FinnWeng opened a new issue #4265: [RELAY][Bug] output type assignment not work for tf.range() in TVM URL: https://github.com/apache/incubator-tvm/issues/4265 This issue happens when converting code with `tf.range()`. My environment is: develop: python3.6, tensorflow1.14 convert to TVM: container of tvmai/demo-gpu The code of issue is ```python my_tensor = tf.reshape(tf.range(1,256+1,1,dtype=tf.float32),[1,256]) ``` the error log is: ``` tvm._ffi.base.TVMError: Traceback (most recent call last): [bt] (8) /usr/tvm/build/libtvm.so(TVMFuncCall+0x61) [0x7f8c084d70f1] [bt] (7) /usr/tvm/build/libtvm.so(+0xb1d64b) [0x7f8c083e264b] [bt] (6) /usr/tvm/build/libtvm.so(tvm::relay::ModuleNode::FromExpr(tvm::relay::Expr const&, tvm::Map<tvm::relay::GlobalVar, tvm::relay::Function, void, void> const&, tvm::Map<tvm::relay::GlobalTypeVar, tvm::relay::TypeData, void, void> const&)+0x17b) [0x7f8c083e236b] [bt] (5) /usr/tvm/build/libtvm.so(tvm::relay::ModuleNode::Add(tvm::relay::GlobalVar const&, tvm::relay::Function const&, bool)+0x344) [0x7f8c083df2d4] [bt] (4) /usr/tvm/build/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x1fd) [0x7f8c082c7ced] [bt] (3) /usr/tvm/build/libtvm.so(tvm::relay::TypeInferencer::Infer(tvm::relay::Expr)+0x55) [0x7f8c082c6e65] [bt] (2) /usr/tvm/build/libtvm.so(tvm::relay::TypeSolver::Solve()+0x4e1) [0x7f8c08306781] [bt] (1) /usr/tvm/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), void tvm::runtime::TypedPackedFunc<bool (tvm::Array<tvm::relay::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::Array<tvm::relay::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>(bool (*)(tvm::Array<tvm::relay::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0xd4) [0x7f8c0809dc74] [bt] (0) /usr/tvm/build/libtvm.so(tvm::relay::BroadcastRel(tvm::Array<tvm::relay::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)+0xb7c) [0x7f8c080d0efc] File "/usr/tvm/src/relay/ir/error.cc", line 133 TVMError: Error(s) have occurred. The program has been annotated with them: In `main`: v0.0.4 fn () { %0 = arange(1f, 257f, 1f, start=meta[relay.Constant][0], stop=meta[relay.Constant][1], step=meta[relay.Constant][2], dtype="int32") unable to unify: `int32` and `float32`; ; %1 = reshape(%0, newshape=[1, 256]); multiply(%1, meta[relay.Constant][3]) an internal invariant was violated while typechecking your program [08:25:28] /usr/tvm/src/relay/op/type_relations.cc:121: Check failed: t0->dtype == t1->dtype (int32 vs. float32) : ; } // meta data omitted. you can use show_meta_data=True to include meta data ``` And I take detour to avoid this issue by: ```python my_tensor = tf.cast(tf.reshape(tf.range(1,256+1,1),[1,256]),tf.float32) ``` It seems fine with code above. So I guess the issue is about type assignment in tf.range. Thanks!
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