Sure. That makes sense.
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Given that there are other folks that are interested in the topic, e.g.
@smallcoscat perhaps it makes sense to land a version with reasonable coverage,
then invite others to contribute and collaborate
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You ar
So we will be adding support for ONNX codegen only.
I will work on adding a codegen for ONNX and then will work on an example ONNX
runtime to demonstrate end to end functionality. I will also be improving
operator coverage for ONNX.
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Adding CoreML codegen with the BYOC feature enables us to offload subgraphs to
Apple’s Neural Engine on iOS devices. There are some approaches how to build a
CoreML model in TVM.
- A0: Build with coremltools
I think this is the most intuitive way to construct CoreML models.
coremltools pr
Hi all!
I'm using TVM for post training quantization and noticed that as of now,
**conv2d_transpose** operations **can not be quantized** and fall back to
float32.
* Is there a limitation behind this or is it simply a missing feature?
* If it's a missing feature, which parts of the code would
