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Subject [GitHub] [incubator-tvm] masahi edited a comment on issue #5243: [Frontend][TensorFlow]Improve TensorFlow Static Shape Tensor Array
Date Mon, 06 Apr 2020 11:02:10 GMT
masahi edited a comment on issue #5243: [Frontend][TensorFlow]Improve TensorFlow Static Shape
Tensor Array
URL: https://github.com/apache/incubator-tvm/pull/5243#issuecomment-609552445
 
 
   Hi @kevinthesun, I started experimenting with how to integrate static tensor array in Torch
frontend. My use case is to support Python tensor list append and stack. I got two problems
below:
   
   1. When I append the tensor to tensor array (by concat), I can do infer shape on the input
tensor to get the fixed shape static tensor array expects. But after I've done some appends
and try to stack the static tensor array, I don't have a way to tell what fixed shape the
input tensor array to stack expects. See
   https://github.com/masahi/tvm/blob/support-more-rnn/python/tvm/relay/frontend/pytorch.py#L989-L990
   Since the shape is fixed, I think there should be an easy way to query the shape associated
with a static array. I see you have such function `check_tensor_array_shape` in this PR (by
parsing op name). Is this the recommended way?
   
   2. The output type of stack is currently `static_tensor_float32_?_2_4_t[]` in my test.
Is there a way to easily unwrap static tensor type wrapper and get  relay `Tensor`? @wweic
had such unwrapper in https://github.com/apache/incubator-tvm/pull/4325 for generic arrays.
We should have something equivalent for static arrays.

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