mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 05:04:58 +08:00
86ae693f20
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
1520 lines
42 KiB
Python
1520 lines
42 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import warnings
|
|
from collections.abc import Mapping
|
|
from typing import Literal, Optional
|
|
|
|
import pytest
|
|
from mistral_common.tokens.tokenizers.base import (SpecialTokenPolicy,
|
|
SpecialTokens)
|
|
from mistral_common.tokens.tokenizers.tekken import (SpecialTokenInfo,
|
|
Tekkenizer)
|
|
|
|
from vllm.assets.audio import AudioAsset
|
|
from vllm.assets.image import ImageAsset
|
|
from vllm.assets.video import VideoAsset
|
|
from vllm.config import ModelConfig
|
|
from vllm.entrypoints.chat_utils import (_try_extract_ast, load_chat_template,
|
|
parse_chat_messages,
|
|
parse_chat_messages_futures,
|
|
resolve_chat_template_content_format,
|
|
resolve_hf_chat_template)
|
|
from vllm.entrypoints.llm import apply_hf_chat_template
|
|
from vllm.multimodal import MultiModalDataDict
|
|
from vllm.multimodal.utils import (encode_audio_base64, encode_image_base64,
|
|
encode_video_base64)
|
|
from vllm.transformers_utils.tokenizer_group import TokenizerGroup
|
|
from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
|
|
|
|
from ..models.registry import HF_EXAMPLE_MODELS
|
|
from ..utils import VLLM_PATH
|
|
|
|
EXAMPLES_DIR = VLLM_PATH / "examples"
|
|
|
|
PHI3V_MODEL_ID = "microsoft/Phi-3.5-vision-instruct"
|
|
ULTRAVOX_MODEL_ID = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
|
|
QWEN2AUDIO_MODEL_ID = "Qwen/Qwen2-Audio-7B-Instruct"
|
|
QWEN2VL_MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct"
|
|
QWEN25VL_MODEL_ID = "Qwen/Qwen2.5-VL-3B-Instruct"
|
|
QWEN25OMNI_MODEL_ID = "Qwen/Qwen2.5-Omni-7B"
|
|
MLLAMA_MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct"
|
|
LLAMA_GUARD_MODEL_ID = "meta-llama/Llama-Guard-3-1B"
|
|
HERMES_MODEL_ID = "NousResearch/Hermes-3-Llama-3.1-8B"
|
|
MISTRAL_MODEL_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
|
|
|
|
|
|
@pytest.fixture(scope="function")
|
|
def phi3v_model_config():
|
|
return ModelConfig(PHI3V_MODEL_ID,
|
|
runner="generate",
|
|
trust_remote_code=True,
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
})
|
|
|
|
|
|
@pytest.fixture(scope="function")
|
|
def phi3v_model_config_mm_interleaved():
|
|
return ModelConfig(PHI3V_MODEL_ID,
|
|
runner="generate",
|
|
trust_remote_code=True,
|
|
interleave_mm_strings=True,
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
})
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def phi3v_tokenizer():
|
|
return TokenizerGroup(
|
|
tokenizer_id=PHI3V_MODEL_ID,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="function")
|
|
def qwen25omni_model_config_mm_interleaved():
|
|
return ModelConfig(QWEN25OMNI_MODEL_ID,
|
|
runner="generate",
|
|
interleave_mm_strings=True,
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
"audio": 1,
|
|
"video": 1,
|
|
})
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def qwen25omni_tokenizer():
|
|
return TokenizerGroup(
|
|
tokenizer_id=QWEN25OMNI_MODEL_ID,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def mllama_model_config():
|
|
return ModelConfig(MLLAMA_MODEL_ID,
|
|
runner="generate",
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
})
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def mllama_tokenizer():
|
|
return TokenizerGroup(
|
|
MLLAMA_MODEL_ID,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="function")
|
|
def mistral_model_config():
|
|
return ModelConfig(MISTRAL_MODEL_ID,
|
|
runner="generate",
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
})
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def mistral_tokenizer():
|
|
return TokenizerGroup(
|
|
tokenizer_id=MISTRAL_MODEL_ID,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def image_url():
|
|
image = ImageAsset('cherry_blossom')
|
|
