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llama.cpp/docs/HOWTO-add-model.md

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Add a new model architecture to llama.cpp

Adding a model requires few steps:

  1. Convert the model to GGUF
  2. Define the model architecture in llama.cpp
  3. Build the GGML graph implementation

After following these steps, you can open PR.

Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:

1. Convert the model to GGUF

This step is done in python with a convert script using the gguf library. Depending on the model architecture, you can use either convert.py or convert-hf-to-gguf.py.

The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.

The required steps to implement for an HF model are:

  1. Define the model Model.register annotation in a new Model subclass, example:
@Model.register("MyModelForCausalLM")
class MyModel(Model):
    model_arch = gguf.MODEL_ARCH.GROK
  1. Define the layout of the GGUF tensors in constants.py

Add an enum entry in MODEL_ARCH, the model human friendly name in MODEL_ARCH_NAMES and the GGUF tensor names in MODEL_TENSORS.

Example for falcon model:

    MODEL_ARCH.FALCON: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_NORM_2,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ]
  1. Map the original tensor names to the standardize equivalent in GGUF

As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.

Once you have found the GGUF tensor name equivalent, add it to the tensor_mapping.py file.

If the tensor name is part of a repetitive layer/block, the key word bid substitutes it.

Example for the normalization tensor in attention layers:

block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
        # Attention norm
        MODEL_TENSOR.ATTN_NORM: (
            "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
            "transformer.blocks.{bid}.norm_1",                      # mpt
            ...
        )
}

transformer.blocks.{bid}.norm_1 will be mapped to blk.{bid}.attn_norm in GGUF.

Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:

  • Model#set_gguf_parameters
  • Model#set_vocab
  • Model#write_tensors

NOTE: Tensor names must end with .weight suffix, that is the convention and several tools like quantize expect this to proceed the weights.

2. Define the model architecture in llama.cpp

The model params and tensors layout must be defined in llama.cpp:

  1. Define a new llm_arch
  2. Define the tensors layout in LLM_TENSOR_NAMES
  3. Add any non standard metadata in llm_load_hparams
  4. Create the tensors for inference in llm_load_tensors
  5. If the model has a RoPE operation, add the rope type in llama_rope_type

NOTE: The dimensions in ggml are typically in the reverse order of the pytorch dimensions.

3. Build the GGML graph implementation

This is the funniest part, you have to provide the inference graph implementation of the new model architecture in llama_build_graph.

Have a look to existing implementation like build_llama, build_dbrx or build_bert.

When implementing a new graph, please note that the underlying ggml backends might not support them all, support of missing backend operations can be added in another PR.

Note: to debug the inference graph: you can use eval-callback.

GGUF specification

https://github.com/ggerganov/ggml/blob/master/docs/gguf.md

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