Cookiee235 opened a new issue, #17222: URL: https://github.com/apache/tvm/issues/17222
The Relax IR in the below test case passed the well-formed checking, but failed when using the DCE unexpectedly! ### Actual behavior ``` Traceback (most recent call last): File "test_sim.py", line 54, in <module> mod = tvm.relax.transform.DeadCodeElimination()(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): 18: 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) 17: tvm::transform::Pass::operator()(tvm::IRModule) const 16: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const 15: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const 14: _ZN3tvm7runtime13PackedFuncObj9ExtractorINS0_16PackedFuncSubObjIZNS0_15TypedPackedFuncIFNS_8IRModuleES5_NS_9transform11PassContextEEE17AssignTypedLambdaIZNS_5relax9transform19DeadCodeEliminationENS0_5ArrayINS0_6StringEvEEEUlS5_S7_E_EEvT_EUlRKNS0_7TVMArgsEPNS0_11TVMRetValueEE_EEE4CallEPKS1_SI_SM_ 13: tvm::relax::DeadCodeElimination(tvm::IRModule const&, tvm::runtime::Array<tvm::runtime::String, void>) 12: tvm::relax::RemoveAllUnused(tvm::RelayExpr) 11: tvm::relax::CollectVarUsage(tvm::RelayExpr const&) 10: tvm::relax::UDChain::Collect(tvm::RelayExpr const&) 9: tvm::relax::ExprVisitor::VisitExpr(tvm::RelayExpr const&) 8: tvm::relax::ExprVisitor::VisitExpr_(tvm::relax::FunctionNode const*) 7: tvm::relax::ExprVisitor::VisitExpr(tvm::RelayExpr const&) 6: tvm::relax::ExprVisitor::VisitExpr_(tvm::relax::SeqExprNode const*) 5: tvm::relax::ExprVisitor::VisitBindingBlock(tvm::relax::BindingBlock const&) 4: tvm::relax::ExprVisitor::VisitBindingBlock_(tvm::relax::BindingBlockNode const*) 3: tvm::relax::ExprVisitor::VisitBinding(tvm::relax::Binding const&) 2: tvm::relax::UDChain::VisitBinding_(tvm::relax::VarBindingNode const*) 1: tvm::relax::ExprVisitor::VisitBinding_(tvm::relax::VarBindingNode const*) 0: tvm::relax::UDChain::VisitVarDef(tvm::relax::Var const&) File "/software/tvm-lunder/src/relax/analysis/udchain.cc", line 75 TVMError: Check failed: (!usage_map.count(var)) is false: Variable while_loop was used before its definition ``` ### 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 add(i: T.Buffer((), "int32"), c: T.Buffer((), "int32"), T_add: T.Buffer((), "int32")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): with T.block("T_add"): vi = T.axis.spatial(1, T.int64(0)) T.reads(i[()], c[()]) T.writes(T_add[()]) T_add[()] = i[()] + c[()] @T.prim_func(private=True) def add1(s: T.Buffer((T.int64(2), T.int64(3)), "float32"), x: T.Buffer((T.int64(2), T.int64(3)), "float32"), T_add: T.Buffer((T.int64(2), T.int64(3)), "float32")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): for ax0, ax1 in T.grid(T.int64(2), T.int64(3)): with T.block("T_add"): v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) T.reads(s[v_ax0, v_ax1], x[v_ax0, v_ax1]) T.writes(T_add[v_ax0, v_ax1]) T_add[v_ax0, v_ax1] = s[v_ax0, v_ax1] + x[v_ax0, v_ax1] @R.function def main(x: R.Tensor((2, 3), dtype="float32")) -> R.Tensor((2, 3), dtype="float32"): cls = Module @R.function def while_loop(i: R.Tensor((), dtype="int32"), s: R.Tensor((2, 3), dtype="float32")) -> R.Tensor((2, 3), dtype="float32"): cond: R.Tensor((), dtype="bool") = R.call_pure_packed("test.vm.less", i, R.const(10, "int32"), sinfo_args=(R.Tensor((), dtype="bool"),)) c: R.Tensor((), dtype="int32") = R.const(1, "int32") if cond: new_i = R.call_tir(cls.add, (i, c), out_sinfo=R.Tensor((), dtype="int32")) new_s = R.call_tir(cls.add1, (s, x), out_sinfo=R.Tensor((2, 3), dtype="float32")) r_then: R.Tensor((2, 3), dtype="float32") = while_loop(new_i, new_s) r: R.Tensor((2, 3), dtype="float32") = r_then else: r: R.Tensor((2, 3), dtype="float32") = s return r gv: R.Tensor((2, 3), dtype="float32") = while_loop(R.const(0, "int32"), x) return gv mod = Module mod.show() assert relax.analysis.well_formed(mod) mod = tvm.relax.transform.DeadCodeElimination()(mod) mod.show() ``` cc @Lunderberg @junrushao -- This is an automated message from the Apache Git Service. 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