repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/pooling/test_dse_qwen2_vl.py | tests/models/multimodal/pooling/test_dse_qwen2_vl.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Callable
import pytest
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import Qwen2VLForConditionalGeneration
from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
from ....utils import large_gpu_test
from ...utils import check_embeddings_close
HF_TEXT_PROMPTS = [
# T -> X
(
"Query: Find me an everyday image that matches the given caption: The label of the object is stop sign", # noqa: E501,
Image.new("RGB", (56, 56)),
),
# T -> X
(
"Query: Retrieve an image of this caption: cherry blossom",
Image.new("RGB", (56, 56)),
),
]
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": "What is shown in this image?",
"cherry_blossom": "What is shown in this image?",
}
)
MODELS = ["MrLight/dse-qwen2-2b-mrl-v1"]
def get_messages(image: Image.Image, text: str, embed_text: bool):
# assert False, 'remember to use outer [] as required'
if embed_text:
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": Image.new("RGB", (56, 56)),
"resized_height": 1,
"resized_width": 1,
}, # need a dummy image here for an easier process.
{"type": "text", "text": text},
],
}
]
else:
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": text},
],
}
]
return messages
def apply_chat_template_and_add_eos(
messages: list[dict],
apply_chat_template_fn: Callable,
):
prompt = (
apply_chat_template_fn(messages, tokenize=False, add_generation_prompt=True)
+ "<|endoftext|>"
)
return prompt
def _run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
input_texts: list[str],
input_images: PromptImageInput,
embed_texts: list[bool],
model: str,
*,
dtype: str,
) -> None:
"""SET PYTHONPATH"""
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
with vllm_runner(
model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=8192
) as vllm_model:
tokenizer = vllm_model.llm.get_tokenizer()
texts = [
# this is necessary because vllm_model.embed will not apply any
# templating to the prompt, and therefore lacks an image_pad
# token unless one is inserted beforehand (the (28,28) image
# above is converted to an image pad token by the chat template).
apply_chat_template_and_add_eos(
get_messages(image, text, False),
apply_chat_template_fn=tokenizer.apply_chat_template,
)
for text, image in zip(input_texts, input_images)
# vllm will replace the pad token with the actual image,
# which may be a placeholder image, later.
]
vllm_outputs = vllm_model.embed(texts, images=input_images)
hf_outputs = []
with hf_runner(
model, dtype=dtype, auto_cls=Qwen2VLForConditionalGeneration
) as hf_model:
prompts = []
for text, image, embed_text in zip(input_texts, input_images, embed_texts):
# dse requires non-standard input processing
# because it needs an image_pad token
messages = get_messages(image, text, embed_text)
prompt = apply_chat_template_and_add_eos(
messages, hf_model.processor.apply_chat_template
)
prompts.append(prompt)
all_inputs = hf_model.get_inputs(
prompts=prompts,
images=input_images,
)
with torch.no_grad():
all_outputs = []
for inputs in all_inputs:
inputs = hf_model.model.prepare_inputs_for_generation(
**inputs,
cache_position=torch.arange(1), # 1 for batch size
use_cache=False,
)
outputs = hf_model.model(
**hf_model.wrap_device(inputs),
return_dict=True,
output_hidden_states=True,
)
pooled_output = F.normalize(
outputs.hidden_states[-1][0, -1], p=2, dim=-1
)
all_outputs.append(pooled_output.tolist())
hf_outputs = all_outputs
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_models_text(
hf_runner,
vllm_runner,
image_assets,
model: str,
dtype: str,
) -> None:
input_texts_images = [
(text, image_placeholder) for text, image_placeholder in HF_TEXT_PROMPTS
]
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]
embed_texts = [True] * len(input_texts)
_run_test(
hf_runner,
vllm_runner,
input_texts,
input_images, # type: ignore
embed_texts,
model,
dtype=dtype,
)
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_models_image(
hf_runner,
vllm_runner,
image_assets,
model: str,
dtype: str,
) -> None:
input_texts_images = [
(text, asset.pil_image) for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
]
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]
embed_texts = [False] * len(input_texts)
_run_test(
hf_runner,
vllm_runner,
input_texts,
input_images,
embed_texts,
model,
dtype=dtype,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/pooling/test_clip.py | tests/models/multimodal/pooling/test_clip.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from transformers import CLIPModel
from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
from ...utils import check_embeddings_close
HF_TEXT_PROMPTS = [
"a photo of a stop sign",
"a photo of a cherry blossom",
]
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": "",
"cherry_blossom": "",
}
)
MODELS = ["openai/clip-vit-base-patch32"]
def _run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
input_texts: list[str],
input_images: PromptImageInput,
model: str,
*,
dtype: str,
) -> None:
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
with vllm_runner(
model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=77
) as vllm_model:
vllm_outputs = vllm_model.embed(input_texts, images=input_images)
with hf_runner(model, dtype=dtype, auto_cls=CLIPModel) as hf_model:
all_inputs = hf_model.get_inputs(input_texts, images=input_images)
all_outputs = []
for inputs in all_inputs:
inputs = hf_model.wrap_device(inputs)
if "pixel_values" in inputs:
pooled_output = hf_model.model.get_image_features(
pixel_values=inputs.pixel_values,
).squeeze(0)
else:
pooled_output = hf_model.model.get_text_features(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
).squeeze(0)
all_outputs.append(pooled_output.tolist())
hf_outputs = all_outputs
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["float"])
def test_models_text(
hf_runner,
vllm_runner,
image_assets,
model: str,
dtype: str,
) -> None:
input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS]
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]
_run_test(
hf_runner,
vllm_runner,
input_texts,
input_images, # type: ignore
model,
dtype=dtype,
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["float"])
def test_models_image(
hf_runner,
vllm_runner,
image_assets,
model: str,
dtype: str,
) -> None:
input_texts_images = [
(text, asset.pil_image) for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
]
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]
_run_test(
hf_runner,
vllm_runner,
input_texts,
input_images,
model,
dtype=dtype,
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["float"])
def test_models_text_image_no_crash(
vllm_runner,
image_assets,
model: str,
dtype: str,
) -> None:
texts = [HF_TEXT_PROMPTS[0]]
images = [image_assets[0].pil_image]
with vllm_runner(
model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=77
) as vllm_model:
with pytest.raises(ValueError, match="not both"):
vllm_model.embed(texts, images=images)
# Should still be able to run subsequent requests
vllm_model.embed(texts)
vllm_model.embed([""], images=images)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/pooling/test_radio.py | tests/models/multimodal/pooling/test_radio.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn as nn
from huggingface_hub import snapshot_download
from transformers import AutoConfig, AutoModel, CLIPImageProcessor
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.model_executor.models.radio import RadioModel
from vllm.transformers_utils.configs.radio import RadioConfig
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
from ....conftest import ImageTestAssets
# we use snapshot_download to prevent conflicts between
# dynamic_module and trust_remote_code for hf_runner
DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
@torch.inference_mode()
def run_radio_test(
image_assets: ImageTestAssets,
model_id: str,
*,
dtype: str,
):
model = snapshot_download(model_id, allow_patterns=DOWNLOAD_PATTERN)
torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
img_processor = CLIPImageProcessor.from_pretrained(model)
images = [asset.pil_image for asset in image_assets]
# Input resolution must be a multiple of `self.min_resolution_step`.
# Using `self.get_nearest_supported_resolution`, for assets 432x642 the
# nearest supported resolution is 432x640.
pixel_values = [
img_processor(image, return_tensors="pt").pixel_values.to(torch_dtype)[
:, :, :, :640
]
for image in images
]
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
# RADIO model on HF does not properly handle torch_dtype argument
# And relies on args["dtype"] which we have to patch manually:
config.args["dtype"] = torch_dtype
hf_model = AutoModel.from_pretrained(
model_id,
config=config,
dtype=torch_dtype,
trust_remote_code=True,
).to("cuda")
hf_model.eval()
# A HF model has image normalization as a part of model's forward
# However in vLLM we don't make normalization a part of the model
# forward step since mean/std stored as model's parameters and
# subject to precision loss (when using fp16/bf16) which negatively
# affects evaluation benchmarks.
hf_model.make_preprocessor_external()
hf_outputs_per_image = [
hf_model(pixel_value.to("cuda")).features for pixel_value in pixel_values
]
radio_config = RadioConfig(
model_name=config.args["model"], reg_tokens=config.args["register_multiple"]
)
vllm_model = RadioModel(radio_config)
vllm_model.load_weights(hf_model.state_dict())
vllm_model = vllm_model.to("cuda", torch_dtype)
vllm_outputs_per_image = [
vllm_model(pixel_values=pixel_value.to("cuda")) for pixel_value in pixel_values
]
del vllm_model, hf_model
cleanup_dist_env_and_memory()
cos_similar = nn.CosineSimilarity(dim=-1)
for vllm_output, hf_output in zip(vllm_outputs_per_image, hf_outputs_per_image):
assert cos_similar(vllm_output, hf_output).mean() > 0.99
@pytest.mark.parametrize(
"model_id",
[
"nvidia/C-RADIOv2-H",
],
)
@pytest.mark.parametrize("dtype", ["half", "bfloat16"])
def test_radio(dist_init, image_assets, model_id, dtype: str) -> None:
run_radio_test(
image_assets,
model_id,
dtype=dtype,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_voxtral.py | tests/models/multimodal/generation/test_voxtral.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
import pytest
import pytest_asyncio
from mistral_common.audio import Audio
from mistral_common.protocol.instruct.chunk import AudioChunk, RawAudio, TextChunk
from mistral_common.protocol.instruct.messages import UserMessage
from vllm.tokenizers.mistral import MistralTokenizer
from ....conftest import AudioTestAssets
from ....utils import RemoteOpenAIServer
from .test_ultravox import MULTI_AUDIO_PROMPT, run_multi_audio_test
MODEL_NAME = "mistralai/Voxtral-Mini-3B-2507"
MISTRAL_FORMAT_ARGS = [
"--tokenizer_mode",
"mistral",
"--config_format",
"mistral",
"--load_format",
"mistral",
]
@pytest.fixture()
def server(request, audio_assets: AudioTestAssets):
args = [
"--enforce-eager",
"--limit-mm-per-prompt",
json.dumps({"audio": len(audio_assets)}),
] + MISTRAL_FORMAT_ARGS
with RemoteOpenAIServer(
MODEL_NAME, args, env_dict={"VLLM_AUDIO_FETCH_TIMEOUT": "30"}
) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def client(server):
async with server.get_async_client() as async_client:
yield async_client
def _get_prompt(audio_assets, question):
tokenizer = MistralTokenizer.from_pretrained(MODEL_NAME)
audios = [
Audio.from_file(str(audio_assets[i].get_local_path()), strict=False)
for i in range(len(audio_assets))
]
audio_chunks = [
AudioChunk(input_audio=RawAudio.from_audio(audio)) for audio in audios
]
text_chunk = TextChunk(text=question)
messages = [UserMessage(content=[*audio_chunks, text_chunk]).to_openai()]
return tokenizer.apply_chat_template(messages=messages)
@pytest.mark.core_model
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models_with_multiple_audios(
vllm_runner,
audio_assets: AudioTestAssets,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
vllm_prompt = _get_prompt(audio_assets, MULTI_AUDIO_PROMPT)
run_multi_audio_test(
vllm_runner,
[(vllm_prompt, [audio.audio_and_sample_rate for audio in audio_assets])],
MODEL_NAME,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tokenizer_mode="mistral",
)
@pytest.mark.asyncio
async def test_online_serving(client, audio_assets: AudioTestAssets):
"""Exercises online serving with/without chunked prefill enabled."""
def asset_to_chunk(asset):
audio = Audio.from_file(str(asset.get_local_path()), strict=False)
audio.format = "wav"
audio_dict = AudioChunk.from_audio(audio).to_openai()
return audio_dict
audio_chunks = [asset_to_chunk(asset) for asset in audio_assets]
text = f"What's happening in these {len(audio_assets)} audio clips?"
messages = [
{
"role": "user",
"content": [*audio_chunks, {"type": "text", "text": text}],
}
]
chat_completion = await client.chat.completions.create(
model=MODEL_NAME, messages=messages, max_tokens=10
)
assert len(chat_completion.choices) == 1
choice = chat_completion.choices[0]
assert choice.message.content == "In the first audio clip, you hear a brief"
assert choice.finish_reason == "length"
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_granite_speech.py | tests/models/multimodal/generation/test_granite_speech.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
import pytest
from transformers import AutoModelForSpeechSeq2Seq
from vllm.logprobs import SampleLogprobs
from vllm.lora.request import LoRARequest
from vllm.platforms import current_platform
from ....conftest import AudioTestAssets, HfRunner, PromptAudioInput, VllmRunner
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
HF_AUDIO_PROMPT = "<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|><|audio|>can you transcribe the speech into a written format?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>" # noqa: E501
def vllm_to_hf_output(
vllm_output: tuple[list[int], str, SampleLogprobs | None],
) -> tuple[list[int], str, SampleLogprobs | None]:
"""Sanitize hf output to be comparable with vllm output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|end_of_text|>"
return output_ids, hf_output_str, out_logprobs
MODEL_NAME = "ibm-granite/granite-speech-3.3-2b"
# Audio lora co-exists directly in the model directory, but
# currently still needs to be passed directly to vLLM.
audio_lora_path = MODEL_NAME
models = [MODEL_NAME]
@pytest.fixture
def granite_speech_attention_config():
"""Return attention config for Granite Speech tests on ROCm."""
if current_platform.is_rocm():
return {"backend": "ROCM_AITER_FA"}
return None
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
inputs: Sequence[tuple[list[str], PromptAudioInput]],
model: str,
*,
max_model_len: int,
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: str | None = None,
attention_config: dict | None = None,
):
"""Inference result should be the same between hf and vllm.
All the audio fixtures for the test are from AUDIO_ASSETS.
For huggingface runner, we provide the audio as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
# max_model_len should be greater than image_feature_size
with vllm_runner(
model,
runner="generate",
max_model_len=max_model_len,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"audio": 1},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enable_lora=True,
max_lora_rank=64,
enforce_eager=True,
attention_config=attention_config,
) as vllm_model:
lora_request = LoRARequest("audio", 1, audio_lora_path)
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=audios,
lora_request=lora_request,
)
for prompts, audios in inputs
]
with hf_runner(model, dtype=dtype, auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
hf_processor = hf_model.processor
eos_token_id = hf_processor.tokenizer.eos_token_id
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(
prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=[audios],
eos_token_id=eos_token_id,
)
for prompts, audios in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=[vllm_to_hf_output(output) for output in vllm_outputs],
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"dtype", ["float16"] if current_platform.is_rocm() else ["bfloat16"]
)
@pytest.mark.parametrize(
"max_model_len", [512] if current_platform.is_rocm() else [2048]
)
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_models(
hf_runner,
vllm_runner,
model: str,
audio_assets: AudioTestAssets,
granite_speech_attention_config,
dtype: str,
max_model_len: int,
max_tokens: int,
num_logprobs: int,
) -> None:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
audio, sr = audio_assets[0].audio_and_sample_rate
# This model expects 16k sample rate, which our test audio
# already is; if this changes, it may break this test,
# so we check it directly
assert sr == 16000
run_test(
hf_runner,
vllm_runner,
[
([HF_AUDIO_PROMPT], [audio]),
],
model,
dtype=dtype,
max_model_len=max_model_len,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tensor_parallel_size=1,
attention_config=granite_speech_attention_config,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_multimodal_gguf.py | tests/models/multimodal/generation/test_multimodal_gguf.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
os.environ["TOKENIZERS_PARALLELISM"] = "true"
from typing import Any, NamedTuple
import pytest
from huggingface_hub import hf_hub_download
from pytest import MarkDecorator
from transformers import AutoModelForImageTextToText
from tests.quantization.utils import is_quant_method_supported
from vllm.assets.image import ImageAsset
from vllm.multimodal.image import rescale_image_size
from vllm.utils.torch_utils import set_default_torch_num_threads
from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner
from ...utils import check_logprobs_close
class GGUFMMTestConfig(NamedTuple):
original_model: str
gguf_repo: str
gguf_backbone: str
gguf_mmproj: str
prompt: list[str]
image_names: list[str] # Store names, load PIL images at runtime
max_model_len: int = 4096
marks: list[MarkDecorator] = []
mm_processor_kwargs: dict[str, Any] = {}
@property
def gguf_model(self):
hf_hub_download(self.gguf_repo, filename=self.gguf_mmproj)
return hf_hub_download(self.gguf_repo, filename=self.gguf_backbone)
# Common prompts aligned with test_common.py "gemma3" entry format
_GEMMA3_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": (
"<bos><start_of_turn>user\n"
"<start_of_image>What's the content in the center of the image?"
"<end_of_turn>\n<start_of_turn>model\n"
),
"cherry_blossom": (
"<bos><start_of_turn>user\n"
"<start_of_image>What is the season?"
"<end_of_turn>\n<start_of_turn>model\n"
),
}
)
# Image asset names - load at runtime to avoid pickle issues with subprocess
_GEMMA3_IMAGE_NAMES = ["stop_sign", "cherry_blossom"]
# Regular multimodal (no pan-and-scan) - uses QAT Q4_0 GGUF
GEMMA3_CONFIG = GGUFMMTestConfig(
original_model="google/gemma-3-4b-it",
gguf_repo="google/gemma-3-4b-it-qat-q4_0-gguf",
gguf_backbone="gemma-3-4b-it-q4_0.gguf",
gguf_mmproj="mmproj-model-f16-4B.gguf",
prompt=_GEMMA3_PROMPTS,
image_names=_GEMMA3_IMAGE_NAMES,
max_model_len=4096,
marks=[pytest.mark.core_model],
mm_processor_kwargs={},
)
# Pan-and-scan multimodal - uses unquantized BF16 GGUF
GEMMA3_CONFIG_PAN_AND_SCAN = GGUFMMTestConfig(
original_model="google/gemma-3-4b-it",
gguf_repo="unsloth/gemma-3-4b-it-GGUF",
gguf_backbone="gemma-3-4b-it-BF16.gguf",
gguf_mmproj="mmproj-BF16.gguf",
prompt=_GEMMA3_PROMPTS,
image_names=_GEMMA3_IMAGE_NAMES,
max_model_len=4096,
marks=[pytest.mark.core_model],
mm_processor_kwargs={"do_pan_and_scan": True},
)
MODELS_TO_TEST = [GEMMA3_CONFIG, GEMMA3_CONFIG_PAN_AND_SCAN]
def run_multimodal_gguf_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
model: GGUFMMTestConfig,
dtype: str,
max_tokens: int,
num_logprobs: int,
):
# Load images at runtime (inside subprocess) to avoid pickle issues
images = [ImageAsset(name).pil_image for name in model.image_names]
size_factors = [0.25, 0.5, 1.0]
inputs_per_image = [
(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
)
for image, prompt in zip(images, model.prompt)
]
# NOTE: Run vLLM first to avoid CUDA init issues with multiprocessing fork.
# Run GGUF model via vLLM.
with (
set_default_torch_num_threads(1),
vllm_runner(
model_name=model.gguf_model,
enforce_eager=True,
tokenizer_name=model.original_model,
dtype=dtype,
max_model_len=model.max_model_len,
mm_processor_kwargs=model.mm_processor_kwargs,
) as gguf_model,
):
gguf_outputs_per_case = [
gguf_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
)
for prompts, images in inputs_per_image
]
# Then run HfRunner for HuggingFace baseline comparison.
with hf_runner(
model.original_model,
dtype=dtype,
auto_cls=AutoModelForImageTextToText,
) as hf_model:
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
)
for prompts, images in inputs_per_image
]
for hf_outputs, gguf_outputs in zip(hf_outputs_per_case, gguf_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=gguf_outputs,
name_0="hf",
name_1="gguf",
)
@pytest.mark.skipif(
not is_quant_method_supported("gguf"),
reason="gguf is not supported on this GPU type.",
)
@pytest.mark.parametrize(
"model",
[
pytest.param(test_config, marks=test_config.marks)
for test_config in MODELS_TO_TEST
],
)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [10])
def test_gemma3_mm_gguf(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
model: GGUFMMTestConfig,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
run_multimodal_gguf_test(
hf_runner, vllm_runner, model, dtype, max_tokens, num_logprobs
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_qwen2_vl.py | tests/models/multimodal/generation/test_qwen2_vl.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any, TypedDict
import numpy.typing as npt
import pytest
import torch
from PIL import Image
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.video import rescale_video_size, sample_frames_from_video
from ....conftest import (
IMAGE_ASSETS,
VIDEO_ASSETS,
PromptImageInput,
PromptVideoInput,
VllmRunner,
)
from ...utils import check_logprobs_close
@pytest.fixture(scope="function", autouse=True)
def enable_pickle(monkeypatch):
"""`LLM.apply_model` requires pickling a function."""
monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
models = ["Qwen/Qwen2-VL-2B-Instruct"]
target_dtype = "half"
IMAGE_PLACEHOLDER = "<|vision_start|><|image_pad|><|vision_end|>"
VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
MODEL_HIDDEN_SIZE = 1536
def qwen2_vl_chat_template(*query):
return f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{''.join(query)}<|im_end|><|im_start|>assistant\n" # noqa: E501
IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": qwen2_vl_chat_template(
IMAGE_PLACEHOLDER,
"What is the biggest text's content in this image?",
),
"cherry_blossom": qwen2_vl_chat_template(
IMAGE_PLACEHOLDER,
"What is the season shown in this image? ",
"Reply with a short sentence (no more than 20 words)",
),
}
)
VIDEO_PROMPTS = VIDEO_ASSETS.prompts(
{
"baby_reading": qwen2_vl_chat_template(
VIDEO_PLACEHOLDER,
"Describe this video with a short sentence ",
"(no more than 20 words)",
),
}
)
MULTIIMAGE_PROMPT = qwen2_vl_chat_template(
IMAGE_PLACEHOLDER,
IMAGE_PLACEHOLDER,
"Describe these two images separately. ",
"For each image, reply with a short sentence ",
"(no more than 10 words).",
)
class Qwen2VLPromptImageEmbeddingInput(TypedDict):
image_embeds: torch.Tensor
image_grid_thw: torch.Tensor
class Qwen2VLPromptVideoEmbeddingInput(TypedDict):
video_embeds: torch.Tensor
video_grid_thw: torch.Tensor
def batch_make_image_embeddings(
image_batches: list[Image.Image | list[Image.Image]],
processor,
llm: VllmRunner,
) -> list[Qwen2VLPromptImageEmbeddingInput]:
"""batched image embeddings for Qwen2-VL
This will infer all images' embeddings in a single batch,
and split the result according to input batches.
image_batches:
- Single-image batches: `list[Image.Image]`
- Multiple-image batches: `list[list[Image.Image]]]`
returns: `list[Qwen2VLPromptImageEmbeddingInput]`
"""
image_batches_: list[Any] = image_batches[:]
# convert single-image batches to multiple-image batches
for idx in range(len(image_batches_)):
if not isinstance(image_batches_[idx], list):
image_batches_[idx] = [image_batches_[idx]]
assert isinstance(image_batches_[idx], list)
# append all images into a list (as a batch)
images: list[Image.Image] = []
for image_batch in image_batches_:
images += image_batch
# image to pixel values
image_processor = processor.image_processor
preprocess_result = image_processor.preprocess(
images=images, return_tensors="pt"
).data
pixel_values = preprocess_result["pixel_values"]
image_grid_thw = preprocess_result["image_grid_thw"]
# pixel values to embeddings & grid_thws
def get_image_embeds(model):
with torch.no_grad():
visual = model.visual
pixel_values_on_device = pixel_values.to(visual.device, dtype=visual.dtype)
return visual(pixel_values_on_device, grid_thw=image_grid_thw).cpu()
image_embeds = torch.concat(llm.apply_model(get_image_embeds))
# split into original batches
result: list[Qwen2VLPromptImageEmbeddingInput] = []
image_counter = 0
embed_counter = 0
for image_batch in image_batches_:
cur_batch_image_count = len(image_batch)
merge_size = image_processor.merge_size
cur_batch_embed_len = sum(
grid_thw.prod(-1) // merge_size // merge_size
for grid_thw in image_grid_thw[
image_counter : image_counter + cur_batch_image_count
]
)
result.append(
{
"image_embeds": image_embeds[
embed_counter : embed_counter + cur_batch_embed_len
],
"image_grid_thw": image_grid_thw[
image_counter : image_counter + cur_batch_image_count
],
}
)
embed_counter += cur_batch_embed_len
image_counter += cur_batch_image_count
# ensure we don't lose any images or embeddings
assert embed_counter == image_embeds.size(0)
assert image_counter == image_grid_thw.size(0)
assert len(image_batches) == len(result)
return result
def batch_make_video_embeddings(
video_batches: PromptVideoInput, processor, llm: VllmRunner
) -> list[Qwen2VLPromptVideoEmbeddingInput]:
"""batched video embeddings for Qwen2-VL
A NDArray represents a single video's all frames.
This will infer all videos' embeddings in a single batch,
and split the result according to input batches.
video_batches:
- Single-video batches: `list[NDArray]`
- Multiple-video batches: `list[list[NDArray]]`
"""
video_batches_: list[Any] = video_batches[:]
for idx in range(len(video_batches_)):
if not isinstance(video_batches_[idx], list):
single_video_batch: list[npt.NDArray] = [video_batches_[idx]]
video_batches_[idx] = single_video_batch
assert isinstance(video_batches_[idx], list)
# append all videos into a list (as a batch)
videos: list[npt.NDArray] = []
for video_batch in video_batches_:
videos += video_batch
# video to pixel values
image_processor = processor.image_processor
preprocess_result = image_processor.preprocess(
images=None, videos=videos, return_tensors="pt"
).data
pixel_values = preprocess_result["pixel_values_videos"]
video_grid_thw = preprocess_result["video_grid_thw"]
# pixel values to embeddings & grid_thws
def get_image_embeds(model):
with torch.no_grad():
visual = model.visual
pixel_values_on_device = pixel_values.to(visual.device, dtype=visual.dtype)
return visual(pixel_values_on_device, grid_thw=video_grid_thw).cpu()
video_embeds = torch.concat(llm.apply_model(get_image_embeds))
# split into original batches
result: list[Qwen2VLPromptVideoEmbeddingInput] = []
video_counter = 0
embed_counter = 0
for video_batch in video_batches_:
cur_batch_video_count = len(video_batch)
merge_size = image_processor.merge_size
cur_batch_embed_len = sum(
grid_thw.prod(-1) // merge_size // merge_size
for grid_thw in video_grid_thw[
video_counter : video_counter + cur_batch_video_count
]
)
result.append(
{
"video_embeds": video_embeds[
embed_counter : embed_counter + cur_batch_embed_len
],
"video_grid_thw": video_grid_thw[
video_counter : video_counter + cur_batch_video_count
],
}
)
embed_counter += cur_batch_embed_len
video_counter += cur_batch_video_count
# ensure we don't lose any videos or embeddings
assert embed_counter == video_embeds.size(0)
assert video_counter == video_grid_thw.size(0)
assert len(video_batches) == len(result)
return result
def run_embedding_input_test(
vllm_runner: type[VllmRunner],
inputs: list[tuple[list[str], PromptImageInput, PromptVideoInput]],
model: str,
*,
dtype: str,
max_tokens: int,
num_logprobs: int,
mm_limit: int,
tensor_parallel_size: int,
distributed_executor_backend: str | None = None,
):
"""Inference result should be the same between
original image/video input and image/video embeddings input.
"""
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(model)
# max_model_len should be greater than image_feature_size
with vllm_runner(
model,
runner="generate",
max_model_len=4000,
max_num_seqs=3,
dtype=dtype,
limit_mm_per_prompt={"image": mm_limit, "video": mm_limit},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
default_torch_num_threads=1,
enable_mm_embeds=True,
) as vllm_model:
outputs_per_case_for_original_input = [
vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images or None,
videos=videos or None,
)
for prompts, images, videos in inputs
]
outputs_per_case_for_embeddings_input = [
vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=batch_make_image_embeddings(images, processor, vllm_model)
if images
else None,
videos=batch_make_video_embeddings(videos, processor, vllm_model)
if videos
else None,
)
for prompts, images, videos in inputs
]
for outputs_for_original_input, outputs_for_embeddings_input in zip(
outputs_per_case_for_original_input, outputs_per_case_for_embeddings_input
):
check_logprobs_close(
outputs_0_lst=outputs_for_original_input,
outputs_1_lst=outputs_for_embeddings_input,
name_0="original_input",
name_1="embeddings_input",
)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# Single-scale
[0.5],
# Single-scale, batched
[0.5, 0.5],
# Multi-scale
[0.25, 0.5, 0.5],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_qwen2_vl_image_embeddings_input(
vllm_runner,
image_assets,
model,
size_factors,
dtype,
max_tokens,
num_logprobs,
monkeypatch,
) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_case: list[tuple[list[str], PromptImageInput, PromptVideoInput]] = [
(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
[],
)
for image, prompt in zip(images, IMAGE_PROMPTS)
]
run_embedding_input_test(
vllm_runner,
inputs_per_case,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
[],
# Single-scale
[0.5],
# Single-scale, batched
[0.5, 0.5],
# Multi-scale
[0.25, 0.5, 0.5],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_qwen2_vl_multiple_image_embeddings_input(
vllm_runner,
image_assets,
model,
size_factors,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_case: list[tuple[list[str], PromptImageInput, PromptVideoInput]] = [
(
[MULTIIMAGE_PROMPT for _ in size_factors],
[
[rescale_image_size(image, factor) for image in images]
for factor in size_factors
],
[],
)
]
run_embedding_input_test(
vllm_runner,
inputs_per_case,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=2,
tensor_parallel_size=1,
)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# Single-scale
[0.5],
# Single-scale, batched
[0.5, 0.5],
# Multi-scale
[0.25, 0.25, 0.5],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_qwen2_vl_video_embeddings_input(
vllm_runner,
video_assets,
model,
size_factors,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
num_frames = 4
sampled_vids = [
sample_frames_from_video(asset.np_ndarrays, num_frames)
for asset in video_assets
]
inputs_per_case: list[tuple[list[str], PromptImageInput, PromptVideoInput]] = [
(
[prompt for _ in size_factors],
[],
[rescale_video_size(video, factor) for factor in size_factors],
)
for video, prompt in zip(sampled_vids, VIDEO_PROMPTS)
]
run_embedding_input_test(
vllm_runner,
inputs_per_case,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_common.py | tests/models/multimodal/generation/test_common.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Common tests for testing .generate() functionality for single / multiple
image, embedding, and video support for different VLMs in vLLM.
"""
import math
from collections import defaultdict
from pathlib import PosixPath
import pytest
from packaging.version import Version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForImageTextToText,
AutoModelForTextToWaveform,
)
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.platforms import current_platform
from vllm.utils.func_utils import identity
from ....conftest import (
IMAGE_ASSETS,
AudioTestAssets,
HfRunner,
ImageTestAssets,
VideoTestAssets,
VllmRunner,
)
from ....utils import create_new_process_for_each_test, large_gpu_mark, multi_gpu_marks
from ...utils import check_outputs_equal
from .vlm_utils import custom_inputs, model_utils, runners
from .vlm_utils.case_filtering import get_parametrized_options
from .vlm_utils.types import (
CustomTestOptions,
ExpandableVLMTestArgs,
VLMTestInfo,
VLMTestType,
)
COMMON_BROADCAST_SETTINGS = {
"test_type": VLMTestType.IMAGE,
"dtype": "half",
"max_tokens": 5,
"tensor_parallel_size": 2,
"hf_model_kwargs": {"device_map": "auto"},
"image_size_factors": [(0.25, 0.5, 1.0)],
"distributed_executor_backend": (
"ray",
"mp",
),
}
### Test configuration for specific models
# NOTE: The convention of the test settings below is to lead each test key
# with the name of the model arch used in the test, using underscores in place
# of hyphens; this makes it more convenient to filter tests for a specific kind
# of model. For example....
#
# To run all test types for a specific key:
# use the k flag to substring match with a leading square bracket; if the
# model arch happens to be a substring of another one, you can add a
# trailing hyphen. E.g.,
# - pytest $TEST_FILE -k "[llava-"
# prevents matching on "[llava_next-" & will match just the enabled cases
# for llava, i.e., single image, image embedding, and custom input tests.
#
# To run a test for a Test Info for just one of multiple models:
# use the k flag to substring match the model name, e.g.,
# - pytest $TEST_FILE -k OpenGVLab/InternVL2-1B
# prevents matching on nGVLab/InternVL2-2B.
#
# You can also combine substrings to match more granularly.
# ex 1:
# pytest $TEST_FILE -k "test_single_image and OpenGVLab/InternVL2-1B"
# will run only test_single_image* for OpenGVLab/InternVL2-1B; this would
# match both wrappers for single image tests, since it also matches
# test_single_image_heavy (which forks if we have a distributed backend)
# ex 2:
# pytest $TEST_FILE -k "[llava- or [intern_vl-"
# will run all of the tests for only llava & internvl.
#
# NOTE you can add --collect-only to any of the above commands to see
# which cases would be selected and deselected by pytest. In general,
# this is a good idea for checking your command first, since tests are slow.
VLM_TEST_SETTINGS = {
#### Core tests to always run in the CI
"llava": VLMTestInfo(
models=["llava-hf/llava-1.5-7b-hf"],
test_type=(VLMTestType.EMBEDDING, VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS),
prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
convert_assets_to_embeddings=model_utils.get_llava_embeddings,
max_model_len=4096,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
custom_test_opts=[
CustomTestOptions(
inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:"
),
limit_mm_per_prompt={"image": 4},
)
],
vllm_runner_kwargs={"enable_mm_embeds": True},
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
"paligemma": VLMTestInfo(
models=["google/paligemma-3b-mix-224"],
test_type=VLMTestType.IMAGE,
prompt_formatter=identity,
img_idx_to_prompt=lambda idx: "",
# Paligemma uses its own sample prompts because the default one fails
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "caption es",
"cherry_blossom": "What is in the picture?",
}
),
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
dtype="bfloat16",
marks=[
pytest.mark.skip(reason="vLLM does not support PrefixLM attention mask")
],
),
"qwen2_5_vl": VLMTestInfo(
models=["Qwen/Qwen2.5-VL-3B-Instruct"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",
enforce_eager=False,
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
"qwen2_5_omni": VLMTestInfo(
models=["Qwen/Qwen2.5-Omni-3B"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|vision_bos|><|IMAGE|><|vision_eos|>",
video_idx_to_prompt=lambda idx: "<|vision_bos|><|VIDEO|><|vision_eos|>",
max_model_len=4096,
max_num_seqs=2,
num_logprobs=6 if current_platform.is_cpu() else 5,
auto_cls=AutoModelForTextToWaveform,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
patch_hf_runner=model_utils.qwen2_5_omni_patch_hf_runner,
image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
"qwen3_vl": VLMTestInfo(
models=["Qwen/Qwen3-VL-4B-Instruct"],
test_type=(
VLMTestType.IMAGE,
VLMTestType.MULTI_IMAGE,
VLMTestType.VIDEO,
),
enforce_eager=False,
needs_video_metadata=True,
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
num_logprobs=20,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
patch_hf_runner=model_utils.qwen3_vl_patch_hf_runner,
vllm_runner_kwargs={
"attention_config": {
"backend": "ROCM_AITER_FA",
},
}
if current_platform.is_rocm()
else None,
image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[
pytest.mark.core_model,
],
),
"ultravox": VLMTestInfo(
models=["fixie-ai/ultravox-v0_5-llama-3_2-1b"],
test_type=VLMTestType.AUDIO,
prompt_formatter=lambda audio_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{audio_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
audio_idx_to_prompt=lambda idx: "<|audio|>",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModel,
hf_output_post_proc=model_utils.ultravox_trunc_hf_output,
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
#### Transformers fallback to test
## To reduce test burden, we only test batching arbitrary image size
# Dynamic image length and number of patches
"llava-onevision-transformers": VLMTestInfo(
models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
max_model_len=16384,
hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
),
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
image_size_factors=[(0.25, 0.5, 1.0)],
vllm_runner_kwargs={
"model_impl": "transformers",
"default_torch_num_threads": 1,
},
# FIXME: Investigate why the test hangs
# when processing the 3rd prompt in vLLM
marks=[pytest.mark.core_model, pytest.mark.skip(reason="Test hangs")],
),
# Gemma3 has bidirectional mask on images
"gemma3-transformers": VLMTestInfo(
models=["google/gemma-3-4b-it"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda vid_prompt: f"<'<bos><start_of_turn>user\n{vid_prompt}<start_of_image><end_of_turn>\n<start_of_turn>model\n", # noqa: E501
max_model_len=4096,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.gemma3_vllm_to_hf_output,
image_size_factors=[(0.25, 0.5, 1.0)],
vllm_runner_kwargs={
"model_impl": "transformers",
},
marks=[pytest.mark.core_model],
),
"idefics3-transformers": VLMTestInfo(
models=["HuggingFaceTB/SmolVLM-256M-Instruct"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:", # noqa: E501
img_idx_to_prompt=lambda idx: "<image>",
max_model_len=8192,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
hf_output_post_proc=model_utils.idefics3_trunc_hf_output,
image_size_factors=[(0.25, 0.5, 1.0)],
vllm_runner_kwargs={
"model_impl": "transformers",
},
marks=[pytest.mark.core_model],
),
# Pixel values from processor are not 4D or 5D arrays
"qwen2_5_vl-transformers": VLMTestInfo(
models=["Qwen/Qwen2.5-VL-3B-Instruct"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
image_size_factors=[(0.25, 0.2, 0.15)],
vllm_runner_kwargs={
"model_impl": "transformers",
# TODO: [ROCm] Revert this once issue #30167 is resolved
**(
{
"mm_processor_kwargs": {
"min_pixels": 256 * 28 * 28,
"max_pixels": 1280 * 28 * 28,
},
}
if current_platform.is_rocm()
else {}
),
},
marks=[large_gpu_mark(min_gb=80 if current_platform.is_rocm() else 32)],
),
#### Extended model tests
"aria": VLMTestInfo(
models=["rhymes-ai/Aria"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n ", # noqa: E501
img_idx_to_prompt=lambda idx: "<fim_prefix><|img|><fim_suffix>\n",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<vlm_image>Please describe the image shortly.",
"cherry_blossom": "<vlm_image>Please infer the season with reason.",
}
),
multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.",
stop_str=["<|im_end|>"],
image_size_factors=[(0.10, 0.15)],
max_tokens=64,
marks=[large_gpu_mark(min_gb=64)],
),
"aya_vision": VLMTestInfo(
models=["CohereLabs/aya-vision-8b"],
test_type=(VLMTestType.IMAGE),
prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<image>What's the content in the center of the image?",
"cherry_blossom": "<image>What is the season?",
}
),
multi_image_prompt="<image><image>Describe the two images in detail.",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}},
),
"aya_vision-multi_image": VLMTestInfo(
models=["CohereLabs/aya-vision-8b"],
test_type=(VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<image>What's the content in the center of the image?",
"cherry_blossom": "<image>What is the season?",
}
),
multi_image_prompt="<image><image>Describe the two images in detail.",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}},
marks=[large_gpu_mark(min_gb=32)],
),
"blip2": VLMTestInfo(
models=["Salesforce/blip2-opt-2.7b"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"Question: {img_prompt} Answer:",
img_idx_to_prompt=lambda idx: "",
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.blip2_vllm_to_hf_output,
# FIXME: https://github.com/huggingface/transformers/pull/38510
marks=[pytest.mark.skip("Model is broken")],
),
"chameleon": VLMTestInfo(
models=["facebook/chameleon-7b"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
# For chameleon, we only compare the sequences
vllm_output_post_proc=lambda vllm_output, model: vllm_output[:2],
hf_output_post_proc=lambda hf_output, model: hf_output[:2],
comparator=check_outputs_equal,
max_tokens=8,
dtype="bfloat16",
),
"deepseek_vl_v2": VLMTestInfo(
models=["Isotr0py/deepseek-vl2-tiny"], # model repo using dynamic module
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|User|>: {img_prompt}\n\n<|Assistant|>: ", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<image>\nWhat's the content in the center of the image?",
"cherry_blossom": "<image>\nPlease infer the season with reason in details.", # noqa: E501
}
),
multi_image_prompt="image_1:<image>\nimage_2:<image>\nWhich image can we see the car and the tower?", # noqa: E501
patch_hf_runner=model_utils.deepseekvl2_patch_hf_runner,
hf_output_post_proc=model_utils.deepseekvl2_trunc_hf_output,
stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"],
image_size_factors=[(1.0,), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
),
"fuyu": VLMTestInfo(
models=["adept/fuyu-8b"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"{img_prompt}\n",
img_idx_to_prompt=lambda idx: "",
max_model_len=2048,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
use_tokenizer_eos=True,
vllm_output_post_proc=model_utils.fuyu_vllm_to_hf_output,
num_logprobs=10,
image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
marks=[large_gpu_mark(min_gb=32)],
),
"gemma3": VLMTestInfo(
models=["google/gemma-3-4b-it"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<start_of_image>What's the content in the center of the image?", # noqa: E501
"cherry_blossom": "<start_of_image>What is the season?",
}
),
multi_image_prompt="<start_of_image><start_of_image>Describe the two images in detail.", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"do_pan_and_scan": True}},
patch_hf_runner=model_utils.gemma3_patch_hf_runner,
),
"glm4v": VLMTestInfo(
models=["zai-org/glm-4v-9b"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<|begin_of_image|><|endoftext|><|end_of_image|>What's the content in the center of the image?", # noqa: E501
"cherry_blossom": "<|begin_of_image|><|endoftext|><|end_of_image|>What is the season?", # noqa: E501
}
),
max_model_len=2048,
max_num_seqs=2,
get_stop_token_ids=lambda tok: [151329, 151336, 151338],
patch_hf_runner=model_utils.glm4v_patch_hf_runner,
# The image embeddings match with HF but the outputs of the language
# decoder are only consistent up to 2 decimal places.
# So, we need to reduce the number of tokens for the test to pass.
max_tokens=8,
num_logprobs=10,
auto_cls=AutoModelForCausalLM,
marks=[large_gpu_mark(min_gb=32)],
),
"glm4_1v": VLMTestInfo(
models=["zai-org/GLM-4.1V-9B-Thinking"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"[gMASK]<|user|>\n{img_prompt}<|assistant|>\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|begin_of_image|><|image|><|end_of_image|>",
video_idx_to_prompt=lambda idx: "<|begin_of_video|><|video|><|end_of_video|>",
max_model_len=2048,
max_num_seqs=2,
get_stop_token_ids=lambda tok: [151329, 151336, 151338],
num_logprobs=10,
image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
auto_cls=AutoModelForImageTextToText,
marks=[large_gpu_mark(min_gb=32)],
),
"glm4_1v-video": VLMTestInfo(
models=["zai-org/GLM-4.1V-9B-Thinking"],
# GLM4.1V require include video metadata for input
test_type=VLMTestType.CUSTOM_INPUTS,
prompt_formatter=lambda vid_prompt: f"[gMASK]<|user|>\n{vid_prompt}<|assistant|>\n", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
patch_hf_runner=model_utils.glm4_1v_patch_hf_runner,
custom_test_opts=[
CustomTestOptions(
inputs=custom_inputs.video_with_metadata_glm4_1v(),
limit_mm_per_prompt={"video": 1},
)
],
marks=[large_gpu_mark(min_gb=32)],
),
"h2ovl": VLMTestInfo(
models=[
"h2oai/h2ovl-mississippi-800m",
"h2oai/h2ovl-mississippi-2b",
],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|prompt|>{img_prompt}<|end|><|answer|>",
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<image>\nWhat's the content in the center of the image?",
"cherry_blossom": "<image>\nWhat is the season?",
}
),
multi_image_prompt="Image-1: <image>\nImage-2: <image>\nDescribe the two images in short.", # noqa: E501
max_model_len=8192,
use_tokenizer_eos=True,
num_logprobs=10,
patch_hf_runner=model_utils.h2ovl_patch_hf_runner,
),
"idefics3": VLMTestInfo(
models=["HuggingFaceTB/SmolVLM-256M-Instruct"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:", # noqa: E501
img_idx_to_prompt=lambda idx: "<image>",
max_model_len=8192,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
hf_output_post_proc=model_utils.idefics3_trunc_hf_output,
),
"intern_vl": VLMTestInfo(
models=[
"OpenGVLab/InternVL2-1B",
"OpenGVLab/InternVL2-2B",
# FIXME: Config cannot be loaded in transformers 4.52
# "OpenGVLab/Mono-InternVL-2B",
],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<image>\nWhat's the content in the center of the image?",
"cherry_blossom": "<image>\nWhat is the season?",
}
),
multi_image_prompt="Image-1: <image>\nImage-2: <image>\nDescribe the two images in short.", # noqa: E501
max_model_len=4096,
use_tokenizer_eos=True,
patch_hf_runner=model_utils.internvl_patch_hf_runner,
),
"intern_vl-video": VLMTestInfo(
models=[
"OpenGVLab/InternVL3-1B",
],
test_type=VLMTestType.VIDEO,
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
video_idx_to_prompt=lambda idx: "<video>",
max_model_len=8192,
use_tokenizer_eos=True,
patch_hf_runner=model_utils.internvl_patch_hf_runner,
num_logprobs=10 if current_platform.is_rocm() else 5,
),
"intern_vl-hf": VLMTestInfo(
models=["OpenGVLab/InternVL3-1B-hf"],
test_type=(
VLMTestType.IMAGE,
VLMTestType.MULTI_IMAGE,
VLMTestType.VIDEO,
),
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<IMG_CONTEXT>",
video_idx_to_prompt=lambda idx: "<video>",
max_model_len=8192,
use_tokenizer_eos=True,
auto_cls=AutoModelForImageTextToText,
),
"isaac": VLMTestInfo(
models=["PerceptronAI/Isaac-0.1"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: (
f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n"
),
img_idx_to_prompt=lambda idx: "<image>",
single_image_prompts=IMAGE_ASSETS.prompts(
{
"stop_sign": "<vlm_image>Please describe the image shortly.",
"cherry_blossom": "<vlm_image>Please infer the season with reason.",
}
),
multi_image_prompt=(
"Picture 1: <vlm_image>\n"
"Picture 2: <vlm_image>\n"
"Describe these two images with one paragraph respectively."
),
enforce_eager=False,
max_model_len=4096,
max_num_seqs=2,
hf_model_kwargs={"device_map": "auto"},
patch_hf_runner=model_utils.isaac_patch_hf_runner,
image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
),
"kimi_vl": VLMTestInfo(
models=["moonshotai/Kimi-VL-A3B-Instruct"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|im_user|>user<|im_middle|>{img_prompt}<|im_end|><|im_assistant|>assistant<|im_middle|>", # noqa: E501
img_idx_to_prompt=lambda _: "<|media_start|>image<|media_content|><|media_pad|><|media_end|>", # noqa: E501
max_model_len=8192,
max_num_seqs=2,
dtype="bfloat16",
tensor_parallel_size=1,
vllm_output_post_proc=model_utils.kimiv_vl_vllm_to_hf_output,
marks=[large_gpu_mark(min_gb=48)],
),
"llama4": VLMTestInfo(
models=["meta-llama/Llama-4-Scout-17B-16E-Instruct"],
prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{img_prompt}<|eot|><|header_start|>assistant<|header_end|>\n\n", # noqa: E501
img_idx_to_prompt=lambda _: "<|image|>",
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
distributed_executor_backend="mp",
image_size_factors=[(0.25, 0.5, 1.0)],
hf_model_kwargs={"device_map": "auto"},
max_model_len=8192,
max_num_seqs=4,
dtype="bfloat16",
auto_cls=AutoModelForImageTextToText,
tensor_parallel_size=4,
marks=multi_gpu_marks(num_gpus=4),
),
"llava_next": VLMTestInfo(
models=["llava-hf/llava-v1.6-mistral-7b-hf"],
test_type=(VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS),
prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]",
max_model_len=10240,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
custom_test_opts=[
CustomTestOptions(
inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]"
),
limit_mm_per_prompt={"image": 4},
)
],
),
"llava_onevision": VLMTestInfo(
models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
test_type=VLMTestType.CUSTOM_INPUTS,
prompt_formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
num_video_frames=16,
max_model_len=16384,
hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
),
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
custom_test_opts=[
CustomTestOptions(
inputs=custom_inputs.multi_video_multi_aspect_ratio_inputs(
formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
),
limit_mm_per_prompt={"video": 4},
)
],
),
"llava_next_video": VLMTestInfo(
models=["llava-hf/LLaVA-NeXT-Video-7B-hf"],
test_type=VLMTestType.VIDEO,
prompt_formatter=lambda vid_prompt: f"USER: {vid_prompt} ASSISTANT:",
num_video_frames=16,
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output,
),
"mantis": VLMTestInfo(
models=["TIGER-Lab/Mantis-8B-siglip-llama3"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
max_model_len=4096,
get_stop_token_ids=lambda tok: [128009],
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.mantis_vllm_to_hf_output,
patch_hf_runner=model_utils.mantis_patch_hf_runner,
),
"minicpmv_25": VLMTestInfo(
models=["openbmb/MiniCPM-Llama3-V-2_5"],
test_type=VLMTestType.IMAGE,
prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
max_model_len=4096,
max_num_seqs=2,
get_stop_token_ids=lambda tok: [tok.eos_id, tok.eot_id],
hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
patch_hf_runner=model_utils.minicpmv_25_patch_hf_runner,
# FIXME: https://huggingface.co/openbmb/MiniCPM-V-2_6/discussions/55
marks=[pytest.mark.skip("HF import fails")],
),
"minicpmo_26": VLMTestInfo(
models=["openbmb/MiniCPM-o-2_6"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
max_model_len=4096,
max_num_seqs=2,
get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(
["<|im_end|>", "<|endoftext|>"]
),
hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
patch_hf_runner=model_utils.minicpmo_26_patch_hf_runner,
# FIXME: https://huggingface.co/openbmb/MiniCPM-o-2_6/discussions/49
marks=[pytest.mark.skip("HF import fails")],
),
"minicpmv_26": VLMTestInfo(
models=["openbmb/MiniCPM-V-2_6"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
max_model_len=4096,
max_num_seqs=2,
get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(
["<|im_end|>", "<|endoftext|>"]
),
hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
patch_hf_runner=model_utils.minicpmv_26_patch_hf_runner,
),
"minimax_vl_01": VLMTestInfo(
models=["MiniMaxAI/MiniMax-VL-01"],
prompt_formatter=lambda img_prompt: f"<beginning_of_sentence>user: {img_prompt} assistant:<end_of_sentence>", # noqa: E501
img_idx_to_prompt=lambda _: "<image>",
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
max_model_len=8192,
max_num_seqs=4,
dtype="bfloat16",
hf_output_post_proc=model_utils.minimax_vl_01_hf_output,
patch_hf_runner=model_utils.minimax_vl_01_patch_hf_runner,
auto_cls=AutoModelForImageTextToText,
marks=[
large_gpu_mark(min_gb=80),
# TODO: [ROCm] Fix pickle issue with ROCm spawn and tp>1
pytest.mark.skipif(
current_platform.is_rocm(),
reason=(
"ROCm: Model too large for single GPU; "
"multi-GPU blocked by HF _LazyConfigMapping pickle issue with spawn"
),
),
],
),
"molmo": VLMTestInfo(
models=["allenai/Molmo-7B-D-0924"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=identity,
max_model_len=4096,
max_num_seqs=2,
patch_hf_runner=model_utils.molmo_patch_hf_runner,
),
"ovis1_6-gemma2": VLMTestInfo(
models=["AIDC-AI/Ovis1.6-Gemma2-9B"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n", # noqa: E501
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | true |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_keye.py | tests/models/multimodal/generation/test_keye.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import asdict
from typing import NamedTuple
import pytest
from PIL.Image import Image
from transformers import AutoProcessor
from vllm import LLM, EngineArgs, SamplingParams
from vllm.multimodal.utils import encode_image_url
MODEL_NAME = "Kwai-Keye/Keye-VL-8B-Preview"
QUESTION = "What is the content of each image?"
class ModelRequestData(NamedTuple):
engine_args: EngineArgs
prompt: str
image_data: list[Image]
stop_token_ids: list[int] | None = None
chat_template: str | None = None
sampling_params: SamplingParams | None = None
@pytest.mark.core_model
@pytest.mark.parametrize("question", [QUESTION])
def test_keye_vl(
image_assets,
question: str,
):
images = [asset.pil_image for asset in image_assets]
image_urls = [encode_image_url(image) for image in images]
engine_args = EngineArgs(
model=MODEL_NAME,
trust_remote_code=True,
max_model_len=8192,
max_num_seqs=5,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
engine_args = asdict(engine_args) | {"seed": 42}
llm = LLM(**engine_args)
sampling_params = SamplingParams(
temperature=0.0, max_tokens=256, stop_token_ids=None
)
outputs = llm.generate(
{
"prompt": prompt,
"multi_modal_data": {"image": images},
},
sampling_params=sampling_params,
)
print("-" * 50)
for o in outputs:
generated_text = o.outputs[0].text
print(generated_text)
assert len(generated_text) > 10, (
f"Generated text is too short: {generated_text}"
)
print("-" * 50)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_ultravox.py | tests/models/multimodal/generation/test_ultravox.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from typing import Any
import numpy as np
import pytest
import pytest_asyncio
from transformers import AutoTokenizer
from ....conftest import AUDIO_ASSETS, AudioTestAssets, VllmRunner
from ....utils import RemoteOpenAIServer
from ...registry import HF_EXAMPLE_MODELS
MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
AUDIO_PROMPTS = AUDIO_ASSETS.prompts(
{
"mary_had_lamb": "Transcribe this into English.",
"winning_call": "What is happening in this audio clip?",
}
)
MULTI_AUDIO_PROMPT = "Describe each of the audios above."
