Updated loss and eval metrics

[wood-b]

This PR built off of #821

Loss Updates:

• Adds loss functions to the registry
• Updates how the loss is actually computed — loss should now be equivalent to not using our DDP wrapper i.e. if you compute the mae/mse loss with torch module you should get the same answer.
• Adds per atom mae loss
• Changes the name of L2MAELoss -> P2NormLoss L2NormLoss. l2mae is still part of registry for backwards compatibility with configs.
• Removes the AtomwiseL2Loss

Evaluator Updates:

• Adds new per-atom and other metrics
• Adds metrics dicts function to the evaluator

Tests:

• A bunch of new unit tests for losses including DDP tests. One note here is that if pytorch ever changes how the gradient averaging is done this won't be captured.
• Comparing first batch E/F loss pre/post changes
• Compare training of OC20 2M pre/post changes (see below)

Energy, forces, and loss all look comparable between eqv2-main (current main) and eqv2-new-loss-test (this PR) trained for 3 epochs on OC20 2M

[codecov]

Codecov Report

Attention: Patch coverage is 97.14286% with 3 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/fairchem/core/modules/evaluator.py	93.54%	2 Missing ⚠️
src/fairchem/core/modules/loss.py	98.14%	1 Missing ⚠️

Files with missing lines	Coverage Δ
src/fairchem/core/common/registry.py	73.22% <100.00%> (+2.53%)	⬆️
src/fairchem/core/common/utils.py	68.26% <ø> (+0.02%)	⬆️
src/fairchem/core/trainers/base_trainer.py	87.75% <100.00%> (+0.16%)	⬆️
src/fairchem/core/trainers/ocp_trainer.py	69.66% <100.00%> (+0.30%)	⬆️
src/fairchem/core/modules/loss.py	95.77% <98.14%> (+29.70%)	⬆️
src/fairchem/core/modules/evaluator.py	91.80% <93.54%> (-1.66%)	⬇️

[lbluque]

Thanks @wood-b ! Just left minor comments.

I double checked training with this, and it looks as expected, energy is slightly better but likely from the stress now being weighted slightly different.

[rayg1234]

thanks for adding this comment, very helpful