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fix subnormal in test_dtype (#17013)

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* fix subnormal in test_dtype

should fix flaky test/backend/test_dtype.py::TestFp8e4m3::test_casts_from

* better
This commit is contained in:
chenyu
2026-07-13 18:53:13 -04:00
committed by GitHub
parent c898dfe150
commit 9ce96c2628
2 changed files with 7 additions and 4 deletions
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test/backend/test_dtype.py
+4 -1
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@@ -8,7 +8,7 @@ from tinygrad.renderer.ptx import PTXRenderer
from tinygrad.renderer.nir import NIRRenderer
from tinygrad import Context, Device, Tensor, dtypes
from hypothesis import given, settings, strategies as strat
from test.helpers import rand_for_dtype
from test.helpers import rand_for_dtype, min_normal
from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX, FP8E4M3FNUZ_MAX, FP8E5M2FNUZ_MAX
import pytest
pytestmark = pytest.mark.filterwarnings("ignore")
@@ -46,6 +46,9 @@ def _test_cast(a:Tensor, target_dtype:DType):
if a.is_floating_point() and dtypes.is_unsigned(target_dtype):
# converting negative float to unsigned integer is undefined
a = a.abs()
if a.is_floating_point() and dtypes.is_float(target_dtype) and (mn:=min_normal(target_dtype)) >= min_normal(a.dtype):
# subnormals are zero, so an input below the target's min normal casts to 0
a = (a.abs() < mn).where(0, a)
expected = list(a.numpy().astype(_to_np_dtype(target_dtype)))
if target_dtype in dtypes.fp8s: expected = [truncate[target_dtype](x) for x in expected]
test/helpers.py
+3 -3
View File
@@ -63,6 +63,8 @@ def assert_jit_cache_len(fxn, expected_len):
else:
assert len(linear.src) == expected_len, f"expected {expected_len}, got {len(linear.src)}"
def min_normal(dt:DType) -> float: return 2.0 ** (2 - (1 << (dtypes.finfo(dt)[0] - 1)))
def rand_for_dtype(dt:DType, size:int, allow_subnormal=True):
if dtypes.is_unsigned(dt):
return np.random.randint(0, 100, size=size, dtype=_to_np_dtype(dt))
@@ -71,9 +73,7 @@ def rand_for_dtype(dt:DType, size:int, allow_subnormal=True):
elif dt == dtypes.bool:
return np.random.choice([True, False], size=size)
ret = np.random.uniform(-10, 10, size=size).astype(_to_np_dtype(dt))
if not allow_subnormal:
min_normal = 2.0 ** (2 - (1 << (dtypes.finfo(dt)[0] - 1)))
ret = np.where(np.abs(ret) < min_normal, 0, ret)
if not allow_subnormal: ret = np.where(np.abs(ret) < min_normal(dt), 0, ret)
return ret
def timeit(fxn:Callable[..., T], *args, **kwargs) -> tuple[T, float]: