utils.py

import os

import torch

from tests.quantization.utils import is_quant_method_supported
from vllm import LLM, SamplingParams
from vllm.compilation.levels import CompilationLevel
from vllm.platforms import current_platform

TEST_MODELS = [
    ("facebook/opt-125m", {}),
    # TODO: add fake implementation for compressed-tensors
    # ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
    #     "dtype": torch.float16,
    #     "quantization": "compressed-tensors"
    # }),
    ("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", {
        "dtype": torch.float16,
        "quantization": "fp8"
    }),
    # TODO: add fake implementation for compressed-tensors
    # ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
    #     "quantization": "compressed-tensors"
    # }),
    ("meta-llama/Meta-Llama-3-8B", {}),
]

# TODO: enable in pytorch 2.5
if False and is_quant_method_supported("aqlm"):  # noqa: SIM223
    TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
        "quantization": "aqlm"
    }))

# TODO: enable in pytorch 2.5
if False and is_quant_method_supported("gguf"):  # noqa: SIM223
    TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", {
        "quantization": "gguf"
    }))

if is_quant_method_supported("gptq"):
    TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {
        "quantization": "gptq"
    }))

if is_quant_method_supported("gptq_marlin"):
    TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", {
        "quantization": "gptq_marlin"
    }))

if is_quant_method_supported("gptq_marlin_24"):
    TEST_MODELS.append(("alexm-nm/tinyllama-24-marlin24-4bit-g128", {
        "quantization": "gptq_marlin_24"
    }))

if is_quant_method_supported("marlin"):
    TEST_MODELS.append(("robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin", {
        "quantization": "marlin"
    }))

if not current_platform.is_rocm() and is_quant_method_supported("awq"):
    TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {
        "quantization": "AWQ"
    }))


def check_full_graph_support(model,
                             model_kwargs,
                             optimization_level,
                             tp_size=1):
    # make sure these models can be captured in full graph mode
    os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(optimization_level)
    os.environ["VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "1"

    # Inductor doesn't support fp8 and the base meta llama uses too
    # much memory.
    quantization = model_kwargs.get("quantization")
    if ((quantization == "fp8" or model == "meta-llama/Meta-Llama-3-8B")
            and optimization_level >= CompilationLevel.PIECEWISE):
        return

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0)
    llm = LLM(model=model,
              enforce_eager=True,
              tensor_parallel_size=tp_size,
              disable_custom_all_reduce=True,
              **model_kwargs)

    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}")