Comments (4)
fengyuentau
commented on June 12, 2024
1
@LaurentBerger I made a patch to fix this problem. The last subgraph in the model (after the last Conv) matches the LayerNorm subgraph pattern but it should not due to axes[-1] is not either -1 nor the axis of the last dimension. This was not considered in the previous matching implementation.
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LaurentBerger
commented on June 12, 2024
Problem is in AttentionLayer.
this
/* Fusion for LayerNormalization.
Graph before fusion
+-> ReduceMean ->+
| |
[Input] -------> Sub -----------------------------------------------> Div -> Mul(B=weight) -> Add(B=bias) -> [Output]
| |
+-> Pow(Y=2) -> ReduceMean -> Add(B=epsilon) -> Sqrt ->+
Graph after fusion
[Input] -> LayerNorm -> [Output]
\
[weight], [bias]
*/
cannot replace this case (extract from sam encoder)
test_attentionlayer (2).zip
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asmorkalov
commented on June 12, 2024
@fengyuentau could you take a look?
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LaurentBerger
commented on June 12, 2024
How to solve? add a flag to disable onnx_graph_simplifier
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