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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You
- 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,
@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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Is int64 tensor fully supported in the main branch now?
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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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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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