AudioTuple = tuple[np.ndarray, int]
VLLM_PLACEHOLDER = "<|audio|>"
HF_PLACEHOLDER = "<|audio|>"
CHUNKED_PREFILL_KWARGS = {
"enable_chunked_prefill": True,
"max_num_seqs": 2,
# Use a very small limit to exercise chunked prefill.
"max_num_batched_tokens": 16,
}
def params_kwargs_to_cli_args(params_kwargs: dict[str, Any]) -> list[str]:
"""Convert kwargs to CLI args."""
args = []
for key, value in params_kwargs.items():
if isinstance(value, bool):
if value:
args.append(f"--{key.replace('_', '-')}")
else:
args.append(f"--{key.replace('_', '-')}={value}")
return args
@pytest.fixture(
params=[
pytest.param({}, marks=pytest.mark.cpu_model),
pytest.param(CHUNKED_PREFILL_KWARGS),
]
)
def server(request, audio_assets: AudioTestAssets):
args = [
"--dtype",
"bfloat16",
"--max-model-len",
"4096",
"--enforce-eager",
"--limit-mm-per-prompt",
json.dumps({"audio": len(audio_assets)}),
"--trust-remote-code",
] + params_kwargs_to_cli_args(request.param)
with RemoteOpenAIServer(
MODEL_NAME, args, env_dict={"VLLM_AUDIO_FETCH_TIMEOUT": "30"}
) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def client(server):
async with server.get_async_client() as async_client:
yield async_client
def _get_prompt(audio_count, question, placeholder):
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
placeholder = f"{placeholder}\n" * audio_count
return tokenizer.apply_chat_template(
[{"role": "user", "content": f"{placeholder}{question}"}],
tokenize=False,
add_generation_prompt=True,
)
def run_multi_audio_test(
vllm_runner: type[VllmRunner],
prompts_and_audios: list[tuple[str, list[AudioTuple]]],
model: str,
*,
dtype: str,
max_tokens: int,
num_logprobs: int,
**kwargs,
):
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
with vllm_runner(
model,
dtype=dtype,
enforce_eager=True,
limit_mm_per_prompt={
"audio": max((len(audio) for _, audio in prompts_and_audios))
},
**kwargs,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
[prompt for prompt, _ in prompts_and_audios],
max_tokens,
num_logprobs=num_logprobs,
audios=[audios for _, audios in prompts_and_audios],
)
# The HuggingFace model doesn't support multiple audios yet, so
# just assert that some tokens were generated.
assert all(tokens for tokens, *_ in vllm_outputs)
@pytest.mark.core_model
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize(
"vllm_kwargs",
[
pytest.param({}, marks=pytest.mark.cpu_model),
pytest.param(CHUNKED_PREFILL_KWARGS),
],
)
def test_models_with_multiple_audios(
vllm_runner,
audio_assets: AudioTestAssets,
dtype: str,
max_tokens: int,
num_logprobs: int,
vllm_kwargs: dict,
) -> None:
vllm_prompt = _get_prompt(len(audio_assets), MULTI_AUDIO_PROMPT, VLLM_PLACEHOLDER)
run_multi_audio_test(
vllm_runner,
[(vllm_prompt, [audio.audio_and_sample_rate for audio in audio_assets])],
MODEL_NAME,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
**vllm_kwargs,
)
@pytest.mark.asyncio
async def test_online_serving(client, audio_assets: AudioTestAssets):
"""Exercises online serving with/without chunked prefill enabled."""
messages = [
{
"role": "user",
"content": [
*[
{"type": "audio_url", "audio_url": {"url": audio.url}}
for audio in audio_assets
],
{
"type": "text",
"text": f"What's happening in these {len(audio_assets)} audio clips?", # noqa: E501
},
],
}
]
chat_completion = await client.chat.completions.create(
model=MODEL_NAME, messages=messages, max_tokens=10
)
assert len(chat_completion.choices) == 1
choice = chat_completion.choices[0]
assert choice.finish_reason == "length"
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_interleaved.py | tests/models/multimodal/generation/test_interleaved.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.multimodal.image import convert_image_mode
models = ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"]
def base_prompt(modalities_str: str) -> str:
return f"<|im_start|>user {modalities_str}\nDescribe what you see from these items.<|im_end|><|im_start|>assistant\n" # noqa: E501
INTERLEAVED_PROMPT = base_prompt("<image><video><image>\n")
NONINTERLEAVED_PROMPT = base_prompt("<image><image><video>\n")
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", ["float16"])
@pytest.mark.parametrize("max_tokens", [128])
def test_models(vllm_runner, model, dtype: str, max_tokens: int) -> None:
"""
This is a simple test to check if interleaved and non-interleaved prompts
give the same result.
"""
image_cherry = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
image_stop = convert_image_mode(ImageAsset("stop_sign").pil_image, "RGB")
images = [image_cherry, image_stop]
video = VideoAsset(name="baby_reading", num_frames=16).np_ndarrays
inputs = [
(
[INTERLEAVED_PROMPT],
[images],
[video],
),
(
[NONINTERLEAVED_PROMPT],
[images],
[video],
),
]
with vllm_runner(
model,
runner="generate",
dtype=dtype,
limit_mm_per_prompt={"image": 2},
max_model_len=32768,
max_num_seqs=2,
tensor_parallel_size=1,
enforce_eager=True,
) as vllm_model:
vllm_outputs_per_case = [
vllm_model.generate_greedy(
prompts, max_tokens, images=images, videos=videos
)
for prompts, images, videos in inputs
]
all_results = [output[0][1] for output in vllm_outputs_per_case]
outputs = [
(total_str, total_str.find("assistant\n") + len("assistant\n"))
for total_str in all_results
]
prompt_lengths = [prompt_len for _, prompt_len in outputs]
generated_strs = [total_str[prompt_len:] for total_str, prompt_len in outputs]
interleaved_prompt_len, noninterleaved_prompt_len = prompt_lengths
interleaved_output_str, noninterleaved_output_str = generated_strs
# The two prompts are identical except for the order of modality tokens.
assert interleaved_prompt_len == noninterleaved_prompt_len
# The two generated strings should be different because of the
# interleaved modality tokens.
assert interleaved_output_str != noninterleaved_output_str
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_maverick.py | tests/models/multimodal/generation/test_maverick.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Create a reduced-layer version of the Maverick model for testing purposes.
This script creates a new model with fewer layers by:
1. Loading the original Maverick model configuration
2. Creating a reduced configuration
3. Generating compatible safetensors files with appropriate weights
4. Creating the necessary index files for vLLM compatibility
"""
import json
import shutil
from pathlib import Path
from typing import Any
import pytest
import torch
from safetensors.torch import save_file
from transformers import AutoConfig, AutoProcessor, AutoTokenizer, GenerationConfig
from vllm import LLM, SamplingParams
from vllm.v1.executor.abstract import Executor
from vllm.v1.kv_cache_interface import ChunkedLocalAttentionSpec, FullAttentionSpec
from ....utils import multi_gpu_test
# Sample prompts for testing
PROMPTS: list[str] = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
def run_maverick_serving(model: str):
"""Test Llama-4-Maverick model with vLLM LLM class using CLI equivalent
options with reduced layers.
"""
try:
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(
model=model,
max_model_len=2048,
enforce_eager=True,
tensor_parallel_size=8,
enable_expert_parallel=True,
trust_remote_code=True,
gpu_memory_utilization=0.4,
kv_cache_dtype="fp8",
)
outputs = llm.generate(PROMPTS, sampling_params)
# Print the outputs
print("\nGenerated Outputs:\n" + "-" * 60)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}")
print(f"Output: {generated_text!r}")
print("-" * 60)
except Exception as e:
print(f"Error initializing or running model: {e}")
raise
def get_rope_layers_config(model_path: str) -> list[int]:
"""
Get the interleaved RoPE configuration from HuggingFace config
Args:
model_path: Path to the local directory containing the reduced
Maverick model checkpoint
Returns:
List of 0 or 1 indicating whether each layer uses RoPE and local attn
0 indicates that RoPE is not used while 1 indicates that RoPE is used.
"""
config_path = Path(model_path) / "config.json"
model_config = json.loads(config_path.read_text())
text_config = model_config["text_config"]
no_rope_layers = text_config["no_rope_layers"]
print(f"Found no_rope_layers: {no_rope_layers}")
return no_rope_layers
def create_reduced_maverick_model(
original_model_name: str = "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
output_dir: str = "/tmp/reduced_maverick",
text_layers: int = 4,
num_experts: int = 4,
vision_layers: int = 2,
force_recreate: bool = False,
) -> str:
"""
Create a reduced-layer version of the Maverick model.
Args:
original_model_name: Name of the original Maverick model
output_dir: Directory to save the reduced model
text_layers: Number of text transformer layers
num_experts: Number of experts per layer
vision_layers: Number of vision transformer layers
force_recreate: Whether to recreate if output_dir already exists
Returns:
Path to the created reduced model directory
"""
print(
f"Creating reduced Maverick model with {text_layers} text layers and "
f"{vision_layers} vision layers..."
)
# Create output directory
output_path = Path(output_dir)
if output_path.exists():
if force_recreate:
shutil.rmtree(output_path)
else:
print(
f"Output directory {output_dir} already exists. "
"Use --force-recreate to overwrite."
)
return str(output_path)
output_path.mkdir(parents=True, exist_ok=True)
try:
print("Loading original model configuration...")
original_config = AutoConfig.from_pretrained(
original_model_name, trust_remote_code=True
)
print("Creating reduced configuration...")
reduced_config = create_reduced_config(
original_config, text_layers, num_experts, vision_layers
)
config_path = output_path / "config.json"
with open(config_path, "w") as f:
json.dump(reduced_config, f, indent=2)
print(f"Saved reduced config to {config_path}")
print("Copying tokenizer files...")
copy_tokenizer_files(original_model_name, output_path)
print("Creating reduced safetensors files...")
create_reduced_safetensors(original_config, reduced_config, output_path)
print("Creating preprocessor config...")
create_preprocessor_config(original_config, output_path)
try:
gen_config = GenerationConfig.from_pretrained(original_model_name)
gen_config.save_pretrained(output_path)
print("Copied generation config")
except Exception as e:
print(f"Could not copy generation config: {e}")
print(f"Successfully created reduced Maverick model at {output_path}")
return str(output_path)
except Exception as e:
print(f"Error creating reduced model: {e}")
# Clean up on failure
if output_path.exists():
shutil.rmtree(output_path)
raise
def create_reduced_config(
original_config: Any, text_layers: int, num_experts: int, vision_layers: int
) -> dict[str, Any]:
"""Create a reduced configuration based on the original."""
# Convert config to dictionary
config_dict = original_config.to_dict()
# Reduce text layers
if "text_config" in config_dict:
original_text_layers = config_dict["text_config"]["num_hidden_layers"]
config_dict["text_config"]["num_hidden_layers"] = text_layers
original_layer_types = config_dict["text_config"]["layer_types"]
config_dict["text_config"]["layer_types"] = original_layer_types[:text_layers]
print(f"Reduced text layers from {original_text_layers} to {text_layers}")
original_num_experts = config_dict["text_config"]["num_local_experts"]
config_dict["text_config"]["num_local_experts"] = num_experts
print(f"Reduced num experts from {original_num_experts} to {num_experts}")
hidden_dim_divisor = 4
original_hidden_size = config_dict["text_config"]["hidden_size"]
new_hidden_size = original_hidden_size // hidden_dim_divisor
config_dict["text_config"]["hidden_size"] = new_hidden_size
print(f"Reduced hidden size from {original_hidden_size} to {new_hidden_size}")
original_head_dim = config_dict["text_config"]["head_dim"]
new_head_dim = original_head_dim // hidden_dim_divisor
config_dict["text_config"]["head_dim"] = new_head_dim
print(f"Reduced head dim from {original_head_dim} to {new_head_dim}")
# Reduce vision layers
if "vision_config" in config_dict:
original_vision_layers = config_dict["vision_config"]["num_hidden_layers"]
config_dict["vision_config"]["num_hidden_layers"] = vision_layers
print(f"Reduced vision layers from {original_vision_layers} to {vision_layers}")
# Update model name to indicate it's a reduced version
config_dict["_name_or_path"] = f"reduced_maverick_{text_layers}t_{vision_layers}v"
return config_dict
def copy_tokenizer_files(original_model_name: str, output_path: Path) -> None:
"""Copy tokenizer files from the original model."""
try:
tokenizer = AutoTokenizer.from_pretrained(
original_model_name, trust_remote_code=True
)
tokenizer.save_pretrained(output_path)
print("Tokenizer files copied successfully")
except Exception as e:
print(f"Warning: Could not copy tokenizer files: {e}")
def create_preprocessor_config(original_config: Any, output_path: Path) -> None:
"""Create preprocessor_config.json for multimodal model."""
# Try to load the original preprocessor config
try:
processor = AutoProcessor.from_pretrained(
original_config._name_or_path
or "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
trust_remote_code=True,
)
processor.save_pretrained(output_path)
print("Copied original preprocessor config")
return
except Exception as e:
print(f"Could not copy original preprocessor config: {e}")
raise
def create_reduced_safetensors(
original_config: Any, reduced_config: dict[str, Any], output_path: Path
) -> None:
"""Create safetensors files with weights for the reduced model."""
print("Generating synthetic weights for reduced model...")
text_config = reduced_config["text_config"]
vision_config = reduced_config["vision_config"]
weights = {}
print("Creating text model weights...")
weights.update(create_text_model_weights(text_config))
print("Creating vision model weights...")
weights.update(create_vision_model_weights(vision_config))
print("Creating shared model weights...")
weights.update(create_shared_weights(text_config, vision_config))
print("Saving weights to safetensors files...")
save_weights_to_safetensors(weights, output_path)
def create_text_model_weights(text_config: dict[str, Any]) -> dict[str, torch.Tensor]:
"""Create synthetic weights for the text model with MoE structure."""
weights = {}
vocab_size = text_config["vocab_size"]
hidden_size = text_config["hidden_size"]
intermediate_size = text_config["intermediate_size"]
intermediate_size_mlp = text_config["intermediate_size_mlp"]
num_layers = text_config["num_hidden_layers"]
num_attention_heads = text_config["num_attention_heads"]
num_key_value_heads = text_config.get("num_key_value_heads", num_attention_heads)
# MoE specific parameters
num_experts = text_config.get("num_local_experts")
assert num_experts is not None, "num_local_experts must be specified for MoE"
head_dim = hidden_size // num_attention_heads
# Embedding layers
weights["language_model.model.embed_tokens.weight"] = torch.randn(
vocab_size, hidden_size, dtype=torch.float16
)
# Transformer layers
for layer_idx in range(num_layers):
layer_prefix = f"language_model.model.layers.{layer_idx}"
print(f"Creating weights for layer {layer_prefix}...")
# Self-attention weights (separate q, k, v projections)
weights[f"{layer_prefix}.self_attn.q_proj.weight"] = torch.randn(
hidden_size, num_attention_heads * head_dim, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.k_proj.weight"] = torch.randn(
hidden_size, num_key_value_heads * head_dim, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.v_proj.weight"] = torch.randn(
num_key_value_heads * head_dim, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.o_proj.weight"] = torch.randn(
hidden_size, num_attention_heads * head_dim, dtype=torch.bfloat16
)
print("Self-attention weights created.")
# Feed-forward weights - MoE pattern based on interleave_moe_layer_step
# For interleave_moe_layer_step=2: layers 1,3,5,... are MoE, layers
# 0,2,4,... are dense
interleave_step = text_config.get("interleave_moe_layer_step", 1)
is_moe_layer = interleave_step > 0 and (layer_idx + 1) % interleave_step == 0
if is_moe_layer:
# MoE layer structure
# 1. Router weights
weights[f"{layer_prefix}.feed_forward.router.weight"] = torch.randn(
num_experts, hidden_size, dtype=torch.float16
)
# 2. Individual expert weights (not fused)
for expert_idx in range(num_experts):
expert_prefix = f"{layer_prefix}.feed_forward.experts.{expert_idx}"
weights[f"{expert_prefix}.gate_proj.weight"] = torch.randn(
intermediate_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{expert_prefix}.up_proj.weight"] = torch.randn(
intermediate_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{expert_prefix}.down_proj.weight"] = torch.randn(
hidden_size, intermediate_size, dtype=torch.bfloat16
)
# Expert weight scales (FP8 quantization)
weights[f"{expert_prefix}.gate_proj.weight_scale"] = torch.ones(
intermediate_size, 1, dtype=torch.bfloat16
)
weights[f"{expert_prefix}.up_proj.weight_scale"] = torch.ones(
intermediate_size, 1, dtype=torch.bfloat16
)
weights[f"{expert_prefix}.down_proj.weight_scale"] = torch.ones(
hidden_size, 1, dtype=torch.bfloat16
)
# 3. Shared expert weights
shared_expert_prefix = f"{layer_prefix}.feed_forward.shared_expert"
weights[f"{shared_expert_prefix}.gate_proj.weight"] = torch.randn(
intermediate_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{shared_expert_prefix}.up_proj.weight"] = torch.randn(
intermediate_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{shared_expert_prefix}.down_proj.weight"] = torch.randn(
hidden_size, intermediate_size, dtype=torch.bfloat16
)
print(f"MoE feed-forward weights created for layer {layer_idx}.")
else:
# Dense layer structure
weights[f"{layer_prefix}.feed_forward.gate_proj.weight"] = torch.randn(
intermediate_size_mlp, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.feed_forward.up_proj.weight"] = torch.randn(
intermediate_size_mlp, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.feed_forward.down_proj.weight"] = torch.randn(
hidden_size, intermediate_size_mlp, dtype=torch.bfloat16
)
print(f"Dense feed-forward weights created for layer {layer_idx}.")
# Layer norms
weights[f"{layer_prefix}.input_layernorm.weight"] = torch.ones(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.post_attention_layernorm.weight"] = torch.ones(
hidden_size, dtype=torch.bfloat16
)
print("Layer norms created.")
# Final layer norm and output projection
weights["language_model.model.norm.weight"] = torch.ones(
hidden_size, dtype=torch.bfloat16
)
weights["language_model.lm_head.weight"] = torch.randn(
vocab_size, hidden_size, dtype=torch.bfloat16
)
return weights
def create_vision_model_weights(
vision_config: dict[str, Any],
) -> dict[str, torch.Tensor]:
"""Create synthetic weights for the vision model."""
weights = {}
hidden_size = vision_config["hidden_size"]
intermediate_size = vision_config["intermediate_size"]
num_layers = vision_config["num_hidden_layers"]
# Vision transformer layers
for layer_idx in range(num_layers):
layer_prefix = f"vision_model.model.layers.{layer_idx}"
weights[f"{layer_prefix}.self_attn.q_proj.weight"] = torch.randn(
hidden_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.q_proj.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.k_proj.weight"] = torch.randn(
hidden_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.k_proj.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.v_proj.weight"] = torch.randn(
hidden_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.v_proj.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.o_proj.weight"] = torch.randn(
hidden_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.self_attn.o_proj.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.mlp.fc1.weight"] = torch.randn(
intermediate_size, hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.mlp.fc1.bias"] = torch.zeros(
intermediate_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.mlp.fc2.weight"] = torch.randn(
hidden_size, intermediate_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.mlp.fc2.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.input_layernorm.weight"] = torch.ones(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.input_layernorm.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.post_attention_layernorm.weight"] = torch.ones(
hidden_size, dtype=torch.bfloat16
)
weights[f"{layer_prefix}.post_attention_layernorm.bias"] = torch.zeros(
hidden_size, dtype=torch.bfloat16
)
return weights
def create_shared_weights(
text_config: dict[str, Any], vision_config: dict[str, Any]
) -> dict[str, torch.Tensor]:
"""Create weights for shared components (vision-language connector)"""
weights = {}
text_hidden_size = text_config["hidden_size"]
projector_input_dim = vision_config["projector_input_dim"]
# Vision-language connector (projects vision features to text space)
weights["multi_modal_projector.linear_1.weight"] = torch.randn(
text_hidden_size, projector_input_dim, dtype=torch.bfloat16
)
return weights
def save_weights_to_safetensors(
weights: dict[str, torch.Tensor], output_path: Path
) -> None:
"""Save weights to safetensors files and create index."""
# Determine how to shard the weights
max_shard_size = 5 * 1024 * 1024 * 1024 # 5GB per shard
# Calculate sizes and create shards
shards = []
current_shard: dict[str, torch.Tensor] = {}
current_size = 0
for name, tensor in weights.items():
tensor_size = tensor.numel() * tensor.element_size()
if current_size + tensor_size > max_shard_size and current_shard:
shards.append(current_shard)
current_shard = {}
current_size = 0
current_shard[name] = tensor
current_size += tensor_size
if current_shard:
shards.append(current_shard)
# Save shards and create index
weight_map = {}
if len(shards) == 1:
# Single file
filename = "model.safetensors"
save_file(shards[0], output_path / filename)
weight_map = {name: filename for name in shards[0]}
print(f"Saved weights to single file: {filename}")
else:
# Multiple shards
for i, shard in enumerate(shards):
filename = f"model-{i + 1:05d}-of-{len(shards):05d}.safetensors"
save_file(shard, output_path / filename)
for name in shard:
weight_map[name] = filename
print(f"Saved shard {i + 1}/{len(shards)}: {filename}")
# Create index file
index_data = {
"metadata": {
"total_size": sum(
tensor.numel() * tensor.element_size() for tensor in weights.values()
)
},
"weight_map": weight_map,
}
index_path = output_path / "model.safetensors.index.json"
with open(index_path, "w") as f:
json.dump(index_data, f, indent=2)
print(f"Created index file: {index_path}")
print(
f"Total model size: {index_data['metadata']['total_size'] / (1024**3):.2f} GB"
)
def check_attention_spec_interleaved_rope(
llm: LLM,
num_attention_layers: int,
num_ranks: int,
rope_layers: list[int],
):
"""Check that the attention spec is correct."""
assert isinstance(llm.llm_engine.model_executor, Executor)
kv_cache_specs_per_rank = llm.llm_engine.model_executor.get_kv_cache_specs()
for rank in range(num_ranks):
kv_cache_specs = kv_cache_specs_per_rank[rank]
assert len(kv_cache_specs.keys()) == num_attention_layers
for i in range(num_attention_layers):
if rope_layers[i] == 0:
expected_spec = FullAttentionSpec
else:
expected_spec = ChunkedLocalAttentionSpec
assert isinstance(
kv_cache_specs[f"language_model.model.layers.{i}.self_attn.attn"],
expected_spec,
)
def run_reduced_model(llm: LLM, should_profile: bool = False) -> None:
"""Test the created reduced model with vLLM."""
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=50)
if should_profile:
llm.start_profile()
outputs = llm.generate(PROMPTS, sampling_params)
if should_profile:
llm.stop_profile()
print("Test generation successful!")
for output in outputs:
print(f"Prompt: {output.prompt}")
print(f"Output: {output.outputs[0].text}")
print("-" * 40)
@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize(
"original_model_name,text_layers,num_experts,vision_layers,",
[("meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", 4, 4, 2)],
)
@pytest.mark.parametrize("enforce_eager", [True, False])
@pytest.mark.parametrize("tp,ep", [(2, True)])
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
def test_dummy_maverick(
monkeypatch,
original_model_name: str,
text_layers: int,
num_experts: int,
vision_layers: int,
enforce_eager: bool,
tp: int,
ep: bool,
output_dir: str = "/tmp/reduced_maverick",
force_recreate: bool = True,
profile: bool = False,
) -> None:
# Disable multiprocessing allows us to access model executor from LLM engine
monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
model_path = create_reduced_maverick_model(
original_model_name=original_model_name,
output_dir=output_dir,
text_layers=text_layers,
num_experts=num_experts,
vision_layers=vision_layers,
force_recreate=force_recreate,
)
print(f"\nReduced model created successfully at: {model_path}")
rope_layers = get_rope_layers_config(model_path)
llm = LLM(
model=model_path,
trust_remote_code=True,
max_model_len=512, # Small context for testing
gpu_memory_utilization=0.3, # Conservative memory usage
enforce_eager=enforce_eager,
tensor_parallel_size=tp,
enable_expert_parallel=ep,
)
check_attention_spec_interleaved_rope(
llm,
text_layers,
tp,
rope_layers,
)
print(f"\nTesting reduced model at {model_path}...")
run_reduced_model(llm=llm, should_profile=profile)
def main():
"""Main function to create and test the reduced model."""
import argparse
parser = argparse.ArgumentParser(
description="Create a reduced-layer Maverick model"
)
parser.add_argument(
"--output-dir",
default="/tmp/reduced_maverick",
help="Output directory for the reduced model",
)
parser.add_argument(
"--text-layers",
type=int,
default=4,
help="Number of text transformer layers",
)
parser.add_argument("--num-experts", type=int, default=4, help="Number of experts")
parser.add_argument(
"--vision-layers",
type=int,
default=2,
help="Number of vision transformer layers",
)
parser.add_argument(
"--force-recreate",
action="store_true",
help="Force recreation if output directory exists",
)
parser.add_argument(
"--test", action="store_true", help="Test the created model with vLLM"
)
parser.add_argument(
"--profile", action="store_true", help="Profile the created model with vLLM"
)
parser.add_argument(
"--test-original",
action="store_true",
help="Test the original model with vLLM",
)
parser.add_argument(
"--original-model",
default="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
help="Original model name to base the reduction on",
)
args = parser.parse_args()
if args.test:
test_dummy_maverick(
original_model_name=args.original_model,
output_dir=args.output_dir,
text_layers=args.text_layers,
num_experts=args.num_experts,
vision_layers=args.vision_layers,
force_recreate=args.force_recreate,
tp=2,
ep=True,
enforce_eager=True,
profile=args.profile,
)
if args.test_original:
run_maverick_serving(args.original_model)
if __name__ == "__main__":
exit(main())
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_whisper.py | tests/models/multimodal/generation/test_whisper.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from typing import Any
import librosa
import pytest
from transformers import AutoModelForSpeechSeq2Seq
from vllm.assets.audio import AudioAsset
from vllm.platforms import current_platform
from ....conftest import HfRunner, PromptAudioInput, VllmRunner
from ....utils import create_new_process_for_each_test, multi_gpu_test
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
VLLM_PROMPT = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
HF_PROMPT = ""
# Whisper expects 16kHz audio
WHISPER_SAMPLE_RATE = 16000
@pytest.fixture(autouse=True)
def use_spawn_for_whisper(monkeypatch):
"""Whisper has issues with forked workers, use spawn instead."""
monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
inputs: Sequence[tuple[list[str], list[str], PromptAudioInput]],
model: str,
*,
max_model_len: int,
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: str | None = None,
enforce_eager: bool = True,
) -> None:
"""Inference result should be the same between hf and vllm.
All the audio fixtures for the test are from AudioAsset.
For huggingface runner, we provide the audio as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
"""
with vllm_runner(
model,
dtype=dtype,
max_model_len=max_model_len,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
limit_mm_per_prompt={"audio": 2},
enforce_eager=enforce_eager,
disable_custom_all_reduce=True,
) as vllm_model:
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(
vllm_prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=audios,
)
for vllm_prompts, _, audios in inputs
]
with hf_runner(model, dtype=dtype, auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(
hf_prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=audios,
)
for _, hf_prompts, audios in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.fixture
def input_audios() -> list[tuple[list[str], list[str], list[tuple[Any, int]]]]:
audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
inputs = []
for asset in audio_assets:
audio, orig_sr = asset.audio_and_sample_rate
# Resample to Whisper's expected sample rate (16kHz)
if orig_sr != WHISPER_SAMPLE_RATE:
audio = librosa.resample(
audio, orig_sr=orig_sr, target_sr=WHISPER_SAMPLE_RATE
)
# vLLM prompts, HF prompts, audio inputs
inputs.append(([VLLM_PROMPT], [HF_PROMPT], [(audio, WHISPER_SAMPLE_RATE)]))
return inputs
def check_model_available(model: str) -> None:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
@pytest.mark.core_model
@pytest.mark.cpu_model
@pytest.mark.parametrize("model", ["openai/whisper-large-v3-turbo"])
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("enforce_eager", [True, False])
@create_new_process_for_each_test("spawn")
def test_models(
hf_runner,
vllm_runner,
model: str,
dtype: str,
num_logprobs: int,
input_audios,
enforce_eager: bool,
) -> None:
check_model_available(model)
if current_platform.is_cpu() and not enforce_eager:
pytest.skip("Skipping test for CPU with non-eager mode")
run_test(
hf_runner,
vllm_runner,
input_audios,
model,
dtype=dtype,
max_model_len=448,
max_tokens=200,
num_logprobs=num_logprobs,
tensor_parallel_size=1,
enforce_eager=enforce_eager,
)
@multi_gpu_test(num_gpus=2)
@pytest.mark.core_model
@pytest.mark.parametrize("model", ["openai/whisper-large-v3-turbo"])
@pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [200])
@pytest.mark.parametrize("num_logprobs", [5])
@create_new_process_for_each_test("spawn")
def test_models_distributed(
hf_runner,
vllm_runner,
model: str,
distributed_executor_backend: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
input_audios,
) -> None:
check_model_available(model)
run_test(
hf_runner,
vllm_runner,
input_audios,
model,
dtype=dtype,
max_model_len=448,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tensor_parallel_size=2,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=False,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_phi4mm.py | tests/models/multimodal/generation/test_phi4mm.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from collections.abc import Sequence
import librosa
import pytest
import regex as re
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer
from vllm.assets.image import ImageAsset
from vllm.logprobs import SampleLogprobs
from vllm.lora.request import LoRARequest
from vllm.multimodal.image import convert_image_mode, rescale_image_size
from ....conftest import (
IMAGE_ASSETS,
HfRunner,
PromptAudioInput,
PromptImageInput,
VllmRunner,
)
from ....utils import large_gpu_test
from ...utils import check_logprobs_close
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": "<|user|>\n<|image_1|>\nWhat's the content of the image?<|end|>\n<|assistant|>\n", # noqa: E501
"cherry_blossom": "<|user|>\n<|image_1|>\nPlease infer the season with reason in details.<|end|>\n<|assistant|>\n", # noqa: E501
}
)
HF_MULTIIMAGE_IMAGE_PROMPT = (
"<|user|>\n<|image_1|>\n<|image_2|>\nDescribe these images.<|end|>\n<|assistant|>\n" # noqa: E501
)
model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
# Since the vision-lora and speech-lora co-exist with the base model,
# we have to manually specify the path of the lora weights.
vision_lora_path = os.path.join(model_path, "vision-lora")
speech_question = os.path.join(
model_path, "examples", "what_is_shown_in_this_image.wav"
)
models = [model_path]
def vllm_to_hf_output(
vllm_output: tuple[list[int], str, SampleLogprobs | None], model: str
):
"""Sanitize vllm output to be comparable with hf output."""
_, output_str, out_logprobs = vllm_output
output_str_without_image = re.sub(r"(<\|image_\d+\|>)+", "", output_str)
assert output_str_without_image[0] == " "
output_str_without_image = output_str_without_image[1:]
hf_output_str = output_str_without_image + "<|end|><|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model)
hf_output_ids = tokenizer.encode(output_str_without_image)
assert hf_output_ids[0] == 1
hf_output_ids = hf_output_ids[1:]
return hf_output_ids, hf_output_str, out_logprobs
target_dtype = "half"
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
inputs: Sequence[tuple[list[str], PromptImageInput, PromptAudioInput | None]],
model: str,
*,
max_model_len: int,
dtype: str,
max_tokens: int,
num_logprobs: int,
mm_limit: int,
tensor_parallel_size: int,
distributed_executor_backend: str | None = None,
):
"""Inference result should be the same between hf and vllm.
All the image fixtures for the test are from IMAGE_ASSETS.
For huggingface runner, we provide the PIL images as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
# max_model_len should be greater than image_feature_size
with vllm_runner(
model,
runner="generate",
max_model_len=max_model_len,
max_num_seqs=2,
dtype=dtype,
limit_mm_per_prompt={"image": mm_limit},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enable_lora=True,
max_lora_rank=320,
gpu_memory_utilization=0.8, # set to 0.8 to avoid OOM in CI
enforce_eager=True,
) as vllm_model:
lora_request = LoRARequest("vision", 1, vision_lora_path)
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
audios=audios,
lora_request=lora_request,
)
for prompts, images, audios in inputs
]
# This error occurs inside `get_peft_model`
# FIXME: https://huggingface.co/microsoft/Phi-4-multimodal-instruct/discussions/75
pytest.skip("HF impl is not compatible with current transformers")
hf_model_kwargs = {"_attn_implementation": "sdpa"}
with hf_runner(model, dtype=dtype, model_kwargs=hf_model_kwargs) as hf_model:
hf_processor = hf_model.processor
eos_token_id = hf_processor.tokenizer.eos_token_id
def patch_hf_processor(
*args, text="", images=None, audio=None, sampling_rate=None, **kwargs
):
audios = None
if audio is not None and sampling_rate is not None:
audios = [(audio, sampling_rate)]
return hf_processor(
*args, text=text, images=images, audios=audios, **kwargs
)
hf_model.processor = patch_hf_processor
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(
prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
audios=audios,
eos_token_id=eos_token_id,
num_logits_to_keep=0,
)
for prompts, images, audios in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# No image
[],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_model_len", [12800])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_models(
hf_runner,
vllm_runner,
image_assets,
model,
size_factors,
dtype: str,
max_model_len: int,
max_tokens: int,
num_logprobs: int,
) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_image = [
(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
None,
)
for image, prompt in zip(images, HF_IMAGE_PROMPTS)
]
run_test(
hf_runner,
vllm_runner,
inputs_per_image,
model,
dtype=dtype,
max_model_len=max_model_len,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# No image
# [],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_model_len", [25600])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_multi_images_models(
hf_runner,
vllm_runner,
image_assets,
model,
size_factors,
dtype: str,
max_model_len: int,
max_tokens: int,
num_logprobs: int,
) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_case = [
(
[HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
[
[rescale_image_size(image, factor) for image in images]
for factor in size_factors
],
None,
),
]
run_test(
hf_runner,
vllm_runner,
inputs_per_case,
model,
dtype=dtype,
max_model_len=max_model_len,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=2,
tensor_parallel_size=1,
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_model_len", [12800])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_vision_speech_models(
hf_runner,
vllm_runner,
model,
dtype: str,
max_model_len: int,
max_tokens: int,
num_logprobs: int,
) -> None:
# use the example speech question so that the model outputs are reasonable
audio = librosa.load(speech_question, sr=None)
image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
inputs_vision_speech = [
(
["<|user|><|image_1|><|audio_1|><|end|><|assistant|>"],
[image],
[audio],
),
]
run_test(
hf_runner,
vllm_runner,
inputs_vision_speech,
model,
dtype=dtype,
max_model_len=max_model_len,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/__init__.py | tests/models/multimodal/generation/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_vit_backend_functionality.py | tests/models/multimodal/generation/test_vit_backend_functionality.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Consolidated test for ViT attention backend functionality across multiple models.
This test validates that each multimodal model can successfully generate outputs
using different ViT attention backends. Tests are parametrized by model and backend.
"""
from dataclasses import asdict
from typing import Any
import pytest
from transformers import AutoProcessor
from vllm import LLM, EngineArgs, SamplingParams
from vllm.attention.backends.registry import AttentionBackendEnum
from vllm.multimodal.utils import encode_image_url
from vllm.multimodal.video import sample_frames_from_video
from vllm.platforms import current_platform
from ....utils import create_new_process_for_each_test
from ...utils import dummy_hf_overrides
# Dots.OCR prompt from official repository
# https://github.com/rednote-hilab/dots.ocr/blob/d72d1d8c5bdd0362eb264f714cdbd1e5daa7cdff/dots_ocr/utils/prompts.py#L3
# ruff: noqa: E501
DOTS_OCR_PROMPT = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
1. Bbox format: [x1, y1, x2, y2]
2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
3. Text Extraction & Formatting Rules:
- Picture: For the 'Picture' category, the text field should be omitted.
- Formula: Format its text as LaTeX.
- Table: Format its text as HTML.
- All Others (Text, Title, etc.): Format their text as Markdown.
4. Constraints:
- The output text must be the original text from the image, with no translation.
- All layout elements must be sorted according to human reading order.
5. Final Output: The entire output must be a single JSON object.
"""
VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
# Model configurations
MODEL_CONFIGS: dict[str, dict[str, Any]] = {
"dots_ocr": {
"model_name": "rednote-hilab/dots.ocr",
"interface": "llm_chat",
"max_model_len": 32768,
"max_num_seqs": 1,
"limit_mm_per_prompt": {"image": 1},
"sampling_params": {
"temperature": 0.1,
"max_tokens": 16384,
"top_p": 0.9,
"stop_token_ids": None,
},
"use_specific_image": "stop_sign",
"prompt_builder": "build_dots_ocr_prompt",
"output_validator": lambda x: len(x) > 10 and "stop" in x.lower(),
},
"ernie45_vl": {
"model_name": "baidu/ERNIE-4.5-VL-28B-A3B-PT",
"interface": "llm_generate",
"max_model_len": 16384,
"max_num_seqs": 2,
"sampling_params": {
"temperature": 0.0,
"max_tokens": 256,
"stop_token_ids": None,
},
"use_processor": True,
"question": "What is the content of each image?",
},
"glm4_1v": {
"model_name": "zai-org/GLM-4.1V-9B-Thinking",
"interface": "llm_generate",
"max_model_len": 32768,
"max_num_seqs": 2,
"sampling_params": {
"temperature": 0.0,
"max_tokens": 256,
"stop_token_ids": None,
},
"use_processor": True,
"question": "What is the content of each image?",
},
"keye_vl": {
"model_name": "Kwai-Keye/Keye-VL-8B-Preview",
"interface": "llm_generate",
"max_model_len": 8192,
"max_num_seqs": 5,
"sampling_params": {
"temperature": 0.0,
"max_tokens": 256,
"stop_token_ids": None,
},
"supported_backends": {
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.ROCM_AITER_FA,
},
"use_processor": True,
"question": "What is the content of each image?",
},
"ovis2_5": {
"model_name": "AIDC-AI/Ovis2.5-2B",
"interface": "llm_generate",
"max_model_len": 8192,
"max_num_seqs": 2,
"sampling_params": {
"temperature": 0.0,
"max_tokens": 256,
"stop_token_ids": None,
},
"prompt_builder": "build_ovis_prompt",
"question": "What is the content of each image?",
},
"qwen2_5_vl": {
"model_name": "Qwen/Qwen2.5-VL-3B-Instruct",
"interface": "vllm_runner",
"media_type": "video",
"max_model_len": 4000,
"max_num_seqs": 1,
"limit_mm_per_prompt": {"video": 1},
"sampling_params": {
"max_tokens": 128,
},
"runner_kwargs": {
"runner": "generate",
"dtype": "bfloat16",
},
"video_params": {
"num_frames": 16,
"pruning_rates": [0.0, 0.75],
},
},
"qwen2_5_omni": {
"model_name": "Qwen/Qwen2.5-Omni-3B",
"interface": "llm_generate",
"max_model_len": 32768,
"max_num_seqs": 2,
"limit_mm_per_prompt": {"image": 3, "video": 3, "audio": 3},
"sampling_params": {
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 16384,
},
"use_processor": True,
"question": "What is the content of each image?",
},
"qwen3_omni": {
"model_name": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
"interface": "llm_generate",
"max_model_len": 32768,
"max_num_seqs": 2,
"limit_mm_per_prompt": {"image": 3, "video": 3, "audio": 3},
"sampling_params": {
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 16384,
},
"use_processor": True,
"question": "What is the content of each image?",
},
}
# Prompt builder functions
def build_dots_ocr_prompt(images, config):
"""Build Dots.OCR specific prompt with OCR instructions."""
# Use only stop_sign image for Dots.OCR
image = images[0] # Already filtered to stop_sign
image_url = encode_image_url(image)
placeholders = [{"type": "image_url", "image_url": {"url": image_url}}]
messages = [
{
"role": "user",
"content": [
*placeholders,
{
"type": "text",
"text": f"<|img|><|imgpad|><|endofimg|>{DOTS_OCR_PROMPT}",
},
],
},
]
return messages
def build_processor_prompt(images, config):
"""Build prompt using AutoProcessor.apply_chat_template()."""
processor = AutoProcessor.from_pretrained(
config["model_name"], trust_remote_code=True
)
image_urls = [encode_image_url(img) for img in images]
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": config["question"]},
],
},
]
return processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
def build_ovis_prompt(images, config):
"""Build Ovis2.5 specific prompt with custom format."""
image_urls = [encode_image_url(img) for img in images]
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
return (
f"<|im_start|>user\n\n{placeholders}\n{config['question']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
def build_qwen2_5_video_prompt():
"""Build Qwen2.5-VL video prompt with EVS placeholder."""
return (
f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
f"<|im_start|>user\n{VIDEO_PLACEHOLDER}"
"Describe this video with a short sentence (no more than 20 words)"
"<|im_end|><|im_start|>assistant\n"
)
# Handler functions
def run_llm_generate_test(config, mm_encoder_attn_backend, image_assets):
"""Standard LLM.generate() interface handler."""
images = [asset.pil_image for asset in image_assets]
# Build prompt
if config.get("use_processor"):
prompt = build_processor_prompt(images, config)
else:
prompt_builder_name = config.get("prompt_builder", "build_ovis_prompt")
prompt_builder = globals()[prompt_builder_name]
prompt = prompt_builder(images, config)
# Determine limit_mm_per_prompt
limit_mm_per_prompt = config.get("limit_mm_per_prompt", {"image": len(images)})
# Create engine
engine_args = EngineArgs(
model=config["model_name"],
trust_remote_code=True,
max_model_len=config["max_model_len"],
max_num_seqs=config["max_num_seqs"],
limit_mm_per_prompt=limit_mm_per_prompt,
mm_encoder_attn_backend=mm_encoder_attn_backend,
hf_overrides=dummy_hf_overrides,
load_format="dummy",
)
engine_dict = asdict(engine_args) | {"seed": 42}
llm = LLM(**engine_dict)
# Generate
sampling_params = SamplingParams(**config["sampling_params"])
outputs = llm.generate(
{
"prompt": prompt,
"multi_modal_data": {"image": images},
},
sampling_params=sampling_params,
)
# Validate
for o in outputs:
generated_text = o.outputs[0].text
validator = config.get("output_validator", lambda x: len(x) > 10)
assert validator(generated_text), (
f"Validation failed for {config['model_name']}: {generated_text}"
)
def run_llm_chat_test(config, mm_encoder_attn_backend, image_assets):
"""LLM.chat() interface handler for Dots.OCR."""
# Filter to stop_sign image only
stop_sign_image = [
asset.pil_image for asset in image_assets if asset.name == "stop_sign"
][0]
# Build messages
messages = build_dots_ocr_prompt([stop_sign_image], config)
# Create engine
engine_args = EngineArgs(
model=config["model_name"],
trust_remote_code=True,
max_model_len=config["max_model_len"],
max_num_seqs=config["max_num_seqs"],
limit_mm_per_prompt=config["limit_mm_per_prompt"],
mm_encoder_attn_backend=mm_encoder_attn_backend,
hf_overrides=dummy_hf_overrides,
load_format="dummy",
)
engine_dict = asdict(engine_args) | {"seed": 42}
llm = LLM(**engine_dict)
# Generate using chat
sampling_params = SamplingParams(**config["sampling_params"])
outputs = llm.chat(messages=messages, sampling_params=sampling_params)
# Validate
for o in outputs:
generated_text = o.outputs[0].text
validator = config.get("output_validator", lambda x: len(x) > 10)
assert validator(generated_text), (
f"Validation failed for {config['model_name']}: {generated_text}"
)
def run_video_test(config, mm_encoder_attn_backend, video_assets, vllm_runner):
"""Video test with EVS (Efficient Video Sampling) handler."""
for pruning_rate in config["video_params"]["pruning_rates"]:
num_frames = config["video_params"]["num_frames"]
# Sample frames from video
sampled_vids = [
sample_frames_from_video(asset.np_ndarrays, num_frames)
for asset in video_assets
]
# Build prompt and prepare video
prompt = build_qwen2_5_video_prompt()
prompts = [prompt]
videos = [sampled_vids[0]]
# Run with vllm_runner context manager
with vllm_runner(
config["model_name"],
max_model_len=config["max_model_len"],
max_num_seqs=config["max_num_seqs"],
limit_mm_per_prompt=config["limit_mm_per_prompt"],
tensor_parallel_size=1,
video_pruning_rate=pruning_rate,
mm_encoder_attn_backend=mm_encoder_attn_backend,
hf_overrides=dummy_hf_overrides,
load_format="dummy",
**config["runner_kwargs"],
) as vllm_model:
outputs = vllm_model.generate_greedy(
prompts,
config["sampling_params"]["max_tokens"],
videos=videos,
)
# Validate output
assert len(outputs) == 1, f"Expected 1 output, got {len(outputs)}"
output_ids, output_text = outputs[0]
assert len(output_ids) > 0, "Generated no output IDs"
assert len(output_text) > 0, "Generated empty text"
assert isinstance(output_text, str), (
f"Output is not string: {type(output_text)}"
)
# Main test function
@pytest.mark.parametrize("model_key", list(MODEL_CONFIGS.keys()))
@pytest.mark.parametrize(
"mm_encoder_attn_backend",
[None] + current_platform.get_supported_vit_attn_backends(),
)
@pytest.mark.skip(reason="Broken test due to memory segmentation fault")
@create_new_process_for_each_test()
def test_vit_backend_functionality(
model_key: str,
mm_encoder_attn_backend: AttentionBackendEnum | None,
image_assets,
video_assets,
vllm_runner,
request,
):
"""Test ViT attention backend functionality for multimodal models.
This test validates that each model can successfully generate outputs
using different ViT attention backends. The test:
1. Filters unsupported backends per model
2. Applies appropriate GPU marks
3. Routes to the correct test handler based on interface
4. Validates output meets minimum requirements
"""
config = MODEL_CONFIGS[model_key]
# Step 1: Backend filtering
if (
"supported_backends" in config
and mm_encoder_attn_backend is not None
and mm_encoder_attn_backend not in config["supported_backends"]
):
pytest.skip(
f"{model_key} does not support {mm_encoder_attn_backend} backend now."
)
# Step 2: Apply GPU marks dynamically
if "gpu_marks" in config:
for mark in config["gpu_marks"]:
request.applymarker(mark)
# Step 3: Route to appropriate handler
if config.get("media_type") == "video":
run_video_test(config, mm_encoder_attn_backend, video_assets, vllm_runner)
elif config["interface"] == "llm_chat":
run_llm_chat_test(config, mm_encoder_attn_backend, image_assets)
elif config["interface"] == "llm_generate":
run_llm_generate_test(config, mm_encoder_attn_backend, image_assets)
else:
raise ValueError(f"Unknown interface: {config['interface']}")
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_pixtral.py | tests/models/multimodal/generation/test_pixtral.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from dataclasses import asdict
from typing import TYPE_CHECKING, Any
import pytest
from mistral_common.multimodal import download_image
from mistral_common.protocol.instruct.chunk import ImageURLChunk
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.tokenizers.multimodal import image_from_chunk
from transformers import AutoProcessor
from vllm import SamplingParams, TextPrompt, TokensPrompt
from vllm.logprobs import Logprob, SampleLogprobs
from vllm.multimodal import MultiModalDataBuiltins
from vllm.platforms import current_platform
from ....utils import VLLM_PATH, large_gpu_test
from ...utils import check_logprobs_close
if TYPE_CHECKING:
from _typeshed import StrPath
PIXTRAL_ID = "mistralai/Pixtral-12B-2409"
MISTRAL_SMALL_3_1_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
MODELS = [PIXTRAL_ID, MISTRAL_SMALL_3_1_ID]
IMG_URLS = [
"237-400x300.jpg", # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
"231-200x300.jpg", # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
"27-500x500.jpg", # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
"17-150x600.jpg", # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
]
PROMPT = "Describe each image in one short sentence."
def _create_msg_format(urls: list[str]) -> list[dict[str, Any]]:
return [
{
"role": "user",
"content": [
{
"type": "text",
"text": PROMPT,
}
]
+ [{"type": "image_url", "image_url": {"url": url}} for url in urls],
}
]
def _create_msg_format_hf(urls: list[str]) -> list[dict[str, Any]]:
return [
{
"role": "user",
"content": [
{
"type": "text",
"content": PROMPT,
},
*({"type": "image", "image": download_image(url)} for url in urls),
],
}
]
def _create_engine_inputs(urls: list[str]) -> TokensPrompt:
msg = _create_msg_format(urls)
tokenizer = MistralTokenizer.from_model("pixtral")
request = ChatCompletionRequest(messages=msg) # type: ignore[type-var]
tokenized = tokenizer.encode_chat_completion(request)
engine_inputs = TokensPrompt(prompt_token_ids=tokenized.tokens)
images = []
for chunk in request.messages[0].content:
if isinstance(chunk, ImageURLChunk):
images.append(image_from_chunk(chunk))
mm_data = MultiModalDataBuiltins(image=images)
engine_inputs["multi_modal_data"] = mm_data
return engine_inputs
def _create_engine_inputs_hf(urls: list[str]) -> TextPrompt:
msg = _create_msg_format_hf(urls)
tokenizer = AutoProcessor.from_pretrained("mistral-community/pixtral-12b")
prompt = tokenizer.apply_chat_template(msg)
images = []
for chunk in msg[0]["content"]:
if chunk["type"] == "image":
images.append(chunk["image"])
mm_data = MultiModalDataBuiltins(image=images)
engine_inputs = TextPrompt(prompt=prompt, multi_modal_data=mm_data)
return engine_inputs
SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
LIMIT_MM_PER_PROMPT = dict(image=4)
MAX_MODEL_LEN = [8192, 65536]
FIXTURES_PATH = VLLM_PATH / "tests/models/fixtures"
assert FIXTURES_PATH.exists()
FIXTURE_LOGPROBS_CHAT = {
PIXTRAL_ID: FIXTURES_PATH / "pixtral_chat.json",
MISTRAL_SMALL_3_1_ID: FIXTURES_PATH / "mistral_small_3_chat.json",
}
OutputsLogprobs = list[tuple[list[int], str, SampleLogprobs | None]]
# For the test author to store golden output in JSON
def _dump_outputs_w_logprobs(
outputs: OutputsLogprobs,
filename: "StrPath",
) -> None:
json_data = [
(
tokens,
text,
[
{k: asdict(v) for k, v in token_logprobs.items()}
for token_logprobs in (logprobs or [])
],
)
for tokens, text, logprobs in outputs
]
with open(filename, "w") as f:
json.dump(json_data, f)
def load_outputs_w_logprobs(filename: "StrPath") -> OutputsLogprobs:
with open(filename, "rb") as f:
json_data = json.load(f)
return [
(
tokens,
text,
[
{int(k): Logprob(**v) for k, v in token_logprobs.items()}
for token_logprobs in logprobs
],
)
for tokens, text, logprobs in json_data
]
@large_gpu_test(min_gb=80)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_model_len", MAX_MODEL_LEN)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_chat(
vllm_runner, max_model_len: int, model: str, dtype: str, local_asset_server
) -> None:
if (
model == MISTRAL_SMALL_3_1_ID
and max_model_len == 65536
and current_platform.is_rocm()
):
pytest.skip(
"OOM on ROCm: 24B model with 65536 context length exceeds GPU memory"
)
EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(FIXTURE_LOGPROBS_CHAT[model])
with vllm_runner(
model,
dtype=dtype,
tokenizer_mode="mistral",
load_format="mistral",
config_format="mistral",
max_model_len=max_model_len,
limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
) as vllm_model:
outputs = []
urls_all = [local_asset_server.url_for(u) for u in IMG_URLS]
msgs = [
_create_msg_format(urls_all[:1]),
_create_msg_format(urls_all[:2]),
_create_msg_format(urls_all),
]
for msg in msgs:
output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)
outputs.extend(output)
logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
# Remove last `None` prompt_logprobs to compare with fixture
for i in range(len(logprobs)):
assert logprobs[i][-1] is None
logprobs[i] = logprobs[i][:-1]
check_logprobs_close(
outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
outputs_1_lst=logprobs,
name_0="h100_ref",
name_1="output",
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_qwen2_5_vl.py | tests/models/multimodal/generation/test_qwen2_5_vl.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm.multimodal.video import sample_frames_from_video
from ....conftest import VIDEO_ASSETS
models = ["Qwen/Qwen2.5-VL-3B-Instruct"]
target_dtype = "bfloat16"
VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
def qwen2_5_vl_chat_template(*query):
return f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{''.join(query)}<|im_end|><|im_start|>assistant\n" # noqa: E501
VIDEO_PROMPTS = VIDEO_ASSETS.prompts(
{
"baby_reading": qwen2_5_vl_chat_template(
VIDEO_PLACEHOLDER,
"Describe this video with a short sentence ",
"(no more than 20 words)",
),
}
)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("video_pruning_rate", [0.0, 0.75])
@pytest.mark.parametrize("num_frames", [16])
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("use_bytecode_hook", [True, False])
def test_qwen2_5_vl_evs_functionality(
vllm_runner,
video_assets,
model,
video_pruning_rate: float,
num_frames: int,
dtype: str,
max_tokens: int,
use_bytecode_hook: bool,
monkeypatch,
) -> None:
"""Test EVS (Efficient Video Sampling) functionality with different
pruning rates.
"""
# Set the environment variable for this test
monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1" if use_bytecode_hook else "0")
# Sample frames from video assets
sampled_vids = [
sample_frames_from_video(asset.np_ndarrays, num_frames)
for asset in video_assets
]
prompts = [VIDEO_PROMPTS[0]]
videos = [sampled_vids[0]]
# Initialize model with EVS configuration
with vllm_runner(
model,
runner="generate",
max_model_len=4000,
dtype=dtype,
limit_mm_per_prompt={"video": 1},
video_pruning_rate=video_pruning_rate,
) as vllm_model:
# Generate output - this should not crash
outputs = vllm_model.generate_greedy(prompts, max_tokens, videos=videos)
# Basic validation that we got a response
assert len(outputs) == 1
output_ids, output_text = outputs[0]
# Ensure we got some output
assert len(output_ids) > 0
assert len(output_text) > 0
# Ensure the output is a string
assert isinstance(output_text, str)
@pytest.mark.core_model
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("video_pruning_rate", [0.0, 0.75])
@pytest.mark.parametrize("num_frames", [16])
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("use_bytecode_hook", [True, False])
def test_qwen2_5_vl_evs_batched_videos(
vllm_runner,
video_assets,
model,
video_pruning_rate: float,
num_frames: int,
dtype: str,
max_tokens: int,
use_bytecode_hook: bool,
monkeypatch,
) -> None:
"""Test EVS functionality with batched videos.
This test validates that:
1. The model handles batched video inputs correctly with EVS
2. Both pruning configurations work with multiple videos
3. The model doesn't crash when processing multiple videos simultaneously
"""
# Set the environment variable for this test
monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1" if use_bytecode_hook else "0")
# Sample frames from video assets
sampled_vids = [
sample_frames_from_video(asset.np_ndarrays, num_frames)
for asset in video_assets
]
# Test batched videos
prompts = [VIDEO_PROMPTS[0], VIDEO_PROMPTS[0]]
videos = [sampled_vids[0], sampled_vids[0]] # Use same video twice for testing
# Initialize model with EVS configuration
with vllm_runner(
model,
runner="generate",
max_model_len=4000,
max_num_seqs=2,
dtype=dtype,
limit_mm_per_prompt={"video": 2},
tensor_parallel_size=1,
video_pruning_rate=video_pruning_rate,
) as vllm_model:
# Generate output - this should not crash
outputs = vllm_model.generate_greedy(prompts, max_tokens, videos=videos)
# Basic validation that we got responses for both videos
assert len(outputs) == 2
for output_ids, output_text in outputs:
# Ensure we got some output for each video
assert len(output_ids) > 0
assert len(output_text) > 0
# Ensure the output is a string
assert isinstance(output_text, str)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/test_audioflamingo3.py | tests/models/multimodal/generation/test_audioflamingo3.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The vLLM team.
