Merge branch 'master' of https://github.com/tensorflow/models into AXg

community/README.md

@@ -42,6 +42,7 @@ This repository provides a curated list of the GitHub repositories with machine
 | [BERT](https://github.com/IntelAI/models/tree/master/benchmarks/language_modeling/tensorflow/bert_large) | [BERT: Pre-training of Deep Bidirectional Transformers<br/>for Language Understanding](https://arxiv.org/pdf/1810.04805) | • FP32 Inference<br/>• FP32 Training | [Intel](https://github.com/IntelAI) |
 | [GNMT](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/mlperf_gnmt) | [Google’s Neural Machine Translation System:<br/>Bridging the Gap between Human and Machine Translation](https://arxiv.org/pdf/1609.08144) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
 | [Transformer-LT](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/transformer_mlperf) | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) | • FP32 Training | [Intel](https://github.com/IntelAI) |
+| [ELECTRA](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/ELECTRA) | [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/forum?id=r1xMH1BtvB) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• Multi-node training on a Pyxis/Enroot Slurm cluster | [NVIDIA](https://github.com/NVIDIA) |
 
 ## Recommendation Systems
 

official/core/__init__.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+

official/core/base_task.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Defines the base task abstraction."""
 import abc
 from typing import Optional

official/core/base_trainer.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Standard Trainer implementation.
 
 The base trainer implements the Orbit `StandardTrainable` and

official/core/base_trainer_test.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Tests for tensorflow_models.core.trainers.trainer."""
 # pylint: disable=g-direct-tensorflow-import
 import os

official/core/config_definitions.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Common configuration settings."""
 
 from typing import Optional, Sequence, Union

official/core/exp_factory.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Experiment factory methods."""
 
 from official.core import config_definitions as cfg

official/core/input_reader.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """A common dataset reader."""
 import random
 from typing import Any, Callable, Optional
@@ -31,6 +30,10 @@ def _get_random_integer():
 class InputReader:
   """Input reader that returns a tf.data.Dataset instance."""
 
+  # A static random number which is the same across different InputReader
+  # instances.
+  static_randnum = _get_random_integer()
+
   def __init__(self,
                params: cfg.DataConfig,
                dataset_fn=tf.data.TFRecordDataset,
@@ -137,7 +140,13 @@ class InputReader:
     self._enable_tf_data_service = (
         params.enable_tf_data_service and params.tf_data_service_address)
     self._tf_data_service_address = params.tf_data_service_address
-    self._tf_data_service_job_name = params.tf_data_service_job_name
+    if self._enable_tf_data_service:
+      # Add a random seed as the tf.data service job name suffix, so tf.data
+      # service doesn't reuse the previous state if TPU worker gets preempted.
+      self._tf_data_service_job_name = (
+          params.tf_data_service_job_name + str(self.static_randnum))
+      self._enable_round_robin_tf_data_service = params.get(
+          'enable_round_robin_tf_data_service', False)
 
   def _shard_files_then_read(
       self, input_context: Optional[tf.distribute.InputContext] = None):
@@ -165,7 +174,8 @@ class InputReader:
         map_func=self._dataset_fn,
         cycle_length=self._cycle_length,
         block_length=self._block_length,
-        num_parallel_calls=tf.data.experimental.AUTOTUNE,
+        num_parallel_calls=(self._cycle_length if self._cycle_length else
+                            tf.data.experimental.AUTOTUNE),
         deterministic=self._deterministic)
     return dataset
 
@@ -277,12 +287,35 @@ class InputReader:
 
     dataset = maybe_map_fn(dataset, self._postprocess_fn)
 
-    if self._enable_tf_data_service:
-      dataset = dataset.apply(
-          tf.data.experimental.service.distribute(
-              processing_mode='parallel_epochs',
-              service=self._tf_data_service_address,
-              job_name=self._tf_data_service_job_name))
+    if self._enable_tf_data_service and input_context:
+      if self._enable_round_robin_tf_data_service:
+        replicas_per_input_pipeline = input_context.num_replicas_in_sync // (
+            input_context.num_input_pipelines)
+        base_consumer_index = input_context.input_pipeline_id * (
+            replicas_per_input_pipeline)
+        num_consumers = input_context.num_input_pipelines * (
+            replicas_per_input_pipeline)
+        range_dataset = tf.data.Dataset.range(replicas_per_input_pipeline)
+        dataset = range_dataset.map(lambda i: dataset.apply(  # pylint: disable=g-long-lambda
+            tf.data.experimental.service.distribute(
+                processing_mode='parallel_epochs',
+                service=self._tf_data_service_address,
+                job_name=self._tf_data_service_job_name,
+                consumer_index=base_consumer_index + i,
+                num_consumers=num_consumers)))
+        # Use parallel interleave to read multiple batches from a tf.data
+        # service worker in parallel.
+        dataset = dataset.interleave(
+            lambda x: x,
+            cycle_length=replicas_per_input_pipeline,
+            num_parallel_calls=replicas_per_input_pipeline,
+            deterministic=True)
+      else:
+        dataset = dataset.apply(
+            tf.data.experimental.service.distribute(
+                processing_mode='parallel_epochs',
+                service=self._tf_data_service_address,
+                job_name=self._tf_data_service_job_name))
 
     if self._deterministic is not None:
       options = tf.data.Options()

official/core/registry.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Registry utility."""
 
