chore: import upstream snapshot with attribution

docs/source/common/callbacks.rst

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+*********
+Callbacks
+*********
+
+Exponential Moving Average (EMA)
+================================
+
+During training, EMA maintains a moving average of the trained parameters.
+EMA parameters can produce significantly better results and faster convergence for a variety of different domains and models.
+
+EMA is a simple calculation. EMA Weights are pre-initialized with the model weights at the start of training.
+
+Every training update, the EMA weights are updated based on the new model weights.
+
+.. math::
+    ema_w = ema_w * decay + model_w * (1-decay)
+
+Enabling EMA is straightforward. We can pass the additional argument to the experiment manager at runtime.
+
+.. code-block:: bash
+
+    python examples/asr/asr_ctc/speech_to_text_ctc.py \
+        model.train_ds.manifest_filepath=/path/to/my/train/manifest.json \
+        model.validation_ds.manifest_filepath=/path/to/my/validation/manifest.json \
+        trainer.devices=2 \
+        trainer.accelerator='gpu' \
+        trainer.max_epochs=50 \
+        exp_manager.ema.enable=True # pass this additional argument to enable EMA
+
+To change the decay rate, pass the additional argument.
+
+.. code-block:: bash
+
+    python examples/asr/asr_ctc/speech_to_text_ctc.py \
+        ...
+        exp_manager.ema.enable=True \
+        exp_manager.ema.decay=0.999
+
+We also offer other helpful arguments.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Argument
+     - Description
+   * - `exp_manager.ema.validate_original_weights=True`
+     - Validate the original weights instead of EMA weights.
+   * - `exp_manager.ema.every_n_steps=2`
+     - Apply EMA every N steps instead of every step.
+   * - `exp_manager.ema.cpu_offload=True`
+     - Offload EMA weights to CPU. May introduce significant slow-downs.