base64 = encode_image_base64(image.pil_image)
|
|
return f"data:image/jpeg;base64,{base64}"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def video_url():
|
|
video = VideoAsset('baby_reading', 1)
|
|
base64 = encode_video_base64(video.np_ndarrays)
|
|
return f"data:video/jpeg;base64,{base64}"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def audio_url():
|
|
audio = AudioAsset('mary_had_lamb')
|
|
base64 = encode_audio_base64(*audio.audio_and_sample_rate)
|
|
return f"data:audio/ogg;base64,{base64}"
|
|
|
|
|
|
def _assert_mm_data_is_image_input(
|
|
mm_data: Optional[MultiModalDataDict],
|
|
image_count: int,
|
|
) -> None:
|
|
assert mm_data is not None
|
|
assert set(mm_data.keys()) == {"image"}
|
|
|
|
image_data = mm_data.get("image")
|
|
assert image_data is not None
|
|
|
|
assert isinstance(image_data, list) and len(image_data) == image_count
|
|
|
|
|
|
ModalityType = Literal["image", "video", "audio"]
|
|
MultiModalDataCounts = Mapping[ModalityType, int]
|
|
|
|
|
|
def _assert_mm_data_inputs(
|
|
mm_data: Optional[MultiModalDataDict],
|
|
data_count: MultiModalDataCounts,
|
|
) -> None:
|
|
assert mm_data is not None
|
|
assert set(data_count.keys()) == (set(mm_data.keys()))
|
|
|
|
for modality, n in data_count.items():
|
|
modality_data = mm_data.get(modality)
|
|
assert modality_data is not None
|
|
assert isinstance(modality_data, list) and len(modality_data) == n
|
|
|
|
|
|
def test_parse_chat_messages_single_image(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in the image?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role": "user",
|
|
"content": "<|image_1|>\nWhat's in the image?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 1)
|
|
|
|
|
|
def test_parse_chat_messages_empty_system(
|
|
mistral_model_config,
|
|
mistral_tokenizer,
|
|
):
|
|
# Test string format
|
|
conversation, _ = parse_chat_messages(
|
|
[{
|
|
"role": "system",
|
|
"content": ""
|
|
}, {
|
|
"role": "user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Who are you?"
|
|
}]
|
|
}],
|
|
mistral_model_config,
|
|
mistral_tokenizer,
|
|
content_format="string",
|
|
)
|
|
assert conversation == [{
|
|
"role": "system",
|
|
"content": ""
|
|
}, {
|
|
"role": "user",
|
|
"content": "Who are you?"
|
|
}]
|
|
|
|
# Test openai format
|
|
conversation, _ = parse_chat_messages(
|
|
[{
|
|
"role": "system",
|
|
"content": ""
|
|
}, {
|
|
"role": "user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Who are you?"
|
|
}]
|
|
}],
|
|
mistral_model_config,
|
|
mistral_tokenizer,
|
|
content_format="openai",
|
|
)
|
|
assert conversation == [{
|
|
"role": "system",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": ""
|
|
}]
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Who are you?"
|
|
}]
|
|
}]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_chat_messages_single_image_async(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_future = parse_chat_messages_futures(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in the image?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role": "user",
|
|
"content": "<|image_1|>\nWhat's in the image?"
|
|
}]
|
|
_assert_mm_data_is_image_input(await mm_future, 1)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_pil",
|
|
"image_pil": ImageAsset('cherry_blossom').pil_image
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in these images?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"<|image_1|>\n<|image_2|>\nWhat's in these images?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_chat_messages_multiple_images_async(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_future = parse_chat_messages_futures(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_pil",
|
|
"image_pil": ImageAsset('cherry_blossom').pil_image
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in these images?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"<|image_1|>\n<|image_2|>\nWhat's in these images?"
