comaniac commented on a change in pull request #7145:
URL: https://github.com/apache/tvm/pull/7145#discussion_r547452564
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File path: python/tvm/auto_scheduler/search_task.py
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@@ -221,10 +221,6 @@ def __init__(
target_host = Target(target_host)
self.dag = compute_dag
Review comment:
@jcf94 the root cause is we have two ComputeDAG constructors in C++, one
takes `compute` and another takes `schedule`. Since we can only register one
`auto_scheduler.ComputeDAG` symbol to Python, I made a constructor dispatching
as follows. Recall that this is to preserve the stage order if we already have
a schedule and wish to use it to create a ComputeDAG.
```
if (tensors) {
return ComputeDAG(tensors.value());
}
ICHECK(sch) << "Both tensors and schedule are null";
return ComputeDAG(sch.value());
```
It means we need both `tensors` (named `compute` on the Python side) and
`sch` to call the ComputeDAG constructor from Python. If we really want to keep
one, then we should keep `sch` because it has more information than `tensors`.
Since `sch` is not stored in the C++ object so we cannot access it via FFI, the
current workaround is having a Python-side specific attribute `self.sche`.
However, this attribute will be wiped out when mapping to the C++ object, so
the current solution is keeping a Python object.
Another solution would be keeping `sch` in the ComputeDAG C++ object. I made
a new commit for this solution. Please comment which one you prefer.
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