Updated internal links with external github links in Optimizer and Learning Rate Scheduler

PiperOrigin-RevId: 639932638

Author: tensorflower-gardener

official/nlp/docs/optimization.md

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+# Optimizer and Learning Rate Scheduler
+
+This page describes the
+[optimization package](https://github.com/tensorflow/models/tree/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization)
+for Tensorflow Official Models (TFM) which includes optimizers, and learning
+rate schedulers.
+
+## Building Optimizer and LR Scheduler
+
+We use an Optimizer factory class to manage optimizer and learning rate
+creation. Optimizer factory takes a config as an input, and it has member
+functions that are used to build optimizer and learning rate schedule. To create
+an optimizer and a LR schedule through OptimizerFactory, you need to do the
+following:
+
+1.  Define optimization config, this includes optimizer, and learning rate
+    schedule.
+2.  Initialize the OptimizerFactory instance using the optimization config.
+3.  Build the learning rate, and the optimizer using the class member functions.
+
+The following is an example for creating an SGD optimizer with stepwise LR
+scheduler with linear warmup:
+
+```python
+params = {'optimizer': { 'type': 'sgd',
+                         'sgd': {'momentum': 0.9}},
+          'learning_rate': {'type': 'stepwise',
+                            'stepwise': {
+                                'boundaries': [10000, 20000],
+                                'values': [0.1, 0.01, 0.001]}},
+          'warmup': {'type': 'linear',
+                     'linear': {'warmup_steps': 500,
+                                'warmup_learning_rate': 0.01}}}
+# Defines optimization config from a dictionary.
+opt_config = optimization.OptimizationConfig(params)
+# Initializes an optimization factory from optimization config.
+opt_factory = optimization.OptimizerFactory(opt_config)
+# Builds the desired learning rate scheduling instance.
+lr = opt_factory.build_learning_rate()
+# Builds the optimizer instance with the desired learning rate schedule.
+optimizer = opt_factory.build_optimizer(lr)
+```
+
+To initialize an OptimizerFactory, `optimizer` and `learning_rate` fields must
+be defined, while `warmup` is an optional field. The field `type` is used to
+define the type of each optimization component. The set of available types are
+explained in details in the following sections.
+
+In the following sections, we explain how to create different optimizers,
+learning rate, and warmup schedulers. We also explain how to add new optimizers,
+or learning rate schedulers.
+
+## Optimizers
+
+The list of supported optimizers can be found
+[here](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/optimizer_factory.py#L31).
+
+```python
+OPTIMIZERS_CLS = {
+    'sgd': tf.keras.optimizers.SGD,
+    'adam': tf.keras.optimizers.Adam,
+    'adamw': nlp_optimization.AdamWeightDecay,
+    'lamb': tfa_optimizers.LAMB,
+    'rmsprop': tf.keras.optimizers.RMSprop
+}
+```
+
+You can specify the type of optimizer to be one of the above using
+[oneof](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/hyperparams/oneof.py)
+config. The available config fields can be found
+[here](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/configs/optimizer_config.py).
+
+All optimizers support gradient clipping methods: clip by value, clip by norm,
+clip by global norm. To specify which method to use, you need to specify the
+appropriate field list
+[here](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/configs/optimizer_config.py#L34).
+
+### Example
+
+We will specify an rmsprop optimizer with discounting factor (rho) of 0.9, and
+global norm gradient clipping of 10.0. Below is the config to be used.
+
+```python
+params = {'optimizer': { 'type': 'rmsprop',
+                         'rmsprop': {'rho': 0.9,
+                                     'global_clipnorm': 10.0}}}
+```
+
+### Adding a New Optimizer
+
+To add a new optimizer, you need to do the following:
+
+1.  Create a
+    [custom](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Optimizer#creating_a_custom_optimizer_2)
+    of tf.keras.optimizers.Optimizer.
+2.  Add the required config fields under
+    [optimization/configs/optimizer_config.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/configs/optimizer_config.py).
+3.  Add the optimizer class to the list of available optimizer classes in
+    (optimizer_factor)[https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/optimizer_factory.py]
+
+## Learning Rate and Warmup Schedules
+
+Learning rate with an optional warmup can be configured by specifying
+`learning_rate`, and `warmup` fields in optimization config. `learning_rate` is
+a required field, while `warmup` is an optional one. The list of supported
+`learning_rate` and `warmup` schedules can be found
+[here](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/modeling/optimization/optimizer_factory.py#L59).
+
+```python
+LR_CLS = {
+    'stepwise': tf.keras.optimizers.schedules.PiecewiseConstantDecay,
+    'polynomial': tf.keras.optimizers.schedules.PolynomialDecay,
+    'exponential': tf.keras.optimizers.schedules.ExponentialDecay,
+    'cosine': tf.keras.experimental.CosineDecay,
+    'power': lr_schedule.DirectPowerDecay,
+}
+
+WARMUP_CLS = {
+    'linear': lr_schedule.LinearWarmup,
+    'polynomial': lr_schedule.PolynomialWarmUp
+}
+```
+
+In addition, a `constant` learning rate can be specified.
+
+## How Learning Rate Works
+
+Learning rate takes `step` as an input, and it returns the learning rate value.
+As the training progresses, usually learning rate value decays. Warmup schedule
+is often used to stablize the training. Warmup schedule starts from a low
+learning rate value, and it gradually increases until it reaches the initial
+value for the regular learning rate decay schedule. We combine `learning_rate`
+(lr) with `warmup` (warmup) schedules as follows
+
+*   Steps [0, warmup_steps): `learning_rate = warmup(step)`
+*   Steps [warmup_steps, train_steps): `learning_rate = lr(step)`
+*   We designed the warmup schedule such that final warmup learning rate is
+    inferred from the learning rate schedule (i.e.
+    `learning_rate(warmup_steps) = warmup(warmup_steps)`). Note that, warmup
+    schedule doesn't delay the regular learning rate decay by warmup_steps,
+    instead it replaces it.
+
+Learning rate value is logged every
+[summary_interval](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/core/config_definitions.py#L262).
+If warmup_steps are less that the `summary_interval`, you won't be able to see
+warmup values.
+
+### Example
+
+We want to specify a cosine learning rate decay with decay_steps of 20000, with
+a linear warmup schedule for the first 500 steps.
+
+```python
+params = {'learning_rate': {'type': 'cosine',
+                            'cosine': {'decay_steps': 20000}},
+          'warmup': {'type': 'linear',
+                     'linear': {'warmup_steps': 500}}}
+```
+
+## Customizing Optimizer Inside Task
+
+Optimizer and learning rate are created inside the
+[task](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/core/base_task.py#L99).
+If different optimizers/learning rate schedulers are needed, they can be defined
+by overriding the class method.
+
+## Important Factors To Consider
+
+*   Batch size: Changing batch size usually requires scaling learning rate
+    values, and number of training steps. Make sure that you change appropriate
+    values as batch size changes.
+*   Train steps: Train steps is highly correlated with fields such as
+    `decay_steps` for cosine learning rate decay. Changing one without changing
+    the other might result in undesired behavior.