 

official/core/registry_test.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Tests for registry."""
 
 from __future__ import absolute_import

official/core/task_factory.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """A global factory to register and access all registered tasks."""
 
 from official.core import registry

official/core/train_lib.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """TFM common training driver library."""
 # pytype: disable=attribute-error
 import os

official/core/train_lib_test.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Tests for train_ctl_lib."""
 import json
 import os

official/core/train_utils.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +11,7 @@
 # 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.
-# ==============================================================================
+
 """Training utils."""
 import copy
 import json
@@ -109,7 +108,7 @@ class BestCheckpointExporter:
     # Saving the best checkpoint might be interrupted if the job got killed.
     for file_to_remove in tf.io.gfile.glob(self.best_ckpt_path + '*'):
       tf.io.gfile.remove(file_to_remove)
-    checkpoint.save(self.best_ckpt_path)
+    checkpoint.write(self.best_ckpt_path)
 
   @property
   def best_ckpt_logs(self):
@@ -218,6 +217,16 @@ def serialize_config(params: config_definitions.ExperimentConfig,
   hyperparams.save_params_dict_to_yaml(params, params_save_path)
 
 
+def save_gin_config(filename_surfix: str, model_dir: str):
+  """Serializes and saves the experiment config."""
+  gin_save_path = os.path.join(
+      model_dir, 'operative_config.{}.gin'.format(filename_surfix))
+  logging.info('Saving gin configurations to %s', gin_save_path)
+  tf.io.gfile.makedirs(model_dir)
+  with tf.io.gfile.GFile(gin_save_path, 'w') as f:
+    f.write(gin.operative_config_str())
+
+
 def read_global_step_from_checkpoint(ckpt_file_path):
   """Read global step from checkpoint, or get global step from its filename."""
   global_step = tf.Variable(-1, dtype=tf.int64)

official/modeling/hyperparams/base_config.py

@@ -30,7 +30,7 @@ from official.modeling.hyperparams import params_dict
 class Config(params_dict.ParamsDict):
   """The base configuration class that supports YAML/JSON based overrides.
 
-  * It recursively enforces a whitelist of basic types and container types, so
+  * It recursively enforces a allowlist of basic types and container types, so
     it avoids surprises with copy and reuse caused by unanticipated types.
   * It converts dict to Config even within sequences,
     e.g. for config = Config({'key': [([{'a': 42}],)]),

official/modeling/hyperparams/params_dict.py

@@ -312,7 +312,7 @@ class ParamsDict(object):
 def read_yaml_to_params_dict(file_path):
   """Reads a YAML file to a ParamsDict."""
   with tf.io.gfile.GFile(file_path, 'r') as f:
-    params_dict = yaml.load(f, Loader=yaml.FullLoader)
+    params_dict = yaml.load(f, Loader=yaml.SafeLoader)
     return ParamsDict(params_dict)
 
 

official/modeling/multitask/configs.py

@@ -18,27 +18,26 @@ from typing import Optional, Tuple
 import dataclasses
 
 from official.core import config_definitions as cfg
-from official.modeling.hyperparams import base_config
+from official.modeling import hyperparams
 
 
 @dataclasses.dataclass
-class TaskRoutine(base_config.Config):
+class TaskRoutine(hyperparams.Config):
   task_name: str = ""
   task_config: cfg.TaskConfig = None
-  mixing_steps: int = 1
   eval_steps: Optional[int] = None
-  task_weight: Optional[float] = None
+  task_weight: Optional[float] = 1.0
 
 
 @dataclasses.dataclass
-class MultiTaskConfig(base_config.Config):
+class MultiTaskConfig(hyperparams.Config):
   init_checkpoint: str = ""
-  model: base_config.Config = None
+  model: hyperparams.Config = None
   task_routines: Tuple[TaskRoutine, ...] = ()
 
 
 @dataclasses.dataclass
-class MultiEvalExperimentConfig(base_config.Config):
+class MultiEvalExperimentConfig(hyperparams.Config):
   """An experiment config for single-task training and multi-task evaluation.
 
   Attributes:

official/modeling/multitask/multitask.py

@@ -32,16 +32,16 @@ class MultiTask(tf.Module, metaclass=abc.ABCMeta):
 
   def __init__(self,
                tasks: Union[Dict[Text, base_task.Task], List[base_task.Task]],
-               task_mixing_steps: Optional[Dict[str, int]] = None,
-               task_weights: Optional[Dict[str, float]] = None,
+               task_weights: Optional[Dict[str, Union[float, int]]] = None,
                task_eval_steps: Optional[Dict[str, int]] = None,
                name: Optional[str] = None):
     """MultiTask initialization.
 
