base_htrans.gin

# Base configuration for the Hierarchical Transformer.

include "trainer_configuration.gin"

# Imports
from transformer import attention
from transformer import decoder_stack
from transformer import models
from transformer import nn_components
from transformer import transformer_base
from transformer import transformer_layer


NUM_LAYERS = 12
NUM_HEADS = 8
HEAD_DIM = 128
EMBED_DIM = 512        # Size of embedding vector for each token
MLP_DIM = 2048         # Number of hidden units in transformer FFN
NUM_EMBEDDINGS = 256   # Number of tokens in vocabulary.
DROPOUT_RATE = 0.05
ATTN_DROPOUT_RATE = 0.05

# For training on TPU.
DTYPE="float32"

# Configure the input task.
decoder_stack.TransformerTaskConfig:
  dataset_name = "synthetic"
  train_split = "train"
  test_split = "test"
  sequence_length = 512
  batch_size = 8
  vocab_size = %NUM_EMBEDDINGS

transformer_layer.TransformerLayer:
  num_heads = %NUM_HEADS
  head_size = %HEAD_DIM
  window_length = 512
  use_long_xl_architecture = True
  max_unrolled_windows = -1           # Always unroll.
  relative_position_type = "t5"       # Can be "fourier", "t5", or None.
  use_causal_mask = True
  attn_dropout_rate = %ATTN_DROPOUT_RATE   # Attention matrix dropout.

  memory_num_neighbors = 0
  compute_importance = False
  dtype = %DTYPE

transformer_base.TransformerBase:
  attn_mlp_factory = @transformer_attn/nn_components.MLP
  ffn_factory = @transformer_ffn/nn_components.MLP
  normalize_keys = True               # More stable with Transformer XL.
  dropout_rate = %DROPOUT_RATE
  pre_attn_dropout = True
  post_attn_dropout = False
  pre_ffn_dropout = False
  post_ffn_dropout = True

transformer_attn/nn_components.MLP:
  num_layers = 1                      # Just a single dense matmul.
  num_hidden_units = 0
  hidden_activation = None
  use_bias = False

transformer_ffn/nn_components.MLP:
  num_layers = 2
  num_hidden_units = %MLP_DIM
  hidden_activation = "relu"
  use_bias = False

decoder_stack.DecoderStack:
  # task_config will be passed in from DecoderOnlyLanguageModel.
  num_layers = %NUM_LAYERS
  embedding_size = %EMBED_DIM
  embedding_stddev = 1.0
  layer_factory = @transformer_layer.TransformerLayer
  dstack_window_length = 0
  use_absolute_positions = False
  use_final_layernorm = True          # Final layernorm before token lookup.
  final_dropout_rate = %DROPOUT_RATE  # Dropout before token lookup.
  final_mlp_factory = None            # Final MLP to predict target tokens.
  recurrent_layer_indices = ()
  memory_factory = None     # e.g. @memory_factory.memory_on_tpu_factory
  memory_layer_indices = ()
  dtype = %DTYPE

models.DecoderOnlyLanguageModel:
  task_config = @decoder_stack.TransformerTaskConfig()
  decoder_factory = @decoder_stack.DecoderStack

nn_components.LayerNorm:
  use_scale = True
  use_bias = False
  use_mean = False   # Calculate and adjust for the mean as well as the scale.
  dtype = %DTYPE