# Copyright 2025 NVIDIA CORPORATION and the HuggingFace Inc. team. All rights
# reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import pytest
from tests.models.registry import HF_EXAMPLE_MODELS
from vllm import LLM, SamplingParams
MODEL_NAME = "nvidia/audio-flamingo-3-hf"
def get_fixture_path(filename):
return os.path.join(
os.path.dirname(__file__), "../../fixtures/audioflamingo3", filename
)
@pytest.fixture(scope="module")
def llm():
# Check if the model is supported by the current transformers version
model_info = HF_EXAMPLE_MODELS.get_hf_info("AudioFlamingo3ForConditionalGeneration")
model_info.check_transformers_version(on_fail="skip")
try:
llm = LLM(
model=MODEL_NAME,
trust_remote_code=True,
dtype="bfloat16",
enforce_eager=True,
limit_mm_per_prompt={"audio": 1},
)
return llm
except Exception as e:
pytest.skip(f"Failed to load model {MODEL_NAME}: {e}")
def test_single_generation(llm):
fixture_path = get_fixture_path("expected_results_single.json")
if not os.path.exists(fixture_path):
pytest.skip(f"Fixture not found: {fixture_path}")
with open(fixture_path) as f:
expected = json.load(f)
audio_url = "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/Why_do_we_ask_questions_converted.wav"
messages = [
{
"role": "user",
"content": [
{"type": "audio_url", "audio_url": {"url": audio_url}},
{"type": "text", "text": "Transcribe the input speech."},
],
}
]
sampling_params = SamplingParams(temperature=0.0, max_tokens=128)
outputs = llm.chat(
messages=messages,
sampling_params=sampling_params,
)
generated_text = outputs[0].outputs[0].text.strip()
expected_text = expected["transcriptions"][0]
assert expected_text in generated_text or generated_text in expected_text
def test_batched_generation(llm):
fixture_path = get_fixture_path("expected_results_batched.json")
if not os.path.exists(fixture_path):
pytest.skip(f"Fixture not found: {fixture_path}")
with open(fixture_path) as f:
expected = json.load(f)
items = [
{
"audio_url": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/dogs_barking_in_sync_with_the_music.wav",
"question": "What is surprising about the relationship "
"between the barking and the music?",
"expected_idx": 0,
},
{
"audio_url": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/Ch6Ae9DT6Ko_00-04-03_00-04-31.wav",
"question": (
"Why is the philosopher's name mentioned in the lyrics? "
"(A) To express a sense of nostalgia "
"(B) To indicate that language cannot express clearly, "
"satirizing the inversion of black and white in the world "
"(C) To add depth and complexity to the lyrics "
"(D) To showcase the wisdom and influence of the philosopher"
),
"expected_idx": 1,
},
]
conversations = []
for item in items:
messages = [
{
"role": "user",
"content": [
{"type": "audio_url", "audio_url": {"url": item["audio_url"]}},
{"type": "text", "text": item["question"]},
],
}
]
conversations.append(messages)
sampling_params = SamplingParams(temperature=0.0, max_tokens=128)
outputs = llm.chat(
messages=conversations,
sampling_params=sampling_params,
)
for i, output in enumerate(outputs):
generated_text = output.outputs[0].text.strip()
expected_text = expected["transcriptions"][i]
assert expected_text in generated_text or generated_text in expected_text
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/case_filtering.py | tests/models/multimodal/generation/vlm_utils/case_filtering.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Utils for determining which subset of model tests belong to a specific
modality, getting all combinations (similar to pytest's parametrization),
handling multimodal placeholder substitution, and so on.
"""
import itertools
from collections import OrderedDict
from collections.abc import Iterable
import pytest
from .types import (
EMBEDDING_SIZE_FACTORS,
ExpandableVLMTestArgs,
ImageSizeWrapper,
SizeType,
VLMTestInfo,
VLMTestType,
)
def get_filtered_test_settings(
test_settings: dict[str, VLMTestInfo],
test_type: VLMTestType,
new_proc_per_test: bool,
) -> dict[str, VLMTestInfo]:
"""Given the dict of potential test settings to run, return a subdict
of tests who have the current test type enabled with the matching val for
fork_per_test.
"""
def matches_test_type(test_info: VLMTestInfo, test_type: VLMTestType):
return test_info.test_type == test_type or (
isinstance(test_info.test_type, Iterable)
and test_type in test_info.test_type
)
matching_tests = {}
for test_name, test_info in test_settings.items():
# Otherwise check if the test has the right type & keep if it does
if matches_test_type(test_info, test_type):
# Embedding tests need to have a conversion func in their test info
if matches_test_type(test_info, VLMTestType.EMBEDDING):
assert test_info.convert_assets_to_embeddings is not None
# Custom test inputs need to explicitly define the mm limit/inputs
if matches_test_type(test_info, VLMTestType.CUSTOM_INPUTS):
assert test_info.custom_test_opts is not None and isinstance(
test_info.custom_test_opts, Iterable
)
# For all types besides custom inputs, we need a prompt formatter
else:
assert test_info.prompt_formatter is not None
# Everything looks okay; keep if this is correct proc handling
if (
test_info.distributed_executor_backend is not None
) == new_proc_per_test:
matching_tests[test_name] = test_info
return matching_tests
def get_model_type_cases(
model_type: str,
test_info: VLMTestInfo,
test_type: VLMTestType,
):
# Ensure that something is wrapped as an iterable it's not already
ensure_wrapped = lambda e: e if isinstance(e, (list, tuple)) else (e,)
# This is essentially the same as nesting a bunch of mark.parametrize
# decorators, but we do it programmatically to allow overrides for on
# a per-model basis, while still being able to execute each of these
# as individual test cases in pytest.
iter_kwargs = OrderedDict(
[
("model", ensure_wrapped(test_info.models)),
("max_tokens", ensure_wrapped(test_info.max_tokens)),
("num_logprobs", ensure_wrapped(test_info.num_logprobs)),
("dtype", ensure_wrapped(test_info.dtype)),
(
"distributed_executor_backend",
ensure_wrapped(test_info.distributed_executor_backend),
),
]
)
# num_frames is video only
if test_type == VLMTestType.VIDEO:
iter_kwargs["num_video_frames"] = ensure_wrapped(test_info.num_video_frames)
iter_kwargs["needs_video_metadata"] = ensure_wrapped(
test_info.needs_video_metadata
)
# No sizes passed for custom inputs, since inputs are directly provided
if test_type not in (
VLMTestType.CUSTOM_INPUTS,
VLMTestType.AUDIO,
):
wrapped_sizes = get_wrapped_test_sizes(test_info, test_type)
if wrapped_sizes is None:
raise ValueError(f"Sizes must be set for test type {test_type}")
iter_kwargs["size_wrapper"] = wrapped_sizes
# Otherwise expand the custom test options instead
elif test_type == VLMTestType.CUSTOM_INPUTS:
if test_info.custom_test_opts is None:
raise ValueError("Test has type CUSTOM_INPUTS, but none given")
iter_kwargs["custom_test_opts"] = test_info.custom_test_opts
# Wrap all model cases in a pytest parameter & pass marks through
return [
pytest.param(
model_type,
ExpandableVLMTestArgs(**{k: v for k, v in zip(iter_kwargs.keys(), case)}),
marks=test_info.marks if test_info.marks is not None else [],
)
for case in list(itertools.product(*iter_kwargs.values()))
]
def get_parametrized_options(
test_settings: dict[str, VLMTestInfo],
test_type: VLMTestType,
create_new_process_for_each_test: bool,
):
"""Converts all of our VLMTestInfo into an expanded list of parameters.
This is similar to nesting pytest parametrize calls, but done directly
through an itertools product so that each test can set things like
size factors etc, while still running in isolated test cases.
"""
matching_tests = get_filtered_test_settings(
test_settings, test_type, create_new_process_for_each_test
)
# Get a list per model type, where each entry contains a tuple of all of
# that model type's cases, then flatten them into the top level so that
# we can consume them in one mark.parametrize call.
cases_by_model_type = [
get_model_type_cases(model_type, test_info, test_type)
for model_type, test_info in matching_tests.items()
]
return list(itertools.chain(*cases_by_model_type))
def get_wrapped_test_sizes(
test_info: VLMTestInfo, test_type: VLMTestType
) -> tuple[ImageSizeWrapper, ...]:
"""Given a test info which may have size factors or fixed sizes, wrap them
and combine them into an iterable, each of which will be used in parameter
expansion.
Args:
test_info: Test configuration to be expanded.
test_type: The type of test being filtered for.
"""
# If it is an embedding test, we always use the EMBEDDING_SIZE_FACTORS
if test_type == VLMTestType.EMBEDDING:
return tuple(
[
ImageSizeWrapper(type=SizeType.SIZE_FACTOR, data=factor)
for factor in EMBEDDING_SIZE_FACTORS
]
)
# Audio and Custom inputs have preprocessed inputs
elif test_type in (VLMTestType.AUDIO, VLMTestType.CUSTOM_INPUTS):
return tuple()
size_factors = test_info.image_size_factors if test_info.image_size_factors else []
fixed_sizes = test_info.image_sizes if test_info.image_sizes else []
wrapped_factors = [
ImageSizeWrapper(type=SizeType.SIZE_FACTOR, data=factor)
for factor in size_factors
]
wrapped_sizes = [
ImageSizeWrapper(type=SizeType.FIXED_SIZE, data=size) for size in fixed_sizes
]
return tuple(wrapped_factors + wrapped_sizes)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/model_utils.py | tests/models/multimodal/generation/vlm_utils/model_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Common utility functions relating to different models that are useful
for manipulating the input / output of HF & vLLM test runners, which are
typically specific to a small subset of models.
"""
import types
from pathlib import PosixPath
import numpy as np
import numpy.typing as npt
import PIL.Image
import pytest
import regex as re
import torch
from PIL.Image import Image
from transformers import (
AutoConfig,
AutoTokenizer,
BatchFeature,
GenerationConfig,
GenerationMixin,
)
from transformers.video_utils import VideoMetadata
from vllm.logprobs import SampleLogprobs
from vllm.platforms import current_platform
from vllm.utils.collection_utils import is_list_of
from .....conftest import HfRunner, ImageAsset, ImageTestAssets
from .types import RunnerOutput
####### vLLM output processors functions
def blip2_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output [blip2 models] to be comparable with hf output."""
_, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "\n"
tokenizer = AutoTokenizer.from_pretrained(model)
hf_output_ids = tokenizer.encode(hf_output_str)
assert hf_output_ids[0] == tokenizer.bos_token_id
hf_output_ids = hf_output_ids[1:]
return hf_output_ids, hf_output_str, out_logprobs
def fuyu_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output [fuyu models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str.lstrip() + "|ENDOFTEXT|"
return output_ids, hf_output_str, out_logprobs
def qwen_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> tuple[list[int], str, SampleLogprobs | None]:
"""Sanitize vllm output [qwen models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|endoftext|>"
return output_ids, hf_output_str, out_logprobs
def qwen2_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> tuple[list[int], str, SampleLogprobs | None]:
"""Sanitize vllm output [qwen2 models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|im_end|>"
return output_ids, hf_output_str, out_logprobs
def kimiv_vl_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> tuple[list[int], str, SampleLogprobs | None]:
"""Sanitize vllm output [kimi_vl models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|im_end|>[EOS]"
return output_ids, hf_output_str, out_logprobs
def llava_image_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> RunnerOutput:
config = AutoConfig.from_pretrained(model)
mm_token_id = config.image_token_index
return _llava_vllm_to_hf_output(vllm_output, model, mm_token_id)
def llava_video_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> tuple[list[int], str, SampleLogprobs | None]:
config = AutoConfig.from_pretrained(model)
mm_token_id = config.video_token_index
return _llava_vllm_to_hf_output(vllm_output, model, mm_token_id)
def _llava_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str, mm_token_id: int
) -> RunnerOutput:
"""Sanitize vllm output [Llava models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
tokenizer = AutoTokenizer.from_pretrained(model)
eos_token_id = tokenizer.eos_token_id
hf_output_ids = [
token_id
for idx, token_id in enumerate(output_ids)
if token_id != mm_token_id or output_ids[idx - 1] != mm_token_id
]
assert output_str[0] == " "
hf_output_str = output_str[1:]
if hf_output_ids[-1] == eos_token_id:
hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
return hf_output_ids, hf_output_str, out_logprobs
def llava_onevision_hf_model_kwargs(model: str) -> dict:
"""Workaround to fix the sliding window issue in llava_onevision."""
config = AutoConfig.from_pretrained(model)
config.text_config.sliding_window = None
return config.to_dict()
def llava_onevision_vllm_to_hf_output(
vllm_output: RunnerOutput, model: str
) -> RunnerOutput:
"""Sanitize vllm output [llava-onevision] to compare with hf output."""
output_ids, output_str, out_logprobs = vllm_output
config = AutoConfig.from_pretrained(model)
video_token_id = config.video_token_index
tokenizer = AutoTokenizer.from_pretrained(model)
eos_token_id = tokenizer.eos_token_id
hf_output_ids = [
token_id
for idx, token_id in enumerate(output_ids)
if token_id != video_token_id or output_ids[idx - 1] != video_token_id
]
hf_output_str = output_str
if hf_output_ids[-1] == eos_token_id:
hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
return hf_output_ids, hf_output_str, out_logprobs
def mantis_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output [mantis] to compare with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|eot_id|>"
return output_ids, hf_output_str, out_logprobs
def phi3v_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output [phi3v] to be comparable with hf output."""
_, output_str, out_logprobs = vllm_output
output_str_without_image = re.sub(r"(<\|image_\d+\|>)+", "", output_str)
assert output_str_without_image[0] == " "
output_str_without_image = output_str_without_image[1:]
hf_output_str = output_str_without_image + "<|end|><|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model)
hf_output_ids = tokenizer.encode(output_str_without_image)
assert hf_output_ids[0] == 1
hf_output_ids = hf_output_ids[1:]
return hf_output_ids, hf_output_str, out_logprobs
def paligemma_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
config = AutoConfig.from_pretrained(model)
image_token_id = config.image_token_index
tokenizer = AutoTokenizer.from_pretrained(model)
eos_token_id = tokenizer.eos_token_id
hf_output_ids = [
token_id
for idx, token_id in enumerate(output_ids)
if token_id != image_token_id or output_ids[idx - 1] != image_token_id
]
hf_output_str = output_str
if hf_output_ids[-1] == eos_token_id:
hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
return hf_output_ids, hf_output_str, out_logprobs
####### Post-processors for HF outputs
def deepseekvl2_trunc_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
output_ids, output_str, out_logprobs = hf_output
if output_str.endswith("<｜end▁of▁sentence｜>"):
output_str = output_str.split("<｜end▁of▁sentence｜>")[0]
return output_ids, output_str, out_logprobs
def idefics3_trunc_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
output_ids, output_str, out_logprobs = hf_output
if output_str.endswith("<end_of_utterance>"):
output_str = output_str.split("<end_of_utterance>")[0]
return output_ids, output_str, out_logprobs
def smolvlm_trunc_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
# Based on Idefics3
return idefics3_trunc_hf_output(hf_output, model)
def minicpmv_trunc_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
output_ids, output_str, out_logprobs = hf_output
if output_str.endswith("<|eot_id|>"):
output_str = output_str.split("<|eot_id|>")[0]
return output_ids, output_str, out_logprobs
def minimax_vl_01_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
output_ids, output_str, out_logprobs = hf_output
if output_str.endswith("<end_of_sentence>"):
output_str = output_str.split("<end_of_sentence>")[0]
return output_ids, output_str, out_logprobs
def ultravox_trunc_hf_output(hf_output: RunnerOutput, model: str) -> RunnerOutput:
output_ids, output_str, out_logprobs = hf_output
tokenizer = AutoTokenizer.from_pretrained(model)
eos_token_id = tokenizer.eos_token_id
eos_token = tokenizer.decode(eos_token_id)
if output_str.endswith(eos_token):
output_str = output_str.split(eos_token)[0]
return output_ids, output_str, out_logprobs
####### Functions for converting image assets to embeddings
def get_llava_embeddings(image_assets: ImageTestAssets):
return [asset.image_embeds for asset in image_assets]
####### Prompt path encoders for models that need models on disk
def qwen_prompt_path_encoder(
tmp_path: PosixPath, prompt: str, assets: list[ImageAsset] | ImageTestAssets
) -> str:
"""Given a temporary dir path, export one or more image assets into the
tempdir & replace its contents with the local path to the string so that
the HF version of Qwen-VL can resolve the path and load the image in its
forward() call.
Args:
tmp_path: Tempdir for test under consideration.
prompt: Prompt with image placeholders.
assets: list of image assets whose len equals the num placeholders.
"""
# Ensure that the number of placeholders matches the number of assets;
# If this is not true, the test is probably written incorrectly.
assert prompt.count("<img></img>") == len(assets)
# Replace the placeholders with local paths to the exported assets
for asset in assets:
image_tmp_path = tmp_path / f"{asset.name}.jpg"
asset.pil_image.save(image_tmp_path)
prompt = prompt.replace(
"<img></img>",
f"<img>{image_tmp_path}</img>",
1,
)
return prompt
####### Model-specific HuggingFace runner patchers
def deepseekvl2_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for GLM4."""
hf_processor = hf_model.processor
def processor(*args, text="", images=None, **kwargs):
if isinstance(images, Image):
images = [images]
# inputs is a custom class instead of dict or BatchFeature
inputs = hf_processor(
*args,
prompt=text,
images=images,
**kwargs,
)
inputs = {
k: inputs[k]
for k in inputs.keys() # noqa
if k not in ("seq_lens", "sft_format")
}
return BatchFeature(data=inputs, tensor_type="pt")
hf_model.processor = processor
hf_model.model.get_output_embeddings = (
lambda: hf_model.model.language.model.embed_tokens
)
return hf_model
def gemma3_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for Gemma 3."""
hf_processor = hf_model.processor
def processor(*args, **kwargs):
return hf_processor(*args, do_pan_and_scan=True, **kwargs)
hf_model.processor = processor
orig_generate = hf_model.model.generate
def _generate(self, *args, **kwargs):
# FIXME: https://github.com/huggingface/transformers/issues/38333
kwargs["disable_compile"] = True
return orig_generate(*args, **kwargs)
hf_model.model.generate = types.MethodType(_generate, hf_model.model)
return hf_model
def gemma3_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput:
"""Sanitize vllm output [gemma-3] to compare with hf output."""
output_ids, output_str, out_logprobs = vllm_output
config = AutoConfig.from_pretrained(model)
image_token_id = config.image_token_id
tokenizer = AutoTokenizer.from_pretrained(model)
eos_token_id = tokenizer.eos_token_id
hf_output_ids = [
token_id
for idx, token_id in enumerate(output_ids)
if token_id != image_token_id
]
hf_output_str = output_str
if hf_output_ids[-1] == eos_token_id:
hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
return hf_output_ids, hf_output_str, out_logprobs
def glm4v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for GLM4V."""
if current_platform.is_rocm():
import types
config = hf_model.model.config
if hasattr(config, "num_layers") and not hasattr(config, "num_hidden_layers"):
config.num_hidden_layers = config.num_layers
config.output_hidden_states = True
def patched_prepare_cache(
self, generation_config, model_kwargs, *args, **kwargs
):
model_kwargs["past_key_values"] = None
model_kwargs["use_cache"] = False
return model_kwargs
hf_model.model._prepare_cache_for_generation = types.MethodType(
patched_prepare_cache, hf_model.model
)
original_generate = hf_model.model.generate
def patched_generate(*args, **kwargs):
kwargs["output_hidden_states"] = True
kwargs["return_dict_in_generate"] = True
return original_generate(*args, **kwargs)
hf_model.model.generate = patched_generate
original_forward = hf_model.model.forward
def patched_forward(*args, **kwargs):
kwargs["output_hidden_states"] = True
return original_forward(*args, **kwargs)
hf_model.model.forward = patched_forward
hf_processor = hf_model.processor
def processor(*args, text="", images=None, **kwargs):
if images is None:
return hf_processor(*args, **kwargs)
images = [images] if isinstance(images, Image) else images
contents = re.findall(
r"<\|begin_of_image\|><\|endoftext\|><\|end_of_image\|>(.*?)<\|assistant\|>",
text,
)
assert len(contents) == len(images)
return hf_processor.apply_chat_template(
[
{"role": "user", "image": image, "content": content}
for image, content in zip(images, contents)
],
add_generation_prompt=True,
tokenize=True,
return_dict=True,
**kwargs,
)
hf_model.processor = processor
hf_model.model.get_output_embeddings = (
lambda: hf_model.model.transformer.output_layer
)
return hf_model
def glm4_1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for GLM4.1V."""
hf_processor = hf_model.processor
def processor(*args, videos=None, **kwargs):
if videos is not None and is_list_of(videos, tuple):
# If videos is a list of tuples, we assume each tuple contains
# (video_array, metadata) as in the case of GLM4.1V.
# Filter out 'do_sample_frames' as it's not a valid VideoMetadata arg
video_metadata = [
[
VideoMetadata(
**{k: v for k, v in video[1].items() if k != "do_sample_frames"}
)
]
for video in videos
]
videos = [[video[0]] for video in videos]
else:
video_metadata = None
return hf_processor(
*args, videos=videos, video_metadata=video_metadata, **kwargs
)
hf_model.processor = processor
return hf_model
def h2ovl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for H2OVL."""
class H2OVLProcessor:
"""A simple processor for H2OVL models."""
def __init__(self, hf_runner: HfRunner):
self.num_image_token = hf_runner.model.num_image_token
self.tokenizer = hf_runner.tokenizer
self.config = AutoConfig.from_pretrained(
hf_runner.model_name, trust_remote_code=True
)
self.vision_config = self.config.vision_config
self.use_thumbnail = self.config.use_thumbnail
self.use_msac = self.config.use_msac
self.min_num = self.config.min_dynamic_patch
self.max_num = self.config.max_dynamic_patch
self.image_size = self.vision_config.image_size
def __call__(self, text: str, images: Image | list[Image], **kwargs):
from vllm.model_executor.models.h2ovl import (
IMG_CONTEXT,
IMG_END,
IMG_START,
image_to_pixel_values_h2ovl,
)
images = [images] if isinstance(images, Image) else images
pixel_values = [
image_to_pixel_values_h2ovl(
image,
input_size=self.image_size,
min_num=self.min_num,
max_num=self.max_num,
use_thumbnail=self.use_thumbnail,
use_msac=self.use_msac,
)
for image in images
]
num_patches_list = [pixel_value.shape[0] for pixel_value in pixel_values]
pixel_values = torch.cat(pixel_values, dim=0)
for num_patches in num_patches_list:
context_tokens = IMG_CONTEXT * self.num_image_token * num_patches
image_tokens = IMG_START + context_tokens + IMG_END
text = text.replace("<image>", image_tokens, 1)
prompt = self.tokenizer(text, return_tensors="pt")
prompt.update({"pixel_values": pixel_values})
return prompt
img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
hf_model.model.img_context_token_id = img_context_token_id
hf_model.processor = H2OVLProcessor(hf_model)
hf_model.model.get_output_embeddings = (
lambda: hf_model.model.language_model.get_output_embeddings()
)
hf_model.model.generate = types.MethodType(_internvl_generate, hf_model.model)
return hf_model
def isaac_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patch HF runner for Isaac:
1) Move processor outputs to model device
2) Ensure IsaacModel.forward returns hidden_states
for compatibility with hidden_states_to_seq_logprobs()
"""
from perceptron.tensorstream import TextType
from perceptron.tensorstream.ops import compute_mrope_pos_tensor, modality_mask
from transformers.modeling_outputs import BaseModelOutputWithPast
def compute_position_ids_input_ids(input_ids: torch.Tensor) -> torch.Tensor:
"""
Create 3D positional indices for token input.
"""
batch_size, seq_length = input_ids.shape
position_ids = torch.arange(seq_length, device=input_ids.device)
position_ids = position_ids.view(1, -1).expand(batch_size, -1)
position_ids = position_ids.unsqueeze(2).expand(-1, -1, 3) # Add 3D for MRoPE
return position_ids
model_device = next(hf_model.model.parameters()).device
# ----------------------------
# 1) Patch processor: move BatchFeature input_ids and TensorStream to model device
# ----------------------------
original_processor = hf_model.processor
def patched_processor(*args, **kwargs):
result = original_processor(*args, **kwargs)
for k, v in result.data.items():
result[k] = v.to(model_device)
return result
hf_model.processor = patched_processor
tokenizer = AutoTokenizer.from_pretrained(
hf_model.model_name, trust_remote_code=True
)
original_generate = hf_model.model.generate
def patched_generate(*args, **kwargs):
kwargs["pad_token_id"] = tokenizer.eos_token_id
kwargs["eos_token_id"] = tokenizer.eos_token_id
return original_generate(*args, **kwargs)
hf_model.model.generate = patched_generate
# ----------------------------
# 2) Patch IsaacModel.forward: add hidden_states to the output
# ----------------------------
isaac_model = hf_model.model.model
def patched_forward(
self,
input_ids=None,
tensor_stream=None,
attention_mask=None,
position_ids=None,
modality_tensor=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_hidden_states=None,
return_dict=None,
cache_position=None,
**kwargs,
):
"""
Forward pass with MRoPE position embeddings.
Computes position embeddings once and passes them through all layers.
"""
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# Get inputs
if tensor_stream is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both tensor_stream and inputs_embeds")
elif tensor_stream is not None:
# Embed TensorStream directly
inputs_embeds = self.embed_stream(tensor_stream)
# Create modality tensor if not provided
if modality_tensor is None:
modality_tensor = modality_mask(tensor_stream)
elif input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
inputs_embeds = self.embed_tokens(input_ids)
# Create text modality tensor if not provided
if modality_tensor is None:
batch_size, seq_length = input_ids.shape
modality_tensor = torch.full(
(batch_size, seq_length),
TextType.text.value,
device=input_ids.device,
dtype=torch.long,
)
elif inputs_embeds is None:
raise ValueError(
"You have to specify either tensor_stream, input_ids or inputs_embeds"
)
# Create default position_ids if not provided
if position_ids is None:
if tensor_stream is not None:
position_ids = compute_mrope_pos_tensor(tensor_stream) # (B,L,3)
else:
position_ids = compute_position_ids_input_ids(input_ids)
# Compute MRoPE position embeddings if we have custom rotary_emb
cos, sin = self.rotary_emb(position_ids, modality_tensor)
cos = cos.to(inputs_embeds.dtype)
sin = sin.to(inputs_embeds.dtype)
# Prepare attention mask
if attention_mask is not None:
attention_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, False
)
# Initialize and collect hidden states
hidden_states = inputs_embeds
hidden_states_list: list[torch.Tensor] = []
if output_hidden_states:
hidden_states_list.append(hidden_states)
for decoder_layer in self.layers:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=(cos, sin),
**kwargs,
)
hidden_states = (
layer_outputs[0] if isinstance(layer_outputs, tuple) else layer_outputs
)
if output_hidden_states:
hidden_states_list.append(hidden_states)
# Final layer norm
hidden_states = self.norm(hidden_states)
if output_hidden_states:
hidden_states_list.append(hidden_states)
# Convert to tuple or None
all_hidden_states = tuple(hidden_states_list) if output_hidden_states else None
# Include hiden_states for compatibility with hidden_states_to_seq_logprobs()
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
)
isaac_model.forward = types.MethodType(patched_forward, isaac_model)
return hf_model
def skyworkr1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for SkyworkR1V."""
class SkyworkR1VProcessor:
"""A simple processor for SkyworkR1V."""
def __init__(self, hf_runner: HfRunner):
self.num_image_token = hf_runner.model.num_image_token
self.tokenizer = hf_runner.tokenizer
self.config = AutoConfig.from_pretrained(
hf_runner.model_name, trust_remote_code=True
)
self.vision_config = self.config.vision_config
self.use_thumbnail = self.config.use_thumbnail
self.min_num = self.config.min_dynamic_patch
self.max_num = self.config.max_dynamic_patch
self.image_size = self.vision_config.image_size
def __call__(self, text: str, images: Image | list[Image], **kwargs):
from vllm.model_executor.models.skyworkr1v import (
IMG_CONTEXT,
IMG_END,
IMG_START,
image_to_pixel_values_skyworkr1v,
)
images = [images] if isinstance(images, Image) else images
pixel_values = [
image_to_pixel_values_skyworkr1v(
image,
input_size=self.image_size,
min_num=self.min_num,
max_num=self.max_num,
use_thumbnail=self.use_thumbnail,
)
for image in images
]
num_patches_list = [pixel_value.shape[0] for pixel_value in pixel_values]
pixel_values = torch.cat(pixel_values, dim=0)
for num_patches in num_patches_list:
context_tokens = IMG_CONTEXT * self.num_image_token * num_patches
image_tokens = IMG_START + context_tokens + IMG_END
text = text.replace("<image>", image_tokens, 1)
prompt = self.tokenizer(text, return_tensors="pt")
prompt.update({"pixel_values": pixel_values})
return prompt
img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
hf_model.model.img_context_token_id = img_context_token_id
hf_model.processor = SkyworkR1VProcessor(hf_model)
hf_model.model.get_output_embeddings = (
lambda: hf_model.model.language_model.get_output_embeddings()
)
hf_model.model.generate = types.MethodType(_internvl_generate, hf_model.model)
return hf_model
def internvl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
"""Patches and returns an instance of the HfRunner to use for InternVL."""
class InternVLProcessor:
"""A simple processor for InternVL2 which misses a processor."""
def __init__(self, hf_runner: HfRunner):
self.num_image_token = hf_runner.model.num_image_token
self.tokenizer = hf_runner.tokenizer
self.config = AutoConfig.from_pretrained(
hf_runner.model_name, trust_remote_code=True
)
self.vision_config = self.config.vision_config
self.use_thumbnail = self.config.use_thumbnail
self.min_num = self.config.min_dynamic_patch
self.max_num = self.config.max_dynamic_patch
self.image_size = self.vision_config.image_size
def __call__(
self,
text: str,
images: Image | list[Image] = None,
videos: npt.NDArray | list[npt.NDArray] = None,
**kwargs,
):
from vllm.model_executor.models.internvl import (
IMG_CONTEXT,
IMG_END,
IMG_START,
image_to_pixel_values_internvl,
video_to_pixel_values_internvl,
)
images = [images] if isinstance(images, Image) else images
videos = [videos] if isinstance(videos, np.ndarray) else videos
if images is not None:
pixel_values_images = [
image_to_pixel_values_internvl(
image,
input_size=self.image_size,
min_num=self.min_num,
max_num=self.max_num,
use_thumbnail=self.use_thumbnail,
)
for image in images
]
num_patches_images = [
pixel_value.shape[0] for pixel_value in pixel_values_images
]
else:
pixel_values_images, num_patches_images = [], []
if videos is not None:
pixel_values_videos = [
video_to_pixel_values_internvl(
video,
input_size=self.image_size,
min_num=1,
max_num=1,
use_thumbnail=False,
)
for video in videos
]
num_patches_videos = [
pixel_value.shape[0] for pixel_value in pixel_values_videos
]
else:
pixel_values_videos, num_patches_videos = [], []
pixel_values = []
while ("<image>" in text) or ("<video>" in text):
image_index = text.find("<image>")
video_index = text.find("<video>")
if image_index == -1 or (
video_index > -1 and video_index < image_index
):
num_patches = num_patches_videos.pop(0)
pixel_values.append(pixel_values_videos.pop(0))
context_tokens = (
IMG_START + IMG_CONTEXT * self.num_image_token + IMG_END
)
video_tokens = "".join(
[f"Frame{i + 1}: {context_tokens}" for i in range(num_patches)]
)
text = text.replace("<video>", video_tokens, 1)
else:
num_patches = num_patches_images.pop(0)
pixel_values.append(pixel_values_images.pop(0))
context_tokens = IMG_CONTEXT * self.num_image_token * num_patches
image_tokens = IMG_START + context_tokens + IMG_END
text = text.replace("<image>", image_tokens, 1)
pixel_values = torch.cat(pixel_values, dim=0)
prompt = self.tokenizer(text, return_tensors="pt")
prompt.update({"pixel_values": pixel_values})
return prompt
img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
hf_model.model.img_context_token_id = img_context_token_id
hf_model.processor = InternVLProcessor(hf_model)
hf_model.model.get_output_embeddings = (
lambda: hf_model.model.language_model.get_output_embeddings()
)
hf_model.model.generate = types.MethodType(_internvl_generate, hf_model.model)
return hf_model
def _internvl_generate(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.FloatTensor,
attention_mask: torch.LongTensor | None = None,
**generate_kwargs,
) -> torch.LongTensor:
"""Generate method for InternVL2 model without fixed use_cache."""
assert self.img_context_token_id is not None
target_dtype = next(self.parameters()).dtype
vit_embeds = self.extract_feature(pixel_values.to(target_dtype))
input_embeds = self.language_model.get_input_embeddings()(input_ids)
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = input_ids == self.img_context_token_id
assert selected.sum() != 0
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | true |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/core.py | tests/models/multimodal/generation/vlm_utils/core.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Core test implementation to be shared across modalities."""
from collections.abc import Callable
from typing import Any
import torch
from transformers.models.auto.auto_factory import _BaseAutoModelClass
from vllm.config.model import RunnerOption
from vllm.tokenizers import TokenizerLike
from .....conftest import HfRunner, VllmRunner
from ....registry import HF_EXAMPLE_MODELS
from .types import PromptWithMultiModalInput, RunnerOutput
def run_test(
*,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
inputs: list[PromptWithMultiModalInput],
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
enforce_eager: bool,
max_model_len: int,
max_num_seqs: int,
hf_output_post_proc: Callable[[RunnerOutput, str], Any] | None,
vllm_output_post_proc: Callable[[RunnerOutput, str], Any] | None,
auto_cls: type[_BaseAutoModelClass],
use_tokenizer_eos: bool,
comparator: Callable[..., None],
get_stop_token_ids: Callable[[TokenizerLike], list[int]] | None,
stop_str: list[str] | None,
limit_mm_per_prompt: dict[str, int],
vllm_runner_kwargs: dict[str, Any] | None,
hf_model_kwargs: dict[str, Any] | None,
patch_hf_runner: Callable[[HfRunner], HfRunner] | None,
runner: RunnerOption = "auto",
distributed_executor_backend: str | None = None,
tensor_parallel_size: int = 1,
vllm_embeddings: torch.Tensor | None = None,
):
"""Modality agnostic test executor for comparing HF/vLLM outputs."""
# In the case of embeddings, vLLM takes separate input tensors
vllm_inputs = vllm_embeddings if vllm_embeddings is not None else inputs
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
# Disable other modalities to save memory
default_limits = {"image": 0, "video": 0, "audio": 0}
limit_mm_per_prompt = default_limits | limit_mm_per_prompt
vllm_outputs_per_mm = []
hf_outputs_per_mm = []
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
vllm_runner_kwargs_: dict[str, Any] = {"mm_processor_cache_gb": 0}
if model_info.tokenizer:
vllm_runner_kwargs_["tokenizer_name"] = model_info.tokenizer
if model_info.tokenizer_mode:
vllm_runner_kwargs_["tokenizer_mode"] = model_info.tokenizer_mode
if model_info.hf_overrides:
vllm_runner_kwargs_["hf_overrides"] = model_info.hf_overrides
if model_info.require_embed_inputs:
for k in ("skip_tokenizer_init", "enable_prompt_embeds", "enable_mm_embeds"):
vllm_runner_kwargs_[k] = model_info.require_embed_inputs
if vllm_runner_kwargs:
vllm_runner_kwargs_.update(vllm_runner_kwargs)
with vllm_runner(
model,
max_model_len=max_model_len,
max_num_seqs=max_num_seqs,
dtype=dtype,
limit_mm_per_prompt=limit_mm_per_prompt,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=enforce_eager,
runner=runner,
**vllm_runner_kwargs_,
) as vllm_model:
tokenizer = vllm_model.llm.get_tokenizer()
vllm_kwargs: dict[str, Any] = {}
if get_stop_token_ids is not None:
vllm_kwargs["stop_token_ids"] = get_stop_token_ids(tokenizer)
if stop_str:
vllm_kwargs["stop"] = stop_str
for prompts, image_data, video_data, audio_data in vllm_inputs:
mm_data = dict(images=image_data, videos=video_data, audios=audio_data)
vllm_kwargs_with_mm_data = vllm_kwargs | mm_data
vllm_output = vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
**vllm_kwargs_with_mm_data,
)
vllm_outputs_per_mm.append(vllm_output)
hf_model = hf_runner(
model, dtype=dtype, auto_cls=auto_cls, model_kwargs=hf_model_kwargs
)
# Some models need to patch things like the model processor, e.g., internvl
if patch_hf_runner is not None:
hf_model = patch_hf_runner(hf_model)
with hf_model, torch.no_grad():
tokenizer = hf_model.tokenizer
# Some models need to explicitly pass the eos_token_id off the tokenizer
# or processor for a good comparison;
# currently assume processor/tokenizer agree on the EOS, and pull it off
# the tokenizer if requested.
hf_kwargs = {}
if use_tokenizer_eos:
hf_kwargs["eos_token_id"] = tokenizer.eos_token_id
if stop_str:
hf_kwargs["stop_strings"] = stop_str
for prompts, image_data, video_data, audio_data in inputs:
mm_data = dict(images=image_data, videos=video_data, audios=audio_data)
hf_kwargs_with_mm_data = hf_kwargs | mm_data
hf_output = hf_model.generate_greedy_logprobs_limit(
prompts,
max_tokens,
num_logprobs=num_logprobs,
tokenizer=tokenizer,
**hf_kwargs_with_mm_data,
)
hf_outputs_per_mm.append(hf_output)
# Apply output processing / sanitation to the vLLM and HF runner results
hf_outputs_per_mm, vllm_outputs_per_mm = process_runner_outputs(
model,
first_runner_outputs=hf_outputs_per_mm,
second_runner_outputs=vllm_outputs_per_mm,
first_runner_processor=hf_output_post_proc,
second_runner_processor=vllm_output_post_proc,
)
for hf_outputs, vllm_outputs in zip(hf_outputs_per_mm, vllm_outputs_per_mm):
# This is usually check_logprobs_close, but it's passed through to
# allow things like check_outputs_equal where needed
comparator(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
def process_runner_outputs(
model,
first_runner_outputs,
second_runner_outputs,
first_runner_processor=None,
second_runner_processor=None,
):
"""Applies the runner processor(s) to the runner outputs, if any."""
if first_runner_processor is not None:
first_runner_outputs = process_outputs(
first_runner_processor, model, first_runner_outputs
)
if second_runner_processor is not None:
second_runner_outputs = process_outputs(
second_runner_processor, model, second_runner_outputs
)
return first_runner_outputs, second_runner_outputs
def process_outputs(output_processor, model, outputs_per_image):
"""Applies a model specific post-processor function to a runner's output"""
return [
[output_processor(res, model) for res in outputs]
for outputs in outputs_per_image
]
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/custom_inputs.py | tests/models/multimodal/generation/vlm_utils/custom_inputs.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Custom input builders for edge-cases in different models."""
from collections.abc import Callable
from vllm.assets.image import ImageAsset
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.video import (
rescale_video_size,
resize_video,
sample_frames_from_video,
)
from .....conftest import IMAGE_ASSETS, VIDEO_ASSETS
from .builders import build_multi_image_inputs, build_single_image_inputs
from .types import ImageSizeWrapper, PromptWithMultiModalInput, SizeType
def multi_image_multi_aspect_ratio_inputs(formatter: Callable[[str], str]):
"""Builds inputs for multi-image (varied sizes/aspect ratio) testing.
Args:
formatter: model-specific prompt formatter.
"""
stop_sign = IMAGE_ASSETS[0].pil_image
cherry_blossom = IMAGE_ASSETS[1].pil_image
# Apply the selected formatter to the base prompts
img_prompts = [
"<image><image>\nDescribe 2 images.",
"<image><image>\nDescribe 2 images.",
"<image><image><image><image>\nDescribe 4 images.",
"<image>\nWhat is the season?",
]
formatted_prompts = [formatter(prompt) for prompt in img_prompts]
aspect_ratio_images = [
[stop_sign, cherry_blossom],
# Images with different sizes and aspect-ratios
[
rescale_image_size(stop_sign, 0.1),
stop_sign,
],
[
stop_sign,
rescale_image_size(stop_sign, 0.25),
cherry_blossom.resize((183, 488)),
cherry_blossom.resize((488, 183)),
],
cherry_blossom,
]
return [
PromptWithMultiModalInput(
prompts=formatted_prompts,
image_data=aspect_ratio_images,
)
]
def multi_video_multi_aspect_ratio_inputs(
formatter: Callable[[str], str], num_frames: int = 16
):
"""Builds inputs for multi-video (varied sizes/aspect ratio) testing.
Args:
formatter: model-specific prompt formatter.
"""
video = sample_frames_from_video(VIDEO_ASSETS[0].np_ndarrays, num_frames)
# Apply the selected formatter to the base prompts
video_prompts = [
"<video><video>\nDescribe 2 videos.",
"<video><video>\nDescribe 2 videos.",
"<video><video><video><video>\nDescribe 4 videos.",
"<video>\nWhy is this video funny?",
]
formatted_prompts = [formatter(prompt) for prompt in video_prompts]
aspect_ratio_videos = [
[video, video],
# Videos with different sizes and aspect-ratios
[
rescale_video_size(video, 0.1),
video,
],
[
video,
rescale_video_size(video, 0.25),
resize_video(video, (183, 488)),
resize_video(video, (488, 183)),
],
video,
]
return [
PromptWithMultiModalInput(
prompts=formatted_prompts,
video_data=aspect_ratio_videos,
)
]
def different_patch_input_cases_internvl():
images = [asset.pil_image.resize((896, 896)) for asset in IMAGE_ASSETS]
formatter = (
lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n" # noqa: E501
)
single_img_prompts = [
"<image>\nWhat's the content in the center of the image?",
"<image>\nWhat is the season?",
]
multi_img_prompts = [
"Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.\n", # noqa: E501
]
formatted_sprompts = [formatter(prompt) for prompt in single_img_prompts]
formatted_mprompts = [formatter(prompt) for prompt in multi_img_prompts]
wrapped_sf = ImageSizeWrapper(type=SizeType.SIZE_FACTOR, data=[0.5, 1.0])
return [
build_single_image_inputs(images, formatted_sprompts, wrapped_sf),
build_multi_image_inputs([images], formatted_mprompts, wrapped_sf),
]
def windows_attention_image_qwen2_5_vl():
# image from regression issue: https://github.com/vllm-project/vllm/issues/15122 # noqa: E501
image = ImageAsset("hato").pil_image
question = "Describe the image."
img_prompt = "<|vision_start|><|image_pad|><|vision_end|>"
prompt = (
f"<|im_start|>User\n{img_prompt}{question}<|im_end|>\n<|im_start|>assistant\n"
)
wrapped_sf = ImageSizeWrapper(type=SizeType.SIZE_FACTOR, data=[0.5])
return build_single_image_inputs([image], [prompt], wrapped_sf)
def video_with_metadata_glm4_1v():
video_array = VIDEO_ASSETS[0].np_ndarrays
metadata = VIDEO_ASSETS[0].metadata
question = "Describe the video."
video_prompt = "<|begin_of_video|><|video|><|end_of_video|>"
formatted_prompt = f"[gMASK]<|user|>\n{video_prompt}{question}<|assistant|>\n"
scales = [0.1, 0.2, 0.25]
video_input = [
[(rescale_video_size(video_array, scale), metadata)] for scale in scales
]
prompts = [formatted_prompt] * len(video_input)
return [
PromptWithMultiModalInput(
prompts=prompts,
video_data=video_input,
)
]
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/builders.py | tests/models/multimodal/generation/vlm_utils/builders.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Helpers for building inputs that can be leveraged for different test types."""
from collections.abc import Callable, Iterable
from pathlib import PosixPath
from typing import Any
import numpy.typing as npt
import torch
from vllm.multimodal.audio import AudioResampler
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.video import (
rescale_video_size,
resize_video,
sample_frames_from_video,
)
from .....conftest import AudioTestAssets, ImageTestAssets, VideoTestAssets
from .types import (
SINGLE_AUDIO_BASE_PROMPT,
SINGLE_IMAGE_BASE_PROMPTS,
TEST_AUDIO_PLACEHOLDER,
TEST_IMG_PLACEHOLDER,
TEST_VIDEO_PLACEHOLDER,
VIDEO_BASE_PROMPT,
ImageSizeWrapper,
PromptWithMultiModalInput,
SizeType,
VLMTestInfo,
)
def replace_test_placeholder(
prompt: str, mm_idx_to_prompt: Callable[[int], str], test_placeholder: str
) -> str:
"""Given a prompt, replaces each test placeholder with the
model-specific tag.
"""
prompt_segments = prompt.split(test_placeholder)
img_prompt = prompt_segments[0]
for placeholder_idx, next_seg in enumerate(prompt_segments[1:], start=1):
img_prompt += mm_idx_to_prompt(placeholder_idx)
img_prompt += next_seg
return img_prompt
def get_model_prompts(
base_prompts: Iterable[str],
img_idx_to_prompt: Callable[[int], str] | None,
video_idx_to_prompt: Callable[[int], str] | None,
audio_idx_to_prompt: Callable[[int], str] | None,
prompt_formatter: Callable[[str], str],
) -> list[str]:
"""Given a model-agnostic base prompt and test configuration for a model(s)
to be tested, update the media placeholders and apply the prompt formatting
to get the test prompt string for this model.
Example for phi3v, given the base_prompt: "<image>What is the season?"
1. Replace img placeholder(s)
-> "<|image_1|>\nWhat is the season?"
2. Apply prompt formatter:
-> <|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n
"""
assert isinstance(base_prompts, (list, tuple))
model_prompts = []
for base_prompt in base_prompts:
# Replace the multimodal placeholders in the base prompt with
# the correct ones for the model that we are testing
if img_idx_to_prompt:
base_prompt = replace_test_placeholder(
base_prompt, img_idx_to_prompt, TEST_IMG_PLACEHOLDER
)
if video_idx_to_prompt:
base_prompt = replace_test_placeholder(
base_prompt, video_idx_to_prompt, TEST_VIDEO_PLACEHOLDER
)
if audio_idx_to_prompt:
base_prompt = replace_test_placeholder(
base_prompt, audio_idx_to_prompt, TEST_AUDIO_PLACEHOLDER
)
# Apply the prompt formatter to wrap the base prompt with
# the correct media placeholders to get the model test prompt
model_prompt = prompt_formatter(base_prompt)
model_prompts.append(model_prompt)
return model_prompts
def build_single_image_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
tmp_path: PosixPath | None = None,
) -> list[PromptWithMultiModalInput]:
if test_info.prompt_formatter is None:
raise ValueError("Prompt formatter must be set to build single image inputs")
model_prompts = get_model_prompts(
test_info.single_image_prompts,
test_info.img_idx_to_prompt,
test_info.video_idx_to_prompt,
test_info.audio_idx_to_prompt,
test_info.prompt_formatter,
)
# For models that require a local path / URL encoded in the image; export
# assets and encode into tmp_path for this test. This should be avoided
# where possible (currently needed for Qwen-VL).
if test_info.prompt_path_encoder is not None:
if tmp_path is None:
raise ValueError("Prompt path encoder requires setting local path")
model_prompts = [
test_info.prompt_path_encoder(tmp_path, prompt, [asset])
for prompt, asset in zip(model_prompts, image_assets)
]
images = [asset.pil_image for asset in image_assets]
assert len(images) == len(model_prompts)
return build_single_image_inputs(images, model_prompts, size_wrapper)
def build_single_image_inputs(
images, model_prompts, size_wrapper: ImageSizeWrapper
) -> list[PromptWithMultiModalInput]:
# For every image / prompt pair, get a pair containing two lists of
# length size_factors, where the first contains duplicates of the model
# prompt [str], and the second contains copies of the image after being
# scaled by one of the size factors.
#
# NOTE: rescaling preserves the image aspect ratio.
return [
PromptWithMultiModalInput(
prompts=[prompt for _ in size_wrapper.data],
image_data=[
apply_image_size_scaling(image, size, size_wrapper.type)
for size in size_wrapper.data
],
)
for image, prompt in zip(images, model_prompts)
]
def build_multi_image_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
tmp_path: PosixPath | None = None,
) -> list[PromptWithMultiModalInput]:
if test_info.prompt_formatter is None:
raise ValueError("Prompt formatter must be set to build multi image inputs")
model_prompts = get_model_prompts(
[test_info.multi_image_prompt],
test_info.img_idx_to_prompt,
test_info.video_idx_to_prompt,
test_info.audio_idx_to_prompt,
test_info.prompt_formatter,
)
if test_info.prompt_path_encoder is not None:
if tmp_path is None:
raise ValueError("Prompt path encoder requires setting local path")
model_prompts = [
test_info.prompt_path_encoder(tmp_path, model_prompt, image_assets)
for model_prompt in model_prompts
]
images = [asset.pil_image for asset in image_assets]
# Currently, we only have one multi-image list & one multi-image prompt
return build_multi_image_inputs(
image_lists=[images],
model_prompts=model_prompts,
size_wrapper=size_wrapper,
)
def build_multi_image_inputs(
image_lists, model_prompts, size_wrapper: ImageSizeWrapper
) -> list[PromptWithMultiModalInput]:
return [
PromptWithMultiModalInput(
prompts=[prompt for _ in size_wrapper.data],
image_data=[
[
apply_image_size_scaling(image, size, size_wrapper.type)
for image in images
]
for size in size_wrapper.data
],
)
for images, prompt in zip(image_lists, model_prompts)
]
def build_embedding_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
):
# These conditions will always be true if invoked through filtering,
# but we still check them in case this is ever called directly
if test_info.prompt_formatter is None:
raise ValueError("Prompt formatter must be set to build image embedding inputs")
if size_wrapper.type != SizeType.SIZE_FACTOR or not all(
factor == 1.0 for factor in size_wrapper.data
):
raise ValueError("Embedding tests require constant (1.0) size factors")
if test_info.convert_assets_to_embeddings is None:
raise ValueError("No conversion func for getting embeddings found")
model_prompts = get_model_prompts(
SINGLE_IMAGE_BASE_PROMPTS,
test_info.img_idx_to_prompt,
test_info.video_idx_to_prompt,
test_info.audio_idx_to_prompt,
test_info.prompt_formatter,
)
images = [asset.pil_image for asset in image_assets]
embeds = test_info.convert_assets_to_embeddings(image_assets)
if test_info.dtype != "auto":
dtype = getattr(torch, test_info.dtype) # type: ignore
embeds = [e.to(dtype=dtype) for e in embeds]
assert len(images) == len(model_prompts)
inputs = build_single_image_inputs(images, model_prompts, size_wrapper)
vllm_embeddings = build_single_image_inputs(embeds, model_prompts, size_wrapper)
return inputs, vllm_embeddings
def build_video_inputs_from_test_info(
test_info: VLMTestInfo,
video_assets: VideoTestAssets,
size_wrapper: ImageSizeWrapper,
num_frames: int,
needs_video_metadata: bool,
) -> list[PromptWithMultiModalInput]:
if test_info.prompt_formatter is None:
raise ValueError("Prompt formatter must be set to build video inputs")
model_prompts = get_model_prompts(
[VIDEO_BASE_PROMPT],
test_info.img_idx_to_prompt,
test_info.video_idx_to_prompt,
test_info.audio_idx_to_prompt,
test_info.prompt_formatter,
)
sampled_vids = [
sample_frames_with_video_metadata(
(asset.np_ndarrays, asset.metadata),
num_frames,
)
for asset in video_assets
]
video_scaler = (
resize_video if size_wrapper.type == SizeType.FIXED_SIZE else rescale_video_size
)
return [
PromptWithMultiModalInput(
prompts=[prompt for _ in size_wrapper.data],
video_data=[
(
video_scaler(video, size)
if not needs_video_metadata
else (video_scaler(video, size), meta)
)
for size in size_wrapper.data
],
)
for (video, meta), prompt in zip(sampled_vids, model_prompts)
]
def sample_frames_with_video_metadata(
video_with_meta: tuple[npt.NDArray, dict[str, Any]],
num_frames: int,
) -> tuple[npt.NDArray, dict[str, Any]]:
video, meta = video_with_meta
video = sample_frames_from_video(video, num_frames)
meta["do_sample_frames"] = meta["total_num_frames"] == num_frames
meta["total_num_frames"] = num_frames
meta["fps"] = meta["duration"] / num_frames
meta["frames_indices"] = list(range(num_frames))
return video, meta
def apply_image_size_scaling(image, size: float | tuple[int, int], size_type: SizeType):
"""Applies a size scaler to one image; this can be an image size factor,
which scales the image while maintaining the aspect ratio"""
# Special case for embeddings; if it's a tensor, it's only valid if we
# are considering size factors at constant scale, i.e., we just clone
# the tensor
if isinstance(image, torch.Tensor):
assert size_type == SizeType.SIZE_FACTOR and size == 1
return image
if size_type == SizeType.SIZE_FACTOR:
# We have a list of image size factors
return rescale_image_size(image, size)
elif size_type == SizeType.FIXED_SIZE:
# We have a list of fixed sizes
return image.resize(size)
raise ValueError("ImageSizeWrapper type must be FIXED_SIZE or SIZE_FACTOR")
def build_audio_inputs_from_test_info(
test_info: VLMTestInfo,
audio_assets: AudioTestAssets,
) -> list[PromptWithMultiModalInput]:
if test_info.prompt_formatter is None:
raise ValueError("Prompt formatter must be set to build audio inputs")
model_prompts = get_model_prompts(
SINGLE_AUDIO_BASE_PROMPT,
test_info.img_idx_to_prompt,
test_info.video_idx_to_prompt,
test_info.audio_idx_to_prompt,
test_info.prompt_formatter,
)
resampler = AudioResampler(
target_sr=16000,
method="librosa",
)
audios = [asset.audio_and_sample_rate for asset in audio_assets]
resampled_audios = [
(
resampler.resample(
audio,
orig_sr=sr,
),
int(resampler.target_sr),
)
for audio, sr in audios
]
return [
PromptWithMultiModalInput(
prompts=model_prompts,
audio_data=resampled_audios,
)
]
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/runners.py | tests/models/multimodal/generation/vlm_utils/runners.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Entrypoints for wrapping the core run_test implementation for specific test
types / modalities.
