hi,
I have a question about the solution code in
[https://github.com/dmlc/tvm/issues/1625](https://github.com/dmlc/tvm/issues/1625)
> import tvm
> import numpy as np
> def intrin_vadd(n):
> x = tvm.placeholder((n, 1, 1), name='vx')
> y = tvm.placeholder((n, 1, 1), name='vy')
> z = tvm.compute(x.shape, lambda i, j, k: x[i, j, k] + y[i, j, k],
> name='z')
> def intrin_func(ins, outs):
> xx, yy = ins
> zz = outs[0]
> return tvm.call_packed("vadd", xx, yy, zz)
>
>
> strides = [tvm.var('so'), tvm.var('si'), 1]
> offset_factor = 1
> xb = tvm.decl_buffer(x.shape, x.dtype,
> name="xb",
> offset_factor=offset_factor,
> strides=strides)
> yb = tvm.decl_buffer(y.shape, y.dtype,
> name="yb",
> offset_factor=offset_factor,
> strides=strides)
> zb = tvm.decl_buffer(z.shape, z.dtype,
> name="zb",
> offset_factor=offset_factor,
> strides=strides)
> binds = {x: xb, y: yb, z: zb}
> return tvm.decl_tensor_intrin(z.op, intrin_func, binds=binds)
> def test_tensori
> ze_vadd():
> m = 16
> n = 16
> l = 16
> x = tvm.placeholder((m,n, l), name='x')
> y = tvm.placeholder((m,n, l), name='y')
> z = tvm.compute(x.shape, lambda i,j, k: x[i,j, k] + y[i,j, k], name='z')
>
> def check(factor):
> s = tvm.create_schedule(z.op)
> xa, xb, xc = s[z].op.axis
> s[z].reorder(xb, xc, xa)
> print(tvm.lower(s, [x, y, z], simple_mode=True))
> vadd = intrin_vadd(factor)
> s[z].tensorize(xa, vadd)
> s = s.normalize()
> print(tvm.lower(s, [x, y, z], simple_mode=True))
>
> check(16)
>
> test_tensorize_vadd()
After the reorder, xa become the innermost axis. Why should we tensorize it
with a tensor with 3 dims. It's just one loop. And if I change the intrin
tensor to 1 dim ,error will occour.
---
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Topic](https://discuss.tvm.ai/t/tensorize-tensorize-failed-after-reorder/722/2)
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Tianqi Chen, UW, Seattle, WA, 98105, United States
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