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Parameters
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----------
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actual : scalar
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The object to check.
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desired : scalar
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The expected object.
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significant : int, optional
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Desired precision, default is 7.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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|
|
|
Raises
|
|
------
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AssertionError
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If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
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|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
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relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
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|
|
|
Examples
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--------
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>>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP
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>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP
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... significant=8)
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>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP
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... significant=8)
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Traceback (most recent call last):
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...
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AssertionError:
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Items are not equal to 8 significant digits:
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ACTUAL: 1.234567e-21
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DESIRED: 1.2345672e-21
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|
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the evaluated condition that raises the exception is
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|
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>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
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True
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"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
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|
|
(actual, desired) = map(float, (actual, desired))
|
|
if desired == actual:
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return
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# Normalized the numbers to be in range (-10.0,10.0)
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# scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))
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scale = 0.5 * (np.abs(desired) + np.abs(actual))
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scale = np.power(10, np.floor(np.log10(scale)))
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try:
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sc_desired = desired / scale
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except ZeroDivisionError:
|
|
sc_desired = 0.0
|
|
try:
|
|
sc_actual = actual / scale
|
|
except ZeroDivisionError:
|
|
sc_actual = 0.0
|
|
msg = build_err_msg(
|
|
[actual, desired],
|
|
err_msg,
|
|
header="Items are not equal to %d significant digits:" % significant,
|
|
verbose=verbose,
|
|
)
|
|
try:
|
|
# If one of desired/actual is not finite, handle it specially here:
|
|
# check that both are nan if any is a nan, and test for equality
|
|
# otherwise
|
|
if not (gisfinite(desired) and gisfinite(actual)):
|
|
if gisnan(desired) or gisnan(actual):
|
|
if not (gisnan(desired) and gisnan(actual)):
|
|
raise AssertionError(msg)
|
|
else:
|
|
if not desired == actual:
|
|
raise AssertionError(msg)
|
|
return
|
|
except (TypeError, NotImplementedError):
|
|
pass
|
|
if np.abs(sc_desired - sc_actual) >= np.power(10.0, -(significant - 1)):
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def assert_array_compare(
|
|
comparison,
|
|
x,
|
|
y,
|
|
err_msg="",
|
|
verbose=True,
|
|
header="",
|
|
precision=6,
|
|
equal_nan=True,
|
|
equal_inf=True,
|
|
*,
|
|
strict=False,
|
|
):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
from torch._numpy import all, array, asarray, bool_, inf, isnan, max
|
|
|
|
x = asarray(x)
|
|
y = asarray(y)
|
|
|
|
def array2string(a):
|
|
return str(a)
|
|
|
|
# original array for output formatting
|
|
ox, oy = x, y
|
|
|
|
def func_assert_same_pos(x, y, func=isnan, hasval="nan"):
|
|
"""Handling nan/inf.
|
|
|
|
Combine results of running func on x and y, checking that they are True
|
|
at the same locations.
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
x_id = func(x)
|
|
y_id = func(y)
|
|
# We include work-arounds here to handle three types of slightly
|
|
# pathological ndarray subclasses:
|
|
# (1) all() on `masked` array scalars can return masked arrays, so we
|
|
# use != True
|
|
# (2) __eq__ on some ndarray subclasses returns Python booleans
|
|
# instead of element-wise comparisons, so we cast to bool_() and
|
|
# use isinstance(..., bool) checks
|
|
# (3) subclasses with bare-bones __array_function__ implementations may
|
|
# not implement np.all(), so favor using the .all() method
|
|
# We are not committed to supporting such subclasses, but it's nice to
|
|
# support them if possible.
|
|
if (x_id == y_id).all().item() is not True:
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg + f"\nx and y {hasval} location mismatch:",
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
# If there is a scalar, then here we know the array has the same
|
|
# flag as it everywhere, so we should return the scalar flag.
|
|
if isinstance(x_id, bool) or x_id.ndim == 0:
|
|
return bool_(x_id)
|
|
elif isinstance(y_id, bool) or y_id.ndim == 0:
|
|
return bool_(y_id)
|
|
else:
|
|
return y_id
|
|
|
|
try:
|
|
if strict:
|
|
cond = x.shape == y.shape and x.dtype == y.dtype
|
|
else:
|
|
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
|
|
if not cond:
|
|
if x.shape != y.shape:
|
|
reason = f"\n(shapes {x.shape}, {y.shape} mismatch)"
|
|
else:
|
|
reason = f"\n(dtypes {x.dtype}, {y.dtype} mismatch)"
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg + reason,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
|
|
flagged = bool_(False)
|
|
|
|
if equal_nan:
|
|
flagged = func_assert_same_pos(x, y, func=isnan, hasval="nan")
|
|
|
|
if equal_inf:
|
|
flagged |= func_assert_same_pos(
|
|
x, y, func=lambda xy: xy == +inf, hasval="+inf"
|
|
)
|
|
flagged |= func_assert_same_pos(
|
|
x, y, func=lambda xy: xy == -inf, hasval="-inf"
|
|
)
|
|
|
|
if flagged.ndim > 0:
|
|
x, y = x[~flagged], y[~flagged]
|
|
# Only do the comparison if actual values are left
|
|
if x.size == 0:
|
|
return
|
|
elif flagged:
|
|
# no sense doing comparison if everything is flagged.
|
|
return
|
|
|
|
val = comparison(x, y)
|
|
|
|
if isinstance(val, bool):
|
|
cond = val
|
|
reduced = array([val])
|
|
else:
|
|
reduced = val.ravel()
|
|
cond = reduced.all()
|
|
|
|
# The below comparison is a hack to ensure that fully masked
|
|
# results, for which val.ravel().all() returns np.ma.masked,
|
|
# do not trigger a failure (np.ma.masked != True evaluates as
|
|
# np.ma.masked, which is falsy).
|
|
if not cond:
|
|
n_mismatch = reduced.size - int(reduced.sum(dtype=intp))
|
|
n_elements = flagged.size if flagged.ndim != 0 else reduced.size
|
|
percent_mismatch = 100 * n_mismatch / n_elements
|
|
remarks = [
|
|
f"Mismatched elements: {n_mismatch} / {n_elements} ({percent_mismatch:.3g}%)"
|
|
]
|
|
|
|
# with errstate(all='ignore'):
|
|
# ignore errors for non-numeric types
|
|
with contextlib.suppress(TypeError, RuntimeError):
|
|
error = abs(x - y)
|
|
if np.issubdtype(x.dtype, np.unsignedinteger):
|
|
error2 = abs(y - x)
|
|
np.minimum(error, error2, out=error)
|
|
max_abs_error = max(error)
|
|
remarks.append(
|
|
"Max absolute difference: " + array2string(max_abs_error.item())
|
|
)
|
|
|
|
# note: this definition of relative error matches that one
|
|
# used by assert_allclose (found in np.isclose)
|
|
# Filter values where the divisor would be zero
|
|
nonzero = bool_(y != 0)
|
|
if all(~nonzero):
|
|
max_rel_error = array(inf)
|
|
else:
|
|
max_rel_error = max(error[nonzero] / abs(y[nonzero]))
|
|
remarks.append(
|
|
"Max relative difference: " + array2string(max_rel_error.item())
|
|
)
|
|
|
|
err_msg += "\n" + "\n".join(remarks)
|
|
msg = build_err_msg(
|
|
[ox, oy],
|
|
err_msg,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
except ValueError:
|
|
import traceback
|
|
|
|
efmt = traceback.format_exc()
|
|
header = f"error during assertion:\n\n{efmt}\n\n{header}"
|
|
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise ValueError(msg) # noqa: B904
|
|
|
|
|
|
def assert_array_equal(x, y, err_msg="", verbose=True, *, strict=False):
|
|
"""
|
|
Raises an AssertionError if two array_like objects are not equal.
