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# mypy: allow-untyped-defs
# Owner(s): ["module: typing"]
from torch.testing._internal.common_utils import (
load_tests,
run_tests,
set_default_dtype,
TEST_NUMPY,
TestCase,
)
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
import sys
import unittest
import torch
if TEST_NUMPY:
import numpy as np
class TestDTypeInfo(TestCase):
def test_invalid_input(self):
for dtype in [
torch.float16,
torch.float32,
torch.float64,
torch.bfloat16,
torch.complex64,
torch.complex128,
torch.bool,
]:
with self.assertRaises(TypeError):
_ = torch.iinfo(dtype)
for dtype in [
torch.int64,
torch.int32,
torch.int16,
torch.int8,
torch.uint8,
torch.bool,
]:
with self.assertRaises(TypeError):
_ = torch.finfo(dtype)
with self.assertRaises(RuntimeError):
dtype.to_complex()
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_iinfo(self):
for dtype in [torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8]:
x = torch.zeros((2, 2), dtype=dtype)
xinfo = torch.iinfo(x.dtype)
xn = x.cpu().numpy()
xninfo = np.iinfo(xn.dtype)
self.assertEqual(xinfo.bits, xninfo.bits)
self.assertEqual(xinfo.max, xninfo.max)
self.assertEqual(xinfo.min, xninfo.min)
self.assertEqual(xinfo.dtype, xninfo.dtype)
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_finfo(self):
for dtype in [
torch.float16,
torch.float32,
torch.float64,
torch.complex64,
torch.complex128,
]:
x = torch.zeros((2, 2), dtype=dtype)
xinfo = torch.finfo(x.dtype)
xn = x.cpu().numpy()
xninfo = np.finfo(xn.dtype)
self.assertEqual(xinfo.bits, xninfo.bits)
self.assertEqual(xinfo.max, xninfo.max)
self.assertEqual(xinfo.min, xninfo.min)
self.assertEqual(xinfo.eps, xninfo.eps)
self.assertEqual(xinfo.tiny, xninfo.tiny)
self.assertEqual(xinfo.resolution, xninfo.resolution)
self.assertEqual(xinfo.dtype, xninfo.dtype)
if not dtype.is_complex:
with set_default_dtype(dtype):
self.assertEqual(torch.finfo(dtype), torch.finfo())
# Special test case for BFloat16 type
x = torch.zeros((2, 2), dtype=torch.bfloat16)
xinfo = torch.finfo(x.dtype)
self.assertEqual(xinfo.bits, 16)
self.assertEqual(xinfo.max, 3.38953e38)
self.assertEqual(xinfo.min, -3.38953e38)
self.assertEqual(xinfo.eps, 0.0078125)
self.assertEqual(xinfo.tiny, 1.17549e-38)
self.assertEqual(xinfo.tiny, xinfo.smallest_normal)
self.assertEqual(xinfo.resolution, 0.01)
self.assertEqual(xinfo.dtype, "bfloat16")
with set_default_dtype(x.dtype):
self.assertEqual(torch.finfo(x.dtype), torch.finfo())
# Special test case for Float8_E5M2
xinfo = torch.finfo(torch.float8_e5m2)
self.assertEqual(xinfo.bits, 8)
self.assertEqual(xinfo.max, 57344.0)
self.assertEqual(xinfo.min, -57344.0)
self.assertEqual(xinfo.eps, 0.25)
self.assertEqual(xinfo.tiny, 6.10352e-05)
self.assertEqual(xinfo.resolution, 1.0)
self.assertEqual(xinfo.dtype, "float8_e5m2")
# Special test case for Float8_E4M3FN
xinfo = torch.finfo(torch.float8_e4m3fn)
self.assertEqual(xinfo.bits, 8)
self.assertEqual(xinfo.max, 448.0)
self.assertEqual(xinfo.min, -448.0)
self.assertEqual(xinfo.eps, 0.125)
self.assertEqual(xinfo.tiny, 0.015625)
self.assertEqual(xinfo.resolution, 1.0)
self.assertEqual(xinfo.dtype, "float8_e4m3fn")
def test_to_complex(self):
# Regression test for https://github.com/pytorch/pytorch/issues/124868
# If reference count is leaked this would be a set of 10 elements
ref_cnt = {sys.getrefcount(torch.float32.to_complex()) for _ in range(10)}
self.assertLess(len(ref_cnt), 3)
self.assertEqual(torch.float64.to_complex(), torch.complex128)
self.assertEqual(torch.float32.to_complex(), torch.complex64)
self.assertEqual(torch.float16.to_complex(), torch.complex32)
def test_to_real(self):
# Regression test for https://github.com/pytorch/pytorch/issues/124868
# If reference count is leaked this would be a set of 10 elements
ref_cnt = {sys.getrefcount(torch.cfloat.to_real()) for _ in range(10)}
self.assertLess(len(ref_cnt), 3)
self.assertEqual(torch.complex128.to_real(), torch.double)
self.assertEqual(torch.complex64.to_real(), torch.float32)
self.assertEqual(torch.complex32.to_real(), torch.float16)
if __name__ == "__main__":
TestCase._default_dtype_check_enabled = True
run_tests()
|