"""
from pathlib import PosixPath
from .....conftest import (
AudioTestAssets,
HfRunner,
ImageTestAssets,
VideoTestAssets,
VllmRunner,
)
from . import builders, core
from .types import ExpandableVLMTestArgs, VLMTestInfo
####### Entrypoints for running different test types
def run_single_image_test(
*,
tmp_path: PosixPath,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: ImageTestAssets,
):
assert test_case.size_wrapper is not None
inputs = builders.build_single_image_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper, tmp_path
)
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"image": 1},
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
def run_multi_image_test(
*,
tmp_path: PosixPath,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: ImageTestAssets,
):
assert test_case.size_wrapper is not None
inputs = builders.build_multi_image_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper, tmp_path
)
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"image": len(image_assets)},
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
def run_embedding_test(
*,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: ImageTestAssets,
):
assert test_case.size_wrapper is not None
inputs, vllm_embeddings = builders.build_embedding_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper
)
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"image": 1},
vllm_embeddings=vllm_embeddings,
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
def run_video_test(
*,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
video_assets: VideoTestAssets,
):
assert test_case.size_wrapper is not None
assert test_case.num_video_frames is not None
inputs = builders.build_video_inputs_from_test_info(
model_test_info,
video_assets,
test_case.size_wrapper,
test_case.num_video_frames,
test_case.needs_video_metadata,
)
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"video": len(video_assets)},
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
def run_audio_test(
*,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
audio_assets: AudioTestAssets,
):
inputs = builders.build_audio_inputs_from_test_info(model_test_info, audio_assets)
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"audio": 1},
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
def run_custom_inputs_test(
*,
model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
):
# Custom test cases can provide inputs directly, but they need to
# explicitly provided a CustomTestConfig, which wraps the inputs and
# the limit_mm_per_prompt
assert test_case.custom_test_opts is not None
inputs = test_case.custom_test_opts.inputs
limit_mm_per_prompt = test_case.custom_test_opts.limit_mm_per_prompt
# Inputs and limit_mm_per_prompt should all be set
assert inputs is not None
assert limit_mm_per_prompt is not None
core.run_test(
hf_runner=hf_runner,
vllm_runner=vllm_runner,
inputs=inputs,
model=test_case.model,
dtype=test_case.dtype,
max_tokens=test_case.max_tokens,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt=limit_mm_per_prompt,
distributed_executor_backend=test_case.distributed_executor_backend,
**model_test_info.get_non_parametrized_runner_kwargs(),
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/__init__.py | tests/models/multimodal/generation/vlm_utils/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/multimodal/generation/vlm_utils/types.py | tests/models/multimodal/generation/vlm_utils/types.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Types for writing multimodal model tests."""
from collections.abc import Callable, Iterable
from enum import Enum
from pathlib import PosixPath
from typing import Any, NamedTuple
import torch
from pytest import MarkDecorator
from transformers import AutoModelForCausalLM
from transformers.models.auto.auto_factory import _BaseAutoModelClass
from vllm.config.model import RunnerOption
from vllm.logprobs import SampleLogprobs
from vllm.tokenizers import TokenizerLike
from .....conftest import (
AUDIO_ASSETS,
IMAGE_ASSETS,
HfRunner,
ImageAsset,
ImageTestAssets,
PromptAudioInput,
PromptImageInput,
PromptVideoInput,
)
from ....utils import check_logprobs_close
# meta image tag; will be replaced by the appropriate tag for the model
TEST_IMG_PLACEHOLDER = "<vlm_image>"
TEST_VIDEO_PLACEHOLDER = "<vlm_video>"
TEST_AUDIO_PLACEHOLDER = "<lmm_audio>"
SINGLE_IMAGE_BASE_PROMPTS = IMAGE_ASSETS.prompts(
{
"stop_sign": f"{TEST_IMG_PLACEHOLDER}What's the content of the image?",
"cherry_blossom": f"{TEST_IMG_PLACEHOLDER}What is the season?",
}
)
SINGLE_AUDIO_BASE_PROMPT = AUDIO_ASSETS.prompts(
{
"mary_had_lamb": f"{TEST_AUDIO_PLACEHOLDER}Transcribe this audio into English.", # noqa: E501
"winning_call": f"{TEST_AUDIO_PLACEHOLDER}What is happening in this audio clip?", # noqa: E501
}
)
MULTI_IMAGE_BASE_PROMPT = f"Image-1: {TEST_IMG_PLACEHOLDER}Image-2: {TEST_IMG_PLACEHOLDER}Describe the two images in detail.\n" # noqa: E501
VIDEO_BASE_PROMPT = f"{TEST_VIDEO_PLACEHOLDER}Why is this video funny?"
IMAGE_SIZE_FACTORS = [(1.0,), (1.0, 1.0, 1.0), (0.25, 0.5, 1.0)]
EMBEDDING_SIZE_FACTORS = [(1.0,), (1.0, 1.0, 1.0)]
RunnerOutput = tuple[list[int], str, SampleLogprobs | None]
class PromptWithMultiModalInput(NamedTuple):
"""Holds the multimodal input for a single test case."""
prompts: list[str]
image_data: PromptImageInput | None = None
video_data: PromptVideoInput | None = None
audio_data: PromptAudioInput | None = None
class VLMTestType(Enum):
IMAGE = 1
MULTI_IMAGE = 2
EMBEDDING = 3
VIDEO = 4
AUDIO = 5
CUSTOM_INPUTS = 6
class SizeType(Enum):
SIZE_FACTOR = 1
FIXED_SIZE = 2
class CustomTestOptions(NamedTuple):
inputs: list[PromptWithMultiModalInput]
limit_mm_per_prompt: dict[str, int]
class ImageSizeWrapper(NamedTuple):
type: SizeType
# A size factor is a wrapper of 0+ floats,
# while a fixed size contains an iterable of integer pairs
data: Iterable[float] | Iterable[tuple[int, int]]
class VLMTestInfo(NamedTuple):
"""Holds the configuration for 1+ tests for one model architecture."""
models: list[str]
test_type: VLMTestType | Iterable[VLMTestType]
# Should be None only if this is a CUSTOM_INPUTS test
prompt_formatter: Callable[[str], str] | None = None
img_idx_to_prompt: Callable[[int], str] = lambda idx: "<image>\n"
video_idx_to_prompt: Callable[[int], str] = lambda idx: "<video>\n"
audio_idx_to_prompt: Callable[[int], str] = lambda idx: "<audio>\n"
# Most models work on the single / multi-image prompts above, but in some
# cases the log prob check fails, e.g., for paligemma. We allow passing
# an override for the single image prompts / multi-image prompt for this
# reason.
single_image_prompts: Iterable[str] = SINGLE_IMAGE_BASE_PROMPTS
multi_image_prompt: str = MULTI_IMAGE_BASE_PROMPT
# Function for converting ImageAssets to image embeddings;
# We need to define this explicitly for embedding tests
convert_assets_to_embeddings: (
Callable[[ImageTestAssets], list[torch.Tensor]] | None
) = None
# Exposed options for vLLM runner; we change these in a several tests,
# but the defaults are derived from VllmRunner & the engine defaults
# These settings are chosen to avoid OOMs when running in the CI
enforce_eager: bool = True
max_model_len: int = 1024
max_num_seqs: int = 256
runner: RunnerOption = "auto"
tensor_parallel_size: int = 1
vllm_runner_kwargs: dict[str, Any] | None = None
# Optional callable which gets a list of token IDs from the model tokenizer
get_stop_token_ids: Callable[[TokenizerLike], list[int]] | None = None
# Optional list of strings to stop generation, useful when stop tokens are
# not special tokens in the tokenizer
stop_str: list[str] | None = None
# Exposed options for HF runner
hf_model_kwargs: dict[str, Any] | None = None
# Indicates we should explicitly pass the EOS from the tokenizer
use_tokenizer_eos: bool = False
auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM
patch_hf_runner: Callable[[HfRunner], HfRunner] | None = None
# Post processors that if defined, will run oun the outputs of the
# vLLM and HF runner, respectively (useful for sanitization, etc).
vllm_output_post_proc: Callable[[RunnerOutput, str], Any] | None = None
hf_output_post_proc: Callable[[RunnerOutput, str], Any] | None = None
# Consumes the output of the callables above and checks if they're equal
comparator: Callable[..., None] = check_logprobs_close
# Default expandable params per test; these defaults can be overridden in
# instances of this object; the complete set of test cases for the model
# is all combinations of .models + all fields below
max_tokens: int = 128
num_logprobs: int = 5
dtype: str = "auto"
distributed_executor_backend: str | None = None
# Only expanded in video tests
num_video_frames: int | tuple[int] = 16
needs_video_metadata: bool = False
# Fixed image sizes / image size factors; most tests use image_size_factors
# The values provided for these two fields will be stacked and expanded
# such that each model will consider each image size factor / image size
# once per tests (much like concatenating and wrapping in one parametrize
# call)
image_size_factors: Iterable[Iterable[float]] = IMAGE_SIZE_FACTORS
image_sizes: Iterable[Iterable[tuple[int, int]]] | None = None
# Hack for updating a prompt to take into a local path; currently only used
# for Qwen-VL, which requires encoding the image path / url into the prompt
# for HF runner
prompt_path_encoder: (
Callable[[PosixPath, str, list[ImageAsset] | ImageTestAssets], str] | None
) = None # noqa: E501
# Allows configuring a test to run with custom inputs
custom_test_opts: list[CustomTestOptions] | None = None
marks: list[MarkDecorator] | None = None
def get_non_parametrized_runner_kwargs(self):
"""Returns a dictionary of expandable kwargs for items that are used
in all test types, which are NOT used when creating the parametrized
test cases.
"""
return {
"enforce_eager": self.enforce_eager,
"max_model_len": self.max_model_len,
"max_num_seqs": self.max_num_seqs,
"runner": self.runner,
"tensor_parallel_size": self.tensor_parallel_size,
"vllm_runner_kwargs": self.vllm_runner_kwargs,
"hf_output_post_proc": self.hf_output_post_proc,
"vllm_output_post_proc": self.vllm_output_post_proc,
"auto_cls": self.auto_cls,
"use_tokenizer_eos": self.use_tokenizer_eos,
"comparator": self.comparator,
"get_stop_token_ids": self.get_stop_token_ids,
"hf_model_kwargs": self.hf_model_kwargs,
"stop_str": self.stop_str,
"patch_hf_runner": self.patch_hf_runner,
}
class ExpandableVLMTestArgs(NamedTuple):
"""The expanded kwargs which correspond to a single test case."""
model: str
max_tokens: int
num_logprobs: int
dtype: str
distributed_executor_backend: str | None
# Sizes are used for everything except for custom input tests
size_wrapper: ImageSizeWrapper | None = None
# Video only
num_video_frames: int | None = None
needs_video_metadata: bool = False
# Custom inputs only
custom_test_opts: CustomTestOptions | None = None
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/__init__.py | tests/models/language/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/mteb_score_utils.py | tests/models/language/pooling_mteb_test/mteb_score_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import tempfile
from pathlib import Path
import mteb
import numpy as np
import requests
from mteb.models import ModelMeta
from torch.utils.data import DataLoader
from tests.models.utils import (
RerankModelInfo,
get_vllm_extra_kwargs,
)
# See #19344
MTEB_RERANK_TASKS = ["NFCorpus"]
MTEB_RERANK_LANGS = ["eng"]
MTEB_RERANK_TOL = 2e-3
template_home = (
Path(__file__).parent.parent.parent.parent.parent
/ "examples/pooling/score/template"
)
_empty_model_meta = ModelMeta(
loader=None,
name="vllm/model",
revision="1",
release_date=None,
languages=None,
framework=[],
similarity_fn_name=None,
n_parameters=None,
memory_usage_mb=None,
max_tokens=None,
embed_dim=None,
license=None,
open_weights=None,
public_training_code=None,
public_training_data=None,
use_instructions=None,
training_datasets=None,
modalities=["text"], # 'image' can be added to evaluate multimodal models
)
class MtebCrossEncoderMixin(mteb.CrossEncoderProtocol):
mteb_model_meta = _empty_model_meta
class VllmMtebCrossEncoder(MtebCrossEncoderMixin):
def __init__(self, vllm_model):
self.llm = vllm_model
self.rng = np.random.default_rng(seed=42)
self.chat_template: str | None = getattr(vllm_model, "chat_template", None)
def predict(
self,
inputs1: DataLoader[mteb.types.BatchedInput],
inputs2: DataLoader[mteb.types.BatchedInput],
*args,
**kwargs,
) -> np.ndarray:
queries = [text for batch in inputs1 for text in batch["text"]]
corpus = [text for batch in inputs2 for text in batch["text"]]
outputs = self.llm.score(
queries,
corpus,
truncate_prompt_tokens=-1,
use_tqdm=False,
chat_template=self.chat_template,
)
scores = np.array(outputs)
return scores
class ScoreClientMtebEncoder(MtebCrossEncoderMixin):
mteb_model_meta = _empty_model_meta
def __init__(self, model_name: str, url):
self.model_name = model_name
self.url = url
self.rng = np.random.default_rng(seed=42)
def predict(
self,
inputs1: DataLoader[mteb.types.BatchedInput],
inputs2: DataLoader[mteb.types.BatchedInput],
*args,
**kwargs,
) -> np.ndarray:
queries = [text for batch in inputs1 for text in batch["text"]]
full_corpus = [text for batch in inputs2 for text in batch["text"]]
outputs = []
for query, corpus in zip(queries, full_corpus):
outputs.append(self.get_score(query, corpus))
scores = np.array(outputs)
return scores
def get_score(self, query, corpus):
response = requests.post(
self.url,
json={
"model": self.model_name,
"text_1": query,
"text_2": corpus,
"truncate_prompt_tokens": -1,
},
).json()
return response["data"][0]["score"]
class RerankClientMtebEncoder(ScoreClientMtebEncoder):
def get_score(self, query, corpus):
response = requests.post(
self.url,
json={
"model": self.model_name,
"query": query,
"documents": [corpus],
"truncate_prompt_tokens": -1,
},
).json()
return response["results"][0]["relevance_score"]
def run_mteb_rerank(cross_encoder: mteb.CrossEncoderProtocol, tasks, languages):
with tempfile.TemporaryDirectory() as prediction_folder:
bm25s = mteb.get_model("bm25s")
eval_splits = ["test"]
mteb_tasks: list[mteb.abstasks.AbsTaskRetrieval] = mteb.get_tasks(
tasks=tasks, languages=languages, eval_splits=eval_splits
)
mteb.evaluate(
bm25s,
mteb_tasks,
prediction_folder=prediction_folder,
show_progress_bar=False,
# don't save results for test runs
cache=None,
overwrite_strategy="always",
)
second_stage_tasks = []
for task in mteb_tasks:
second_stage_tasks.append(
task.convert_to_reranking(
prediction_folder,
top_k=10,
)
)
results = mteb.evaluate(
cross_encoder,
second_stage_tasks,
show_progress_bar=False,
cache=None,
)
main_score = results[0].scores["test"][0]["main_score"]
return main_score
def mteb_test_rerank_models_hf(
hf_runner, model_name, hf_dtype="float32", hf_model_callback=None
):
with hf_runner(model_name, is_cross_encoder=True, dtype=hf_dtype) as hf_model:
if hf_model_callback is not None:
hf_model_callback(hf_model)
st_main_score = run_mteb_rerank(
hf_model, tasks=MTEB_RERANK_TASKS, languages=MTEB_RERANK_LANGS
)
st_dtype = next(hf_model.model.model.parameters()).dtype
return st_main_score, st_dtype
def mteb_test_rerank_models(
hf_runner,
vllm_runner,
model_info: RerankModelInfo,
vllm_extra_kwargs=None,
hf_model_callback=None,
vllm_mteb_encoder=VllmMtebCrossEncoder,
atol=MTEB_RERANK_TOL,
):
vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
with vllm_runner(
model_info.name,
runner="pooling",
max_model_len=None,
max_num_seqs=8,
**vllm_extra_kwargs,
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
# Confirm whether vllm is using the correct architecture
if model_info.architecture:
assert model_info.architecture in model_config.architectures
# Score API is only enabled for num_labels == 1
assert model_config.hf_config.num_labels == 1
# Maybe load chat_template.
chat_template: str | None = None
if model_info.chat_template_name is not None:
chat_template = (template_home / model_info.chat_template_name).read_text()
vllm_model.chat_template = chat_template
# Confirm whether the important configs in model_config are correct.
if model_info.pooling_type is not None:
assert model_config.pooler_config.pooling_type == model_info.pooling_type
if model_info.attn_type is not None:
assert model_config.attn_type == model_info.attn_type
if model_info.is_prefix_caching_supported is not None:
assert (
model_config.is_prefix_caching_supported
== model_info.is_prefix_caching_supported
)
if model_info.is_chunked_prefill_supported is not None:
assert (
model_config.is_chunked_prefill_supported
== model_info.is_chunked_prefill_supported
)
vllm_main_score = run_mteb_rerank(
vllm_mteb_encoder(vllm_model),
tasks=MTEB_RERANK_TASKS,
languages=MTEB_RERANK_LANGS,
)
vllm_dtype = model_config.dtype
head_dtype = model_config.head_dtype
# Accelerate mteb test by setting
# SentenceTransformers mteb score to a constant
if model_info.mteb_score is None:
st_main_score, st_dtype = mteb_test_rerank_models_hf(
hf_runner, model_info.name, model_info.hf_dtype, hf_model_callback
)
else:
st_main_score = model_info.mteb_score
st_dtype = "Constant"
print("Model:", model_info.name)
print("VLLM:", f"dtype:{vllm_dtype}", f"head_dtype:{head_dtype}", vllm_main_score)
print("SentenceTransformers:", st_dtype, st_main_score)
print("Difference:", st_main_score - vllm_main_score)
# We are not concerned that the vllm mteb results are better
# than SentenceTransformers, so we only perform one-sided testing.
assert st_main_score - vllm_main_score < atol
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_snowflake_arctic_embed.py | tests/models/language/pooling_mteb_test/test_snowflake_arctic_embed.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling.embed_utils import correctness_test_embed_models
from tests.models.utils import EmbedModelInfo
from .mteb_embed_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-xs",
is_matryoshka=False,
architecture="BertModel",
mteb_score=0.714927797,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-s",
is_matryoshka=False,
architecture="BertModel",
enable_test=False,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-m",
is_matryoshka=False,
architecture="BertModel",
enable_test=False,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-m-long",
is_matryoshka=False,
architecture="NomicBertModel",
mteb_score=0.681146831,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-l",
is_matryoshka=False,
architecture="BertModel",
enable_test=False,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-m-v1.5",
is_matryoshka=True,
architecture="BertModel",
mteb_score=0.649088363,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-l-v2.0",
is_matryoshka=True,
architecture="XLMRobertaModel",
mteb_score=0.712258299,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"Snowflake/snowflake-arctic-embed-m-v2.0",
is_matryoshka=True,
architecture="GteModel",
mteb_score=0.706622444,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_nemotron.py | tests/models/language/pooling_mteb_test/test_nemotron.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling_mteb_test.mteb_embed_utils import (
mteb_test_embed_models,
)
from tests.models.language.pooling_mteb_test.mteb_score_utils import (
mteb_test_rerank_models,
)
from tests.models.utils import (
EmbedModelInfo,
RerankModelInfo,
)
EMBEDDING_MODELS = [
EmbedModelInfo(
"nvidia/llama-nemotron-embed-1b-v2",
architecture="LlamaBidirectionalModel",
mteb_score=0.689164662128673,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
)
]
RERANK_MODELS = [
RerankModelInfo(
"nvidia/llama-nemotron-rerank-1b-v2",
architecture="LlamaBidirectionalForSequenceClassification",
chat_template_name="nemotron-rerank.jinja",
mteb_score=0.33994,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
),
]
@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(
hf_runner, vllm_runner, model_info: RerankModelInfo
) -> None:
mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_nomic.py | tests/models/language/pooling_mteb_test/test_nomic.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling.embed_utils import correctness_test_embed_models
from tests.models.utils import EmbedModelInfo
from .mteb_embed_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo(
"nomic-ai/nomic-embed-text-v1",
architecture="NomicBertModel",
mteb_score=0.737568559,
enable_test=True,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
),
EmbedModelInfo(
"nomic-ai/nomic-embed-text-v1.5",
architecture="NomicBertModel",
enable_test=False,
),
EmbedModelInfo(
"nomic-ai/CodeRankEmbed", architecture="NomicBertModel", enable_test=False
),
EmbedModelInfo(
"nomic-ai/nomic-embed-text-v2-moe",
architecture="NomicBertModel",
mteb_score=0.715488912,
enable_test=True,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_baai.py | tests/models/language/pooling_mteb_test/test_baai.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling.embed_utils import correctness_test_embed_models
from tests.models.utils import (
EmbedModelInfo,
RerankModelInfo,
)
from .mteb_embed_utils import mteb_test_embed_models
from .mteb_score_utils import mteb_test_rerank_models
MODELS = [
########## BertModel
EmbedModelInfo(
"BAAI/bge-base-en",
architecture="BertModel",
mteb_score=0.779336792,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo("BAAI/bge-base-zh", architecture="BertModel", enable_test=False),
EmbedModelInfo("BAAI/bge-small-en", architecture="BertModel", enable_test=False),
EmbedModelInfo("BAAI/bge-small-zh", architecture="BertModel", enable_test=False),
EmbedModelInfo("BAAI/bge-large-en", architecture="BertModel", enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh", architecture="BertModel", enable_test=False),
EmbedModelInfo(
"BAAI/bge-large-zh-noinstruct", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-base-en-v1.5", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-base-zh-v1.5", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-small-en-v1.5", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-small-zh-v1.5", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-large-en-v1.5", architecture="BertModel", enable_test=False
),
EmbedModelInfo(
"BAAI/bge-large-zh-v1.5", architecture="BertModel", enable_test=False
),
########## XLMRobertaModel
EmbedModelInfo(
"BAAI/bge-m3",
architecture="XLMRobertaModel",
mteb_score=0.787343078,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
########## Qwen2Model
EmbedModelInfo(
"BAAI/bge-code-v1",
architecture="Qwen2Model",
mteb_score=0.75724465,
dtype="float32",
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
enable_test=True,
),
]
RERANK_MODELS = [
########## XLMRobertaForSequenceClassification
RerankModelInfo(
"BAAI/bge-reranker-base",
architecture="XLMRobertaForSequenceClassification",
mteb_score=0.32398,
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
RerankModelInfo(
"BAAI/bge-reranker-large",
architecture="XLMRobertaForSequenceClassification",
enable_test=False,
),
RerankModelInfo(
"BAAI/bge-reranker-v2-m3",
architecture="XLMRobertaForSequenceClassification",
enable_test=False,
),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(
hf_runner, vllm_runner, model_info: RerankModelInfo
) -> None:
mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_cross_encoder.py | tests/models/language/pooling_mteb_test/test_cross_encoder.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.utils import (
RerankModelInfo,
)
from .mteb_score_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo(
"cross-encoder/ms-marco-TinyBERT-L-2-v2",
mteb_score=0.32898,
architecture="BertForSequenceClassification",
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
),
RerankModelInfo(
"tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
mteb_score=0.25736,
architecture="Qwen3ForSequenceClassification",
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
),
]
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(
hf_runner, vllm_runner, model_info: RerankModelInfo
) -> None:
mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_intfloat.py | tests/models/language/pooling_mteb_test/test_intfloat.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling.embed_utils import correctness_test_embed_models
from tests.models.utils import EmbedModelInfo
from .mteb_embed_utils import mteb_test_embed_models
MODELS = [
########## BertModel
EmbedModelInfo(
"intfloat/e5-small",
architecture="BertModel",
mteb_score=0.742285423,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo("intfloat/e5-base", architecture="BertModel", enable_test=False),
EmbedModelInfo("intfloat/e5-large", architecture="BertModel", enable_test=False),
EmbedModelInfo(
"intfloat/multilingual-e5-small", architecture="BertModel", enable_test=False
),
########## XLMRobertaModel
EmbedModelInfo(
"intfloat/multilingual-e5-base",
architecture="XLMRobertaModel",
mteb_score=0.779325955,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"intfloat/multilingual-e5-large",
architecture="XLMRobertaModel",
enable_test=False,
),
EmbedModelInfo(
"intfloat/multilingual-e5-large-instruct",
architecture="XLMRobertaModel",
enable_test=False,
),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_bge_reranker_v2_gemma.py | tests/models/language/pooling_mteb_test/test_bge_reranker_v2_gemma.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
import mteb
import numpy as np
import pytest
import torch
from torch.utils.data import DataLoader
from tests.conftest import HfRunner
from tests.models.utils import RerankModelInfo
from .mteb_score_utils import VllmMtebCrossEncoder, mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo(
"BAAI/bge-reranker-v2-gemma",
architecture="GemmaForSequenceClassification",
mteb_score=0.33757,
hf_overrides={
"architectures": ["GemmaForSequenceClassification"],
"classifier_from_token": ["Yes"],
"method": "no_post_processing",
},
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
),
]
PROMPT = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." # noqa: E501
class GemmaRerankerHfRunner(HfRunner):
def __init__(
self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
) -> None:
from transformers import AutoModelForCausalLM, AutoTokenizer
super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self.yes_loc = self.tokenizer.convert_tokens_to_ids("Yes")
@torch.no_grad()
def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
def get_inputs(pairs, tokenizer, prompt=None):
if prompt is None:
prompt = PROMPT
sep = "\n"
prompt_inputs = tokenizer(
prompt, return_tensors=None, add_special_tokens=False
)["input_ids"]
sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)[
"input_ids"
]
inputs = []
for query, passage in pairs:
query_inputs = tokenizer(
f"A: {query}",
return_tensors=None,
add_special_tokens=False,
truncation=True,
)
passage_inputs = tokenizer(
f"B: {passage}",
return_tensors=None,
add_special_tokens=False,
truncation=True,
)
item = tokenizer.prepare_for_model(
[tokenizer.bos_token_id] + query_inputs["input_ids"],
sep_inputs + passage_inputs["input_ids"],
truncation="only_second",
padding=False,
return_attention_mask=False,
return_token_type_ids=False,
add_special_tokens=False,
)
item["input_ids"] = item["input_ids"] + sep_inputs + prompt_inputs
item["attention_mask"] = [1] * len(item["input_ids"])
inputs.append(item)
return tokenizer.pad(
inputs,
padding=True,
return_tensors="pt",
)
scores = []
for query, doc, *_ in prompts:
pairs = [(query, doc)]
inputs = get_inputs(pairs, self.tokenizer)
inputs = inputs.to(self.model.device)
_n_tokens = inputs["input_ids"].shape[1]
logits = self.model(**inputs, return_dict=True).logits
_scores = (
logits[:, -1, self.yes_loc]
.view(
-1,
)
.float()
.sigmoid()
)
scores.append(_scores[0].item())
return torch.Tensor(scores)
class GemmaMtebEncoder(VllmMtebCrossEncoder):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.query_template = "A: {query}\n"
self.document_template = "B: {doc}\n{prompt}"
def predict(
self,
inputs1: DataLoader[mteb.types.BatchedInput],
inputs2: DataLoader[mteb.types.BatchedInput],
*args,
**kwargs,
) -> np.ndarray:
queries = [
self.query_template.format(query=text)
for batch in inputs1
for text in batch["text"]
]
corpus = [
self.document_template.format(doc=text, prompt=PROMPT)
for batch in inputs2
for text in batch["text"]
]
outputs = self.llm.score(
queries, corpus, truncate_prompt_tokens=-1, use_tqdm=False
)
scores = np.array(outputs)
return scores
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
mteb_test_rerank_models(
GemmaRerankerHfRunner,
vllm_runner,
model_info,
vllm_mteb_encoder=GemmaMtebEncoder,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/__init__.py | tests/models/language/pooling_mteb_test/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_st_projector.py | tests/models/language/pooling_mteb_test/test_st_projector.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.utils import (
EmbedModelInfo,
)
from .mteb_embed_utils import mteb_test_embed_models
# ST models with projector (Dense) layers
ST_PROJECTOR_MODELS = [
EmbedModelInfo(
"TencentBAC/Conan-embedding-v1",
architecture="BertModel",
mteb_score=0.688611955,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"google/embeddinggemma-300m",
architecture="Gemma3TextModel",
mteb_score=0.7473819294684156,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
dtype="float32",
),
]
@pytest.mark.parametrize("model_info", ST_PROJECTOR_MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_qwen3_reranker.py | tests/models/language/pooling_mteb_test/test_qwen3_reranker.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
import pytest
import torch
from tests.conftest import HfRunner
from tests.models.utils import RerankModelInfo
from tests.utils import multi_gpu_test
from .mteb_score_utils import mteb_test_rerank_models
qwen3_reranker_hf_overrides = {
"architectures": ["Qwen3ForSequenceClassification"],
"classifier_from_token": ["no", "yes"],
"is_original_qwen3_reranker": True,
}
RERANK_MODELS = [
RerankModelInfo(
"Qwen/Qwen3-Reranker-0.6B",
architecture="Qwen3ForSequenceClassification",
mteb_score=0.25736,
hf_overrides=qwen3_reranker_hf_overrides,
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
enable_test=True,
),
RerankModelInfo(
"Qwen/Qwen3-Reranker-4B",
architecture="Qwen3ForSequenceClassification",
hf_overrides=qwen3_reranker_hf_overrides,
enable_test=False,
),
]
class Qwen3RerankerHfRunner(HfRunner):
def __init__(
self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
) -> None:
from transformers import AutoModelForCausalLM, AutoTokenizer
super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self.token_false_id = self.tokenizer.convert_tokens_to_ids("no")
self.token_true_id = self.tokenizer.convert_tokens_to_ids("yes")
def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
def process_inputs(pairs):
inputs = self.tokenizer(
pairs,
padding=False,
truncation="longest_first",
return_attention_mask=False,
)
for i, ele in enumerate(inputs["input_ids"]):
inputs["input_ids"][i] = ele
inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt")
for key in inputs:
inputs[key] = inputs[key].to(self.model.device)
return inputs
@torch.no_grad()
def compute_logits(inputs):
batch_scores = self.model(**inputs).logits[:, -1, :]
true_vector = batch_scores[:, self.token_true_id]
false_vector = batch_scores[:, self.token_false_id]
batch_scores = torch.stack([false_vector, true_vector], dim=1)
batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
scores = batch_scores[:, 1].exp()
return scores
scores = []
for query, doc, *_ in prompts:
pairs = [(query, doc)]
inputs = process_inputs(pairs)
score = compute_logits(inputs)
scores.append(score[0].item())
return torch.Tensor(scores)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
mteb_test_rerank_models(Qwen3RerankerHfRunner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
@multi_gpu_test(num_gpus=2)
def test_rerank_models_mteb_tp(vllm_runner, model_info: RerankModelInfo) -> None:
assert model_info.architecture == "Qwen3ForSequenceClassification"
vllm_extra_kwargs: dict[str, Any] = {
"tensor_parallel_size": 2,
}
mteb_test_rerank_models(
Qwen3RerankerHfRunner, vllm_runner, model_info, vllm_extra_kwargs
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_jina.py | tests/models/language/pooling_mteb_test/test_jina.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from functools import partial
import pytest
from tests.models.language.pooling.embed_utils import (
check_embeddings_close,
correctness_test_embed_models,
matryoshka_fy,
)
from tests.models.utils import (
EmbedModelInfo,
RerankModelInfo,
)
from vllm import PoolingParams
from .mteb_embed_utils import mteb_test_embed_models
from .mteb_score_utils import mteb_test_rerank_models
EMBEDDING_MODELS = [
EmbedModelInfo(
"jinaai/jina-embeddings-v3",
mteb_score=0.824413164,
architecture="XLMRobertaModel",
is_matryoshka=True,
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
dtype="float32",
)
]
RERANK_MODELS = [
RerankModelInfo(
"jinaai/jina-reranker-v2-base-multilingual",
mteb_score=0.33643,
architecture="XLMRobertaForSequenceClassification",
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
)
]
@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
def hf_model_callback(model):
model.encode = partial(model.encode, task="text-matching")
mteb_test_embed_models(
hf_runner, vllm_runner, model_info, hf_model_callback=hf_model_callback
)
@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
def hf_model_callback(model):
model.encode = partial(model.encode, task="text-matching")
correctness_test_embed_models(
hf_runner,
vllm_runner,
model_info,
example_prompts,
hf_model_callback=hf_model_callback,
)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(
hf_runner, vllm_runner, model_info: RerankModelInfo
) -> None:
mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("dimensions", [16, 32])
def test_matryoshka(
hf_runner,
vllm_runner,
model_info,
dtype: str,
dimensions: int,
example_prompts,
monkeypatch,
) -> None:
if not model_info.is_matryoshka:
pytest.skip("Model is not matryoshka")
# ST will strip the input texts, see test_embedding.py
example_prompts = [str(s).strip() for s in example_prompts]
with hf_runner(
model_info.name,
dtype=dtype,
is_sentence_transformer=True,
) as hf_model:
hf_outputs = hf_model.encode(example_prompts, task="text-matching")
hf_outputs = matryoshka_fy(hf_outputs, dimensions)
with vllm_runner(
model_info.name, runner="pooling", dtype=dtype, max_model_len=None
) as vllm_model:
assert vllm_model.llm.llm_engine.model_config.is_matryoshka
matryoshka_dimensions = (
vllm_model.llm.llm_engine.model_config.matryoshka_dimensions
)
assert matryoshka_dimensions is not None
if dimensions not in matryoshka_dimensions:
with pytest.raises(ValueError):
vllm_model.embed(
example_prompts, pooling_params=PoolingParams(dimensions=dimensions)
)
else:
vllm_outputs = vllm_model.embed(
example_prompts, pooling_params=PoolingParams(dimensions=dimensions)
)
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_gte.py | tests/models/language/pooling_mteb_test/test_gte.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.language.pooling.embed_utils import correctness_test_embed_models
from tests.models.utils import (
EmbedModelInfo,
RerankModelInfo,
)
from .mteb_embed_utils import mteb_test_embed_models
from .mteb_score_utils import mteb_test_rerank_models
MODELS = [
########## BertModel
EmbedModelInfo(
"thenlper/gte-large",
mteb_score=0.76807651,
architecture="BertModel",
pooling_type="MEAN",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo("thenlper/gte-base", architecture="BertModel", enable_test=False),
EmbedModelInfo("thenlper/gte-small", architecture="BertModel", enable_test=False),
EmbedModelInfo(
"thenlper/gte-large-zh", architecture="BertModel", enable_test=False
),
EmbedModelInfo("thenlper/gte-base-zh", architecture="BertModel", enable_test=False),
EmbedModelInfo(
"thenlper/gte-small-zh", architecture="BertModel", enable_test=False
),
########### NewModel
# These three architectures are almost the same, but not exactly the same.
# For example,
# - whether to use token_type_embeddings
# - whether to use context expansion
# So only test one (the most widely used) model
EmbedModelInfo(
"Alibaba-NLP/gte-multilingual-base",
architecture="GteNewModel",
mteb_score=0.775074696,
hf_overrides={"architectures": ["GteNewModel"]},
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
EmbedModelInfo(
"Alibaba-NLP/gte-base-en-v1.5",
architecture="GteNewModel",
hf_overrides={"architectures": ["GteNewModel"]},
enable_test=False,
),
EmbedModelInfo(
"Alibaba-NLP/gte-large-en-v1.5",
architecture="GteNewModel",
hf_overrides={"architectures": ["GteNewModel"]},
enable_test=False,
),
########### Qwen2ForCausalLM
EmbedModelInfo(
"Alibaba-NLP/gte-Qwen2-1.5B-instruct",
mteb_score=0.758473459018872,
architecture="Qwen2ForCausalLM",
pooling_type="LAST",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
########## ModernBertModel
EmbedModelInfo(
"Alibaba-NLP/gte-modernbert-base",
mteb_score=0.748193353,
architecture="ModernBertModel",
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
########## Qwen3ForCausalLM
EmbedModelInfo(
"Qwen/Qwen3-Embedding-0.6B",
mteb_score=0.771163695,
architecture="Qwen3ForCausalLM",
dtype="float32",
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
enable_test=True,
),
EmbedModelInfo(
"Qwen/Qwen3-Embedding-4B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=False,
),
]
RERANK_MODELS = [
RerankModelInfo(
# classifier_pooling: mean
"Alibaba-NLP/gte-reranker-modernbert-base",
mteb_score=0.33386,
architecture="ModernBertForSequenceClassification",
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
RerankModelInfo(
"Alibaba-NLP/gte-multilingual-reranker-base",
mteb_score=0.33062,
architecture="GteNewForSequenceClassification",
hf_overrides={"architectures": ["GteNewForSequenceClassification"]},
pooling_type="CLS",
attn_type="encoder_only",
is_prefix_caching_supported=False,
is_chunked_prefill_supported=False,
enable_test=True,
),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
@pytest.mark.parametrize("model_info", MODELS)
def test_embed_models_correctness(
hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
) -> None:
correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(
hf_runner, vllm_runner, model_info: RerankModelInfo
) -> None:
mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/test_mxbai_rerank.py | tests/models/language/pooling_mteb_test/test_mxbai_rerank.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
import pytest
import torch
from tests.conftest import HfRunner
from tests.models.utils import RerankModelInfo
from .mteb_score_utils import mteb_test_rerank_models
mxbai_rerank_hf_overrides = {
"architectures": ["Qwen2ForSequenceClassification"],
"classifier_from_token": ["0", "1"],
"method": "from_2_way_softmax",
}
RERANK_MODELS = [
RerankModelInfo(
"mixedbread-ai/mxbai-rerank-base-v2",
architecture="Qwen2ForSequenceClassification",
hf_overrides=mxbai_rerank_hf_overrides,
mteb_score=0.273,
pooling_type="LAST",
attn_type="decoder",
is_prefix_caching_supported=True,
is_chunked_prefill_supported=True,
enable_test=True,
),
RerankModelInfo(
"mixedbread-ai/mxbai-rerank-large-v2",
architecture="Qwen2ForSequenceClassification",
hf_overrides=mxbai_rerank_hf_overrides,
enable_test=False,
),
]
class MxbaiRerankerHfRunner(HfRunner):
def __init__(
self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
) -> None:
from transformers import AutoModelForCausalLM, AutoTokenizer
super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self.yes_loc = self.tokenizer.convert_tokens_to_ids("1")
self.no_loc = self.tokenizer.convert_tokens_to_ids("0")
def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
def process_inputs(pairs):
inputs = self.tokenizer(
pairs,
padding=False,
truncation="longest_first",
return_attention_mask=False,
)
for i, ele in enumerate(inputs["input_ids"]):
inputs["input_ids"][i] = ele
inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt")
for key in inputs:
inputs[key] = inputs[key].to(self.model.device)
return inputs
@torch.no_grad()
def compute_logits(inputs):
logits = self.model(**inputs).logits[:, -1, :]
yes_logits = logits[:, self.yes_loc]
no_logits = logits[:, self.no_loc]
logits = yes_logits - no_logits
scores = logits.float().sigmoid()
return scores
scores = []
for query, doc, *_ in prompts:
pairs = [(query, doc)]
inputs = process_inputs(pairs)
score = compute_logits(inputs)
scores.append(score[0].item())
return torch.Tensor(scores)
@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
mteb_test_rerank_models(MxbaiRerankerHfRunner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling_mteb_test/mteb_embed_utils.py | tests/models/language/pooling_mteb_test/mteb_embed_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import mteb
import numpy as np
import torch
from mteb.models import ModelMeta
from mteb.types import Array
from torch.utils.data import DataLoader
import tests.ci_envs as ci_envs
from tests.models.utils import (
EmbedModelInfo,
check_embeddings_close,
get_vllm_extra_kwargs,
)
# Most embedding models on the STS12 task (See #17175):
# - Model implementation and minor changes in tensor dtype
# results in differences less than 1e-4
# - Different model results in differences more than 1e-3
# 1e-4 is a good tolerance threshold
MTEB_EMBED_TASKS = ["STS12"]
MTEB_EMBED_TOL = 1e-4
_empty_model_meta = ModelMeta(
loader=None,
name="vllm/model",
revision="1",
release_date=None,
languages=None,
framework=[],
similarity_fn_name=None,
n_parameters=None,
memory_usage_mb=None,
max_tokens=None,
embed_dim=None,
license=None,
open_weights=None,
public_training_code=None,
public_training_data=None,
use_instructions=None,
training_datasets=None,
modalities=["text"], # 'image' can be added to evaluate multimodal models
)
class MtebEmbedMixin(mteb.EncoderProtocol):
mteb_model_meta = _empty_model_meta
def similarity(
self,
embeddings1: np.ndarray,
embeddings2: np.ndarray,
) -> np.ndarray:
# Cosine similarity
norm1 = np.linalg.norm(embeddings1, axis=1, keepdims=True)
norm2 = np.linalg.norm(embeddings2, axis=1, keepdims=True)
sim = np.dot(embeddings1, embeddings2.T) / (norm1 * norm2.T)
return sim
def similarity_pairwise(
self,
embeddings1: Array,
embeddings2: Array,
) -> Array:
# Cosine similarity
norm1 = np.linalg.norm(embeddings1, axis=1, keepdims=True)
norm2 = np.linalg.norm(embeddings2, axis=1, keepdims=True)
sim = np.sum(embeddings1 * embeddings2, axis=1) / (
norm1.flatten() * norm2.flatten()
)
return sim
class VllmMtebEncoder(MtebEmbedMixin):
def __init__(self, vllm_model):
self.llm = vllm_model
self.rng = np.random.default_rng(seed=42)
def encode(
self,
inputs: DataLoader[mteb.types.BatchedInput],
*args,
**kwargs,
) -> np.ndarray:
# Hoping to discover potential scheduling
# issues by randomizing the order.
sentences = [text for batch in inputs for text in batch["text"]]
r = self.rng.permutation(len(sentences))
sentences = [sentences[i] for i in r]
outputs = self.llm.embed(sentences, use_tqdm=False)
embeds = np.array(outputs)
embeds = embeds[np.argsort(r)]
return embeds
class OpenAIClientMtebEncoder(MtebEmbedMixin):
def __init__(self, model_name: str, client):
self.model_name = model_name
self.client = client
self.rng = np.random.default_rng(seed=42)
def encode(
self,
inputs: DataLoader[mteb.types.BatchedInput],
*args,
**kwargs,
) -> np.ndarray:
# Hoping to discover potential scheduling
# issues by randomizing the order.
sentences = [text for batch in inputs for text in batch["text"]]
r = self.rng.permutation(len(sentences))
sentences = [sentences[i] for i in r]
embeddings = self.client.embeddings.create(
model=self.model_name, input=sentences
)
outputs = [d.embedding for d in embeddings.data]
embeds = np.array(outputs)
embeds = embeds[np.argsort(r)]
return embeds
def run_mteb_embed_task(encoder: mteb.EncoderProtocol, tasks):
tasks = mteb.get_tasks(tasks=tasks)
results = mteb.evaluate(
encoder,
tasks,
cache=None,
show_progress_bar=False,
)
main_score = results[0].scores["test"][0]["main_score"]
return main_score
def mteb_test_embed_models(
hf_runner,
vllm_runner,
model_info: EmbedModelInfo,
vllm_extra_kwargs=None,
hf_model_callback=None,
atol=MTEB_EMBED_TOL,
):
vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
# Test embed_dims, isnan and whether to use normalize
example_prompts = ["The chef prepared a delicious meal." * 1000]
with vllm_runner(
model_info.name,
runner="pooling",
max_model_len=model_info.max_model_len,
**vllm_extra_kwargs,
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
# Confirm whether vllm is using the correct architecture
if model_info.architecture:
assert model_info.architecture in model_config.architectures
# Confirm whether the important configs in model_config are correct.
if model_info.pooling_type is not None:
assert model_config.pooler_config.pooling_type == model_info.pooling_type
if model_info.attn_type is not None:
assert model_config.attn_type == model_info.attn_type
if model_info.is_prefix_caching_supported is not None:
assert (
model_config.is_prefix_caching_supported
== model_info.is_prefix_caching_supported
)
if model_info.is_chunked_prefill_supported is not None:
assert (
model_config.is_chunked_prefill_supported
== model_info.is_chunked_prefill_supported
)
vllm_main_score = run_mteb_embed_task(
VllmMtebEncoder(vllm_model), MTEB_EMBED_TASKS
)
vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
head_dtype = model_config.head_dtype
# Test embedding_size, isnan and whether to use normalize
vllm_outputs = vllm_model.embed(example_prompts, truncate_prompt_tokens=-1)
outputs_tensor = torch.tensor(vllm_outputs)
assert not torch.any(torch.isnan(outputs_tensor))
embedding_size = model_config.embedding_size
assert torch.tensor(vllm_outputs).shape[-1] == embedding_size
# Accelerate mteb test by setting
# SentenceTransformers mteb score to a constant
if model_info.mteb_score is None:
with hf_runner(
model_info.name,
is_sentence_transformer=True,
dtype=ci_envs.VLLM_CI_HF_DTYPE or model_info.hf_dtype,
) as hf_model:
# e.g. setting default parameters for the encode method of hf_runner
if hf_model_callback is not None:
hf_model_callback(hf_model)
st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
st_dtype = next(hf_model.model.parameters()).dtype
# Check embeddings close to hf outputs
hf_outputs = hf_model.encode(example_prompts)
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
else:
st_main_score = model_info.mteb_score
st_dtype = "Constant"
print("Model:", model_info.name)
print("VLLM:", f"dtype:{vllm_dtype}", f"head_dtype:{head_dtype}", vllm_main_score)
print("SentenceTransformers:", st_dtype, st_main_score)
print("Difference:", st_main_score - vllm_main_score)
# We are not concerned that the vllm mteb results are better
# than SentenceTransformers, so we only perform one-sided testing.
assert st_main_score - vllm_main_score < atol
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_multilabel_classification_support.py | tests/models/language/pooling/test_multilabel_classification_support.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
@pytest.mark.parametrize(
"model",
["Rami/multi-label-class-classification-on-github-issues"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.classify(example_prompts)
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output)
vllm_output = torch.tensor(vllm_output)
assert torch.allclose(
hf_output, vllm_output, 1e-3 if dtype == "float" else 1e-2
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_classification.py | tests/models/language/pooling/test_classification.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
from vllm.platforms import current_platform
@pytest.mark.parametrize(
"model",
[
pytest.param(
"jason9693/Qwen2.5-1.5B-apeach",
marks=[
pytest.mark.core_model,
pytest.mark.cpu_model,
pytest.mark.slow_test,
],
),
],
)
@pytest.mark.parametrize("dtype", ["half"] if current_platform.is_rocm() else ["float"])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.classify(example_prompts)
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
# check logits difference
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output)
vllm_output = torch.tensor(vllm_output)
# the tolerance value of 1e-2 is selected based on the
# half datatype tests in
# tests/models/language/pooling/test_embedding.py
assert torch.allclose(
hf_output, vllm_output, 1e-3 if dtype == "float" else 1e-2
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_multi_vector_retrieval.py | tests/models/language/pooling/test_multi_vector_retrieval.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModel
from tests.models.utils import check_embeddings_close
@pytest.mark.parametrize(
"model",
["BAAI/bge-m3"],
)
@pytest.mark.parametrize("dtype", ["half"])
@torch.inference_mode
def test_embed_models(hf_runner, vllm_runner, example_prompts, model: str, dtype: str):
with vllm_runner(
model,
runner="pooling",
max_model_len=None,
) as vllm_model:
vllm_outputs = vllm_model.token_embed(example_prompts)
with hf_runner(
model,
auto_cls=AutoModel,
) as hf_model:
tokenizer = hf_model.tokenizer
hf_outputs = []
for prompt in example_prompts:
inputs = tokenizer([prompt], return_tensors="pt")
inputs = hf_model.wrap_device(inputs)
output = hf_model.model(**inputs)
embedding = output.last_hidden_state[0].float()
# normal
hf_outputs.append(embedding.cpu())
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
check_embeddings_close(
embeddings_0_lst=hf_output,
embeddings_1_lst=vllm_output,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_head_dtype.py | tests/models/language/pooling/test_head_dtype.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
@pytest.mark.parametrize(
"model",
["nie3e/sentiment-polish-gpt2-small"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
for head_dtype_str in ["float32", "model"]:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
hf_overrides={"head_dtype": head_dtype_str},
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
model_dtype = model_config.dtype
head_dtype = model_config.head_dtype
if head_dtype_str == "float32":
assert head_dtype == torch.float32
elif head_dtype_str == "model":
assert head_dtype == model_dtype
vllm_outputs = vllm_model.classify(example_prompts)
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output).float()
vllm_output = torch.tensor(vllm_output).float()
assert torch.allclose(hf_output, vllm_output, atol=1e-2)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py | tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModel
from tests.models.utils import check_embeddings_close
from vllm import TokensPrompt
@pytest.mark.parametrize(
"model",
["Qwen/Qwen3-Embedding-0.6B"],
)
@torch.inference_mode
def test_embed_models(hf_runner, vllm_runner, model: str):
chunk_size = 10
n_prompt_tokens = [55, 56, 57]
token_prompts = [[1024 + i for i in range(n)] for n in n_prompt_tokens]
with vllm_runner(
model,
runner="pooling",
max_model_len=128,
max_num_batched_tokens=chunk_size,
enforce_eager=True,
# `enable_chunked_prefill`: Set to `False` instead of `None` in VllmRunner
enable_chunked_prefill=True,
enable_prefix_caching=True,
) as vllm_model:
vllm_outputs = vllm_model.token_embed(
[TokensPrompt(prompt_token_ids=t) for t in token_prompts],
)
with hf_runner(
model,
auto_cls=AutoModel,
) as hf_model:
hf_outputs = []
for token_prompt in token_prompts:
inputs = hf_model.wrap_device({"input_ids": torch.tensor([token_prompt])})
input_ids = inputs["input_ids"]
output = hf_model.model(input_ids)
hf_outputs.append(output.last_hidden_state.cpu().float()[0])
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
check_embeddings_close(
embeddings_0_lst=hf_output,
embeddings_1_lst=vllm_output,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_truncation_control.py | tests/models/language/pooling/test_truncation_control.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
MODEL_NAME = "sentence-transformers/all-MiniLM-L12-v2"
max_model_len = 128
input_str = """Immerse yourself in the enchanting chronicle of calculus, a
mathematical domain that has radically transformed our comprehension of
change and motion. Despite its roots in ancient civilizations, the
formal birth of calculus predominantly occurred in the 17th century,
primarily under the influential guidance of Sir Isaac Newton and Gottfried
Wilhelm Leibniz. The earliest traces of calculus concepts are found in
ancient Greek mathematics,most notably in the works of Eudoxus and
Archimedes, around 300 BCE. They utilized the 'method of exhaustion'—a
technique for computing areas and volumes through the use of finite sums.
This methodology laid crucial foundational work for integral calculus.
In the 17th century, both Newton and Leibniz independently pioneered
calculus, each contributing unique perspectives that would shape this new
field."""
def test_smaller_truncation_size(
vllm_runner, model_name=MODEL_NAME, input_str=input_str
):
truncate_prompt_tokens = 10
with vllm_runner(
model_name, runner="pooling", max_model_len=max_model_len
) as vllm_model:
vllm_output = vllm_model.llm.embed(
input_str, truncate_prompt_tokens=truncate_prompt_tokens
)
prompt_tokens = vllm_output[0].prompt_token_ids
assert len(prompt_tokens) == truncate_prompt_tokens
def test_max_truncation_size(vllm_runner, model_name=MODEL_NAME, input_str=input_str):
truncate_prompt_tokens = -1
with vllm_runner(
model_name, runner="pooling", max_model_len=max_model_len
) as vllm_model:
vllm_output = vllm_model.llm.embed(
input_str, truncate_prompt_tokens=truncate_prompt_tokens
)
prompt_tokens = vllm_output[0].prompt_token_ids
assert len(prompt_tokens) == max_model_len
def test_bigger_truncation_size(
vllm_runner, model_name=MODEL_NAME, input_str=input_str
):
truncate_prompt_tokens = max_model_len + 1
with (
pytest.raises(ValueError),
vllm_runner(
model_name, runner="pooling", max_model_len=max_model_len
) as vllm_model,
):
llm_output = vllm_model.llm.embed(
input_str, truncate_prompt_tokens=truncate_prompt_tokens
)
assert (
llm_output
== f"""truncate_prompt_tokens value
({truncate_prompt_tokens}) is greater than
max_model_len ({max_model_len}). Please, select
a smaller truncation size."""
