Milestone2.2: Optimize transposes in XNNPACK partition by removing redundant to_copy ops #11316
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| def input_dim_order( | ||
| self, input_node: torch.fx.Node, input_order: InputDimOrder | ||
| ) -> bool: | ||
| if input_node.name == "x": |
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Can you replace this with checking if the input_node is a placeholder?
| from executorch.exir.passes.memory_format_ops_pass import DimOrderOpsRevertPass | ||
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| class TestChannelsLastTaggedReshapePass(unittest.TestCase): |
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Can we add a test that includes implicitly created dim order conversions? This will check to make sure that user created and pass-created converts get optimized out correctly. I expected it will work, but it would be nice to cover it since this is a common use case.
Maybe something like:
to_channels_last
upsample_nearest2d (not partitioned)
to_channels_first
conv
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| # If we encounter a to_copy node, check if it is preceded by an opposite to_copy node | ||
| if node.target == exir_ops.edge.aten._to_copy.default: | ||
| if prev and ChannelsLastTaggedReshapePass.is_nchw_node( |
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I think that there may be cases where the using the previous node in the iteration order might not actually be the first arg, especially in more complex graphs. Can you try replacing prev with node.args[0]? That should be sound in all cases.
Summary
Optimize transposes in XNNPACK partition
Test plan
Constructed graphs with multiple redundant to_copy ops. Asserted their removal after pass