LeiWang1999 commented on PR #15264:
URL: https://github.com/apache/tvm/pull/15264#issuecomment-1864872348
Thanks tq, I found that under `index_map =
IndexMap.from_func(ldmatrix_trans_permutation_16x16_32x8_16x16,
index_dtype="int32")` though, the inverse map is not ok, the output is:
```bash
========================inject transform=============================
# from tvm.script import tir as T
@T.prim_func
def main(A: T.Buffer((16, 16), "float16"), B: T.Buffer((16, 16), "float16")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i, j in T.grid(16, 16):
with T.block("B"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[(vi * 2 + vj // 8) // 16 * 8 + (vi * 2 + vj // 8) % 8,
(vi * 2 + vj // 8) % 16 // 8 * 8 + vj % 8])
T.writes(A[vi, vj])
A[vi, vj] = B[(vi * 2 + vj // 8) // 16 * 8 + (vi * 2 + vj // 8)
% 8, (vi * 2 + vj // 8) % 16 // 8 * 8 + vj % 8]
========================inverse inject transform=============================
# from tvm.script import tir as T
@T.prim_func
def main(A: T.Buffer((16, 16), "float16"), B: T.Buffer((16, 16), "float16")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i, j in T.grid(16, 16):yu
with T.block("B"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[(vi * 2 + vj // 8) // 2, vj % 16])
T.writes(A[vi, vj])
A[vi, vj] = B[(vi * 2 + vj // 8) // 2, vj % 16]
```
After applying the inverse_map to the map, the layout should remain
consistent with its state prior to the transformation.
Before this pull request, the code was functioning as expected.
```bash
========================inject transform=============================
# from tvm.script import tir as T
@T.prim_func
def func(A: T.Buffer[(16, 16), "float16"], B: T.Buffer[(16, 16), "float16"]):
# function attr dict
T.func_attr({"tir.noalias": True, "global_symbol": "main"})
# body
# with T.block("root")
for i, j in T.grid(16, 16):
with T.block("B"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[vi // 8 * 8 + vi % 4 * 2 + vj // 8, vi % 8 // 4 * 8 +
vj % 8])
T.writes(A[vi, vj])
A[vi, vj] = B[vi // 8 * 8 + vi % 4 * 2 + vj // 8, vi % 8 // 4 *
8 + vj % 8]
========================inverse inject transform=============================
# from tvm.script import tir as T
@T.prim_func
def func(A: T.Buffer[(16, 16), "float16"], B: T.Buffer[(16, 16), "float16"]):
# function attr dict
T.func_attr({"tir.noalias": True, "global_symbol": "main"})
# body
# with T.block("root")
for i, j in T.grid(16, 16):
with T.block("B"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[vi, vj])
T.writes(A[vi, vj])
A[vi, vj] = B[vi, vj]
```
I'll take a look tomorrow.
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