kk2049 commented on issue #9715:
URL: https://github.com/apache/tvm/issues/9715#issuecomment-997443436
@comaniac Thanks for your reply! I have tried to run this workload with
`tvm.target.Target("llvm")`. This workload can be successfully launched. So I
select `target("cuda")` again and tried to print the compute DAG. It looks like
this:
```
Computational DAG:
kernel = PLACEHOLDER [512, 512, 3, 3]
G(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 3) == 2)), 1f,
select(((floormod(i, 4) == 3) && (floormod(j, 3) == 1)), ..(OMITTED)..
(floormod(i, 4) == 0) && (floormod(j, 3) == 1)), 0f, select(((floormod(i, 4) ==
0) && (floormod(j, 3) == 0)), 1f, 0f))))))))))))
my_kernel_pack(eps, nu, ci, co) += ((kernel[co, ci, r_kh, r_kw]*G[eps,
r_kh])*G[nu, r_kw])
data = PLACEHOLDER [1, 512, 7, 7]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i2 >= 1) && (i2 < 8)) && (i3
>= 1)) && (i3 < 8)), data[i0, i1, (i2 - 1), (i3 - 1)], 0f)
my_d(c, p, eps_1, nu_1) = data_pad[floordiv(p, 16), c,
((floormod(floordiv(p, 4), 4)*2) + eps_1), ((floormod(p, 4)*2) + nu_1)]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f,
select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), ..(OMITTED)..
ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0)
&& (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
my_data_pack(eps, nu, ci, p) += ((my_d[ci, p, r_a, r_b]*B[r_a, eps])*B[r_b,
nu])
my_bgemm(eps, nu, co, p) += (my_kernel_pack[eps, nu, ci,
co]*my_data_pack[eps, nu, ci, p])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f,
select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), ..(OMITTED)..
ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i,
4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
my_inverse(co, p, vh, vw) += ((my_bgemm[r_a_2, r_b_2, co, p]*A[r_a_2,
vh])*A[r_b_2, vw])
my_output(n, co, h, w) = my_inverse[co, ((((n*4)*4) + (floordiv(h, 2)*4)) +
floordiv(w, 2)), floormod(h, 2), floormod(w, 2)]
input2_dy = PLACEHOLDER [1, 512, 7, 7]
my_output.my_inverse.grad(ax0, ax1, ax2, ax3) = select((((((((ax2*4) +
(floordiv((7 + (ax1*-2)), 8)*-8)) <= 24) && (((ax1*-2) + ..(OMITTED).. ) <=
15)), input2_dy[0, ax0, (ax2 + (floordiv((7 + (ax1*-2)), 8)*-2)),
(((floordiv((7 + (ax1*-2)), 8)*8) + (ax1*2)) + ax3)], 0f)
extracted_tensor(n0_n0_vh.shifted.shifted, n1_n1_vw.shifted.shifted,
n2_n2_jac_i0.shifted.shifted, n3_n3_jac_i1.shifted.shifted) =
(A[n2_n2_jac_i0.shifted.shifted,
n0_n0_vh.shifted.shifted]*A[n3_n3_jac_i1.shifted.shifted,
n1_n1_vw.shifted.shifted])
my_inverse.my_bgemm.grad(ax0, ax1, ax2, ax3) +=
(my_output.my_inverse.grad[ax2, ax3, n0_n0_k2.shifted.shifted,
n1_n1_k3.shifted.shifted]*extracted_tensor[n0_n0_k2.shifted.shifted,
n1_n1_k3.shifted.shifted, ax0, ax1])
my_bgemm.my_data_pack.grad(ax0, ax1, ax2, ax3) +=
(my_inverse.my_bgemm.grad[ax0, ax1, n0_n0_k2.shifted.shifted,
ax3]*my_kernel_pack[ax0, ax1, ax2, n0_n0_k2.shifted.shifted])
extracted_tensor(n0_n0_eps.shifted.shifted, n1_n1_nu.shifted.shifted,
n4_n4_jac_i2.shifted.shifted, n5_n5_jac_i3.shifted.shifted) =
(B[n4_n4_jac_i2.shifted.shifted,
n0_n0_eps.shifted.shifted]*B[n5_n5_jac_i3.shifted.shifted,
n1_n1_nu.shifted.shifted])
my_data_pack.my_d.grad(ax0, ax1, ax2, ax3) +=
(my_bgemm.my_data_pack.grad[n0_n0_k0.shifted.shifted, n1_n1_k1.shifted.shifted,
ax0, ax1]*extracted_tensor[n0_n0_k0.shifted.shifted, n1_n1_k1.shifted.shifted,
ax2, ax3])
data_pad.data.grad(ax0, ax1, ax2, ax3) += my_data_pack.my_d.grad[ax1,
(((((floordiv((ax2 + 1), 2) + n0_n0_fdiv1.shifted.shifted) ..(OMITTED)..
ormod((ax2 + 1), 2) + (n0_n0_fdiv1.shifted.shifted*-2)) + 2), ((floormod((ax3 +
1), 2) + (n1_n1_fmod1.shifted.shifted*-2)) + 2)]
```
I have tried to check this DAG info myself but failed to anything useful.
Maybe you can find something in it?
Thanks a lot for your help!!!
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