Cookiee235 opened a new issue, #17255:
URL: https://github.com/apache/tvm/issues/17255
### Actual behavior
```
Traceback (most recent call last):
File "test.py", line 88, in <module>
ex = relax.build(mod, target='llvm')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm-lunder/python/tvm/relax/vm_build.py", line 335, in
build
mod = pipeline(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
File "/software/tvm-lunder/python/tvm/relax/pipeline.py", line 101, in
_pipeline
mod = seq(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):
10:
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)
9: tvm::transform::Pass::operator()(tvm::IRModule) const
8: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
7: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
6: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
5: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
4:
_ZN3tvm7runtime13PackedFuncObj9ExtractorINS0_16PackedFuncSubObjIZNS0_15TypedPackedFuncIFNS_8IRModuleES5_NS_9transform11PassContextEEE17AssignTypedLambdaIZNS_5relax9transform21StaticPlanBlockMemoryEvEUlS5_S7_E_EEvT_EUlRKNS0_7TVMArgsEPNS0_11TVMRetValueEE_EEE4CallEPKS1_SF_SJ_
3: tvm::relax::StaticPlanBlockMemory(tvm::IRModule)
2: tvm::relax::ExprVisitor::VisitExpr(tvm::RelayExpr const&)
1: tvm::relax::StorageAllocatorInit::VisitExpr_(tvm::relax::FunctionNode
const*)
0: tvm::relax::SetTIRVarUpperBound(tvm::relax::Function,
tvm::arith::Analyzer*, tvm::runtime::Map<tvm::tir::Var, tvm::arith::IntSet,
void, void>*)
File
"/software/tvm-lunder/src/relax/transform/static_plan_block_memory.cc", line 378
TVMError: Check failed: (value != nullptr) is false: The entry value of attr
`tir_var_upper_bound` should be integer. However Array is got.
```
### Steps to reproduce
```python
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
def add(rxplaceholder: T.Buffer((T.int64(8),), "float32"),
rxplaceholder_1: T.Buffer((), "float32"), T_add: T.Buffer((T.int64(8),),
"float32")):
T.evaluate(0)
@T.prim_func
def exp(rxplaceholder: T.Buffer((T.int64(2), T.int64(4)), "float32"),
compute: T.Buffer((T.int64(2), T.int64(4)), "float32")):
T.evaluate(0)
@T.prim_func
def log(rxplaceholder: T.Buffer((T.int64(10),), "float32"), compute:
T.Buffer((T.int64(10),), "float32")):
T.evaluate(0)
@T.prim_func
def pad(rxplaceholder: T.Buffer((T.int64(8),), "float32"), PadInput:
T.Buffer((T.int64(10),), "float32")):
T.evaluate(0)
@T.prim_func
def relu(rxplaceholder: T.Buffer((T.int64(8),), "float32"), compute:
T.Buffer((T.int64(8),), "float32")):
T.evaluate(0)
@T.prim_func
def reshape(rxplaceholder: T.Buffer((T.int64(2), T.int64(4)),
"float32"), T_reshape: T.Buffer((T.int64(8),), "float32")):
T.evaluate(0)
@T.prim_func(private=True)
def reshape1(alloc: T.Buffer((T.int64(2), T.int64(4)), "float32"),
T_reshape: T.Buffer((T.int64(8),), "float32")):
T.func_attr({"tir.noalias": T.bool(True)})
# with T.block("root"):
for ax0 in range(T.int64(8)):
with T.block("T_reshape"):
v_ax0 = T.axis.spatial(T.int64(8), ax0)
T.reads(alloc[v_ax0 % T.int64(8) // T.int64(4), v_ax0 %
T.int64(4)])
T.writes(T_reshape[v_ax0])
T_reshape[v_ax0] = alloc[v_ax0 % T.int64(8) // T.int64(4),
v_ax0 % T.int64(4)]
@R.function
def main_2(x: R.Tensor((2, 4), dtype="float32")) -> R.Tensor((10,),
dtype="float32"):
R.func_attr({"relax.force_pure": 1})
cls = Module
storage: R.Object = R.memory.alloc_storage(R.shape([32]),
R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc: R.Tensor((2, 4), dtype="float32") =
R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([2, 4]),
R.dtype("float32"))
cls.exp(x, alloc)
lv1 = R.call_tir(cls.reshape1, (alloc,), out_sinfo=R.Tensor((8,),
dtype="float32"))
R.memory.kill_tensor(alloc)
storage1: R.Object = R.memory.alloc_storage(R.shape([40]),
R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc1: R.Tensor((8,), dtype="float32") =
R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([8]),
R.dtype("float32"))
cls.relu(lv1, alloc1)
R.memory.kill_tensor(lv1)
alloc2: R.Tensor((8,), dtype="float32") =
R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([8]),
R.dtype("float32"))
R.memory.kill_storage(storage)
cls.add(alloc1, R.const(1, "float32"), alloc2)
R.memory.kill_tensor(alloc1)
alloc3: R.Tensor((10,), dtype="float32") =
R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([10]),
R.dtype("float32"))
R.memory.kill_storage(storage1)
cls.pad(alloc2, alloc3)
R.memory.kill_tensor(alloc2)
storage_1: R.Object = R.memory.alloc_storage(R.shape([40]),
R.prim_value(0), R.str("global"), R.dtype("uint8"))
alloc4: R.Tensor((10,), dtype="float32") =
R.memory.alloc_tensor(storage_1, R.prim_value(0), R.shape([10]),
R.dtype("float32"))
R.memory.kill_storage(storage_1)
cls.log(alloc3, alloc4)
R.memory.kill_tensor(alloc3)
return alloc4
@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor((10,),
dtype="float32"):
n = T.int64()
R.func_attr({"relax.force_pure": 1, "tir_var_upper_bound": {"n":
[4]}})
cls = Module
para0: R.Tensor((2, n), dtype="float32") = x
res: R.Tensor((10,), dtype="float32") = cls.main_2(para0)
return res
mod = Module
ex = relax.build(mod, target='llvm')
vm = relax.VirtualMachine(ex, tvm.cpu())
```
cc @Lunderberg @junrushao
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