|
|
|
|
Given two array_like objects, check that the shape is equal and all
|
|
elements of these objects are equal (but see the Notes for the special
|
|
handling of a scalar). An exception is raised at shape mismatch or
|
|
conflicting values. In contrast to the standard usage in numpy, NaNs
|
|
are compared like numbers, no assertion is raised if both objects have
|
|
NaNs in the same positions.
|
|
|
|
The usual caution for verifying equality with floating point numbers is
|
|
advised.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The actual object to check.
|
|
y : array_like
|
|
The desired, expected object.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
strict : bool, optional
|
|
If True, raise an AssertionError when either the shape or the data
|
|
type of the array_like objects does not match. The special
|
|
handling for scalars mentioned in the Notes section is disabled.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired objects are not equal.
|
|
|
|
See Also
|
|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
|
relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
|
|
|
|
Notes
|
|
-----
|
|
When one of `x` and `y` is a scalar and the other is array_like, the
|
|
function checks that each element of the array_like object is equal to
|
|
the scalar. This behaviour can be disabled with the `strict` parameter.
|
|
|
|
Examples
|
|
--------
|
|
The first assert does not raise an exception:
|
|
|
|
>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
|
|
... [np.exp(0),2.33333, np.nan])
|
|
|
|
Use `assert_allclose` or one of the nulp (number of floating point values)
|
|
functions for these cases instead:
|
|
|
|
>>> np.testing.assert_allclose([1.0,np.pi,np.nan],
|
|
... [1, np.sqrt(np.pi)**2, np.nan],
|
|
... rtol=1e-10, atol=0)
|
|
|
|
As mentioned in the Notes section, `assert_array_equal` has special
|
|
handling for scalars. Here the test checks that each value in `x` is 3:
|
|
|
|
>>> x = np.full((2, 5), fill_value=3)
|
|
>>> np.testing.assert_array_equal(x, 3)
|
|
|
|
Use `strict` to raise an AssertionError when comparing a scalar with an
|
|
array:
|
|
|
|
>>> np.testing.assert_array_equal(x, 3, strict=True)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not equal
|
|
<BLANKLINE>
|
|
(shapes (2, 5), () mismatch)
|
|
x: torch.ndarray([[3, 3, 3, 3, 3],
|
|
[3, 3, 3, 3, 3]])
|
|
y: torch.ndarray(3)
|
|
|
|
The `strict` parameter also ensures that the array data types match:
|
|
|
|
>>> x = np.array([2, 2, 2])
|
|
>>> y = np.array([2., 2., 2.], dtype=np.float32)
|
|
>>> np.testing.assert_array_equal(x, y, strict=True)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not equal
|
|
<BLANKLINE>
|
|
(dtypes dtype("int64"), dtype("float32") mismatch)
|
|
x: torch.ndarray([2, 2, 2])
|
|
y: torch.ndarray([2., 2., 2.])
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
assert_array_compare(
|
|
operator.__eq__,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header="Arrays are not equal",
|
|
strict=strict,
|
|
)
|
|
|
|
|
|
def assert_array_almost_equal(x, y, decimal=6, err_msg="", verbose=True):
|
|
"""
|
|
Raises an AssertionError if two objects are not equal up to desired
|
|
precision.
|
|
|
|
.. note:: It is recommended to use one of `assert_allclose`,
|
|
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
|
|
instead of this function for more consistent floating point
|
|
comparisons.
|
|
|
|
The test verifies identical shapes and that the elements of ``actual`` and
|
|
``desired`` satisfy.
|
|
|
|
``abs(desired-actual) < 1.5 * 10**(-decimal)``
|
|
|
|
That is a looser test than originally documented, but agrees with what the
|
|
actual implementation did up to rounding vagaries. An exception is raised
|
|
at shape mismatch or conflicting values. In contrast to the standard usage
|
|
in numpy, NaNs are compared like numbers, no assertion is raised if both
|
|
objects have NaNs in the same positions.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The actual object to check.
|
|
y : array_like
|
|
The desired, expected object.
|
|
decimal : int, optional
|
|
Desired precision, default is 6.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
|
|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
|
relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
|
|
|
|
Examples
|
|
--------
|
|
the first assert does not raise an exception
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
|
|
... [1.0,2.333,np.nan])
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
|
|
... [1.0,2.33339,np.nan], decimal=5)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not almost equal to 5 decimals
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 3 (33.3%)
|
|
Max absolute difference: 5.999999999994898e-05
|
|
Max relative difference: 2.5713661239633743e-05
|
|
x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64)
|
|
y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64)
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
|
|
... [1.0,2.33333, 5], decimal=5)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not almost equal to 5 decimals
|
|
<BLANKLINE>
|
|
x and y nan location mismatch:
|
|
x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64)
|
|
y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
from torch._numpy import any as npany, float_, issubdtype, number, result_type
|
|
|
|
def compare(x, y):
|
|
try:
|
|
if npany(gisinf(x)) or npany(gisinf(y)):
|
|
xinfid = gisinf(x)
|
|
yinfid = gisinf(y)
|
|
if not (xinfid == yinfid).all():
|
|
return False
|
|
# if one item, x and y is +- inf
|
|
if x.size == y.size == 1:
|
|
return x == y
|
|
x = x[~xinfid]
|
|
y = y[~yinfid]
|
|
except (TypeError, NotImplementedError):
|
|
pass
|
|
|
|
# make sure y is an inexact type to avoid abs(MIN_INT); will cause
|
|
# casting of x later.
|
|
dtype = result_type(y, 1.0)
|
|
y = asanyarray(y, dtype)
|
|
z = abs(x - y)
|
|
|
|
if not issubdtype(z.dtype, number):
|
|
z = z.astype(float_) # handle object arrays
|
|
|
|
return z < 1.5 * 10.0 ** (-decimal)
|
|
|
|
assert_array_compare(
|
|
compare,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header=("Arrays are not almost equal to %d decimals" % decimal),
|
|
precision=decimal,
|
|
)
|
|
|
|
|
|
def assert_array_less(x, y, err_msg="", verbose=True):
|
|
"""
|
|
Raises an AssertionError if two array_like objects are not ordered by less
|
|
than.
|
|
|
|
Given two array_like objects, check that the shape is equal and all
|
|
elements of the first object are strictly smaller than those of the
|
|
second object. An exception is raised at shape mismatch or incorrectly
|
|
ordered values. Shape mismatch does not raise if an object has zero
|
|
dimension. In contrast to the standard usage in numpy, NaNs are
|
|
compared, no assertion is raised if both objects have NaNs in the same
|
|
positions.
