Compute gating for mixture-of-experts in TensorFlow.

  Note: until the algorithm and inferface solidify, we pass in a hyperparameters
  dictionary in order not to complicate the interface in mtf_transformer.py .
  Once this code moves out of "research", we should pass the hyperparameters
  separately.

  Hyperparameters used:
    hparams.moe_use_second_place_loss: a boolean
    hparams.moe_second_policy_train: a string
    hparams.moe_second_policy_eval: a string
    hparams.moe_second_threshold: a float

  The returned forward assignment is a tensor used to map (via einsum) from the
  inputs to the expert_inputs.  Likewise, the returned combine_tensor is
  used to map (via einsum) from the expert outputs to the outputs.  Both the
  forward and backward assignments are mostly zeros.  The shapes of the tensors
  are as follows.

  inputs: [<batch_dims>, group_size_dim, input_dim]
  importance: [<batch_dims>, group_size_dim]
  dispatch_tensor:
    [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
  expert_inputs:
    [<batch_dims>, experts_dim, expert_capacity_dim, input_dim]

  expert_outputs: [<batch_dims>, experts_dim, expert_capacity_dim, output_dim]
  combine_tensor:
    [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
  outputs: [<batch_dims>, group_size_dim, output_dim]

  "importance" is an optional tensor with one floating-point value for each
  input vector.  If the importance of an input is 1.0, then we send it to
  up to 2 experts.  If 0.0 < importance < 1.0, then we send it to at most
  one expert.  If importance == 0.0, then we send it to no experts.

  We use "importance" at the second-level gating function of a hierarchical
  mixture of experts.  Inputs to the first-choice expert-group get importance
  1.0.  Inputs to the second-choice expert group get importance 0.5.
  Inputs that represent padding get importance 0.0.

  Args:
    inputs: a mtf.Tensor with shape [<batch_dims>, group_size_dim, input_dim]
    outer_expert_dims: an optional list of dimensions.  This is for the case
      where we are at an inner level of a hierarchical MoE.
    experts_dim: a Dimension (the number of experts)
    expert_capacity_dim: a Dimension (number of examples per group per expert)
    hparams: model hyperparameters.
    train: a boolean
    importance: an optional tensor with shape [<batch_dims>, group_size_dim]

  Returns:
    dispatch_tensor: a Tensor with shape
      [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
    combine_tensor: a Tensor with shape
      [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
    loss: a mtf scalar

  Raises:
    ValueError: on illegal hyperparameters

def _top_2_gating(
    inputs, outer_expert_dims, experts_dim, expert_capacity_dim,
    hparams, train, importance=None):
  """Compute gating for mixture-of-experts in TensorFlow.

  Note: until the algorithm and inferface solidify, we pass in a hyperparameters
  dictionary in order not to complicate the interface in mtf_transformer.py .
  Once this code moves out of "research", we should pass the hyperparameters
  separately.

  Hyperparameters used:
    hparams.moe_use_second_place_loss: a boolean
    hparams.moe_second_policy_train: a string
    hparams.moe_second_policy_eval: a string
    hparams.moe_second_threshold: a float

  The returned forward assignment is a tensor used to map (via einsum) from the
  inputs to the expert_inputs.  Likewise, the returned combine_tensor is
  used to map (via einsum) from the expert outputs to the outputs.  Both the
  forward and backward assignments are mostly zeros.  The shapes of the tensors
  are as follows.

  inputs: [<batch_dims>, group_size_dim, input_dim]
  importance: [<batch_dims>, group_size_dim]
  dispatch_tensor:
    [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
  expert_inputs:
    [<batch_dims>, experts_dim, expert_capacity_dim, input_dim]

  expert_outputs: [<batch_dims>, experts_dim, expert_capacity_dim, output_dim]
  combine_tensor:
    [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
  outputs: [<batch_dims>, group_size_dim, output_dim]

  "importance" is an optional tensor with one floating-point value for each
  input vector.  If the importance of an input is 1.0, then we send it to
  up to 2 experts.  If 0.0 < importance < 1.0, then we send it to at most
  one expert.  If importance == 0.0, then we send it to no experts.

