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move transcendental fuzzer test to test_transcendental (#7511)

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chenyu
2024-11-03 12:36:50 -05:00
committed by GitHub
parent 84592225d8
commit 2f70fb893e
2 changed files with 45 additions and 44 deletions
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40
test/test_dtype_alu.py
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@@ -173,45 +173,5 @@ class TestDTypeALU(unittest.TestCase):
@given(ht.int32, strat.sampled_from(dtypes_float+dtypes_int+dtypes_bool))
def test_int32_cast(self, a, dtype): universal_test_cast(a, dtypes.int32, dtype)
class TestFromFuzzer(unittest.TestCase):
@given(strat.sampled_from(dtypes_float))
def test_sin(self, dtype):
if not is_dtype_supported(dtype): return
if dtype == dtypes.float64:
# crashes in CI CUDA
if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
def _test_value(n: float, unit: float=1.0):
next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
ulp = next_float - 1.0
ulp = unit * ulp
np.testing.assert_allclose(Tensor([n], dtype=dtype).sin().numpy(), np.sin(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
_test_value(-35.0)
_test_value(-25.0)
_test_value(25.0)
_test_value(30.0) # 30.0 == switch_over
_test_value(35.0)
_test_value(0.0)
_test_value(np.pi / 2)
# worst case of ulp 1.5
_test_value(np.pi * 2, unit=1.5)
@given(strat.sampled_from(dtypes_float))
def test_log2(self, dtype):
if not is_dtype_supported(dtype): return
if dtype == dtypes.float64:
# crashes in CI CUDA
if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
def _test_value(n: float, unit: float=1.0):
next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
ulp = next_float - 1.0
ulp = unit * ulp
np.testing.assert_allclose(Tensor([n], dtype=dtype).log2().numpy(), np.log2(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
fmin = np.finfo(_to_np_dtype(dtype)).tiny
for scale in [1.0, 1e10, 1e20, 1e30]:
_test_value(fmin * scale)
_test_value(-fmin * scale)
_test_value(0)
_test_value(0.0000009)
if __name__ == '__main__':
unittest.main()

49
test/test_transcendental.py
View File

@@ -3,7 +3,7 @@ from tinygrad import Tensor, Device, dtypes
from tinygrad.tensor import _to_np_dtype
from tinygrad.helpers import Context, getenv
from test.test_schedule import check_schedule
from test.test_dtype_alu import ht
from test.test_dtype_alu import ht, dtypes_float
from test.helpers import is_dtype_supported
import numpy as np
from hypothesis import given, settings, strategies as strat
@@ -40,10 +40,10 @@ class TestTranscendentalMath(unittest.TestCase):
op[1](np.array([x], dtype=_to_np_dtype(dtypes.float16))),
atol=1e-2, rtol=5e-3) # exp can have bigger rtol
@unittest.skipIf(Device.DEFAULT=="LLVM", "FIXME: LLVM might change computer")
@unittest.skipIf(Device.DEFAULT=="LLVM", "FIXME: LLVM might change compute")
@given(strat.sampled_from([(dtypes.float64, 709.5), (dtypes.float32, 88.7), (dtypes.float16, 11)]))
def test_exp_near_inf(self, dtype_x):
# reordering compute might give inf result
# reordering compute might return inf
dtype, x = dtype_x
if not is_dtype_supported(dtype): return
with Context(TRANSCENDENTAL=2):
@@ -51,8 +51,49 @@ class TestTranscendentalMath(unittest.TestCase):
expected = np.exp(np.array([x], dtype=_to_np_dtype(dtype)))
np.testing.assert_allclose(y, expected, rtol=5e-3)
class TestFromFuzzer(unittest.TestCase):
@given(strat.sampled_from(dtypes_float))
def test_sin(self, dtype):
if not is_dtype_supported(dtype): return
if dtype == dtypes.float64:
# crashes in CI CUDA
if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
def _test_value(n: float, unit: float=1.0):
next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
ulp = next_float - 1.0
ulp = unit * ulp
with Context(TRANSCENDENTAL=2):
np.testing.assert_allclose(Tensor([n], dtype=dtype).sin().numpy(), np.sin(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
_test_value(-35.0)
_test_value(-25.0)
_test_value(25.0)
_test_value(30.0) # 30.0 == switch_over
_test_value(35.0)
_test_value(0.0)
_test_value(np.pi / 2)
# worst case of ulp 1.5
_test_value(np.pi * 2, unit=1.5)
@given(strat.sampled_from(dtypes_float))
def test_log2(self, dtype):
if not is_dtype_supported(dtype): return
if dtype == dtypes.float64:
# crashes in CI CUDA
if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
def _test_value(n: float, unit: float=1.0):
next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
ulp = next_float - 1.0
ulp = unit * ulp
with Context(TRANSCENDENTAL=2):
np.testing.assert_allclose(Tensor([n], dtype=dtype).log2().numpy(), np.log2(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
fmin = np.finfo(_to_np_dtype(dtype)).tiny
for scale in [1.0, 1e10, 1e20, 1e30]:
_test_value(fmin * scale)
_test_value(-fmin * scale)
_test_value(0)
_test_value(0.0000009)
class TestTranscendentalSchedule(unittest.TestCase):
# w/ payne_hanek_reduction (fp32)
def test_transcendental_sin_fusion(self):
with Context(TRANSCENDENTAL=2):
a = Tensor.empty(10)