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| input_shape=nodes[0].input_shape, | ||
| output_shape=nodes[-1].output_shape, | ||
| weights={}, | ||
| weights={}, # TODO: update with weights of all nodes |
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Is the todo here planned for this PR?
is it necessary actually? because you can always retrieve the original weights from the original graph
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No, in the PR that handles the MP.
model_compression_toolkit/core/common/mixed_precision/mixed_precision_search_facade.py
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consider adding an e2e tests to verify that none of our substitutions fails the fusing metadata.
tests/keras_tests/function_tests/test_activation_weights_composition_substitution.py
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This commit introduces handling graph fusion by encapsulating fusion metadata in the graph. This ensures that after access to the graph that modify the graph, a validation check is performed to verify that the fusion information remains consistent and that no modifications have introduced inconsistencies. Additionally, the fusion-related logic has been refactored into a new class called GraphFuser, which takes the graph along with its fusion metadata and creates a new graph where fused operations are represented as single nodes. --------- Co-authored-by: reuvenp <reuvenp@altair-semi.com>
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Pull Request Description:
This PR introduces handling graph fusion by encapsulating fusion metadata within a new wrapper class for the graph. This class ensures that after every access to the graph, a validation check is performed to verify that the fusion information remains consistent and that no modifications have introduced inconsistencies. Additionally, the fusion-related logic has been refactored into a new class called
GraphFuser, which takes the graph along with its fusion metadata and creates a new graph where fused operations are represented as single nodes.Checklist before requesting a review: