Add more fine-tuning advice

docs/beware.md

@@ -17,6 +17,11 @@ exactly the right variables
 at exactly the right pressure levels
 from exactly the right source.
 
+This also means that the performance of the model will be sensitive to how the
+data is regridded.
+For optimal performance, you should ensure that the data is regridded
+exactly like the data seen during pretraining and fine-tuning.
+
 (t0-vs-analysis)=
 ## HRES IFS T0 Versus HRES IFS Analysis
 

docs/finetuning.md

@@ -37,6 +37,37 @@ loss = ...
 loss.backward()
 ```
 
+## Exploding Gradients
+
+When fine-tuning, you may run into very large gradient values.
+Gradient clipping and internal layer normalisation layers mitigate the impact
+of large gradients,
+meaning that large gradients will not immediately lead to abnormal model outputs and loss values.
+Nevertheless, if gradients do blow up, the model will not learn anymore and eventually the loss value
+will also blow up.
+You should carefully monitor the value of the gradients to detect exploding gradients.
+
+One cause of exploding gradients is too large values for internal activations.
+Typically this can be fixed by judiciously inserting a layer normalisation layer.
+
+We have identified the level aggregation as weak point of the model that can be susceptible
+to exploding gradients.
+You can stabilise the level aggregation of the model
+by setting the following flag in the constructor: `stabilise_level_agg=True`.
+Note that `stabilise_level_agg=True` will considerably perturb the model,
+so significant additional fine-tuning may be required to get to the desired level of performance.
+
+```python
+from aurora import Aurora
+from aurora.normalisation import locations, scales
+
+model = Aurora(
+    use_lora=False,
+    stabilise_level_agg=True,  # Insert extra layer norm. to mitigate exploding gradients.
+)
+model.load_checkpoint("microsoft/aurora", "aurora-0.25-pretrained.ckpt", strict=False)
+```
+
 ## Extending Aurora with New Variables
 
 Aurora can be extended with new variables by adjusting the keyword arguments `surf_vars`,
@@ -66,6 +97,18 @@ scales["new_static_var"] = 1.0
 scales["new_atmos_var"] = 1.0
 ```
 
+To more efficiently learn new variables, it is recommended to use a separate learning rate for
+the patch embeddings of the new variables in the encoder and decoder.
+For example, if you are using Adam, you can try `1e-3` for the new patch embeddings
+and `3e-4` for the other parameters.
+
+By default, patch embeddings in the encoder for new variables are initialised randomly.
+This means that adding new variables to the model perturbs the predictions for the existing
+variables.
+If you do not want this, you can alternatively initialise the new patch embeddings in the encoder
+to zero.
+The relevant parameter dictionaries are `model.encoder.{surf,atmos}_token_embeds.weights`.
+
 ## Other Model Extensions
 
 It is possible to extend to model in any way you like.
@@ -83,3 +126,17 @@ model = Aurora(...)
 
 model.load_checkpoint("microsoft/aurora", "aurora-0.25-pretrained.ckpt", strict=False)
 ```
+
+## Triple Check Your Fine-Tuning Data!
+
+When fine-tuning the model, it is absolutely essential to carefully check your fine-tuning data.
+
+* Are the old (and possibly new) normalisation statistics appropriate for the new data?
+
+* Is any data missing?
+
+* Does the data contains zeros or NaNs?
+
+* Does the data contain any outliers that could possibly interfere with fine-tuning?
+
+_Et cetera._