[Core] Support loading GGUF model

[Isotr0py]

FIX #1002

Features:

• This PR adds support for loading GGUF format model
• This PR will also add gguf to requirements.
• Support k-quants and imatrix-quants inference.
• Currently, LLaMa, Mistral and Qwen2 are supported.

Some issues:

• To use imatrix-quants, gguf installation from source is required.
• To run Qwen2 gguf model, gguf installation from source is required as well.

TODO:

• Support loading model from GGUF format
• Implement dequantize Q8_0 and Q4_0 tensors during inference instead of pre-dequantize in model loading
• Add gguf and update transformers in requirements.txt
• Add GGUF model tests

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---

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[AlpinDale]

Hi @Isotr0py, I can help with this PR if needed. I've already done some work implementing all GGUF quants + related kernels in vLLM. Let me know if you'd like to collaborate on this!

[Isotr0py]

@AlpinDale Thanks! I'm glad to push this forward by adding quants kernels!

I'm not familiar with the quantization in ggml and it's difficult for me to implement the mmq/mmvq ops.

And about this, I have several questions/ideas about the kernel implements:

1. Can we also implement CPU quants kernel besides CUDA kernel? So that CPU backend can also take advantage from this.
2. Can we get potential performance improvement by implementing the kernel with Triton?

[mgoin]

@Isotr0py I'm fine with the initial support being just naive dequantization, as long as we change this to dequant at runtime for each forward pass so there is a memory usage benefit. I think it is worth starting with the CUDA kernels for Linear modules first so it is easily accessible to users, then tackling the CPU implementation. The interesting starting point IMO is supporting the main quantization methods i.e. Q4, Q8, and especially the new IQ methods.

@AlpinDale it would be great if you could help get this into a similar state as you've implemented before!

[AlpinDale]

Great job @Isotr0py, sorry I was away for a while.

Would it be better to directly port the dequantization kernels to vLLM instead of relying on the transformers integration? They seem to have only the simple quants, and I don't imagine they're very performant. Aphrodite Engine already implements GGUF loading, so we can pull the kernels directly here if needed. Please see here for the kernels, and here for the GGUF linear method. One of the biggest problems I've had with GGUF (and exllamav2) are the quantized embeddings. If we can find an elegant solution to that, it'd do us a lot of good. GGUF quantizes both input and output embeddings, I believe. For that, please check out the linear.py and the vocab_parallel_embedding.py.

Let me know how I can help with the integration. Cheers!

[Isotr0py]

@AlpinDale I agree that we can directly port the Aphrodite Engine's dequantization kernels to vLLM. But I think we can also keep the transformers integration dequantization for CPU backend until we add the optimized kernel for CPU. (If I haven't missed something, Aphrodite Engine should only have CUDA kernels implemented for GGUF, right?)

IMO, I think vocab_parallel_embedding.py in Aphrodite Engine has been a good enough solution for quantized embeddings.

I also wonder if we can optimize the kernel's ops for merged linear, because we need to unpack the quantized weights in merged linear layer, collect and re-pack the outputs currently. (If this can be solved, we can support many models without modifying their implementation!)

(I will update this PR once I finish the work in another PR.)

[AlpinDale]

Yeah, the kernels are CUDA only (and they don't work with ROCm for now). It'd be exciting if this PR can be merged with the proper dequant kernels, so I can switch over my implementation to this too. GGUF in particular has made catching up with vLLM upstream a nightmare for me :)

Are you on slack or discord? We can discuss this in further depth if you'd like.

[Isotr0py]

@AlpinDale I think we can discuss this further on discord. How I can communicate with you on discord?

[AlpinDale]

My username is alpindale

[Isotr0py]

OK, I have added a check to raise exception for tp_size>1 when initialize GGUFConfig.

[mgoin]

Thanks for the nice work @Isotr0py and @AlpinDale - let's keep it up with improvements!

[inuwamobarak]

Nice update. I was wondering how I can load my .gguf model directly from my local directory with this new update.

# Initialize the model
model_path = "/content/qwen1_5-0_5b-chat-q2_k.gguf"
llm = LLM(model=model_path)

sampling_params = SamplingParams(temperature=0.5, max_tokens=200)
prompt = "How many helicopters can a human eat in one sitting?"
response = llm.generate(prompt, sampling_params)
output_tokens = len(response[0].outputs[0].token_ids)

generated_text = response[0].outputs[0].text
print(generated_text)

Error:
OSError: It looks like the config file at '/content/qwen1_5-0_5b-chat-q2_k.gguf' is not a valid JSON file.

