[tuner] Add direct TD spec generation for candidates #606
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This PR is broken right now because of an issue tracked here iree-org/iree#19269. Reviews are appreciated, but it won't be able to land until after this issue is resolved. |
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| # Index 0 is reserved for default config, so it gets no td spec. | ||
| with ir.Location.unknown() as loc: | ||
| empty_module = ir.Module.create(loc) | ||
| config_specs: list[ir.Module] = [empty_module] |
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To avoid this special case, we could also look up the lowering_config and translation_info from the original module and re-materialize it here, but I don't think it's worth it...
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Or we could generate a placeholder spec that never matches anything.
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I kind of like the empty module for now. It seems relatively clean to me, and it makes it clear to someone looking through the specs that it is a placeholder. Having an explicit placeholder spec would also be good.
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Signed-off-by: Max Dawkins <[email protected]>
Signed-off-by: Max Dawkins <[email protected]>
Signed-off-by: Max Dawkins <[email protected]>
Signed-off-by: Max Dawkins <[email protected]>
Signed-off-by: Max Dawkins <[email protected]>
Signed-off-by: Max Dawkins <[email protected]>
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Signed-off-by: Max Dawkins <[email protected]>
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| if contraction_op is None: | ||
| assert False, f"contraction op not found" |
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| if contraction_op is None: | |
| assert False, f"contraction op not found" | |
| assert contraction is not None, f"contraction op not found" |
| if conv_op is None: | ||
| assert False, f"convolution op not found" |
This PR adds direct transform dialect spec generation for candidate configurations. This is the first part of the large refactoring described in #577. The way TD specs are generated is by matching against certain types of operations, and then creating a named sequence with
transform.iree.match.cast_compatible_dag_from_rootbased on the matched operation. This is done for each configuration found, and the specs are saved to the temporary tuning directory to be used later in tuning.One main difference in the flow of candidate generation is that state is no longer tracked by saving files to a temporary directory. Instead, ir modules are passed to each function, and only at the very end of candidate generation are the transform dialect specs written to files. This makes things cleaner, since there no longer needs to be a coordination of file paths.