[Apache TVM Discuss] [Development] How to deploy model to iOS device?

[kindlehe via Apache TVM Discuss]

There is android_deploy and android_camera demo telling how to deploy model to 
android device, but there is no friendly iOS version.
Could you please give me some advice?
@tqchen





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[Apache TVM Discuss] [Development/RFC] [RFC] CSE Optimization

[Jack Zheng via Apache TVM Discuss]

- The user API looks like the following:
  ```C++
  /*!
   * \brief Eliminate common subexpressions among \p in_args and between them 
and \p output .
   *
   * \param output The output tensor.
   * \param input_grads The gradients of input tensors.
   */
  std::pair >
  CSE(const Tensor& output, const std::vector& input_grads);
  ```
- The tensor expression tree does not provide extra information. It is just an 
useful data structure for comparison purposes.





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[Apache TVM Discuss] [Development/RFC] [RFC] CSE Optimization

[Yizhi Liu via Apache TVM Discuss]

@tqchen @junrushao1994  how much of the work do you think we can reuse once we 
move to https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm?





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[Apache TVM Discuss] [Development/RFC] [RFC] Support for large tensors

[Yi Wang via Apache TVM Discuss]

Is int64 tensor fully supported in the main branch now?





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[Apache TVM Discuss] [Development/RFC] [RFC] CSE Optimization

[tqchen via Apache TVM Discuss]

 I think there are two potential ways to think about it. We can either try to 
do CSE in the Expr level, or we can do CSE in the TE level. I think both will 
bring some of the benefit, so it would be helpful to support both variants





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[Apache TVM Discuss] [Development/RFC] [RFC] CSE Optimization

[Junru Shao via Apache TVM Discuss]

I agree with @tqchen. CSE is not hard to implement, and CSE on both sides 
provides different benefits. So we can potentially support both variants.





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