Hello,
In some frameworks (maybe all?) we can define a layer/operator name.
Looking at the[ Relay Python API
documentation](https://tvm.apache.org/docs/api/python/relay/nn.html#module-tvm.relay.nn),
I dont see that Relay operators have a 'name' (except Bitpack which uses this
parameter for a
Hey @comaniac Thanks. Are the hardware parameters like (num of threads, cache
size) taken into consideration when auto scheduling is used. Also how does auto
scheduling generate schedules from scratch. Does it look at every loop and try
to figure out the best transformation based on execution
@jwfromm Thanks very much, I will update and have a try.
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The fix is now merged, if you update to master your model should work.
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I'm very interested in this too.
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* The search space is defined in the schedule template, such as:
https://github.com/apache/incubator-tvm/blob/master/python/tvm/topi/cuda/conv2d_direct.py#L31
This line defines a search parameter, and the candidates are the factors of
the length of `f`.
* While AutoTVM needs schedule templa
Sorry for the delay in response. @blacklong617, you're right that we should
support broadcasting alpha but currently dont. This PR:
https://github.com/apache/incubator-tvm/pull/6549 fixes the issue.
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How do you set the number of CPUs in the model runtime？
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After looking at the tutorial for auto tuning in auto tvm, there seems to two
methods to create a search space, one where the user gives an array if possible
values to search and the other where tvm generates this space. In the latter
case how does tvm model the search space equation, or is ju
