ORTDiffusionPipelines with IO Binding
#2056
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| if self.use_io_binding is False and provider == "CUDAExecutionProvider": | ||
| self.use_io_binding = True |
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This overrides use_io_binding choice from user. What if user want to run performance test with io binding disabled?
I suggest that:
if use_io_binding is None: change it to True
if not use_io_binding and it is cuda provider, log a warning.
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This is already the default behavior in ORTModels, I kept it for consistency (I'm not a fan of it tbh) to not break stuff for old users.
| def providers(self) -> Tuple[str]: | ||
| return self._validate_same_attribute_value_across_components("providers") | ||
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| @property | ||
| def provider(self) -> str: | ||
| return self._validate_same_attribute_value_across_components("provider") | ||
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| @property | ||
| def providers_options(self) -> Dict[str, Dict[str, Any]]: | ||
| return self._validate_same_attribute_value_across_components("providers_options") | ||
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| @property | ||
| def provider_options(self) -> Dict[str, Any]: | ||
| return self._validate_same_attribute_value_across_components("provider_options") |
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It is not necessary to validate same value across components.
I think it is feasible to use different provider and different provider options for components. For example, we can run text_encoder with CPU, and unet with CUDA provider. Or we want to enable cuda graph in one component but not the other in provider option.
May add some comments and loose the constraint later.
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there's a comment in _validate_same_attribute_value_across_components definition explaining the reasoning behind these checks, which is exactly what you said. Pipeline attributes can be accessed but they only make sense when they're consistent, for now this is my proposition for multi model parts pipelines, an alternative would be to return that of the main component (unet/transformer) or not supporting these attributes at all for the main pipeline (replace them with provider_map for example like device vs device_map).
What does this PR do?
This is also my attempt to create a generalizable io binding framework, the idea is to always have
output_shapes = fn(input_shapes, known_shapes)whereknown_shapesis mostly stuff we find in the config, we the use this information at runtime with a simple symbolic resolver, keeping the shape inference time minimal, to create output tensors in torch and thus accelerate inference without the need to pass by ort values / cupy / numpy.Before submitting
Who can review?