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Port NumPy typing testing style to PyTorch (#52408)
Summary: ref: https://github.com/pytorch/pytorch/issues/16574 Pull Request resolved: https://github.com/pytorch/pytorch/pull/52408 Reviewed By: anjali411 Differential Revision: D26654687 Pulled By: malfet fbshipit-source-id: 6feb603d8fb03c2ba2a01468bfde1a9901e889fd
This commit is contained in:
Guilherme Leobas
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@ -172,6 +172,7 @@ USE_PYTEST_LIST = [
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'distributions/test_constraints',
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'distributions/test_transforms',
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'distributions/test_utils',
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'test_typing',
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]
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WINDOWS_BLOCKLIST = [
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149
test/test_typing.py
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149
test/test_typing.py
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@ -0,0 +1,149 @@
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# based on NumPy numpy/typing/tests/test_typing.py
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import itertools
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import os
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import re
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import shutil
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from typing import IO, Dict, List
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import pytest
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try:
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from mypy import api
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except ImportError:
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NO_MYPY = True
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else:
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NO_MYPY = False
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DATA_DIR = os.path.join(os.path.dirname(__file__), "typing")
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REVEAL_DIR = os.path.join(DATA_DIR, "reveal")
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MYPY_INI = os.path.join(DATA_DIR, os.pardir, os.pardir, "mypy.ini")
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CACHE_DIR = os.path.join(DATA_DIR, ".mypy_cache")
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#: A dictionary with file names as keys and lists of the mypy stdout as values.
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#: To-be populated by `run_mypy`.
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OUTPUT_MYPY: Dict[str, List[str]] = {}
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def _key_func(key: str) -> str:
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"""Split at the first occurance of the ``:`` character.
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Windows drive-letters (*e.g.* ``C:``) are ignored herein.
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"""
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drive, tail = os.path.splitdrive(key)
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return os.path.join(drive, tail.split(":", 1)[0])
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@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
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@pytest.fixture(scope="module", autouse=True)
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def run_mypy() -> None:
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"""Clears the cache and run mypy before running any of the typing tests.
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The mypy results are cached in `OUTPUT_MYPY` for further use.
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"""
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if os.path.isdir(CACHE_DIR):
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shutil.rmtree(CACHE_DIR)
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for directory in (REVEAL_DIR,):
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# Run mypy
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stdout, stderr, _ = api.run(
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[
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"--show-absolute-path",
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"--config-file",
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MYPY_INI,
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"--cache-dir",
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CACHE_DIR,
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directory,
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]
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)
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assert not stderr, directory
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stdout = stdout.replace("*", "")
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# Parse the output
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iterator = itertools.groupby(stdout.split("\n"), key=_key_func)
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OUTPUT_MYPY.update((k, list(v)) for k, v in iterator if k)
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def get_test_cases(directory):
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for root, _, files in os.walk(directory):
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for fname in files:
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if os.path.splitext(fname)[-1] == ".py":
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fullpath = os.path.join(root, fname)
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# Use relative path for nice py.test name
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relpath = os.path.relpath(fullpath, start=directory)
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yield pytest.param(
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fullpath,
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# Manually specify a name for the test
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id=relpath,
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)
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#: A dictionary with all supported format keys (as keys)
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#: and matching values
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FORMAT_DICT: Dict[str, str] = {}
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def _parse_reveals(file: IO[str]) -> List[str]:
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"""Extract and parse all ``" # E: "`` comments from the passed file-like object.
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All format keys will be substituted for their respective value from `FORMAT_DICT`,
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*e.g.* ``"{float64}"`` becomes ``"numpy.floating[numpy.typing._64Bit]"``.
