nice job! If it works out, it will be really helpful!
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Congrats @junrushao1994!!!
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Thank you SO much for offering such a great opportunity!! I am super thrilled
to serve the community as an Apache TVM committer! It is always great
experience chatting and sharing ideas in the friendly TVM forum, and let's
continue to work together to make TVM more accessible, easy-to-use as wel
Congrats @junrushao1994!
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Please join us to welcome Junru Shao(@junrushao1994) as a new Committer. Junru
has been actively contributing to various aspects of the TVM codebase. He
reimplemented and refactored the Target system which greatly helped code
lowering and code generation. Junru also largely contributed to the
Please join us to welcome Junru Shao(@junrushao1994) as a new Committer. Junru
has been actively contributing to various aspects of the TVM codebase. He
reimplemented and refactored the Target system which greatly helped code
lowering and code generation. Junru also largely contributed to the ru
## Motivation
In order to enrich the support of TF operator and the need of practical work,
we add 15 operators to TVM.
as follows:
* Expm1, Rint, Softsign, Cumprod, Cumsum
* SegmentMax, SegmentMin, SegmentMean, SegmentPord, SegmentSum
* UnsortedSegmentMax, UnsortedSegmen
In current relay pass, we can merge two consecutive reshape op by
`SimplifyExpr` pass. Sometimes, We may produce useless reshape op in the
process of importing models. For example:
#[version = "0.0.5"]
def @main(%Placeholder: Tensor[(1, 3, 227, 227), float32], %conv1/weights:
Tensor[(
