# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Compares the outputs of gptq vs gptq_marlin.

Note: GPTQ and Marlin do not have bitwise correctness.
As a result, in this test, we just confirm that the top selected tokens of the
Marlin/GPTQ models are in the top 5 selections of each other.
Note: Marlin internally uses locks to synchronize the threads. This can
result in very slight nondeterminism for Marlin. As a result, we re-run the test
up to 3 times to see if we pass.
"""

import os

import pytest

from tests.quantization.utils import is_quant_method_supported
from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
from vllm.platforms import current_platform

from ..utils import check_logprobs_close
from ...utils import models_path_prefix

os.environ["TOKENIZERS_PARALLELISM"] = "true"

MAX_MODEL_LEN = 1024

MODELS = [
    # act_order==True, group_size=128
    (os.path.join(models_path_prefix, "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ"), "main"),
    # 8-bit, act_order==True, group_size=channelwise
    (os.path.join(models_path_prefix, "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ"), "gptq-8bit--1g-actorder_True"),
    # 4-bit, act_order==True, group_size=128
    (os.path.join(models_path_prefix, "TechxGenus/gemma-1.1-2b-it-GPTQ"), "main"),
]


@pytest.mark.flaky(reruns=3)
@pytest.mark.skipif(
    not is_quant_method_supported("gptq_marlin")
    or current_platform.is_rocm()
    or not current_platform.is_cuda(),
    reason="gptq_marlin is not supported on this GPU type.",
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half", "bfloat16"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    vllm_runner,
    example_prompts,
    model,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    model_name, revision = model

    # Run marlin.
    with vllm_runner(
        model_name=model_name,
        revision=revision,
        dtype=dtype,
        quantization="marlin",
        max_model_len=MAX_MODEL_LEN,
        tensor_parallel_size=1,
    ) as gptq_marlin_model:
        gptq_marlin_outputs = gptq_marlin_model.generate_greedy_logprobs(
            example_prompts[:-1], max_tokens, num_logprobs
        )
    _ROPE_DICT.clear()  # clear rope cache to avoid rope dtype error

    # Run gptq.
    # The naive gptq kernel doesn't support bf16 yet.
    # Here we always compare fp16/bf16 gpt marlin kernel
    # to fp16 gptq kernel.
    with vllm_runner(
        model_name=model_name,
        revision=revision,
        dtype="half",
        quantization="gptq",
        max_model_len=MAX_MODEL_LEN,
        tensor_parallel_size=1,
    ) as gptq_model:
        gptq_outputs = gptq_model.generate_greedy_logprobs(
            example_prompts[:-1], max_tokens, num_logprobs
        )

    check_logprobs_close(
        outputs_0_lst=gptq_outputs,
        outputs_1_lst=gptq_marlin_outputs,
        name_0="gptq",
        name_1="gptq_marlin",
    )
