[Usage]: Does class LLM support inference quantization on CPU?

[rsong0606]

Your current environment

Hey Team,

I was experimenting with class LLM using gptq_marlin on the GPU, and it is incredibly fast. However, when I tried running it on the CPU, it seems that the class does not support CPU usage. Is there an alternative that supports CPU, in case I missed something? Thanks.

class LLM:
    """An LLM for generating texts from given prompts and sampling parameters.

    This class includes a tokenizer, a language model (possibly distributed
    across multiple GPUs), and GPU memory space allocated for intermediate
    states (aka KV cache). Given a batch of prompts and sampling parameters,
    this class generates texts from the model, using an intelligent batching
    mechanism and efficient memory management.

    NOTE: This class is intended to be used for offline inference. For online
    serving, use the `AsyncLLMEngine` class instead.
    NOTE: For the comprehensive list of arguments, see `EngineArgs`.

    Args:
        model: The name or path of a HuggingFace Transformers model.
        tokenizer: The name or path of a HuggingFace Transformers tokenizer.
        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
            if available, and "slow" will always use the slow tokenizer.
        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer. Expect valid prompt_token_ids and None for prompt
            from the input.
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
        tensor_parallel_size: The number of GPUs to use for distributed
            execution with tensor parallelism.
        dtype: The data type for the model weights and activations. Currently,
            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
            the `torch_dtype` attribute specified in the model config file.
            However, if the `torch_dtype` in the config is `float32`, we will
            use `float16` instead.
        quantization: The method used to quantize the model weights. Currently,
            we support "awq", "gptq", "squeezellm", and "fp8" (experimental).
            If None, we first check the `quantization_config` attribute in the
            model config file. If that is None, we assume the model weights are
            not quantized and use `dtype` to determine the data type of
            the weights.
        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id.
        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id.
        seed: The seed to initialize the random number generator for sampling.
        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
            reserve for the model weights, activations, and KV cache. Higher
            values will increase the KV cache size and thus improve the model's
            throughput. However, if the value is too high, it may cause out-of-
            memory (OOM) errors.
        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
            This can be used for temporarily storing the states of the requests
            when their `best_of` sampling parameters are larger than 1. If all
            requests will have `best_of=1`, you can safely set this to 0.
            Otherwise, too small values may cause out-of-memory (OOM) errors.
        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode (DEPRECATED. Use `max_seq_len_to_capture` instead).
        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode.
        disable_custom_all_reduce: See ParallelConfig
    """

    def __init__(
        self,
        model: str,
        tokenizer: Optional[str] = None,
        tokenizer_mode: str = "auto",
        skip_tokenizer_init: bool = False,
        trust_remote_code: bool = False,
        tensor_parallel_size: int = 1,
        dtype: str = "auto",
        quantization: Optional[str] = None,
        revision: Optional[str] = None,
        tokenizer_revision: Optional[str] = None,
        seed: int = 0,
        gpu_memory_utilization: float = 0.9,
        swap_space: int = 4,
        enforce_eager: bool = False,
        max_context_len_to_capture: Optional[int] = None,
        max_seq_len_to_capture: int = 8192,
        disable_custom_all_reduce: bool = False,
        **kwargs,
    ) -> None:
        if "disable_log_stats" not in kwargs:
            kwargs["disable_log_stats"] = True
        engine_args = EngineArgs(
            model=model,
            tokenizer=tokenizer,
            tokenizer_mode=tokenizer_mode,
            skip_tokenizer_init=skip_tokenizer_init,
            trust_remote_code=trust_remote_code,
            tensor_parallel_size=tensor_parallel_size,
            dtype=dtype,
            quantization=quantization,
            revision=revision,
            tokenizer_revision=tokenizer_revision,
            seed=seed,
            gpu_memory_utilization=gpu_memory_utilization,
            swap_space=swap_space,
            enforce_eager=enforce_eager,
            max_context_len_to_capture=max_context_len_to_capture,
            max_seq_len_to_capture=max_seq_len_to_capture,
            disable_custom_all_reduce=disable_custom_all_reduce,
            **kwargs,
        )
        self.llm_engine = LLMEngine.from_engine_args(
            engine_args, usage_context=UsageContext.LLM_CLASS)
        self.request_counter = Counter()

my specs are attached:

CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.29.3
Libc version: glibc-2.31

Python version: 3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21)  [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA L4
Nvidia driver version: 535.86.10
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Byte Order:                           Little Endian
Address sizes:                        46 bits physical, 48 bits virtual
CPU(s):                               12
On-line CPU(s) list:                  0-11
Thread(s) per core:                   2
Core(s) per socket:                   6
Socket(s):                            1
NUMA node(s):                         1
Vendor ID:                            GenuineIntel
CPU family:                           6
Model:                                85
Model name:                           Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping:                             7
CPU MHz:                              2200.182
BogoMIPS:                             4400.36
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            192 KiB
L1i cache:                            192 KiB
L2 cache:                             6 MiB
L3 cache:                             38.5 MiB
NUMA node0 CPU(s):                    0-11

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.

[lukehare]

Same question.

I have been looking through the docs and CPU inference with any quantization method doesn't appear to be possible, but is it on the roadmap?

Thanks!