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[tests] add test for hotswapping + compilation on resolution changes #11825
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Cc: @StrongerXi and @anijain2305 as well. |
@@ -1350,7 +1350,6 @@ def test_model_parallelism(self): | |||
new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) | |||
# Making sure part of the model will actually end up offloaded | |||
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1}) | |||
print(f" new_model.hf_device_map:{new_model.hf_device_map}") |
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Unrelated but hopefully okay :-)
tests/models/test_modeling_common.py
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if different_resolutions is not None: | ||
for height, width in self.different_shapes_for_compilation: | ||
new_inputs_dict = self.prepare_dummy_input(height=height, width=width) | ||
_ = model(**new_inputs_dict) | ||
else: | ||
output0_after = model(**inputs_dict)["sample"] | ||
assert torch.allclose(output0_before, output0_after, atol=tol, rtol=tol) |
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We don't need assertions when testing for different shapes as that would make the test unnecessarily complicated. We just check for recompilations.
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
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Thanks for extending the hotswapping tests. Overall looks good, but I have some suggestions.
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Thanks, I learned something new about torch.compile
. LGTM.
@@ -315,6 +315,8 @@ pipeline.load_lora_weights( | |||
> [!TIP] | |||
> Move your code inside the `with torch._dynamo.config.patch(error_on_recompile=True)` context manager to detect if a model was recompiled. If a model is recompiled despite following all the steps above, please open an [issue](https://github.com/huggingface/diffusers/issues) with a reproducible example. | |||
If you expect to varied resolutions during inference with this feature, then make sure set `dynamic=True` during compilation. Refer to [this document](../optimization/fp16#dynamic-shape-compilation) for more details. |
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I wonder if we should publicize use_duck_shape = False
as well...
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thanks
What does this PR do?
As promised :)