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_embedding.py | tests/models/language/pooling/test_embedding.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm.config import PoolerConfig
from ...utils import check_embeddings_close
@pytest.mark.parametrize(
"model",
[
# Be careful of the order of models, decoder-only models should be
# placed before encoder-only models, otherwise `Qwen2.5-0.5B-Instruct`
# case won't pass because gte-Qwen2-1.5B-instruct will cache custom
# model code with bidirectional attention.
# [Decoder-only]
pytest.param(
"BAAI/bge-multilingual-gemma2",
marks=[pytest.mark.core_model, pytest.mark.slow_test],
),
pytest.param(
"intfloat/e5-mistral-7b-instruct",
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
pytest.param(
"ssmits/Qwen2-7B-Instruct-embed-base", marks=[pytest.mark.cpu_model]
),
# [Encoder-only]
pytest.param(
"BAAI/bge-base-en-v1.5",
marks=[
pytest.mark.core_model,
pytest.mark.cpu_model,
pytest.mark.slow_test,
],
),
pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
pytest.param("intfloat/multilingual-e5-small"),
# [Cross-Encoder]
pytest.param(
"sentence-transformers/stsb-roberta-base-v2",
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
],
)
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model,
) -> None:
vllm_extra_kwargs = {}
if model == "ssmits/Qwen2-7B-Instruct-embed-base":
vllm_extra_kwargs["pooler_config"] = PoolerConfig(
pooling_type="MEAN", normalize=False
)
max_model_len: int | None = 512
if model in [
"sentence-transformers/all-MiniLM-L12-v2",
"sentence-transformers/stsb-roberta-base-v2",
]:
max_model_len = None
# The example_prompts has ending "\n", for example:
# "Write a short story about a robot that dreams for the first time.\n"
# sentence_transformers will strip the input texts, see:
# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
# This makes the input_ids different between hf_model and vllm_model.
# So we need to strip the input texts to avoid test failing.
example_prompts = [str(s).strip() for s in example_prompts]
with hf_runner(model, is_sentence_transformer=True) as hf_model:
hf_outputs = hf_model.encode(example_prompts)
with vllm_runner(
model, runner="pooling", max_model_len=max_model_len, **vllm_extra_kwargs
) as vllm_model:
vllm_outputs = vllm_model.embed(example_prompts)
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_auto_prefix_cache_support.py | tests/models/language/pooling/test_auto_prefix_cache_support.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
from tests.models.language.pooling.embed_utils import run_embedding_correctness_test
@pytest.mark.parametrize(
"model",
["jason9693/Qwen2.5-1.5B-apeach"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
# example_prompts is too short for testing prefix_caching
example_prompts = [s * 10 for s in example_prompts]
with vllm_runner(
model, max_model_len=512, dtype=dtype, enable_prefix_caching=True
) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
# First Run
vllm_model.classify(example_prompts)
# assert prefix_caching works
pooling_outputs = vllm_model.llm.encode(
example_prompts, pooling_task="classify"
)
for output in pooling_outputs:
assert output.num_cached_tokens > 0
vllm_outputs = [req_output.outputs.data for req_output in pooling_outputs]
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output)
vllm_output = torch.tensor(vllm_output)
assert torch.allclose(
hf_output, vllm_output, 1e-3 if dtype == "float" else 1e-2
)
@pytest.mark.parametrize(
"model",
["Qwen/Qwen3-Embedding-0.6B"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_embed_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
):
# example_prompts is too short for testing prefix_caching
example_prompts = [str(s).strip() * 10 for s in example_prompts]
with vllm_runner(
model,
runner="pooling",
max_model_len=None,
enable_prefix_caching=True,
) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
# First Run
vllm_model.embed(example_prompts)
# assert prefix_caching works
pooling_outputs = vllm_model.llm.encode(example_prompts, pooling_task="embed")
for output in pooling_outputs:
assert output.num_cached_tokens > 0
vllm_outputs = [req_output.outputs.data for req_output in pooling_outputs]
with hf_runner(
model,
is_sentence_transformer=True,
) as hf_model:
run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
@pytest.mark.parametrize(
"model",
[
"intfloat/e5-small",
"Alibaba-NLP/gte-Qwen2-1.5B-instruct", # is_causal == False
"papluca/xlm-roberta-base-language-detection",
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_non_causal_models(
hf_runner, vllm_runner, example_prompts, model: str, dtype: str
) -> None:
with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert not cache_config.enable_prefix_caching
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/embed_utils.py | tests/models/language/pooling/embed_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
import pytest
from tests.conftest import HfRunner
from tests.models.utils import EmbedModelInfo, check_embeddings_close, matryoshka_fy
def run_embedding_correctness_test(
hf_model: "HfRunner",
inputs: list[str],
vllm_outputs: Sequence[list[float]],
dimensions: int | None = None,
):
hf_outputs = hf_model.encode(inputs)
if dimensions:
hf_outputs = matryoshka_fy(hf_outputs, dimensions)
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
tol=1e-2,
)
def correctness_test_embed_models(
hf_runner,
vllm_runner,
model_info: EmbedModelInfo,
example_prompts,
vllm_extra_kwargs=None,
hf_model_callback=None,
):
pytest.skip("Debug only, ci prefers to use mteb test.")
# The example_prompts has ending "\n", for example:
# "Write a short story about a robot that dreams for the first time.\n"
# sentence_transformers will strip the input texts, see:
# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
# This makes the input_ids different between hf_model and vllm_model.
# So we need to strip the input texts to avoid test failing.
example_prompts = [str(s).strip() for s in example_prompts]
vllm_extra_kwargs = vllm_extra_kwargs or {}
vllm_extra_kwargs["dtype"] = model_info.dtype
if model_info.hf_overrides is not None:
vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
with vllm_runner(
model_info.name, runner="pooling", max_model_len=None, **vllm_extra_kwargs
) as vllm_model:
vllm_outputs = vllm_model.embed(example_prompts)
with hf_runner(
model_info.name,
dtype=model_info.hf_dtype,
is_sentence_transformer=True,
) as hf_model:
if hf_model_callback is not None:
hf_model_callback(hf_model)
run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_scoring.py | tests/models/language/pooling/test_scoring.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
CROSS_ENCODER_MODELS = [
"cross-encoder/ms-marco-MiniLM-L-6-v2", # Bert
"BAAI/bge-reranker-v2-m3", # Roberta
]
EMBEDDING_MODELS = [
"sentence-transformers/all-MiniLM-L12-v2",
]
TEXTS_1 = [
"What is the capital of France?",
"What is the capital of Germany?",
]
TEXTS_2 = [
"The capital of France is Paris.",
"The capital of Germany is Berlin.",
]
DTYPE = "half"
@pytest.fixture(scope="module", params=CROSS_ENCODER_MODELS)
def model_name(request):
yield request.param
def test_cross_encoder_1_to_1(vllm_runner, hf_runner, model_name):
text_pair = [TEXTS_1[0], TEXTS_2[0]]
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict([text_pair]).tolist()
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
assert len(vllm_outputs) == 1
assert len(hf_outputs) == 1
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
def test_cross_encoder_1_to_N(vllm_runner, hf_runner, model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[0], TEXTS_2[1]],
]
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict(text_pairs).tolist()
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
assert len(vllm_outputs) == 2
assert len(hf_outputs) == 2
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
def test_cross_encoder_N_to_N(vllm_runner, hf_runner, model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[1], TEXTS_2[1]],
]
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict(text_pairs).tolist()
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
assert len(vllm_outputs) == 2
assert len(hf_outputs) == 2
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
def emb_model_name(request):
yield request.param
def test_embedding_1_to_1(vllm_runner, hf_runner, emb_model_name):
text_pair = [TEXTS_1[0], TEXTS_2[0]]
with hf_runner(
emb_model_name, dtype=DTYPE, is_sentence_transformer=True
) as hf_model:
hf_embeddings = hf_model.encode(text_pair)
hf_outputs = [F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)]
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
assert len(vllm_outputs) == 1
assert len(hf_outputs) == 1
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
def test_embedding_1_to_N(vllm_runner, hf_runner, emb_model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[0], TEXTS_2[1]],
]
with hf_runner(
emb_model_name, dtype=DTYPE, is_sentence_transformer=True
) as hf_model:
hf_embeddings = [hf_model.encode(text_pair) for text_pair in text_pairs]
hf_outputs = [
F.cosine_similarity(*map(torch.tensor, pair), dim=0)
for pair in hf_embeddings
]
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
assert len(vllm_outputs) == 2
assert len(hf_outputs) == 2
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
def test_embedding_N_to_N(vllm_runner, hf_runner, emb_model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[1], TEXTS_2[1]],
]
with hf_runner(
emb_model_name, dtype=DTYPE, is_sentence_transformer=True
) as hf_model:
hf_embeddings = [hf_model.encode(text_pair) for text_pair in text_pairs]
hf_outputs = [
F.cosine_similarity(*map(torch.tensor, pair), dim=0)
for pair in hf_embeddings
]
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
assert len(vllm_outputs) == 2
assert len(hf_outputs) == 2
assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_reward.py | tests/models/language/pooling/test_reward.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
from transformers import AutoModel
from vllm.platforms import current_platform
from ....conftest import HfRunner
from ...utils import check_transformers_version
@pytest.fixture
def math_step_prompts():
# ruff: noqa: E501
data = {
"system": "Please reason step by step, and put your final answer within \\boxed{}. ",
"query": "Sue lives in a fun neighborhood. One weekend, the neighbors decided to play a prank on Sue. On Friday morning, the neighbors placed 18 pink plastic flamingos out on Sue's front yard. On Saturday morning, the neighbors took back one third of the flamingos, painted them white, and put these newly painted white flamingos back out on Sue's front yard. Then, on Sunday morning, they added another 18 pink plastic flamingos to the collection. At noon on Sunday, how many more pink plastic flamingos were out than white plastic flamingos?",
"response": [
"To find out how many more pink plastic flamingos were out than white plastic flamingos at noon on Sunday, we can break down the problem into steps. First, on Friday, the neighbors start with 18 pink plastic flamingos.",
"On Saturday, they take back one third of the flamingos. Since there were 18 flamingos, (1/3 \\times 18 = 6) flamingos are taken back. So, they have (18 - 6 = 12) flamingos left in their possession. Then, they paint these 6 flamingos white and put them back out on Sue's front yard. Now, Sue has the original 12 pink flamingos plus the 6 new white ones. Thus, by the end of Saturday, Sue has (12 + 6 = 18) pink flamingos and 6 white flamingos.",
"On Sunday, the neighbors add another 18 pink plastic flamingos to Sue's front yard. By the end of Sunday morning, Sue has (18 + 18 = 36) pink flamingos and still 6 white flamingos.",
"To find the difference, subtract the number of white flamingos from the number of pink flamingos: (36 - 6 = 30). Therefore, at noon on Sunday, there were 30 more pink plastic flamingos out than white plastic flamingos. The answer is (\\boxed{30}).",
],
}
answer = "<extra_0>".join(data["response"]) + "<extra_0>"
prompt = f"<im_start>system\n{data['system']}<im_end>\n<im_start>user\n{data['query']}<im_end>\n<im_start>assistant\n{answer}<im_end><|endoftext|>"
return [prompt]
def step_reward_patch_hf_model(hf_model: HfRunner):
# Patch the hf_runner to use the step reward function
def make_step_rewards(
logits: torch.Tensor, token_masks: torch.Tensor
) -> list[list[float]]:
probabilities = F.softmax(logits, dim=-1)
probabilities = probabilities * token_masks.unsqueeze(-1)
all_scores_res: list[list[float]] = []
for i in range(probabilities.size(0)):
sample = probabilities[i] # seq_len, num_labels
positive_probs = sample[sample != 0].view(-1, 2)
non_zero_elements_list = positive_probs.cpu().tolist()
all_scores_res.append(non_zero_elements_list)
return all_scores_res
def reward(prompts: list[str]) -> list[list[float]]:
input_ids = hf_model.tokenizer(prompts, return_tensors="pt").input_ids
input_ids = hf_model.wrap_device(input_ids)
outputs = hf_model.model(input_ids=input_ids)
step_sep_id = hf_model.tokenizer.encode("<extra_0>")[0]
token_masks = input_ids == step_sep_id
return make_step_rewards(outputs[0], token_masks)
hf_model.reward = reward # type: ignore[attr-defined]
return hf_model
@pytest.mark.parametrize(
"model",
[
pytest.param(
"Qwen/Qwen2.5-Math-PRM-7B",
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_prm_models(
hf_runner,
vllm_runner,
math_step_prompts,
model: str,
dtype: str,
) -> None:
check_transformers_version(
"Qwen/Qwen2.5-Math-PRM-7B", max_transformers_version="4.53.2"
)
if current_platform.is_cpu():
pytest.skip("CPU only supports V1")
with vllm_runner(model, max_model_len=1024, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.reward(math_step_prompts)
with hf_runner(model, dtype=dtype, auto_cls=AutoModel) as hf_model:
hf_model = step_reward_patch_hf_model(hf_model)
hf_outputs = hf_model.reward(math_step_prompts)
# check logits difference
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output).float()
vllm_output = torch.tensor(vllm_output).float()
assert torch.allclose(hf_output, vllm_output, 1.5e-2)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_gritlm.py | tests/models/language/pooling/test_gritlm.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import numpy as np
import openai
import pytest
from scipy.spatial.distance import cosine
from vllm import LLM, SamplingParams
from vllm.config import ModelConfig
from ....utils import RemoteOpenAIServer
MODEL_NAME = "parasail-ai/GritLM-7B-vllm"
MAX_MODEL_LEN = 4000
ATOL = 0.002
def _arr(arr):
"""
Convert a list of integers to an array of integers.
"""
return np.array(arr)
def test_find_array():
from vllm.model_executor.models.gritlm import GritLMMeanPool
model_config = ModelConfig(
MODEL_NAME,
runner="pooling",
dtype="bfloat16",
seed=0,
)
pooling = GritLMMeanPool(model_config=model_config)
arr = _arr([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
assert pooling._find_array(arr, _arr([3, 4, 5]), start_idx=0) == 3
assert pooling._find_array(arr, _arr([3, 4, 5]), start_idx=1) == 3
assert pooling._find_array(arr, _arr([3, 4, 5]), start_idx=5) == -1
assert pooling._find_array(arr, _arr([3, 4, 5]), end_idx=3) == -1
assert pooling._find_array(arr, _arr([3, 4, 5]), end_idx=4) == 3
assert pooling._find_array(arr, _arr([3, 5]), start_idx=0) == -1
with pytest.raises(ValueError):
pooling._find_array(arr, _arr([3, 4, 5]), start_idx=-1)
def run_llm_encode(
llm: LLM,
queries: list[str],
instruction: str,
) -> list[list[float]]:
outputs = llm.embed([instruction + q for q in queries])
return [output.outputs.embedding for output in outputs]
async def run_client_embeddings(
client: openai.AsyncOpenAI,
queries: list[str],
instruction: str,
) -> list[list[float]]:
outputs = await client.embeddings.create(
model=MODEL_NAME,
input=[instruction + q for q in queries],
)
return [data.embedding for data in outputs.data]
def gritlm_instruction(instruction):
return (
"<|user|>\n" + instruction + "\n<|embed|>\n" if instruction else "<|embed|>\n"
)
def get_test_data():
"""
Grabbed this test data and the expected values from
README.md in https://github.com/ContextualAI/gritlm
"""
q_instruction = gritlm_instruction(
"Given a scientific paper title, retrieve the paper's abstract",
)
queries = [
"Bitcoin: A Peer-to-Peer Electronic Cash System",
"Generative Representational Instruction Tuning",
]
d_instruction = gritlm_instruction("")
documents = [
# ruff: noqa: E501
"A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
"All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
]
return queries, q_instruction, documents, d_instruction
def validate_embed_output(q_rep: list[list[float]], d_rep: list[list[float]]):
cosine_sim_q0_d0 = 1 - cosine(q_rep[0], d_rep[0])
assert cosine_sim_q0_d0 == pytest.approx(0.609, abs=ATOL)
cosine_sim_q0_d1 = 1 - cosine(q_rep[0], d_rep[1])
assert cosine_sim_q0_d1 == pytest.approx(0.101, abs=ATOL)
cosine_sim_q1_d0 = 1 - cosine(q_rep[1], d_rep[0])
assert cosine_sim_q1_d0 == pytest.approx(0.120, abs=ATOL)
cosine_sim_q1_d1 = 1 - cosine(q_rep[1], d_rep[1])
assert cosine_sim_q1_d1 == pytest.approx(0.534, abs=ATOL)
def test_gritlm_offline_embedding(vllm_runner):
queries, q_instruction, documents, d_instruction = get_test_data()
with vllm_runner(
MODEL_NAME,
runner="pooling",
max_model_len=MAX_MODEL_LEN,
) as vllm_model:
llm = vllm_model.llm
d_rep = run_llm_encode(
llm,
documents,
d_instruction,
)
q_rep = run_llm_encode(
llm,
queries,
q_instruction,
)
validate_embed_output(q_rep, d_rep)
@pytest.mark.asyncio
async def test_gritlm_api_server_embedding():
queries, q_instruction, documents, d_instruction = get_test_data()
args = ["--runner", "pooling", "--max_model_len", str(MAX_MODEL_LEN)]
with RemoteOpenAIServer(MODEL_NAME, args) as server:
client_embedding = server.get_async_client()
d_rep = await run_client_embeddings(
client_embedding,
documents,
d_instruction,
)
q_rep = await run_client_embeddings(
client_embedding,
queries,
q_instruction,
)
validate_embed_output(q_rep, d_rep)
def test_gritlm_offline_generate(monkeypatch: pytest.MonkeyPatch, vllm_runner):
input = "<|user|>\nWhat is the capital of France?\n<|assistant|>\n"
with vllm_runner(
MODEL_NAME,
runner="generate",
max_model_len=MAX_MODEL_LEN,
) as vllm_model:
llm = vllm_model.llm
sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
outputs = llm.generate(input, sampling_params=sampling_params)
assert outputs[0].outputs[0].text == "The capital of France is Paris."
@pytest.mark.asyncio
async def test_gritlm_api_server_generate():
input = "<|user|>\nWhat is the capital of France?\n<|assistant|>\n"
args = ["--runner", "generate", "--max_model_len", str(MAX_MODEL_LEN)]
with RemoteOpenAIServer(MODEL_NAME, args) as server:
client_generate = server.get_async_client()
outputs = await client_generate.completions.create(
model=MODEL_NAME,
prompt=input,
max_tokens=256,
temperature=0.0,
)
assert outputs.choices[0].text == "The capital of France is Paris."
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_extract_hidden_states.py | tests/models/language/pooling/test_extract_hidden_states.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm import TokensPrompt
@pytest.mark.parametrize(
"model",
["Qwen/Qwen3-0.6B"],
)
@torch.inference_mode
def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
n_prompt_tokens = [55, 56, 57]
token_prompts = [[1024 + i for i in range(n)] for n in n_prompt_tokens]
with vllm_runner(
model,
max_model_len=128,
enforce_eager=True,
runner="pooling",
enable_prefix_caching=True,
) as vllm_model:
pooling_outputs = vllm_model.llm.encode(
[TokensPrompt(prompt_token_ids=t) for t in token_prompts],
pooling_task="token_embed",
)
for n, output in zip(n_prompt_tokens, pooling_outputs):
assert len(output.prompt_token_ids) == n
assert len(output.outputs.data) == n
assert output.num_cached_tokens == 0
# test enable_prefix_caching plus all pooling
# we need to skip reading cache at this request by
# request.skip_reading_prefix_cache
pooling_outputs = vllm_model.llm.encode(
[TokensPrompt(prompt_token_ids=t) for t in token_prompts],
pooling_task="token_embed",
)
for n, output in zip(n_prompt_tokens, pooling_outputs):
assert len(output.prompt_token_ids) == n
assert len(output.outputs.data) == n
assert output.num_cached_tokens == 0
# skip_reading_prefix_cache can still write to cache
# to accelerate following requests
pooling_outputs = vllm_model.llm.encode(
[TokensPrompt(prompt_token_ids=t) for t in token_prompts],
pooling_task="embed",
)
for n, output in zip(n_prompt_tokens, pooling_outputs):
assert len(output.prompt_token_ids) == n
assert output.num_cached_tokens > 0
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_nomic_max_model_len.py | tests/models/language/pooling/test_nomic_max_model_len.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa: SIM117
from typing import Any
import pytest
from ...utils import EmbedModelInfo
MODELS = [
EmbedModelInfo("nomic-ai/nomic-embed-text-v1"),
# EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5"),
# EmbedModelInfo("nomic-ai/CodeRankEmbed"),
EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe"),
# EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long"),
]
rope_theta = 1000
factor = 4.0
original_max_position_embeddings = 2048
max_model_len = int(original_max_position_embeddings * factor)
@pytest.mark.parametrize("model_info", MODELS)
def test_default(model_info, vllm_runner):
with vllm_runner(
model_info.name, runner="pooling", max_model_len=None
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
if model_info.name == "nomic-ai/nomic-embed-text-v2-moe":
# For nomic-embed-text-v2-moe the length is set to 512
# by sentence_bert_config.json.
assert model_config.max_model_len == 512
else:
assert model_config.max_model_len == original_max_position_embeddings
@pytest.mark.parametrize("model_info", MODELS)
def test_set_max_model_len_legal(model_info, vllm_runner):
# set max_model_len <= 512
with vllm_runner(
model_info.name, runner="pooling", max_model_len=256
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
assert model_config.max_model_len == 256
# set 512 < max_model_len <= 2048
if model_info.name == "nomic-ai/nomic-embed-text-v2-moe":
# For nomic-embed-text-v2-moe the length is set to 512
# by sentence_bert_config.json.
with pytest.raises(ValueError):
with vllm_runner(model_info.name, runner="pooling", max_model_len=1024):
pass
else:
with vllm_runner(
model_info.name, runner="pooling", max_model_len=1024
) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
assert model_config.max_model_len == 1024
@pytest.mark.parametrize("model_info", MODELS)
def test_set_max_model_len_illegal(model_info, vllm_runner):
# set max_model_len > 2048
with pytest.raises(ValueError):
with vllm_runner(model_info.name, runner="pooling", max_model_len=4096):
pass
# set max_model_len > 2048 by hf_overrides
hf_overrides = {"max_model_len": 4096}
with pytest.raises(ValueError):
with vllm_runner(
model_info.name,
runner="pooling",
max_model_len=None,
hf_overrides=hf_overrides,
):
pass
@pytest.mark.parametrize("model_info", MODELS)
def test_use_rope_scaling_legal(model_info, vllm_runner):
hf_overrides = {
"rope_parameters": {
"rope_theta": rope_theta,
"rope_type": "yarn",
"factor": factor,
"original_max_position_embeddings": original_max_position_embeddings,
},
"max_model_len": max_model_len,
}
with vllm_runner(
model_info.name, runner="pooling", max_model_len=None, hf_overrides=hf_overrides
):
pass
@pytest.mark.parametrize("model_info", MODELS)
def test_use_rope_scaling_illegal(model_info, vllm_runner):
hf_overrides: dict[str, Any] = {
"rope_parameters": {
"rope_theta": rope_theta,
"rope_type": "yarn",
"factor": factor,
"original_max_position_embeddings": original_max_position_embeddings,
},
}
# illegal max_model_len
with pytest.raises(ValueError):
with vllm_runner(
model_info.name,
runner="pooling",
max_model_len=max_model_len + 1,
hf_overrides=hf_overrides,
):
pass
hf_overrides = {
"rope_parameters": {
"rope_theta": rope_theta,
"rope_type": "yarn",
"factor": factor,
"original_max_position_embeddings": original_max_position_embeddings,
},
"max_model_len": max_model_len + 1,
}
# illegal max_model_len by hf_overrides
with pytest.raises(ValueError):
with vllm_runner(
model_info.name,
runner="pooling",
max_model_len=None,
hf_overrides=hf_overrides,
):
pass
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_token_classification.py | tests/models/language/pooling/test_token_classification.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForTokenClassification
from tests.models.utils import softmax
@pytest.mark.parametrize("model", ["boltuix/NeuroBERT-NER"])
# The float32 is required for this tiny model to pass the test.
@pytest.mark.parametrize("dtype", ["float"])
@torch.inference_mode
def test_bert_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(model, max_model_len=None, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.token_classify(example_prompts)
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForTokenClassification
) as hf_model:
tokenizer = hf_model.tokenizer
hf_outputs = []
for prompt in example_prompts:
inputs = tokenizer([prompt], return_tensors="pt")
inputs = hf_model.wrap_device(inputs)
output = hf_model.model(**inputs)
hf_outputs.append(softmax(output.logits[0]))
# check logits difference
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = hf_output.detach().clone().cpu().float()
vllm_output = vllm_output.detach().clone().cpu().float()
assert torch.allclose(hf_output, vllm_output, 1e-2)
@pytest.mark.parametrize("model", ["disham993/electrical-ner-ModernBERT-base"])
@pytest.mark.parametrize("dtype", ["float"])
@torch.inference_mode
def test_modernbert_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
from vllm.platforms import current_platform
with vllm_runner(model, max_model_len=None, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.token_classify(example_prompts)
# Use eager attention on ROCm to avoid HF Transformers flash attention
# accuracy issues: https://github.com/vllm-project/vllm/issues/30167
hf_model_kwargs = {}
if current_platform.is_rocm():
hf_model_kwargs["attn_implementation"] = "eager"
with hf_runner(
model,
dtype=dtype,
auto_cls=AutoModelForTokenClassification,
model_kwargs=hf_model_kwargs,
) as hf_model:
tokenizer = hf_model.tokenizer
hf_outputs = []
for prompt in example_prompts:
inputs = tokenizer([prompt], return_tensors="pt")
inputs = hf_model.wrap_device(inputs)
output = hf_model.model(**inputs)
hf_outputs.append(softmax(output.logits[0]))
# check logits difference
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = hf_output.detach().clone().cpu().float()
vllm_output = vllm_output.detach().clone().cpu().float()
assert torch.allclose(hf_output, vllm_output, atol=1e-2)
@pytest.mark.parametrize("model", ["bd2lcco/Qwen3-0.6B-finetuned"])
@pytest.mark.parametrize("dtype", ["float"])
@torch.inference_mode
def test_auto_conversion(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(model, max_model_len=1024, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.token_classify(example_prompts)
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForTokenClassification
) as hf_model:
tokenizer = hf_model.tokenizer
hf_outputs = []
for prompt in example_prompts:
inputs = tokenizer([prompt], return_tensors="pt")
inputs = hf_model.wrap_device(inputs)
output = hf_model.model(**inputs)
hf_outputs.append(softmax(output.logits[0]))
# check logits difference
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = hf_output.detach().clone().cpu().float()
vllm_output = vllm_output.detach().clone().cpu().float()
assert torch.allclose(hf_output, vllm_output, atol=1e-2)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/__init__.py | tests/models/language/pooling/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_splade_sparse_pooler.py | tests/models/language/pooling/test_splade_sparse_pooler.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import types
import pytest
import torch
import torch.nn as nn
from vllm.model_executor.models.bert import (
BertMLMHead,
SPLADESparsePooler,
)
# ---------------------------------------------------------------------
# Functional test: SPLADE formula correctness (no HF download needed)
# ---------------------------------------------------------------------
@pytest.mark.parametrize("B,T,H,V", [(2, 3, 5, 7)])
@torch.inference_mode
def test_splade_pooler_matches_reference_formula(B, T, H, V):
"""Ensure SPLADESparsePooler forward() matches the mathematical formula:
log1p(relu(logits)) -> max over sequence length (after masking)."""
torch.manual_seed(0)
# Prepare [B] sequences of shape [T, H]
hs_list = [torch.randn(T, H) for _ in range(B)]
hs_tenser = torch.cat(hs_list)
# Simulate PoolingMetadata (only required fields)
prompt_lens = [T, T - 1]
prompt_lens_tenser = torch.tensor(prompt_lens, dtype=torch.int32)
token_ids = torch.tensor(
[
[101, 5, 102], # Batch 0: [CLS], token, [SEP]
[101, 6, 6], # Batch 1: [CLS], token, token (last token ignored)
],
dtype=torch.long,
)
meta = types.SimpleNamespace(
prompt_lens=prompt_lens_tenser, prompt_token_ids=token_ids
)
# MLM head (prefer BertMLMHead, fallback to Linear if unavailable)
try:
mlm_head = BertMLMHead(hidden_size=H, vocab_size=V, layer_norm_eps=1e-12)
except Exception:
mlm_head = nn.Linear(H, V, bias=True)
# Forward pass through SPLADE pooler
pooler = SPLADESparsePooler(mlm_head=mlm_head, pooling="max", remove_cls_sep=True)
pooled = pooler(hidden_states=hs_tenser, pooling_metadata=meta) # list of [V]
# Basic output checks
assert isinstance(pooled, torch.Tensor) and len(pooled) == B
for vec in pooled:
assert vec.shape == (V,)
assert torch.isfinite(vec).all()
assert (vec >= 0).all(), "SPLADE outputs must be non-negative."
# Reference implementation for comparison
def ref_one(hs: torch.Tensor, L: int, tid_row: torch.Tensor) -> torch.Tensor:
keep = torch.ones(L, dtype=torch.bool)
if L > 0 and tid_row[0].item() == 101: # remove CLS
keep[0] = False
if L > 0 and tid_row[L - 1].item() == 102: # remove SEP
keep[L - 1] = False
valid = hs[:L][keep[:L]]
if valid.numel() == 0:
return torch.zeros(V, dtype=torch.float32)
logits = mlm_head(valid) # [L', V]
scores = torch.log1p(torch.relu(logits)) # [L', V]
return scores.max(dim=0).values.to(torch.float32)
torch.testing.assert_close(
pooled[0],
ref_one(hs_list[0], prompt_lens[0], token_ids[0]),
rtol=1e-4,
atol=1e-4,
)
torch.testing.assert_close(
pooled[1],
ref_one(hs_list[1], prompt_lens[1], token_ids[1]),
rtol=1e-4,
atol=1e-4,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_pooler_config_init_behaviour.py | tests/models/language/pooling/test_pooler_config_init_behaviour.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
from tests.models.utils import softmax
from vllm.config import PoolerConfig
@pytest.mark.parametrize(
"model",
["jason9693/Qwen2.5-1.5B-apeach", "papluca/xlm-roberta-base-language-detection"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models_using_activation(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(use_activation=False),
) as vllm_model:
wo_activation_out = vllm_model.classify(example_prompts)
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(use_activation=True),
) as vllm_model:
w_activation_out = vllm_model.classify(example_prompts)
for wo_activation, w_activation in zip(wo_activation_out, w_activation_out):
wo_activation = torch.tensor(wo_activation)
w_activation = torch.tensor(w_activation)
assert not torch.allclose(wo_activation, w_activation, atol=1e-2), (
"pooler_config is not working"
)
assert torch.allclose(
softmax(wo_activation), w_activation, 1e-3 if dtype == "float" else 1e-2
)
@pytest.mark.parametrize(
"model",
[
"intfloat/multilingual-e5-small",
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_embed_models_using_normalize(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=False),
) as vllm_model:
wo_normalize = torch.tensor(vllm_model.embed(example_prompts))
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=True),
) as vllm_model:
w_normalize = torch.tensor(vllm_model.embed(example_prompts))
assert not torch.allclose(wo_normalize, w_normalize, atol=1e-2), (
"pooler_config normalize is not working"
)
assert torch.allclose(
F.normalize(wo_normalize, p=2, dim=-1), w_normalize, atol=1e-2
), "w_normal should be close to normal(wo_normal)."
@pytest.mark.parametrize(
"model",
[
"internlm/internlm2-1_8b-reward",
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_reward_models_using_activation(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=1024,
dtype=dtype,
pooler_config=PoolerConfig(use_activation=False),
) as vllm_model:
wo_activation = vllm_model.reward(example_prompts)
with vllm_runner(
model,
max_model_len=1024,
dtype=dtype,
pooler_config=PoolerConfig(use_activation=True),
) as vllm_model:
w_activation = vllm_model.reward(example_prompts)
for wo, w in zip(wo_activation, w_activation):
wo = torch.tensor(wo)
w = torch.tensor(w)
assert not torch.allclose(wo, w, atol=1e-2), (
"pooler_config activation is not working"
)
assert torch.allclose(softmax(wo), w, atol=1e-2), (
"w_activation should be close to activation(wo_activation)."
)
@pytest.mark.parametrize(
"model",
[
"intfloat/multilingual-e5-small",
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_multi_vector_retrieval_models_using_normalize(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=False),
) as vllm_model:
wo_normalize = vllm_model.token_embed(example_prompts)
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=True),
) as vllm_model:
w_normalize = vllm_model.token_embed(example_prompts)
for wo, w in zip(wo_normalize, w_normalize):
assert not torch.allclose(wo, w, atol=1e-2), (
"pooler_config normalize is not working"
)
assert torch.allclose(F.normalize(wo, p=2, dim=-1), w, atol=1e-2), (
"w_normal should be close to normal(wo_normal)."
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/pooling/test_mm_classifier_conversion.py | tests/models/language/pooling/test_mm_classifier_conversion.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm.config.pooler import PoolerConfig
def test_idefics_multimodal(
vllm_runner,
) -> None:
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
with vllm_runner(
model_name="HuggingFaceM4/Idefics3-8B-Llama3",
runner="pooling",
convert="classify",
load_format="dummy",
max_model_len=512,
enforce_eager=True,
tensor_parallel_size=1,
disable_log_stats=True,
dtype="bfloat16",
) as vllm_model:
llm = vllm_model.get_llm()
outputs = llm.classify(prompts)
for output in outputs:
assert len(output.outputs.probs) == 2
def update_config(config):
config.text_config.update(
{
"architectures": ["Gemma3ForSequenceClassification"],
"classifier_from_token": ["A", "B", "C", "D", "E"],
"method": "no_post_processing",
"id2label": {
"A": "Chair",
"B": "Couch",
"C": "Table",
"D": "Bed",
"E": "Cupboard",
},
}
)
return config
def test_gemma_multimodal(
vllm_runner,
) -> None:
messages = [
{
"role": "system",
"content": """
You are a helpful assistant. You will be given a product description
which may also include an image. Classify the following product into
one of the categories:
A = chair
B = couch
C = table
D = bed
E = cupboard
You'll answer with exactly one letter (A, B, C, D, or E).""",
},
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/red_chair.jpg"
},
},
{"type": "text", "text": "A fine 19th century piece of furniture."},
],
},
]
with vllm_runner(
model_name="google/gemma-3-4b-it",
runner="pooling",
convert="classify",
load_format="auto",
hf_overrides=update_config,
pooler_config=PoolerConfig(pooling_type="LAST"),
max_model_len=512,
enforce_eager=True,
tensor_parallel_size=1,
disable_log_stats=True,
dtype="bfloat16",
) as vllm_model:
llm = vllm_model.get_llm()
prompts = llm.preprocess_chat(messages)
result = llm.classify(prompts)
assert result[0].outputs.probs[0] > 0.95
assert all(c < 0.05 for c in result[0].outputs.probs[1:])
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_granite.py | tests/models/language/generation/test_granite.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from ...utils import check_logprobs_close
MODELS = [
# TODO(sang): Sliding window should be tested separately.
"ibm/PowerLM-3b",
"ibm/PowerMoE-3b",
]
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(model, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_common.py | tests/models/language/generation/test_common.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.platforms import current_platform
from ....utils import large_gpu_mark
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
# Models that require embedding scaling for prompt_embeds test
EMBED_SCALING_MODELS = {
"openbmb/MiniCPM4.1-8B",
}
# This list contains the model that are using AITER kernel.
# Skip model that are not using AITER tests.
# When more AITER kernels are added, this list will not be
# needed as all the models will be calling AITER kernels
# in parts of the operators
AITER_MODEL_LIST = [
"meta-llama/Llama-3.2-1B-Instruct",
"openbmb/MiniCPM3-4B",
"Qwen/Qwen-7B-Chat",
"Qwen/Qwen2.5-0.5B-Instruct",
"TitanML/tiny-mixtral",
"Qwen/Qwen3-8B",
]
# @maybe_test_rocm_aiter
@pytest.mark.parametrize(
"model",
[
pytest.param(
"bigscience/bloom-560m", # bloom - testing alibi slopes
marks=[
pytest.mark.core_model,
pytest.mark.slow_test,
pytest.mark.cpu_model,
],
),
pytest.param(
"openai-community/gpt2", # gpt2
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
pytest.param("Milos/slovak-gpt-j-405M"), # gptj
pytest.param("bigcode/tiny_starcoder_py"), # gpt_bigcode
pytest.param("EleutherAI/pythia-70m"), # gpt_neox
pytest.param(
"google/gemma-1.1-2b-it", # gemma
marks=[
pytest.mark.core_model,
pytest.mark.cpu_model,
pytest.mark.slow_test,
],
),
pytest.param(
"google/gemma-2-2b-it", # test hybrid attention
marks=[pytest.mark.cpu_model],
),
pytest.param(
"zai-org/chatglm3-6b", # chatglm (text-only)
),
pytest.param(
"meta-llama/Llama-3.2-1B-Instruct", # llama
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
pytest.param(
"openbmb/MiniCPM4.1-8B", # minicpm
marks=[pytest.mark.core_model, large_gpu_mark(min_gb=48)],
),
pytest.param(
"facebook/opt-125m", # opt
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
pytest.param(
"microsoft/phi-2", # phi
marks=[pytest.mark.core_model, pytest.mark.slow_test],
),
pytest.param(
"Qwen/Qwen-7B-Chat", # qwen (text-only)
),
pytest.param(
"Qwen/Qwen2.5-0.5B-Instruct", # qwen2
marks=[
pytest.mark.core_model,
pytest.mark.cpu_model,
pytest.mark.slow_test,
],
),
pytest.param(
"Qwen/Qwen3-8B", # qwen (text-only)
),
pytest.param("stabilityai/stablelm-3b-4e1t"), # stablelm
pytest.param("bigcode/starcoder2-3b"), # starcoder2
pytest.param(
"TitanML/tiny-mixtral", # mixtral
marks=[pytest.mark.core_model, pytest.mark.cpu_model],
),
pytest.param("swiss-ai/Apertus-8B-Instruct-2509"), # apertus
],
)
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize(
"use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False]
)
@pytest.mark.parametrize("use_prompt_embeds", [True, False])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
max_tokens: int,
num_logprobs: int,
use_rocm_aiter: bool,
use_prompt_embeds: bool,
monkeypatch,
) -> None:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
if use_rocm_aiter and (model in AITER_MODEL_LIST):
monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
elif use_rocm_aiter and model not in AITER_MODEL_LIST:
# Skip model that are not using AITER tests.
# When more AITER kernels are added, this list will not be
# needed as all the models will be calling AITER kernels
# in parts of the operators
pytest.skip(f"Skipping '{model}' model test with AITER kernel.")
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
prompt_embeds: list[torch.Tensor] | None = [] if use_prompt_embeds else None
for prompt in example_prompts:
token_ids = hf_model.tokenizer(prompt, return_tensors="pt").input_ids.to(
hf_model.model.device
)
if prompt_embeds is not None:
embed = hf_model.model.get_input_embeddings()(token_ids)
# MiniCPM models apply scale_emb to embeddings internally.
# vLLM expects pre-scaled embeddings when using inputs_embeds.
if model in EMBED_SCALING_MODELS:
config = hf_model.model.config
embed = embed * config.scale_emb
prompt_embeds.append(embed.squeeze(0))
with vllm_runner(
model,
tokenizer_name=model_info.tokenizer or model,
tokenizer_mode=model_info.tokenizer_mode,
trust_remote_code=model_info.trust_remote_code,
max_num_seqs=2,
enable_prompt_embeds=use_prompt_embeds,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
if prompt_embeds is not None:
vllm_outputs_from_embeds = vllm_model.generate_greedy_logprobs(
prompt_embeds, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
if prompt_embeds is not None:
check_logprobs_close(
outputs_0_lst=vllm_outputs,
outputs_1_lst=vllm_outputs_from_embeds,
name_0="vllm",
name_1="vllm_from_embeds",
)
if use_rocm_aiter:
# this is to ensure that vllm engine
# has deallocated the memory before running the next
# unit tests. On ROCm, when using AITER
# the memory might not be deallocated completely
# before running the next test case
torch.cuda.synchronize()
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_mistral.py | tests/models/language/generation/test_mistral.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import copy
import json
import pytest
from vllm.sampling_params import SamplingParams
from vllm.tokenizers.mistral import MistralTokenizer
from vllm.tool_parsers.mistral_tool_parser import (
MistralToolCall,
MistralToolParser,
)
from ...utils import check_logprobs_close
MODELS = [
"mistralai/Mistral-7B-Instruct-v0.3",
]
MISTRAL_FORMAT_MODELS = [
"mistralai/Mistral-7B-Instruct-v0.3",
# uses the v3-Tekken tokenizer
"mistralai/Ministral-8B-Instruct-2410",
# Mistral-Nemo is too big for CI, but passes locally
# "mistralai/Mistral-Nemo-Instruct-2407"
]
SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
SYMBOLIC_LANG_PROMPTS = [
"勇敢な船乗りについての詩を書く", # japanese
"寫一首關於勇敢的水手的詩", # chinese
"ပုံပြင်လေးပြောပြပါ်:\n", # burmese
"Repeat the phrase 'URGENCY🌶️':\nURGENCY🌶️\nURGENCY🌶️\n", # see https://github.com/vllm-project/vllm/pull/9625
]
# for function calling
TOOLS = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. "
"'San Francisco'",
},
"state": {
"type": "string",
"description": "the two-letter abbreviation for the state that "
"the city is in, e.g. 'CA' which would mean 'California'",
},
"unit": {
"type": "string",
"description": "The unit to fetch the temperature in",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrites text",
"parameters": {
"type": "object",
"required": [],
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite.",
}
},
},
},
},
]
MSGS = [
{"role": "system", "content": "You are an assistant."},
{
"role": "user",
"content": "Could you please rewrite the below article? \n\n My English needs "
"improvving, maybe I make errors.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text":"My English needs improvving, maybe '
'I make errors."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"My English needs improving, maybe '
'I make errors."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nMy English needs improving, maybe I make errors",
},
{
"role": "user",
"content": (
"Can you tell me what the temperate will be in Dallas, in fahrenheit?"
),
},
]
SAMPLE_JSON_SCHEMA = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"skills": {
"type": "array",
"items": {"type": "string", "maxLength": 10},
"minItems": 3,
},
"work_history": {
"type": "array",
"items": {
"type": "object",
"properties": {
"company": {"type": "string"},
"duration": {"type": "number"},
"position": {"type": "string"},
},
"required": ["company", "position"],
},
},
},
"required": ["name", "age", "skills", "work_history"],
}
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
# TODO(sang): Sliding window should be tested separately.
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(model, dtype=dtype, tokenizer_mode="mistral") as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_mistral_format(
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with vllm_runner(
model,
dtype=dtype,
tokenizer_mode="mistral",
load_format="mistral",
config_format="mistral",
) as mistral_format_model:
mistral_format_outputs = mistral_format_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(
model,
dtype=dtype,
tokenizer_mode="hf",
load_format="safetensors",
config_format="hf",
) as hf_format_model:
hf_format_outputs = hf_format_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_format_outputs,
outputs_1_lst=mistral_format_outputs,
name_0="hf",
name_1="mistral",
)
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_mistral_symbolic_languages(vllm_runner, model: str, dtype: str) -> None:
with vllm_runner(
model,
dtype=dtype,
max_model_len=8192,
tokenizer_mode="mistral",
config_format="mistral",
load_format="mistral",
) as vllm_model:
for prompt in SYMBOLIC_LANG_PROMPTS:
msg = {"role": "user", "content": prompt}
outputs = vllm_model.llm.chat([msg], sampling_params=SAMPLING_PARAMS)
assert "�" not in outputs[0].outputs[0].text.strip()
@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
with vllm_runner(
model,
dtype=dtype,
tokenizer_mode="mistral",
config_format="mistral",
load_format="mistral",
) as vllm_model:
msgs = copy.deepcopy(MSGS)
outputs = vllm_model.llm.chat(
msgs, tools=TOOLS, sampling_params=SAMPLING_PARAMS
)
tokenizer = vllm_model.llm.get_tokenizer()
tool_parser = MistralToolParser(tokenizer)
model_output = outputs[0].outputs[0].text.strip()
assert model_output.startswith(tool_parser.bot_token), model_output
parsed_message = tool_parser.extract_tool_calls(model_output, None)
assert parsed_message.tools_called
assert MistralToolCall.is_valid_id(parsed_message.tool_calls[0].id)
assert parsed_message.tool_calls[0].function.name == "get_current_weather"
assert (
parsed_message.tool_calls[0].function.arguments
== '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'
) # noqa
assert parsed_message.content is None
def test_mistral_function_call_nested_json():
"""Ensure that the function-name regex captures the entire outermost
JSON block, including nested braces."""
# Create a minimal stub tokenizer that provides the few attributes the
# parser accesses (`version` and `get_vocab`).
class _StubMistralTokenizer(MistralTokenizer):
version = 11 # Satisfy the version check
def __init__(self):
pass
@staticmethod
def get_vocab():
# Provide the special TOOL_CALLS token expected by the parser.
return {"[TOOL_CALLS]": 0}
tokenizer = _StubMistralTokenizer()
parser = MistralToolParser(tokenizer)
# Craft a model output featuring nested JSON inside the arguments.
args_dict = {
"city": "Dallas",
"state": "TX",
"unit": "fahrenheit",
"sub_dict": {"foo": "bar", "inner": {"x": 1, "y": 2}},
}
model_output = f"{parser.bot_token}get_current_weather{json.dumps(args_dict)}"
parsed = parser.extract_tool_calls(model_output, None)
# Assertions: the tool call is detected and the full nested JSON is parsed
# without truncation.
assert parsed.tools_called
assert MistralToolCall.is_valid_id(parsed.tool_calls[0].id)
assert parsed.tool_calls[0].function.name == "get_current_weather"
assert json.loads(parsed.tool_calls[0].function.arguments) == args_dict
# No additional content outside the tool call should be returned.
assert parsed.content is None
# multiple calls
multiple_args_dict = [
{
"city": "Dallas",
"state": "TX",
"unit": "fahrenheit",
"sub_dict": {"foo": "bar", "inner": {"x": 1, "y": 2}},
},
{},
{"a": 0},
{"a": 1, "b": "c"},
]
names = ["get_current_weather", "get_current_weather_2", "random", "random_2"]
model_output = "".join(
[
f"{parser.bot_token}{name}{json.dumps(args)}"
for name, args in zip(names, multiple_args_dict)
]
)
parsed = parser.extract_tool_calls(model_output, None)
# Assertions: the tool call is detected and the full nested JSON is parsed
# without truncation.
assert parsed.tools_called
assert len(parsed.tool_calls) == len(multiple_args_dict)
for i, tool_call in enumerate(parsed.tool_calls):
assert MistralToolCall.is_valid_id(tool_call.id)
assert tool_call.function.name == names[i]
assert json.loads(tool_call.function.arguments) == multiple_args_dict[i]
# No additional content outside the tool call should be returned.
assert parsed.content is None
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_hybrid.py | tests/models/language/generation/test_hybrid.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Callable
import pytest
from tests.models.registry import HF_EXAMPLE_MODELS
from tests.utils import multi_gpu_test
from vllm.engine.arg_utils import EngineArgs
from vllm.sampling_params import SamplingParams
from ...utils import check_logprobs_close, check_outputs_equal
# Mark all tests as hybrid
pytestmark = pytest.mark.hybrid_model
# NOTE: The first model in each list is taken as the primary model,
# meaning that it will be used in all tests in this file
# The rest of the models will only be tested by test_models
APC_MULTIPLY_BY = 300
SSM_MODELS = [
"state-spaces/mamba-130m-hf",
"tiiuae/falcon-mamba-tiny-dev",
# mamba2-codestral in transformers is broken pending:
# https://github.com/huggingface/transformers/pull/40861
# "yujiepan/mamba2-codestral-v0.1-tiny-random",
]
HYBRID_MODELS = [
"ai21labs/Jamba-tiny-dev",
"pfnet/plamo-2-1b",
"Zyphra/Zamba2-1.2B-instruct",
"hmellor/tiny-random-BambaForCausalLM",
"ibm-granite/granite-4.0-tiny-preview",
"tiiuae/Falcon-H1-0.5B-Base",
"LiquidAI/LFM2-1.2B",
"tiny-random/qwen3-next-moe",
]
FULL_CUDA_GRAPH_MODELS = [
"ai21labs/Jamba-tiny-dev",
"pfnet/plamo-2-1b",
"Zyphra/Zamba2-1.2B-instruct",
]
FP32_STATE_MODELS = [
"state-spaces/mamba-130m-hf",
"Zyphra/Zamba2-1.2B-instruct",
]
# Avoid OOM
MAX_NUM_SEQS = 4
@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
num_logprobs: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_batching(
vllm_runner,
example_prompts,
model: str,
max_tokens: int,
num_logprobs: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
for_loop_outputs = []
with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
for prompt in example_prompts:
(single_output,) = vllm_model.generate_greedy_logprobs(
[prompt], max_tokens, num_logprobs
)
for_loop_outputs.append(single_output)
batched_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=for_loop_outputs,
outputs_1_lst=batched_outputs,
name_0="for_loop_vllm",
name_1="batched_vllm",
)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [10])
def test_chunked_prefill_with_parallel_sampling(
vllm_runner,
example_prompts,
model: str,
max_tokens: int,
) -> None:
"""
Tests chunked prefill in conjunction with n > 1.
In this case, prefill is populated with decoding tokens and
we test that it doesn't fail.
This test might fail if cache is not allocated correctly for n > 1
decoding steps inside a chunked prefill forward pass
(where we have both prefill and decode together)
"""
sampling_params = SamplingParams(n=3, temperature=1, seed=0, max_tokens=max_tokens)
with vllm_runner(
model,
enable_chunked_prefill=True,
# forces prefill chunks with decoding
max_num_batched_tokens=MAX_NUM_SEQS * 3,
max_num_seqs=MAX_NUM_SEQS,
) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [20])
def test_mamba_cache_cg_padding(
vllm_runner,
example_prompts,
model: str,
max_tokens: int,
) -> None:
"""
This test is for verifying that mamba cache is padded to CG captured
batch size. If it's not, a torch RuntimeError will be raised because
tensor dimensions aren't compatible.
"""
vllm_config = EngineArgs(model=model, trust_remote_code=True).create_engine_config()
while len(example_prompts) == vllm_config.pad_for_cudagraph(len(example_prompts)):
example_prompts.append(example_prompts[0])
try:
with vllm_runner(model) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
except RuntimeError:
pytest.fail(
"Couldn't run batch size which is not equal to a Cuda Graph "
"captured batch size. "
"Could be related to mamba cache not padded correctly"
)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
vllm_runner,
example_prompts,
model: str,
) -> None:
"""
This test is for verifying that the hybrid inner state management doesn't
collapse in case where the number of incoming requests and
finished_requests_ids is larger than the maximum mamba block capacity.
This could generally happen due to the fact that hybrid does support
statelessness mechanism where it can clean up new incoming requests in
a single step.
"""
try:
with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
except ValueError:
pytest.fail(
"Hybrid inner state wasn't cleaned up properly between"
"steps finished requests registered unnecessarily "
)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
def test_state_cleanup(
vllm_runner,
example_prompts,
model: str,
) -> None:
"""
This test is for verifying that the Hybrid state is cleaned up between
steps.
If it's not cleaned, an error would be expected.
