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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/08/11 09:15:25 UTC
[GitHub] [tvm] cxx122 opened a new issue, #12378: [Bug] Inconsistent caused by 127i8*127i8*numi64 after using compute_inline
cxx122 opened a new issue, #12378:
URL: https://github.com/apache/tvm/issues/12378
```
TENSOR_0 = te.compute([2,2], lambda pcg,wcv:te.max_value("int8"), name ="TENSOR_0")
TENSOR_1 = te.placeholder([2,2], dtype="int64", name="TENSOR_1")
TENSOR_2 = te.compute([2,2], lambda zcv,tcu:TENSOR_0[zcv,tcu]*TENSOR_0[zcv,tcu]*TENSOR_1[zcv,tcu], name ="TENSOR_2")
```
The tir program before compute_inline:
```
@main = primfn(TENSOR_0_1: handle, TENSOR_1_1: handle, TENSOR_2_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {TENSOR_0: Buffer(TENSOR_0_2: Pointer(int8), int8, [4], []),
TENSOR_1: Buffer(TENSOR_1_2: Pointer(int64), int64, [4], []),
TENSOR_2: Buffer(TENSOR_2_2: Pointer(int64), int64, [4], [])}
buffer_map = {TENSOR_0_1: TENSOR_0, TENSOR_1_1: TENSOR_1, TENSOR_2_1: TENSOR_2}
preflattened_buffer_map = {TENSOR_0_1: TENSOR_0_3: Buffer(TENSOR_0_2, int8, [2, 2], []), TENSOR_1_1: TENSOR_1_3: Buffer(TENSOR_1_2, int64, [2, 2], []), TENSOR_2_1: TENSOR_2_3: Buffer(TENSOR_2_2, int64, [2, 2], [])} {
for (pcg: int32, 0, 2) {
for (wcv: int32, 0, 2) {
TENSOR_0[((pcg*2) + wcv)] = 127i8
}
}
for (zcv: int32, 0, 2) {
for (tcu: int32, 0, 2) {
let cse_var_1: int32 = ((zcv*2) + tcu)
TENSOR_2[cse_var_1] = (cast(int64, (TENSOR_0[cse_var_1]*TENSOR_0[cse_var_1]))*TENSOR_1[cse_var_1])
}
}
}
```
The tir program after compute_inline:
```
@main = primfn(TENSOR_0_1: handle, TENSOR_1_1: handle, TENSOR_2_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {TENSOR_0: Buffer(TENSOR_0_2: Pointer(int8), int8, [4], []),
TENSOR_1: Buffer(TENSOR_1_2: Pointer(int64), int64, [4], []),
TENSOR_2: Buffer(TENSOR_2_2: Pointer(int64), int64, [4], [])}
buffer_map = {TENSOR_0_1: TENSOR_0, TENSOR_1_1: TENSOR_1, TENSOR_2_1: TENSOR_2}
preflattened_buffer_map = {TENSOR_0_1: TENSOR_0_3: Buffer(TENSOR_0_2, int8, [2, 2], []), TENSOR_1_1: TENSOR_1_3: Buffer(TENSOR_1_2, int64, [2, 2], []), TENSOR_2_1: TENSOR_2_3: Buffer(TENSOR_2_2, int64, [2, 2], [])} {
for (zcv: int32, 0, 2) {
for (tcu: int32, 0, 2) {
let cse_var_1: int32 = ((zcv*2) + tcu)
TENSOR_2[cse_var_1] = (TENSOR_1[cse_var_1]*16129i64)
}
}
}
```
### Actual behavior
```
Traceback (most recent call last):
File "/Scuzer/src/bugs/bug16/IncorrectResult__3ce1533f-dbeb-42c9-bb3a-5fe018abe989/Incorrect_bug.py", line 46, in <module>
tvm.testing.assert_allclose(pre_list[2].numpy(), after_list[2].numpy(),rtol=1e-5)
File "/Scuzer/tvm_cov_patch/tvm/python/tvm/testing/utils.py", line 114, in assert_allclose
np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True)
File "/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 1527, in assert_allclose
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
File "/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 840, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=1e-05, atol=1e-07
Mismatched elements: 3 / 4 (75%)
Max absolute difference: 64512
Max relative difference: 0.999938
x: array([[0, 1],
[3, 4]])
y: array([[ 0, 16129],
[48387, 64516]])
```
### Environment
Operating System: Ubuntu 18.04, TVM version: tag0.9.0 [d361585]
### Steps to reproduce
```
import os
