Cookiee235 opened a new issue, #17235:
URL: https://github.com/apache/tvm/issues/17235
### Actual behavior
```
Traceback (most recent call last):
File "/share_container/optfuzz/res/bugs/simple/obj_int.py", line 59, in
<module>
compile_mod(mod, input_0)
File "/share_container/optfuzz/res/bugs/simple/obj_int.py", line 56, in
compile_mod
mod_outputs = vm['main'](*inputs)
^^^^^^^^^^^^^^^^^^^
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):
13:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::relax_vm::VirtualMachineImpl::_LookupFunction(tvm::runtime::String
const&)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#1}>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
12:
tvm::runtime::relax_vm::VirtualMachineImpl::InvokeClosurePacked(tvm::runtime::ObjectRef
const&, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
11:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::relax_vm::VirtualMachineImpl::GetClosureInternal(tvm::runtime::String
const&, bool)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#1}>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
10: tvm::runtime::relax_vm::VirtualMachineImpl::InvokeBytecode(long,
std::vector<tvm::runtime::TVMRetValue,
std::allocator<tvm::runtime::TVMRetValue> > const&)
9: tvm::runtime::relax_vm::VirtualMachineImpl::RunLoop()
8:
tvm::runtime::relax_vm::VirtualMachineImpl::RunInstrCall(tvm::runtime::relax_vm::VMFrame*,
tvm::runtime::relax_vm::Instruction)
7:
tvm::runtime::relax_vm::VirtualMachineImpl::InvokeClosurePacked(tvm::runtime::ObjectRef
const&, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
6:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::relax_vm::VirtualMachineImpl::GetClosureInternal(tvm::runtime::String
const&, bool)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#1}>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
5: tvm::runtime::relax_vm::VirtualMachineImpl::InvokeBytecode(long,
std::vector<tvm::runtime::TVMRetValue,
std::allocator<tvm::runtime::TVMRetValue> > const&)
4: tvm::runtime::relax_vm::VirtualMachineImpl::RunLoop()
3:
tvm::runtime::relax_vm::VirtualMachineImpl::RunInstrCall(tvm::runtime::relax_vm::VMFrame*,
tvm::runtime::relax_vm::Instruction)
2:
tvm::runtime::relax_vm::VirtualMachineImpl::InvokeClosurePacked(tvm::runtime::ObjectRef
const&, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
1:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::relax_vm::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#11}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
0: tvm::runtime::ObjectRef
tvm::runtime::TVMPODValue_::AsObjectRef<tvm::runtime::ObjectRef>() const
File "/software/tvm/include/tvm/runtime/packed_func.h", line 2080
InternalError: Check failed: type_code_ == kTVMObjectHandle (0 vs. 8) :
expected Object but got int
```
### Steps to reproduce
```
import tvm
from tvm import relax
import numpy as np
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 add1(C: T.Buffer((T.int64(16), T.int64(16)), "float32"), B:
T.Buffer((T.int64(16), T.int64(16)), "float32"), T_add: T.Buffer((T.int64(16),
T.int64(16)), "float32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for ax0, ax1 in T.grid(T.int64(16), T.int64(16)):
with T.block("T_add"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads(C[v_ax0, v_ax1], B[v_ax0, v_ax1])
T.writes(T_add[v_ax0, v_ax1])
T_add[v_ax0, v_ax1] = C[v_ax0, v_ax1] + B[v_ax0, v_ax1]
@T.prim_func(private=True)
def multiply(A: T.Buffer((T.int64(16), T.int64(16)), "float32"),
T_multiply: T.Buffer((T.int64(16), T.int64(16)), "float32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for ax0, ax1 in T.grid(T.int64(16), T.int64(16)):
with T.block("T_multiply"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads(A[v_ax0, v_ax1])
T.writes(T_multiply[v_ax0, v_ax1])
T_multiply[v_ax0, v_ax1] = A[v_ax0, v_ax1] * T.float32(2)
@R.function
def transform_params(A: R.Tensor((16, 16), dtype="float32"), B:
R.Tensor((16, 16), dtype="float32")) -> R.Tuple(R.Tensor((16, 16),
dtype="float32"), R.Tensor((16, 16), dtype="float32"), R.Prim(value=42),
R.Tensor((), dtype="float16")):
cls = Module
C = R.call_tir(cls.multiply, (A,), out_sinfo=R.Tensor((16, 16),
dtype="float32"))
D = R.call_tir(cls.add1, (C, B), out_sinfo=R.Tensor((16, 16),
dtype="float32"))
return (C, D, R.prim_value(42), R.const(17.5, "float16"))
@R.function
def main(para0: R.Tensor((16, 16), dtype="float32")) ->
R.Tuple(R.Tensor((16, 16), dtype="float32"), R.Tensor((16, 16),
dtype="float32"), R.Prim(value=42), R.Tensor((), dtype="float16")):
cls = Module
with R.dataflow():
res: R.Tuple(R.Tensor((16, 16), dtype="float32"), R.Tensor((16,
16), dtype="float32"), R.Prim(value=42), R.Tensor((), dtype="float16")) =
cls.transform_params(para0, para0)
R.output(res)
return res
mod = Module
mod = tvm.relax.transform.LegalizeOps()(mod)
def compile_mod(mod, *inputs):
mod = relax.transform.FuseTIR()(mod)
mod = relax.transform.LambdaLift()(mod)
ex = relax.build(mod, target='llvm')
vm = relax.VirtualMachine(ex, tvm.cpu())
mod_outputs = vm['main'](*inputs)
input_0 = tvm.nd.array(np.random.randint(10, size=[16,
16]).astype('float32'))
compile_mod(mod, input_0)
```
cc @Lunderberg @junrushao
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