  We use "importance" at the second-level gating function of a hierarchical
  mixture of experts.  Inputs to the first-choice expert-group get importance
  1.0.  Inputs to the second-choice expert group get importance 0.5.
  Inputs that represent padding get importance 0.0.

  Args:
    inputs: a mtf.Tensor with shape [<batch_dims>, group_size_dim, input_dim]
    outer_expert_dims: an optional list of dimensions.  This is for the case
      where we are at an inner level of a hierarchical MoE.
    experts_dim: a Dimension (the number of experts)
    expert_capacity_dim: a Dimension (number of examples per group per expert)
    hparams: model hyperparameters.
    train: a boolean
    importance: an optional tensor with shape [<batch_dims>, group_size_dim]

  Returns:
    dispatch_tensor: a Tensor with shape
      [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
    combine_tensor: a Tensor with shape
      [<batch_dims>, group_size_dim, experts_dim, expert_capacity_dim]
    loss: a mtf scalar

  Raises:
    ValueError: on illegal hyperparameters
  """
  group_size_dim, unused_input_dim = inputs.shape.dims[-2:]

  raw_gates = mtf.softmax(mtf.layers.dense(
      inputs, experts_dim, use_bias=False,
      expert_dims=outer_expert_dims), experts_dim)

  # The internals of this function run in float32.
  #   bfloat16 seems to reduce quality.
  raw_gates = mtf.to_float(raw_gates)

  expert_capacity_f = float(expert_capacity_dim.size)

  # FIND TOP 2 EXPERTS PER POSITON
  # Find the top expert for each position. shape=[batch, group]
  index_1, gate_1 = mtf.top_1(raw_gates, experts_dim)
  # [batch, group, experts]
  mask_1 = mtf.one_hot(index_1, experts_dim, dtype=raw_gates.dtype)
  density_1_proxy = raw_gates
  if importance is not None:
    mask_1 *= mtf.to_float(mtf.equal(importance, 1.0))
    gate_1 *= mtf.to_float(mtf.equal(importance, 1.0))
    density_1_proxy *= mtf.to_float(mtf.equal(importance, 1.0))
  gates_without_top_1 = raw_gates * (1.0 - mask_1)
  # [batch, group]
  index_2, gate_2 = mtf.top_1(gates_without_top_1, experts_dim)
  # [batch, group, experts]
  mask_2 = mtf.one_hot(index_2, experts_dim, dtype=raw_gates.dtype)
  if importance is not None:
    mask_2 *= mtf.to_float(mtf.greater(importance, 0.0))

  denom = gate_1 + gate_2 + 1e-9
  gate_1 /= denom
  gate_2 /= denom

  # BALANCING LOSSES
  # shape = [batch, experts]
  # We want to equalize the fraction of the batch assigned to each expert
  density_1 = mtf.reduce_mean(mask_1, reduced_dim=group_size_dim)
  # Something continuous that is correlated with what we want to equalize.
  density_1_proxy = mtf.reduce_mean(density_1_proxy, reduced_dim=group_size_dim)
  density_1 = mtf.Print(
      density_1, [mtf.reduce_mean(density_1, output_shape=[experts_dim])],
      "density_1", summarize=1000)
  loss = (mtf.reduce_mean(density_1_proxy * density_1)
          * float(experts_dim.size * experts_dim.size))

  if hparams.moe_use_second_place_loss:
    # Also add a loss to encourage all experts to be used equally also as the
    # second-place expert.  Experimentally, this seems to be a wash.
    # We want to equalize the fraction of the batch assigned to each expert:
    density_2 = mtf.reduce_mean(mask_2, reduced_dim=group_size_dim)
    # As a proxy for density_2, we renormalize the raw gates after the top one
    # has been removed.
    normalized = gates_without_top_1 / (
        mtf.reduce_sum(gates_without_top_1, reduced_dim=experts_dim) + 1e-9)
    density_2_proxy = mtf.reduce_mean(normalized, reduced_dim=group_size_dim)
    loss_2 = (mtf.reduce_mean(density_2_proxy * density_2)
              * float(experts_dim.size * experts_dim.size))
    loss += loss_2 * 0.5

