Separate CLI t2t_decoder

PiperOrigin-RevId: 166920562

Author: Ryan Sepassi

README.md

@@ -29,15 +29,26 @@ You can chat with us and other users on
 with T2T announcements.
 
 Here is a one-command version that installs tensor2tensor, downloads the data,
-trains an English-German translation model, and lets you use it interactively:
+trains an English-German translation model, and evaluates it:
 ```
 pip install tensor2tensor && t2t-trainer \
   --generate_data \
   --data_dir=~/t2t_data \
   --problems=translate_ende_wmt32k \
   --model=transformer \
   --hparams_set=transformer_base_single_gpu \
-  --output_dir=~/t2t_train/base \
+  --output_dir=~/t2t_train/base
+```
+
+You can decode from the model interactively:
+
+```
+t2t-decoder \
+  --data_dir=~/t2t_data \
+  --problems=translate_ende_wmt32k \
+  --model=transformer \
+  --hparams_set=transformer_base_single_gpu \
+  --output_dir=~/t2t_train/base
   --decode_interactive
 ```
 
@@ -106,14 +117,12 @@ echo "Goodbye world" >> $DECODE_FILE
 BEAM_SIZE=4
 ALPHA=0.6
 
-t2t-trainer \
+t2t-decoder \
   --data_dir=$DATA_DIR \
   --problems=$PROBLEM \
   --model=$MODEL \
   --hparams_set=$HPARAMS \
   --output_dir=$TRAIN_DIR \
-  --train_steps=0 \
-  --eval_steps=0 \
   --decode_beam_size=$BEAM_SIZE \
   --decode_alpha=$ALPHA \
   --decode_from_file=$DECODE_FILE

docs/walkthrough.md

@@ -10,15 +10,26 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO
 [![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
 Here is a one-command version that installs tensor2tensor, downloads the data,
-trains an English-German translation model, and lets you use it interactively:
+trains an English-German translation model, and evaluates it:
 ```
 pip install tensor2tensor && t2t-trainer \
   --generate_data \
   --data_dir=~/t2t_data \
   --problems=translate_ende_wmt32k \
   --model=transformer \
   --hparams_set=transformer_base_single_gpu \
-  --output_dir=~/t2t_train/base \
+  --output_dir=~/t2t_train/base
+```
+
+You can decode from the model interactively:
+
+```
+t2t-decoder \
+  --data_dir=~/t2t_data \
+  --problems=translate_ende_wmt32k \
+  --model=transformer \
+  --hparams_set=transformer_base_single_gpu \
+  --output_dir=~/t2t_train/base
   --decode_interactive
 ```
 

setup.py

@@ -22,6 +22,7 @@
     scripts=[
         'tensor2tensor/bin/t2t-trainer',
         'tensor2tensor/bin/t2t-datagen',
+        'tensor2tensor/bin/t2t-decoder',
         'tensor2tensor/bin/t2t-make-tf-configs',
     ],
     install_requires=[

tensor2tensor/bin/t2t-decoder

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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"""Decode from trained T2T models.
+
+This binary performs inference using the Estimator API.
+
+Example usage to decode from dataset:
+
+  t2t-decoder \
+      --data_dir ~/data \
+      --problems=algorithmic_identity_binary40 \
+      --model=transformer
+      --hparams_set=transformer_base
+
+Set FLAGS.decode_interactive or FLAGS.decode_from_file for alternative decode
+sources.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+# Dependency imports
+
+from tensor2tensor.utils import decoding
+from tensor2tensor.utils import trainer_utils
+from tensor2tensor.utils import usr_dir
+
+import tensorflow as tf
+
+flags = tf.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string("t2t_usr_dir", "",
+                    "Path to a Python module that will be imported. The "
+                    "__init__.py file should include the necessary imports. "
+                    "The imported files should contain registrations, "
+                    "e.g. @registry.register_model calls, that will then be "
+                    "available to the t2t-decoder.")
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
+  trainer_utils.log_registry()
+  trainer_utils.validate_flags()
+  data_dir = os.path.expanduser(FLAGS.data_dir)
+  output_dir = os.path.expanduser(FLAGS.output_dir)
+
+  hparams = trainer_utils.create_hparams(
+      FLAGS.hparams_set, FLAGS.problems, data_dir, passed_hparams=FLAGS.hparams)
+  estimator, _ = trainer_utils.create_experiment_components(
+      hparams=hparams,
+      output_dir=output_dir,
+      data_dir=data_dir,
+      model_name=FLAGS.model)
+
+  if FLAGS.decode_interactive:
+    decoding.decode_interactively(estimator)
+  elif FLAGS.decode_from_file:
+    decoding.decode_from_file(estimator, FLAGS.decode_from_file)
+  else:
+    decoding.decode_from_dataset(
+        estimator,
+        FLAGS.problems.split("-"),
+        return_beams=FLAGS.decode_return_beams,
+        beam_size=FLAGS.decode_beam_size,
+        max_predictions=FLAGS.decode_num_samples,
+        decode_to_file=FLAGS.decode_to_file,
+        save_images=FLAGS.decode_save_images,
+        identity_output=FLAGS.identity_output)
+
+
+if __name__ == "__main__":
+  tf.app.run()

