Move core configs to core/ folder. Leave the configs for legacy models.

PiperOrigin-RevId: 336795303

official/core/base_trainer.py

@@ -25,9 +25,9 @@ import orbit
 import tensorflow as tf
 
 from official.core import base_task
+from official.core import config_definitions
 from official.modeling import optimization
 from official.modeling import performance
-from official.modeling.hyperparams import config_definitions
 
 ExperimentConfig = config_definitions.ExperimentConfig
 TrainerConfig = config_definitions.TrainerConfig

official/core/base_trainer_test.py

@@ -23,8 +23,8 @@ import tensorflow as tf
 from tensorflow.python.distribute import combinations
 from tensorflow.python.distribute import strategy_combinations
 from official.core import base_trainer as trainer_lib
+from official.core import config_definitions as cfg
 from official.core import train_lib
-from official.modeling.hyperparams import config_definitions as cfg
 from official.utils.testing import mock_task
 
 

official/core/config_definitions.py

+# Lint as: python3
+# Copyright 2020 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.
+# ==============================================================================
+"""Common configuration settings."""
+
+from typing import Optional, Sequence, Union
+
+import dataclasses
+
+from official.modeling.hyperparams import base_config
+from official.modeling.optimization.configs import optimization_config
+
+OptimizationConfig = optimization_config.OptimizationConfig
+
+
+@dataclasses.dataclass
+class DataConfig(base_config.Config):
+  """The base configuration for building datasets.
+
+  Attributes:
+    input_path: The path to the input. It can be either (1) a str indicating
+      a file path/pattern, or (2) a str indicating multiple file paths/patterns
+      separated by comma (e.g "a, b, c" or no spaces "a,b,c"), or
+      (3) a list of str, each of which is a file path/pattern or multiple file
+      paths/patterns separated by comma.
+      It should not be specified when the following `tfds_name` is specified.
+    tfds_name: The name of the tensorflow dataset (TFDS). It should not be
+      specified when the above `input_path` is specified.
+    tfds_split: A str indicating which split of the data to load from TFDS. It
+      is required when above `tfds_name` is specified.
+    global_batch_size: The global batch size across all replicas.
+    is_training: Whether this data is used for training or not.
+    drop_remainder: Whether the last batch should be dropped in the case it has
+      fewer than `global_batch_size` elements.
+    shuffle_buffer_size: The buffer size used for shuffling training data.
+    cache: Whether to cache dataset examples. Can be used to avoid re-reading
+      from disk on the second epoch. Requires significant memory overhead.
+    cycle_length: The number of files that will be processed concurrently when
+      interleaving files.
+    block_length: The number of consecutive elements to produce from each input
+      element before cycling to another input element when interleaving files.
+    deterministic: A boolean controlling whether determinism should be enforced.
+    sharding: Whether sharding is used in the input pipeline.
+    enable_tf_data_service: A boolean indicating whether to enable tf.data
+      service for the input pipeline.
+    tf_data_service_address: The URI of a tf.data service to offload
+      preprocessing onto during training. The URI should be in the format
+      "protocol://address", e.g. "grpc://tf-data-service:5050". It can be
+      overridden by `FLAGS.tf_data_service` flag in the binary.
+    tf_data_service_job_name: The name of the tf.data service job. This
+      argument makes it possible for multiple datasets to share the same job.
+      The default behavior is that the dataset creates anonymous, exclusively
+      owned jobs.
+    tfds_data_dir: A str specifying the directory to read/write TFDS data.
+    tfds_download: A bool to indicate whether to download data using TFDS.
+    tfds_as_supervised: A bool. When loading dataset from TFDS, if True, the
+      returned tf.data.Dataset will have a 2-tuple structure (input, label)
+      according to builder.info.supervised_keys; if False, the default, the
+      returned tf.data.Dataset will have a dictionary with all the features.
+    tfds_skip_decoding_feature: A str to indicate which features are skipped for
+      decoding when loading dataset from TFDS. Use comma to separate multiple
+      features. The main use case is to skip the image/video decoding for better
+      performance.
+  """
+  input_path: Union[Sequence[str], str] = ""
+  tfds_name: str = ""
+  tfds_split: str = ""
+  global_batch_size: int = 0
+  is_training: bool = None
+  drop_remainder: bool = True
+  shuffle_buffer_size: int = 100
+  cache: bool = False
+  cycle_length: Optional[int] = None
+  block_length: int = 1
+  deterministic: Optional[bool] = None
+  sharding: bool = True
+  enable_tf_data_service: bool = False
+  tf_data_service_address: Optional[str] = None
+  tf_data_service_job_name: Optional[str] = None
+  tfds_data_dir: str = ""
+  tfds_download: bool = False
+  tfds_as_supervised: bool = False
+  tfds_skip_decoding_feature: str = ""
+
+
+@dataclasses.dataclass
+class RuntimeConfig(base_config.Config):
+  """High-level configurations for Runtime.
+
+  These include parameters that are not directly related to the experiment,
