Invalid argument: No OpKernel was registered to support Op 'PyFunc'

[Sathya R]

I can successfully export the seq2seq library based model and use it in Tensorflow serving.
To mention, when it is successfully working, I have turned off beam_search by supplying,
FLAGS_model_params["inference.beam_search.beam_width"]=0 upon exporting.

When I turn on beam search by,
FLAGS_model_params["inference.beam_search.beam_width"]=5 upon exporting,
I get the following error while loading the exported model through Tensorflow Serving,

2019-03-13 18:48:11.664016: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:285] SavedModel load for tags { serve }; Status: fail. Took 97350 microseconds.
2019-03-13 18:48:11.664075: E tensorflow_serving/util/retrier.cc:37] Loading servable: {name: dmodel version: 1551934425} failed: Invalid argument: No OpKernel was registered to support Op 'PyFunc' used by {{node model/att_seq2seq/decode/attention_decoder/PyFunc}}with these attrs: [_output_shapes=[[?,5]], Tin=[DT_INT32, DT_INT32], Tout=[DT_INT32], token="pyfunc_0"]
Registered devices: [CPU]
Registered kernels:
  <no registered kernels>

	 [[{{node model/att_seq2seq/decode/attention_decoder/PyFunc}}]]

Upon searching for the solution, I could see py_func are supported only in Python interpreter.
Since, Tensorflow Serving runs in C++, the error message appears.

The possible solution is to convert py_func based operations to TF operations.
If so, could some one point out which function exactly should I convert to TF Ops (the above error message could possibly help)?

Since, node model/att_seq2seq/decode/attention_decoder/PyFunc seems to occur within decoder, I cannot get rid of this by pruning or something else.

Or is there any alternate solutions possible?

Thanks in advance

[Sathya R]

If so, could some one point out which function exactly should I convert to TF Ops (the above error message could possibly help)?

py_func is used here

[Sathya R]

I have been trying to implement this function in pure TF ops,

def gather_tree_py(values, parents):
  beam_length = values.shape[0]
  num_beams = values.shape[1]
  res = np.zeros_like(values)
  res[-1, :] = values[-1, :]        # Point 1
  for beam_id in range(num_beams):
    parent = parents[-1][beam_id]
    for level in reversed(range(beam_length - 1)):
      res[level, beam_id] = values[level][parent]        # Point 2
      parent = parents[level][parent]
  return np.array(res).astype(values.dtype)

However, I'm stuck in converting lines mentioned as Point 1 & Point 2
They're numpy based array operations.
Is that possible to do that in native TF ops?
If not, any alternate solutions possible?
Will writing a Custom Operation in C++ suffice the scenario?

Tensorflow based Code so far,

beam_length = tf.shape(values)[0]
num_beams = tf.shape(values)[1]
res = tf.zeros_like(values)

beam_id = tf.constant(0)
while_condition = lambda beam_id: tf.less(beam_id, num_beams)
def body(beam_id):
    parent = parents[-1][beam_id]

    # internal while loop
    level = tf.subtract(beam_length, 2)
    while_condition2 = lambda level, beam_id, parent: tf.greater(level, -1)
    def body2(level, beam_id, parent):
        res[level, beam_id] = values[level][parent]        # Point 2
        parent.assign(parents[level][parent])
        return[tf.subtract(level, 1), beam_id, parent]
    tf.while_loop(while_condition2, body2, [level, beam_id, parent])
    # internal while loop

    return [tf.add(beam_id, 1)]

# do the loop:
tf.while_loop(while_condition, body, [beam_id])

[ghtwht]

you could just comment out the related code: line 68~73 of attention_seq2seq.py and "if self.use_beam_search: **" of basic_seq2seq.py to avoid using beam search since the related code almost based in pyhton.

[Sathya R]

you could just comment out the related code: line 68~73 of attention_seq2seq.py and "if self.use_beam_search: **" of basic_seq2seq.py to avoid using beam search since the related code almost based in pyhton.

I could manage to implement that py_func as a Custom Operator.
For those of you interested with the code for Custom Operation, see here