"""
try:
with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
for _ in range(10):
vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
except ValueError:
pytest.fail(
"Hybrid inner state wasn't cleaned up between states, "
"could be related to finished_requests_ids"
)
@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_distributed_correctness(
vllm_runner,
example_prompts,
model: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with vllm_runner(
model, tensor_parallel_size=1, max_num_seqs=MAX_NUM_SEQS
) as vllm_model:
vllm_outputs_tp_1 = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(
model, tensor_parallel_size=2, max_num_seqs=MAX_NUM_SEQS
) as vllm_model:
vllm_outputs_tp_2 = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=vllm_outputs_tp_1,
outputs_1_lst=vllm_outputs_tp_2,
name_0="vllm_tp_1",
name_1="vllm_tp_2",
)
@pytest.mark.parametrize("model", FULL_CUDA_GRAPH_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_full_cuda_graph(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
num_logprobs: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", FP32_STATE_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize(
"cache_dtype_param", ["mamba_ssm_cache_dtype", "mamba_cache_dtype"]
)
def test_fp32_cache_state(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
num_logprobs: int,
cache_dtype_param: str,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(
model, max_num_seqs=MAX_NUM_SEQS, **{cache_dtype_param: "float32"}
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
# Helper functions for the APC tests
def _get_vllm_runner_params(
model: str,
max_model_len: int,
tensor_parallel_size: int = 1,
):
return {
"model_name": model,
"enable_chunked_prefill": True,
"enable_prefix_caching": False,
"max_model_len": max_model_len,
"tensor_parallel_size": tensor_parallel_size,
"gpu_memory_utilization": 0.4,
}
def _get_vLLM_output(
vllm_runner,
kwargs,
prompts,
max_tokens,
num_logprobs,
num_repetitions=1,
vllm_model=None,
):
outs = []
if vllm_model is None:
vllm_model = vllm_runner(**kwargs)
for _ in range(num_repetitions):
if num_logprobs < 0:
vllm_output = vllm_model.generate_greedy(prompts, max_tokens)
else:
vllm_output = vllm_model.generate_greedy_logprobs(
prompts, max_tokens, num_logprobs
)
outs.append(vllm_output)
return outs, vllm_model
@pytest.mark.parametrize("model", [HYBRID_MODELS[0], HYBRID_MODELS[3]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("n_repetitions", [2])
# If num_logprobs is set to -1, then the stringent version
# of the test is executed using `check_outputs_equal`
# instead of `check_logprobs_close`
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_apc_single_prompt(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
n_repetitions: int,
num_logprobs: int,
tensor_parallel_size: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
compare_operator: Callable = (
check_logprobs_close if num_logprobs > 0 else check_outputs_equal # type: ignore
)
# Sample prompts.
generated_prompts = [APC_MULTIPLY_BY * example_prompts[0]]
max_model_len = max(len(prompt) + max_tokens for prompt in generated_prompts)
vllm_runner_kwargs = _get_vllm_runner_params(
model, max_model_len, tensor_parallel_size=tensor_parallel_size
)
vllm_runner_kwargs["mamba_ssm_cache_dtype"] = "float32"
vllm_outputs_no_cache, _ = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts, max_tokens, num_logprobs
)
vllm_runner_kwargs["enable_prefix_caching"] = True
vllm_outputs_cache_rep, _ = _get_vLLM_output(
vllm_runner,
vllm_runner_kwargs,
generated_prompts,
max_tokens,
num_logprobs,
n_repetitions,
)
for r_idx, vllm_outputs_cache_itn in enumerate(vllm_outputs_cache_rep):
# In the first repetition, the caches are filled
# In the second repetition, these caches are reused
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0],
outputs_1_lst=vllm_outputs_cache_itn,
name_0="vllm_no_cache",
name_1=f"vllm_cache_it_{r_idx + 1}",
)
@pytest.mark.parametrize("model", [HYBRID_MODELS[0], HYBRID_MODELS[3]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("n_repetitions", [2])
# If num_logprobs is set to -1, then the stringent version
# of the test is executed using `check_outputs_equal`
# instead of `check_logprobs_close`
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_apc_single_prompt_block_align_alignment(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
n_repetitions: int,
num_logprobs: int,
tensor_parallel_size: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
compare_operator: Callable = (
check_logprobs_close if num_logprobs > 0 else check_outputs_equal # type: ignore
)
# Sample prompts. This custom prompt is used, as it causes the most issues
generated_prompts = ["The president of the United States is " * APC_MULTIPLY_BY]
max_model_len = max(len(prompt) + max_tokens for prompt in generated_prompts)
vllm_runner_kwargs = _get_vllm_runner_params(
model, max_model_len, tensor_parallel_size=tensor_parallel_size
)
vllm_runner_kwargs["mamba_ssm_cache_dtype"] = "float32"
vllm_outputs_no_cache, _ = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts, max_tokens, num_logprobs
)
vllm_runner_kwargs["enable_prefix_caching"] = True
with vllm_runner(**vllm_runner_kwargs) as vllm_model:
# Retrieve the default mamba state block size
mamba_block_size = vllm_model.llm.llm_engine.cache_config.mamba_block_size
# In case the hybrid model does not have the
# "mamba_block_size" assume a fixed constant
if mamba_block_size is None:
mamba_block_size = 512
mamba_block_size_multiplier = 10
for offsets in [-3, 3, mamba_block_size // 4 + 3, mamba_block_size // 2 - 3]:
vllm_runner_kwargs["max_num_batched_tokens"] = (
mamba_block_size_multiplier * mamba_block_size - offsets
)
vllm_outputs_cache_rep, _ = _get_vLLM_output(
vllm_runner,
vllm_runner_kwargs,
generated_prompts,
max_tokens,
num_logprobs,
n_repetitions,
)
# Check alignment of the output logits when using APC
for r_idx, vllm_outputs_cache_itn in enumerate(vllm_outputs_cache_rep):
# In the first repetition, the caches are filled
# In the second repetition, these caches are reused
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0],
outputs_1_lst=vllm_outputs_cache_itn,
name_0="vllm_no_cache",
name_1=f"vllm_cache_it_{r_idx + 1}",
)
@pytest.mark.parametrize("model", [HYBRID_MODELS[0], HYBRID_MODELS[3]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("n_repetitions", [2])
# If num_logprobs is set to -1, then the stringent version
# of the test is executed using `check_outputs_equal`
# instead of `check_logprobs_close`
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_apc_multiple_prompts_all_cached_outputs(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
n_repetitions: int,
num_logprobs: int,
tensor_parallel_size: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
compare_operator: Callable = (
check_logprobs_close if num_logprobs > 0 else check_outputs_equal # type: ignore
)
# Sample prompts.
generated_prompts = [APC_MULTIPLY_BY * prompt for prompt in example_prompts]
max_model_len = max(len(prompt) + max_tokens for prompt in generated_prompts)
vllm_runner_kwargs = _get_vllm_runner_params(
model, max_model_len, tensor_parallel_size=tensor_parallel_size
)
vllm_runner_kwargs["mamba_ssm_cache_dtype"] = "float32"
vllm_outputs_no_cache, _ = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts, max_tokens, num_logprobs
)
vllm_runner_kwargs["enable_prefix_caching"] = True
vllm_outputs_cache_rep, _ = _get_vLLM_output(
vllm_runner,
vllm_runner_kwargs,
generated_prompts,
max_tokens,
num_logprobs,
n_repetitions,
)
for r_idx, vllm_outputs_cache_itn in enumerate(vllm_outputs_cache_rep):
# In the first repetition, the caches are filled
# In the second repetition, these caches are reused
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0],
outputs_1_lst=vllm_outputs_cache_itn,
name_0="vllm_no_cache",
name_1=f"vllm_cache_it_{r_idx + 1}",
)
@pytest.mark.parametrize("model", [HYBRID_MODELS[0], HYBRID_MODELS[3]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("n_repetitions", [2])
# If num_logprobs is set to -1, then the stringent version
# of the test is executed using `check_outputs_equal`
# instead of `check_logprobs_close`
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_apc_multiple_prompts_block_align_alignment(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
n_repetitions: int,
num_logprobs: int,
tensor_parallel_size: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
compare_operator: Callable = (
check_logprobs_close if num_logprobs > 0 else check_outputs_equal # type: ignore
)
# Sample prompts. This custom prompt is used, as it causes the most issues
prompt_text = "The president of the United States is "
prompt_offsets = [0, 3, 7, 13, 17, 22, 25, 31]
generated_prompts = [
prompt_text[offset:] * APC_MULTIPLY_BY for offset in prompt_offsets
]
max_model_len = max(len(prompt) + max_tokens for prompt in generated_prompts)
vllm_runner_kwargs = _get_vllm_runner_params(
model, max_model_len, tensor_parallel_size
)
vllm_runner_kwargs["mamba_ssm_cache_dtype"] = "float32"
vllm_outputs_no_cache, _ = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts, max_tokens, num_logprobs
)
vllm_runner_kwargs["enable_prefix_caching"] = True
with vllm_runner(**vllm_runner_kwargs) as vllm_model:
# Retrieve the default mamba state block size
mamba_block_size = vllm_model.llm.llm_engine.cache_config.mamba_block_size
# In case the hybrid model does not have the
# "mamba_block_size" assume a fixed constant
if mamba_block_size is None:
mamba_block_size = 512
mamba_block_size_multiplier = 10
for offsets in [-3, 3, mamba_block_size // 4 + 3, mamba_block_size // 2 - 3]:
vllm_runner_kwargs["max_num_batched_tokens"] = (
mamba_block_size_multiplier * mamba_block_size - offsets
)
vllm_outputs_cache_rep, _ = _get_vLLM_output(
vllm_runner,
vllm_runner_kwargs,
generated_prompts,
max_tokens,
num_logprobs,
n_repetitions,
)
# Check alignment of the output logits when using APC
for r_idx, vllm_outputs_cache_itn in enumerate(vllm_outputs_cache_rep):
# In the first repetition, the caches are filled
# In the second repetition, these caches are reused
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0],
outputs_1_lst=vllm_outputs_cache_itn,
name_0="vllm_no_cache",
name_1=f"vllm_cache_it_{r_idx + 1}",
)
@pytest.mark.parametrize("model", [HYBRID_MODELS[0], HYBRID_MODELS[3]])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("n_repetitions", [2])
# If num_logprobs is set to -1, then the stringent version
# of the test is executed using `check_outputs_equal`
# instead of `check_logprobs_close`
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_apc_multiple_prompts_partial_cached_outputs(
hf_runner,
vllm_runner,
example_prompts,
monkeypatch,
model: str,
max_tokens: int,
n_repetitions: int,
num_logprobs: int,
tensor_parallel_size: int,
) -> None:
try:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")
except ValueError:
pass
compare_operator: Callable = (
check_logprobs_close if num_logprobs > 0 else check_outputs_equal # type: ignore
)
# Sample prompts.
generated_prompts = [APC_MULTIPLY_BY * prompt for prompt in example_prompts]
max_model_len = max(len(prompt) + max_tokens for prompt in generated_prompts)
vllm_runner_kwargs = _get_vllm_runner_params(
model, max_model_len, tensor_parallel_size=tensor_parallel_size
)
vllm_runner_kwargs["mamba_ssm_cache_dtype"] = "float32"
vllm_outputs_no_cache, _ = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts, max_tokens, num_logprobs
)
# Cache only part of all the prompts
vllm_runner_kwargs["enable_prefix_caching"] = True
vllm_outputs_partial_cache, vllm_model = _get_vLLM_output(
vllm_runner, vllm_runner_kwargs, generated_prompts[:3], max_tokens, num_logprobs
)
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0][:3],
outputs_1_lst=vllm_outputs_partial_cache[0],
name_0="vllm_no_cache",
name_1="vllm_partial_cache",
)
vllm_outputs_cache_rep, _ = _get_vLLM_output(
vllm_runner,
vllm_runner_kwargs,
generated_prompts,
max_tokens,
num_logprobs,
n_repetitions,
vllm_model=vllm_model,
)
for r_idx, vllm_outputs_cache_itn in enumerate(vllm_outputs_cache_rep):
# In the first repetition, the caches are filled
# In the second repetition, these caches are reused
compare_operator(
outputs_0_lst=vllm_outputs_no_cache[0],
outputs_1_lst=vllm_outputs_cache_itn,
name_0="vllm_no_cache",
name_1=f"vllm_cache_it_{r_idx + 1}",
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_gemma.py | tests/models/language/generation/test_gemma.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import numpy as np
import pytest
MODELS = ["google/gemma-2b", "google/gemma-2-2b", "google/gemma-3-4b-it"]
@pytest.mark.parametrize("model", MODELS)
def test_dummy_loader(vllm_runner, monkeypatch, model: str) -> None:
with monkeypatch.context() as m:
m.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
with vllm_runner(
model,
load_format="dummy",
) as llm:
if model == "google/gemma-3-4b-it":
normalizers = llm.llm.collective_rpc(
lambda self: self.model_runner.model.language_model.model.normalizer.cpu().item() # noqa: E501
)
config = llm.llm.llm_engine.model_config.hf_config.text_config
else:
normalizers = llm.llm.collective_rpc(
lambda self: self.model_runner.model.model.normalizer.cpu().item()
)
config = llm.llm.llm_engine.model_config.hf_config
assert np.allclose(normalizers, config.hidden_size**0.5, rtol=2e-3)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/__init__.py | tests/models/language/generation/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation/test_phimoe.py | tests/models/language/generation/test_phimoe.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.platforms import current_platform
from ....utils import large_gpu_test
from ...utils import check_logprobs_close
MODELS = [
"microsoft/Phi-3.5-MoE-instruct",
]
def test_phimoe_routing_function():
from vllm.model_executor.models.phimoe import phimoe_routing_function
test_case = {
0: {
"hidden_states": torch.tensor(
[1, 2, 3, 4, 5, 6, 7, 8], dtype=torch.float32, requires_grad=False
).view(4, 2),
"gating_output": torch.tensor(
[0.1, 0.2, 0.3, 0.4], dtype=torch.float32, requires_grad=False
),
"topk": 2,
"renormalize": False,
},
1: {
"hidden_states": torch.tensor(
[1, 2, 3, 4, 5, 6, 7, 8], dtype=torch.float32, requires_grad=False
).view(4, 2),
"gating_output": torch.tensor(
[0.4, 0.2, 0.3, 0.4], dtype=torch.float32, requires_grad=False
),
"topk": 2,
"renormalize": False,
},
}
ground_truth = {
0: {
"topk_weights": torch.tensor(
[1.0, 1.0], dtype=torch.float32, requires_grad=False
),
"topk_ids": torch.tensor([3, 2], dtype=torch.long, requires_grad=False),
},
1: {
"topk_weights": torch.tensor(
[0.5, 1.0], dtype=torch.float32, requires_grad=False
),
"topk_ids": torch.tensor([0, 3], dtype=torch.long, requires_grad=False),
},
}
for test_id in test_case:
topk_weights, topk_ids = phimoe_routing_function(**test_case[test_id])
assert torch.allclose(topk_weights, ground_truth[test_id]["topk_weights"])
assert torch.equal(topk_ids, ground_truth[test_id]["topk_ids"])
@pytest.mark.skipif(
condition=current_platform.is_cpu(),
reason="This test takes a lot time to run on CPU, "
"and vllm CI's disk space is not enough for this model.",
)
@large_gpu_test(min_gb=80)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs
)
with vllm_runner(model, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs
)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation_ppl_test/test_qwen.py | tests/models/language/generation_ppl_test/test_qwen.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.utils import GenerateModelInfo
from .ppl_utils import wikitext_ppl_test
MODELS = [
GenerateModelInfo("Qwen/Qwen3-0.6B"),
GenerateModelInfo("Qwen/Qwen3-0.6B-FP8"),
# transformers:
# Loading a GPTQ quantized model requires optimum, gptqmodel
# GenerateModelInfo("Qwen/Qwen3-0.6B-GPTQ-Int8"),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_ppl(hf_runner, vllm_runner, model_info: GenerateModelInfo):
wikitext_ppl_test(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation_ppl_test/ppl_utils.py | tests/models/language/generation_ppl_test/ppl_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/docs/transformers/perplexity
from typing import cast
import torch
from datasets import load_dataset
import tests.ci_envs as ci_envs
from tests.models.utils import (
GenerateModelInfo,
TokensTextLogprobsPromptLogprobs,
get_vllm_extra_kwargs,
)
from vllm.logprobs import Logprob
# See #24485
PPL_TOL = 0.01
MAX_LENGTH = 1024
@torch.inference_mode
def wikitext_ppl_test(
hf_runner,
vllm_runner,
model_info: GenerateModelInfo,
max_length=MAX_LENGTH,
vllm_extra_kwargs=None,
atol=PPL_TOL,
):
vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
with vllm_runner(
model_info.name,
gpu_memory_utilization=0.7,
max_model_len=max_length,
max_num_seqs=1,
**vllm_extra_kwargs,
) as vllm_model:
# Use max_num_seqs=1 to avoid OOM,
# and avoid batch different requests together.
model_config = vllm_model.llm.llm_engine.model_config
# Confirm whether vllm is using the correct architecture
if model_info.architecture:
assert model_info.architecture in model_config.architectures
max_length = min(model_config.max_model_len - 1, max_length)
stride = max_length
tokenizer = vllm_model.llm.get_tokenizer()
tokens = tokenizer.encode("\n\n".join(dataset["text"]))
n_tokens = len(tokens)
chunks = []
for begin_loc in range(0, n_tokens, stride):
end_loc = min(begin_loc + max_length, n_tokens)
chunks.append(tokens[begin_loc:end_loc])
outputs = vllm_model.generate_greedy_logprobs(
prompts=chunks,
max_tokens=1,
num_logprobs=None,
num_prompt_logprobs=0,
use_tqdm=False,
)
nll_sum = torch.tensor(0.0, dtype=torch.float32, device="cpu")
n_tokens = 0
for output in outputs:
output = cast(TokensTextLogprobsPromptLogprobs, output)
token_datas = cast(list[dict[int, Logprob] | None], output[3])
assert token_datas[0] is None
token_log_probs = []
for token_data in token_datas[1:]:
assert token_data is not None
assert len(token_data) == 1
token_log_prob = list(token_data.values())[0].logprob
token_log_probs.append(token_log_prob)
neg_log_likelihood = -torch.tensor(
token_log_probs, dtype=torch.float32, device="cpu"
).sum()
nll_sum += neg_log_likelihood
n_tokens += len(token_log_probs)
vllm_ppl = float(torch.exp(nll_sum / n_tokens))
vllm_dtype = model_config.dtype
head_dtype = model_config.head_dtype
# Accelerate ppl test by setting Transformers ppl score to a constant
if model_info.hf_ppl is None:
with hf_runner(
model_info.name,
dtype=ci_envs.VLLM_CI_HF_DTYPE or model_info.hf_dtype,
) as hf_model:
nll_sum = torch.tensor(0.0, dtype=torch.float32, device="cpu")
n_tokens = 0
for chunk in chunks:
inputs = hf_model.wrap_device({"input_ids": torch.tensor([chunk])})
input_ids = inputs["input_ids"]
outputs = hf_model.model(input_ids, labels=input_ids)
neg_log_likelihood = outputs.loss
neg_log_likelihood = neg_log_likelihood.to(torch.float32).cpu()
num_loss_tokens = len(chunk) - 1
nll_sum += neg_log_likelihood * num_loss_tokens
n_tokens += num_loss_tokens
hf_ppl = float(torch.exp(nll_sum / n_tokens))
hf_dtype = next(hf_model.model.parameters()).dtype
else:
hf_ppl = model_info.hf_ppl
hf_dtype = "Constant"
differ = (vllm_ppl - hf_ppl) / hf_ppl
print("Model:", model_info.name)
print("VLLM:", f"dtype:{vllm_dtype}", f"head_dtype:{head_dtype}", vllm_ppl)
print("Transformers:", hf_dtype, hf_ppl)
print("Difference (%):", differ * 100)
# PPL the smaller, the better
# We are not concerned that the vllm PPL is less than Transformers,
# so we only perform one-sided testing.
assert differ < atol
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation_ppl_test/test_gpt.py | tests/models/language/generation_ppl_test/test_gpt.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.utils import GenerateModelInfo
from .ppl_utils import wikitext_ppl_test
MODELS = [GenerateModelInfo("openai-community/gpt2-large")]
@pytest.mark.parametrize("model_info", MODELS)
def test_ppl(hf_runner, vllm_runner, model_info: GenerateModelInfo):
wikitext_ppl_test(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation_ppl_test/test_gemma.py | tests/models/language/generation_ppl_test/test_gemma.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from tests.models.utils import GenerateModelInfo
from .ppl_utils import wikitext_ppl_test
MODELS = [
GenerateModelInfo("google/gemma-2b"),
GenerateModelInfo("google/gemma-2-2b"),
GenerateModelInfo("google/gemma-3-4b-it"),
]
@pytest.mark.parametrize("model_info", MODELS)
def test_ppl(hf_runner, vllm_runner, model_info: GenerateModelInfo):
wikitext_ppl_test(hf_runner, vllm_runner, model_info)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/models/language/generation_ppl_test/__init__.py | tests/models/language/generation_ppl_test/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_ca_buffer_sharing.py | tests/distributed/test_ca_buffer_sharing.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# can only run on machines with p2p access across GPUs
# can only run with torchrun:
# torchrun --nproc_per_node=2 tests/distributed/test_ca_buffer_sharing.py
import ctypes
import torch
import torch.distributed as dist
from vllm.distributed.device_communicators.cuda_wrapper import CudaRTLibrary
from vllm.distributed.device_communicators.custom_all_reduce import ( # noqa
CustomAllreduce,
)
# create a cpu process group for communicating metadata (ipc handle)
dist.init_process_group(backend="gloo")
rank = local_rank = dist.get_rank()
world_size = dist.get_world_size()
# every process sets its own device (differently)
lib = CudaRTLibrary()
lib.cudaSetDevice(rank)
buffer_size_in_bytes = 1024
byte_value = 2 # the value we write to the buffer for verification
pointers = CustomAllreduce.create_shared_buffer(buffer_size_in_bytes)
print(f"Rank {rank} has pointers {pointers}")
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
# the first rank tries to write to all buffers
for p in pointers:
pointer = ctypes.c_void_p(p)
lib.cudaMemset(pointer, byte_value, buffer_size_in_bytes)
dist.barrier()
torch.cuda.synchronize()
host_data = (ctypes.c_char * buffer_size_in_bytes)()
# all ranks read from all buffers, and check if the data is correct
for p in pointers:
pointer = ctypes.c_void_p(p)
lib.cudaMemcpy(host_data, pointer, buffer_size_in_bytes)
for i in range(buffer_size_in_bytes):
assert ord(host_data[i]) == byte_value, (
f"Rank {rank} failed"
f" to verify buffer {p}. Expected {byte_value}, "
f"got {ord(host_data[i])}"
)
print(f"Rank {rank} verified all buffers")
dist.barrier()
torch.cuda.synchronize()
CustomAllreduce.free_shared_buffer(pointers)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_node_count.py | tests/distributed/test_node_count.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
import torch.distributed as dist
from vllm.distributed.parallel_state import _node_count
from vllm.distributed.utils import StatelessProcessGroup
from vllm.utils.network_utils import get_ip, get_open_port
if __name__ == "__main__":
dist.init_process_group(backend="gloo")
rank = dist.get_rank()
world_size = dist.get_world_size()
if rank == 0:
port = get_open_port()
ip = get_ip()
dist.broadcast_object_list([ip, port], src=0)
else:
recv = [None, None]
dist.broadcast_object_list(recv, src=0)
ip, port = recv
stateless_pg = StatelessProcessGroup.create(ip, port, rank, world_size)
for pg in [dist.group.WORLD, stateless_pg]:
test_result = _node_count(pg)
# Expected node count based on environment variable)
expected = int(os.environ.get("NUM_NODES", "1"))
assert test_result == expected, f"Expected {expected} nodes, got {test_result}"
if pg == dist.group.WORLD:
print(
f"Node count test passed! Got {test_result} nodes "
f"when using torch distributed!"
)
else:
print(
f"Node count test passed! Got {test_result} nodes "
f"when using StatelessProcessGroup!"
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_quick_all_reduce.py | tests/distributed/test_quick_all_reduce.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import multiprocessing
import random
import pytest
import ray
import torch
import torch.distributed as dist
from vllm import _custom_ops as ops
from vllm.distributed.communication_op import tensor_model_parallel_all_reduce # noqa
from vllm.distributed.parallel_state import get_tp_group, graph_capture
from vllm.platforms import current_platform
from ..utils import (
ensure_model_parallel_initialized,
init_test_distributed_environment,
multi_process_parallel,
)
torch.manual_seed(42)
random.seed(44)
# Size over 8MB is sufficient for custom quick allreduce.
test_sizes = [random.randint(8 * 1024 * 1024, 10 * 1024 * 1024) for _ in range(8)]
for i, v in enumerate(test_sizes):
test_sizes[i] -= v % 8
@ray.remote(num_gpus=1, max_calls=1)
def graph_quickreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pp_size,
rank,
distributed_init_port,
):
with monkeypatch.context() as m:
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
ensure_model_parallel_initialized(tp_size, pp_size)
group = get_tp_group().device_group
# A small all_reduce for warmup.
# this is needed because device communicators might be created lazily
# (e.g. NCCL). This will ensure that the communicator is initialized
# before any communication happens, so that this group can be used for
# graph capture immediately.
data = torch.zeros(1)
data = data.to(device=device)
torch.distributed.all_reduce(data, group=group)
torch.cuda.synchronize()
del data
# we use the first group to communicate once
# and the second group to communicate twice
# and so on
# this is used to demonstrate that each group can
# communicate independently
num_communication = rank // tp_size + 1
for sz in test_sizes:
for dtype in [torch.float16, torch.bfloat16]:
with graph_capture(device=device) as graph_capture_context:
inp1 = torch.randint(
1, 23, (sz,), dtype=dtype, device=torch.cuda.current_device()
)
inp2 = torch.randint(
-23, 1, (sz,), dtype=dtype, device=torch.cuda.current_device()
)
torch.cuda.synchronize()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph, stream=graph_capture_context.stream):
for _ in range(num_communication):
out1 = tensor_model_parallel_all_reduce(inp1)
dist.all_reduce(inp1, group=group)
out2 = tensor_model_parallel_all_reduce(inp2)
dist.all_reduce(inp2, group=group)
graph.replay()
torch.testing.assert_close(out1, inp1, atol=2.5, rtol=0.1)
torch.testing.assert_close(out2, inp2, atol=2.5, rtol=0.1)
@ray.remote(num_gpus=1, max_calls=1)
def eager_quickreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pp_size,
rank,
distributed_init_port,
):
with monkeypatch.context() as m:
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
# Size over 8MB is sufficient for custom quick allreduce.
sz = 16 * 1024 * 1024
fa = get_tp_group().device_communicator.qr_comm
inp = torch.tensor(
[1.0 * ((i) % 23) for i in range(sz)], dtype=torch.float16, device=device
)
out = fa.quick_all_reduce(inp)
torch.testing.assert_close(out, inp * tp_size, atol=2.5, rtol=0.1)
inp = torch.tensor(
[1.0 * ((i) % 23) for i in range(sz)], dtype=torch.bfloat16, device=device
)
out = fa.quick_all_reduce(inp)
torch.testing.assert_close(out, inp * tp_size, atol=2.5, rtol=0.1)
@pytest.mark.skipif(
not current_platform.is_rocm(), reason="only test quick allreduce for rocm"
)
@pytest.mark.parametrize("quant_mode", ["FP", "INT8", "INT6", "INT4"])
@pytest.mark.parametrize("tp_size", [2])
@pytest.mark.parametrize("pipeline_parallel_size", [1, 2])
@pytest.mark.parametrize("test_target", [graph_quickreduce, eager_quickreduce])
def test_custom_quick_allreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pipeline_parallel_size,
test_target,
quant_mode,
):
world_size = tp_size * pipeline_parallel_size
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
monkeypatch.setenv("VLLM_ROCM_QUICK_REDUCE_QUANTIZATION", quant_mode)
multi_process_parallel(monkeypatch, tp_size, pipeline_parallel_size, test_target)
def qr_variable_input(rank, world_size):
"""
When the tensor parallelism is set to 4 or 8, frequent changes
in the input shape can cause QuickReduce to hang (this issue
has been observed with the gpt_oss model).
"""
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
qr_max_size = None # MB
_ptr = ops.init_custom_qr(rank, world_size, qr_max_size)
ranks = []
for i in range(world_size):
ranks.append(i)
dist.init_process_group(
backend="nccl",
init_method="tcp://127.0.0.1:29500",
rank=rank,
world_size=world_size,
)
cpu_group = torch.distributed.new_group(ranks, backend="nccl")
handle = ops.qr_get_handle(_ptr)
world_size = dist.get_world_size(group=cpu_group)
handles = [None] * world_size
dist.all_gather_object(handles, handle, group=cpu_group)
ops.qr_open_handles(_ptr, handles)
num = 1
s1 = 1024
while num < 50000: # 50000 is sufficient to identify issues.
dtype = torch.float16
if num % 2 == 0:
s2 = 1024
inp1 = torch.zeros(
(s1, s2), dtype=dtype, device=torch.cuda.current_device()
)
else:
s2 = 2048
inp1 = torch.ones((s1, s2), dtype=dtype, device=torch.cuda.current_device())
result = torch.empty_like(inp1)
# FP = 0 INT8 = 1 INT6 = 2 INT4 = 3 NONE = 4
ops.qr_all_reduce(_ptr, inp1, result, 3, cast_bf2half=True)
try:
if inp1[0, 0] == 0:
assert torch.all(result == 0)
else:
assert torch.all(result == world_size)
except AssertionError:
print("Assertion failed! Allreduce results are incorrect.")
raise
num += 1
@pytest.mark.skipif(
not current_platform.is_rocm(), reason="only test quick allreduce for rocm"
)
@pytest.mark.parametrize("tp_size", [4, 8])
@pytest.mark.parametrize("pipeline_parallel_size", [1])
def test_custom_quick_allreduce_variable_input(tp_size, pipeline_parallel_size):
world_size = tp_size * pipeline_parallel_size
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
multiprocessing.set_start_method("spawn", force=True)
# 60s is enough
timeout = 60
processes = []
for rank in range(tp_size):
p = multiprocessing.Process(target=qr_variable_input, args=(rank, tp_size))
p.start()
processes.append((rank, p))
for rank, p in processes:
p.join(timeout=timeout)
if p.is_alive():
for r, proc in processes:
if proc.is_alive():
proc.terminate()
proc.join()
raise RuntimeError(f"QuickReduce hang detected after {timeout} seconds!")
if __name__ == "__main__":
test_custom_quick_allreduce_variable_input(tp_size=4, pipeline_parallel_size=1)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_eplb_fused_moe_layer_dep_nvfp4.py | tests/distributed/test_eplb_fused_moe_layer_dep_nvfp4.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Test that the interaction between EPLB and FusedMoE Layer is okay for DP w/ NVFP4
from dataclasses import dataclass
import pytest
import torch
from tests.kernels.moe.utils import make_test_quant_config
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.distributed.eplb.rebalance_execute import rearrange_expert_weights_inplace
from vllm.distributed.parallel_state import (
ensure_model_parallel_initialized,
get_dp_group,
)
from vllm.forward_context import set_forward_context
from vllm.model_executor.layers.fused_moe.layer import FusedMoE
from vllm.model_executor.layers.quantization.modelopt import (
ModelOptNvFp4Config,
ModelOptNvFp4FusedMoE,
)
from .eplb_utils import distributed_run, set_env_vars_and_device
@dataclass
class TestConfig:
num_layers: int
num_experts: int
num_local_experts: int
num_topk: int
hidden_size: int
intermediate_size: int
num_tokens: int
def make_fused_moe_layer(
rank: int,
layer_idx: int,
test_config: TestConfig,
) -> FusedMoE:
quant_config = None
device = torch.device(f"cuda:{rank}")
quant_config = ModelOptNvFp4Config(
is_checkpoint_nvfp4_serialized=True,
kv_cache_quant_algo=None,
exclude_modules=[],
)
fml = FusedMoE(
num_experts=test_config.num_experts,
top_k=test_config.num_topk,
hidden_size=test_config.hidden_size,
intermediate_size=test_config.intermediate_size,
prefix=f"dummy_layer_{layer_idx}",
activation="silu",
is_act_and_mul=True,
params_dtype=torch.bfloat16,
quant_config=quant_config,
)
nvfp4_fused_moe = ModelOptNvFp4FusedMoE(quant_config, fml)
nvfp4_fused_moe.create_weights(
fml,
test_config.num_local_experts,
test_config.hidden_size,
test_config.intermediate_size,
params_dtype=torch.uint8,
global_num_experts=test_config.num_experts,
)
fml = fml.to(device)
w1_q, w2_q, quant_config = make_test_quant_config(
test_config.num_local_experts,
test_config.intermediate_size,
test_config.hidden_size,
in_dtype=torch.bfloat16,
quant_dtype="nvfp4",
block_shape=None,
per_act_token_quant=False,
)
fml.w13_weight.data = w1_q
fml.w2_weight.data = w2_q
fml.w2_input_scale.data = torch.randn_like(fml.w2_input_scale.data) / 5
fml.w13_input_scale.data = torch.randn_like(fml.w13_input_scale.data) / 5
fml.w2_weight_scale_2.data = torch.randn_like(fml.w2_weight_scale_2.data) / 5
fml.w13_weight_scale_2.data = torch.randn_like(fml.w13_weight_scale_2.data) / 5
fml.w2_weight_scale.data = (
torch.randn(fml.w2_weight_scale.data.shape, device=device) / 5
).to(fml.w2_weight_scale.data.dtype)
fml.w13_weight_scale.data = (
torch.randn(fml.w13_weight_scale.data.shape, device=device) / 5
).to(fml.w13_weight_scale.data.dtype)
nvfp4_fused_moe.process_weights_after_loading(fml)
fml.maybe_init_modular_kernel()
return fml
def _test_eplb_fml(env, world_size: int, test_config: TestConfig):
set_env_vars_and_device(env)
vllm_config = VllmConfig()
vllm_config.parallel_config.data_parallel_size = world_size
vllm_config.parallel_config.enable_expert_parallel = True
with set_current_vllm_config(vllm_config):
ensure_model_parallel_initialized(
tensor_model_parallel_size=1, pipeline_model_parallel_size=1
)
ep_group = get_dp_group().cpu_group
ep_rank = torch.distributed.get_rank()
device = torch.device(f"cuda:{ep_rank}")
fml_layers = [
make_fused_moe_layer(ep_rank, layer_idx, test_config).to(device)
for layer_idx in range(test_config.num_layers)
]
rank_expert_weights = [fml.get_expert_weights() for fml in fml_layers]
hidden_states = []
router_logits = []
for layer_idx in range(test_config.num_layers):
hidden_states.append(
torch.randn(
(test_config.num_tokens, test_config.hidden_size),
dtype=torch.bfloat16,
device=device,
)
)
router_logits.append(
torch.randn(
(test_config.num_tokens, test_config.num_experts),
dtype=torch.bfloat16,
device=device,
)
)
out_before_shuffle = []
with set_forward_context(
{},
num_tokens=test_config.num_tokens,
num_tokens_across_dp=torch.tensor(
[test_config.num_tokens] * world_size, device="cpu", dtype=torch.int
),
vllm_config=vllm_config,
):
for lidx, fml in enumerate(fml_layers):
out_before_shuffle.append(
fml(hidden_states[lidx].clone(), router_logits[lidx].clone())
)
indices = torch.zeros(
test_config.num_layers, test_config.num_experts, dtype=torch.long
)
for lidx in range(test_config.num_layers):
indices[lidx] = torch.Tensor(range(test_config.num_experts))
shuffled_indices = torch.zeros_like(indices)
for lidx in range(test_config.num_layers):
shuffled_indices[lidx] = torch.randperm(test_config.num_experts)
rearrange_expert_weights_inplace(
indices,
shuffled_indices,
rank_expert_weights,
ep_group,
is_profile=False,
)
num_global_experts = test_config.num_experts
logical_to_physical_map_list = []
for lidx, fml in enumerate(fml_layers):
physical_to_logical_map = shuffled_indices[lidx].to(device)
logical_to_physical_map = torch.empty(
(num_global_experts,), dtype=torch.int32, device=device
)
logical_to_physical_map[physical_to_logical_map] = torch.arange(
0, num_global_experts, dtype=torch.int32, device=device
)
logical_to_physical_map_list.append(
logical_to_physical_map.reshape(num_global_experts, 1)
)
logical_to_physical_map = torch.stack(logical_to_physical_map_list)
for lidx, fml in enumerate(fml_layers):
logical_replica_count = torch.ones(
(test_config.num_layers, num_global_experts),
dtype=torch.int32,
device=device,
)
fml.enable_eplb = True
fml.set_eplb_state(
lidx,
torch.zeros(
(test_config.num_layers, num_global_experts),
dtype=torch.int32,
device=device,
),
logical_to_physical_map,
logical_replica_count,
)
out_after_shuffle = []
with set_forward_context(
{},
num_tokens=test_config.num_tokens,
num_tokens_across_dp=torch.tensor(
[test_config.num_tokens] * world_size, device="cpu", dtype=torch.int
),
vllm_config=vllm_config,
):
for lidx, fml in enumerate(fml_layers):
out_after_shuffle.append(
fml(hidden_states[lidx].clone(), router_logits[lidx].clone())
)
for lidx in range(test_config.num_layers):
torch.testing.assert_close(
out_before_shuffle[lidx], out_after_shuffle[lidx], atol=1e-1, rtol=1e-1
)
@pytest.mark.parametrize("world_size", [2, 4])
@pytest.mark.parametrize("num_layers", [8])
@pytest.mark.parametrize("num_experts", [32])
@pytest.mark.parametrize("hidden_size", [256])
@pytest.mark.parametrize("intermediate_size", [256])
@pytest.mark.parametrize("num_tokens", [256])
@pytest.mark.parametrize("backend", ["latency", "throughput"])
def test_eplb_fml(
world_size: int,
num_layers: int,
num_experts: int,
hidden_size: int,
intermediate_size: int,
num_tokens: int,
backend: str,
monkeypatch,
):
monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1")
monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", backend)
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
num_local_experts = num_experts // world_size
num_topk = 4
test_config = TestConfig(
num_layers=num_layers,
num_experts=num_experts,
num_local_experts=num_local_experts,
num_topk=num_topk,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
num_tokens=num_tokens,
)
distributed_run(
_test_eplb_fml,
world_size,
test_config,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_eplb_execute.py | tests/distributed/test_eplb_execute.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import random
import pytest
import torch
import torch.distributed
from vllm.distributed.eplb.rebalance_execute import (
move_from_buffer,
rearrange_expert_weights_inplace,
transfer_layer,
)
from vllm.distributed.parallel_state import (
ensure_model_parallel_initialized,
get_tp_group,
)
from .eplb_utils import distributed_run, set_env_vars_and_device
def create_expert_indices_with_redundancy(
num_layers: int,
num_logical_experts: int,
total_physical_experts: int,
redundancy_config: list[int], # redundancy for each logical expert
) -> torch.Tensor:
"""
Create expert indices with redundancy.
Args:
num_layers: number of layers
num_logical_experts: number of logical experts
total_physical_experts: total number of physical experts
redundancy_config: redundancy for each logical expert
Returns:
indices: Shape (num_layers, total_physical_experts)
"""
assert sum(redundancy_config) == total_physical_experts
assert len(redundancy_config) == num_logical_experts
indices = torch.zeros(num_layers, total_physical_experts, dtype=torch.long)
for layer in range(num_layers):
physical_pos = 0
for logical_expert_id, redundancy in enumerate(redundancy_config):
for _ in range(redundancy):
indices[layer, physical_pos] = logical_expert_id
physical_pos += 1
# Shuffle the indices at dim 1
for layer in range(num_layers):
indices[layer] = indices[layer][torch.randperm(indices.shape[1])]
return indices
def create_expert_weights(
num_layers: int,
num_local_experts: int,
hidden_sizes: list[int],
rank: int,
device: torch.device,
physical_to_logical_mapping: torch.Tensor,
) -> list[list[torch.Tensor]]:
"""
Create fake expert weights tensor for testing.
Use `arange` to generate predictable weights values, based on logical
expert ID.
All replicas of the same logical expert should have the same weights.
Args:
physical_to_logical_mapping: Shape (num_layers, num_local_experts)
mapping[layer, physical_pos] = logical_expert_id
"""
expert_weights = []
for layer in range(num_layers):
layer_weights = []
for weight_idx, hidden_size in enumerate(hidden_sizes):
weight_tensor = torch.zeros(
num_local_experts, hidden_size, device=device, dtype=torch.float32
)
for local_expert in range(num_local_experts):
# Get the logical expert ID for this physical expert
global_pos = rank * num_local_experts + local_expert
logical_expert_id = physical_to_logical_mapping[
layer, global_pos
].item()
# Generate weights based on logical expert ID
# (so that all replicas of the same logical expert have the
# same weights)
base_value = logical_expert_id * 1000 + layer * 100 + weight_idx * 10
weight_tensor[local_expert] = torch.arange(
base_value,
base_value + hidden_size,
device=device,
dtype=torch.float32,
)
layer_weights.append(weight_tensor)
expert_weights.append(layer_weights)
return expert_weights
def create_redundancy_config(
num_logical_experts: int,
num_physical_experts: int,
) -> list[int]:
"""Create a redundancy configuration."""
redundancy_config = [1] * num_logical_experts
remaining = num_physical_experts - num_logical_experts
# Randomly assign the remaining physical experts to the logical experts
for _ in range(remaining):
redundancy_config[random.choice(range(num_logical_experts))] += 1
return redundancy_config
def verify_expert_weights_after_shuffle(
expert_weights: list[list[torch.Tensor]],
new_indices: torch.Tensor,
hidden_sizes: list[int],
ep_rank: int,
num_local_experts: int,
):
"""Verify the weights after shuffling are correct."""
num_layers = len(expert_weights)
for layer in range(num_layers):
for weight_idx, hidden_size in enumerate(hidden_sizes):
weight_tensor = expert_weights[layer][weight_idx]
for local_expert in range(num_local_experts):
# Calculate the global expert ID for this local expert
global_pos = ep_rank * num_local_experts + local_expert
expected_logical_expert = new_indices[layer, global_pos].item()
# Check if the weights are correct
actual_weights = weight_tensor[local_expert]
expected_base = (
expected_logical_expert * 1000 + layer * 100 + weight_idx * 10
)
expected_weights = torch.arange(
expected_base,
expected_base + hidden_size,
device=actual_weights.device,
dtype=actual_weights.dtype,
)
torch.testing.assert_close(
actual_weights,
expected_weights,
msg=f"Layer {layer}, weight {weight_idx},"
f"local expert {local_expert}: "
f"weights do not match. "
f"Expected logical expert {expected_logical_expert}",
)
def verify_redundant_experts_have_same_weights(
expert_weights: list[list[torch.Tensor]],
indices: torch.Tensor,
hidden_sizes: list[int],
world_size: int,
num_local_experts: int,
):
"""
Verify that all replicas of the same logical expert have the same weights.
"""
num_layers = len(expert_weights)
total_physical_experts = world_size * num_local_experts
for layer in range(num_layers):
# Collect weights for all physical experts for each weight matrix
all_weights: list[torch.Tensor] = []
for weight_idx, hidden_size in enumerate(hidden_sizes):
# Create tensor to store all expert weights
# Shape: [total_physical_experts, hidden_size]
gathered_weights = torch.zeros(
total_physical_experts,
hidden_size,
device=expert_weights[layer][weight_idx].device,
dtype=expert_weights[layer][weight_idx].dtype,
)
# Use all_gather to collect expert weights from current node
# expert_weights[layer][weight_idx] shape:
# [num_local_experts, hidden_size]
local_weights = expert_weights[layer][
weight_idx
] # [num_local_experts, hidden_size]
# Split tensor along dim 0 into a list for all_gather
gathered_weights_list = torch.chunk(gathered_weights, world_size, dim=0)
torch.distributed.all_gather(
# Output list: each element corresponds to one rank's weights
list(gathered_weights_list),
local_weights, # Input: current rank's local weights
)
all_weights.append(gathered_weights)
# Verify that all replicas of the same logical expert have the same
# weights
logical_expert_weights: dict[int, dict[int, torch.Tensor]] = {}
for physical_pos in range(total_physical_experts):
logical_expert_id = int(indices[layer, physical_pos].item())
if logical_expert_id not in logical_expert_weights:
# First time encountering this logical expert, save its weights
logical_expert_weights[logical_expert_id] = {
weight_idx: all_weights[weight_idx][physical_pos]
for weight_idx in range(len(hidden_sizes))
}
else:
# Verify that current physical expert's weights match the
# previously saved logical expert weights
for weight_idx in range(len(hidden_sizes)):
torch.testing.assert_close(
all_weights[weight_idx][physical_pos],
logical_expert_weights[logical_expert_id][weight_idx],
msg=f"Layer {layer}, weight {weight_idx},"
f"logical expert {logical_expert_id}: "
f"Physical expert {physical_pos} has different weights"
f"than expected",
)
def _test_async_transfer_layer_without_mtp_worker(
env,
world_size: int,
num_layers: int,
num_local_experts: int,
num_logical_experts: int,
) -> None:
set_env_vars_and_device(env)
ensure_model_parallel_initialized(
tensor_model_parallel_size=world_size, pipeline_model_parallel_size=1
)
tp_group = get_tp_group()
ep_group = tp_group.device_group
ep_rank = torch.distributed.get_rank()
device = torch.device(f"cuda:{ep_rank}")
total_physical_experts = world_size * num_local_experts
hidden_sizes = [16, 32]
redundancy_config = create_redundancy_config(
num_logical_experts,
total_physical_experts,
)
old_indices = create_expert_indices_with_redundancy(
num_layers,
num_logical_experts,
total_physical_experts,
redundancy_config,
)
new_redundancy_config = create_redundancy_config(
num_logical_experts,
total_physical_experts,
)
new_indices = create_expert_indices_with_redundancy(
num_layers,
num_logical_experts,
total_physical_experts,
new_redundancy_config,
)
expert_weights = create_expert_weights(
num_layers,
num_local_experts,
hidden_sizes,
ep_rank,
device,
old_indices,
)
expert_buffer = [torch.empty_like(w) for w in expert_weights[0]]
cuda_stream = torch.cuda.Stream(device=device)
for layer_idx in range(num_layers):
is_unchanged, is_received_locally, experts_recv_loc = asyncio.run(
transfer_layer(
old_global_expert_indices=old_indices,
new_global_expert_indices=new_indices,
expert_weights=expert_weights,
expert_weights_buffer=expert_buffer,
ep_group=ep_group,
layer=layer_idx,
cuda_stream=cuda_stream,
)
)
cuda_stream.synchronize()
move_from_buffer(
expert_weights=expert_weights[layer_idx],
expert_weights_buffer=expert_buffer,
is_unchanged=is_unchanged,
is_received_locally=is_received_locally,
experts_recv_loc=experts_recv_loc,
new_indices=new_indices[layer_idx].tolist(),
ep_group=ep_group,
)
verify_expert_weights_after_shuffle(
expert_weights,
new_indices,
hidden_sizes,
ep_rank,
num_local_experts,
)
verify_redundant_experts_have_same_weights(
expert_weights,
new_indices,
hidden_sizes,
world_size,
num_local_experts,
)
def _test_rearrange_expert_weights_with_redundancy(
env, world_size, num_layers, num_local_experts, num_logical_experts
) -> None:
# Initialize model parallel (using tensor parallel as an entrypoint
# to expert parallel)
set_env_vars_and_device(env)
ensure_model_parallel_initialized(
tensor_model_parallel_size=world_size, pipeline_model_parallel_size=1
)
ep_group = get_tp_group().cpu_group
ep_rank = torch.distributed.get_rank()
device = torch.device(f"cuda:{ep_rank}")
# Test parameters
total_physical_experts = world_size * num_local_experts
hidden_sizes = [32, 64] # Two different weight matrices
# Create old expert indices (with redundancy)
redundancy_config = create_redundancy_config(
num_logical_experts, total_physical_experts
)
old_indices = create_expert_indices_with_redundancy(
num_layers,
num_logical_experts,
total_physical_experts,
redundancy_config,
)
# Create new expert indices (with redundancy)
new_redundancy_config = create_redundancy_config(
num_logical_experts, total_physical_experts
)
new_indices = create_expert_indices_with_redundancy(
num_layers,
num_logical_experts,
total_physical_experts,
new_redundancy_config,
)
# Create expert weights
expert_weights = create_expert_weights(
num_layers, num_local_experts, hidden_sizes, ep_rank, device, old_indices
)
# Execute weight rearrangement
rearrange_expert_weights_inplace(
old_indices,
new_indices,
expert_weights,
ep_group,
is_profile=False,
)
# Verify the rearrangement result
verify_expert_weights_after_shuffle(
expert_weights,
new_indices,
hidden_sizes,
ep_rank,
num_local_experts,
)
verify_redundant_experts_have_same_weights(
expert_weights,
new_indices,
hidden_sizes,
world_size,
num_local_experts,
)
@pytest.mark.parametrize(
"world_size,num_layers,num_local_experts,num_logical_experts",
[
# 2 GPU, 2 experts per GPU
# 3 logical experts, 4 physical experts, 1 redundant experts
(2, 1, 2, 3),
# 2 GPU, 3 experts per GPU
# 4 logical experts, 6 physical experts, 2 redundant experts
(2, 2, 3, 4),
# 2 GPU, 8 experts per GPU
# 16 logical experts, 16 physical experts, 0 redundant experts
(2, 4, 8, 16),
# 4 GPU, 2 experts per GPU
# 6 logical experts, 8 physical experts, 2 redundant experts
(4, 1, 2, 6),
# 4 GPU, 2 experts per GPU
# 5 logical experts, 8 physical experts, 3 redundant experts
(4, 2, 2, 5),
# 4 GPU, 8 experts per GPU
# 16 logical experts, 32 physical experts, 16 redundant experts
(4, 8, 8, 16),
],
)
def test_rearrange_expert_weights_with_redundancy(
world_size, num_layers, num_local_experts, num_logical_experts
):
"""Test the functionality of rearranging expert weights with redundancy."""
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
distributed_run(
_test_rearrange_expert_weights_with_redundancy,
world_size,
num_layers,
num_local_experts,
num_logical_experts,
)
def _test_rearrange_expert_weights_no_change(env, world_size) -> None:
set_env_vars_and_device(env)
ensure_model_parallel_initialized(
tensor_model_parallel_size=world_size, pipeline_model_parallel_size=1
)
ep_group = get_tp_group().cpu_group
ep_rank = torch.distributed.get_rank()
device = torch.device(f"cuda:{ep_rank}")
num_layers = 2
num_local_experts = 2
total_physical_experts = world_size * num_local_experts
num_logical_experts = total_physical_experts // 2 # Some redundancy
hidden_sizes = [32, 64]
# Create redundancy configuration
redundancy_config = [2] * num_logical_experts
# Same indices - no change
indices = create_expert_indices_with_redundancy(
num_layers, num_logical_experts, total_physical_experts, redundancy_config
)
expert_weights = create_expert_weights(
num_layers, num_local_experts, hidden_sizes, ep_rank, device, indices
)
# Save original weights
original_weights = []
for layer_weights in expert_weights:
layer_copy = []
for weight in layer_weights:
layer_copy.append(weight.clone())
original_weights.append(layer_copy)
# Execute rearrangement (should be no change)
rearrange_expert_weights_inplace(
indices,
indices, # Same indices
expert_weights,
ep_group,
is_profile=False,
)
# Verify that the weights have not changed
for layer in range(num_layers):
for weight_idx in range(len(hidden_sizes)):
torch.testing.assert_close(
expert_weights[layer][weight_idx],
original_weights[layer][weight_idx],
msg=f"""Layer {layer}, weight {weight_idx}
should remain unchanged""",
)
@pytest.mark.parametrize(
"world_size,num_layers,num_local_experts,num_logical_experts",
[
(2, 2, 2, 3),
],
)
def test_async_transfer_layer_without_mtp(
world_size: int,
num_layers: int,
num_local_experts: int,
num_logical_experts: int,
):
"""Exercise async EPLB transfer path without MTP/spec decode."""
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
distributed_run(
_test_async_transfer_layer_without_mtp_worker,
world_size,
num_layers,
num_local_experts,
num_logical_experts,
)
@pytest.mark.parametrize("world_size", [2, 4])
def test_rearrange_expert_weights_no_change(world_size):
"""
Test that when the indices do not change, the weights should remain
unchanged.
"""
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
distributed_run(_test_rearrange_expert_weights_no_change, world_size)
def _test_rearrange_expert_weights_profile_mode(env, world_size) -> None:
set_env_vars_and_device(env)
ensure_model_parallel_initialized(
tensor_model_parallel_size=world_size, pipeline_model_parallel_size=1
)
ep_group = get_tp_group().cpu_group
ep_rank = torch.distributed.get_rank()
device = torch.device(f"cuda:{ep_rank}")
num_layers = 1
num_local_experts = 2
total_physical_experts = world_size * num_local_experts
num_logical_experts = total_physical_experts // 2
hidden_sizes = [32]
# Create different index distributions
old_redundancy = create_redundancy_config(
num_logical_experts, total_physical_experts
)
new_redundancy = create_redundancy_config(
num_logical_experts, total_physical_experts
)
old_indices = create_expert_indices_with_redundancy(
num_layers, num_logical_experts, total_physical_experts, old_redundancy
)
new_indices = create_expert_indices_with_redundancy(
num_layers, num_logical_experts, total_physical_experts, new_redundancy
)
expert_weights = create_expert_weights(
num_layers, num_local_experts, hidden_sizes, ep_rank, device, old_indices
)
# Save original weights
original_weights = []
for layer_weights in expert_weights:
layer_copy = []
for weight in layer_weights:
layer_copy.append(weight.clone())
original_weights.append(layer_copy)
# Execute profile mode rearrangement
rearrange_expert_weights_inplace(
old_indices,
new_indices,
expert_weights,
ep_group,
is_profile=True, # Profile mode
)
# In profile mode, the weights should remain unchanged
for layer in range(num_layers):
for weight_idx in range(len(hidden_sizes)):
torch.testing.assert_close(
expert_weights[layer][weight_idx],
original_weights[layer][weight_idx],
msg="In profile mode, the weights should remain unchanged",
)
@pytest.mark.parametrize("world_size", [2, 4])
def test_rearrange_expert_weights_profile_mode(world_size):
"""Test profile mode (should not copy actual weights)"""
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
distributed_run(_test_rearrange_expert_weights_profile_mode, world_size)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_eplb_fused_moe_layer.py | tests/distributed/test_eplb_fused_moe_layer.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Test that the interaction between EPLB and FusedMoE Layer is okay
from dataclasses import dataclass
import pytest
import torch
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.distributed.eplb.rebalance_execute import rearrange_expert_weights_inplace
from vllm.distributed.parallel_state import (
ensure_model_parallel_initialized,
get_tp_group,
)
from vllm.model_executor.layers.fused_moe.layer import FusedMoE
from .eplb_utils import distributed_run, set_env_vars_and_device
@dataclass
class TestConfig:
num_layers: int
num_experts: int
num_local_experts: int
num_topk: int
hidden_size: int
intermediate_size: int
weight_dtype: torch.dtype
weight_scale_dtype: torch.dtype | None
column_major_scales: bool
def make_expert_weights(
layer_idx: int,
global_expert_idx: int,
global_num_experts: int,
tensor_shape: tuple[int, ...],
tensor_dtype: torch.dtype,
tensor_device: torch.device,
is_column_major: bool,
) -> torch.Tensor:
assert len(tensor_shape) == 2
if is_column_major:
tensor_shape = (tensor_shape[1], tensor_shape[0])
x = torch.empty(tensor_shape, dtype=tensor_dtype, device=tensor_device)
value_offset = (layer_idx * global_num_experts + global_expert_idx) * x.numel()
x.view(-1).copy_(
torch.arange(
value_offset,
value_offset + x.numel(),
dtype=tensor_dtype,
device=tensor_device,
)
)
if is_column_major:
x = torch.transpose(x, 1, 0)
assert not x.is_contiguous()
return x
def make_fused_moe_layer(
rank: int,
layer_idx: int,
test_config: TestConfig,
) -> FusedMoE:
fml = FusedMoE(
num_experts=test_config.num_experts,
top_k=test_config.num_topk,
hidden_size=test_config.hidden_size,
intermediate_size=test_config.intermediate_size,
prefix=f"dummy_layer_{layer_idx}",
activation="silu",
is_act_and_mul=True,
params_dtype=test_config.weight_dtype,
)
device = torch.device(f"cuda:{rank}")
from functools import partial
_make_expert_weights = partial(
make_expert_weights,
layer_idx=layer_idx,
global_num_experts=test_config.num_experts,
tensor_device=device,
)
assert isinstance(fml.w13_weight.data, torch.Tensor)
assert isinstance(fml.w2_weight.data, torch.Tensor)
fml.w13_weight.data = fml.w13_weight.data.to(device=device)
fml.w2_weight.data = fml.w2_weight.data.to(device=device)
w13_weight = fml.w13_weight.data
w2_weight = fml.w2_weight.data
assert w13_weight.size(0) == test_config.num_local_experts
for i in range(test_config.num_local_experts):
g_i = rank * test_config.num_local_experts + i
w13_weight_e = w13_weight[i]
w2_weight_e = w2_weight[i]
w13_weight_e.copy_(
_make_expert_weights(
global_expert_idx=g_i,
tensor_shape=w13_weight_e.shape,
tensor_dtype=w13_weight_e.dtype,
is_column_major=False,
)
)
w2_weight_e.copy_(
_make_expert_weights(
global_expert_idx=g_i,
tensor_shape=w2_weight_e.shape,
tensor_dtype=w2_weight_e.dtype,
is_column_major=False,
)
)
block_size = 16
def block_quant_scales_shape(
shape: tuple[int, ...], is_column_major: bool
) -> tuple[int, ...]:
assert len(shape) == 3
if not is_column_major:
return (shape[0], shape[1] // block_size, shape[2] // block_size)
else:
return (shape[0], shape[2] // block_size, shape[1] // block_size)
is_column_major = test_config.column_major_scales
w13_weight_scale_inv = torch.empty(
block_quant_scales_shape(w13_weight.shape, is_column_major),
dtype=test_config.weight_dtype,
device=device,
)
w2_weight_scale_inv = torch.empty(
block_quant_scales_shape(w2_weight.shape, is_column_major),
dtype=test_config.weight_dtype,
device=device,
)
for i in range(test_config.num_local_experts):
g_i = rank * test_config.num_local_experts + i
w13_s_e = w13_weight_scale_inv[i]
w2_s_e = w2_weight_scale_inv[i]
w13_s_e.copy_(
_make_expert_weights(
global_expert_idx=g_i,
tensor_shape=w13_s_e.shape,
tensor_dtype=w13_s_e.dtype,
# Fill data in row-major and then
# transpose if test_config requires col-major.
is_column_major=False,
)
)
w2_s_e.copy_(
_make_expert_weights(
global_expert_idx=g_i,
tensor_shape=w2_s_e.shape,
tensor_dtype=w2_s_e.dtype,
is_column_major=False,
)
)
if is_column_major:
w13_weight_scale_inv = torch.transpose(w13_weight_scale_inv, 1, 2)
w2_weight_scale_inv = torch.transpose(w2_weight_scale_inv, 1, 2)
assert not w13_weight_scale_inv.is_contiguous()
assert not w2_weight_scale_inv.is_contiguous()
# Add scales to the parameter list
fml.w13_weight_scale_inv = torch.nn.Parameter(
w13_weight_scale_inv, requires_grad=False
)
fml.w2_weight_scale_inv = torch.nn.Parameter(
w2_weight_scale_inv, requires_grad=False
)
return fml
def _test_eplb_fml(env, world_size: int, test_config: TestConfig):
# Initialize model parallel (using tensor parallel as an entrypoint
# to expert parallel)
set_env_vars_and_device(env)
vllm_config = VllmConfig()
vllm_config.parallel_config.tensor_parallel_size = world_size
vllm_config.parallel_config.enable_expert_parallel = True
with set_current_vllm_config(vllm_config):
ensure_model_parallel_initialized(
tensor_model_parallel_size=world_size, pipeline_model_parallel_size=1
)
ep_group = get_tp_group().cpu_group
ep_rank = torch.distributed.get_rank()
fml_layers = [
make_fused_moe_layer(ep_rank, layer_idx, test_config)
for layer_idx in range(test_config.num_layers)
]
rank_expert_weights = [fml.get_expert_weights() for fml in fml_layers]
indices = torch.zeros(
test_config.num_layers, test_config.num_experts, dtype=torch.long
)
for lidx in range(test_config.num_layers):
indices[lidx] = torch.Tensor(range(test_config.num_experts))
shuffled_indices = torch.zeros_like(indices)
for lidx in range(test_config.num_layers):
shuffled_indices[lidx] = torch.randperm(test_config.num_experts)
rearrange_expert_weights_inplace(
indices,
shuffled_indices,
rank_expert_weights,
ep_group,
is_profile=False,
)
num_local_experts = test_config.num_local_experts
num_global_experts = test_config.num_experts
for lidx, fml in enumerate(fml_layers):
for name, w in fml.named_parameters():
for e in range(num_local_experts):
g_e = shuffled_indices[lidx][ep_rank * num_local_experts + e]
ref = make_expert_weights(
layer_idx=lidx,
global_expert_idx=int(g_e.item()),
global_num_experts=num_global_experts,
tensor_shape=w[e].shape,
tensor_dtype=w[e].dtype,
tensor_device=w[e].device,
is_column_major=not w[e].is_contiguous(),
)
assert w[e].shape == ref.shape and w[e].stride() == ref.stride(), (
f"w[{e}] {w[e].size()} {w[e].stride()} vs "
f"ref {ref.size()} {ref.stride()}"
)
torch.testing.assert_close(w[e], ref)
@pytest.mark.parametrize("world_size", [2])
@pytest.mark.parametrize("num_layers", [4])
@pytest.mark.parametrize("num_experts", [16])
@pytest.mark.parametrize("hidden_size", [256])
@pytest.mark.parametrize("intermediate_size", [256])
@pytest.mark.parametrize("column_major_scales", [True, False])
def test_eplb_fml(
world_size: int,
num_layers: int,
num_experts: int,
hidden_size: int,
intermediate_size: int,
column_major_scales: bool,
):
if torch.cuda.device_count() < world_size:
pytest.skip(f"Need at least {world_size} GPUs to run the test")
num_local_experts = num_experts // world_size
num_topk = 4
# The dtypes are fine as we are essentially just checking data-copies
weight_dtype = torch.bfloat16
weight_scale_dtype = torch.bfloat16
test_config = TestConfig(
num_layers=num_layers,
num_experts=num_experts,
num_local_experts=num_local_experts,
num_topk=num_topk,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
weight_dtype=weight_dtype,
weight_scale_dtype=weight_scale_dtype,
column_major_scales=column_major_scales,
)
distributed_run(
_test_eplb_fml,
world_size,
test_config,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_custom_all_reduce.py | tests/distributed/test_custom_all_reduce.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import random
import pytest
import ray
import torch
import torch.distributed as dist
from vllm.distributed.communication_op import tensor_model_parallel_all_reduce # noqa
from vllm.distributed.parallel_state import get_tp_group, graph_capture
from ..utils import (
ensure_model_parallel_initialized,
init_test_distributed_environment,
multi_process_parallel,
)
random.seed(42)
test_sizes = [random.randint(1024, 2048 * 1024) for _ in range(8)]
for i, v in enumerate(test_sizes):
test_sizes[i] -= v % 8
@ray.remote(num_gpus=1, max_calls=1)
def graph_allreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pp_size,
rank,
distributed_init_port,
):
with monkeypatch.context() as m:
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
ensure_model_parallel_initialized(tp_size, pp_size)
group = get_tp_group().device_group
# A small all_reduce for warmup.
# this is needed because device communicators might be created lazily
# (e.g. NCCL). This will ensure that the communicator is initialized
# before any communication happens, so that this group can be used for
# graph capture immediately.
data = torch.zeros(1)
data = data.to(device=device)
torch.distributed.all_reduce(data, group=group)
torch.cuda.synchronize()
del data
# we use the first group to communicate once
# and the second group to communicate twice
# and so on
# this is used to demonstrate that each group can
# communicate independently
num_communication = rank // tp_size + 1
for sz in test_sizes:
for dtype in [torch.float32, torch.float16, torch.bfloat16]:
with graph_capture(device=device) as graph_capture_context:
# use integers so result matches NCCL exactly
inp1 = torch.randint(
1, 16, (sz,), dtype=dtype, device=torch.cuda.current_device()
)
inp2 = torch.randint(
1, 16, (sz,), dtype=dtype, device=torch.cuda.current_device()
)
torch.cuda.synchronize()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph, stream=graph_capture_context.stream):
for i in range(num_communication):
out1 = tensor_model_parallel_all_reduce(inp1)
# the input buffer is immediately modified to test
# synchronization
dist.all_reduce(inp1, group=group)
out2 = tensor_model_parallel_all_reduce(inp2)
dist.all_reduce(inp2, group=group)
graph.replay()
torch.testing.assert_close(out1, inp1)
torch.testing.assert_close(out2, inp2)
@ray.remote(num_gpus=1, max_calls=1)
def eager_allreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pp_size,
rank,
distributed_init_port,
):
with monkeypatch.context() as m:
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
# we use the first group to communicate once
# and the second group to communicate twice
# and so on
# this is used to demonstrate that each group can
# communicate independently
num_communication = rank // tp_size + 1
sz = 1024
fa = get_tp_group().device_communicator.ca_comm
inp = torch.ones(sz, dtype=torch.float32, device=device)
out = inp
for _ in range(num_communication):
out = fa.all_reduce(out, registered=False)
torch.testing.assert_close(out, inp * (tp_size**num_communication))
inp = torch.ones(sz * 4, dtype=torch.bfloat16, device=device)
out = inp
for _ in range(num_communication):
out = fa.all_reduce(out, registered=False)
torch.testing.assert_close(out, inp * (tp_size**num_communication))
@pytest.mark.parametrize("tp_size", [2])
@pytest.mark.parametrize("pipeline_parallel_size", [1, 2])
@pytest.mark.parametrize("test_target", [eager_allreduce, graph_allreduce])
def test_custom_allreduce(
monkeypatch: pytest.MonkeyPatch,
tp_size,
pipeline_parallel_size,
test_target,
):
world_size = tp_size * pipeline_parallel_size
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
multi_process_parallel(monkeypatch, tp_size, pipeline_parallel_size, test_target)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_context_parallel.py | tests/distributed/test_context_parallel.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
WARNING: This test runs in both single-node (4 GPUs) and multi-node
(2 node with 2 GPUs each) modes. If the test only uses 2 GPUs, it is
important to set the distributed backend to "mp" to avoid Ray scheduling
all workers in a node other than the head node, which can cause the test
to fail.