+  e.g. directories, accelerator type, etc.
+
+  Attributes:
+    distribution_strategy: e.g. 'mirrored', 'tpu', etc.
+    enable_xla: Whether or not to enable XLA.
+    per_gpu_thread_count: thread count per GPU.
+    gpu_thread_mode: Whether and how the GPU device uses its own threadpool.
+    dataset_num_private_threads: Number of threads for a private threadpool
+      created for all datasets computation.
+    tpu: The address of the TPU to use, if any.
+    num_gpus: The number of GPUs to use, if any.
+    worker_hosts: comma-separated list of worker ip:port pairs for running
+      multi-worker models with DistributionStrategy.
+    task_index: If multi-worker training, the task index of this worker.
+    all_reduce_alg: Defines the algorithm for performing all-reduce.
+    num_packs: Sets `num_packs` in the cross device ops used in
+      MirroredStrategy.  For details, see tf.distribute.NcclAllReduce.
+    mixed_precision_dtype: dtype of mixed precision policy. It can be 'float32',
+      'float16', or 'bfloat16'.
+    loss_scale: The type of loss scale, or 'float' value. This is used when
+      setting the mixed precision policy.
+    run_eagerly: Whether or not to run the experiment eagerly.
+    batchnorm_spatial_persistent: Whether or not to enable the spatial
+      persistent mode for CuDNN batch norm kernel for improved GPU performance.
+  """
+  distribution_strategy: str = "mirrored"
+  enable_xla: bool = False
+  gpu_thread_mode: Optional[str] = None
+  dataset_num_private_threads: Optional[int] = None
+  per_gpu_thread_count: int = 0
+  tpu: Optional[str] = None
+  num_gpus: int = 0
+  worker_hosts: Optional[str] = None
+  task_index: int = -1
+  all_reduce_alg: Optional[str] = None
+  num_packs: int = 1
+  mixed_precision_dtype: Optional[str] = None
+  loss_scale: Optional[Union[str, float]] = None
+  run_eagerly: bool = False
+  batchnorm_spatial_persistent: bool = False
+
+  # Global model parallelism configurations.
+  num_cores_per_replica: int = 1
+  default_shard_dim: int = -1
+
+  def model_parallelism(self):
+    return dict(
+        num_cores_per_replica=self.num_cores_per_replica,
+        default_shard_dim=self.default_shard_dim)
+
+
+@dataclasses.dataclass
+class TrainerConfig(base_config.Config):
+  """Configuration for trainer.
+
+  Attributes:
+    optimizer_config: optimizer config, it includes optimizer, learning rate,
+      and warmup schedule configs.
+    train_tf_while_loop: whether or not to use tf while loop.
+    train_tf_function: whether or not to use tf_function for training loop.
+    eval_tf_function: whether or not to use tf_function for eval.
+    allow_tpu_summary: Whether to allow summary happen inside the XLA program
+      runs on TPU through automatic outside compilation.
+    steps_per_loop: number of steps per loop.
+    summary_interval: number of steps between each summary.
+    checkpoint_interval: number of steps between checkpoints.
+    max_to_keep: max checkpoints to keep.
+    continuous_eval_timeout: maximum number of seconds to wait between
+      checkpoints, if set to None, continuous eval will wait indefinitely. This
+      is only used continuous_train_and_eval and continuous_eval modes. Default
+      value is 1 hrs.
+    train_steps: number of train steps.
+    validation_steps: number of eval steps. If `None`, the entire eval dataset
+      is used.
+    validation_interval: number of training steps to run between evaluations.
+    best_checkpoint_export_subdir: if set, the trainer will keep track of the
+      best evaluation metric, and export the corresponding best checkpoint under
+      `model_dir/best_checkpoint_export_subdir`. Note that this only works if
+      mode contains eval (such as `train_and_eval`, `continuous_eval`, and
+      `continuous_train_and_eval`).
+    best_checkpoint_eval_metric: for exporting the best checkpoint, which
+      evaluation metric the trainer should monitor. This can be any evaluation
+      metric appears on tensorboard.
+    best_checkpoint_metric_comp: for exporting the best checkpoint, how the
+      trainer should compare the evaluation metrics. This can be either `higher`
+      (higher the better) or `lower` (lower the better).
+  """
+  optimizer_config: OptimizationConfig = OptimizationConfig()
+  # Orbit settings.
+  train_tf_while_loop: bool = True
+  train_tf_function: bool = True
+  eval_tf_function: bool = True
+  allow_tpu_summary: bool = False
+  # Trainer intervals.
+  steps_per_loop: int = 1000
+  summary_interval: int = 1000
+  checkpoint_interval: int = 1000
+  # Checkpoint manager.
+  max_to_keep: int = 5
+  continuous_eval_timeout: int = 60 * 60
+  # Train/Eval routines.
+  train_steps: int = 0
+  # Sets validation steps to be -1 to evaluate the entire dataset.
+  validation_steps: int = -1
+  validation_interval: int = 1000
+  # Best checkpoint export.
+  best_checkpoint_export_subdir: str = ""
+  best_checkpoint_eval_metric: str = ""
+  best_checkpoint_metric_comp: str = "higher"
+
+
+@dataclasses.dataclass
+class TaskConfig(base_config.Config):
+  init_checkpoint: str = ""
+  model: base_config.Config = None
+  train_data: DataConfig = DataConfig()
+  validation_data: DataConfig = DataConfig()
+
+
+@dataclasses.dataclass
+class ExperimentConfig(base_config.Config):
+  """Top-level configuration."""
+  task: TaskConfig = TaskConfig()
+  trainer: TrainerConfig = TrainerConfig()
+  runtime: RuntimeConfig = RuntimeConfig()

official/core/exp_factory.py

@@ -15,8 +15,8 @@
 # ==============================================================================
 """Experiment factory methods."""
 
+from official.core import config_definitions as cfg
 from official.core import registry
-from official.modeling.hyperparams import config_definitions as cfg
 
 
 _REGISTERED_CONFIGS = {}

official/core/input_reader.py

@@ -21,7 +21,7 @@ from typing import Any, Callable, Optional
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-from official.modeling.hyperparams import config_definitions as cfg
+from official.core import config_definitions as cfg
 
 
 def _get_random_integer():

official/core/train_lib.py

@@ -27,7 +27,7 @@ import tensorflow as tf
 
 from official.core import train_utils
 from official.core import base_task
-from official.modeling.hyperparams import config_definitions
+from official.core import config_definitions
 
 
 class BestCheckpointExporter:

official/core/train_utils.py

@@ -27,9 +27,9 @@ import tensorflow as tf
 
 from official.core import base_task
 from official.core import base_trainer
+from official.core import config_definitions
 from official.core import exp_factory
 from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
 
 
 def create_trainer(params: config_definitions.ExperimentConfig,

official/modeling/hyperparams/config_definitions.py

@@ -14,143 +14,16 @@
 # limitations under the License.
 # ==============================================================================
 """Common configuration settings."""
-
-from typing import Optional, Sequence, Union
-
+# pylint:disable=wildcard-import
 import dataclasses
 