tensor2tensor/utils/trainer_utils.py

@@ -27,7 +27,6 @@
 from tensor2tensor.data_generators import problem_hparams
 from tensor2tensor.models import models  # pylint: disable=unused-import
 from tensor2tensor.utils import data_reader
-from tensor2tensor.utils import decoding
 from tensor2tensor.utils import devices
 from tensor2tensor.utils import input_fn_builder
 from tensor2tensor.utils import metrics
@@ -101,16 +100,13 @@
 flags.DEFINE_string("ps_job", "/job:ps", "name of ps job")
 flags.DEFINE_integer("ps_replicas", 0, "How many ps replicas.")
 
-# Decode flags
-# Set one of {decode_from_dataset, decode_interactive, decode_from_file} to
-# decode.
-flags.DEFINE_bool("decode_from_dataset", False, "Decode from dataset on disk.")
-flags.DEFINE_bool("decode_use_last_position_only", False,
-                  "In inference, use last position only for speedup.")
+# Decoding flags
+flags.DEFINE_string("decode_from_file", None, "Path to decode file")
 flags.DEFINE_bool("decode_interactive", False,
                   "Interactive local inference mode.")
+flags.DEFINE_bool("decode_use_last_position_only", False,
+                  "In inference, use last position only for speedup.")
 flags.DEFINE_bool("decode_save_images", False, "Save inference input images.")
-flags.DEFINE_string("decode_from_file", None, "Path to decode file")
 flags.DEFINE_string("decode_to_file", None, "Path to inference output file")
 flags.DEFINE_integer("decode_shards", 1, "How many shards to decode.")
 flags.DEFINE_integer("decode_problem_id", 0, "Which problem to decode.")
@@ -128,7 +124,7 @@
                      "Maximum number of ids in input. Or <= 0 for no max.")
 flags.DEFINE_bool("identity_output", False, "To print the output as identity")
 flags.DEFINE_integer("decode_num_samples", -1,
-                     "Number of samples to decode. Currently used in"
+                     "Number of samples to decode. Currently used in "
                      "decode_from_dataset. Use -1 for all.")
 
 
@@ -303,7 +299,6 @@ def run(data_dir, model, output_dir, train_steps, eval_steps, schedule):
     if exp.train_steps > 0 or exp.eval_steps > 0:
       tf.logging.info("Performing local training and evaluation.")
       exp.train_and_evaluate()
-    decode(exp.estimator)
   else:
     # Perform distributed training/evaluation.
     learn_runner.run(
@@ -350,20 +345,3 @@ def session_config():
 
 def get_data_filepatterns(data_dir, mode):
   return data_reader.get_data_filepatterns(FLAGS.problems, data_dir, mode)
-
-
-def decode(estimator):
-  if FLAGS.decode_interactive:
-    decoding.decode_interactively(estimator)
-  elif FLAGS.decode_from_file is not None and FLAGS.decode_from_file is not "":
-    decoding.decode_from_file(estimator, FLAGS.decode_from_file)
-  elif FLAGS.decode_from_dataset:
-    decoding.decode_from_dataset(
-        estimator,
-        FLAGS.problems.split("-"),
-        return_beams=FLAGS.decode_return_beams,
-        beam_size=FLAGS.decode_beam_size,
-        max_predictions=FLAGS.decode_num_samples,
-        decode_to_file=FLAGS.decode_to_file,
-        save_images=FLAGS.decode_save_images,
-        identity_output=FLAGS.identity_output)