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add support fo hpu in float8 base and compile test for torch ao

9780d09

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ankurneog reviewed

View reviewed changes

Have given some general comments , I think we can refine it further, for skips lets use the if not supported_device()

test/float8/test_base.py Outdated

@@ @@ -239,11 +240,11 @@ def test_axiswise_reshape(self): @@
 (ScalingGranularity.TENSORWISE, ScalingGranularity.AXISWISE),
 ],
 )
-@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-@unittest.skipIf(not is_sm_at_least_90(), "Requires CUDA capability >= 9.0")
+@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ -272,7 +273,7 @@ def test_axiswise_gemm(self, a_shape, a_granularity, b_granularity):
sqnr = compute_error(c_ref, c_fp8_compute)
assert sqnr >= 25.0

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ -337,7 +338,7 @@ def _test_linear_impl(
@pytest.mark.parametrize("linear_dtype", [torch.bfloat16, torch.float32])
@pytest.mark.parametrize("linear_bias", [False, True])
@pytest.mark.parametrize("use_ac", [False, True])
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ -379,23 +380,23 @@ def test_linear_from_config_params(
)
@pytest.mark.parametrize("x_shape", [(16, 16), (2, 16, 16), (3, 2, 16, 16)])
@pytest.mark.parametrize("linear_bias", [True, False])
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ -409,32 +410,32 @@ def test_linear_from_recipe(
@pytest.mark.parametrize(
"linear_dtype", [torch.float16, torch.bfloat16, torch.float32]
)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ @@ -590,8 +592,9 @@ def test_different_configs_error(self): @@
 a @ b
 @unittest.skipIf(
+not torch.hpu.is_available() and

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_base.py Outdated

@@ -681,7 +684,7 @@ class TestNumerics:
torch.float8_e5m2fnuz,
],
)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_compile.py Outdated

@@ -88,7 +90,7 @@ def _test_compile_base(
)
@pytest.mark.parametrize("emulate", [False, True] if is_sm_at_least_89() else [True])
@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float32])
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

test/float8/test_compile.py Outdated

@@ -124,7 +126,7 @@ def test_eager_only(
[ScalingType.DYNAMIC],
)
@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float32])
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@unittest.skipIf(not torch.hpu.is_available() and not torch.cuda.is_available(), "Accelerator not available")

ankurneog Jun 10, 2025

can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html

torchao/utils.py

		torch.finfo = wrap_torch_finfo


		def supported_device_available():

ankurneog Jun 10, 2025

for the skips you can use this as well, in conjunction to this can use the accelerator APIs directly : https://docs.pytorch.org/docs/stable/generated/torch.accelerator.is_available.html
eg:
supported accelerator:
if torch.accelerator.is_available() :
if hpu or cuda

akansehl marked this pull request as draft

June 12, 2025 08:35

use torch accelerator APIs

db647ff

akansehl changed the title ~~DUMMY PR: add support for hpu in float8 base and compile test for torch ao~~ WIP PR: add support for hpu in float8 base and compile test for torch ao

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