[Torch][WeightCompression] Add Scale Estimation data-aware support #3179
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kshpv
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| @pytest.mark.parametrize("axes", [None, (0,), (0, 1), (0, 1, 2)]) |
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Comments regarding the test_reducers_and_aggregators:
- Please join this three tests to one
- Please use reference inputs/outputs for the reducers as it done in all other tests (instead of a random input and reference numpy implementation)
- Please move the joined test above the aggregators tests
MaximProshin
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weight compression build - 291 |
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The proposed example can be added as a follow-up PR - will be excluded from this PR |
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| [None, np.array([[[1, 2, 0], [1, -3, 10]], [[-1, 2, -3], [4, 5, -6]]]), 16.1666], | ||
| [(0,), np.array([[[1, 2, 0], [1, -3, 10]], [[-1, 2, -3], [4, 5, -6]]]), 14.25], | ||
| [(0, 1), np.array([[[1, 2, 0], [1, -3, 10]], [[-1, 2, -3], [4, 5, -6]]]), 15.875], |
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As each ref input is equal to each other, I believe we can create a global var like DEFAULT_VARIANCE_REF_INPUT for it😀
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Changes
Add data-aware support for Torch backend for WeightCompression with Scale Estimatiaon support
Reason for changes
Add support of Torch
Related tickets
158974
Tests
Add unittest
Add usecases for Torch to WeightCompression job