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[tests] fix torchao tests #13330

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[tests] fix torchao tests #13330

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5e5b575
fix torchao tests
sayakpaul Mar 25, 2026
4e01e02
add mslk for additional dependencies.
sayakpaul Mar 25, 2026
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2 changes: 1 addition & 1 deletion .github/workflows/nightly_tests.yml
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Expand Up @@ -341,7 +341,7 @@ jobs:
additional_deps: ["peft", "kernels"]
- backend: "torchao"
test_location: "torchao"
additional_deps: []
additional_deps: [mslk-cuda]
- backend: "optimum_quanto"
test_location: "quanto"
additional_deps: []
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6 changes: 6 additions & 0 deletions tests/models/testing_utils/quantization.py
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Expand Up @@ -177,6 +177,11 @@ def _test_quantization_inference(self, config_kwargs):
model_quantized.to(torch_device)

inputs = self.get_dummy_inputs()
model_dtype = next(model_quantized.parameters()).dtype
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@DN6 DN6 Mar 25, 2026

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This would affect all quantization backends? e.g. With a GGUF backend the dtype could end up as int8 and potentially cast inputs into int8?

Also prefer to avoid casting inputs post fetching from self.get_dummy_inputs() within a test if we can avoid it.

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@sayakpaul sayakpaul Mar 25, 2026

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Where should it go then? Should we implement a custom get_dummy_inputs() for torchao tests? I think it's reasonably safe to keep the dtypes of the inputs to bfloat16 there because that will replicate what we do in actual pipelines. LMK.

inputs = {
k: v.to(dtype=model_dtype) if torch.is_tensor(v) and torch.is_floating_point(v) else v
for k, v in inputs.items()
}
output = model_quantized(**inputs, return_dict=False)[0]

assert output is not None, "Model output is None"
Expand Down Expand Up @@ -930,6 +935,7 @@ def test_torchao_device_map(self):
"""Test that device_map='auto' works correctly with quantization."""
self._test_quantization_device_map(TorchAoConfigMixin.TORCHAO_QUANT_TYPES["int8wo"])

@pytest.mark.xfail(reason="dequantize is not implemented in torchao")
def test_torchao_dequantize(self):
"""Test that dequantize() works correctly."""
self._test_dequantize(TorchAoConfigMixin.TORCHAO_QUANT_TYPES["int8wo"])
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