mirror of https://github.com/vllm-project/vllm.git
42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# flake8: noqa
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"""Tests Quark mxfp4 models against ground truth generation
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"""
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import pytest
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from vllm import LLM, SamplingParams
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MODELS = ["amd/Llama-2-7b-chat-hf-wmxfp4-amxfp4-kvfp8-scale-uint8"]
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EXPECTED_STRS_MAP = {
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"amd/Llama-2-7b-chat-hf-wmxfp4-amxfp4-kvfp8-scale-uint8": [
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'\n### Key Features\n\n* **High-throughput Inference**: vLL',
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'\nArtificial intelligence (AI) has evolved significantly since its inception in the 1',
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'Artificial intelligence (AI) and human intelligence (HI) are two distinct concepts that have been',
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'A neural network is a machine learning model inspired by the structure of the human brain. It consists of',
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'\nTitle: The Dreaming Robot\n\nAs the sun set on the bustling metropol',
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'\nThe COVID-19 pandemic has had a profound impact on global economic structures and business',
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'The Mona Lisa painting, created by Leonardo da Vinci in the early 16th',
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" everybody knows this proverbial saying, but did you know that it's not entirely accurate?",
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]
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}
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@pytest.mark.skip(reason="Model to be released in the future")
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@pytest.mark.quant_model
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@pytest.mark.parametrize("model_name", MODELS)
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def test_models(example_prompts, model_name) -> None:
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sampling_params = SamplingParams(max_tokens=20, temperature=0)
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llm = LLM(
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model=model_name,
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kv_cache_dtype="fp8",
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quantization="quark",
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)
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outputs = llm.generate(example_prompts, sampling_params)
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for i, output in enumerate(outputs):
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output_str = output.outputs[0].text
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expected_str = EXPECTED_STRS_MAP[model_name][i]
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assert expected_str == output_str, (
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f"Expected: {expected_str!r}\nvLLM: {output_str!r}")
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