Invalid argument: No OpKernel was registered to support Op 'PyFunc' about seq2seq

[sathyarr]

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

[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.

[sathyarr]

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

[sathyarr]

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])