Cookiee235 opened a new issue, #17222:
URL: https://github.com/apache/tvm/issues/17222
The Relax IR in the below test case passed the well-formed checking, but
failed when using the DCE unexpectedly!
### Actual behavior
```
Traceback (most recent call last):
File "test_sim.py", line 54, in <module>
mod = tvm.relax.transform.DeadCodeElimination()(mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm-lunder/python/tvm/ir/transform.py", line 238, in
__call__
return _ffi_transform_api.RunPass(self, mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm-lunder/python/tvm/_ffi/_ctypes/packed_func.py", line
240, in __call__
raise_last_ffi_error()
File "/software/tvm-lunder/python/tvm/_ffi/base.py", line 481, in
raise_last_ffi_error
raise py_err
tvm._ffi.base.TVMError: Traceback (most recent call last):
18:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::transform::Pass,
tvm::IRModule)>::AssignTypedLambda<tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}>(tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}, std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
>, tvm::runtime::TVMRetValue)
17: tvm::transform::Pass::operator()(tvm::IRModule) const
16: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
15: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
14:
_ZN3tvm7runtime13PackedFuncObj9ExtractorINS0_16PackedFuncSubObjIZNS0_15TypedPackedFuncIFNS_8IRModuleES5_NS_9transform11PassContextEEE17AssignTypedLambdaIZNS_5relax9transform19DeadCodeEliminationENS0_5ArrayINS0_6StringEvEEEUlS5_S7_E_EEvT_EUlRKNS0_7TVMArgsEPNS0_11TVMRetValueEE_EEE4CallEPKS1_SI_SM_
13: tvm::relax::DeadCodeElimination(tvm::IRModule const&,
tvm::runtime::Array<tvm::runtime::String, void>)
12: tvm::relax::RemoveAllUnused(tvm::RelayExpr)
11: tvm::relax::CollectVarUsage(tvm::RelayExpr const&)
10: tvm::relax::UDChain::Collect(tvm::RelayExpr const&)
9: tvm::relax::ExprVisitor::VisitExpr(tvm::RelayExpr const&)
8: tvm::relax::ExprVisitor::VisitExpr_(tvm::relax::FunctionNode const*)
7: tvm::relax::ExprVisitor::VisitExpr(tvm::RelayExpr const&)
6: tvm::relax::ExprVisitor::VisitExpr_(tvm::relax::SeqExprNode const*)
5: tvm::relax::ExprVisitor::VisitBindingBlock(tvm::relax::BindingBlock
const&)
4:
tvm::relax::ExprVisitor::VisitBindingBlock_(tvm::relax::BindingBlockNode const*)
3: tvm::relax::ExprVisitor::VisitBinding(tvm::relax::Binding const&)
2: tvm::relax::UDChain::VisitBinding_(tvm::relax::VarBindingNode const*)
1: tvm::relax::ExprVisitor::VisitBinding_(tvm::relax::VarBindingNode
const*)
0: tvm::relax::UDChain::VisitVarDef(tvm::relax::Var const&)
File "/software/tvm-lunder/src/relax/analysis/udchain.cc", line 75
TVMError: Check failed: (!usage_map.count(var)) is false: Variable
while_loop was used before its definition
```
### Steps to reproduce
```
import tvm
from tvm import relax
from tvm.script import ir as I
from tvm.script import tir as T
from tvm.script import relax as R
@I.ir_module
class Module:
@T.prim_func(private=True)
def add(i: T.Buffer((), "int32"), c: T.Buffer((), "int32"), T_add:
T.Buffer((), "int32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
with T.block("T_add"):
vi = T.axis.spatial(1, T.int64(0))
T.reads(i[()], c[()])
T.writes(T_add[()])
T_add[()] = i[()] + c[()]
@T.prim_func(private=True)
def add1(s: T.Buffer((T.int64(2), T.int64(3)), "float32"), x:
T.Buffer((T.int64(2), T.int64(3)), "float32"), T_add: T.Buffer((T.int64(2),
T.int64(3)), "float32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for ax0, ax1 in T.grid(T.int64(2), T.int64(3)):
with T.block("T_add"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads(s[v_ax0, v_ax1], x[v_ax0, v_ax1])
T.writes(T_add[v_ax0, v_ax1])
T_add[v_ax0, v_ax1] = s[v_ax0, v_ax1] + x[v_ax0, v_ax1]
@R.function
def main(x: R.Tensor((2, 3), dtype="float32")) -> R.Tensor((2, 3),
dtype="float32"):
cls = Module
@R.function
def while_loop(i: R.Tensor((), dtype="int32"), s: R.Tensor((2, 3),
dtype="float32")) -> R.Tensor((2, 3), dtype="float32"):
cond: R.Tensor((), dtype="bool") =
R.call_pure_packed("test.vm.less", i, R.const(10, "int32"),
sinfo_args=(R.Tensor((), dtype="bool"),))
c: R.Tensor((), dtype="int32") = R.const(1, "int32")
if cond:
new_i = R.call_tir(cls.add, (i, c), out_sinfo=R.Tensor((),
dtype="int32"))
new_s = R.call_tir(cls.add1, (s, x), out_sinfo=R.Tensor((2,
3), dtype="float32"))
r_then: R.Tensor((2, 3), dtype="float32") =
while_loop(new_i, new_s)
r: R.Tensor((2, 3), dtype="float32") = r_then
else:
r: R.Tensor((2, 3), dtype="float32") = s
return r
gv: R.Tensor((2, 3), dtype="float32") = while_loop(R.const(0,
"int32"), x)
return gv
mod = Module
mod.show()
assert relax.analysis.well_formed(mod)
mod = tvm.relax.transform.DeadCodeElimination()(mod)
mod.show()
```
cc @Lunderberg @junrushao
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