add configurable unique layer init, clean up lr and loss display #64
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Small PR: 1 - add configurable init style in model_args - 'use_unique_init' will use the layer_id in the init stddev denom, otherwise uses the original init style of total layer count. (verified both work on 7B llama...not clear yet if one is better vs other). 2 - clean up lr and loss display formatting - lr display was spanning out to 12+ digits which isn't that informative, and was wrapped in list format. This PR rounds it to max of 8 digits precision and removes the []'s that were around the lr rate display. (note this is purely UI...the full float precision is still used in actual lr calcs). 3 - clean up loss display - rounds the loss display to 4 digits precision to make it more readable and informative. previously: <img width="1198" alt="Screenshot 2024-02-16 at 2 33 34 PM" src="https://github.com/pytorch-labs/torchtrain/assets/46302957/77733af0-42db-4fab-a047-fccc7d404278"> Now: <img width="1063" alt="Screenshot 2024-02-16 at 2 51 53 PM" src="https://github.com/pytorch-labs/torchtrain/assets/46302957/4eb75b98-67f4-41ec-83d8-dd84a0e8b29e">
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…orch#64) Small PR: 1 - add configurable init style in model_args - 'use_unique_init' will use the layer_id in the init stddev denom, otherwise uses the original init style of total layer count. (verified both work on 7B llama...not clear yet if one is better vs other). 2 - clean up lr and loss display formatting - lr display was spanning out to 12+ digits which isn't that informative, and was wrapped in list format. This PR rounds it to max of 8 digits precision and removes the []'s that were around the lr rate display. (note this is purely UI...the full float precision is still used in actual lr calcs). 3 - clean up loss display - rounds the loss display to 4 digits precision to make it more readable and informative. previously: <img width="1198" alt="Screenshot 2024-02-16 at 2 33 34 PM" src="https://github.com/pytorch-labs/torchtrain/assets/46302957/77733af0-42db-4fab-a047-fccc7d404278"> Now: <img width="1063" alt="Screenshot 2024-02-16 at 2 51 53 PM" src="https://github.com/pytorch-labs/torchtrain/assets/46302957/4eb75b98-67f4-41ec-83d8-dd84a0e8b29e">
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Small PR:
1 - add configurable init style in model_args - 'use_unique_init' will use the layer_id in the init stddev denom, otherwise uses the original init style of total layer count. (verified both work on 7B llama...not clear yet if one is better vs other).
2 - clean up lr and loss display formatting - lr display was spanning out to 12+ digits which isn't that informative, and was wrapped in list format. This PR rounds it to max of 8 digits precision and removes the []'s that were around the lr rate display.
(note this is purely UI...the full float precision is still used in actual lr calcs).
3 - clean up loss display - rounds the loss display to 4 digits precision to make it more readable and informative.
previously:
Now:
