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adjust unit tests for test_save_load_float16 #12500

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adjust unit tests for test_save_load_float16 #12500

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18 changes: 9 additions & 9 deletions tests/pipelines/test_pipelines_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1438,19 +1438,19 @@ def test_save_load_float16(self, expected_max_diff=1e-2):
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
for component in pipe_loaded.components.values():
for name, component in pipe_loaded.components.items():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
if hasattr(component, "dtype"):
self.assertTrue(
component.dtype == torch.float16,
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
)
if hasattr(component, "half"):
# Although all components for pipe_loaded should be float16 now, some submodules still use fp32, like in https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/t5/modeling_t5.py#L783, so we need to do the conversion again manally to align with the datatype we use in pipe exactly
component = component.to(torch_device).half()
pipe_loaded.set_progress_bar_config(disable=None)

for name, component in pipe_loaded.components.items():
if hasattr(component, "dtype"):
self.assertTrue(
component.dtype == torch.float16,
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
)

inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
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