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skip segfaults on ROCM

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This commit is contained in:
Michael Melesse
2022-10-26 20:46:47 +00:00
parent 648e4cfe89
commit 8ecab462f6
3 changed files with 18 additions and 4 deletions
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15
python/test/unit/language/test_core.py
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@@ -679,6 +679,8 @@ def test_tensor_atomic_rmw(axis, device="cuda"):
def test_atomic_cas():
if torch.version.hip is not None:
pytest.skip(f"test_atomic_cas currently has segfaults on ROCM")
# 1. make sure that atomic_cas changes the original value (Lock)
@triton.jit
def change_value(Lock):
@@ -788,6 +790,8 @@ def test_store_bool():
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
def test_f8_xf16_roundtrip(dtype):
if torch.version.hip is not None:
pytest.skip(f"test_f8_xf16_roundtrip currently has segfaults on ROCM")
"""Tests that converting an f8 to f16 and back to f8 doesn't change its value"""
check_type_supported(dtype)
@@ -814,6 +818,8 @@ def test_f8_xf16_roundtrip(dtype):
def test_f16_to_f8_rounding():
if torch.version.hip is not None:
pytest.skip(f"test_atomic_cas currently has segfaults on ROCM")
"""Takes all float16s, converts them to float8 and back to float16. Checks that the absolute
error is the minimum over all float8.
Or the same explanation a bit mathier:
@@ -881,6 +887,8 @@ def test_f16_to_f8_rounding():
for dtype in dtypes_with_bfloat16
for shape in [32, 64, 128, 512]])
def test_reduce1d(op, dtype_str, shape, device='cuda'):
if torch.version.hip is not None:
pytest.skip(f"test_reduce1d currently has segfaults on ROCM")
check_type_supported(dtype_str) # bfloat16 on cc < 80 will not be tested
# triton kernel
@@ -940,6 +948,8 @@ reduce_configs2 = [
@pytest.mark.parametrize("op, dtype_str, shape, axis", reduce_configs1 + reduce_configs2)
def test_reduce2d(op, dtype_str, shape, axis, device='cuda'):
if torch.version.hip is not None:
pytest.skip(f"test_reduce2d currently has segfaults on ROCM")
# triton kernel
@triton.jit
def kernel(X, Z, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, AXIS: tl.constexpr):
@@ -1237,6 +1247,8 @@ def test_masked_load(dtype_str, size, size_diff, device='cuda'):
@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
def test_masked_load_shared_memory(dtype, device='cuda'):
if torch.version.hip is not None:
pytest.skip(f"test_masked_load_shared_memory currently has segfaults on ROCM")
check_type_supported(dtype) # bfloat16 on cc < 80 will not be tested
M = 32
@@ -1557,7 +1569,8 @@ def test_num_warps_pow2():
('float32', 'libdevice.pow', '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'),
('float64', 'libdevice.norm4d', '')])
def test_libdevice(dtype_str, expr, lib_path):
if torch.version.hip is not None:
pytest.skip(f"test_libdevice currently has segfaults on ROCM")
@triton.jit
def kernel(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))