[AutoScheduler] Register workload when deserializing tasks #6927
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comaniac
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| func_name = json.loads(self.workload_key)[0] | ||
| except Exception: # pylint: disable=broad-except | ||
| raise RuntimeError("Invalid workload key %s" % self.workload_key) | ||
| register_workload_tensors(func_name, self.dag.tensors) |
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We should do more checks here.
There are two kinds of workloads.
You can find the note here
https://github.com/apache/incubator-tvm/blob/51d81fb71781eb606f4b563ec28290dbd8d04faf/python/tvm/auto_scheduler/workload_registry.py#L42-L52
We have to handle two cases correctly without incorrect overwriting
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Changed the logic accordingly. Now we only register workloads extracted from Relay programs.
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* [AutoScheduler] Register workload when deserializing tasks * fix name * format * merge * fix test * more checks
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* [AutoScheduler] Register workload when deserializing tasks * fix name * format * merge * fix test * more checks
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* [AutoScheduler] Register workload when deserializing tasks * fix name * format * merge * fix test * more checks
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One issue of the current auto_scheduler task serialization is that the workload won't be registered when deserializing a task so users have to manually call
register_workload_tensorsafter deserialization. However,register_workload_tensorsalways uses compute DAG hash as the workload key. This could cause workload key mismatching if the task was from a TE function that uses function name as the workload key. This PR makes the following changes to make sureWORKLOAD_FUNC_REGISTRYis consist after deserializing a task.register_workload_tensorsby moving the ComputeDAG out.cc @merrymercy @jcf94