Can you extend support for gemma-7b as well?

convert_hf_checkpoint.py fails with following error for gemma-7b -

Model config {'block_size': 2048, 'vocab_size': 256000, 'n_layer': 28, 'n_head': 16, 'dim': 3072, 'intermediate_size': 24576, 'n_local_heads': 16, 'head_dim': 192, 'rope_base': 10000, 'norm_eps': 1e-05}Traceback (most recent call last):  File "scripts/convert_hf_checkpoint.py", line 111, in <module>    convert_hf_checkpoint(  File "......../site-packages/torch/utils/_contextlib.py", line 115, in decorate_context    return func(*args, **kwargs)  File "scripts/convert_hf_checkpoint.py", line 91, in convert_hf_checkpoint    q = permute(q, config.n_head)  File "scripts/convert_hf_checkpoint.py", line 62, in permute    w.view(n_head, 2, config.head_dim // 2, dim)RuntimeError: shape '[16, 2, 96, 3072]' is invalid for input of size 12582912

The issue is withModelArgs.head_dim being computed as 192 but HF config dictates it to be 256. I tried by forcing it but then it fails during inferencing with the following error for each layer -

size mismatch for layers.0.attention.wo.weight: copying a param with shape torch.Size([3072, 4096]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).

Same error as@shaahji saw above. I tried with config{'dim': 3072, 'vocab_size': 256000, 'n_layer': 28, 'n_head': 16, 'n_local_heads': 16, 'intermediate_size': 24576} according to gemma-7b/config.json

{  "architectures": [    "GemmaForCausalLM"  ],  "attention_bias": false,  "attention_dropout": 0.0,  "bos_token_id": 2,  "eos_token_id": 1,  "head_dim": 256,  "hidden_act": "gelu",  "hidden_size": 3072,  "initializer_range": 0.02,  "intermediate_size": 24576,  "max_position_embeddings": 8192,  "model_type": "gemma",  "num_attention_heads": 16,  "num_hidden_layers": 28,  "num_key_value_heads": 16,  "pad_token_id": 0,  "rms_norm_eps": 1e-06,  "rope_scaling": null,  "rope_theta": 10000.0,  "torch_dtype": "bfloat16",  "transformers_version": "4.38.0.dev0",  "use_cache": true,  "vocab_size": 256000}

It seems like we force setting'head_dim':256, the 'dim' will be bumped to 4096. Also I've no idea where the number "12582912" comes from. It's like a puzzle to figure out how those numbers are mapped and determined.@Chillee could you elaborate?

I added support for gemma-7b. The main non-trivial component here was thathead_dim * n_heads != dim, so some parts of the model definition needed to be patched.

I'm getting 83 tok/s for fp16

cc:@guangy10@shaahji

Hi@Chillee!

Thank you for your pull request.

Werequire contributors to sign ourContributor License Agreement, and yours needs attention.

You currently have a record in our system, but the CLA is no longer valid, and will need to beresubmitted.

Process

In order for us to review and merge your suggested changes, please sign athttps://code.facebook.com/cla.If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA.

Once the CLA is signed, our tooling will perform checks and validations. Afterwards, thepull request will be tagged withCLA signed. The tagging process may take up to 1 hour after signing. Please give it that time before contacting us about it.

If you have received this in error or have any questions, please contact us atcla@meta.com. Thanks!