t5x/configs/runs/infer.gin

# Defaults for infer.py.
#
#
# You must also include a binding for MODEL.
#
# Required to be set:
#
# - MIXTURE_OR_TASK_NAME: The SeqIO Task/Mixture to use for inference
# - TASK_FEATURE_LENGTHS: The lengths per key in the SeqIO Task to trim features
#                         to.
# - CHECKPOINT_PATH: The model checkpoint to use for inference
# - INFER_OUTPUT_DIR: The dir to write results to.
#
#
# Commonly overridden options:
#
# - infer.mode
# - infer.checkpoint_period
# - infer.shard_id
# - infer.num_shards
# - DatasetConfig.split
# - DatasetConfig.batch_size
# - DatasetConfig.use_cached
# - RestoreCheckpointConfig.is_tensorflow
# - RestoreCheckpointConfig.mode
# - PjitPartitioner.num_partitions
from __gin__ import dynamic_registration

import __main__ as infer_script
from t5x import partitioning
from t5x import utils

# Must be overridden
MIXTURE_OR_TASK_NAME = %gin.REQUIRED
TASK_FEATURE_LENGTHS = %gin.REQUIRED
CHECKPOINT_PATH = %gin.REQUIRED
INFER_OUTPUT_DIR = %gin.REQUIRED

# DEPRECATED: Import the this module in your gin file.
MIXTURE_OR_TASK_MODULE = None

infer_script.infer:
  mode = 'predict'
  model = %MODEL  # imported from separate gin file
  output_dir = %INFER_OUTPUT_DIR
  dataset_cfg = @utils.DatasetConfig()
  partitioner = @partitioning.PjitPartitioner()
  restore_checkpoint_cfg = @utils.RestoreCheckpointConfig()
  checkpoint_period = 100
  shard_id = 0
  num_shards = 1

partitioning.PjitPartitioner:
  num_partitions = 1
  logical_axis_rules = @partitioning.standard_logical_axis_rules()

utils.DatasetConfig:
  mixture_or_task_name = %MIXTURE_OR_TASK_NAME
  module = %MIXTURE_OR_TASK_MODULE
  task_feature_lengths = %TASK_FEATURE_LENGTHS
  use_cached = False
  split = 'test'
  batch_size = 32
  shuffle = False
  seed = 0
  pack = False

utils.RestoreCheckpointConfig:
  path = %CHECKPOINT_PATH
  mode = 'specific'
  dtype = 'bfloat16'