MellowArtisan opened a new issue, #17200:
URL: https://github.com/apache/tvm/issues/17200
### Actual behavior
```
Traceback (most recent call last):
File "/share_container/optfuzz/res/bugs/18_check.py", line 35, in <module>
mod = tvm.transform.Sequential([relax.transform.LiftTransformParams(),
relax.transform.LiftTransformParams()])(mod) # crash here
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm/python/tvm/ir/transform.py", line 238, in __call__
return _ffi_transform_api.RunPass(self, mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 239, in
__call__
raise_last_ffi_error()
File "/software/tvm/python/tvm/_ffi/base.py", line 481, in
raise_last_ffi_error
raise py_err
tvm.error.InternalError: Traceback (most recent call last):
11:
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*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
10: tvm::transform::Pass::operator()(tvm::IRModule) const
9: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
8: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
7: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
6: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
5: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
4: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
3:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relax::transform::PartitionTransformParams(tvm::runtime::Variant<tvm::Bool,
tvm::runtime::Array<tvm::runtime::String, void> >)::{lambda(tvm::IRModule,
tvm::transform::PassContext)#1}>(tvm::relax::transform::PartitionTransformParams(tvm::runtime::Variant<tvm::Bool,
tvm::runtime::Array<tvm::runtime::String, void> >)::{lambda(tvm::IRModule,
tvm::transform::PassContext)#1})::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
2:
tvm::relax::transform::PartitionTransformParams(tvm::runtime::Variant<tvm::Bool,
tvm::runtime::Array<tvm::runtime::String, void> >)::{lambda(tvm::IRModule,
tvm::transform::PassContext)#1}::operator()(tvm::IRModule,
tvm::transform::PassContext) const [clone .isra.0]
1: tvm::IRModuleNode::Add(tvm::GlobalVar const&, tvm::BaseFunc const&,
bool)
0: tvm::IRModuleNode::AddUnchecked(tvm::GlobalVar const&, tvm::BaseFunc
const&)
File "/software/tvm/src/ir/module.cc", line 233
InternalError: Check failed: (*it).second == var
(I.GlobalVar("main_transform_params") vs. I.GlobalVar("main_transform_params"))
:
```
### Environment
* TVM: 0.17.dev0
* OS: Ubuntu20.04
### 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 matmul1(x: T.Buffer((T.int64(256), T.int64(256)), "float32"), w1_t:
T.Buffer((T.int64(256), T.int64(256)), "float32"), matmul:
T.Buffer((T.int64(256), T.int64(256)), "float32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for i0, i1, k in T.grid(T.int64(256), T.int64(256), T.int64(256)):
with T.block("matmul"):
v_i0, v_i1, v_k = T.axis.remap("SSR", [i0, i1, k])
T.reads(x[v_i0, v_k], w1_t[v_k, v_i1])
T.writes(matmul[v_i0, v_i1])
with T.init():
matmul[v_i0, v_i1] = T.float32(0)
matmul[v_i0, v_i1] = matmul[v_i0, v_i1] + x[v_i0, v_k] *
w1_t[v_k, v_i1]
@R.function
def main(x: R.Tensor((256, 256), dtype="float32"), ) -> R.Tensor((256,
256), dtype="float32"):
R.func_attr({"num_input": 1})
cls = Module
with R.dataflow():
y1 = R.call_tir(cls.matmul1, (x, x), out_sinfo=R.Tensor((256,
256), dtype="float32"))
R.output(y1)
return y1
mod = Module
mod.show()
mod = tvm.relax.transform.LegalizeOps()(mod)
#mod = tvm.transform.Sequential([relax.transform.LiftTransformParams()]) #
run well
mod = tvm.transform.Sequential([relax.transform.LiftTransformParams(),
relax.transform.LiftTransformParams()])(mod) # crash here
```
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