|
|
|
|
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The smaller object to check.
|
|
y : array_like
|
|
The larger object to compare.
|
|
err_msg : string
|
|
The error message to be printed in case of failure.
|
|
verbose : bool
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired objects are not equal.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_equal: tests objects for equality
|
|
assert_array_almost_equal: test objects for equality up to precision
|
|
|
|
|
|
|
|
Examples
|
|
--------
|
|
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
|
|
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 3 (33.3%)
|
|
Max absolute difference: 1.0
|
|
Max relative difference: 0.5
|
|
x: torch.ndarray([1., 1., nan], dtype=float64)
|
|
y: torch.ndarray([1., 2., nan], dtype=float64)
|
|
|
|
>>> np.testing.assert_array_less([1.0, 4.0], 3)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 2 (50%)
|
|
Max absolute difference: 2.0
|
|
Max relative difference: 0.6666666666666666
|
|
x: torch.ndarray([1., 4.], dtype=float64)
|
|
y: torch.ndarray(3)
|
|
|
|
>>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
(shapes (3,), (1,) mismatch)
|
|
x: torch.ndarray([1., 2., 3.], dtype=float64)
|
|
y: torch.ndarray([4])
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
assert_array_compare(
|
|
operator.__lt__,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header="Arrays are not less-ordered",
|
|
equal_inf=False,
|
|
)
|
|
|
|
|
|
def assert_string_equal(actual, desired):
|
|
"""
|
|
Test if two strings are equal.
|
|
|
|
If the given strings are equal, `assert_string_equal` does nothing.
|
|
If they are not equal, an AssertionError is raised, and the diff
|
|
between the strings is shown.
|
|
|
|
Parameters
|
|
----------
|
|
actual : str
|
|
The string to test for equality against the expected string.
|
|
desired : str
|
|
The expected string.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.testing.assert_string_equal('abc', 'abc') # doctest: +SKIP
|
|
>>> np.testing.assert_string_equal('abc', 'abcd') # doctest: +SKIP
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
...
|
|
AssertionError: Differences in strings:
|
|
- abc+ abcd? +
|
|
|
|
"""
|
|
# delay import of difflib to reduce startup time
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import difflib
|
|
|
|
if not isinstance(actual, str):
|
|
raise AssertionError(repr(type(actual)))
|
|
if not isinstance(desired, str):
|
|
raise AssertionError(repr(type(desired)))
|
|
if desired == actual:
|
|
return
|
|
|
|
diff = list(
|
|
difflib.Differ().compare(actual.splitlines(True), desired.splitlines(True))
|
|
)
|
|
diff_list = []
|
|
while diff:
|
|
d1 = diff.pop(0)
|
|
if d1.startswith(" "):
|
|
continue
|
|
if d1.startswith("- "):
|
|
l = [d1]
|
|
d2 = diff.pop(0)
|
|
if d2.startswith("? "):
|
|
l.append(d2)
|
|
d2 = diff.pop(0)
|
|
if not d2.startswith("+ "):
|
|
raise AssertionError(repr(d2))
|
|
l.append(d2)
|
|
if diff:
|
|
d3 = diff.pop(0)
|
|
if d3.startswith("? "):
|
|
l.append(d3)
|
|
else:
|
|
diff.insert(0, d3)
|
|
if d2[2:] == d1[2:]:
|
|
continue
|
|
diff_list.extend(l)
|
|
continue
|
|
raise AssertionError(repr(d1))
|
|
if not diff_list:
|
|
return
|
|
msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}"
|
|
if actual != desired:
|
|
raise AssertionError(msg)
|
|
|
|
|
|
import unittest
|
|
|
|
|
|
class _Dummy(unittest.TestCase):
|
|
def nop(self):
|
|
pass
|
|
|
|
|
|
_d = _Dummy("nop")
|
|
|
|
|
|
def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
|
|
"""
|
|
assert_raises_regex(exception_class, expected_regexp, callable, *args,
|
|
**kwargs)
|
|
assert_raises_regex(exception_class, expected_regexp)
|
|
|
|
Fail unless an exception of class exception_class and with message that
|
|
matches expected_regexp is thrown by callable when invoked with arguments
|
|
args and keyword arguments kwargs.
|
|
|
|
Alternatively, can be used as a context manager like `assert_raises`.
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.9.0
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs)
|
|
|
|
|
|
def decorate_methods(cls, decorator, testmatch=None):
|
|
"""
|
|
Apply a decorator to all methods in a class matching a regular expression.
|
|
|
|
The given decorator is applied to all public methods of `cls` that are
|
|
matched by the regular expression `testmatch`
|
|
(``testmatch.search(methodname)``). Methods that are private, i.e. start
|
|
with an underscore, are ignored.
|
|
|
|
Parameters
|
|
----------
|
|
cls : class
|
|
Class whose methods to decorate.
|
|
decorator : function
|
|
Decorator to apply to methods
|
|
testmatch : compiled regexp or str, optional
|
|
The regular expression. Default value is None, in which case the
|
|
nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
|
|
is used.
|
|
If `testmatch` is a string, it is compiled to a regular expression
|
|
first.
|
|
|
|
"""
|
|
if testmatch is None:
|
|
testmatch = re.compile(rf"(?:^|[\\b_\\.{os.sep}-])[Tt]est")
|
|
else:
|
|
testmatch = re.compile(testmatch)
|
|
cls_attr = cls.__dict__
|
|
|
|
# delayed import to reduce startup time
|
|
from inspect import isfunction
|
|
|
|
methods = [_m for _m in cls_attr.values() if isfunction(_m)]
|
|
for function in methods:
|
|
try:
|
|
if hasattr(function, "compat_func_name"):
|
|
funcname = function.compat_func_name
|
|
else:
|
|
funcname = function.__name__
|
|
except AttributeError:
|
|
# not a function
|
|
continue
|
|
if testmatch.search(funcname) and not funcname.startswith("_"):
|
|
setattr(cls, funcname, decorator(function))
|
|
return
|
|
|
|
|
|
def _assert_valid_refcount(op):
|
|
"""
|
|
Check that ufuncs don't mishandle refcount of object `1`.
|
|
Used in a few regression tests.
|
|
"""
|
|
if not HAS_REFCOUNT:
|
|
return True
|
|
|
|
import gc
|
|
|
|
import numpy as np
|
|
|
|
b = np.arange(100 * 100).reshape(100, 100)
|
|
c = b
|
|
i = 1
|
|
|
|
gc.disable()
|
|
try:
|
|
rc = sys.getrefcount(i)
|
|
for j in range(15):
|
|
d = op(b, c)
|
|
assert_(sys.getrefcount(i) >= rc)
|
|
finally:
|
|
gc.enable()
|
|
del d # for pyflakes
|
|
|
|
|
|
def assert_allclose(
|
|
actual,
|
|
desired,
|
|
rtol=1e-7,
|
|
atol=0,
|
|
equal_nan=True,
|
|
err_msg="",
|
|
verbose=True,
|
|
check_dtype=False,
|
|
):
|
|
"""
|
|
Raises an AssertionError if two objects are not equal up to desired
|
|
tolerance.