  # Depending on the policy in the hparams, we may drop out some of the
  # second-place experts.
  policy = (
      hparams.moe_second_policy_train if train else
      hparams.moe_second_policy_eval)
  threshold = (
      hparams.moe_second_threshold_train if train else
      hparams.moe_second_threshold_eval)
  if policy == "all":
    # Use second-place experts for all examples.
    pass
  elif policy == "none":
    # Never use second-place experts for all examples.
    mask_2 = mtf.zeros_like(mask_2)
  elif policy == "threshold":
    # Use second-place experts if gate_2 > threshold.
    mask_2 *= mtf.to_float(mtf.greater(gate_2, threshold))
  elif policy == "random":
    # Use second-place experts with probablity min(1.0, gate_2 / threshold).
    mask_2 *= mtf.to_float(
        mtf.less(mtf.random_uniform(gate_2.mesh, gate_2.shape),
                 gate_2 / max(threshold, 1e-9)))
  else:
    raise ValueError("Unknown policy %s" % policy)
  mask_2 = mtf.Print(
      mask_2, [mtf.reduce_mean(mask_2, output_shape=[experts_dim])],
      "density_2", summarize=1000)

  # COMPUTE ASSIGNMENT TO EXPERTS
  # [batch, group, experts]
  # This is the position within the expert's mini-batch for this sequence
  position_in_expert_1 = mtf.cumsum(
      mask_1, group_size_dim, exclusive=True) * mask_1
  # Remove the elements that don't fit. [batch, group, experts]
  mask_1 *= mtf.to_float(mtf.less(position_in_expert_1, expert_capacity_f))
  # [batch, experts]
  # How many examples in this sequence go to this expert
  mask_1_count = mtf.reduce_sum(mask_1, reduced_dim=group_size_dim)
  # [batch, group] - mostly ones, but zeros where something didn't fit
  mask_1_flat = mtf.reduce_sum(mask_1, reduced_dim=experts_dim)
  # [batch, group]
  position_in_expert_1 = mtf.reduce_sum(
      position_in_expert_1, reduced_dim=experts_dim)
  # Weight assigned to first expert.  [batch, group]
  gate_1 *= mask_1_flat

  # [batch, group, experts]
  position_in_expert_2 = (
      mtf.cumsum(mask_2, group_size_dim, exclusive=True) + mask_1_count)
  position_in_expert_2 *= mask_2
  mask_2 *= mtf.to_float(mtf.less(position_in_expert_2, expert_capacity_f))
  # mask_2_count = mtf.reduce_sum(mask_2, reduced_dim=experts_dim)
  mask_2_flat = mtf.reduce_sum(mask_2, reduced_dim=experts_dim)
  gate_2 *= mask_2_flat
  position_in_expert_2 = mtf.reduce_sum(
      position_in_expert_2, reduced_dim=experts_dim)

  # [batch, group, experts, expert_capacity]
  combine_tensor = (
      gate_1 * mask_1_flat
      * mtf.one_hot(index_1, experts_dim)
      * mtf.one_hot(mtf.to_int32(position_in_expert_1), expert_capacity_dim) +
      gate_2 * mask_2_flat
      * mtf.one_hot(index_2, experts_dim)
      * mtf.one_hot(mtf.to_int32(position_in_expert_2), expert_capacity_dim))

  combine_tensor = mtf.cast(combine_tensor, inputs.dtype)
  loss = mtf.cast(loss, inputs.dtype)

  dispatch_tensor = mtf.cast(
      mtf.cast(combine_tensor, tf.bool), combine_tensor.dtype)

  return dispatch_tensor, combine_tensor, loss