82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# 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.quant_model
|
|
@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:
|
|
model = 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 = model.generate(prompt, params)
|
|
generations.append(outputs[0].outputs[0].text)
|
|
del model
|
|
|
|
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}")
|