Could you provide these parameters?
b = tvm.var('b') # batch size -> 12 or n
n = tvm.var('n') # sequence length -> 512
h = tvm.var('h') # number of heads -> ?
m = tvm.var('m') # hidden dimension -> 768
w = tvm.var('w') # window size -> 512
w_upper = tvm.var('w_upper') # window size to the right of the word.
Should be `0` or `w` -> ?
padding = tvm.var('padding') # padding -> ?
transpose_t1 = tvm.var('transpose_t1') # t1 should be transposed -> True /
False
t1d3 = tvm.var('t1d3') # last dimension of t1 -> ?
t3d3 = tvm.var('t3d3') # last dimension of t3 (the result tensor) -> ?
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