Thanks

[inuwamobarak]

testing this file https://github.com/vllm-project/vllm/blob/main/examples/gguf_inference.py also gave a similar issue:

OSError: It looks like the config file at '/root/.cache/huggingface/hub/models--TheBloke--TinyLlama-1.1B-Chat-v1.0-GGUF/snapshots/52e7645ba7c309695bec7ac98f4f005b139cf465/tinyllama-1.1b-chat-v1.0.Q4_0.gguf' is not a valid JSON file.

[kalebeasilvadev]

@inuwamobarak I have the same message

[inuwamobarak]

Yes @kalebeasilvadev I prefer to load my model locally as .gguf file but it seems not to work.

I saw this commit from June but it gave the same JSON error too:

from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(
    model="/content/tinyllama-1.1b-chat-v0.3.Q2_K.gguf",
    tokenizer="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    load_format="gguf"
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

---

OSError: It looks like the config file at '/content/tinyllama-1.1b-chat-v0.3.Q2_K.gguf' is not a valid JSON file.

[mgoin]

@inuwamobarak @kalebeasilvadev
This model works fine from my testing just now. I am able to spin up a vLLM server:

wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q2_K.gguf
vllm serve tinyllama-1.1b-chat-v1.0.Q2_K.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0

Are you building from source or using the nightly build? This feature is not in a release yet, it didn't make it in 0.5.4 as you can see in the release notes.

If you want to try the GGUF support you must build main from source or use the nightly build:

export VLLM_VERSION=0.5.4 # vLLM's main branch version is currently set to latest released tag
pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/nightly/vllm-${VLLM_VERSION}-cp38-abi3-manylinux1_x86_64.whl
# You can also access a specific commit
# export VLLM_COMMIT=fd95e026e0f9f50bacf1a63ef419df8bacfc99c0
# pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/${VLLM_COMMIT}/vllm-${VLLM_VERSION}-cp38-abi3-manylinux1_x86_64.whl

[vbiral]

@Isotr0py
I found one problem when loading some GGUF versions of Llama 3.1.
Looks like that RoPE weights in the GGUF model don't have a conversion to the HF model.

I changed the code from the function "gguf_quant_weights_iterator" to remove this weight and after that the LLM load and run worked:

    for tensor in reader.tensors:
        if(tensor.name != "rope_freqs.weight"):
            weight_type = tensor.tensor_type
            name = gguf_to_hf_name_map[tensor.name]

            if weight_type.name != "F32":
                weight_type_name = name.replace("weight", "qweight_type")
                weight_type = torch.tensor(weight_type)
                yield weight_type_name, weight_type

    for tensor in reader.tensors:
        if(tensor.name != "rope_freqs.weight"):
            weight = tensor.data
            weight_type = tensor.tensor_type
            name = gguf_to_hf_name_map[tensor.name]

            if weight_type.name != "F32":
                name = name.replace("weight", "qweight")
            param = torch.tensor(weight)
            yield name, param

I'm not a specialist in LLM architecture and don't know if this can impact the results, could you evaluate if what I did makes sense?

[inuwamobarak]

@inuwamobarak @kalebeasilvadev This model works fine from my testing just now. I am able to spin up a vLLM server:

wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q2_K.gguf
vllm serve tinyllama-1.1b-chat-v1.0.Q2_K.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0

Are you building from source or using the nightly build? This feature is not in a release yet, it didn't make it in 0.5.4 as you can see in the release notes.

If you want to try the GGUF support you must build main from source or use the nightly build:

export VLLM_VERSION=0.5.4 # vLLM's main branch version is currently set to latest released tag
pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/nightly/vllm-${VLLM_VERSION}-cp38-abi3-manylinux1_x86_64.whl
# You can also access a specific commit
# export VLLM_COMMIT=fd95e026e0f9f50bacf1a63ef419df8bacfc99c0
# pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/${VLLM_COMMIT}/vllm-${VLLM_VERSION}-cp38-abi3-manylinux1_x86_64.whl

Thank you @mgoin it worked now

[Isotr0py]

@vbiral Thanks for reporting!
Seems that the gguf_to_hf_name_map didn't handle rope_freqs correctly. I will have a look and fix it.

[yiakwy-xpu-ml-framework-team]

Yeah, the kernels are CUDA only (and they don't work with ROCm for now). It'd be exciting if this PR can be merged with the proper dequant kernels, so I can switch over my implementation to this too. GGUF in particular has made catching up with vLLM upstream a nightmare for me :)

Are you on slack or discord? We can discuss this in further depth if you'd like.

Has the kernels been verified with ROCm in MI30X machines ? Better to have a micro benchmark datasheet.