Sync from v0.13

tests/models/quantization/test_modelopt.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# flake8: noqa
+"""Tests Model Optimizer fp8 models against ground truth generation
+Note: these tests will only pass on H100
+"""
+
+import os
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.quantization.utils import is_quant_method_supported
+from vllm import LLM, SamplingParams
+
+os.environ["TOKENIZERS_PARALLELISM"] = "true"
+
+MAX_MODEL_LEN = 1024
+
+MODELS = ["nvidia/Llama-3.1-8B-Instruct-FP8"]
+
+EXPECTED_STRS_MAP = {
+    "nvidia/Llama-3.1-8B-Instruct-FP8": [
+        "You're referring to VLLM, a high-performance Large Language Model (LLM) inference and",
+        "Here are the major milestones in the development of artificial intelligence (AI) from 1950 to ",
+        "The comparison between artificial intelligence (AI) and human intelligence in terms of processing information is a complex and",
+        'A neural network is a complex system modeled after the human brain, consisting of interconnected nodes or "ne',
+        "**The Spark of Imagination**\n\nZeta-5, a sleek and efficient robot, whir",
+        "The COVID-19 pandemic has had a profound impact on global economic structures and business models, leading to",
+        "The Mona Lisa, painted by Leonardo da Vinci in the early 16th century, is one of",
+        "Here are the translations:\n\n**Japanese:** 「早起きは早く獲物をとる",
+    ]
+}
+
+
+# This test compares against golden strings for exact match since
+# there is no baseline implementation to compare against
+# and is unstable w.r.t specifics of the fp8 implementation or
+# the hardware being run on.
+# Disabled to prevent it from breaking the build
+@pytest.mark.skip(
+    reason="Prevent unstable test based on golden strings from breaking the build."
+)
+@pytest.mark.skipif(
+    not is_quant_method_supported("fp8"),
+    reason="fp8 is not supported on this GPU type.",
+)
+@pytest.mark.parametrize("model_name", MODELS)
+def test_models(example_prompts, model_name) -> None:
+    llm = LLM(
+        model=model_name,
+        max_model_len=MAX_MODEL_LEN,
+        trust_remote_code=True,
+        enforce_eager=True,
+        quantization="modelopt",
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    formatted_prompts = [
+        tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        for prompt in example_prompts
+    ]
+    params = SamplingParams(max_tokens=20, temperature=0)
+    generations: list[str] = []
+    # Note: these need to be run 1 at a time due to numerical precision,
+    # since the expected strs were generated this way.
+    for prompt in formatted_prompts:
+        outputs = llm.generate(prompt, params)
+        generations.append(outputs[0].outputs[0].text)
+    del llm
+
+    print(model_name, generations)
+    expected_strs = EXPECTED_STRS_MAP[model_name]
+    for i in range(len(example_prompts)):
+        generated_str = generations[i]
+        expected_str = expected_strs[i]
+        assert expected_str == generated_str, (
+            f"Test{i}:\nExpected: {expected_str!r}\nvLLM: {generated_str!r}"
+        )