Cookiee235 commented on issue #17211:
URL: https://github.com/apache/tvm/issues/17211#issuecomment-2256589902
A similar bug occurs as shown below.
Based on what I saw. The well-formed checker commonly corrects the return
type and shape. However, when the type of relax function return var is
`R.Tuple()`, the well-formed checker seems not to work.
### Actual behavior
```
Traceback (most recent call last):
File "/share_container/optfuzz/res/bugs/res_type.py", line 82, in <module>
mod_outputs = vm['main'](input_0, input_1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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
ValueError: Traceback (most recent call last):
8:
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*)
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: _ZN3tvm7runtime13PackedFuncObj9ExtractorINS0_16Pack
0: tvm::runtime::relax_vm::CheckTensorInfo(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
File "/software/tvm/src/runtime/relax_vm/builtin.cc", line 247
ValueError: Check failed: (DataType(ptr->dl_tensor.dtype) == dtype) is
false: ErrorContext(fn=main, loc=return, annotation=R.Tuple(R.Tensor((16, 16),
dtype="int32"), R.Tensor((32, 32), dtype="float32"))) expect Tensor with dtype
float32 but get int32
```
### 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 ones(T_full: T.Buffer((T.int64(16), T.int64(16)), "int32")):
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_full"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads()
T.writes(T_full[v_ax0, v_ax1])
T_full[v_ax0, v_ax1] = 1
@T.prim_func(private=True)
def zeros(T_full: T.Buffer((T.int64(16), T.int64(16)), "int32")):
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_full"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads()
T.writes(T_full[v_ax0, v_ax1])
T_full[v_ax0, v_ax1] = 0
@T.prim_func(private=True)
def zeros1(T_full: T.Buffer((T.int64(32), T.int64(32)), "int32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for ax0, ax1 in T.grid(T.int64(32), T.int64(32)):
with T.block("T_full"):
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads()
T.writes(T_full[v_ax0, v_ax1])
T_full[v_ax0, v_ax1] = 0
@R.function(private=True)
def func() -> R.Tuple(R.Tensor((16, 16), dtype="int32"), R.Tensor((16,
16), dtype="int32"), R.Tensor((32, 32), dtype="int32")):
cls = Module
A = R.call_tir(cls.zeros, R.tuple(), out_sinfo=R.Tensor((16, 16),
dtype="int32"))
B = R.call_tir(cls.ones, R.tuple(), out_sinfo=R.Tensor((16, 16),
dtype="int32"))
C = R.call_tir(cls.zeros1, R.tuple(), out_sinfo=R.Tensor((32, 32),
dtype="int32"))
return (A, B, C)
@R.function
def main_2() -> R.Tuple(R.Tensor, R.Tensor):
cls = Module
args: R.Tuple(R.Tensor, R.Tensor, R.Tensor) = cls.func()
gv1: R.Tensor = args[0]
gv2: R.Tensor = args[2]
return (gv1, gv2)
@R.function
def main(v3_0: R.Tensor((1, 22, 1), dtype="float16"), v6_0: R.Tensor((1,
37), dtype="float16")) -> R.Tuple(R.Tensor((16, 16), dtype="int32"),
R.Tensor((32, 32), dtype="float32")): # if return value is a tuple, well_form
checker cannot correct it!
R.func_attr({"num_input": 1})
cls = Module
with R.dataflow():
res: R.Tuple(R.Tensor, R.Tensor) = cls.main_2()
R.output(res)
return res
mod = Module
mod.show()
mod = tvm.relax.transform.LegalizeOps()(mod)
mod = relax.transform.FuseTIR()(mod)
mod = relax.transform.LambdaLift()(mod)
ex = relax.build(mod, target='llvm')
vm = relax.VirtualMachine(ex, tvm.cpu())
input_0 = tvm.nd.array(10 * np.random.random([1, 22, 1]).astype('float16'))
input_1 = tvm.nd.array(10 * np.random.random([1, 37]).astype('float16'))
mod_outputs = vm['main'](input_0, input_1)
```
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