import numpy as np
import tvm
from tvm import te, auto_scheduler, topi
import tvm.testing
TENSOR_0 = te.compute([2,2], lambda pcg,wcv:te.max_value("int8"), name ="TENSOR_0")
TENSOR_1 = te.placeholder([2,2], dtype="int64", name="TENSOR_1")
TENSOR_2 = te.compute([2,2], lambda zcv,tcu:TENSOR_0[zcv,tcu]*TENSOR_0[zcv,tcu]*TENSOR_1[zcv,tcu], name ="TENSOR_2")
s = te.create_schedule(TENSOR_2.op)
tensor_list = [TENSOR_0,TENSOR_1,TENSOR_2]
dev = tvm.cpu(0)
pre_list = []
after_list = []
for tensor in tensor_list:
shape = [x.value if 'value' in dir(x) and isinstance(x.value, int) else 1 for x in tensor.shape]
params = (5*np.random.uniform(size=shape)).astype(tensor.dtype)
pre_list.append(tvm.nd.array(params.copy(), dev))
after_list.append(tvm.nd.array(params.copy(), dev))
pre_mod = tvm.lower(s, tensor_list, simple_mode=True)
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(pre_mod)
f(*pre_list)
# Schedule
s[TENSOR_0].compute_inline()
now_mod = tvm.lower(s, tensor_list, simple_mode=True)
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(now_mod)
f(*after_list)
tvm.testing.assert_allclose(pre_list[2].numpy(), after_list[2].numpy(),rtol=1e-5)
```
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[GitHub] [tvm] cxx122 closed issue #12378: [Bug] Inconsistency caused by 127i8*127i8*numi64 after using compute_inline
Posted by GitBox <gi...@apache.org>.
cxx122 closed issue #12378: [Bug] Inconsistency caused by 127i8*127i8*numi64 after using compute_inline
URL: https://github.com/apache/tvm/issues/12378
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[GitHub] [tvm] ganler commented on issue #12378: [Bug] Inconsistency caused by 127i8*127i8*numi64 after using compute_inline
Posted by GitBox <gi...@apache.org>.
ganler commented on issue #12378:
URL: https://github.com/apache/tvm/issues/12378#issuecomment-1218628413
Not sure if this is real bug or an undefined behavior similar to https://github.com/apache/tvm/issues/12377. In C standard (which I guess TVM should be consistent with), signed integer overflow leads to undefined behavior. Therefore, if there is any signed overflow happening, the results are not guaranteed to be "consistent".
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[GitHub] [tvm] ganler commented on issue #12378: [Bug] Inconsistency caused by 127i8*127i8*numi64 after using compute_inline
Posted by GitBox <gi...@apache.org>.
ganler commented on issue #12378:
URL: https://github.com/apache/tvm/issues/12378#issuecomment-1219008499
Thanks for confirming the UB. I think this level of guarantee (i.e., consistency over UB) is often not available in TVM as well as traditional compilers that have been engineered for decades due to its cost. Maybe you can tune the fuzzer a bit to filter false alarms by invalid numerics as well as integer overflows. :-)
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[GitHub] [tvm] cxx122 commented on issue #12378: [Bug] Inconsistency caused by 127i8*127i8*numi64 after using compute_inline
Posted by GitBox <gi...@apache.org>.
cxx122 commented on issue #12378:
URL: https://github.com/apache/tvm/issues/12378#issuecomment-1218962255
I just tried it and it may be the same as #12377. Some optimizations may not be suitable to be applied when there is an overflow.
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