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# Copyright 2022 Google.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Program to run a transformer model over a single article.
"""
# This program is currently a template, which can be expanded to do more
# sophisticated analysis.
from typing import Sequence
from absl import app
from absl import flags
from clu import platform
import jax
from transformer import inference_utils
from transformer import tasks # pylint: disable=unused-import
import tensorflow.compat.v2 as tf
flags.DEFINE_string("workdir", "", "Directory to save model checkpoints.")
flags.DEFINE_string("load_dir", "", "Directory to load pre-trained model.")
flags.DEFINE_integer("num_steps", 110, "Number of steps.")
flags.DEFINE_list(
"gin_search_paths",
["transformer/configs"],
"List of paths where the Gin config files are located.")
flags.DEFINE_multi_string(
"gin_file", ["base_htrans.gin"], "List of Gin config files.")
flags.DEFINE_multi_string(
"gin_param", None, "Newline separated list of Gin parameter bindings.")
FLAGS = flags.FLAGS
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
# Hide any GPUs from TensorFlow. Otherwise TF might reserve memory and make
# it unavailable to JAX.
tf.config.experimental.set_visible_devices([], "GPU")
# Add a note so that we can tell which task is which JAX host.
# (Depending on the platform task 0 is not guaranteed to be host 0)
platform.work_unit().set_task_status(f"process_index: {jax.process_index()}, "
f"process_count: {jax.process_count()}")
platform.work_unit().create_artifact(platform.ArtifactType.DIRECTORY,
FLAGS.workdir, "workdir")
inference_utils.parse_gin_configuration(FLAGS.gin_file, FLAGS.gin_param,
gin_paths=FLAGS.gin_search_paths)
article_data = inference_utils.read_article(True)
(_, vocab) = article_data
(task, task_state, _) = inference_utils.create_model_and_task(
vocab, load_dir=FLAGS.load_dir)
outs = inference_utils.run_model(task, task_state, article_data,
verbose=True)
inference_utils.get_token_losses(outs)
if __name__ == "__main__":
app.run(main)
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