Model export failure

ExecuTorch
1

Hi,
I am trying to export my model for use in my Android app using Executorch.
I set up the executorch environment as per
https://pytorch.org/executorch/stable/getting-started-setup.html

I am confused as to how to proceed.

I tried using this

        m = tf_model
        print(exir.capture(m, m.get_random_inputs()).to_edge())
        open("tfmodel.pte", "wb").write(exir.capture(m,   
        m.get_random_inputs()).to_edge().to_executorch().buffer)

It failed with this error

Traceback (most recent call last):
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/train.py", line 280, in <module>
    print(exir.capture(m, m.get_random_inputs()).to_edge())
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1696, in __getattr__
    raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
AttributeError: 'TFModel' object has no attribute 'get_random_inputs'

Should I use this
https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html

I would appreciate guidance non how to proceed.

Thanks

3

UPDATE
I resolved the problem. However, now the application fails with another error (Please see below).
Please note that the model runs without problems when run without the executorch code.
I would appreciate any suggestions as to what I should do to resolve this latest error.
Thank you

Traceback (most recent call last):
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/executorch/exir/tracer.py", line 667, in dynamo_trace
    return torchdynamo.export(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 1213, in inner
    result_traced = opt_f(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 401, in _fn
    return fn(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 549, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 142, in _fn
    return fn(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 384, in _convert_frame_assert
    return _compile(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 570, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 221, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 492, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/bytecode_transformation.py", line 1028, in transform_code_object
    transformations(instructions, code_options)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 462, in transform
    tracer.run()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2107, in run
    super().run()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 747, in run
    and self.step()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 710, in step
    getattr(self, inst.opname)(inst)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 405, in wrapper
    return inner_fn(self, inst)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1143, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/functions.py", line 307, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/functions.py", line 261, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 618, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2234, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 2358, in inline_call_
    tracer.run()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 747, in run
    and self.step()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 710, in step
    getattr(self, inst.opname)(inst)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 405, in wrapper
    return inner_fn(self, inst)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1143, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/nn_module.py", line 309, in call_function
    return wrap_fx_proxy(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 1304, in wrap_fx_proxy
    return wrap_fx_proxy_cls(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 1391, in wrap_fx_proxy_cls
    example_value = get_fake_value(proxy.node, tx)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 1422, in get_fake_value
    raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 1383, in get_fake_value
    return wrap_fake_exception(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 952, in wrap_fake_exception
    return fn()
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 1384, in <lambda>
    lambda: run_node(tx.output, node, args, kwargs, nnmodule)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 1483, in run_node
    raise RuntimeError(fn_str + str(e)).with_traceback(e.__traceback__) from e
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 1467, in run_node
    return nnmodule(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1323, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1529, in dispatch
    return decomposition_table[func](*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_prims_common/wrappers.py", line 240, in _fn
    result = fn(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_decomp/decompositions.py", line 72, in inner
    r = f(*tree_map(increase_prec, args), **tree_map(increase_prec, kwargs))
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_decomp/decompositions.py", line 1306, in addmm
    out = alpha * torch.mm(mat1, mat2)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1323, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1621, in dispatch
    r = func(*args, **kwargs)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_ops.py", line 516, in __call__
    return self._op(*args, **kwargs or {})
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/_meta_registrations.py", line 1891, in meta_mm
    torch._check(
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/__init__.py", line 1028, in _check
    _check_with(RuntimeError, cond, message)
  File "/home/adonnini1/anaconda3/lib/python3.9/site-packages/torch/__init__.py", line 1011, in _check_with
    raise error_type(message_evaluated)
torch._dynamo.exc.TorchRuntimeError: Failed running call_module L__self___encoder_embedding_linear_embd(*(FakeTensor(..., size=(0,), dtype=torch.float64),), **{}):
a and b must have same reduction dim, but got [1, 0] X [2, 512].

from user code:
   File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/model.py", line 473, in forward
    enc_embed = self.encoder_embedding.forward(enc_input)
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/model.py", line 384, in forward
    x = self.linear_embd(x) * math.sqrt(self.emb_size)     # Shape = (B, N, C)


The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/train.py", line 292, in <module>
    print(exir.capture(m, (VAL_INPUT, DEC_INPUT, DEC_SOURCE_MASK, DEC_TARGET_MASK)).to_edge())
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/executorch/exir/capture/_capture.py", line 146, in capture
    graph_module, _ = dynamo_trace(
  File "/home/adonnini1/Development/ContextQSourceCode/NeuralNetworks/trajectory-prediction-transformers-master/executorch/exir/tracer.py", line 686, in dynamo_trace
    raise InternalError(
executorch.exir.error.InternalError: torchdynamo internal error occured. Please see above stacktrace
4

2nd UPDATE

I made further progress.

Instead of using exir.capture, I used torch.export it produced the exported model successfully.

My question is what is the equivalent of

exir.capture.to_edge().to_executorch().buffer when using torch.export?

Thanks