Trainer callbacks

docs/source/index.rst

@@ -213,6 +213,7 @@ conversion utilities for the following models:
     :maxdepth: 2
     :caption: Main Classes
 
+    main_classes/callback
     main_classes/configuration
     main_classes/logging
     main_classes/model
@@ -270,3 +271,4 @@ conversion utilities for the following models:
     internal/modeling_utils
     internal/pipelines_utils
     internal/tokenization_utils
+    internal/trainer_utils

docs/source/internal/trainer_utils.rst

@@ -0,0 +1,21 @@
+Utilities for Trainer
+-----------------------------------------------------------------------------------------------------------------------
+
+This page lists all the utility functions used by :class:`~transformers.Trainer`.
+
+Most of those are only useful if you are studying the code of the Trainer in the library.
+
+Utilities
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EvalPrediction
+
+.. autofunction:: transformers.set_seed
+
+.. autofunction:: transformers.torch_distributed_zero_first
+
+
+Callbacks internals
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.trainer_callback.CallbackHandler

docs/source/main_classes/callback.rst

@@ -0,0 +1,68 @@
+Callbacks
+-----------------------------------------------------------------------------------------------------------------------
+
+Callbacks are objects that can customize the behavior of the training loop in the PyTorch
+:class:`~transformers.Trainer` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
+state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
+stopping).
+
+Callbacks are "read only" pieces of code, apart from the :class:`~transformers.TrainerControl` object they return, they
+cannot change anything in the training loop. For customizations that require changes in the training loop, you should
+subclass :class:`~transformers.Trainer` and override the methods you need (see :doc:`trainer` for examples).
+
+By default a :class:`~transformers.Trainer` will use the following callbacks:
+
+- :class:`~transformers.DefaultFlowCallback` which handles the default beahvior for logging, saving and evaluation.
+- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProrgressCallback` to display progress and print the
+  logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
+  it's the second one).
+- :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
+  or tensorboardX).
+- :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
+- :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
+
+The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
+:class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
+Trainer's internal state via :class:`~transformers.TrainerState`, and can take some actions on the training loop via
+:class:`~transformers.TrainerControl`.
+
+
+Available Callbacks
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Here is the list of the available :class:`~transformers.TrainerCallback` in the library:
+
+.. autoclass:: transformers.integrations.CometCallback
+    :members: setup
+
+.. autoclass:: transformers.DefaultFlowCallback
+
+.. autoclass:: transformers.PrinterCallback
+
+.. autoclass:: transformers.ProgressCallback
+
+.. autoclass:: transformers.integrations.TensorBoardCallback
+
+.. autoclass:: transformers.integrations.WandbCallback
+    :members: setup
+
+
+TrainerCallback
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TrainerCallback
+    :members:
+
+
+TrainerState
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TrainerState
+    :members:
+
+
+TrainerControl
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TrainerControl
+    :members:

docs/source/main_classes/trainer.rst

@@ -18,7 +18,6 @@ previous features. To inject custom behavior you can subclass them and override
 - **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
 - **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
 - **log** -- Logs information on the various objects watching training.
-- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
 - **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
   init.
 - **compute_loss** - Computes the loss on a batch of training inputs.
@@ -40,36 +39,34 @@ Here is an example of how to customize :class:`~transformers.Trainer` using a cu
             logits = outputs[0]
             return my_custom_loss(logits, labels)
 
+Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
+:doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
+other ML platforms...) and take decisions (like early stopping).
+
 
 Trainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.Trainer
     :members:
 
+
 TFTrainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTrainer
     :members:
 
+
 TrainingArguments
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TrainingArguments
     :members:
 
+
 TFTrainingArguments
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTrainingArguments
     :members:
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first

examples/seq2seq/seq2seq_trainer.py

@@ -9,7 +9,7 @@
 from transformers.configuration_fsmt import FSMTConfig
 from transformers.file_utils import is_torch_tpu_available
 from transformers.optimization import Adafactor, AdamW, get_linear_schedule_with_warmup
-from transformers.trainer import get_tpu_sampler
+from transformers.trainer_pt_utils import get_tpu_sampler
 
 
 try:

examples/seq2seq/test_finetune_trainer.py

@@ -4,7 +4,8 @@
 from unittest.mock import patch
 
 from transformers.testing_utils import slow
-from transformers.trainer_utils import TrainerState, set_seed
+from transformers.trainer_callback import TrainerState
+from transformers.trainer_utils import set_seed
 
 from .finetune_trainer import main
 from .test_seq2seq_examples import MBART_TINY

src/transformers/__init__.py

@@ -205,7 +205,15 @@
 from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
 
 # Trainer
-from .trainer_utils import EvalPrediction, TrainerState, set_seed
+from .trainer_callback import (
+    DefaultFlowCallback,
+    PrinterCallback,
+    ProgressCallback,
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+)
+from .trainer_utils import EvalPrediction, EvaluationStrategy, set_seed
 from .training_args import TrainingArguments
 from .training_args_tf import TFTrainingArguments
 from .utils import logging
@@ -528,7 +536,8 @@
     from .tokenization_marian import MarianTokenizer
 
     # Trainer
-    from .trainer import EvalPrediction, Trainer, set_seed, torch_distributed_zero_first
+    from .trainer import Trainer
+    from .trainer_pt_utils import torch_distributed_zero_first
 else:
     from .utils.dummy_pt_objects import *