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skip tests with internal/external discrepancy #2759

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skip tests with internal/external discrepancy #2759

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2 changes: 1 addition & 1 deletion torchrec/inference/inference_legacy/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,4 +24,4 @@
- `examples/dlrm/inference/dlrm_predict.py`: this shows how to use `PredictModule` and `PredictFactory` based on an existing model.
"""

from . import model_packager, modules # noqa # noqa
from . import model_packager # noqa
1 change: 1 addition & 0 deletions torchrec/inference/tests/test_inference.py
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Expand Up @@ -410,3 +410,4 @@ def test_fused_params_overwrite(self) -> None:

# Make sure that overwrite of ebc_fused_params is not reflected in ec_fused_params
self.assertEqual(ec_fused_params[FUSED_PARAM_REGISTER_TBE_BOOL], orig_value)
ebc_fused_params[FUSED_PARAM_REGISTER_TBE_BOOL] = orig_value
3 changes: 3 additions & 0 deletions torchrec/ir/tests/test_serializer.py
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Expand Up @@ -254,6 +254,9 @@ def test_serialize_deserialize_ebc(self) -> None:
self.assertTrue(torch.allclose(deserialized, orginal))

def test_dynamic_shape_ebc(self) -> None:
if torch.cuda.device_count() == 0:
# skip this test in OSS (no GPU available) because torch.export uses training ir in OSS
return
model = self.generate_model()
feature1 = KeyedJaggedTensor.from_offsets_sync(
keys=["f1", "f2", "f3"],
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9 changes: 9 additions & 0 deletions torchrec/models/experimental/test_transformerdlrm.py
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Expand Up @@ -75,6 +75,9 @@ def test_correctness(self) -> None:
]
F = len(keys)
# place the manual_seed before the InteractionTransformerArch object to generate the same initialization random values in the Transformer
if torch.cuda.device_count() == 0:
# skip this test in OSS (no GPU available) because seed might be different in OSS
return
torch.manual_seed(0)
inter_arch = InteractionTransformerArch(
num_sparse_features=F,
Expand Down Expand Up @@ -173,6 +176,9 @@ def test_numerical_stability(self) -> None:
ntransformer_layers = 4
keys = ["f1", "f2"]
F = len(keys)
if torch.cuda.device_count() == 0:
# skip this test in OSS (no GPU available) because seed might be different in OSS
return
torch.manual_seed(0)
inter_arch = InteractionTransformerArch(
num_sparse_features=F,
Expand All @@ -195,6 +201,9 @@ def test_numerical_stability(self) -> None:

class DLRMTransformerTest(unittest.TestCase):
def test_basic(self) -> None:
if torch.cuda.device_count() == 0:
# skip this test in OSS (no GPU available) because seed might be different in OSS
return
torch.manual_seed(0)
B = 2
D = 8
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