|
|
}]
|
|
_assert_mm_data_is_image_input(await mm_future, 2)
|
|
|
|
|
|
def test_parse_chat_messages_placeholder_already_in_prompt(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type":
|
|
"text",
|
|
"text":
|
|
"What's in <|image_1|> and how does it compare to <|image_2|>?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"What's in <|image_1|> and how does it compare to <|image_2|>?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_placeholder_one_already_in_prompt(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type":
|
|
"text",
|
|
"text":
|
|
"What's in <|image_1|> and how does it compare to the other one?" # noqa: E501
|
|
}
|
|
]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"<|image_2|>\nWhat's in <|image_1|> and how does it compare to the "
|
|
"other one?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images_across_messages(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in this image?"
|
|
}]
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What about this one?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [
|
|
{
|
|
"role": "user",
|
|
"content": "<|image_1|>\nWhat's in this image?"
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "<|image_2|>\nWhat about this one?"
|
|
},
|
|
]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_context_text_format(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role": "user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What's in this text?"
|
|
}]
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role": "user",
|
|
"content": "What about this one?"
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="openai",
|
|
)
|
|
|
|
assert conversation == [
|
|
{
|
|
"role": "user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What's in this text?"
|
|
}]
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Some stuff."
|
|
}]
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What about this one?"
|
|
}]
|
|
},
|
|
]
|
|
|
|
|
|
def test_parse_chat_messages_rejects_too_many_images_in_one_message(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings(
|
|
"ignore",
|
|
message="coroutine 'async_get_and_parse_image' was never awaited")
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="At most 2 image\\(s\\) may be provided in one request\\."
|
|
):
|
|
parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in these images?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
|
|
def test_parse_chat_messages_rejects_too_many_images_across_messages(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings(
|
|
"ignore",
|
|
message="coroutine 'async_get_and_parse_image' was never awaited")
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="At most 2 image\\(s\\) may be provided in one request\\."
|
|
):
|
|
parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What's in this image?"
|
|
}]
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "What about these two?"
|
|
}]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images_uncommon_input(
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
"What's in these images?", {
|
|
"image_url": image_url
|
|
}, {
|
|
"image_url": image_url
|
|
}
|
|
]
|
|
}],
|
|
phi3v_model_config,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"<|image_1|>\n<|image_2|>\nWhat's in these images?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images_interleave(
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "I need you to compare this image"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "and this one"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "Do they have differences?"
|
|
}]
|
|
}],
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"I need you to compare this image\n<|image_1|>\nand this one\n<|image_2|>\n" # noqa: E501
|
|
"Do they have differences?"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_chat_messages_multiple_images_interleave_async(
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages_futures(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "I need you to compare this image"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "and this one"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "Do they have differences?"
|
|
}]
|
|
}],
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"I need you to compare this image\n<|image_1|>\nand this one\n<|image_2|>\n" # noqa: E501
|
|
"Do they have differences?"
|
|
}]
|
|
_assert_mm_data_is_image_input(await mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images_multiple_messages_interleave(
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "What's on this image?"
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "Be accurate."
|
|
},
|
|
]
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What's on this image?"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}]
|
|
}],
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"What's on this image?\n<|image_1|>\nBe accurate."
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role": "user",
|
|
"content": "What's on this image?\n<|image_2|>"
|
|
}]
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
|
|
|
|
def test_parse_chat_messages_multiple_modals_multiple_messages_interleave(
|
|
qwen25omni_model_config_mm_interleaved, qwen25omni_tokenizer,
|
|
image_url, video_url, audio_url):
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "What's on this image?"
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "Now listen to this audio"
|
|
},
|
|
{
|
|
"type": "audio_url",
|
|
"audio_url": {
|
|
"url": audio_url
|
|
}
|
|
},
|
|
]
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What's on this image?"
|
|
}, {
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
}, {
|
|
"type": "text",
|
|
"text": "And what's in the video?"
|
|
}, {
|
|
"type": "video_url",
|
|
"video_url": {
|
|
"url": video_url
|
|
}
|
|
}]
|
|
}],
|
|
qwen25omni_model_config_mm_interleaved,
|
|
qwen25omni_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
assert conversation == [{
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
|
|
"Now listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>"
|
|
}, {
|
|
"role": "assistant",
|
|
"content": "Some stuff."