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"""
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string = file.read().replace("*", "")
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# Grab all `# E:`-based comments
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comments_array = list(map(lambda str: str.partition(" # E: ")[2], string.split("\n")))
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comments = "/n".join(comments_array)
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# Only search for the `{*}` pattern within comments,
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# otherwise there is the risk of accidently grabbing dictionaries and sets
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key_set = set(re.findall(r"\{(.*?)\}", comments))
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kwargs = {
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k: FORMAT_DICT.get(k, f"<UNRECOGNIZED FORMAT KEY {k!r}>") for k in key_set
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}
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fmt_str = comments.format(**kwargs)
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return fmt_str.split("/n")
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@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
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@pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR))
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def test_reveal(path):
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__tracebackhide__ = True
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with open(path) as fin:
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lines = _parse_reveals(fin)
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output_mypy = OUTPUT_MYPY
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assert path in output_mypy
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for error_line in output_mypy[path]:
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match = re.match(
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r"^.+\.py:(?P<lineno>\d+): note: .+$",
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error_line,
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)
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if match is None:
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raise ValueError(f"Unexpected reveal line format: {error_line}")
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lineno = int(match.group("lineno")) - 1
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assert "Revealed type is" in error_line
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marker = lines[lineno]
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_test_reveal(path, marker, error_line, 1 + lineno)
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_REVEAL_MSG = """Reveal mismatch at line {}
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Expected reveal: {!r}
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Observed reveal: {!r}
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"""
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def _test_reveal(path: str, reveal: str, expected_reveal: str, lineno: int) -> None:
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if reveal not in expected_reveal:
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raise AssertionError(_REVEAL_MSG.format(lineno, expected_reveal, reveal))
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if __name__ == '__main__':
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pytest.main([__file__])
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114
test/typing/reveal/tensor_constructors.py
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114
test/typing/reveal/tensor_constructors.py
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@ -0,0 +1,114 @@
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import torch
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from torch.testing._internal.common_utils import TEST_NUMPY
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if TEST_NUMPY:
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import numpy as np
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# From the docs, there are quite a few ways to create a tensor:
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# https://pytorch.org/docs/stable/tensors.html
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# torch.tensor()
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reveal_type(torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])) # E: torch.tensor.Tensor
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reveal_type(torch.tensor([0, 1])) # E: torch.tensor.Tensor
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reveal_type(torch.tensor([[0.11111, 0.222222, 0.3333333]],
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dtype=torch.float64,
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device=torch.device('cuda:0'))) # E: torch.tensor.Tensor
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reveal_type(torch.tensor(3.14159)) # E: torch.tensor.Tensor
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# torch.sparse_coo_tensor
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i = torch.tensor([[0, 1, 1],
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[2, 0, 2]]) # E: torch.tensor.Tensor
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v = torch.tensor([3, 4, 5], dtype=torch.float32) # E: torch.tensor.Tensor
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reveal_type(torch.sparse_coo_tensor(i, v, [2, 4])) # E: torch.tensor.Tensor
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reveal_type(torch.sparse_coo_tensor(i, v)) # E: torch.tensor.Tensor
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reveal_type(torch.sparse_coo_tensor(i, v, [2, 4],
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dtype=torch.float64,
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device=torch.device('cuda:0'))) # E: torch.tensor.Tensor
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reveal_type(torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])) # E: torch.tensor.Tensor
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reveal_type(torch.sparse_coo_tensor(torch.empty([1, 0]),
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torch.empty([0, 2]), [1, 2])) # E: torch.tensor.Tensor
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# torch.as_tensor
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if TEST_NUMPY:
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a = np.array([1, 2, 3])
