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85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Compare the outputs of a GPTQ model to a Marlin_24 model.
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Note: GPTQ and Marlin_24 do not have bitwise correctness.
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As a result, in this test, we just confirm that the top selected tokens of the
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Marlin/GPTQ models are in the top 3 selections of each other.
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"""
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from dataclasses import dataclass
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import pytest
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from tests.quantization.utils import is_quant_method_supported
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from vllm.platforms import current_platform
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from ..utils import check_logprobs_close
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@dataclass
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class ModelPair:
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model_marlin: str
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model_gptq: str
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model_pairs = [
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# 4-bit, group_size == 128
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ModelPair(
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model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-g128",
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model_gptq="alexm-nm/tinyllama-24-gptq-4bit-g128",
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),
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# # 4-bit, group_size == channelwise
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# ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-channelwise",
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# model_gptq="alexm-nm/tinyllama-24-gptq-4bit-channelwise"),
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# 8-bit, group_size == 128
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ModelPair(
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model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-g128",
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model_gptq="alexm-nm/tinyllama-24-gptq-8bit-g128",
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),
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# # 8-bit, group_size == channelwise
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# ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-channelwise",
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# model_gptq="alexm-nm/tinyllama-24-gptq-8bit-channelwise"),
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]
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@pytest.mark.flaky(reruns=2)
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@pytest.mark.skipif(
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not is_quant_method_supported("gptq_marlin_24")
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or current_platform.is_rocm()
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or not current_platform.is_cuda(),
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reason="Marlin24 is not supported on this GPU type.",
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)
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@pytest.mark.parametrize("model_pair", model_pairs)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [8])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(
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vllm_runner,
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example_prompts,
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model_pair: ModelPair,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with vllm_runner(
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model_pair.model_marlin, dtype=dtype, quantization="gptq_marlin_24"
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) as marlin_24_model:
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marlin_24_outputs = marlin_24_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs
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)
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with vllm_runner(
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model_pair.model_gptq, dtype=dtype, quantization="gptq"
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) as gptq_model:
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gptq_outputs = gptq_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs
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)
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check_logprobs_close(
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outputs_0_lst=gptq_outputs,
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outputs_1_lst=marlin_24_outputs,
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name_0="gptq",
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name_1="marlin_24",
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)
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