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[ExecuTorch] Arm Ethos: Do not depend on torch.testing._internal #8893

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[ExecuTorch] Arm Ethos: Do not depend on torch.testing._internal #8893

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5 changes: 2 additions & 3 deletions backends/arm/test/passes/test_rescale_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@
from executorch.backends.arm.test import common, conftest
from executorch.backends.arm.test.tester.arm_tester import ArmTester
from parameterized import parameterized
from torch.testing._internal import optests


def test_rescale_op():
Expand Down Expand Up @@ -64,7 +63,7 @@ def test_nonzero_zp_for_int32():
),
]
for sample_input in sample_inputs:
with pytest.raises(optests.generate_tests.OpCheckError):
with pytest.raises(Exception, match="opcheck"):
torch.library.opcheck(torch.ops.tosa._rescale, sample_input)


Expand All @@ -87,7 +86,7 @@ def test_zp_outside_range():
),
]
for sample_input in sample_inputs:
with pytest.raises(optests.generate_tests.OpCheckError):
with pytest.raises(Exception, match="opcheck"):
torch.library.opcheck(torch.ops.tosa._rescale, sample_input)


Expand Down
27 changes: 24 additions & 3 deletions backends/arm/test/runner_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,12 +34,33 @@
from torch.fx.node import Node

from torch.overrides import TorchFunctionMode
from torch.testing._internal.common_utils import torch_to_numpy_dtype_dict
from tosa import TosaGraph

logger = logging.getLogger(__name__)
logger.setLevel(logging.CRITICAL)

# Copied from PyTorch.
# From torch/testing/_internal/common_utils.py:torch_to_numpy_dtype_dict
# To avoid a dependency on _internal stuff.
_torch_to_numpy_dtype_dict = {
torch.bool: np.bool_,
torch.uint8: np.uint8,
torch.uint16: np.uint16,
torch.uint32: np.uint32,
torch.uint64: np.uint64,
torch.int8: np.int8,
torch.int16: np.int16,
torch.int32: np.int32,
torch.int64: np.int64,
torch.float16: np.float16,
torch.float32: np.float32,
torch.float64: np.float64,
torch.bfloat16: np.float32,
torch.complex32: np.complex64,
torch.complex64: np.complex64,
torch.complex128: np.complex128,
}


class QuantizationParams:
__slots__ = ["node_name", "zp", "scale", "qmin", "qmax", "dtype"]
Expand Down Expand Up @@ -335,7 +356,7 @@ def run_corstone(
output_dtype = node.meta["val"].dtype
tosa_ref_output = np.fromfile(
os.path.join(intermediate_path, f"out-{i}.bin"),
torch_to_numpy_dtype_dict[output_dtype],
_torch_to_numpy_dtype_dict[output_dtype],
)

output_np.append(torch.from_numpy(tosa_ref_output).reshape(output_shape))
Expand All @@ -349,7 +370,7 @@ def prep_data_for_save(
):
if isinstance(data, torch.Tensor):
data_np = np.array(data.detach(), order="C").astype(
torch_to_numpy_dtype_dict[data.dtype]
_torch_to_numpy_dtype_dict[data.dtype]
)
else:
data_np = np.array(data)
Expand Down
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