@jwfromm: Thank you very much for sharing your view on this.
As i recall ONNX-Chainer has limited support in their current code. Please
refer
[Code](https://github.com/chainer/chainer/blob/master/onnx_chainer/functions/__init__.py)
or
[Tutorial](https://docs.chainer.org/en/stable/onnx_chaine
Your proposal is reasonable from perspective of existing Chainer users looking
for deployment solution. However, I see little benefit from TVM side.
First of all, compared to other framework like PyTorch, the number of Chainer
user had been very tiny even during Chainer was in active developme
Thank you very much for sharing your views on this. It was really insightful.
Few points i want to reply as below:
[quote="masahi, post:7, topic:6129"]
First of all, compared to other framework like PyTorch, the number of Chainer
user had been very tiny even during Chainer was in active develo
@anijain2305 Thank you for bringing this up. Would you mind listing the current
pain point of managing different categories of conv2d in Relay, and listing
pros and cons for keeping the current method VS separating?
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[quote="ANSHUMAN.TRIPATHY, post:8, topic:6129"]
But don’t you think the **“out of scope from TVM”** is applicable for all
frontends. As all frontends first need to beakdown the model received and put
all the pieces together to in terms of TVM.
[/quote]
No. For example, both Torchscript and ONNX
Okay, I've implemented these APIs and converted a couple of passes to use them,
please take a look and review. https://github.com/apache/incubator-tvm/pull/4886
@haichen @MarisaKirisame I'm noticing that the passes with these edge cases all
seem to be doing more complicated things than the sim
Yes, that sounds good to me.
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Closed #4648.
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Close for now as most relay and tir nodes has been updated
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