[BUG] Incorrect buffer offset for vectorized computation

[wrongtest-intellif]

Hi there, we are working on vectorized computation in some tagged storage scope. We encountered the problem on vectorize:
The example is as below:

# before vectorize
for (i: int32, 0, 1024) {
    C[i] = ((float32*)A[i] + (float32*)B[(i])
}
 
# after vectorize (correct now)
C[ramp(0, 1, 1024)] = ((float32x1024*)A[ramp(0, 1, 1024)] + (float32x1024*)B[ramp(0, 1, 1024)])
 
# after storage rewrite (problematic)
Merged[ramp(0, 1, 1024)] = ((float32x1024*)Merged[ramp(0, 1, 1024)] + (float32x1024*)Merged[ramp(1, 1, 1024)])

# after storage rewrite (correct version if we do not vectorize)
for (i: int32, 0, 1024) {
    Merged[i] = ((float32*)Merged[i] + (float32*)Merge[(i + 1024)])
}

Here we use tagged storage scope, thus A,B,C can be merge into single buffer Merged and A and C share same region:

• A -> Merged[0: 1024]
• B -> Merged[1024; 2048]
• C -> Merge[0: 1024]

It seems that after buffer merging and buffer index remap, the ramp node is incorrectly rewrite to
ramp(origin + 1, 1, 1024) instead of ramp(origin + 1024, 1, 1024)

The related implementation seems to be here:
https://github.com/apache/tvm/blob/main/src/tir/transforms/storage_rewrite.cc#L507-L512
where the offset is divided by a factor of datatype lanes (in our case, 1024)

The code to reproduce the problem is as below:

import tvm
from tvm import te
from tvm import testing
import numpy as np

@tvm.register_func("tvm.info.mem.global_mycategory")
def my_mem_info():
    return tvm.ir.make_node(
        "MemoryInfo",
        unit_bits=8,
        max_simd_bits=512,
        max_num_bits=99999,
        head_address=None,
    )

A = te.placeholder([1024], name="A")
B = te.placeholder([1024], name="B")
C = te.compute([1024], lambda i: A[i] + B[i], name="C")
s = te.create_schedule(C.op)
AA = s.cache_read(A, "global_mycategory", readers=[C])
BB = s.cache_read(B, "global_mycategory", readers=[C])
CC = s.cache_write(C, "global_mycategory")
s[CC].vectorize(s[CC].op.axis[0])
print(tvm.lower(s, [A, B, C]))
f = tvm.build(s, [A, B, C], "llvm")
arr_A = tvm.nd.array(np.linspace(0, 1024, 1024).astype("float32"))
arr_B = tvm.nd.array(np.linspace(1024, 2048, 1024).astype("float32"))
arr_C = tvm.nd.array(np.zeros([1024], dtype="float32"))
f(arr_A, arr_B, arr_C)
print(arr_A.asnumpy())
print(arr_B.asnumpy())
print(arr_C.asnumpy())
tvm.testing.assert_allclose(
    arr_C.asnumpy(), arr_A.asnumpy() + arr_B.asnumpy(), rtol=1e-5)

[tqchen]

Thanks @wrongtest for reporting the issue, can you help to suggest a fix and send a PR?

[tqchen]

also cc @vinx13 @masahi who can help manage the related PRs

[wrongtest-intellif]

Hi, there is a fix at #8338. @tqchen @vinx13 @masahi