ganler opened a new issue, #13029: URL: https://github.com/apache/tvm/issues/13029
### Expected behavior TVM should successfully compile a model whose operators are supoprted. ### Actual behavior The compilation could fail when the model contains the [recently](https://github.com/apache/tvm/pull/12124) supported `trilu` operator. In the `Steps to reproduce` section, the minimal reproducible is derived from a ONNX model exported by PyTorch which uses a mix a `int64` as shape arguments, mixing with `int32` constants in TVM's frontend translator, causing the compilation to fail due to int32-int64 mismatch in `check_op`: https://github.com/apache/tvm/blob/bdcfa01eae3ffe8c6d39aa26d0d1e5b311d47efb/python/tvm/topi/transform.py#L1057 A quick fix could just be aligning integer types of `row_index` and `col_index - k` before doing `check_op`. ### Environment `fa17da22c73fb9e95c27e4c28130835b628caf6b` on Ubuntu 20.04. ### Steps to reproduce Minimized reproducible. ```python import tvm from tvm import relay x1 = relay.var("x1", shape=[2, 1], dtype="float32") x2 = relay.var("x2", shape=(1, 1, 1, 1), dtype="float32") x3 = relay.var("x3", shape=(), dtype="int64") v0 = relay.broadcast_to(x1, shape=relay.const([2, 1], dtype="int64")) v2 = relay.divide(x2, v0) v3 = relay.trilu(v0, x3) f = relay.Function([x1, x2, x3], relay.Tuple([v2, v3])) relay.create_executor("graph", device=tvm.cpu(), target="llvm").evaluate(f) ``` <details><summary>Log. Click to expand!</summary> ```python """ Traceback (most recent call last): File "test.py", line 12, in <module> relay.create_executor("graph", device=tvm.cpu(), target="llvm").evaluate(f) ... 25: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::FunctionNode const*) 24: tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::FunctionNode const*) 23: _ZN3tvm5relay9 22: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::FunctionNode const*) 21: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&) 20: tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&) 19: tvm::NodeFunctor<tvm::RelayExpr (tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*) const 18: _ZZN3tvm5relay11ExprFunc 17: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::TupleNode const*) 16: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&) 15: tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&) 14: tvm::NodeFunctor<tvm::RelayExpr (tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*) const 13: _ZZN3tvm5relay11ExprFunc 12: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::CallNode const*) 11: tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::CallNode const*) 10: tvm::relay::tec::TECompilerImpl::Lower(tvm::relay::tec::CCacheKey const&) 9: tvm::relay::tec::TECompilerImpl::LowerInternal(tvm::relay::tec::CCacheKey const&, tvm::GlobalVarSupply) 8: tvm::relay::tec::PrimFuncFor(tvm::relay::Function const&, tvm::Target const&, tvm::GlobalVarSupply) 7: tvm::relay::tec::ScheduleBuilder::Create(tvm::relay::Function const&, tvm::GlobalVarSupply) 6: tvm::relay::tec::LowerToTECompute::Lower(tvm::relay::Function const&) 5: tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&) 4: tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&) 3: tvm::NodeFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>*) const 2: _ZZN3tvm5relay11ExprFunc 1: tvm::relay::tec::LowerToTECompute::VisitExpr_(tvm::relay::CallNode const*) 0: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<TVMFuncCreateFromCFunc::$_2> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) File "/home/jiawei/dev/tvm-official-release/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun rv = local_pyfunc(*pyargs) File "/home/jiawei/dev/tvm-official-release/python/tvm/relay/backend/te_compiler.py", line 317, in lower_call best_impl, outputs = select_implementation(op, call.attrs, inputs, ret_type, target) File "/home/jiawei/dev/tvm-official-release/python/tvm/relay/backend/te_compiler.py", line 207, in select_implementation outs = impl.compute(attrs, inputs, out_type) File "/home/jiawei/dev/tvm-official-release/python/tvm/relay/op/op.py", line 126, in compute return _OpImplementationCompute(self, attrs, inputs, out_type) File "/home/jiawei/dev/tvm-official-release/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__ raise get_last_ffi_error() 3: TVMFuncCall 2: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::relay::$_3> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) 1: tvm::relay::OpImplementation::Compute(tvm::Attrs const&, tvm::runtime::Array<tvm::te::Tensor, void> const&, tvm::Type const&) 0: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<TVMFuncCreateFromCFunc::$_2> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) File "/home/jiawei/dev/tvm-official-release/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun rv = local_pyfunc(*pyargs) File "/home/jiawei/dev/tvm-official-release/python/tvm/relay/op/strategy/generic.py", line 1489, in _compute_trilu topi_compute( File "/home/jiawei/dev/tvm-official-release/python/tvm/topi/transform.py", line 1061, in trilu return te.compute(data.shape, _apply_trilu, name="trilu") File "/home/jiawei/dev/tvm-official-release/python/tvm/te/operation.py", line 132, in compute body = fcompute(*[v.var for v in dim_var]) File "/home/jiawei/dev/tvm-official-release/python/tvm/topi/transform.py", line 1057, in _apply_trilu check_position = check_op(row_index, col_index - k) File "/home/jiawei/dev/tvm-official-release/python/tvm/tir/expr.py", line 881, in __init__ self.__init_handle_by_constructor__(_ffi_api.LE, a, b, span) # type: ignore File "/home/jiawei/dev/tvm-official-release/python/tvm/_ffi/_ctypes/object.py", line 145, in __init_handle_by_constructor__ handle = __init_by_constructor__(fconstructor, args) File "/home/jiawei/dev/tvm-official-release/python/tvm/_ffi/_ctypes/packed_func.py", line 260, in __init_handle_by_constructor__ raise get_last_ffi_error() 2: TVMFuncCall 1: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::tir::LE (tvm::PrimExpr, tvm::PrimExpr, tvm::Span)>::AssignTypedLambda<tvm::tir::$_51>(tvm::tir::$_51, 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*) 0: tvm::tir::LE::LE(tvm::PrimExpr, tvm::PrimExpr, tvm::Span) File "/home/jiawei/dev/tvm-official-release/src/tir/ir/expr.cc", line 459 TypeError: Check failed: (a.dtype() == b.dtype()) is false: mismatched types. int32 vs. int64 """ ``` </details> ### Triage Please refer to the list of label tags linked above to find the relevant tags and add them here in a bullet format (example below). * needs-triage cc: @jwfromm -- This is an automated message from the Apache Git Service. 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