"""Compare the outputs of a GPTQ model to a Marlin model.

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 3 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.

Run `pytest tests/models/test_marlin.py`.
"""

from dataclasses import dataclass

import pytest
import torch

from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS

capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
marlin_not_supported = (capability <
                        QUANTIZATION_METHODS["marlin"].get_min_capability())


@dataclass
class ModelPair:
    model_marlin: str
    model_gptq: str


model_pairs = [
    ModelPair(model_marlin="nm-testing/zephyr-beta-7b-marlin-g128",
              model_gptq="nm-testing/zephyr-beta-7b-gptq-g128"),
    ModelPair(model_marlin="robertgshaw2/zephyr-7b-beta-channelwise-marlin",
              model_gptq="robertgshaw2/zephyr-7b-beta-channelwise-gptq"),
    ModelPair(model_marlin="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin",
              model_gptq="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-gptq")
]


@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(marlin_not_supported,
                    reason="Marlin is not supported on this GPU type.")
@pytest.mark.parametrize("model_pair", model_pairs)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    vllm_runner,
    example_prompts,
    model_pair: ModelPair,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    marlin_model = vllm_runner(model_pair.model_marlin, dtype=dtype)
    marlin_outputs = marlin_model.generate_greedy_logprobs(
        example_prompts, max_tokens, num_logprobs)

    # Note: not sure why, but deleting just the model on Ada Lovelace
    #   does not free the GPU memory. On Ampere, deleting the just model
    #   frees the memory.
    del marlin_model

    gptq_model = vllm_runner(model_pair.model_gptq, dtype=dtype)
    gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
                                                       max_tokens,
                                                       num_logprobs)

    # Note: not sure why, but deleting just the model on Ada Lovelace
    #   does not free the GPU memory. On Ampere, deleting the just model
    #   frees the memory.
    del gptq_model

    # loop through the prompts
    for prompt_idx in range(len(example_prompts)):
        gptq_output_ids, gptq_output_str, gptq_logprobs = gptq_outputs[
            prompt_idx]
        marlin_output_ids, marlin_output_str, marlin_logprobs = marlin_outputs[
            prompt_idx]

        for idx, (gptq_output_id, marlin_output_id) in enumerate(
                zip(gptq_output_ids, marlin_output_ids)):
            # If sequence is not an exact match,
            if marlin_output_id != gptq_output_id:
                # Each predicted token must be in top 5 of the other's
                assert gptq_output_id in marlin_logprobs[idx], (
                    f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\n"
                    f"Marlin:\t{marlin_output_str!r}")
                assert marlin_output_id in gptq_logprobs[idx], (
                    f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\n"
                    f"Marlin:\t{marlin_output_str!r}")

                # Break out since sequences will now diverge.
                break