[GitHub] [tvm] jikechao opened a new issue, #15430: [Bug] [Relay][Frontend][Keras] Dot operator lead to a crash when crate executor

["jikechao (via GitHub)" <gi...@apache.org>]

jikechao opened a new issue, #15430:
URL: https://github.com/apache/tvm/issues/15430

   A model with `Dot` operator can be loaded to TVM but crashes when creating the graph executor.
   
   
   ### model structure and RelayIR infor:
   ![image](https://github.com/apache/tvm/assets/29506758/9b21b7ee-a5a3-4125-b988-e965b7f8400c)
   
   
   ### Actual behavior
   
   ```
   Traceback (most recent call last):
     File "26_crash_Dot.py", line 25, in <module>
       model = relay.build_module.create_executor("graph", mod, tvm.cpu(0), 'llvm', params).evaluate()
     File "/workplace/software/tvm/tvm_/python/tvm/relay/backend/interpreter.py", line 171, in evaluate
       return self._make_executor()
     File "/workplace/software/tvm/tvm_/python/tvm/relay/build_module.py", line 513, in _make_executor
       self.mod = InferType()(self.mod)
     File "/workplace/software/tvm/tvm_/python/tvm/ir/transform.py", line 160, in __call__
       return _ffi_transform_api.RunPass(self, mod)
     File "/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__
       raise get_last_ffi_error()
   tvm.error.DiagnosticError: Traceback (most recent call last):
     7: TVMFuncCall
     6: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::transform::Pass, tvm::IRModule)>::AssignTypedLambda<tvm::transform::$_6>(tvm::transform::$_6, 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*)
     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: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::$_2>(tvm::relay::transform::InferType()::$_2)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
     1: tvm::DiagnosticContext::Render()
     0: _ZN3tvm7runtime6detail
     File "/workplace/software/tvm/tvm_/src/ir/diagnostic.cc", line 131
   DiagnosticError: one or more error diagnostics were emitted, please check diagnostic render for output.
   
   ```
   
   ### Environment
   
   Any environment details, such as: Operating System, TVM version, etc
   
   ### Steps to reproduce
   
   ```
   import tvm
   import tvm.relay as relay
   import numpy as np
   from tensorflow import keras
   from tensorflow.keras import layers, models
   
   
   input_shape1 = (2, 2, 2)
   input_shape2 = (2, 2, 2)
   
   x1 = layers.Input(shape=input_shape1[1:], dtype='float32')
   x2 = layers.Input(shape=input_shape2[1:], dtype='float32')
   
   layer = keras.layers.Dot(axes=1)
   layer.set_weights(layer.get_weights())
   
   y = layer([x1, x2])
   model = models.Model([x1, x2], y)
   model.summary()
   
   shape_dict = {'input_1': input_shape1, 'input_2':input_shape2}
   mod, params = relay.frontend.from_keras(model,layout='NWC')
   print(mod)
   with tvm.transform.PassContext(opt_level=3):
       model = relay.build_module.create_executor("graph", mod, tvm.cpu(0), 'llvm', params).evaluate()
   ```
   
   ### Triage
   * frontend:keras
   * needs-triage
   


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[GitHub] [tvm] jikechao commented on issue #15430: [Bug] [Relay][Frontend][Keras] Dot operator lead to a crash when crate executor

["jikechao (via GitHub)" <gi...@apache.org>]

jikechao commented on issue #15430:
URL: https://github.com/apache/tvm/issues/15430#issuecomment-1655272375

   @Hzfengsy @vvchernov 
   is this a TVM bug? 


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