Set of hyperparameters.

def mtf_image_transformer_base():
  """Set of hyperparameters."""
  hparams = common_hparams.basic_params1()
  hparams.no_data_parallelism = True
  hparams.use_fixed_batch_size = True
  hparams.batch_size = 1
  hparams.max_length = 3072
  hparams.hidden_size = 256
  hparams.label_smoothing = 0.0
  # 8-way model-parallelism
  hparams.add_hparam("mesh_shape", "batch:8")
  hparams.add_hparam("layout", "batch:batch")
  hparams.add_hparam("mtf_mode", True)
  hparams.add_hparam("num_heads", 8)
  hparams.add_hparam("filter_size", 1024)
  hparams.add_hparam("num_encoder_layers", 0)
  hparams.add_hparam("num_decoder_layers", 6)
  hparams.add_hparam("attention_key_size", 256)
  hparams.add_hparam("attention_value_size", 256)
  # Share weights between input and target embeddings
  hparams.shared_embedding = True

  # mixture of experts hparams
  hparams.add_hparam("ffn_layer", "dense_relu_dense")
  hparams.add_hparam("moe_overhead_train", 1.0)
  hparams.add_hparam("moe_overhead_eval", 2.0)
  hparams.moe_num_experts = 16
  hparams.moe_loss_coef = 1e-3

  hparams.shared_embedding_and_softmax_weights = True
  hparams.optimizer = "Adafactor"
  hparams.learning_rate_schedule = "rsqrt_decay"
  hparams.learning_rate_warmup_steps = 10000
  hparams.add_hparam("d_kv", 64)
  hparams.add_hparam("d_ff", 2048)

  # Image related hparams
  hparams.add_hparam("img_len", 32)
  hparams.add_hparam("num_channels", 3)
  hparams.add_hparam("unconditional", True)

  # Local Attention related params
  hparams.add_hparam("block_length", 128)
  hparams.add_hparam("block_height", 16)
  hparams.add_hparam("block_width", 16)
  hparams.add_hparam("attention_type", "local1d")
  return hparams