When doing reduction the predicate guarding the
[initialization](https://github.com/apache/tvm/blob/adf560ebed8465c22bf58f406d0a8d20663cdd1d/src/te/operation/compute_op.cc#L488)
skips the boundary checking of the original itervars, by setting
`skip_ivar_domain` argument in
[MakeBoundCheck](ht
If you are using `relay.build()` -> `graph_executor.GraphModule` path, the
point I remember is that it should pass a multi-target dict into `target`
argument of build and pass a device list into GraphModule like
```python
lib = relay.build(relay_mod, target={"cpu": "llvm", "gpu": "cuda"},
par
Hi~ Can this unittest case help you?
https://github.com/apache/tvm/blob/be03d62e5b0afd607964365bc73e94f72fdfaaef/tests/python/relay/test_vm.py#L1071
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Same question when trying to convert ConvBERT. Any help?
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We use threads for parallelism within an operator. By "concurrency" I meant
something like asynchronous execution among operators (also called
inter-operator parallelism).
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You a
Hello,
I have read some posts from forum,but I still confused about that.
1) If I want to using Relay to build a simple network and heterogeneous
execution some Ops on gpu and others on cpu. There seem to be two different
ways.
* One is through relay.annotation.on_device, relay.device_copy
@grant-arm
Thanks a lot for the link. I'll have a look at the repo and see if standalone
execution is possible for any MCUs.
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Hi @sho ,
If you're looking for a demo of a standalone application running on an MCU, you
could take a look at
https://github.com/apache/tvm/tree/main/apps/microtvm/ethosu . Although this
demo is an example of how to use TVM to run a model and offload operators to
the microNPU, it should pro
Thank you for visiting my question.
I'm now trying to run inference on bare-metal devices. More specifically, I'd
like to use micro tvm(or the outputs from micro tvm) on **MCUs**.
However, I went through some tutorials or notebooks created by developers:
https://github.com/areusch/microtvm-bl
Currently, I try to learn and understand TVM. I have a mxnet model and I want
to do AutoTVM, so I try to use `autotvm.task.extract_from_program`. The code is
similar to this
```
target = tvm.target.cuda()
tasks = autotvm.task.extract_from_program(
mod["main"], target=target, target_host
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