I just write a matrix multiplication computation, and here is my code, it is
easy
A = te.placeholder((49, 576))
B = te.placeholder((64, 576))
D = topi.nn.pad(A, (0, 0), (15, 0))
k = te.reduce_axis((0, 576))
C = te.compute(
(64, 64),
lambda i, j: te.sum(
D(i, k) * B(j, k),
axis=[k]
)
)
E = topi.nn.pad(C, (0, 0), (-15, 0))
s = te.create_schedule(E.op)
code = tvm.lower(s, [A, B, E], simple_mode=True)
print(code)
I want to pad D to (64, 576) then I can do tensorize of (16, 16) in the matrix
multiplication. But I got the tir like this:
primfn(placeholder_2: handle, placeholder_3: handle, PadInput_1: handle) ->
()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {PadInput: Buffer(PadInput_2: Pointer(float32), float32, [49,
64], []),
placeholder: Buffer(placeholder_4: Pointer(float32), float32,
[49, 576], []),
placeholder_1: Buffer(placeholder_5: Pointer(float32),
float32, [64, 576], [])}
buffer_map = {placeholder_2: placeholder, placeholder_3: placeholder_1,
PadInput_1: PadInput} {
attr [PadInput_3: Pointer(float32)] "storage_scope" = "global";
allocate(PadInput_3, float32, [28224]);
attr [compute: Pointer(float32)] "storage_scope" = "global";
allocate(compute, float32, [3136]) {
for (i0: int32, 0, 49) {
for (i1: int32, 0, 576) {
PadInput_3[((i0*576) + i1)] = (float32*)placeholder_4[((i0*576) +
i1)]
}
}
for (i: int32, 0, 49) {
for (j: int32, 0, 64) {
compute[((i*64) + j)] = 0f32
for (rv: int32, 0, 576) {
compute[((i*64) + j)] = ((float32*)compute[((i*64) + j)] +
((float32*)PadInput_3[((i*576) + rv)]*(float32*)placeholder_5[((j*576) + rv)]))
}
}
}
for (i0_1: int32, 0, 49) {
for (i1_1: int32, 0, 64) {
PadInput_2[((i0_1*64) + i1_1)] = (float32*)compute[((i0_1*64) +
i1_1)]
}
}
}
}
It seems like the padding I did was eliminated because tvm might think it is
useless. But it is important in my scheduling. So is there anybody can tell me,
is there an option to shut down the elimination? Thanks a lot!
---
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