[GitHub] [incubator-tvm] wweic opened a new issue #4291: [Prelude][Relay] Add utility function to extract tensor from prelude tensor_t

[GitBox <gi...@apache.org>]

wweic opened a new issue #4291: [Prelude][Relay] Add utility function to extract tensor from prelude tensor_t
URL: https://github.com/apache/incubator-tvm/issues/4291
 
 
   `tensor_t` in prelude is a type that wraps variable rank tensor with variable shape. Like, `tensor_array_concat` will return object of type `tensor_t`. 
   
   But for most of the other TF operators, they expect tensors with fixed rank. For example `sum(tensor_array_concat(tensor_array))`, it won't work right now since `sum` can't accept `tensor_t` returned by `tensor_array_concat`. We need functions to convert `tensor_t` to fixed rank tensors. They are:
   ````
   let get_data_tensor1: tensor_t -> Tensor[(Any), dtype]
   let get_data_tensor2: tensor_t -> Tensor[(Any, Any), dtype]
   let get_data_tensor3: tensor_t -> Tensor[(Any, Any, Any), dtype]
   let get_data_tensor4: tensor_t -> Tensor[(Any, Any, Any, Any), dtype]
   let get_data_tensor5: tensor_t -> Tensor[(Any, Any, Any, Any, Any), dtype]
   let get_data_tensor6: tensor_t -> Tensor[(Any, Any, Any, Any, Any, Any), dtype]
   ````
   The reason we need 6 functions because relay can not represent a function has the following type:
   ````
   let get_data_tensor: tensor_t -> Tensor[(Any), dtype] or Tensor[(Any, Any), dtype] or Tensor[(Any, Any, Any), dtype]
   ````
   
   We also need a function `get_tensor_rank: tensor_t -> Tensor[(), int32]` so we can dispatch to the right `get_data_tensor` function given the rank of `tensor_t`.
   
   Since we start to look into models with tensor array ops, we will have to implement these functions.

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