Support StableLM From StabilityAI

[MarkSchmidty]

Blog Post Announcement (It may be using the same architecture as GPT-NeoX)

GitHub Repo

In case these links 404 due to being posted early by accident: https://archive.is/ZQszO https://archive.ph/U0Pr8

(Checkpoint links are Hugging Face repos with model weights)

Size	StableLM-Base-Alpha	StableLM-Tuned-Alpha	Training Tokens [in progress]	Context Window	Web Demo
3B	checkpoint	checkpoint	800B [1.5T]*	4096
7B	checkpoint	checkpoint	800B [1.5T]*	4096	HuggingFace
15B	(in progress)	(pending)	1.5T*
30B	(in progress)	(pending)	1.5T*
65B	(in progress)	(pending)	1.5T*
175B	(planned)

*3T Planned

[Green-Sky]

are they just new GPT-NeoX models? or did they forget to update the model cards on hf ? :smile:

[Green-Sky]

related https://github.com/ggerganov/ggml/issues/10

[jessejohnson]

This was quick! 😅

They've included a bit in the ReadMe indicating that compatibility with llama.cpp is actively desired. :)

EDIT: related HN thread https://news.ycombinator.com/item?id=35629127

[NoNamedCat]

This models will be compatible with llama.cpp?

[rabidcopy]

Definitely interested in this. Interesting that they specifically highlight wanting llama.cpp/ggml support.

[rabidcopy]

If it really is GPT NeoX, this repo has conversion, quantization, and support for basic inference for GPT NeoX and other model formats. https://github.com/NolanoOrg/cformers/blob/master/cformers/cpp/converters/convert_gptneox_to_ggml.py https://github.com/NolanoOrg/cformers/blob/master/cformers/cpp/quantize_gptneox.cpp

[ggerganov]

Here is a very quick and dirty implementation using ggml:

https://github.com/ggerganov/ggml/pull/96

Also, found a bug in multi-threaded ggml_cpy():

https://github.com/ggerganov/ggml/pull/96/files#diff-b4a500ab2765c31526c5541f3e51e21e46990b87d9774cac6f3089db315bdc5bR5655-R5660

[acheong08]

are they just new GPT-NeoX models? or did they forget to update the model cards on hf ? smile

Is it?

[MarkSchmidty]

Yes it's using GPT-NeoX architecture. The model details can be seen here: https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-base-alpha-7b.yaml

  # model settings
  "num-layers": 16,
  "hidden-size": 6144,
  "num-attention-heads": 48,
  "seq-length": 4096,
  "max-position-embeddings": 4096,

  # architecture design
  "norm": "layernorm",
  "pos-emb": "rotary",
  "rotary_pct": 0.25,
  "activation": "gelu",
  "no-weight-tying": true,
  "gpt_j_residual": true,
  "output_layer_parallelism": "column",

[ggerganov]

Merged in ggml: https://github.com/ggerganov/ggml/tree/master/examples/stablelm

[mhkhung]

The q4_x files output from ggml are not compatible with llama.cpp?

[fgdfgfthgr-fox]

The q4_x files output from ggml are not compatible with llama.cpp?

It seems so currently.

[magicrobotmonkey]

I've converted/quantized stablelm-tuned-alpha-7b to Q4_3 and it works great with ggml, but llama.cpp throws error loading model: missing tok_embeddings.weight, seems like some support is missing.

[AndreiSva]

I am getting the same error

[mikeggh]

Are you using the specific binary for stablelm? It seems separated from the looks of it in https://github.com/ggerganov/ggml/tree/master/examples/stablelm

[wkkautas]

Are there plan to integrate ggml/examples/stablelm into llama.cpp? Also it would be great if a single llama.cpp binary is able to use also gpt-2 and gpt-j.

[ggerganov]

There seems to be a bug in the existing StableLM implementation in ggml. See the updated README for more details:

https://github.com/ggerganov/ggml/tree/master/examples/stablelm#warning

Best way to fix this is to compare outputs with the reference implementation. Any help will be appreciated.

[ggerganov]

So, I ran the HF transformers implementation and I observe the same "increasing magnitude" behaviour as in the ggml implementation.

To do this, I changed the following line:

https://github.com/huggingface/transformers/blob/c2c99dc7ef5edab8f7674a1eb00cf6ac6996fd0f/src/transformers/models/gpt_neox/modeling_gpt_neox.py#L234

to:

        print(attn_scores);
        attn_weights = nn.functional.softmax(attn_scores, dim=-1)

Here is the output log from a sample run:

softmax-stablelm.txt

For comparison, here is running GPT-2 using HF transformers with the same change:

softmax-gpt-2.txt

Notice how the GPT-2 values are all well below 1e1 for each layer, while the StableLM numbers jump all the way up to 1e3. The GPT-2 behaviour is also observed for GPT-J and LLaMA models (these are the models that I currently play with the most). To me, it kind of makes sense to be this way and it seems to be correct, while the StableLM numbers are weird.

---

So is my understanding incorrect or is there something wrong with the StableLM model? In any case, I no longer think there is a bug in the ggml implementation.

[byroneverson]

I believe this behavior is correct and is a result of how the models were trained. The text output seems to be coherent and the values only rarely converge to -inf. I may be out of line, but is it possible this is normal? I will continue to look further into this but I doubt softmax would work at all if this was a major issue. If you have any further insight I would love to dive deeper.

[ggerganov]

is it possible this is normal?

Absolutely. It's just my intuitive understanding that the scaling before the soft max layer has the purpose of preventing exactly this kind of magnitude increase. But I could be wrong and this is fine.