HI @hjiang,
Sorry for the late response, I've had some other work to do. Thanks for the
proposed solutions, I'll try this implementations with my model and I'll keep
you updated.
Regards
Augusto
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Some comments on the dtype, the dtype field in Tensor is actually quite
flexible(goes beyond the enumeration since arbitary vector length, bitwidth and
customized data type is also allowed). So perhaps string, or making a
structured variant makes sense. So we can continue use string for simpli
## Motivation
As part of the [Standalone µTVM
Roadmap](https://discuss.tvm.ai/t/rfc-tvm-standalone-tvm-roadmap/6987), the TVM
RPC server is being implemented on bare metal devices. The overall approach is
to link MinRPCServer against the MISRA-C runtime plus additional compiled TVM
functions
I see. In my experience, it is worth making this a structured type, even if it
seems painful at first. In the long run, having to maintain custom parsing
logic for just one of your fields (where the others are all structured) ends up
being a maintenance burden. I'm a strong advocate for using
In this case the parsing is already necessary and builtin, because the numpy
convention uses the string for dtype. So we are trying to build compatibility
for interpolating with something that already exists. The types on the c++ side
is structured.
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Gotcha. In that case I think it's important to document that the format of the
field is the type string used by numpy.
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## Motivation
Although TVM provides quantization flow for pre-quantized models, we do find
some developers would prefer to use their own quantization flow for their
accelerators, since they may have specialized calibration and quantization
flows other than TVM QNN. However, current BYOC flow
Also cc @JoeyChou @abergeron
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cc @anijain2305 as well
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working on it
TQ
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cc @liangfu @tgall_foo
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Hi @areusch, thanks for proposing RPC support to MISRA-C runtime.
Regarding to the `TVMFuncRegistry` design, the `names` field are designed to be
rather compact, and requires a special handling of the list of strings. I would
rather propose an alternative way that we could use fixed-length st
hi @liangfu, thanks for taking a look at my proposal!
I agree the names field is a little complex. here are some alternatives:
N1. use an array of `const char[MAX_FUNC_NAME_LENGTH][num_funcs]`. The positive
is that you can traverse the list without scanning; the negative is it could
waste spa
## Difference between the logs for Ansor and AutoTVM
There are two major differences between ansor's log and autotvm's log
1. The workload for Ansor is a subgraph defined by multiple `tvm.compute`,
while the workload for autotvm is a single operator.
To index log quickly, Ansor stores a hash
Here's a list of fixes we applied to v0.6 branch. I will cut a tag this Friday.
* Fixed process termination routine in windows #4844
* [Runtime] Fix NDArray SaveDLTensor declaration and implementation signature
different #4586
* [NODE][Serialization]fix serialization precision loss in float #45
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