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reveal_type(torch.as_tensor(a)) # E: torch.tensor.Tensor
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reveal_type(torch.as_tensor(a, device=torch.device('cuda'))) # E: torch.tensor.Tensor
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# torch.as_strided
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x = torch.randn(3, 3)
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reveal_type(torch.as_strided(x, (2, 2), (1, 2))) # E: torch.tensor.Tensor
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reveal_type(torch.as_strided(x, (2, 2), (1, 2), 1)) # E: torch.tensor.Tensor
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# torch.from_numpy
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if TEST_NUMPY:
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a = np.array([1, 2, 3])
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reveal_type(torch.from_numpy(a)) # E: torch.tensor.Tensor
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# torch.zeros/zeros_like
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reveal_type(torch.zeros(2, 3)) # E: torch.tensor.Tensor
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reveal_type(torch.zeros(5)) # E: torch.tensor.Tensor
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reveal_type(torch.zeros_like(torch.empty(2, 3))) # E: torch.tensor.Tensor
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# torch.ones/ones_like
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reveal_type(torch.ones(2, 3)) # E: torch.tensor.Tensor
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reveal_type(torch.ones(5)) # E: torch.tensor.Tensor
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reveal_type(torch.ones_like(torch.empty(2, 3))) # E: torch.tensor.Tensor
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# torch.arange
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reveal_type(torch.arange(5)) # E: torch.tensor.Tensor
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reveal_type(torch.arange(1, 4)) # E: torch.tensor.Tensor
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reveal_type(torch.arange(1, 2.5, 0.5)) # E: torch.tensor.Tensor
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# torch.range
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reveal_type(torch.range(1, 4)) # E: torch.tensor.Tensor
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reveal_type(torch.range(1, 4, 0.5)) # E: torch.tensor.Tensor
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# torch.linspace
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reveal_type(torch.linspace(3, 10, steps=5)) # E: torch.tensor.Tensor
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reveal_type(torch.linspace(-10, 10, steps=5)) # E: torch.tensor.Tensor
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reveal_type(torch.linspace(start=-10, end=10, steps=5)) # E: torch.tensor.Tensor
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reveal_type(torch.linspace(start=-10, end=10, steps=1)) # E: torch.tensor.Tensor
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# torch.logspace
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reveal_type(torch.logspace(start=-10, end=10, steps=5)) # E: torch.tensor.Tensor
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reveal_type(torch.logspace(start=0.1, end=1.0, steps=5)) # E: torch.tensor.Tensor
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reveal_type(torch.logspace(start=0.1, end=1.0, steps=1)) # E: torch.tensor.Tensor
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reveal_type(torch.logspace(start=2, end=2, steps=1, base=2)) # E: torch.tensor.Tensor
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# torch.eye
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reveal_type(torch.eye(3)) # E: torch.tensor.Tensor
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# torch.empty/empty_like/empty_strided
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reveal_type(torch.empty(2, 3)) # E: torch.tensor.Tensor
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reveal_type(torch.empty_like(torch.empty(2, 3), dtype=torch.int64)) # E: torch.tensor.Tensor
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reveal_type(torch.empty_strided((2, 3), (1, 2))) # E: torch.tensor.Tensor
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# torch.full/full_like
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reveal_type(torch.full((2, 3), 3.141592)) # E: torch.tensor.Tensor
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reveal_type(torch.full_like(torch.full((2, 3), 3.141592), 2.71828)) # E: torch.tensor.Tensor
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# torch.quantize_per_tensor
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reveal_type(torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8)) # E: torch.tensor.Tensor
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# torch.quantize_per_channel
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x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
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quant = torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8)
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reveal_type(x) # E: torch.tensor.Tensor
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# torch.dequantize
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reveal_type(torch.dequantize(x)) # E: torch.tensor.Tensor
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# torch.complex
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real = torch.tensor([1, 2], dtype=torch.float32)
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imag = torch.tensor([3, 4], dtype=torch.float32)
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reveal_type(torch.complex(real, imag)) # E: torch.tensor.Tensor
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# torch.polar
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abs = torch.tensor([1, 2], dtype=torch.float64)
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pi = torch.acos(torch.zeros(1)).item() * 2
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angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
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reveal_type(torch.polar(abs, angle)) # E: torch.tensor.Tensor
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# torch.heaviside
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inp = torch.tensor([-1.5, 0, 2.0])
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values = torch.tensor([0.5])
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reveal_type(torch.heaviside(inp, values)) # E: torch.tensor.Tensor
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