     Args:
       tasks: a list or a flat dict of Task.
-      task_mixing_steps: a dict of (task, mixing steps).
-      task_weights: a dict of (task, loss weight).
+      task_weights: a dict of (task, task weight), task weight can be applied
+        directly during loss summation in a joint backward step, or it can be
+        used to sample task among interleaved backward step.
       task_eval_steps: a dict of (task, eval steps).
       name: the instance name of a MultiTask object.
     """
@@ -62,31 +62,24 @@ class MultiTask(tf.Module, metaclass=abc.ABCMeta):
     self._task_eval_steps = dict([
         (name, self._task_eval_steps.get(name, None)) for name in self.tasks
     ])
-    self._task_mixing_steps = task_mixing_steps or {}
-    self._task_mixing_steps = dict([
-        (name, self._task_mixing_steps.get(name, 1)) for name in self.tasks
-    ])
     self._task_weights = task_weights or {}
     self._task_weights = dict([
-        (name, self._task_weights.get(name, None)) for name in self.tasks
+        (name, self._task_weights.get(name, 1.0)) for name in self.tasks
     ])
 
   @classmethod
   def from_config(cls, config: configs.MultiTaskConfig, logging_dir=None):
     tasks = {}
     task_eval_steps = {}
-    task_mixing_steps = {}
     task_weights = {}
     for task_routine in config.task_routines:
       task_name = task_routine.task_name
       tasks[task_name] = task_factory.get_task(
           task_routine.task_config, logging_dir=logging_dir)
       task_eval_steps[task_name] = task_routine.eval_steps
-      task_mixing_steps[task_name] = task_routine.mixing_steps
       task_weights[task_name] = task_routine.task_weight
     return cls(
         tasks,
-        task_mixing_steps=task_mixing_steps,
         task_eval_steps=task_eval_steps,
         task_weights=task_weights)
 
@@ -97,12 +90,13 @@ class MultiTask(tf.Module, metaclass=abc.ABCMeta):
   def task_eval_steps(self, task_name):
     return self._task_eval_steps[task_name]
 
-  def task_mixing_steps(self, task_name):
-    return self._task_mixing_steps[task_name]
-
   def task_weight(self, task_name):
     return self._task_weights[task_name]
 
+  @property
+  def task_weights(self):
+    return self._task_weights
+
   @classmethod
   def create_optimizer(cls,
                        optimizer_config: OptimizationConfig,

official/modeling/multitask/train_lib.py

@@ -25,12 +25,16 @@ from official.modeling.multitask import evaluator as evaluator_lib
 from official.modeling.multitask import multitask
 
 
-def run_experiment_wtih_multitask_eval(
+def run_experiment_with_multitask_eval(
     *,
-    distribution_strategy: tf.distribute.Strategy, train_task: base_task.Task,
-    eval_tasks: multitask.MultiTask, mode: str,
+    distribution_strategy: tf.distribute.Strategy,
+    train_task: base_task.Task,
+    eval_tasks: multitask.MultiTask,
+    mode: str,
     params: configs.MultiEvalExperimentConfig,
-    model_dir: str) -> tf.keras.Model:
+    model_dir: str,
+    run_post_eval: bool = False,
+    save_summary: bool = True) -> tf.keras.Model:
   """Runs train/eval configured by the experiment params.
 
   Args:
@@ -41,6 +45,9 @@ def run_experiment_wtih_multitask_eval(
       or 'continuous_eval'.
     params: MultiEvalExperimentConfig instance.
     model_dir: A 'str', a path to store model checkpoints and summaries.
+    run_post_eval: Whether to run post eval once after training, metrics logs
+      are returned.
+    save_summary: Whether to save train and validation summary.
 
   Returns:
       model: `tf.keras.Model` instance.
@@ -92,9 +99,11 @@ def run_experiment_wtih_multitask_eval(
       global_step=global_step,
       steps_per_loop=params.trainer.steps_per_loop,
       checkpoint_manager=checkpoint_manager,
-      summary_dir=os.path.join(model_dir, 'train'),
-      eval_summary_dir=os.path.join(model_dir, 'validation'),
-      summary_interval=params.trainer.summary_interval)
+      summary_dir=os.path.join(model_dir, 'train') if save_summary else None,
+      eval_summary_dir=os.path.join(model_dir, 'validation') if
+      (save_summary) else None,
+      summary_interval=params.trainer.summary_interval if
+      (save_summary) else None)
 
   logging.info('Starts to execute mode: %s', mode)
   with distribution_strategy.scope():
@@ -121,4 +130,8 @@ def run_experiment_wtih_multitask_eval(
     else:
       raise NotImplementedError('The mode is not implemented: %s' % mode)
 
-    return model
+    if run_post_eval:
+      return model, evaluator.evaluate(
+          tf.convert_to_tensor(params.trainer.validation_steps))
+    else:
+      return model, {}

official/modeling/performance.py

@@ -40,7 +40,7 @@ def configure_optimizer(optimizer,
           optimizer, dynamic=False, initial_scale=loss_scale)
   if use_graph_rewrite:
     # Note: the model dtype must be 'float32', which will ensure
-    # tf.ckeras.mixed_precision and
+    # tf.keras.mixed_precision and
     # tf.train.experimental.enable_mixed_precision_graph_rewrite do not double
     # up.
     optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(