1c75176947730de8322acf6ad996096625e92e3a,tensorflow_lattice/python/rtl_layer.py,RTL,call,#RTL#Any#,217

Before Change

        // Concat into (-1, lattice_rank) for a single lattice
        lattice_inputs.append(
            tf.concat([input_tensors[i] for i in inputs_for_unit], axis=1))
      if len(lattice_inputs) > 1:
        // Stack into (-1, units, lattice_rank) for multi-unit lattice layer
        lattice_inputs = tf.stack(lattice_inputs, axis=1)
      else:
        lattice_inputs = lattice_inputs[0]
      output_monotonicity = max(monotonicities)
      // Call each lattice layer and store based on output monotonicy.
      outputs_for_monotonicity[output_monotonicity].append(
          self._lattice_layers[monotonicities](lattice_inputs))

After Change

      else:
        input_tensors.append(items)
    if len(input_tensors) == 1:
      flattened_input = input_tensors[0]
    else:
      flattened_input = tf.concat(input_tensors, axis=1)

    // outputs_for_monotonicity[0] are non-monotonic outputs
    // outputs_for_monotonicity[1] are monotonic outputs
    outputs_for_monotonicity = [[], []]
    for monotonicities, inputs_for_units in self._rtl_structure:
      if len(inputs_for_units) == 1:
        inputs_for_units = inputs_for_units[0]
      lattice_inputs = tf.gather(flattened_input, inputs_for_units, axis=1)
      output_monotonicity = max(monotonicities)
      // Call each lattice layer and store based on output monotonicy.
      outputs_for_monotonicity[output_monotonicity].append(
          self._lattice_layers[str(monotonicities)](lattice_inputs))

Instances