"""
import json
import os
from dataclasses import dataclass
from typing import Literal, NamedTuple
import pytest
import torch
from tests.evals.gsm8k.gsm8k_eval import evaluate_gsm8k
from tests.utils import RemoteOpenAIServer, create_new_process_for_each_test
from vllm.config.model import RunnerOption
from vllm.logger import init_logger
from ..models.registry import HF_EXAMPLE_MODELS
logger = init_logger("test_context_parallel")
VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
CP_TEST_MODELS = [
# TODO support other models
# [LANGUAGE GENERATION]
"deepseek-ai/DeepSeek-V2-Lite-Chat",
"Qwen/Qwen2.5-1.5B-Instruct",
]
# GSM8K eval configuration
NUM_QUESTIONS = 256 # Fast eval for CI
NUM_SHOTS = 5 # Few-shot examples
# tp accuracy with 2% buffer
MIN_ACCURACY = {
# .buildkite/lm-eval-harness/configs/DeepSeek-V2-Lite-Chat.yaml
"deepseek-ai/DeepSeek-V2-Lite-Chat": 0.64,
# .buildkite/lm-eval-harness/configs/Qwen2.5-1.5B-Instruct.yaml
"Qwen/Qwen2.5-1.5B-Instruct": 0.52,
}
class ParallelSetup(NamedTuple):
tp_size: int
pp_size: int
dcp_size: int
cp_kv_cache_interleave_size: int
eager_mode: bool
chunked_prefill: bool
class CPTestOptions(NamedTuple):
multi_node_only: bool
attn_backend: str | None = None
@dataclass
class CPTestSettings:
parallel_setups: list[ParallelSetup]
distributed_backends: list[str]
runner: RunnerOption
test_options: CPTestOptions
@staticmethod
def detailed(
*,
tp_base: int = 4,
pp_base: int = 1,
dcp_multipliers: list[float] | None = None,
cp_kv_cache_interleave_size: int = 1,
multi_node_only: bool = False,
runner: RunnerOption = "auto",
attn_backend: str | None = None,
):
parallel_setups = []
if dcp_multipliers is None:
dcp_multipliers = [
0.5,
]
for eager_mode_val in [False]:
for pp_multiplier in [1]:
for dcp_multiplier in dcp_multipliers:
for chunked_prefill_val in [True]:
parallel_setups.append(
ParallelSetup(
tp_size=tp_base,
pp_size=pp_multiplier * pp_base,
dcp_size=int(dcp_multiplier * tp_base),
cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
eager_mode=eager_mode_val,
chunked_prefill=chunked_prefill_val,
)
)
return CPTestSettings(
parallel_setups=parallel_setups,
distributed_backends=["mp"],
runner=runner,
test_options=CPTestOptions(
multi_node_only=multi_node_only,
attn_backend=attn_backend,
),
)
def iter_params(self, model_id: str):
opts = self.test_options
for parallel_setup in self.parallel_setups:
for backend in self.distributed_backends:
yield (
model_id,
parallel_setup,
backend,
self.runner,
opts,
)
CP_TEXT_GENERATION_MODELS = {
"deepseek-ai/DeepSeek-V2-Lite-Chat": [
CPTestSettings.detailed(dcp_multipliers=[1]),
CPTestSettings.detailed(
dcp_multipliers=[0.5],
cp_kv_cache_interleave_size=64,
attn_backend="FLASHMLA",
),
],
"Qwen/Qwen2.5-1.5B-Instruct": [
CPTestSettings.detailed(
cp_kv_cache_interleave_size=16, attn_backend="FLASH_ATTN"
),
CPTestSettings.detailed(
cp_kv_cache_interleave_size=16, attn_backend="FLASHINFER"
),
],
}
def _test_cp_gsm8k(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: CPTestOptions,
num_gpus_available: int,
*,
method: Literal["generate"],
is_multimodal: bool,
):
(
tp_size,
pp_size,
dcp_size,
cp_kv_cache_interleave_size,
eager_mode,
chunked_prefill,
) = parallel_setup
multi_node_only, attn_backend = test_options
model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
model_info.check_transformers_version(on_fail="skip")
trust_remote_code = model_info.trust_remote_code
tokenizer_mode = model_info.tokenizer_mode
hf_overrides = model_info.hf_overrides
model_info.check_available_online(on_fail="skip")
if num_gpus_available < tp_size * pp_size:
pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
if VLLM_MULTI_NODE and distributed_backend == "mp":
pytest.skip(
"Skipping multi-node pipeline parallel test for "
"multiprocessing distributed backend"
)
if multi_node_only and not VLLM_MULTI_NODE:
pytest.skip("Not in multi-node setting")
server_args = [
# use half precision for speed and memory savings in CI environment
"--dtype",
"bfloat16",
"--max-model-len",
"4096",
"--max-num-seqs",
"64",
]
if chunked_prefill:
server_args.append("--enable-chunked-prefill")
if eager_mode:
server_args.append("--enforce-eager")
if runner != "auto":
server_args.extend(["--runner", runner])
if trust_remote_code:
server_args.append("--trust-remote-code")
if tokenizer_mode:
server_args.extend(["--tokenizer-mode", tokenizer_mode])
if hf_overrides:
server_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
server_args.extend(
[
"--tensor-parallel-size",
str(tp_size),
"--pipeline-parallel-size",
str(pp_size),
"--decode-context-parallel-size",
str(dcp_size),
"--dcp-kv-cache-interleave-size",
str(cp_kv_cache_interleave_size),
"--distributed-executor-backend",
distributed_backend,
]
)
if attn_backend:
server_args.append(f"--attention-backend={attn_backend}")
with RemoteOpenAIServer(
model_id,
server_args,
max_wait_seconds=720,
) as remote_server:
host = f"http://{remote_server.host}"
port = remote_server.port
# Run GSM8K evaluation
results = evaluate_gsm8k(
num_questions=NUM_QUESTIONS,
num_shots=NUM_SHOTS,
host=host,
port=port,
)
# Validate accuracy is reasonable
accuracy = results["accuracy"]
min_accuracy = MIN_ACCURACY[model_id]
assert accuracy >= min_accuracy, (
f"TP+DCP accuracy too low: {accuracy:.3f} < {min_accuracy:.3f}"
)
@pytest.mark.parametrize(
(
"model_id",
"parallel_setup",
"distributed_backend",
"runner",
"test_options",
),
[
params
for model_id, settings in CP_TEXT_GENERATION_MODELS.items()
for setting in settings
for params in setting.iter_params(model_id)
if model_id in CP_TEST_MODELS
],
)
@create_new_process_for_each_test()
def test_cp_generation(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: CPTestOptions,
num_gpus_available,
):
if (
model_id == "deepseek-ai/DeepSeek-V2-Lite-Chat"
and torch.cuda.get_device_capability() < (9, 0)
):
pytest.skip(reason="MLA+DCP requires compute capability of 9.0 or higher")
if (
model_id == "Qwen/Qwen2.5-1.5B-Instruct"
and torch.cuda.get_device_capability() != (9, 0)
):
pytest.skip(reason="GQA+DCP currently requires compute capability of 9.0")
_test_cp_gsm8k(
model_id,
parallel_setup,
distributed_backend,
runner,
test_options,
num_gpus_available,
method="generate",
is_multimodal=False,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_kvlayout.py | tests/distributed/test_kvlayout.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm.config import (
DeviceConfig,
KVTransferConfig,
ModelConfig,
VllmConfig,
set_current_vllm_config,
)
from vllm.distributed.kv_transfer.kv_connector.utils import (
get_kv_connector_cache_layout,
)
from vllm.logger import init_logger
logger = init_logger("test_expert_parallel")
def test_get_kv_connector_cache_layout_without_kv_connector():
vllm_config = VllmConfig(device_config=DeviceConfig("cpu"))
with set_current_vllm_config(vllm_config):
# Test with default settings
layout = get_kv_connector_cache_layout()
assert layout == "NHD"
def test_get_kv_connector_cache_layout_with_lmcache_connector():
kv_transfer_config = KVTransferConfig(
kv_connector="LMCacheConnectorV1",
kv_role="kv_both",
)
vllm_config = VllmConfig(
device_config=DeviceConfig("cpu"), kv_transfer_config=kv_transfer_config
)
with set_current_vllm_config(vllm_config):
# Test with default settings
layout = get_kv_connector_cache_layout()
assert layout == "NHD"
def test_get_kv_connector_cache_layout_with_nixl_connector():
kv_transfer_config = KVTransferConfig(
kv_connector="NixlConnector",
kv_role="kv_both",
)
model_config = ModelConfig()
vllm_config = VllmConfig(
device_config=DeviceConfig("cpu"),
model_config=model_config,
kv_transfer_config=kv_transfer_config,
)
with set_current_vllm_config(vllm_config):
# Test with default settings
layout = get_kv_connector_cache_layout()
assert layout == "HND"
def test_get_kv_connector_cache_layout_with_multi_connector():
kv_transfer_config = KVTransferConfig(
kv_connector="MultiConnector",
kv_role="kv_both",
kv_connector_extra_config={
"connectors": [
{"kv_connector": "ExampleConnector", "kv_role": "kv_both"},
{"kv_connector": "NixlConnector", "kv_role": "kv_both"},
]
},
)
model_config = ModelConfig()
vllm_config = VllmConfig(
device_config=DeviceConfig("cpu"),
model_config=model_config,
kv_transfer_config=kv_transfer_config,
)
with set_current_vllm_config(vllm_config):
# Test with default settings
layout = get_kv_connector_cache_layout()
assert layout == "HND"
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_shm_storage.py | tests/distributed/test_shm_storage.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import multiprocessing
import random
import time
import traceback
import unittest
from multiprocessing import Lock
import torch
# Assuming these are imported from your module
from vllm.distributed.device_communicators.shm_object_storage import (
MsgpackSerde,
SingleWriterShmObjectStorage,
SingleWriterShmRingBuffer,
)
from vllm.multimodal.inputs import (
MultiModalFieldElem,
MultiModalKwargsItem,
MultiModalSharedField,
)
def _dummy_elem(modality: str, key: str, size: int):
return MultiModalFieldElem(
modality=modality,
key=key,
data=torch.empty((size,), dtype=torch.int8),
field=MultiModalSharedField(batch_size=1),
)
def _dummy_item(modality: str, size_by_key: dict[str, int]):
return MultiModalKwargsItem.from_elems(
[_dummy_elem(modality, key, size) for key, size in size_by_key.items()]
)
class TestSingleWriterShmObjectStorage(unittest.TestCase):
def setUp(self):
"""Set up test fixtures before each test method."""
ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=1024 * 100,
create=True, # 10 MB buffer
)
self.storage = SingleWriterShmObjectStorage(
max_object_size=1024 * 10, # 10KB max object
n_readers=2,
ring_buffer=ring_buffer,
serde_class=MsgpackSerde,
reader_lock=Lock(),
)
def tearDown(self):
"""Clean up after each test."""
if self.storage:
del self.storage
def test_minimal_put_get_cycle(self):
"""Test basic put and get operations."""
key = "test_key"
value = _dummy_item("text", {"field1": 10, "field2": 20})
# Put operation
address, monotonic_id = self.storage.put(key, value)
# Verify key is in index
self.assertIn(key, self.storage.key_index)
self.assertEqual(self.storage.key_index[key], (address, monotonic_id))
self.assertEqual(self.storage.id_index[monotonic_id], key)
# Get operation
result = self.storage.get(address, monotonic_id)
# Verify result
self.assertEqual(result, value)
def test_put_same_key_twice(self):
"""Test behavior when putting the same key multiple times."""
key = "duplicate_key"
value1 = "first value"
value2 = "second value"
# First put
address1, id1 = self.storage.put(key, value1)
retrieved1 = self.storage.get(address1, id1)
self.assertEqual(retrieved1, value1)
# should raise an error on second put
with self.assertRaises(ValueError) as context:
self.storage.put(key, value2)
self.assertIn("already exists in the storage", str(context.exception))
def test_large_object_rejection(self):
"""Test that objects exceeding max_object_size are rejected."""
# Create an object larger than max_object_size
large_data = "x" * (self.storage.max_object_size + 100)
with self.assertRaises(ValueError) as context:
self.storage.put("large_key", large_data)
self.assertIn("exceeds max object size", str(context.exception))
def test_buffer_overflow_and_cleanup(self):
"""Test behavior when buffer fills up and needs cleanup."""
# Fill up the buffer with many small objects
stored_items = []
try:
for i in range(1000): # Try to store many items
key = f"item_{i}"
value = f"data_{i}" * 100 # Make it reasonably sized
address, monotonic_id = self.storage.put(key, value)
stored_items.append((key, value, address, monotonic_id))
except MemoryError:
print(f"Buffer filled after {len(stored_items)} items")
# Verify that some items are still accessible
accessible_count = 0
for key, original_value, address, monotonic_id in stored_items:
for i in range(self.storage.n_readers):
retrieved = self.storage.get(address, monotonic_id)
if retrieved == original_value:
accessible_count += 1
self.assertEqual(accessible_count, len(stored_items))
try:
for i in range(len(stored_items), 1000): # Try to store many items
key = f"item_{i}"
value = f"data_{i}" * 100 # Make it reasonably sized
address, monotonic_id = self.storage.put(key, value)
stored_items.append((key, value, address, monotonic_id))
except MemoryError:
print(f"Buffer filled after {len(stored_items)} items")
# Verify that some items are still accessibles
for key, original_value, address, monotonic_id in stored_items:
try:
for i in range(self.storage.n_readers):
retrieved = self.storage.get(address, monotonic_id)
if retrieved == original_value:
accessible_count += 1
except ValueError as e:
print(f"Error retrieving {key}: {e}")
# some items from the first batch may still be accessible
self.assertGreaterEqual(accessible_count, len(stored_items))
def test_blocking_unread_object(self):
"""Test behavior when buffer fills up and needs cleanup."""
# Fill up the buffer with many small objects
stored_items = []
try:
for i in range(1000): # Try to store many items
key = f"item_{i}"
value = f"data_{i}" * 100 # Make it reasonably sized
address, monotonic_id = self.storage.put(key, value)
stored_items.append((key, value, address, monotonic_id))
except MemoryError:
print(f"Buffer filled after {len(stored_items)} items")
# read all items except the first one
# to simulate a blocking situation
accessible_count = 0
for key, original_value, address, monotonic_id in stored_items[1:]:
for i in range(self.storage.n_readers):
retrieved = self.storage.get(address, monotonic_id)
if retrieved == original_value:
accessible_count += 1
self.assertEqual(accessible_count, len(stored_items) - 1)
try:
key = f"item_{len(stored_items)}"
value = f"data_{len(stored_items)}" * 100
address, monotonic_id = self.storage.put(key, value)
except MemoryError:
print(f"Buffer filled after {len(stored_items)} items")
# read the first item
for i in range(self.storage.n_readers):
key, original_value, address, monotonic_id = stored_items[0]
retrieved = self.storage.get(address, monotonic_id)
self.assertEqual(retrieved, original_value)
try:
for i in range(len(stored_items), 1000): # Try to store many items
key = f"item_{i}"
value = f"data_{i}" * 100 # Make it reasonably sized
address, monotonic_id = self.storage.put(key, value)
stored_items.append((key, value, address, monotonic_id))
except MemoryError:
print(f"Buffer filled after {len(stored_items)} items")
# some items from the first batch may still be accessible
self.assertGreaterEqual(len(stored_items), accessible_count + 10)
def test_invalid_get_operations(self):
"""Test various invalid get operations."""
# Test with non-existent address
with self.assertRaises(ValueError): # Could be various exceptions
self.storage.get(99999, 1)
# Store something first
address, monotonic_id = self.storage.put("test", "value")
# Test with wrong monotonic_id
with self.assertRaises(ValueError) as context:
self.storage.get(address, monotonic_id + 100)
self.assertIn("has been modified or is invalid", str(context.exception))
def test_clear_storage(self):
"""Test clearing the storage."""
# Store some items
for i in range(5):
self.storage.put(f"item_{i}", f"value_{i}")
# Clear the storage
self.storage.clear()
# Verify that all indices are empty
self.assertEqual(len(self.storage.key_index), 0)
self.assertEqual(len(self.storage.id_index), 0)
self.assertEqual(len(self.storage.ring_buffer.metadata), 0)
# Verify that new items can be added after clearing
address, monotonic_id = self.storage.put("new_item", "new_value")
self.assertIn("new_item", self.storage.key_index)
self.assertEqual((address, monotonic_id), (0, 0))
# Reader process function
def reader_process(process_id, storage_handle, items_to_read):
"""Reader process that connects to existing shared memory and reads data."""
reader_storage = SingleWriterShmObjectStorage.create_from_handle(storage_handle)
print(f"Reader {process_id} started")
errors = []
for key, original_value, address, monotonic_id in items_to_read:
time.sleep(random.random() / 100)
try:
# Read data from shared memory
retrieved_value = reader_storage.get(address, monotonic_id)
# Verify data integrity
assert retrieved_value == original_value
print(f"Reader {process_id} retrieved {key}: {retrieved_value}")
except Exception as e:
errors.append((key, str(e), type(e).__name__))
def run_multiprocess_example():
"""Run a minimal working example with real shared memory."""
print("=== Minimal Object Storage Example ===")
try:
# Create storage instance
ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=1024 * 100,
create=True, # 10 MB buffer
)
storage = SingleWriterShmObjectStorage(
max_object_size=1024,
n_readers=3,
ring_buffer=ring_buffer,
serde_class=MsgpackSerde,
reader_lock=Lock(),
)
print(f"Created storage (writer: {storage.is_writer})")
# Test basic data types
test_data = [
("user_data", {"name": "Alice", "age": 30, "scores": [95, 87, 92]}),
("simple_string", "Hello, World!"),
("number", 42),
("list_data", [1, 2, 3, "four", 5.0]),
]
stored_items = []
# Store all data
for key, value in test_data:
print(f"Storing {key}: {value}")
address, monotonic_id = storage.put(key, value)
stored_items.append((key, value, address, monotonic_id))
print(f" -> Stored at address {address}, ID {monotonic_id}")
print("\n--- Retrieving Data ---")
processes = []
handle = storage.handle()
# initialize lock for reader processes
handle.reader_lock = Lock()
for i in range(storage.n_readers):
p = multiprocessing.Process(
target=reader_process, args=(i, handle, stored_items)
)
processes.append(p)
p.start()
for p in processes:
p.join(timeout=10)
if p.is_alive():
p.terminate()
p.join()
except Exception as e:
print(f"Error in minimal example: {e}")
traceback.print_exc()
if __name__ == "__main__":
# Run the minimal example first
run_multiprocess_example()
print("\n" + "=" * 50 + "\n")
# Run the test suite
print("Running comprehensive test suite...")
unittest.main(verbosity=2, exit=False)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_shm_broadcast.py | tests/distributed/test_shm_broadcast.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import multiprocessing
import random
import time
import numpy as np
import torch.distributed as dist
from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
from vllm.distributed.utils import StatelessProcessGroup
from vllm.utils.network_utils import get_open_port
from vllm.utils.system_utils import update_environment_variables
def get_arrays(n: int, seed: int = 0) -> list[np.ndarray]:
np.random.seed(seed)
sizes = np.random.randint(1, 10_000, n)
# on average, each array will have 5k elements
# with int64, each array will have 40kb
return [np.random.randint(1, 100, i) for i in sizes]
def distributed_run(fn, world_size):
number_of_processes = world_size
processes = []
for i in range(number_of_processes):
env = {}
env["RANK"] = str(i)
env["LOCAL_RANK"] = str(i)
env["WORLD_SIZE"] = str(number_of_processes)
env["LOCAL_WORLD_SIZE"] = str(number_of_processes)
env["MASTER_ADDR"] = "localhost"
env["MASTER_PORT"] = "12345"
p = multiprocessing.Process(target=fn, args=(env,))
processes.append(p)
p.start()
for p in processes:
p.join()
for p in processes:
assert p.exitcode == 0
def worker_fn_wrapper(fn):
# `multiprocessing.Process` cannot accept environment variables directly
# so we need to pass the environment variables as arguments
# and update the environment variables in the function
def wrapped_fn(env):
update_environment_variables(env)
dist.init_process_group(backend="gloo")
fn()
return wrapped_fn
@worker_fn_wrapper
def worker_fn():
rank = dist.get_rank()
if rank == 0:
port = get_open_port()
ip = "127.0.0.1"
dist.broadcast_object_list([ip, port], src=0)
else:
recv = [None, None]
dist.broadcast_object_list(recv, src=0)
ip, port = recv # type: ignore
stateless_pg = StatelessProcessGroup.create(ip, port, rank, dist.get_world_size())
for pg in [dist.group.WORLD, stateless_pg]:
writer_rank = 2
broadcaster = MessageQueue.create_from_process_group(
pg, 40 * 1024, 2, writer_rank
)
if rank == writer_rank:
seed = random.randint(0, 1000)
dist.broadcast_object_list([seed], writer_rank)
else:
recv = [None]
dist.broadcast_object_list(recv, writer_rank)
seed = recv[0] # type: ignore
if pg == dist.group.WORLD:
dist.barrier()
else:
pg.barrier()
# in case we find a race condition
# print the seed so that we can reproduce the error
print(f"Rank {rank} got seed {seed}")
# test broadcasting with about 400MB of data
N = 10_000
if rank == writer_rank:
arrs = get_arrays(N, seed)
for x in arrs:
broadcaster.broadcast_object(x)
time.sleep(random.random() / 1000)
else:
arrs = get_arrays(N, seed)
for x in arrs:
y = broadcaster.broadcast_object(None)
assert np.array_equal(x, y)
time.sleep(random.random() / 1000)
if pg == dist.group.WORLD:
dist.barrier()
print(f"torch distributed passed the test! Rank {rank}")
else:
pg.barrier()
print(f"StatelessProcessGroup passed the test! Rank {rank}")
def test_shm_broadcast():
distributed_run(worker_fn, 4)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_utils.py | tests/distributed/test_utils.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import socket
import pytest
import ray
import torch
import vllm.envs as envs
from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
from vllm.distributed.utils import StatelessProcessGroup
from vllm.utils.network_utils import get_open_port
from vllm.utils.system_utils import update_environment_variables
from vllm.utils.torch_utils import cuda_device_count_stateless
from ..utils import multi_gpu_test
@ray.remote
class _CUDADeviceCountStatelessTestActor:
def get_count(self):
return cuda_device_count_stateless()
def set_cuda_visible_devices(self, cuda_visible_devices: str):
update_environment_variables({"CUDA_VISIBLE_DEVICES": cuda_visible_devices})
def get_cuda_visible_devices(self):
return envs.CUDA_VISIBLE_DEVICES
def test_cuda_device_count_stateless():
"""Test that cuda_device_count_stateless changes return value if
CUDA_VISIBLE_DEVICES is changed."""
actor = _CUDADeviceCountStatelessTestActor.options( # type: ignore
num_gpus=2
).remote()
assert len(sorted(ray.get(actor.get_cuda_visible_devices.remote()).split(","))) == 2
assert ray.get(actor.get_count.remote()) == 2
ray.get(actor.set_cuda_visible_devices.remote("0"))
assert ray.get(actor.get_count.remote()) == 1
ray.get(actor.set_cuda_visible_devices.remote(""))
assert ray.get(actor.get_count.remote()) == 0
def cpu_worker(rank, WORLD_SIZE, port1, port2):
pg1 = StatelessProcessGroup.create(
host="127.0.0.1", port=port1, rank=rank, world_size=WORLD_SIZE
)
if rank <= 2:
pg2 = StatelessProcessGroup.create(
host="127.0.0.1", port=port2, rank=rank, world_size=3
)
data = torch.tensor([rank])
data = pg1.broadcast_obj(data, src=2)
assert data.item() == 2
if rank <= 2:
data = torch.tensor([rank + 1])
data = pg2.broadcast_obj(data, src=2)
assert data.item() == 3
pg2.barrier()
pg1.barrier()
def gpu_worker(rank, WORLD_SIZE, port1, port2):
torch.cuda.set_device(rank)
pg1 = StatelessProcessGroup.create(
host="127.0.0.1", port=port1, rank=rank, world_size=WORLD_SIZE
)
pynccl1 = PyNcclCommunicator(pg1, device=rank)
if rank <= 2:
pg2 = StatelessProcessGroup.create(
host="127.0.0.1", port=port2, rank=rank, world_size=3
)
pynccl2 = PyNcclCommunicator(pg2, device=rank)
data = torch.tensor([rank]).cuda()
pynccl1.all_reduce(data)
pg1.barrier()
torch.cuda.synchronize()
if rank <= 2:
pynccl2.all_reduce(data)
pg2.barrier()
torch.cuda.synchronize()
item = data[0].item()
print(f"rank: {rank}, item: {item}")
if rank == 3:
assert item == 6
else:
assert item == 18
def broadcast_worker(rank, WORLD_SIZE, port1, port2):
pg1 = StatelessProcessGroup.create(
host="127.0.0.1", port=port1, rank=rank, world_size=WORLD_SIZE
)
if rank == 2:
pg1.broadcast_obj("secret", src=2)
else:
obj = pg1.broadcast_obj(None, src=2)
assert obj == "secret"
pg1.barrier()
def allgather_worker(rank, WORLD_SIZE, port1, port2):
pg1 = StatelessProcessGroup.create(
host="127.0.0.1", port=port1, rank=rank, world_size=WORLD_SIZE
)
data = pg1.all_gather_obj(rank)
assert data == list(range(WORLD_SIZE))
pg1.barrier()
@pytest.mark.skip(reason="This test is flaky and prone to hang.")
@multi_gpu_test(num_gpus=4)
@pytest.mark.parametrize(
"worker", [cpu_worker, gpu_worker, broadcast_worker, allgather_worker]
)
def test_stateless_process_group(worker):
port1 = get_open_port()
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(("", port1))
port2 = get_open_port()
WORLD_SIZE = 4
from multiprocessing import get_context
ctx = get_context("fork")
processes = []
for i in range(WORLD_SIZE):
rank = i
processes.append(
ctx.Process(target=worker, args=(rank, WORLD_SIZE, port1, port2))
)
for p in processes:
p.start()
for p in processes:
p.join()
for p in processes:
assert not p.exitcode
print("All processes finished.")
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_shm_buffer.py | tests/distributed/test_shm_buffer.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import traceback
import unittest
import numpy as np
from vllm.distributed.device_communicators.shm_object_storage import (
SingleWriterShmRingBuffer,
)
class TestSingleWriterShmRingBuffer(unittest.TestCase):
"""Test suite for the ring buffer implementation"""
def setUp(self):
"""Set up test fixtures"""
self.buffer_size = 4096
self.ring_buffer = None
def tearDown(self):
"""Clean up after tests"""
if self.ring_buffer:
del self.ring_buffer
def test_buffer_opening(self):
"""Test opening an existing buffer"""
# First create a buffer
self.ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=self.buffer_size, create=True
)
# Then open it with another instance
reader_buffer = SingleWriterShmRingBuffer(*self.ring_buffer.handle())
self.assertFalse(reader_buffer.is_writer)
self.assertEqual(
reader_buffer.shared_memory.name, self.ring_buffer.shared_memory.name
)
def test_buffer_access(self):
"""Test accessing allocated buffers"""
self.ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=self.buffer_size, create=True
)
size = 100
address, monotonic_id = self.ring_buffer.allocate_buf(size)
# Write some test data
test_data = b"Hello, World!" * 7 # 91 bytes
with self.ring_buffer.access_buf(address) as (data_buf, metadata):
data_buf[0 : len(test_data)] = test_data
# Read it back
with self.ring_buffer.access_buf(address) as (data_buf2, metadata2):
read_data = bytes(data_buf2[0 : len(test_data)])
read_id = metadata2[0]
self.assertEqual(read_data, test_data)
self.assertEqual(read_id, monotonic_id)
def test_memory_error_on_full_buffer(self):
"""Test that MemoryError is raised when buffer is full"""
small_buffer_size = 200
self.ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=small_buffer_size, create=True
)
# Fill up the buffer
self.ring_buffer.allocate_buf(100)
self.ring_buffer.allocate_buf(80) # Total: 196 bytes used
# This should fail
with self.assertRaises(MemoryError):
self.ring_buffer.allocate_buf(1) # Would exceed buffer capacity
def test_allocation_and_free(self):
"""Test allocation and freeing of buffers"""
small_buffer_size = 200
self.ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=small_buffer_size, create=True
)
size = 80
# Write some data
test_data = b"Repeated test data"
for i in range(5):
address, monotonic_id = self.ring_buffer.allocate_buf(size)
with self.ring_buffer.access_buf(address) as (data_buf, metadata):
data_buf[0:4] = (0).to_bytes(4, "little") # 0 for not in-use
data_buf[4 : len(test_data) + 4] = test_data
print(self.ring_buffer.metadata)
freed_ids = self.ring_buffer.free_buf(lambda *args: True)
print(f" Freed IDs: {freed_ids}")
self.assertEqual(freed_ids[0], i)
def test_clear_buffer(self):
"""Test clearing the buffer"""
self.ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=self.buffer_size, create=True
)
# Allocate some buffers
for _ in range(3):
self.ring_buffer.allocate_buf(100)
# Clear the buffer
self.ring_buffer.clear()
# Check that metadata is empty and IDs reset
self.assertEqual(len(self.ring_buffer.metadata), 0)
self.assertEqual(self.ring_buffer.monotonic_id_start, 0)
self.assertEqual(self.ring_buffer.monotonic_id_end, 0)
self.assertEqual(self.ring_buffer.data_buffer_start, 0)
self.assertEqual(self.ring_buffer.data_buffer_end, 0)
def test_allocation_cycles(self):
buffer_size = 100
ring = SingleWriterShmRingBuffer(data_buffer_size=buffer_size, create=True)
# tracking allocations for assertions
allocated_bitmap = np.zeros(
(buffer_size,), dtype=np.bool_
) # addr -> is_allocated
allocation_map = dict() # monotonic_id -> (addr, size)
def count_allocated(bitmap) -> int:
return np.sum(bitmap).item()
def is_free_fn(a, b) -> bool:
return True
def mark_allocated_with_assertion(id, addr, size):
addr = addr % buffer_size
self.assertEqual(count_allocated(allocated_bitmap[addr : addr + size]), 0)
allocated_bitmap[addr : addr + size] = True
allocation_map[id] = (addr, size)
def mark_freed_with_assertion(id):
self.assertTrue(id in allocation_map)
addr, size = allocation_map.pop(id)
addr = addr % buffer_size
self.assertEqual(
count_allocated(allocated_bitmap[addr : addr + size]), size
)
allocated_bitmap[addr : addr + size] = False
def ring_free(free_size=None):
freed_ids = ring.free_buf(is_free_fn, free_size)
for freed_id in freed_ids:
mark_freed_with_assertion(freed_id)
def ring_allocate(allocate_size):
allocate_size_with_md = allocate_size + ring.MD_SIZE
try:
addr, monotonic_id = ring.allocate_buf(allocate_size)
mark_allocated_with_assertion(monotonic_id, addr, allocate_size_with_md)
except MemoryError:
# free 2x size for enough space if wrapping happened
ring_free(allocate_size_with_md * 2)
# retry allocating
addr, monotonic_id = ring.allocate_buf(allocate_size)
mark_allocated_with_assertion(monotonic_id, addr, allocate_size_with_md)
# 1. allocation & free cycles
for _ in range(33):
# will consume 2 + 8 = 10 bytes per allocation
ring_allocate(2)
# 2. free all allocations
ring_free()
# 3. try allocate the largest possible buffer
ring_allocate(buffer_size - ring.MD_SIZE)
def main():
"""Main function demonstrating usage and running tests"""
print("=== SingleWriterShmRingBuffer Test Suite ===\n")
# Run unit tests
print("Running unit tests...")
unittest.main(argv=[""], exit=False, verbosity=2)
print("\n" + "=" * 50)
print("=== Manual Demo ===\n")
# Manual demonstration
try:
print("Creating ring buffer...")
writer_buffer = SingleWriterShmRingBuffer(data_buffer_size=2048, create=True)
reader_buffer = SingleWriterShmRingBuffer(*writer_buffer.handle())
print(f"Buffer created with name: {writer_buffer.shared_memory.name}")
# Allocate some buffers
print("\nAllocating buffers...")
address_array = []
for i in range(3):
size = 100 + i * 50
try:
writer_buffer.free_buf(lambda *args: True)
address, monotonic_id = writer_buffer.allocate_buf(size)
address_array.append((address, size, monotonic_id))
# Write some test data
with writer_buffer.access_buf(address) as (data_buf, metadata):
test_message = f"Test message {i}".encode()
data_buf[0 : len(test_message)] = test_message
except MemoryError as e:
print(f" Failed to allocate {size} bytes: {e}")
print("\nBuffer state:")
print(f" Data buffer start: {writer_buffer.data_buffer_start}")
print(f" Data buffer end: {writer_buffer.data_buffer_end}")
print(f" Monotonic ID start: {writer_buffer.monotonic_id_start}")
print(f" Monotonic ID end: {writer_buffer.monotonic_id_end}")
print(f" Metadata entries: {len(writer_buffer.metadata)}")
# Try to read back the data
print("\nReading back data...")
for address, size, monotonic_id in address_array:
with reader_buffer.access_buf(address) as (data_buf, metadata):
# Find null terminator or read first 50 chars
data_bytes = bytes(data_buf[0:size])
message = data_bytes.decode()
print(f" ID {monotonic_id}: '{message}'")
except Exception as e:
print(f"Demo error: {e}")
traceback.print_exc()
print("\n=== Demo Complete ===")
if __name__ == "__main__":
main()
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_torchrun_example_moe.py | tests/distributed/test_torchrun_example_moe.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# unit test for `examples/offline_inference/torchrun_example.py`
import os
import random
import torch.distributed as dist
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import get_tp_group, get_world_group
dist.init_process_group(backend="gloo")
# Create prompts
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
] * 10
dp_size = int(os.getenv("DP_SIZE", "1"))
dp_rank = int(os.getenv("DP_RANK", "0"))
if dp_size > 1:
# distribute the prompts across the data parallel ranks
prompts = [prompt for idx, prompt in enumerate(prompts) if idx % dp_size == dp_rank]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# set different `gpu_memory_utilization` and `swap_space` for different ranks,
# to test if all ranks agree on the same kv cache configuration.
llm = LLM(
model="microsoft/Phi-mini-MoE-instruct",
tensor_parallel_size=int(os.getenv("TP_SIZE", "1")),
pipeline_parallel_size=int(os.getenv("PP_SIZE", "1")),
enable_expert_parallel=int(os.getenv("ENABLE_EP", "0")) == 1,
distributed_executor_backend="external_launcher",
gpu_memory_utilization=random.uniform(0.7, 0.9),
swap_space=random.randint(1, 4),
seed=0,
)
outputs = llm.generate(prompts, sampling_params)
group = get_world_group() if dp_size == 1 else get_tp_group()
cpu_group = group.cpu_group
group_rank = dist.get_rank(group=cpu_group)
def test_consistent_across_ranks(obj):
if group_rank == 0:
dist.broadcast_object_list([obj], src=group.ranks[0], group=cpu_group)
else:
container = [None]
dist.broadcast_object_list(container, src=group.ranks[0], group=cpu_group)
assert container[0] == obj
test_consistent_across_ranks(llm.llm_engine.vllm_config.cache_config.num_cpu_blocks)
test_consistent_across_ranks(llm.llm_engine.vllm_config.cache_config.num_gpu_blocks)
# make sure we can access the model parameters from the calling process
# of the `LLM` instance.
params = list(
llm.llm_engine.model_executor.driver_worker.worker.model_runner.model.parameters()
)
test_consistent_across_ranks(len(params))
# all ranks should have the same outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
test_consistent_across_ranks(prompt)
test_consistent_across_ranks(generated_text)
print(f"Rank {group_rank}, Prompt: {prompt!r}, Generated text: {generated_text!r}")
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_nccl_symm_mem_allreduce.py | tests/distributed/test_nccl_symm_mem_allreduce.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import random
import typing
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import vllm.envs as envs
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.distributed.device_communicators.cuda_communicator import CudaCommunicator
from vllm.distributed.device_communicators.pynccl import register_nccl_symmetric_ops
from vllm.distributed.device_communicators.pynccl_allocator import (
get_nccl_mem_pool,
is_symmetric_memory_enabled,
)
from vllm.distributed.parallel_state import (
get_tp_group,
init_distributed_environment,
initialize_model_parallel,
)
from vllm.platforms import current_platform
from vllm.utils.system_utils import update_environment_variables
torch.manual_seed(42)
random.seed(44)
test_size_elements = 4 * 1024 * 1024
def nccl_symm_mem_allreduce_worker(local_rank: int, world_size: int):
monkeypatch = pytest.MonkeyPatch()
with monkeypatch.context() as m:
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
dtype = torch.bfloat16
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)
torch.set_default_device(device)
torch.set_default_dtype(dtype)
update_environment_variables(
{
"RANK": str(local_rank),
"LOCAL_RANK": str(local_rank),
"WORLD_SIZE": str(world_size),
"MASTER_ADDR": "localhost",
"MASTER_PORT": "12345",
}
)
init_distributed_environment()
initialize_model_parallel(tensor_model_parallel_size=world_size)
cuda_communicator = typing.cast(
CudaCommunicator, get_tp_group().device_communicator
)
pynccl_comm = cuda_communicator.pynccl_comm
if get_nccl_mem_pool() is None:
pytest.skip(
"NCCL allocator compilation failed (probably missing NCCL headers)."
)
if not is_symmetric_memory_enabled():
pytest.skip("NCCL symmetric memory allreduce is disabled.")
register_nccl_symmetric_ops(pynccl_comm)
input = torch.randint(1, 23, (test_size_elements,), dtype=dtype, device=device)
input_clone = input.clone()
output = torch.ops.vllm.all_reduce_symmetric_with_copy(input)
assert output is not None
group = get_tp_group().device_group
dist.all_reduce(input_clone, group=group)
torch.testing.assert_close(output, input_clone, atol=2.5, rtol=0.1)
@pytest.mark.skipif(
not current_platform.is_cuda(),
reason="NCCLSymmMemAllreduce is only available for CUDA platforms.",
)
@pytest.mark.parametrize("world_size", [2])
@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"], reason="Only test on CUDA")
def test_nccl_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch, world_size):
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
# Enable SymmMemCommunicator
monkeypatch.setenv("VLLM_USE_NCCL_SYMM_MEM", "1")
monkeypatch.setenv("NCCL_NVLS_ENABLE", "1")
monkeypatch.setenv("NCCL_CUMEM_ENABLE", "1")
mp.spawn(nccl_symm_mem_allreduce_worker, args=(world_size,), nprocs=world_size)
cleanup_dist_env_and_memory()
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_pipeline_parallel.py | tests/distributed/test_pipeline_parallel.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
WARNING: This test runs in both single-node (4 GPUs) and multi-node
(2 node with 2 GPUs each) modes. If the test only uses 2 GPUs, it is
important to set the distributed backend to "mp" to avoid Ray scheduling
all workers in a node other than the head node, which can cause the test
to fail.
"""
import json
import os
from dataclasses import dataclass
from typing import Literal, NamedTuple
import pytest
from vllm.config.model import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
from vllm.logger import init_logger
from vllm.transformers_utils.config import get_config
from ..models.registry import HF_EXAMPLE_MODELS
from ..utils import compare_two_settings, create_new_process_for_each_test
logger = init_logger("test_pipeline_parallel")
VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
class ParallelSetup(NamedTuple):
tp_size: int
pp_size: int
eager_mode: bool
class PPTestOptions(NamedTuple):
multi_node_only: bool
load_format: str | None = None
@dataclass
class PPTestSettings:
parallel_setups: list[ParallelSetup]
distributed_backends: list[str]
runner: RunnerOption
test_options: PPTestOptions
@staticmethod
def detailed(
*,
tp_base: int = 1,
pp_base: int = 2,
multi_node_only: bool = False,
runner: RunnerOption = "auto",
load_format: str | None = None,
):
return PPTestSettings(
parallel_setups=[
ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=False),
ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=False),
ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=True),
ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=False),
ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=True),
],
distributed_backends=["mp", "ray"],
runner=runner,
test_options=PPTestOptions(
multi_node_only=multi_node_only, load_format=load_format
),
)
@staticmethod
def fast(
*,
tp_base: int = 1,
pp_base: int = 2,
runner: RunnerOption = "auto",
multi_node_only: bool = False,
load_format: str | None = None,
):
return PPTestSettings(
parallel_setups=[
ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=True),
],
distributed_backends=["mp"],
runner=runner,
test_options=PPTestOptions(
multi_node_only=multi_node_only, load_format=load_format
),
)
def iter_params(self, model_id: str):
opts = self.test_options
for parallel_setup in self.parallel_setups:
for backend in self.distributed_backends:
yield (model_id, parallel_setup, backend, self.runner, opts)
# NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU
# The values displayed here are only a rough indicator of the size of the model
TEXT_GENERATION_MODELS = {
# [Decoder-only]
# Uses Llama
# "BAAI/AquilaChat-7B": PPTestSettings.fast(),
"Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(load_format="dummy"),
"baichuan-inc/Baichuan-7B": PPTestSettings.fast(),
"baichuan-inc/Baichuan2-13B-Chat": PPTestSettings.fast(),
"bigscience/bloomz-1b1": PPTestSettings.fast(),
"zai-org/chatglm3-6b": PPTestSettings.fast(),
"CohereLabs/c4ai-command-r-v01": PPTestSettings.fast(load_format="dummy"),
"databricks/dbrx-instruct": PPTestSettings.fast(load_format="dummy"),
"Deci/DeciLM-7B-instruct": PPTestSettings.fast(),
"deepseek-ai/deepseek-llm-7b-chat": PPTestSettings.fast(),
"deepseek-ai/DeepSeek-V2-Lite-Chat": PPTestSettings.fast(tp_base=2),
"LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct": PPTestSettings.fast(),
"tiiuae/falcon-7b": PPTestSettings.fast(),
"google/gemma-1.1-2b-it": PPTestSettings.fast(),
"google/gemma-2-9b": PPTestSettings.fast(),
"gpt2": PPTestSettings.fast(),
"bigcode/starcoder": PPTestSettings.fast(),
"EleutherAI/gpt-j-6b": PPTestSettings.fast(),
"EleutherAI/pythia-1.4b": PPTestSettings.fast(),
"ibm/PowerLM-3b": PPTestSettings.fast(),
"ibm/PowerMoE-3b": PPTestSettings.fast(),
# Uses Llama
# "internlm/internlm-chat-7b": PPTestSettings.fast(),
"internlm/internlm2-chat-7b": PPTestSettings.fast(),
"inceptionai/jais-13b-chat": PPTestSettings.fast(),
"ai21labs/Jamba-tiny-dev": PPTestSettings.fast(),
"pfnet/plamo-2-1b": PPTestSettings.fast(),
"pfnet/plamo-3-nict-2b-base": PPTestSettings.fast(),
"meta-llama/Llama-3.2-1B-Instruct": PPTestSettings.detailed(),
# Tests TransformersForCausalLM
"hmellor/Ilama-3.2-1B": PPTestSettings.fast(),
"openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(),
"openbmb/MiniCPM3-4B": PPTestSettings.fast(),
# Uses Llama
# "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(),
"state-spaces/mamba-130m-hf": PPTestSettings.fast(),
"mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(load_format="dummy"),
"mosaicml/mpt-7b": PPTestSettings.fast(),
"nvidia/Minitron-8B-Base": PPTestSettings.fast(),
"allenai/OLMo-1B-hf": PPTestSettings.fast(),
"allenai/OLMo-2-0425-1B": PPTestSettings.fast(),
"allenai/OLMoE-1B-7B-0924-Instruct": PPTestSettings.fast(),
"facebook/opt-iml-max-1.3b": PPTestSettings.fast(),
"OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(),
"adept/persimmon-8b-chat": PPTestSettings.fast(),
"microsoft/phi-2": PPTestSettings.fast(),
"microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(),
"microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(
multi_node_only=True, load_format="dummy"
),
"Qwen/Qwen-7B-Chat": PPTestSettings.fast(),
"Qwen/Qwen2.5-0.5B-Instruct": PPTestSettings.fast(),
"Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(),
"stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(),
"bigcode/starcoder2-3b": PPTestSettings.fast(),
"upstage/solar-pro-preview-instruct": PPTestSettings.fast(load_format="dummy"),
# FIXME: Cannot load tokenizer in latest transformers version.
# Need to use tokenizer from `meta-llama/Llama-2-7b-chat-hf`
# "xverse/XVERSE-7B-Chat": PPTestSettings.fast(),
# [Encoder-only]
# TODO: Implement PP
# "facebook/bart-base": PPTestSettings.fast(),
}
EMBEDDING_MODELS = { # type: ignore[var-annotated]
# [Text-only]
"intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(runner="pooling"),
"BAAI/bge-multilingual-gemma2": PPTestSettings.fast(runner="pooling"),
"Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(
load_format="dummy", runner="pooling"
),
}
MULTIMODAL_MODELS = {
# [Decoder-only]
"Salesforce/blip2-opt-6.7b": PPTestSettings.fast(),
"facebook/chameleon-7b": PPTestSettings.fast(),
"adept/fuyu-8b": PPTestSettings.fast(),
"zai-org/glm-4v-9b": PPTestSettings.fast(),
"OpenGVLab/InternVL2-1B": PPTestSettings.fast(),
"llava-hf/llava-1.5-7b-hf": PPTestSettings.fast(),
"llava-hf/llava-v1.6-mistral-7b-hf": PPTestSettings.fast(),
"llava-hf/LLaVA-NeXT-Video-7B-hf": PPTestSettings.fast(),
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf": PPTestSettings.fast(),
"openbmb/MiniCPM-Llama3-V-2_5": PPTestSettings.fast(),
"allenai/Molmo-7B-D-0924": PPTestSettings.fast(),
"AIDC-AI/Ovis2-1B": PPTestSettings.fast(),
"AIDC-AI/Ovis2.5-2B": PPTestSettings.fast(),
"microsoft/Phi-3.5-vision-instruct": PPTestSettings.fast(),
"mistralai/Pixtral-12B-2409": PPTestSettings.fast(load_format="dummy"),
"Qwen/Qwen-VL-Chat": PPTestSettings.fast(),
"Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(),
"Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(),
"fixie-ai/ultravox-v0_5-llama-3_2-1b": PPTestSettings.fast(),
}
# NOTE: You can update this on your local machine to run specific tests
TEST_MODELS = [
# [LANGUAGE GENERATION]
"microsoft/Phi-3.5-MoE-instruct",
"meta-llama/Llama-3.2-1B-Instruct",
"hmellor/Ilama-3.2-1B",
"ibm/PowerLM-3b",
"deepseek-ai/DeepSeek-V2-Lite-Chat",
# [LANGUAGE EMBEDDING]
"intfloat/e5-mistral-7b-instruct",
"BAAI/bge-multilingual-gemma2",
# [MULTIMODAL GENERATION]
"OpenGVLab/InternVL2-1B",
"microsoft/Phi-3.5-vision-instruct",
"fixie-ai/ultravox-v0_5-llama-3_2-1b",
# [LANGUAGE GENERATION - HYBRID ARCH]
"ai21labs/Jamba-tiny-dev",
]
def _compare_tp(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: PPTestOptions,
num_gpus_available: int,
*,
method: Literal["generate", "encode"],
is_multimodal: bool,
):
(
tp_size,
pp_size,
eager_mode,
) = parallel_setup
multi_node_only, load_format = test_options
model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
model_info.check_transformers_version(on_fail="skip")
trust_remote_code = model_info.trust_remote_code
tokenizer_mode = model_info.tokenizer_mode
hf_overrides = model_info.hf_overrides
hf_config = get_config(model_id, trust_remote_code)
require_embed_inputs = model_info.require_embed_inputs
max_num_seqs = model_info.max_num_seqs
dtype = "float16"
if hf_config.model_type in _FLOAT16_NOT_SUPPORTED_MODELS:
dtype = "bfloat16"
if load_format == "dummy":
# Avoid OOM
text_overrides = {
"num_hidden_layers": 4,
"hidden_size": 512,
"intermediate_size": 800,
"num_attention_heads": 4,
"num_key_value_heads": 1,
}
if is_multimodal:
hf_overrides.update({"text_config": text_overrides})
else:
hf_overrides.update(text_overrides)
else:
model_info.check_available_online(on_fail="skip")
if num_gpus_available < tp_size * pp_size:
pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
if VLLM_MULTI_NODE and distributed_backend == "mp":
pytest.skip(
"Skipping multi-node pipeline parallel test for "
"multiprocessing distributed backend"
)
if multi_node_only and not VLLM_MULTI_NODE:
pytest.skip("Not in multi-node setting")
common_args = [
# use half precision for speed and memory savings in CI environment
"--dtype",
dtype,
"--max-model-len",
"2048",
"--max-num-seqs",
"8",
]
if eager_mode:
common_args.append("--enforce-eager")
if runner != "auto":
common_args.extend(["--runner", runner])
if trust_remote_code:
common_args.append("--trust-remote-code")
if tokenizer_mode:
common_args.extend(["--tokenizer-mode", tokenizer_mode])
if load_format:
common_args.extend(["--load-format", load_format])
if hf_overrides:
common_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
if require_embed_inputs:
common_args.extend(
[
"--skip-tokenizer-init",
"--enable-prompt-embeds",
"--enable-mm-embeds",
]
)
if max_num_seqs:
common_args.extend(["--max-num-seqs", f"{max_num_seqs}"])
if distributed_backend == "ray":
# Test Ray Compiled Graph for all the tests
pp_env = {
"VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": "1",
}
# Temporary. Currently when zeromq + SPMD is used, it does not properly
# terminate because of a Ray Compiled Graph issue.
common_args.append("--disable-frontend-multiprocessing")
elif distributed_backend == "mp":
pp_env = None
else:
pp_env = None
tp_env = None
pp_args = [
*common_args,
"--pipeline-parallel-size",
str(pp_size),
"--tensor-parallel-size",
str(tp_size),
"--distributed-executor-backend",
distributed_backend,
]
# compare without pipeline parallelism
# NOTE: use mp backend for TP
# PP tests might involve multiple nodes, and ray might
# schedule all workers in a node other than the head node,
# which can cause the test to fail.
tp_args = [
*common_args,
"--tensor-parallel-size",
str(tp_size),
"--distributed-executor-backend",
"mp",
]
compare_two_settings(model_id, pp_args, tp_args, pp_env, tp_env, method=method)
@pytest.mark.parametrize(
("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
[
params
for model_id, settings in TEXT_GENERATION_MODELS.items()
for params in settings.iter_params(model_id)
if model_id in TEST_MODELS
],
)
@create_new_process_for_each_test()
def test_tp_language_generation(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: PPTestOptions,
num_gpus_available,
):
_compare_tp(
model_id,
parallel_setup,
distributed_backend,
runner,
test_options,
num_gpus_available,
method="generate",
is_multimodal=False,
)
@pytest.mark.parametrize(
("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
[
params
for model_id, settings in EMBEDDING_MODELS.items()
for params in settings.iter_params(model_id)
if model_id in TEST_MODELS
],
)
@create_new_process_for_each_test()
def test_tp_language_embedding(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: PPTestOptions,
num_gpus_available,
):
_compare_tp(
model_id,
parallel_setup,
distributed_backend,
runner,
test_options,
num_gpus_available,
method="encode",
is_multimodal=False,
)
@pytest.mark.parametrize(
("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
[
params
for model_id, settings in MULTIMODAL_MODELS.items()
for params in settings.iter_params(model_id)
if model_id in TEST_MODELS
],
)
@create_new_process_for_each_test()
def test_tp_multimodal_generation(
model_id: str,
parallel_setup: ParallelSetup,
distributed_backend: str,
runner: RunnerOption,
test_options: PPTestOptions,
num_gpus_available,
):
_compare_tp(
model_id,
parallel_setup,
distributed_backend,
runner,
test_options,
num_gpus_available,
method="generate",
is_multimodal=True,
)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_multi_node_assignment.py | tests/distributed/test_multi_node_assignment.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Make sure ray assigns GPU workers to the correct node.