+from official.core.config_definitions import *
 from official.modeling.hyperparams import base_config
-from official.modeling.optimization.configs import optimization_config
-
-OptimizationConfig = optimization_config.OptimizationConfig
-
-
-@dataclasses.dataclass
-class DataConfig(base_config.Config):
-  """The base configuration for building datasets.
-
-  Attributes:
-    input_path: The path to the input. It can be either (1) a str indicating
-      a file path/pattern, or (2) a str indicating multiple file paths/patterns
-      separated by comma (e.g "a, b, c" or no spaces "a,b,c"), or
-      (3) a list of str, each of which is a file path/pattern or multiple file
-      paths/patterns separated by comma.
-      It should not be specified when the following `tfds_name` is specified.
-    tfds_name: The name of the tensorflow dataset (TFDS). It should not be
-      specified when the above `input_path` is specified.
-    tfds_split: A str indicating which split of the data to load from TFDS. It
-      is required when above `tfds_name` is specified.
-    global_batch_size: The global batch size across all replicas.
-    is_training: Whether this data is used for training or not.
-    drop_remainder: Whether the last batch should be dropped in the case it has
-      fewer than `global_batch_size` elements.
-    shuffle_buffer_size: The buffer size used for shuffling training data.
-    cache: Whether to cache dataset examples. Can be used to avoid re-reading
-      from disk on the second epoch. Requires significant memory overhead.
-    cycle_length: The number of files that will be processed concurrently when
-      interleaving files.
-    block_length: The number of consecutive elements to produce from each input
-      element before cycling to another input element when interleaving files.
-    deterministic: A boolean controlling whether determinism should be enforced.
-    sharding: Whether sharding is used in the input pipeline.
-    enable_tf_data_service: A boolean indicating whether to enable tf.data
-      service for the input pipeline.
-    tf_data_service_address: The URI of a tf.data service to offload
-      preprocessing onto during training. The URI should be in the format
-      "protocol://address", e.g. "grpc://tf-data-service:5050". It can be
-      overridden by `FLAGS.tf_data_service` flag in the binary.
-    tf_data_service_job_name: The name of the tf.data service job. This
-      argument makes it possible for multiple datasets to share the same job.
-      The default behavior is that the dataset creates anonymous, exclusively
-      owned jobs.
-    tfds_data_dir: A str specifying the directory to read/write TFDS data.
-    tfds_download: A bool to indicate whether to download data using TFDS.
-    tfds_as_supervised: A bool. When loading dataset from TFDS, if True, the
-      returned tf.data.Dataset will have a 2-tuple structure (input, label)
-      according to builder.info.supervised_keys; if False, the default, the
-      returned tf.data.Dataset will have a dictionary with all the features.
-    tfds_skip_decoding_feature: A str to indicate which features are skipped for
-      decoding when loading dataset from TFDS. Use comma to separate multiple
-      features. The main use case is to skip the image/video decoding for better
-      performance.
-  """
-  input_path: Union[Sequence[str], str] = ""
-  tfds_name: str = ""
-  tfds_split: str = ""
-  global_batch_size: int = 0
-  is_training: bool = None
-  drop_remainder: bool = True
-  shuffle_buffer_size: int = 100
-  cache: bool = False
-  cycle_length: Optional[int] = None
-  block_length: int = 1
-  deterministic: Optional[bool] = None
-  sharding: bool = True
-  enable_tf_data_service: bool = False
-  tf_data_service_address: Optional[str] = None
-  tf_data_service_job_name: Optional[str] = None
-  tfds_data_dir: str = ""
-  tfds_download: bool = False
-  tfds_as_supervised: bool = False
-  tfds_skip_decoding_feature: str = ""
-
-
-@dataclasses.dataclass
-class RuntimeConfig(base_config.Config):
-  """High-level configurations for Runtime.
-
-  These include parameters that are not directly related to the experiment,
-  e.g. directories, accelerator type, etc.
-
-  Attributes:
-    distribution_strategy: e.g. 'mirrored', 'tpu', etc.
-    enable_xla: Whether or not to enable XLA.
-    per_gpu_thread_count: thread count per GPU.
-    gpu_thread_mode: Whether and how the GPU device uses its own threadpool.
-    dataset_num_private_threads: Number of threads for a private threadpool
-      created for all datasets computation.
-    tpu: The address of the TPU to use, if any.
-    num_gpus: The number of GPUs to use, if any.
-    worker_hosts: comma-separated list of worker ip:port pairs for running
-      multi-worker models with DistributionStrategy.
-    task_index: If multi-worker training, the task index of this worker.
-    all_reduce_alg: Defines the algorithm for performing all-reduce.
-    num_packs: Sets `num_packs` in the cross device ops used in
-      MirroredStrategy.  For details, see tf.distribute.NcclAllReduce.
-    mixed_precision_dtype: dtype of mixed precision policy. It can be 'float32',
-      'float16', or 'bfloat16'.
-    loss_scale: The type of loss scale, or 'float' value. This is used when
-      setting the mixed precision policy.
-    run_eagerly: Whether or not to run the experiment eagerly.
-    batchnorm_spatial_persistent: Whether or not to enable the spatial
-      persistent mode for CuDNN batch norm kernel for improved GPU performance.
-  """
-  distribution_strategy: str = "mirrored"
-  enable_xla: bool = False
-  gpu_thread_mode: Optional[str] = None
-  dataset_num_private_threads: Optional[int] = None
-  per_gpu_thread_count: int = 0
-  tpu: Optional[str] = None
-  num_gpus: int = 0
-  worker_hosts: Optional[str] = None
-  task_index: int = -1
-  all_reduce_alg: Optional[str] = None
-  num_packs: int = 1
-  mixed_precision_dtype: Optional[str] = None
-  loss_scale: Optional[Union[str, float]] = None
-  run_eagerly: bool = False
-  batchnorm_spatial_persistent: bool = False
-
-  # Global model parallelism configurations.
-  num_cores_per_replica: int = 1
-  default_shard_dim: int = -1
-
-  def model_parallelism(self):
-    return dict(
-        num_cores_per_replica=self.num_cores_per_replica,
-        default_shard_dim=self.default_shard_dim)
 
