Gemma 2: 9b and 27b versions

[Andrei-Aksionov]

Hi there 👋

Fixes #1535

Google released the latest and greatest Gemma model - v2.
This time it comes in three sizes:

• 2b (not yet released)
• 9b
• 27b

Based on the technical report and the official implementation here are the main changes that I've spotted:

1. Embeddings scaler needs to be casted down before applying.
2. Needs to be careful with attention scores scaler: it's not equal to head_size, but rather n_embd/n_head. In case of Gemma head_size might not be equal to n_embd/n_head.
3. Logits soft-caping for attention scores and for final logits. Soft-caping is needed only for training (looks like it's more important for a larger model) and not so much for inference. Since flash attention doesn't support soft-caping, it needs to be disabled if not in training mode.
4. Sliding window attention is used instead of a global window on every odd (idx) layer with half the size.
5. RMSNorm does downcasting right at the end. That was the behavior before I added support for Gemma v1.
6. Transformer block now has two more normalization layers: right after attention layer (before residual connection) and right after MLP (also before residual connection). Previously we had norm -> attn -> residual -> norm -> MLP -> residual. Now: norm -> attn -> norm -> residual -> norm -> MLP -> norm -> residual.
7. Both 9b and 27b use grouped query attention. In Gemma v1 7b had a regular multi-head attention, while 2b variant had multi-query attention (single key-value pair is shared across all query heads).

[rasbt]

Nice summary. I think this touches all the main points. The others (knowledge distillation for the small models; tied embeddings) would not affect the architecture, it's more of a pretraining method. So yeah, looks great! Many thanks for taking this on!

[rasbt]

@Andrei-Aksionov
Sliding window attention (an ugly one, but hey, it works)

Cool! We can also add that to the existing Mistral/Mixtral models then 😊

[Andrei-Aksionov]

Cool! We can also add that to the existing Mistral/Mixtral models then

I believe only Mistral v0.1 supported sliding window attention, all the subsequent models by Mistral.ai don't use it.
But after this PR is merged, adding SLA would be just a matter of an additional line in a config.

[rasbt]

I believe only Mistral v0.1 supported sliding window attention, all the subsequent models by Mistral.ai don't use it.

I think you are right.

But after this PR is merged, adding SLA would be just a matter of an additional line in a config.

Nice!

[Andrei-Aksionov]

Gemma 2 9b/9b-it now has an initial support (with a lot of “scaffolding”).

Generation returns plausible results, but chat does a couple of strange things:

1. OOM. Don't understand why if a regular generate script consumes ~20 GB.
   Update: It's not a Gemma specific problem Chat consumes more VRAM than Generate #1558, so it's not a blocker.
2. Had to use quantization (bnb.nf4) and the model was very restrictive, often didn't want to respond and instead asked to rephrase the question. I know that LlaMA 3, because of a very long training, has saturated bf16 dtype up to the very last digit and thus quantization affects more than other models. Maybe here we have the same (thanks to a “teacher”)? 🤷
   Update # 1: if to use generate script with quantization, then I get a proper output. Something else is broken in chat script, besides a higher memory consumption.
   Update # 2: KV-cache needs to be change to support sliding window attention. Chat script pre-allocates too much memory (up to model.max_seq_length), so a layer with sliding window has a wrong kv-cache.

---

Anyway, there is a lot of work that needs to be done (besides what I've mentioned above) before I can open this PR for a review:

• 1. Only final_softcapping affects tests. Need to make tests fail if attention_logit_softcapping is messed up.
• 2. Deal with all TODOs. The code works, but is very ugly and non-performant.
• 3. Use torch profiler to make sure that there are no shady device syncs happen in the background.
• 4. Add code and a test for LitGPT --> HF format conversion.
• 5. Does the torch.compile work with softcapping? If not, a clear error message needs to be printed.
• 6. Do a short training as a sanity-check.
• 7. Figure out what to do with CausalSelfAttention from adapter.py. Tests for adapter don't fail because of # 1.
• 8. Add support for 27b variant.

[rasbt]

This is a great PR. It's crazy that you pulled this off. Really awesome.

[Andrei-Aksionov]

One more thing. Due to time constraints, I didn't test Gemma v2 27b version.
Tests are running fine, but it would be nice to check the generated output.

@rasbt could you do this?

[rasbt]

One more thing. Due to time constraints, I didn't test Gemma v2 27b version.
Tests are running fine, but it would be nice to check the generated output.

@rasbt could you do this?

Yes, I am happy to do this. The other thing is I will also generate config files for the smaller models

[rasbt]

Works great!

[rasbt]

Based on the config file run, the train and val loss look great. It's a surprisingly low MMLU though. There's nothing wrong with the finetuned model though and it works fine during chat:

(Not 100% sure, but maybe the MMLU scores in the README were created with --num_fewshot greater than 1.

Anyways, I think everything else seems to be fine though and good to merge now right?

[Andrei-Aksionov]

Yep, let's merge.

[rasbt]

Awesome, this is great! Thanks for this amazing PR!