|
|
|
|
Given two array_like objects, check that their shapes and all elements
|
|
are equal (but see the Notes for the special handling of a scalar). An
|
|
exception is raised if the shapes mismatch or any values conflict. In
|
|
contrast to the standard usage in numpy, NaNs are compared like numbers,
|
|
no assertion is raised if both objects have NaNs in the same positions.
|
|
|
|
The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note
|
|
that ``allclose`` has different default values). It compares the difference
|
|
between `actual` and `desired` to ``atol + rtol * abs(desired)``.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Parameters
|
|
----------
|
|
actual : array_like
|
|
Array obtained.
|
|
desired : array_like
|
|
Array desired.
|
|
rtol : float, optional
|
|
Relative tolerance.
|
|
atol : float, optional
|
|
Absolute tolerance.
|
|
equal_nan : bool, optional.
|
|
If True, NaNs will compare equal.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp
|
|
|
|
Notes
|
|
-----
|
|
When one of `actual` and `desired` is a scalar and the other is
|
|
array_like, the function checks that each element of the array_like
|
|
object is equal to the scalar.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = [1e-5, 1e-3, 1e-1]
|
|
>>> y = np.arccos(np.cos(x))
|
|
>>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
|
|
def compare(x, y):
|
|
return np.isclose(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan)
|
|
|
|
actual, desired = asanyarray(actual), asanyarray(desired)
|
|
header = f"Not equal to tolerance rtol={rtol:g}, atol={atol:g}"
|
|
|
|
if check_dtype:
|
|
assert actual.dtype == desired.dtype
|
|
|
|
assert_array_compare(
|
|
compare,
|
|
actual,
|
|
desired,
|
|
err_msg=str(err_msg),
|
|
verbose=verbose,
|
|
header=header,
|
|
equal_nan=equal_nan,
|
|
)
|
|
|
|
|
|
def assert_array_almost_equal_nulp(x, y, nulp=1):
|
|
"""
|
|
Compare two arrays relatively to their spacing.
|
|
|
|
This is a relatively robust method to compare two arrays whose amplitude
|
|
is variable.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Input arrays.
|
|
nulp : int, optional
|
|
The maximum number of unit in the last place for tolerance (see Notes).
|
|
Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If the spacing between `x` and `y` for one or more elements is larger
|
|
than `nulp`.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_max_ulp : Check that all items of arrays differ in at most
|
|
N Units in the Last Place.
|
|
spacing : Return the distance between x and the nearest adjacent number.
|
|
|
|
Notes
|
|
-----
|
|
An assertion is raised if the following condition is not met::
|
|
|
|
abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1., 1e-10, 1e-20])
|
|
>>> eps = np.finfo(x.dtype).eps
|
|
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x) # doctest: +SKIP
|
|
|
|
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x) # doctest: +SKIP
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError: X and Y are not equal to 1 ULP (max is 2)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
|
|
|
|
ax = np.abs(x)
|
|
ay = np.abs(y)
|
|
ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
|
|
if not np.all(np.abs(x - y) <= ref):
|
|
if np.iscomplexobj(x) or np.iscomplexobj(y):
|
|
msg = "X and Y are not equal to %d ULP" % nulp
|
|
else:
|
|
max_nulp = np.max(nulp_diff(x, y))
|
|
msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
|
|
"""
|
|
Check that all items of arrays differ in at most N Units in the Last Place.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : array_like
|
|
Input arrays to be compared.
|
|
maxulp : int, optional
|
|
The maximum number of units in the last place that elements of `a` and
|
|
`b` can differ. Default is 1.
|
|
dtype : dtype, optional
|
|
Data-type to convert `a` and `b` to if given. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
ret : ndarray
|
|
Array containing number of representable floating point numbers between
|
|
items in `a` and `b`.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If one or more elements differ by more than `maxulp`.
|
|
|
|
Notes
|
|
-----
|
|
For computing the ULP difference, this API does not differentiate between
|
|
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
|
|
is zero).
|
|
|
|
See Also
|
|
--------
|
|
assert_array_almost_equal_nulp : Compare two arrays relatively to their
|
|
spacing.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.linspace(0., 1., 100)
|
|
>>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a))) # doctest: +SKIP
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
|
|
|
|
ret = nulp_diff(a, b, dtype)
|
|
if not np.all(ret <= maxulp):
|
|
raise AssertionError(
|
|
f"Arrays are not almost equal up to {maxulp:g} "
|
|
f"ULP (max difference is {np.max(ret):g} ULP)"
|
|
)
|
|
return ret
|
|
|
|
|
|
def nulp_diff(x, y, dtype=None):
|
|
"""For each item in x and y, return the number of representable floating
|
|
points between them.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
first input array
|
|
y : array_like
|
|
second input array
|
|
dtype : dtype, optional
|
|
Data-type to convert `x` and `y` to if given. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
nulp : array_like
|
|
number of representable floating point numbers between each item in x
|
|
and y.
|
|
|
|
Notes
|
|
-----
|
|
For computing the ULP difference, this API does not differentiate between
|
|
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
|
|
is zero).
|
|
|
|
Examples
|
|
--------
|
|
# By definition, epsilon is the smallest number such as 1 + eps != 1, so
|
|
# there should be exactly one ULP between 1 and 1 + eps
|
|
>>> nulp_diff(1, 1 + np.finfo(x.dtype).eps) # doctest: +SKIP
|
|
1.0
|
|
"""
|
|
import numpy as np
|
|
|
|
if dtype:
|
|
x = np.asarray(x, dtype=dtype)
|
|
y = np.asarray(y, dtype=dtype)
|
|
else:
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
|
|
t = np.common_type(x, y)
|
|
if np.iscomplexobj(x) or np.iscomplexobj(y):
|
|
raise NotImplementedError("_nulp not implemented for complex array")
|
|
|
|
x = np.array([x], dtype=t)
|
|
y = np.array([y], dtype=t)
|
|
|
|
x[np.isnan(x)] = np.nan
|
|
y[np.isnan(y)] = np.nan
|
|
|
|
if not x.shape == y.shape:
|
|
raise ValueError(f"x and y do not have the same shape: {x.shape} - {y.shape}")
|
|
|
|
def _diff(rx, ry, vdt):
|
|
diff = np.asarray(rx - ry, dtype=vdt)
|
|
return np.abs(diff)
|
|
|
|
rx = integer_repr(x)
|
|
ry = integer_repr(y)
|
|
return _diff(rx, ry, t)
|
|
|
|
|
|
def _integer_repr(x, vdt, comp):
|
|
# Reinterpret binary representation of the float as sign-magnitude:
|
|
# take into account two-complement representation
|
|
# See also
|
|
# https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
|
|
rx = x.view(vdt)
|
|
if not (rx.size == 1):
|
|
rx[rx < 0] = comp - rx[rx < 0]
|
|
else:
|
|
if rx < 0:
|
|
rx = comp - rx
|
|
|
|
return rx
|
|
|
|
|
|
def integer_repr(x):
|
|
"""Return the signed-magnitude interpretation of the binary representation
|
|
of x."""
|
|
import numpy as np
|
|
|
|
if x.dtype == np.float16:
|
|
return _integer_repr(x, np.int16, np.int16(-(2**15)))
|
|
elif x.dtype == np.float32:
|
|
return _integer_repr(x, np.int32, np.int32(-(2**31)))
|
|
elif x.dtype == np.float64:
|
|
return _integer_repr(x, np.int64, np.int64(-(2**63)))
|
|
else:
|
|
raise ValueError(f"Unsupported dtype {x.dtype}")
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_warns_context(warning_class, name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
with suppress_warnings() as sup:
|
|
l = sup.record(warning_class)
|
|
yield
|
|
if not len(l) > 0:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError("No warning raised" + name_str)
|
|
|
|
|
|
def assert_warns(warning_class, *args, **kwargs):
|
|
"""
|
|
Fail unless the given callable throws the specified warning.
|
|
|
|
A warning of class warning_class should be thrown by the callable when
|
|
invoked with arguments args and keyword arguments kwargs.
|
|
If a different type of warning is thrown, it will not be caught.
|
|
|
|
If called with all arguments other than the warning class omitted, may be
|
|
used as a context manager:
|
|
|
|
with assert_warns(SomeWarning):
|
|
do_something()
|
|
|
|
The ability to be used as a context manager is new in NumPy v1.11.0.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
warning_class : class
|
|
The class defining the warning that `func` is expected to throw.
|
|
func : callable, optional
|
|
Callable to test
|
|
*args : Arguments
|
|
Arguments for `func`.
|
|
**kwargs : Kwargs
|
|
Keyword arguments for `func`.
|
|
|
|
Returns
|
|
-------
|
|
The value returned by `func`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import warnings
|
|
>>> def deprecated_func(num):
|
|
... warnings.warn("Please upgrade", DeprecationWarning)
|
|
... return num*num
|
|
>>> with np.testing.assert_warns(DeprecationWarning):
|
|
... assert deprecated_func(4) == 16
|
|
>>> # or passing a func
|
|
>>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)
|
|
>>> assert ret == 16
|
|
"""
|
|
if not args:
|
|
return _assert_warns_context(warning_class)
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_warns_context(warning_class, name=func.__name__):
|
|
return func(*args, **kwargs)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_no_warnings_context(name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
with warnings.catch_warnings(record=True) as l:
|
|
warnings.simplefilter("always")
|
|
yield
|
|
if len(l) > 0:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError(f"Got warnings{name_str}: {l}")
|
|
|
|
|
|
def assert_no_warnings(*args, **kwargs):
|
|
"""
|
|
Fail if the given callable produces any warnings.
|
|
|
|
If called with all arguments omitted, may be used as a context manager:
|
|
|
|
with assert_no_warnings():
|
|
do_something()
|
|
|
|
The ability to be used as a context manager is new in NumPy v1.11.0.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
The callable to test.
|
|
\\*args : Arguments
|
|
Arguments passed to `func`.
|
|
\\*\\*kwargs : Kwargs
|
|
Keyword arguments passed to `func`.
|
|
|
|
Returns
|
|
-------
|
|
The value returned by `func`.
|
|
|
|
"""
|
|
if not args:
|
|
return _assert_no_warnings_context()
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_no_warnings_context(name=func.__name__):
|
|
return func(*args, **kwargs)
|
|
|
|
|
|
def _gen_alignment_data(dtype=float32, type="binary", max_size=24):
|
|
"""
|
|
generator producing data with different alignment and offsets
|
|
to test simd vectorization
|
|
|
|
Parameters
|
|
----------
|
|
dtype : dtype
|
|
data type to produce
|
|
type : string
|
|
'unary': create data for unary operations, creates one input
|
|
and output array
|
|
'binary': create data for unary operations, creates two input
|
|
and output array
|
|
max_size : integer
|
|
maximum size of data to produce
|
|
|
|
Returns
|
|
-------
|
|
if type is 'unary' yields one output, one input array and a message
|
|
containing information on the data
|
|
if type is 'binary' yields one output array, two input array and a message
|
|
containing information on the data
|
|
|
|
"""
|
|
ufmt = "unary offset=(%d, %d), size=%d, dtype=%r, %s"
|
|
bfmt = "binary offset=(%d, %d, %d), size=%d, dtype=%r, %s"
|
|
for o in range(3):
|
|
for s in range(o + 2, max(o + 3, max_size)):
|
|
if type == "unary":
|
|
|
|
def inp():
|
|
return arange(s, dtype=dtype)[o:]
|
|
|
|
out = empty((s,), dtype=dtype)[o:]
|
|
yield out, inp(), ufmt % (o, o, s, dtype, "out of place")
|
|
d = inp()
|
|
yield d, d, ufmt % (o, o, s, dtype, "in place")
|
|
yield out[1:], inp()[:-1], ufmt % (
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp()[1:], ufmt % (
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield inp()[:-1], inp()[1:], ufmt % (o, o + 1, s - 1, dtype, "aliased")
|
|
yield inp()[1:], inp()[:-1], ufmt % (o + 1, o, s - 1, dtype, "aliased")
|
|
if type == "binary":
|
|
|
|
def inp1():
|
|
return arange(s, dtype=dtype)[o:]
|
|
|
|
inp2 = inp1
|
|
out = empty((s,), dtype=dtype)[o:]
|
|
yield out, inp1(), inp2(), bfmt % (o, o, o, s, dtype, "out of place")
|
|
d = inp1()
|
|
yield d, d, inp2(), bfmt % (o, o, o, s, dtype, "in place1")
|
|
d = inp2()
|
|
yield d, inp1(), d, bfmt % (o, o, o, s, dtype, "in place2")
|
|
yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % (
|
|
o + 1,
|
|
o,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % (
|
|
o,
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % (
|
|
o,
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % (
|
|
o + 1,
|
|
o,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % (
|
|
o,
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % (
|
|
o,
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
|
|
|
|
class IgnoreException(Exception):
|
|
"Ignoring this exception due to disabled feature"
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def tempdir(*args, **kwargs):
|
|
"""Context manager to provide a temporary test folder.
|
|
|
|
All arguments are passed as this to the underlying tempfile.mkdtemp
|
|
function.
|
|
|
|
"""
|
|
tmpdir = mkdtemp(*args, **kwargs)
|
|
try:
|
|
yield tmpdir
|
|
finally:
|
|
shutil.rmtree(tmpdir)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def temppath(*args, **kwargs):
|
|
"""Context manager for temporary files.
|
|
|
|
Context manager that returns the path to a closed temporary file. Its
|
|
parameters are the same as for tempfile.mkstemp and are passed directly
|
|
to that function. The underlying file is removed when the context is
|
|
exited, so it should be closed at that time.
|
|
|
|
Windows does not allow a temporary file to be opened if it is already
|
|
open, so the underlying file must be closed after opening before it
|
|
can be opened again.
