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[SCHEDULE] Generate Lower Bound Conditions #1014
Description
given the following algorithm and schedule
import tvm
A = tvm.placeholder((64,), name='A', dtype="float32")
Apad = tvm.compute((66,), lambda i: tvm.select(tvm.all(i>0, i < 65), A[i-1], tvm.const(0.)), name='Apad')
Apad2 = tvm.compute((66,), lambda i: Apad[i]*2, name='Apad2')
s = tvm.create_schedule(Apad2.op)
AL1 = s.cache_read(A,"local",[Apad])
print(tvm.lower(s, [A, Apad2], simple_mode=True))I get the following lowered code. The stage that produces A.local seems incorrect since it accesses element A[-1] in the first iteration. Is there a solution or a workaround?
// attr [A.local] storage_scope = "local"
allocate A.local[float32 * 66]
// attr [Apad] storage_scope = "global"
allocate Apad[float32 * 66]
produce A.local {
for (ax0, 0, 66) {
if (likely((ax0 < 65))) {
A.local[ax0] = A[(ax0 + -1)]
}
}
}
produce Apad {
for (i, 0, 66) {
Apad[i] = tvm_if_then_else(((0 < i) && (i < 65)), A.local[i], 0.000000f)
}
}
produce Apad2 {
for (i, 0, 66) {
Apad2[i] = (Apad[i]*2.000000f)
}
}
Activity
tqchen commentedon Mar 17, 2018
this is a problem, we should have add guard for the beginning condition of A as well
[-]combining cache_read and select (possible bug)[/-][+][SCHEDULE] Generate Lower Bound Conditions[/+]kun-zh commentedon Mar 19, 2018
Seems the size of A.local should be 64, and don't need to generate the condition assignment. Am I right?
kun-zh commentedon Mar 19, 2018
Sorry, the size for A.local is 66 because TVM will schedule A.local with the scheduling for the reader tensor. However, we want to get the expected lower code:
// attr [A.local] storage_scope = "local"
allocate A.local[float32 * 66]
// attr [Apad] storage_scope = "global"
allocate Apad[float32 * 66]
produce A.local {
for (ax0, 0, 64) {
A.local[ax0] = A[(ax0)]
}
}
produce Apad {
for (i, 0, 66) {
Apad[i] = tvm_if_then_else(((0 < i) && (i < 65)), A.local[i + -1], 0.000000f)
}
}
produce Apad2 {
for (i, 0, 66) {
Apad2[i] = (Apad[i]*2.000000f)
}
}
I tried:
A = tvm.placeholder((64,), name='A', dtype="float32")
AL = tvm.compute((64,), lambda i: A[i], name='AL')
Apad = tvm.compute((66,), lambda i: tvm.select(tvm.all(i>0, i < 65), A[i-1], tvm.const(0.)), name='Apad')
Apad2 = tvm.compute((66,), lambda i: Apad[i]*2, name='Apad2')
s = tvm.create_schedule(Apad2.op)
s[AL].set_scope("local")
#AL = s.cache_read(A,"local",[Apad])
print(tvm.lower(s, [A, Apad2], simple_mode=True))
And get the error:
raise ValueError("Cannot find the operation %s in schedule" % (str(k)))
ValueError: Cannot find the operation compute(AL, 0x15ff280) in schedule
Seems AL has been removed because of the memory optimization. I think if the scopes are different between A and AL, AL should not be removed.
derisavi-huawei commentedon Mar 19, 2018
@kun-zh
The reason for the error message is that
ALis not used any of the computations. If you replaceAinApad's expression withALin your code, then your code compiles but it will still have a similar problem. The lowered code is