Run:
```sh
cd $VLLM_PATH/tests
pytest distributed/test_multi_node_assignment.py
```
"""
import os
import pytest
import ray
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from vllm import initialize_ray_cluster
from vllm.config import ParallelConfig
from vllm.utils.network_utils import get_ip
from vllm.v1.executor.ray_utils import _wait_until_pg_removed
VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
@pytest.mark.skipif(
not VLLM_MULTI_NODE, reason="Need at least 2 nodes to run the test."
)
def test_multi_node_assignment() -> None:
# NOTE: important to keep this class definition here
# to let ray use cloudpickle to serialize it.
class Actor:
def get_ip(self):
return get_ip()
for _ in range(10):
config = ParallelConfig(1, 2)
initialize_ray_cluster(config)
current_ip = get_ip()
workers = []
for bundle_id, bundle in enumerate(config.placement_group.bundle_specs):
if not bundle.get("GPU", 0):
continue
scheduling_strategy = PlacementGroupSchedulingStrategy(
placement_group=config.placement_group,
placement_group_capture_child_tasks=True,
placement_group_bundle_index=bundle_id,
)
worker = ray.remote(
num_cpus=0,
num_gpus=1,
scheduling_strategy=scheduling_strategy,
)(Actor).remote()
worker_ip = ray.get(worker.get_ip.remote())
assert worker_ip == current_ip
workers.append(worker)
for worker in workers:
ray.kill(worker)
_wait_until_pg_removed(config.placement_group)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_symm_mem_allreduce.py | tests/distributed/test_symm_mem_allreduce.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import queue
import random
import typing
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import vllm.envs as envs
from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
from vllm.distributed.device_communicators.cuda_communicator import CudaCommunicator
from vllm.distributed.parallel_state import (
get_tp_group,
init_distributed_environment,
initialize_model_parallel,
)
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.llm_engine import LLMEngine
from vllm.platforms import current_platform
from vllm.utils.system_utils import update_environment_variables
torch.manual_seed(42)
random.seed(44)
test_size_elements = 1024 * 1024
def symm_mem_allreduce_worker(local_rank: int, world_size: int, q: mp.Queue):
monkeypatch = pytest.MonkeyPatch()
config = VllmConfig(parallel_config=ParallelConfig(tensor_parallel_size=world_size))
with monkeypatch.context() as m, set_current_vllm_config(config):
m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
dtype = torch.bfloat16
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)
torch.set_default_device(device)
torch.set_default_dtype(dtype)
update_environment_variables(
{
"RANK": str(local_rank),
"LOCAL_RANK": str(local_rank),
"WORLD_SIZE": str(world_size),
"MASTER_ADDR": "localhost",
"MASTER_PORT": "12345",
}
)
init_distributed_environment()
initialize_model_parallel(tensor_model_parallel_size=world_size)
cuda_communicator = typing.cast(
CudaCommunicator, get_tp_group().device_communicator
)
symm_mem_comm = cuda_communicator.symm_mem_comm
if symm_mem_comm is None or symm_mem_comm.disabled:
# can't use skip under multiprocessing
q.put("SymmMemCommunicator is not available or disabled.")
return
inp_direct_symm_mem = torch.randint(
1, 23, (test_size_elements,), dtype=dtype, device=device
)
if not symm_mem_comm.should_use_symm_mem(inp_direct_symm_mem):
# can't use skip under multiprocessing
q.put("SymmMemCommunicator isn't used for this world and input size.")
return
original_inp_direct_symm_mem = inp_direct_symm_mem.clone()
out_direct_symm_mem = symm_mem_comm.all_reduce(inp_direct_symm_mem)
assert out_direct_symm_mem is not None
group = get_tp_group().device_group
dist.all_reduce(original_inp_direct_symm_mem, group=group)
torch.testing.assert_close(
out_direct_symm_mem, original_inp_direct_symm_mem, atol=2.5, rtol=0.1
)
# Test tensor_model_parallel_all_reduce which should use symm_mem
inp_tensor_parallel = torch.randint(
-23, 1, (test_size_elements,), dtype=dtype, device=device
)
original_inp_tensor_parallel = inp_tensor_parallel.clone()
out_tensor_parallel = tensor_model_parallel_all_reduce(inp_tensor_parallel)
dist.all_reduce(original_inp_tensor_parallel, group=group)
torch.testing.assert_close(
out_tensor_parallel, original_inp_tensor_parallel, atol=2.5, rtol=0.1
)
@pytest.mark.skipif(
not current_platform.is_cuda(),
reason="SymmMemAllreduce is only available for CUDA platforms.",
)
@pytest.mark.parametrize("tp_size", [2])
@pytest.mark.parametrize("pipeline_parallel_size", [1])
@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"], reason="Only test on CUDA")
def test_symm_mem_allreduce(
monkeypatch: pytest.MonkeyPatch, tp_size, pipeline_parallel_size
):
world_size = tp_size * pipeline_parallel_size
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
q = mp.get_context("spawn").Queue()
mp.spawn(symm_mem_allreduce_worker, args=(world_size, q), nprocs=world_size)
try:
val = q.get(timeout=1)
except queue.Empty:
val = None
finally:
cleanup_dist_env_and_memory()
if val is not None:
pytest.skip(val)
@pytest.mark.skipif(
not current_platform.is_cuda(),
reason="SymmMemAllreduce is only available for CUDA platforms.",
)
@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"], reason="Only test on CUDA")
def test_dp_with_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch):
world_size = 4
if world_size > torch.cuda.device_count():
pytest.skip("Not enough GPUs to run the test.")
# Verify that the DataParallel runs without error
engine_args = EngineArgs(
model="distilbert/distilgpt2",
enforce_eager=True,
enable_prefix_caching=True,
data_parallel_size=2,
tensor_parallel_size=2,
data_parallel_backend="mp",
)
LLMEngine.from_engine_args(engine_args)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/conftest.py | tests/distributed/conftest.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import random
import msgspec
import msgspec.msgpack
import pytest
import zmq
from vllm.config.kv_events import KVEventsConfig
from vllm.distributed.kv_events import EventPublisherFactory
from .test_events import SampleBatch
DP_RANK = 0
@pytest.fixture
def random_port():
"""Generate a random port number for testing"""
return random.randint(10000, 59900)
@pytest.fixture
def publisher_config(random_port, request):
"""Create a publisher config with inproc transport"""
how = request.param if hasattr(request, "param") else "inproc"
if how == "inproc":
endpoint = f"inproc://test-{random_port}"
replay_endpoint = endpoint + "-replay"
else:
endpoint = f"tcp://*:{random_port}"
replay_endpoint = f"tcp://*:{random_port + 100}"
return KVEventsConfig(
enable_kv_cache_events=True,
publisher="zmq",
endpoint=endpoint,
replay_endpoint=replay_endpoint,
buffer_steps=100,
hwm=1000,
topic="test",
)
@pytest.fixture
def publisher(publisher_config):
"""Create and return a publisher instance"""
pub = EventPublisherFactory.create(publisher_config, DP_RANK)
yield pub
pub.shutdown()
@pytest.fixture
def subscriber(publisher_config):
"""Create and return a subscriber for testing"""
endpoint = publisher_config.endpoint
replay_endpoint = publisher_config.replay_endpoint
if endpoint.startswith("tcp://*"):
endpoint = endpoint.replace("*", "127.0.0.1")
if replay_endpoint and replay_endpoint.startswith("tcp://*"):
replay_endpoint = replay_endpoint.replace("*", "127.0.0.1")
sub = MockSubscriber(
[endpoint],
[replay_endpoint] if replay_endpoint else None,
publisher_config.topic,
)
yield sub
sub.close()
class MockSubscriber:
"""Helper class to receive and verify published events"""
def __init__(
self,
pub_endpoints: str | list[str],
replay_endpoints: str | list[str] | None = None,
topic: str = "",
decode_type=SampleBatch,
):
self.ctx = zmq.Context.instance()
# Convert single endpoint to list for consistency
if isinstance(pub_endpoints, str):
pub_endpoints = [pub_endpoints]
if isinstance(replay_endpoints, str):
replay_endpoints = [replay_endpoints]
# Set up subscriber socket - connect to all endpoints
self.sub = self.ctx.socket(zmq.SUB)
self.sub.setsockopt(zmq.SUBSCRIBE, topic.encode("utf-8"))
for endpoint in pub_endpoints:
self.sub.connect(endpoint)
# Set up replay sockets if provided
self.replay_sockets = []
if replay_endpoints:
for replay_endpoint in replay_endpoints:
replay = self.ctx.socket(zmq.REQ)
replay.connect(replay_endpoint)
self.replay_sockets.append(replay)
self.topic = topic
self.topic_bytes = topic.encode("utf-8")
self.received_msgs: list[tuple[int, SampleBatch]] = []
self.last_seq = -1
self.decoder = msgspec.msgpack.Decoder(type=decode_type)
def receive_one(self, timeout=1000) -> tuple[int, SampleBatch] | None:
"""Receive a single message with timeout"""
if not self.sub.poll(timeout):
return None
topic_bytes, seq_bytes, payload = self.sub.recv_multipart()
assert topic_bytes == self.topic_bytes
seq = int.from_bytes(seq_bytes, "big")
data = self.decoder.decode(payload)
self.last_seq = seq
self.received_msgs.append((seq, data))
return seq, data
def request_replay(self, start_seq: int, socket_idx: int = 0) -> None:
"""Request replay of messages starting from start_seq"""
if not self.replay_sockets:
raise ValueError("Replay sockets not initialized")
if socket_idx >= len(self.replay_sockets):
raise ValueError(f"Invalid socket index {socket_idx}")
self.replay_sockets[socket_idx].send(start_seq.to_bytes(8, "big"))
def receive_replay(self, socket_idx: int = 0) -> list[tuple[int, SampleBatch]]:
"""Receive replayed messages from a specific replay socket"""
if not self.replay_sockets:
raise ValueError("Replay sockets not initialized")
if socket_idx >= len(self.replay_sockets):
raise ValueError(f"Invalid socket index {socket_idx}")
replay_socket = self.replay_sockets[socket_idx]
replayed: list[tuple[int, SampleBatch]] = []
while True:
try:
if not replay_socket.poll(1000):
break
frames = replay_socket.recv_multipart()
if not frames or not frames[-1]:
# End of replay marker
break
seq_bytes, payload = frames
seq = int.from_bytes(seq_bytes, "big")
data = self.decoder.decode(payload)
replayed.append((seq, data))
except zmq.ZMQError as _:
break
return replayed
def close(self):
"""Clean up resources"""
self.sub.close()
for replay in self.replay_sockets:
replay.close()
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_eplb_spec_decode.py | tests/distributed/test_eplb_spec_decode.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
import lm_eval
import pytest
from tests.utils import large_gpu_mark
from vllm.platforms import current_platform
def get_model_args(
model_name: str,
spec_model_name: str | None,
spec_method: str,
tp_size: int,
model_max_len: int,
use_async: bool = False,
) -> dict:
speculative_config = {
"method": spec_method,
"model": spec_model_name,
"num_speculative_tokens": 1,
"max_model_len": model_max_len,
}
eplb_config = {
"num_redundant_experts": tp_size,
"window_size": 128,
"step_interval": 1024,
"log_balancedness": False,
}
if use_async:
eplb_config["use_async"] = True
model_args = {
"pretrained": model_name,
"dtype": "auto",
"add_bos_token": True,
"tensor_parallel_size": tp_size,
"gpu_memory_utilization": 0.7,
"speculative_config": speculative_config,
"enable_expert_parallel": True,
"eplb_config": eplb_config,
"enable_eplb": True,
"max_model_len": model_max_len,
}
return model_args
pytestmark = pytest.mark.skipif(
current_platform.is_rocm(),
reason="EPLB with Spec Decode is a work in progress on ROCm.",
)
@pytest.mark.parametrize(
"model_setup",
[
pytest.param(
("mtp", "Qwen/Qwen3-Next-80B-A3B-Instruct", None, 4, 0.86),
marks=large_gpu_mark(min_gb=80),
),
pytest.param(
(
"eagle",
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
"morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct",
4,
0.92,
),
marks=pytest.mark.skip(reason="Skipping due to CI OOM issues"),
),
],
ids=["qwen3_next_mtp", "llama4_eagle"],
)
def test_eplb_spec_decode(
monkeypatch: pytest.MonkeyPatch,
model_setup: tuple[str, str, str, int, float],
):
"""
Test the correctness of EPLB speculative decoding with GSM8K dataset.
Applicable to MoE models with mtp or eagle spec decode.
"""
method, model_name, spec_model_name, tp_size, expected_gsm8k_value = model_setup
TASK = "gsm8k"
FILTER = "exact_match,strict-match"
RTOL = 0.03
model_args = get_model_args(
model_name=model_name,
spec_model_name=spec_model_name,
spec_method=method,
tp_size=tp_size,
model_max_len=4096,
)
results = lm_eval.simple_evaluate(
model="vllm",
model_args=model_args,
tasks=TASK,
batch_size=64,
num_fewshot=8,
)
measured_value = results["results"][TASK][FILTER]
assert (
measured_value - RTOL < expected_gsm8k_value
and measured_value + RTOL > expected_gsm8k_value
), f"Expected: {expected_gsm8k_value} | Measured: {measured_value}"
@large_gpu_mark(min_gb=80)
def test_eplb_spec_decode_qwen3_next_mtp_async() -> None:
"""
Ensure async EPLB works with MTP speculative decoding for Qwen3-Next.
"""
TASK = "gsm8k"
FILTER = "exact_match,strict-match"
RTOL = 0.03
expected_gsm8k_value = 0.86
model_args = get_model_args(
model_name="Qwen/Qwen3-Next-80B-A3B-Instruct",
spec_model_name=None,
spec_method="mtp",
tp_size=4,
model_max_len=4096,
use_async=True,
)
results = lm_eval.simple_evaluate(
model="vllm",
model_args=model_args,
tasks=TASK,
batch_size=64,
num_fewshot=8,
)
measured_value = results["results"][TASK][FILTER]
assert (
measured_value - RTOL < expected_gsm8k_value
and measured_value + RTOL > expected_gsm8k_value
), f"Expected: {expected_gsm8k_value} | Measured: {measured_value}"
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/__init__.py | tests/distributed/__init__.py | python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false | |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_same_node.py | tests/distributed/test_same_node.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
import torch
import torch.distributed as dist
from vllm.distributed.parallel_state import in_the_same_node_as
from vllm.distributed.utils import StatelessProcessGroup
from vllm.utils.network_utils import get_ip, get_open_port
def _run_test(pg):
test_result = all(in_the_same_node_as(pg, source_rank=0))
expected = os.environ.get("VLLM_TEST_SAME_HOST", "1") == "1"
assert test_result == expected, f"Expected {expected}, got {test_result}"
if pg == dist.group.WORLD:
print("Same node test passed! when using torch distributed!")
else:
print("Same node test passed! when using StatelessProcessGroup!")
if __name__ == "__main__":
dist.init_process_group(backend="gloo")
rank = dist.get_rank()
if rank == 0:
port = get_open_port()
ip = get_ip()
dist.broadcast_object_list([ip, port], src=0)
else:
recv = [None, None]
dist.broadcast_object_list(recv, src=0)
ip, port = recv
stateless_pg = StatelessProcessGroup.create(ip, port, rank, dist.get_world_size())
for pg in [dist.group.WORLD, stateless_pg]:
if os.environ.get("VLLM_TEST_WITH_DEFAULT_DEVICE_SET", "0") == "1":
default_devices = ["cpu"]
if torch.cuda.is_available():
default_devices.append("cuda")
for device in default_devices:
torch.set_default_device(device)
_run_test(pg)
else:
_run_test(pg)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_pynccl.py | tests/distributed/test_pynccl.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
import multiprocess as mp
import numpy as np
import pytest
import torch
import torch.distributed
from vllm.distributed.communication_op import tensor_model_parallel_all_reduce # noqa
from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
from vllm.distributed.device_communicators.pynccl_wrapper import NCCLLibrary
from vllm.distributed.parallel_state import (
ensure_model_parallel_initialized,
get_world_group,
graph_capture,
init_distributed_environment,
)
from vllm.utils.system_utils import update_environment_variables
mp.set_start_method("spawn", force=True)
def distributed_run(fn, world_size):
number_of_processes = world_size
processes: list[mp.Process] = []
for i in range(number_of_processes):
env: dict[str, str] = {}
env["RANK"] = str(i)
env["LOCAL_RANK"] = str(i)
env["WORLD_SIZE"] = str(number_of_processes)
env["LOCAL_WORLD_SIZE"] = str(number_of_processes)
env["MASTER_ADDR"] = "localhost"
env["MASTER_PORT"] = "12345"
p = mp.Process(target=fn, args=(env,))
processes.append(p)
p.start()
for p in processes:
p.join()
for p in processes:
assert p.exitcode == 0
def worker_fn_wrapper(fn):
# `multiprocessing.Process` cannot accept environment variables directly
# so we need to pass the environment variables as arguments
# and update the environment variables in the function
def wrapped_fn(env):
update_environment_variables(env)
local_rank = os.environ["LOCAL_RANK"]
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)
init_distributed_environment()
fn()
return wrapped_fn
@worker_fn_wrapper
def worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
tensor = torch.ones(16, 1024, 1024, dtype=torch.float32).cuda(pynccl_comm.rank)
tensor = pynccl_comm.all_reduce(tensor)
torch.cuda.synchronize()
assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl():
distributed_run(worker_fn, 2)
@worker_fn_wrapper
def multiple_allreduce_worker_fn():
device = torch.device(f"cuda:{torch.distributed.get_rank()}")
groups = [
torch.distributed.new_group(ranks=[0, 1], backend="gloo"),
torch.distributed.new_group(ranks=[2, 3], backend="gloo"),
]
group = groups[0] if torch.distributed.get_rank() in [0, 1] else groups[1]
pynccl_comm = PyNcclCommunicator(group=group, device=device)
tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
# two groups can communicate independently
if torch.distributed.get_rank() in [0, 1]:
tensor = pynccl_comm.all_reduce(tensor)
tensor = pynccl_comm.all_reduce(tensor)
torch.cuda.synchronize()
assert torch.all(tensor == 4).cpu().item()
else:
tensor = pynccl_comm.all_reduce(tensor)
torch.cuda.synchronize()
assert torch.all(tensor == 2).cpu().item()
@pytest.mark.skipif(
torch.cuda.device_count() < 4, reason="Need at least 4 GPUs to run the test."
)
def test_pynccl_multiple_allreduce():
# this tests pynccl for multiple tp groups, in a standalone way
# i.e. call `pynccl_comm.all_reduce` directly
distributed_run(multiple_allreduce_worker_fn, 4)
@worker_fn_wrapper
def multiple_allreduce_with_vllm_worker_fn():
device = torch.device(f"cuda:{torch.distributed.get_rank()}")
ensure_model_parallel_initialized(2, 2)
tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
with graph_capture(device=device):
# two tp groups can communicate independently
if torch.distributed.get_rank() in [0, 1]:
tensor = tensor_model_parallel_all_reduce(tensor)
tensor = tensor_model_parallel_all_reduce(tensor)
torch.cuda.synchronize()
assert torch.all(tensor == 4).cpu().item()
else:
tensor = tensor_model_parallel_all_reduce(tensor)
torch.cuda.synchronize()
assert torch.all(tensor == 2).cpu().item()
@pytest.mark.skipif(
torch.cuda.device_count() < 4, reason="Need at least 4 GPUs to run the test."
)
def test_pynccl_multiple_allreduce_with_vllm():
# this tests pynccl for multiple tp groups, together with vllm
# i.e. call `tensor_model_parallel_all_reduce`
distributed_run(multiple_allreduce_with_vllm_worker_fn, 4)
@worker_fn_wrapper
def worker_fn_with_cudagraph():
with torch.no_grad():
graph = torch.cuda.CUDAGraph()
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
# run something in the default stream to initialize torch engine
a = torch.ones((4, 4), device=f"cuda:{pynccl_comm.rank}")
torch.cuda.synchronize()
with torch.cuda.graph(graph):
a_out = pynccl_comm.all_reduce(a)
torch.cuda.synchronize()
graph.replay()
torch.cuda.synchronize()
assert torch.all(a_out == pynccl_comm.world_size).cpu().item()
@worker_fn_wrapper
def all_gather_worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
rank = pynccl_comm.rank
world_size = pynccl_comm.world_size
device = f"cuda:{pynccl_comm.rank}"
num_elems = 1000
tensor = (
torch.arange(num_elems, dtype=torch.float32, device=device) + rank * num_elems
)
result = torch.zeros(num_elems * world_size, dtype=torch.float32, device=device)
expected = torch.cat(
[
torch.arange(num_elems, dtype=torch.float32) + r * num_elems
for r in range(world_size)
]
).to(device)
pynccl_comm.all_gather(result, tensor)
torch.cuda.synchronize()
torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_all_gather():
distributed_run(all_gather_worker_fn, 2)
@worker_fn_wrapper
def all_gatherv_worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
rank = pynccl_comm.rank
world_size = pynccl_comm.world_size
device = f"cuda:{pynccl_comm.rank}"
assert world_size <= 8
sizes = [81, 20, 57, 52, 81, 5, 49, 49][:world_size]
num_elems = sizes[rank]
tensor = torch.arange(num_elems, dtype=torch.float32, device=device) + rank * 100
result = torch.zeros(sum(sizes), dtype=torch.float32, device=device)
expected = torch.cat(
[
torch.arange(sizes[r], dtype=torch.float32) + r * 100
for r in range(world_size)
]
).to(device)
pynccl_comm.all_gatherv(result, tensor, sizes=sizes)
torch.cuda.synchronize()
torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_all_gatherv():
distributed_run(all_gatherv_worker_fn, 2)
@worker_fn_wrapper
def reduce_scatter_worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
rank = pynccl_comm.rank
world_size = pynccl_comm.world_size
device = f"cuda:{pynccl_comm.rank}"
num_elems = 1000
tensor = (
torch.arange(num_elems, dtype=torch.float32, device=device) + rank * num_elems
)
assert num_elems % world_size == 0
result = torch.zeros(num_elems // world_size, dtype=torch.float32, device=device)
# Calculate expected result for this rank's chunk
scattered_size = num_elems // world_size
all_tensors = [
torch.arange(num_elems, dtype=torch.float32) + r * num_elems
for r in range(world_size)
]
expected = sum(
tensor[rank * scattered_size : (rank + 1) * scattered_size]
for tensor in all_tensors
).to(device)
pynccl_comm.reduce_scatter(result, tensor)
torch.cuda.synchronize()
torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_reduce_scatter():
distributed_run(reduce_scatter_worker_fn, 2)
@worker_fn_wrapper
def reduce_scatterv_worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
rank = pynccl_comm.rank
world_size = pynccl_comm.world_size
device = f"cuda:{pynccl_comm.rank}"
assert world_size <= 8
sizes = [81, 20, 57, 52, 81, 5, 49, 49][:world_size]
num_elems = sum(sizes)
tensor = torch.arange(num_elems, dtype=torch.float32, device=device) + rank * 100
result = torch.zeros(sizes[rank], dtype=torch.float32, device=device)
# Calculate expected result for this rank's chunk
all_tensors = [
torch.arange(num_elems, dtype=torch.float32) + r * 100
for r in range(world_size)
]
sizes_cumsum = np.cumsum(sizes)
start = 0 if rank == 0 else sizes_cumsum[rank - 1]
end = sizes_cumsum[rank]
expected = sum(tensor[start:end] for tensor in all_tensors).to(device)
pynccl_comm.reduce_scatterv(result, tensor, sizes=sizes)
torch.cuda.synchronize()
torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_reduce_scatterv():
distributed_run(reduce_scatterv_worker_fn, 2)
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_with_cudagraph():
distributed_run(worker_fn_with_cudagraph, 2)
@worker_fn_wrapper
def send_recv_worker_fn():
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
if pynccl_comm.rank == 0:
tensor = torch.ones(16, 1024, 1024, dtype=torch.float32).cuda(pynccl_comm.rank)
else:
tensor = torch.empty(16, 1024, 1024, dtype=torch.float32).cuda(pynccl_comm.rank)
if pynccl_comm.rank == 0:
pynccl_comm.send(tensor, dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
else:
pynccl_comm.recv(tensor, src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
torch.cuda.synchronize()
assert torch.all(tensor == 1).cpu().item()
@pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="Need at least 2 GPUs to run the test."
)
def test_pynccl_send_recv():
distributed_run(send_recv_worker_fn, 2)
@worker_fn_wrapper
def multiple_send_recv_worker_fn():
device = torch.device(f"cuda:{torch.distributed.get_rank()}")
groups = [
torch.distributed.new_group(ranks=[0, 2], backend="gloo"),
torch.distributed.new_group(ranks=[1, 3], backend="gloo"),
]
group = groups[0] if torch.distributed.get_rank() in [0, 2] else groups[1]
pynccl_comm = PyNcclCommunicator(group=group, device=device)
if torch.distributed.get_rank() == 0:
tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
elif torch.distributed.get_rank() == 1:
tensor = 2 * torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
else:
tensor = torch.empty(16, 1024, 1024, dtype=torch.float32, device=device)
if torch.distributed.get_rank() in [0, 1]:
pynccl_comm.send(tensor, dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
else:
pynccl_comm.recv(tensor, src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
torch.cuda.synchronize()
if torch.distributed.get_rank() in [0, 2]:
assert torch.all(tensor == 1).cpu().item()
else:
assert torch.all(tensor == 2).cpu().item()
@pytest.mark.skipif(
torch.cuda.device_count() < 4, reason="Need at least 4 GPUs to run the test."
)
def test_pynccl_multiple_send_recv():
distributed_run(multiple_send_recv_worker_fn, 4)
@pytest.mark.skipif(
torch.cuda.device_count() < 4, reason="Need at least 4 GPUs to run the test."
)
def test_pynccl_broadcast():
distributed_run(broadcast_worker_fn, 4)
@worker_fn_wrapper
def broadcast_worker_fn():
# Test broadcast for every root rank.
# Essentially this is an all-gather operation.
pynccl_comm = PyNcclCommunicator(
get_world_group().cpu_group, device=get_world_group().device
)
recv_tensors = [
torch.empty(16, 1024, 1024, dtype=torch.float32, device=pynccl_comm.device)
for i in range(pynccl_comm.world_size)
]
recv_tensors[pynccl_comm.rank] = (
torch.ones(16, 1024, 1024, dtype=torch.float32, device=pynccl_comm.device)
* pynccl_comm.rank
)
for i in range(pynccl_comm.world_size):
pynccl_comm.broadcast(recv_tensors[i], src=i)
# the broadcast op might be launched in a different stream
# need to synchronize to make sure the tensor is ready
torch.cuda.synchronize()
assert torch.all(recv_tensors[i] == i).cpu().item()
def test_ncclGetUniqueId():
lib = NCCLLibrary()
unique_id = lib.ncclGetUniqueId()
# `list(unique_id.internal)` is something like this:
# [34, -16, 23, 83, 109, -19, 59, 95, 2, 0, -86, 55, 10, -128, 0, 29, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# as long as the function doesn't raise an exception, we're good
assert unique_id is not None
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_eplb_algo.py | tests/distributed/test_eplb_algo.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.distributed.eplb.policy.default import DefaultEplbPolicy
def test_basic_rebalance():
"""Test basic rebalancing functionality"""
# Example from https://github.com/deepseek-ai/eplb
weight = torch.tensor(
[
[90, 132, 40, 61, 104, 165, 39, 4, 73, 56, 183, 86],
[20, 107, 104, 64, 19, 197, 187, 157, 172, 86, 16, 27],
]
)
num_layers = weight.shape[0]
num_replicas = 16
num_groups = 4
num_nodes = 2
num_gpus = 8
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify output shapes
assert phy2log.shape == (
2,
16,
), f"Expected `phy2log` shape (2, 16), got {phy2log.shape}"
assert log2phy.shape[0] == 2, (
f"Expected `log2phy` first dimension 2, got {log2phy.shape[0]}"
)
assert log2phy.shape[1] == 12, (
f"Expected `log2phy` second dimension 12, got {log2phy.shape[1]}"
)
assert logcnt.shape == (
2,
12,
), f"Expected `logcnt` shape (2, 12), got {logcnt.shape}"
# Verify physical to logical expert mapping range is correct
assert torch.all(phy2log >= 0) and torch.all(phy2log < 12), (
"Physical to logical mapping should be in range [0, 12)"
)
# Verify expert count reasonableness
assert torch.all(logcnt >= 1), "Each logical expert should have at least 1 replica"
assert torch.sum(logcnt, dim=1).sum() == num_replicas * num_layers, (
f"Total replicas should be {num_replicas * num_layers}"
)
# Verify expected output
expected_phy2log = torch.tensor(
[
[5, 6, 5, 7, 8, 4, 3, 4, 10, 9, 10, 2, 0, 1, 11, 1],
[7, 10, 6, 8, 6, 11, 8, 9, 2, 4, 5, 1, 5, 0, 3, 1],
]
)
assert torch.all(phy2log == expected_phy2log)
expected_logcnt = torch.tensor(
[[1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1], [1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1]]
)
assert torch.all(logcnt == expected_logcnt)
def test_single_gpu_case():
"""Test single GPU case"""
weight = torch.tensor([[10, 20, 30, 40]])
num_replicas = 4
num_groups = 1
num_nodes = 1
num_gpus = 1
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify shapes
assert phy2log.shape == (1, 4)
assert log2phy.shape[0] == 1
assert log2phy.shape[1] == 4
assert logcnt.shape == (1, 4)
# Verify all logical experts are mapped
assert set(phy2log[0].tolist()) == {0, 1, 2, 3}
def test_equal_weights():
"""Test case with equal weights"""
weight = torch.tensor([[50, 50, 50, 50, 50, 50, 50, 50]])
num_replicas = 8
num_groups = 2
num_nodes = 2
num_gpus = 4
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify shapes
assert phy2log.shape == (1, 8)
assert logcnt.shape == (1, 8)
# With equal weights, each expert should have exactly one replica
assert torch.all(logcnt == 1), (
"With equal weights and no replication, "
"each expert should have exactly 1 replica"
)
def test_extreme_weight_imbalance():
"""Test extreme weight imbalance case"""
weight = torch.tensor([[1000, 1, 1, 1, 1, 1, 1, 1]])
num_replicas = 12
num_groups = 2
num_nodes = 2
num_gpus = 4
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify shapes
assert phy2log.shape == (1, 12)
assert logcnt.shape == (1, 8)
# Expert with highest weight (index 0) should have more replicas
assert logcnt[0, 0] > logcnt[0, 1], (
"Expert with highest weight should have more replicas"
)
def test_multiple_layers():
"""Test multiple layers case"""
weight = torch.tensor(
[
[10, 20, 30, 40, 50, 60], # First layer
[60, 50, 40, 30, 20, 10], # Second layer (opposite weight pattern)
[25, 25, 25, 25, 25, 25], # Third layer (equal weights)
]
)
num_replicas = 8
num_groups = 2
num_nodes = 2
num_gpus = 4
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify shapes
assert phy2log.shape == (3, 8)
assert logcnt.shape == (3, 6)
# Verify expert allocation is reasonable for each layer
for layer in range(3):
assert torch.all(phy2log[layer] >= 0) and torch.all(phy2log[layer] < 6), (
f"Layer {layer} physical to logical mappingshould be in range [0, 6)"
)
assert torch.sum(logcnt[layer]) == num_replicas, (
f"Layer {layer} total replicas should be {num_replicas}"
)
def test_parameter_validation():
"""Test parameter validation"""
weight = torch.tensor([[10, 20, 30, 40]])
# Test non-divisible case - this should handle normally without throwing
# errors because the function will fall back to global load balancing
# strategy
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(weight, 8, 3, 2, 4)
assert phy2log.shape == (1, 8)
assert logcnt.shape == (1, 4)
# Test cases that will actually cause errors:
# num_physical_experts not divisible by num_gpus
with pytest.raises(AssertionError):
DefaultEplbPolicy.rebalance_experts(weight, 7, 2, 2, 4) # 7 not divisible by 4
def test_small_scale_hierarchical():
"""Test small-scale hierarchical load balancing"""
weight = torch.tensor(
[
[100, 50, 200, 75, 150, 25, 300, 80], # 8 experts
]
)
num_replicas = 12
num_groups = 4 # 4 groups, 2 experts each
num_nodes = 2 # 2 nodes
num_gpus = 4 # 4 GPUs
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Verify basic constraints
assert phy2log.shape == (1, 12)
assert logcnt.shape == (1, 8)
assert torch.sum(logcnt) == num_replicas
assert torch.all(logcnt >= 1)
# Expert with highest weight should have more replicas
max_weight_expert = torch.argmax(weight[0])
assert logcnt[0, max_weight_expert] >= 2, (
"Highest weight expert should have multiple replicas"
)
def test_global_load_balance_fallback():
"""Test global load balancing fallback case"""
# When num_groups % num_nodes != 0, should fall back to global load
# balancing
weight = torch.tensor([[10, 20, 30, 40, 50, 60]])
num_replicas = 8
num_groups = 3 # Cannot be divided evenly by num_nodes=2
num_nodes = 2
num_gpus = 4
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Should work normally, just using global load balancing strategy
assert phy2log.shape == (1, 8)
assert logcnt.shape == (1, 6)
assert torch.sum(logcnt) == num_replicas
@pytest.mark.parametrize("device", ["cpu", "cuda"])
def test_device_compatibility(device):
"""Test device compatibility"""
if device == "cuda" and not torch.cuda.is_available():
pytest.skip("CUDA not available")
weight = torch.tensor([[10, 20, 30, 40]], device=device)
num_replicas = 6
num_groups = 2
num_nodes = 1
num_gpus = 2
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
# Function will convert to CPU internally, but should handle different
# device inputs normally
assert phy2log.shape == (1, 6)
assert logcnt.shape == (1, 4)
def test_additional_cases():
"""Test more edge cases and different parameter combinations"""
# Test case 1: Large-scale distributed setup
weight1 = torch.tensor(
[[50, 100, 75, 120, 90, 60, 80, 110, 40, 70, 95, 85, 65, 55, 45, 35]]
)
phy2log1, log2phy1, logcnt1 = DefaultEplbPolicy.rebalance_experts(
weight1, 24, 8, 4, 8
)
assert phy2log1.shape == (1, 24)
assert logcnt1.shape == (1, 16)
assert torch.sum(logcnt1) == 24
# Test case 2: Different weight distributions
weight2 = torch.tensor(
[
[200, 150, 100, 50, 25, 12], # Decreasing weights
[12, 25, 50, 100, 150, 200], # Increasing weights
]
)
phy2log2, log2phy2, logcnt2 = DefaultEplbPolicy.rebalance_experts(
weight2, 10, 3, 1, 2
)
assert phy2log2.shape == (2, 10)
assert logcnt2.shape == (2, 6)
# Verify high-weight experts have more replicas
for layer in range(2):
max_weight_idx = torch.argmax(weight2[layer])
assert logcnt2[layer, max_weight_idx] >= 2
if __name__ == "__main__":
weight = torch.tensor(
[
[90, 132, 40, 61, 104, 165, 39, 4, 73, 56, 183, 86],
[20, 107, 104, 64, 19, 197, 187, 157, 172, 86, 16, 27],
]
)
num_replicas = 16
num_groups = 4
num_nodes = 2
num_gpus = 8
phy2log, log2phy, logcnt = DefaultEplbPolicy.rebalance_experts(
weight, num_replicas, num_groups, num_nodes, num_gpus
)
print(phy2log)
test_basic_rebalance()
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_multiproc_executor.py | tests/distributed/test_multiproc_executor.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Integration tests for MultiprocExecutor at the executor level.
This test directly tests the executor without going through the LLM interface,
focusing on executor initialization, RPC calls, and distributed execution.
"""
import multiprocessing
import os
from tests.utils import multi_gpu_test
from vllm.config import VllmConfig
from vllm.engine.arg_utils import EngineArgs
from vllm.utils import get_open_port
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.executor.multiproc_executor import MultiprocExecutor
MODEL = "facebook/opt-125m"
def create_vllm_config(
tensor_parallel_size: int = 1,
pipeline_parallel_size: int = 1,
max_model_len: int = 256,
gpu_memory_utilization: float = 0.3,
distributed_executor_backend: str = "mp",
nnodes: int = 1,
node_rank: int = 0,
master_port: int = 0,
) -> VllmConfig:
"""Create a VllmConfig for testing using EngineArgs."""
engine_args = EngineArgs(
model=MODEL,
tensor_parallel_size=tensor_parallel_size,
pipeline_parallel_size=pipeline_parallel_size,
max_model_len=max_model_len,
gpu_memory_utilization=gpu_memory_utilization,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True,
)
vllm_config = engine_args.create_engine_config()
# Override distributed node settings if needed
if nnodes > 1 or node_rank > 0:
vllm_config.parallel_config.nnodes = nnodes
vllm_config.parallel_config.node_rank = node_rank
vllm_config.parallel_config.master_port = master_port
if nnodes > 1:
vllm_config.parallel_config.disable_custom_all_reduce = True
return vllm_config
def create_test_scheduler_output(num_requests: int = 1) -> SchedulerOutput:
"""Create a minimal SchedulerOutput for testing."""
# This is a simplified version - in practice you'd need proper
# SchedulerOutput construction based on the actual vLLM v1 API
return SchedulerOutput(
scheduled_new_reqs=[],
scheduled_resumed_reqs=[],
scheduled_running_reqs=[],
num_scheduled_tokens={},
total_num_scheduled_tokens=0,
)
def test_multiproc_executor_initialization():
"""Test that MultiprocExecutor can be initialized with proper config."""
vllm_config = create_vllm_config(
tensor_parallel_size=1,
pipeline_parallel_size=1,
)
# Create executor - this should initialize workers
executor = MultiprocExecutor(vllm_config=vllm_config)
# Verify executor properties
assert executor.world_size == 1, "World size should be 1 for single GPU"
assert executor.local_world_size == 1, "Local world size should be 1"
assert hasattr(executor, "workers"), "Executor should have workers"
assert len(executor.workers) == 1, "Should have 1 worker for single GPU"
# Clean up
executor.shutdown()
@multi_gpu_test(num_gpus=2)
def test_multiproc_executor_initialization_tensor_parallel():
"""Test MultiprocExecutor initialization with tensor parallelism."""
vllm_config = create_vllm_config(
tensor_parallel_size=2,
pipeline_parallel_size=1,
)
# Create executor
executor = MultiprocExecutor(vllm_config=vllm_config)
# Verify executor properties
assert executor.world_size == 2, "World size should be 2 for TP=2"
assert executor.local_world_size == 2, "Local world size should be 2"
assert len(executor.workers) == 2, "Should have 2 workers for TP=2"
# Verify output rank calculation
output_rank = executor._get_output_rank()
assert output_rank == 0, "Output rank should be 0 for TP=2, PP=1"
# Clean up
executor.shutdown()
@multi_gpu_test(num_gpus=2)
def test_multiproc_executor_collective_rpc():
"""Test collective RPC calls to all workers."""
vllm_config = create_vllm_config(
tensor_parallel_size=2,
pipeline_parallel_size=1,
)
# Create executor
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Test check_health RPC - should work without errors
executor.check_health()
# Test that RPC works correctly
# Note: We're just testing that the RPC mechanism works,
# not testing actual model execution here
assert not executor.is_failed, "Executor should not be in failed state"
finally:
# Clean up
executor.shutdown()
def test_multiproc_executor_failure_callback():
"""Test failure callback registration and invocation."""
vllm_config = create_vllm_config(
tensor_parallel_size=1,
pipeline_parallel_size=1,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Test callback registration
callback_invoked = []
def test_callback():
callback_invoked.append(True)
# Register callback
executor.register_failure_callback(test_callback)
# Callback should not be invoked yet
assert len(callback_invoked) == 0, "Callback should not be invoked immediately"
# Simulate failure
executor.is_failed = True
# Register another callback - should be invoked immediately
executor.register_failure_callback(test_callback)
assert len(callback_invoked) == 1, (
"Callback should be invoked when executor is failed"
)
finally:
# Clean up
executor.shutdown()
@multi_gpu_test(num_gpus=2)
def test_multiproc_executor_worker_monitor():
"""Test that worker monitor is set up correctly."""
vllm_config = create_vllm_config(
tensor_parallel_size=2,
pipeline_parallel_size=1,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Verify all worker processes are alive
for worker in executor.workers:
assert worker.proc.is_alive(), f"Worker rank {worker.rank} should be alive"
# Verify executor is not in failed state
assert not executor.is_failed, "Executor should not be in failed state"
finally:
# Clean up
executor.shutdown()
# After shutdown, workers should be terminated
import time
time.sleep(0.5) # Give processes time to terminate
for worker in executor.workers:
assert not worker.proc.is_alive(), (
f"Worker rank {worker.rank} should terminate after shutdown"
)
@multi_gpu_test(num_gpus=2)
def test_multiproc_executor_get_response_message_queues():
"""Test message queue retrieval for different ranks."""
vllm_config = create_vllm_config(
tensor_parallel_size=2,
pipeline_parallel_size=1,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Get all message queues
all_queues = executor.get_response_mqs()
assert len(all_queues) == 2, "Should have 2 message queues for 2 workers"
# Get message queue for specific rank
rank0_queue = executor.get_response_mqs(unique_reply_rank=0)
assert len(rank0_queue) == 1, "Should have 1 message queue for rank 0"
rank1_queue = executor.get_response_mqs(unique_reply_rank=1)
assert len(rank1_queue) == 1, "Should have 1 message queue for rank 1"
finally:
# Clean up
executor.shutdown()
def test_multiproc_executor_shutdown_cleanup():
"""Test that shutdown properly cleans up resources."""
vllm_config = create_vllm_config(
tensor_parallel_size=1,
pipeline_parallel_size=1,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
# Verify executor is set up
assert hasattr(executor, "workers"), "Executor should have workers"
assert len(executor.workers) > 0, "Should have at least one worker"
# Shutdown
executor.shutdown()
# Verify cleanup
import time
time.sleep(0.5) # Give processes time to terminate
for worker in executor.workers:
assert not worker.proc.is_alive(), "Worker processes should be terminated"
# Verify shutdown event is set
assert executor.shutdown_event.is_set(), "Shutdown event should be set"
# Multiple shutdowns should be safe (idempotent)
executor.shutdown()
executor.shutdown()
@multi_gpu_test(num_gpus=4)
def test_multiproc_executor_pipeline_parallel():
"""Test MultiprocExecutor with pipeline parallelism."""
vllm_config = create_vllm_config(
tensor_parallel_size=2,
pipeline_parallel_size=2,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Verify executor properties
assert executor.world_size == 4, "World size should be 4 for TP=2, PP=2"
assert len(executor.workers) == 4, "Should have 4 workers"
# Verify output rank calculation
# For TP=2, PP=2: output should be from the last PP stage (ranks 2-3)
# Specifically rank 2 (first rank of last PP stage)
output_rank = executor._get_output_rank()
assert output_rank == 2, "Output rank should be 2 (first rank of last PP stage)"
# Verify max_concurrent_batches for pipeline parallel
assert executor.max_concurrent_batches == 2, (
"Max concurrent batches should equal PP size"
)
finally:
# Clean up
executor.shutdown()
def test_multiproc_executor_properties():
"""Test various executor properties and configurations."""
vllm_config = create_vllm_config(
tensor_parallel_size=1,
pipeline_parallel_size=1,
)
executor = MultiprocExecutor(vllm_config=vllm_config)
try:
# Test supports_pp property
assert MultiprocExecutor.supports_pp is True, (
"MultiprocExecutor should support pipeline parallelism"
)
# Test world_size calculation
assert executor.world_size == (
executor.parallel_config.tensor_parallel_size
* executor.parallel_config.pipeline_parallel_size
), "World size should equal TP * PP"
# Test local_world_size calculation
assert executor.local_world_size == (
executor.parallel_config.world_size // executor.parallel_config.nnodes
), "Local world size should be world_size / nnodes"
finally:
# Clean up
executor.shutdown()
@multi_gpu_test(num_gpus=4)
def test_multiproc_executor_multi_node():
"""
Test MultiprocExecutor with multi-node configuration.
This simulates 2 nodes with TP=4:
- Node 0 (rank 0): Uses GPUs 0,1 (CUDA_VISIBLE_DEVICES=0,1) with TP=2
- Node 1 (rank 1): Uses GPUs 2,3 (CUDA_VISIBLE_DEVICES=2,3) with TP=2
Total world_size = 4, nnodes = 2
"""
port = get_open_port()
# symm_mem does not work for simulating multi instance in single node
os.environ["VLLM_ALLREDUCE_USE_SYMM_MEM"] = "0"
def run_node(node_rank: int, result_queue: multiprocessing.Queue, port: int):
"""Run a single node's executor."""
executor = None
try:
# Set CUDA_VISIBLE_DEVICES for this node
if node_rank == 0:
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
else:
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
# Create config for this node
vllm_config = create_vllm_config(
tensor_parallel_size=4, # Total TP across all nodes
pipeline_parallel_size=1,
nnodes=2, # 2 nodes
node_rank=node_rank,
master_port=port, # same port
)
# Create executor for this node
executor = MultiprocExecutor(vllm_config=vllm_config)
# Verify node-specific properties
assert executor.world_size == 4, (
f"World size should be 4 on node {node_rank}"
)
assert executor.local_world_size == 2, (
f"Local world size should be 2 on node {node_rank}"
)
assert len(executor.workers) == 2, (
f"Should have 2 local workers on node {node_rank}"
)
# Verify worker ranks are correct for this node
expected_ranks = [node_rank * 2, node_rank * 2 + 1]
actual_ranks = sorted([w.rank for w in executor.workers])
assert actual_ranks == expected_ranks, (
f"Node {node_rank} should have workers "
f"with ranks {expected_ranks}, got {actual_ranks}"
)
# Verify all workers are alive
for worker in executor.workers:
assert worker.proc.is_alive(), (
f"Worker rank {worker.rank} should be alive on node {node_rank}"
)
# executor.gen
# Put success result in queue BEFORE shutdown to avoid hanging
result_queue.put({"node": node_rank, "success": True})
import time
time.sleep(2)
executor.shutdown()
except Exception as e:
# Put failure result in queue
result_queue.put({"node": node_rank, "success": False, "error": str(e)})
raise e
finally:
if executor is not None:
executor.shutdown()
# Create a queue to collect results from both processes
result_queue: multiprocessing.Queue[dict[str, int | bool]] = multiprocessing.Queue()
# Start both node processes
processes = []
for node_rank in range(2):
p = multiprocessing.Process(
target=run_node,
args=(node_rank, result_queue, port),
name=f"Node{node_rank}",
)
p.start()
processes.append(p)
# Wait for both processes to complete
all_completed = True
for p in processes:
p.join(timeout=60)
if p.is_alive():
p.terminate()
p.join(timeout=20)
if p.is_alive():
p.kill()
p.join()
all_completed = False
# Check results from both nodes
results: list[dict[str, int | bool]] = []
while len(results) < 2:
try:
result = result_queue.get(timeout=1)
results.append(result)
except Exception:
pass
assert all_completed, "Not all processes completed successfully"
assert len(results) == 2, f"Expected 2 results, got {len(results)}"
assert results[0]["success"], f"Node 0 failed: {results[0]}"
assert results[1]["success"], f"Node 1 failed: {results[1]}"
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
vllm-project/vllm | https://github.com/vllm-project/vllm/blob/0d4044edd85de30d7d4558aeea4d1e95c7c556d6/tests/distributed/test_pipeline_partition.py | tests/distributed/test_pipeline_partition.py | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
import pytest
from vllm.distributed.utils import get_pp_indices
def test_custom_layer_partition(monkeypatch: pytest.MonkeyPatch):
with monkeypatch.context() as m:
def _verify(partition_str, num_layers, pp_size, goldens):
bak = os.environ.get("VLLM_PP_LAYER_PARTITION", None)
m.setenv("VLLM_PP_LAYER_PARTITION", partition_str)
for pp_rank, golden in enumerate(goldens):
assert get_pp_indices(num_layers, pp_rank, pp_size) == golden
if bak is not None:
m.setenv("VLLM_PP_LAYER_PARTITION", bak)
# Even partition
_verify("5,5,5,5", 20, 4, [(0, 5), (5, 10), (10, 15), (15, 20)])
# Balanced partition
_verify("4,6,6,4", 20, 4, [(0, 4), (4, 10), (10, 16), (16, 20)])
# Put reminder somewhere
_verify("5,6,5,6", 22, 4, [(0, 5), (5, 11), (11, 16), (16, 22)])
# Invalid partition strings
with pytest.raises(ValueError):
_verify("5,5,5,5,", 20, 4, [(0, 5), (5, 10), (10, 15), (15, 20)])
with pytest.raises(ValueError):
_verify("5,5,5,a", 20, 4, [(0, 5), (5, 10), (10, 15), (15, 20)])
# Wrong number of partitions
with pytest.raises(ValueError):
_verify("5,5,5", 20, 4, [(0, 5), (5, 10), (10, 15), (15, 20)])
# Wrong number of layers
with pytest.raises(ValueError):
_verify("5,5,5,5", 21, 4, [(0, 5), (5, 10), (10, 15), (15, 20)])
@pytest.mark.parametrize(
"num_hidden_layers,pp_size,pp_rank,indices",
[
# pp_size 2
(2, 2, 0, (0, 1)),
(2, 2, 1, (1, 2)),
(3, 2, 0, (0, 2)),
(3, 2, 1, (2, 3)),
# pp_size 3
(3, 3, 0, (0, 1)),
(3, 3, 1, (1, 2)),
(3, 3, 2, (2, 3)),
(4, 3, 0, (0, 1)),
(4, 3, 1, (1, 3)),
(4, 3, 2, (3, 4)),
(5, 3, 0, (0, 2)),
(5, 3, 1, (2, 4)),
(5, 3, 2, (4, 5)),
],
)
def test_uneven_auto_partition(
num_hidden_layers: int,
pp_size: int,
pp_rank: int,
indices: tuple[int, int],
):
assert indices == get_pp_indices(num_hidden_layers, pp_rank, pp_size)
| python | Apache-2.0 | 0d4044edd85de30d7d4558aeea4d1e95c7c556d6 | 2026-01-04T14:38:19.902011Z | false |
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