|
|
|
|
"""
|
|
fd, path = mkstemp(*args, **kwargs)
|
|
os.close(fd)
|
|
try:
|
|
yield path
|
|
finally:
|
|
os.remove(path)
|
|
|
|
|
|
class clear_and_catch_warnings(warnings.catch_warnings):
|
|
"""Context manager that resets warning registry for catching warnings
|
|
|
|
Warnings can be slippery, because, whenever a warning is triggered, Python
|
|
adds a ``__warningregistry__`` member to the *calling* module. This makes
|
|
it impossible to retrigger the warning in this module, whatever you put in
|
|
the warnings filters. This context manager accepts a sequence of `modules`
|
|
as a keyword argument to its constructor and:
|
|
|
|
* stores and removes any ``__warningregistry__`` entries in given `modules`
|
|
on entry;
|
|
* resets ``__warningregistry__`` to its previous state on exit.
|
|
|
|
This makes it possible to trigger any warning afresh inside the context
|
|
manager without disturbing the state of warnings outside.
|
|
|
|
For compatibility with Python 3.0, please consider all arguments to be
|
|
keyword-only.
|
|
|
|
Parameters
|
|
----------
|
|
record : bool, optional
|
|
Specifies whether warnings should be captured by a custom
|
|
implementation of ``warnings.showwarning()`` and be appended to a list
|
|
returned by the context manager. Otherwise None is returned by the
|
|
context manager. The objects appended to the list are arguments whose
|
|
attributes mirror the arguments to ``showwarning()``.
|
|
modules : sequence, optional
|
|
Sequence of modules for which to reset warnings registry on entry and
|
|
restore on exit. To work correctly, all 'ignore' filters should
|
|
filter by one of these modules.
|
|
|
|
Examples
|
|
--------
|
|
>>> import warnings
|
|
>>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP
|
|
... modules=[np.core.fromnumeric]):
|
|
... warnings.simplefilter('always')
|
|
... warnings.filterwarnings('ignore', module='np.core.fromnumeric')
|
|
... # do something that raises a warning but ignore those in
|
|
... # np.core.fromnumeric
|
|
"""
|
|
|
|
class_modules = ()
|
|
|
|
def __init__(self, record=False, modules=()):
|
|
self.modules = set(modules).union(self.class_modules)
|
|
self._warnreg_copies = {}
|
|
super().__init__(record=record)
|
|
|
|
def __enter__(self):
|
|
for mod in self.modules:
|
|
if hasattr(mod, "__warningregistry__"):
|
|
mod_reg = mod.__warningregistry__
|
|
self._warnreg_copies[mod] = mod_reg.copy()
|
|
mod_reg.clear()
|
|
return super().__enter__()
|
|
|
|
def __exit__(self, *exc_info):
|
|
super().__exit__(*exc_info)
|
|
for mod in self.modules:
|
|
if hasattr(mod, "__warningregistry__"):
|
|
mod.__warningregistry__.clear()
|
|
if mod in self._warnreg_copies:
|
|
mod.__warningregistry__.update(self._warnreg_copies[mod])
|
|
|
|
|
|
class suppress_warnings:
|
|
"""
|
|
Context manager and decorator doing much the same as
|
|
``warnings.catch_warnings``.
|
|
|
|
However, it also provides a filter mechanism to work around
|
|
https://bugs.python.org/issue4180.
|
|
|
|
This bug causes Python before 3.4 to not reliably show warnings again
|
|
after they have been ignored once (even within catch_warnings). It
|
|
means that no "ignore" filter can be used easily, since following
|
|
tests might need to see the warning. Additionally it allows easier
|
|
specificity for testing warnings and can be nested.
|
|
|
|
Parameters
|
|
----------
|
|
forwarding_rule : str, optional
|
|
One of "always", "once", "module", or "location". Analogous to
|
|
the usual warnings module filter mode, it is useful to reduce
|
|
noise mostly on the outmost level. Unsuppressed and unrecorded
|
|
warnings will be forwarded based on this rule. Defaults to "always".
|
|
"location" is equivalent to the warnings "default", match by exact
|
|
location the warning warning originated from.
|
|
|
|
Notes
|
|
-----
|
|
Filters added inside the context manager will be discarded again
|
|
when leaving it. Upon entering all filters defined outside a
|
|
context will be applied automatically.
|
|
|
|
When a recording filter is added, matching warnings are stored in the
|
|
``log`` attribute as well as in the list returned by ``record``.
|
|
|
|
If filters are added and the ``module`` keyword is given, the
|
|
warning registry of this module will additionally be cleared when
|
|
applying it, entering the context, or exiting it. This could cause
|
|
warnings to appear a second time after leaving the context if they
|
|
were configured to be printed once (default) and were already
|
|
printed before the context was entered.
|
|
|
|
Nesting this context manager will work as expected when the
|
|
forwarding rule is "always" (default). Unfiltered and unrecorded
|
|
warnings will be passed out and be matched by the outer level.
|
|
On the outmost level they will be printed (or caught by another
|
|
warnings context). The forwarding rule argument can modify this
|
|
behaviour.
|
|
|
|
Like ``catch_warnings`` this context manager is not threadsafe.
|
|
|
|
Examples
|
|
--------
|
|
|
|
With a context manager::
|
|
|
|
with np.testing.suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "Some text")
|
|
sup.filter(module=np.ma.core)
|
|
log = sup.record(FutureWarning, "Does this occur?")
|
|
command_giving_warnings()
|
|
# The FutureWarning was given once, the filtered warnings were
|
|
# ignored. All other warnings abide outside settings (may be
|
|
# printed/error)
|
|
assert_(len(log) == 1)
|
|
assert_(len(sup.log) == 1) # also stored in log attribute
|
|
|
|
Or as a decorator::
|
|
|
|
sup = np.testing.suppress_warnings()
|
|
sup.filter(module=np.ma.core) # module must match exactly
|
|
@sup
|
|
def some_function():
|
|
# do something which causes a warning in np.ma.core
|
|
pass
|
|
"""
|
|
|
|
def __init__(self, forwarding_rule="always"):
|
|
self._entered = False
|
|
|
|
# Suppressions are either instance or defined inside one with block:
|
|
self._suppressions = []
|
|
|
|
if forwarding_rule not in {"always", "module", "once", "location"}:
|
|
raise ValueError("unsupported forwarding rule.")
|
|
self._forwarding_rule = forwarding_rule
|
|
|
|
def _clear_registries(self):
|
|
if hasattr(warnings, "_filters_mutated"):
|
|
# clearing the registry should not be necessary on new pythons,
|
|
# instead the filters should be mutated.
|
|
warnings._filters_mutated()
|
|
return
|
|
# Simply clear the registry, this should normally be harmless,
|
|
# note that on new pythons it would be invalidated anyway.
|
|
for module in self._tmp_modules:
|
|
if hasattr(module, "__warningregistry__"):
|
|
module.__warningregistry__.clear()
|
|
|
|
def _filter(self, category=Warning, message="", module=None, record=False):
|
|
if record:
|
|
record = [] # The log where to store warnings
|
|
else:
|
|
record = None
|
|
if self._entered:
|
|
if module is None:
|
|
warnings.filterwarnings("always", category=category, message=message)
|
|
else:
|
|
module_regex = module.__name__.replace(".", r"\.") + "$"
|
|
warnings.filterwarnings(
|
|
"always", category=category, message=message, module=module_regex
|
|
)
|
|
self._tmp_modules.add(module)
|
|
self._clear_registries()
|
|
|
|
self._tmp_suppressions.append(
|
|
(category, message, re.compile(message, re.IGNORECASE), module, record)
|
|
)
|
|
else:
|
|
self._suppressions.append(
|
|
(category, message, re.compile(message, re.IGNORECASE), module, record)
|
|
)
|
|
|
|
return record
|
|
|
|
def filter(self, category=Warning, message="", module=None):
|
|
"""
|
|
Add a new suppressing filter or apply it if the state is entered.