 
+# TODO(hongkuny): These configs are used in models that are going to deprecate.
+# Once those models are removed, we should delete this file to avoid confusion.
+# Users should not use this file anymore.
 @dataclasses.dataclass
 class TensorboardConfig(base_config.Config):
   """Configuration for Tensorboard.
@@ -183,79 +56,3 @@ class CallbacksConfig(base_config.Config):
   enable_backup_and_restore: bool = False
   enable_tensorboard: bool = True
   enable_time_history: bool = True
-
-
-@dataclasses.dataclass
-class TrainerConfig(base_config.Config):
-  """Configuration for trainer.
-
-  Attributes:
-    optimizer_config: optimizer config, it includes optimizer, learning rate,
-      and warmup schedule configs.
-    train_tf_while_loop: whether or not to use tf while loop.
-    train_tf_function: whether or not to use tf_function for training loop.
-    eval_tf_function: whether or not to use tf_function for eval.
-    allow_tpu_summary: Whether to allow summary happen inside the XLA program
-      runs on TPU through automatic outside compilation.
-    steps_per_loop: number of steps per loop.
-    summary_interval: number of steps between each summary.
-    checkpoint_interval: number of steps between checkpoints.
-    max_to_keep: max checkpoints to keep.
-    continuous_eval_timeout: maximum number of seconds to wait between
-      checkpoints, if set to None, continuous eval will wait indefinitely. This
-      is only used continuous_train_and_eval and continuous_eval modes. Default
-      value is 1 hrs.
-    train_steps: number of train steps.
-    validation_steps: number of eval steps. If `None`, the entire eval dataset
-      is used.
-    validation_interval: number of training steps to run between evaluations.
-    best_checkpoint_export_subdir: if set, the trainer will keep track of the
-      best evaluation metric, and export the corresponding best checkpoint under
-      `model_dir/best_checkpoint_export_subdir`. Note that this only works if
-      mode contains eval (such as `train_and_eval`, `continuous_eval`, and
-      `continuous_train_and_eval`).
-    best_checkpoint_eval_metric: for exporting the best checkpoint, which
-      evaluation metric the trainer should monitor. This can be any evaluation
-      metric appears on tensorboard.
-    best_checkpoint_metric_comp: for exporting the best checkpoint, how the
-      trainer should compare the evaluation metrics. This can be either `higher`
-      (higher the better) or `lower` (lower the better).
-  """
-  optimizer_config: OptimizationConfig = OptimizationConfig()
-  # Orbit settings.
-  train_tf_while_loop: bool = True
-  train_tf_function: bool = True
-  eval_tf_function: bool = True
-  allow_tpu_summary: bool = False
-  # Trainer intervals.
-  steps_per_loop: int = 1000
-  summary_interval: int = 1000
-  checkpoint_interval: int = 1000
-  # Checkpoint manager.
-  max_to_keep: int = 5
-  continuous_eval_timeout: int = 60 * 60
-  # Train/Eval routines.
-  train_steps: int = 0
-  # Sets validation steps to be -1 to evaluate the entire dataset.
-  validation_steps: int = -1
-  validation_interval: int = 1000
-  # Best checkpoint export.
-  best_checkpoint_export_subdir: str = ""
-  best_checkpoint_eval_metric: str = ""
-  best_checkpoint_metric_comp: str = "higher"
-
-
-@dataclasses.dataclass
-class TaskConfig(base_config.Config):
-  init_checkpoint: str = ""
-  model: base_config.Config = None
-  train_data: DataConfig = DataConfig()
-  validation_data: DataConfig = DataConfig()
-
-
-@dataclasses.dataclass
-class ExperimentConfig(base_config.Config):
-  """Top-level configuration."""
-  task: TaskConfig = TaskConfig()
-  trainer: TrainerConfig = TrainerConfig()
-  runtime: RuntimeConfig = RuntimeConfig()

official/nlp/data/data_loader_factory_test.py

@@ -18,7 +18,7 @@
 import dataclasses
 import tensorflow as tf
 
-from official.modeling.hyperparams import config_definitions as cfg
+from official.core import config_definitions as cfg
 from official.nlp.data import data_loader_factory
 
