[Apache TVM Discuss] [Questions] Support for dynamic_rnn tensorflow

[Zhonhel via Apache TVM Discuss <no...@discuss.tvm.ai>]

I compile a network also use tf dynamic_rnn with TVM.

I meet an another error:

when convert this op in the frontend:
```
name: "rnn_1/gru1/while/TensorArrayWrite/TensorArrayWriteV3"
op: "TensorArrayWriteV3"
input: "rnn_1/gru1/while/TensorArrayWrite/TensorArrayWriteV3/Enter"
input: "rnn_1/gru1/while/Identity"
input: "rnn_1/gru1/while/Select"
input: "rnn_1/gru1/while/Identity_1"
attr {
  key: "T"
  value {
    type: DT_FLOAT
  }
}
```
it shows this error:
```
Traceback (most recent call last):
  File "/tvm_test_pb.py", line 44, in <module>
    mod, params = relay.frontend.from_tensorflow(graph_def, layout=None, shape=shape_dict)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 1264, in from_tensorflow
    mod, params = g.from_tensorflow(graph, layout, shape, outputs)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 659, in from_tensorflow
    func = self._get_relay_func(graph, layout=layout, shape=shape, outputs=outputs)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 619, in _get_relay_func
    self._backtrack_construct(node.name)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 1130, in _backtrack_construct
    node, [], attr, self._control_flow_node_map
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 895, in _convert_control_flow_operator
    op = self._licm_construct(plname, node.input[0])
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 1068, in _licm_construct
    actual_expr = self._backtrack_construct(node_name)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 1183, in _backtrack_construct
    op = self._convert_operator(node.op, node.name, inputs, attr)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow.py", line 1023, in _convert_operator
    sym = convert_map[op_name](inputs, attrs, self._params, self._prelude)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/tensorflow_ops.py", line 1660, in _impl
    input_t_shape = _infer_shape(inputs[2], prelude.mod)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/common.py", line 512, in infer_shape
    out_type = infer_type(inputs, mod=mod)
  File "/tvm-2021.8.13/tvm/python/tvm/relay/frontend/common.py", line 479, in infer_type
    mod = _transform.InferType()(mod)
  File "/tvm-2021.8.13/tvm/python/tvm/ir/transform.py", line 161, in __call__
    return _ffi_transform_api.RunPass(self, mod)
  File "/tvm-2021.8.13/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__
    raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
  7: TVMFuncCall
  6: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), 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::string)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
  5: tvm::transform::Pass::operator()(tvm::IRModule) const
  4: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  3: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  2: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1}>(tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
  1: tvm::relay::TypeInferencer::Infer(tvm::GlobalVar, tvm::relay::Function)
  0: tvm::relay::TypeSolver::Solve()
  10: TVMFuncCall
  9: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), 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::string)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
  8: tvm::transform::Pass::operator()(tvm::IRModule) const
  7: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  6: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  5: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1}>(tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
  4: tvm::relay::TypeInferencer::Infer(tvm::GlobalVar, tvm::relay::Function)
  3: tvm::relay::TypeSolver::Solve()
  2: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<bool (tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>(bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
  1: bool tvm::relay::ConcatenateRel<tvm::relay::ConcatenateAttrs>(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)
  0: tvm::TensorType tvm::runtime::Downcast<tvm::TensorType, tvm::Type>(tvm::Type)
  File "/tvm-2021.8.13/tvm/src/relay/analysis/type_solver.cc", line 624
TVMError: 
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
  Check failed: (false) is false: [10:08:17] /tvm-2021.8.13/tvm/include/tvm/runtime/object.h:886: 
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
  Check failed: (ref->template IsInstance<typename SubRef::ContainerType>()) is false: Downcast from TypeCall to relay.TensorType failed.


Process finished with exit code 1
```





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