Optimize Transpose->Pad when 'pads' input is not a constant #1331
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When the second input is not a constant, let's shuffle it with Split followed by Concat. There are examples of models, where this non-constant input gets constant folded anyway by a framework. Even if that's not the case. Split+Concat of 8 (or 10) element tensor should be a good trade for Transpose pair. Signed-off-by: Mateusz Tabaka <mateusz.tabaka@intel.com>
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@TomWildenhain-Microsoft this one is ready for review. Thanks. |
TomWildenhain-Microsoft
approved these changes
Feb 10, 2021
| # when the second input is not a constant, let's shuffle it with Split followed by Concat | ||
| # there are examples of models, where this non-constant input | ||
| # gets constant folded anyway by a framework. | ||
| split = self._g.make_node("Split", inputs=[node.input[1]], attr={}, output_count=trans_rank * 2) |
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Nice. When the pad is non-const, it is often constructed by concating some tensors together. This optimization might introduce a concat -> split -> concat sequence that can be further optimized. Still, it is worth it for removing a transpose and the concat -> split -> concat will be operating on very small tensors. We might want to add a new optimizer for it in the future though.
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When the second input is not a constant, let's shuffle it with Split followed by Concat.
There are examples of models, where this non-constant input gets constant folded anyway by a framework.
Even if that's not the case, Split+Concat of 8 (or 10) element tensor should be a good trade for Transpose pair.
Signed-off-by: Mateusz Tabaka mateusz.tabaka@intel.com