 

official/nlp/data/pretrain_dataloader.py

@@ -18,9 +18,8 @@ from typing import Mapping, Optional
 
 import dataclasses
 import tensorflow as tf
-
+from official.core import config_definitions as cfg
 from official.core import input_reader
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.data import data_loader
 from official.nlp.data import data_loader_factory
 

official/nlp/data/question_answering_dataloader.py

@@ -18,9 +18,8 @@ from typing import Mapping, Optional
 
 import dataclasses
 import tensorflow as tf
-
+from official.core import config_definitions as cfg
 from official.core import input_reader
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.data import data_loader
 from official.nlp.data import data_loader_factory
 

official/nlp/data/sentence_prediction_dataloader.py

@@ -18,9 +18,8 @@ from typing import Mapping, Optional
 
 import dataclasses
 import tensorflow as tf
-
+from official.core import config_definitions as cfg
 from official.core import input_reader
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.data import data_loader
 from official.nlp.data import data_loader_factory
 

official/nlp/data/tagging_dataloader.py

@@ -18,9 +18,8 @@ from typing import Mapping, Optional
 
 import dataclasses
 import tensorflow as tf
-
+from official.core import config_definitions as cfg
 from official.core import input_reader
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.data import data_loader
 from official.nlp.data import data_loader_factory
 

official/nlp/data/wmt_dataloader.py

@@ -41,9 +41,8 @@ from typing import Optional
 
 import dataclasses
 import tensorflow as tf
-
+from official.core import config_definitions as cfg
 from official.core import input_reader
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.data import data_loader
 from official.nlp.data import data_loader_factory
 

official/nlp/tasks/electra_task.py

@@ -19,9 +19,9 @@ import dataclasses
 import tensorflow as tf
 
 from official.core import base_task
+from official.core import config_definitions as cfg
 from official.core import task_factory
 from official.modeling import tf_utils
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.configs import bert
 from official.nlp.configs import electra
 from official.nlp.configs import encoders

official/nlp/tasks/masked_lm.py

@@ -19,9 +19,9 @@ import dataclasses
 import tensorflow as tf
 
 from official.core import base_task
+from official.core import config_definitions as cfg
 from official.core import task_factory
 from official.modeling import tf_utils
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.configs import bert
 from official.nlp.configs import encoders
 from official.nlp.data import data_loader_factory

official/nlp/tasks/question_answering.py

@@ -25,9 +25,9 @@ import tensorflow as tf
 import tensorflow_hub as hub
 
 from official.core import base_task
+from official.core import config_definitions as cfg
 from official.core import task_factory
 from official.modeling.hyperparams import base_config
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.bert import squad_evaluate_v1_1
 from official.nlp.bert import squad_evaluate_v2_0
 from official.nlp.bert import tokenization

official/nlp/tasks/sentence_prediction.py

@@ -26,10 +26,10 @@ import tensorflow as tf
 import tensorflow_hub as hub
 
 from official.core import base_task
+from official.core import config_definitions as cfg
 from official.core import task_factory
 from official.modeling import tf_utils
 from official.modeling.hyperparams import base_config
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.configs import encoders
 from official.nlp.data import data_loader_factory
 from official.nlp.modeling import models

official/nlp/tasks/tagging.py

@@ -25,9 +25,9 @@ import tensorflow as tf
 import tensorflow_hub as hub
 
 from official.core import base_task
+from official.core import config_definitions as cfg
 from official.core import task_factory
 from official.modeling.hyperparams import base_config
-from official.modeling.hyperparams import config_definitions as cfg
 from official.nlp.configs import encoders
 from official.nlp.data import data_loader_factory
 from official.nlp.modeling import models

official/nlp/train_ctl_continuous_finetune.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 # ==============================================================================
 """TFM continuous finetuning+eval training driver."""
-
 import gc
 import os
 import time
@@ -31,11 +30,11 @@ from official.common import registry_imports
 # pylint: enable=unused-import
 from official.common import distribute_utils
 from official.common import flags as tfm_flags
+from official.core import config_definitions
 from official.core import task_factory
 from official.core import train_lib
 from official.core import train_utils
 from official.modeling import performance
-from official.modeling.hyperparams import config_definitions
 
 FLAGS = flags.FLAGS