|
|
|
|
Parameters
|
|
----------
|
|
category : class, optional
|
|
Warning class to filter
|
|
message : string, optional
|
|
Regular expression matching the warning message.
|
|
module : module, optional
|
|
Module to filter for. Note that the module (and its file)
|
|
must match exactly and cannot be a submodule. This may make
|
|
it unreliable for external modules.
|
|
|
|
Notes
|
|
-----
|
|
When added within a context, filters are only added inside
|
|
the context and will be forgotten when the context is exited.
|
|
"""
|
|
self._filter(category=category, message=message, module=module, record=False)
|
|
|
|
def record(self, category=Warning, message="", module=None):
|
|
"""
|
|
Append a new recording filter or apply it if the state is entered.
|
|
|
|
All warnings matching will be appended to the ``log`` attribute.
|
|
|
|
Parameters
|
|
----------
|
|
category : class, optional
|
|
Warning class to filter
|
|
message : string, optional
|
|
Regular expression matching the warning message.
|
|
module : module, optional
|
|
Module to filter for. Note that the module (and its file)
|
|
must match exactly and cannot be a submodule. This may make
|
|
it unreliable for external modules.
|
|
|
|
Returns
|
|
-------
|
|
log : list
|
|
A list which will be filled with all matched warnings.
|
|
|
|
Notes
|
|
-----
|
|
When added within a context, filters are only added inside
|
|
the context and will be forgotten when the context is exited.
|
|
"""
|
|
return self._filter(
|
|
category=category, message=message, module=module, record=True
|
|
)
|
|
|
|
def __enter__(self):
|
|
if self._entered:
|
|
raise RuntimeError("cannot enter suppress_warnings twice.")
|
|
|
|
self._orig_show = warnings.showwarning
|
|
self._filters = warnings.filters
|
|
warnings.filters = self._filters[:]
|
|
|
|
self._entered = True
|
|
self._tmp_suppressions = []
|
|
self._tmp_modules = set()
|
|
self._forwarded = set()
|
|
|
|
self.log = [] # reset global log (no need to keep same list)
|
|
|
|
for cat, mess, _, mod, log in self._suppressions:
|
|
if log is not None:
|
|
del log[:] # clear the log
|
|
if mod is None:
|
|
warnings.filterwarnings("always", category=cat, message=mess)
|
|
else:
|
|
module_regex = mod.__name__.replace(".", r"\.") + "$"
|
|
warnings.filterwarnings(
|
|
"always", category=cat, message=mess, module=module_regex
|
|
)
|
|
self._tmp_modules.add(mod)
|
|
warnings.showwarning = self._showwarning
|
|
self._clear_registries()
|
|
|
|
return self
|
|
|
|
def __exit__(self, *exc_info):
|
|
warnings.showwarning = self._orig_show
|
|
warnings.filters = self._filters
|
|
self._clear_registries()
|
|
self._entered = False
|
|
del self._orig_show
|
|
del self._filters
|
|
|
|
def _showwarning(
|
|
self, message, category, filename, lineno, *args, use_warnmsg=None, **kwargs
|
|
):
|
|
for cat, _, pattern, mod, rec in (self._suppressions + self._tmp_suppressions)[
|
|
::-1
|
|
]:
|
|
if issubclass(category, cat) and pattern.match(message.args[0]) is not None:
|
|
if mod is None:
|
|
# Message and category match, either recorded or ignored
|
|
if rec is not None:
|
|
msg = WarningMessage(
|
|
message, category, filename, lineno, **kwargs
|
|
)
|
|
self.log.append(msg)
|
|
rec.append(msg)
|
|
return
|
|
# Use startswith, because warnings strips the c or o from
|
|
# .pyc/.pyo files.
|
|
elif mod.__file__.startswith(filename):
|
|
# The message and module (filename) match
|
|
if rec is not None:
|
|
msg = WarningMessage(
|
|
message, category, filename, lineno, **kwargs
|
|
)
|
|
self.log.append(msg)
|
|
rec.append(msg)
|
|
return
|
|
|
|
# There is no filter in place, so pass to the outside handler
|
|
# unless we should only pass it once
|
|
if self._forwarding_rule == "always":
|
|
if use_warnmsg is None:
|
|
self._orig_show(message, category, filename, lineno, *args, **kwargs)
|
|
else:
|
|
self._orig_showmsg(use_warnmsg)
|
|
return
|
|
|
|
if self._forwarding_rule == "once":
|
|
signature = (message.args, category)
|
|
elif self._forwarding_rule == "module":
|
|
signature = (message.args, category, filename)
|
|
elif self._forwarding_rule == "location":
|
|
signature = (message.args, category, filename, lineno)
|
|
|
|
if signature in self._forwarded:
|
|
return
|
|
self._forwarded.add(signature)
|
|
if use_warnmsg is None:
|
|
self._orig_show(message, category, filename, lineno, *args, **kwargs)
|
|
else:
|
|
self._orig_showmsg(use_warnmsg)
|
|
|
|
def __call__(self, func):
|
|
"""
|
|
Function decorator to apply certain suppressions to a whole
|
|
function.
|
|
"""
|
|
|
|
@wraps(func)
|
|
def new_func(*args, **kwargs):
|
|
with self:
|
|
return func(*args, **kwargs)
|
|
|
|
return new_func
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_no_gc_cycles_context(name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
|
|
# not meaningful to test if there is no refcounting
|
|
if not HAS_REFCOUNT:
|
|
yield
|
|
return
|
|
|
|
assert_(gc.isenabled())
|
|
gc.disable()
|
|
gc_debug = gc.get_debug()
|
|
try:
|
|
for i in range(100):
|
|
if gc.collect() == 0:
|
|
break
|
|
else:
|
|
raise RuntimeError(
|
|
"Unable to fully collect garbage - perhaps a __del__ method "
|
|
"is creating more reference cycles?"
|
|
)
|
|
|
|
gc.set_debug(gc.DEBUG_SAVEALL)
|
|
yield
|
|
# gc.collect returns the number of unreachable objects in cycles that
|
|
# were found -- we are checking that no cycles were created in the context
|
|
n_objects_in_cycles = gc.collect()
|
|
objects_in_cycles = gc.garbage[:]
|
|
finally:
|
|
del gc.garbage[:]
|
|
gc.set_debug(gc_debug)
|
|
gc.enable()
|
|
|
|
if n_objects_in_cycles:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError(
|
|
"Reference cycles were found{}: {} objects were collected, "
|
|
"of which {} are shown below:{}".format(
|
|
name_str,
|
|
n_objects_in_cycles,
|
|
len(objects_in_cycles),
|
|
"".join(
|
|
"\n {} object with id={}:\n {}".format(
|
|
type(o).__name__,
|
|
id(o),
|
|
pprint.pformat(o).replace("\n", "\n "),
|
|
)
|
|
for o in objects_in_cycles
|
|
),
|
|
)
|
|
)
|
|
|
|
|
|
def assert_no_gc_cycles(*args, **kwargs):
|
|
"""
|
|
Fail if the given callable produces any reference cycles.
|
|
|
|
If called with all arguments omitted, may be used as a context manager:
|
|
|
|
with assert_no_gc_cycles():
|
|
do_something()
|
|
|
|
.. versionadded:: 1.15.0
|
|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
The callable to test.
|
|
\\*args : Arguments
|
|
Arguments passed to `func`.
|
|
\\*\\*kwargs : Kwargs
|
|
Keyword arguments passed to `func`.
|
|
|
|
Returns
|
|
-------
|
|
Nothing. The result is deliberately discarded to ensure that all cycles
|
|
are found.
|
|
|
|
"""
|
|
if not args:
|
|
return _assert_no_gc_cycles_context()
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_no_gc_cycles_context(name=func.__name__):
|
|
func(*args, **kwargs)
|
|
|
|
|
|
def break_cycles():
|
|
"""
|
|
Break reference cycles by calling gc.collect
|
|
Objects can call other objects' methods (for instance, another object's
|
|
__del__) inside their own __del__. On PyPy, the interpreter only runs
|
|
between calls to gc.collect, so multiple calls are needed to completely
|
|
release all cycles.
|
|
"""
|
|
|
|
gc.collect()
|
|
if IS_PYPY:
|
|
# a few more, just to make sure all the finalizers are called
|
|
gc.collect()
|
|
gc.collect()
|
|
gc.collect()
|
|
gc.collect()
|
|
|
|
|
|
def requires_memory(free_bytes):
|
|
"""Decorator to skip a test if not enough memory is available"""
|
|
import pytest
|
|
|
|
def decorator(func):
|
|
@wraps(func)
|
|
def wrapper(*a, **kw):
|
|
msg = check_free_memory(free_bytes)
|
|
if msg is not None:
|
|
pytest.skip(msg)
|
|
|
|
try:
|
|
return func(*a, **kw)
|
|
except MemoryError:
|
|
# Probably ran out of memory regardless: don't regard as failure
|
|
pytest.xfail("MemoryError raised")
|
|
|
|
return wrapper
|
|
|
|
return decorator
|
|
|
|
|
|
def check_free_memory(free_bytes):
|
|
"""
|
|
Check whether `free_bytes` amount of memory is currently free.
|
|
Returns: None if enough memory available, otherwise error message
|
|
"""
|
|
env_var = "NPY_AVAILABLE_MEM"
|
|
env_value = os.environ.get(env_var)
|
|
if env_value is not None:
|
|
try:
|
|
mem_free = _parse_size(env_value)
|
|
except ValueError as exc:
|
|
raise ValueError( # noqa: B904
|
|
f"Invalid environment variable {env_var}: {exc}"
|
|
)
|
|
|
|
msg = (
|
|
f"{free_bytes/1e9} GB memory required, but environment variable "
|
|
f"NPY_AVAILABLE_MEM={env_value} set"
|
|
)
|
|
else:
|
|
mem_free = _get_mem_available()
|
|
|
|
if mem_free is None:
|
|
msg = (
|
|
"Could not determine available memory; set NPY_AVAILABLE_MEM "
|
|
"environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run "
|
|
"the test."
|
|
)
|
|
mem_free = -1
|
|
else:
|
|
msg = (
|
|
f"{free_bytes/1e9} GB memory required, but {mem_free/1e9} GB available"
|
|
)
|
|
|
|
return msg if mem_free < free_bytes else None
|
|
|
|
|
|
def _parse_size(size_str):
|
|
"""Convert memory size strings ('12 GB' etc.) to float"""
|
|
suffixes = {
|
|
"": 1,
|
|
"b": 1,
|
|
"k": 1000,
|
|
"m": 1000**2,
|
|
"g": 1000**3,
|
|
"t": 1000**4,
|
|
"kb": 1000,
|
|
"mb": 1000**2,
|
|
"gb": 1000**3,
|
|
"tb": 1000**4,
|
|
"kib": 1024,
|
|
"mib": 1024**2,
|
|
"gib": 1024**3,
|
|
"tib": 1024**4,
|
|
}
|
|
|
|
size_re = re.compile(
|
|
r"^\s*(\d+|\d+\.\d+)\s*({})\s*$".format("|".join(suffixes.keys())),
|
|
re.IGNORECASE,
|
|
)
|
|
|
|
m = size_re.match(size_str.lower())
|
|
if not m or m.group(2) not in suffixes:
|
|
raise ValueError(f"value {size_str!r} not a valid size")
|
|
return int(float(m.group(1)) * suffixes[m.group(2)])
|
|
|
|
|
|
def _get_mem_available():
|
|
"""Return available memory in bytes, or None if unknown."""
|
|
try:
|
|
import psutil
|
|
|
|
return psutil.virtual_memory().available
|
|
except (ImportError, AttributeError):
|
|
pass
|
|
|
|
if sys.platform.startswith("linux"):
|
|
info = {}
|
|
with open("/proc/meminfo") as f:
|
|
for line in f:
|
|
p = line.split()
|
|
info[p[0].strip(":").lower()] = int(p[1]) * 1024
|
|
|
|
if "memavailable" in info:
|
|
# Linux >= 3.14
|
|
return info["memavailable"]
|
|
else:
|
|
return info["memfree"] + info["cached"]
|
|
|
|
return None
|
|
|
|
|
|
def _no_tracing(func):
|
|
"""
|
|
Decorator to temporarily turn off tracing for the duration of a test.
|
|
Needed in tests that check refcounting, otherwise the tracing itself
|
|
influences the refcounts
|
|
"""
|
|
if not hasattr(sys, "gettrace"):
|
|
return func
|
|
else:
|
|
|
|
@wraps(func)
|
|
def wrapper(*args, **kwargs):
|
|
original_trace = sys.gettrace()
|
|
try:
|
|
sys.settrace(None)
|
|
return func(*args, **kwargs)
|
|
finally:
|
|
sys.settrace(original_trace)
|
|
|
|
return wrapper
|
|
|
|
|
|
def _get_glibc_version():
|
|
try:
|
|
ver = os.confstr("CS_GNU_LIBC_VERSION").rsplit(" ")[1]
|
|
except Exception as inst:
|
|
ver = "0.0"
|
|
|
|
return ver
|
|
|
|
|
|
_glibcver = _get_glibc_version()
|
|
|
|
|
|
def _glibc_older_than(x):
|
|
return _glibcver != "0.0" and _glibcver < x
|