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content":
|
|
"What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
|
|
"And what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>"
|
|
}]
|
|
|
|
_assert_mm_data_inputs(mm_data, {"image": 2, "video": 1, "audio": 1})
|
|
|
|
|
|
def test_parse_chat_messages_multiple_images_interleave_with_placeholders(
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
image_url,
|
|
):
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=r"Found more '<|image_1|>' placeholders in input prompt "
|
|
"than actual multimodal data items."):
|
|
parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": image_url
|
|
}
|
|
},
|
|
{
|
|
"type":
|
|
"text",
|
|
"text":
|
|
"I need you to compare this image\n<|image_1|>\nand this one\n<|image_2|>\n" # noqa: E501
|
|
"Do they have differences?"
|
|
},
|
|
]
|
|
}],
|
|
phi3v_model_config_mm_interleaved,
|
|
phi3v_tokenizer,
|
|
content_format="string",
|
|
)
|
|
|
|
|
|
### Mllama currently wraps images / texts as interleaved dictionaries
|
|
def test_mllama_single_image(
|
|
mllama_model_config,
|
|
mllama_tokenizer,
|
|
image_url,
|
|
):
|
|
"""Ensures that a single image is parsed correctly mllama."""
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
'type': 'text',
|
|
'text': 'The content of this image is:'
|
|
}, {
|
|
"image_url": image_url
|
|
}]
|
|
}],
|
|
mllama_model_config,
|
|
mllama_tokenizer,
|
|
content_format="openai",
|
|
)
|
|
_assert_mm_data_is_image_input(mm_data, 1)
|
|
assert conversation == [{
|
|
'role':
|
|
'user',
|
|
'content': [{
|
|
'type': 'text',
|
|
'text': 'The content of this image is:'
|
|
}, {
|
|
'type': 'image'
|
|
}]
|
|
}]
|
|
|
|
|
|
def test_mllama_interleaved_images(
|
|
mllama_model_config,
|
|
mllama_tokenizer,
|
|
image_url,
|
|
):
|
|
"""Ensures that multiple image are parsed as interleaved dicts."""
|
|
conversation, mm_data = parse_chat_messages(
|
|
[{
|
|
"role":
|
|
"user",
|
|
"content": [
|
|
{
|
|
'type': 'text',
|
|
'text': 'The content of the first image is:'
|
|
},
|
|
{
|
|
"image_url": image_url
|
|
},
|
|
{
|
|
'type': 'text',
|
|
'text': 'The content of the second image is:'
|
|
},
|
|
{
|
|
"image_url": image_url
|
|
},
|
|
]
|
|
}],
|
|
mllama_model_config,
|
|
mllama_tokenizer,
|
|
content_format="openai",
|
|
)
|
|
_assert_mm_data_is_image_input(mm_data, 2)
|
|
assert conversation == [{
|
|
'role':
|
|
'user',
|
|
'content': [{
|
|
'type': 'text',
|
|
'text': 'The content of the first image is:'
|
|
}, {
|
|
'type': 'image'
|
|
}, {
|
|
'type': 'text',
|
|
'text': 'The content of the second image is:'
|
|
}, {
|
|
'type': 'image'
|
|
}]
|
|
}]
|
|
|
|
|
|
@pytest.mark.parametrize("model", [MLLAMA_MODEL_ID])
|
|
def test_multimodal_image_parsing_matches_hf(model, image_url):
|
|
"""Checks end to end hf alignment for multimodal [image] parsing."""
|
|
|
|
def get_conversation(is_hf: bool):
|
|
img_part = {"type": "image_url", "image_url": {"url": image_url}}
|
|
if is_hf:
|
|
img_part = {'type': 'image'}
|
|
return [{
|
|
'role':
|
|
'user',
|
|
'content': [
|
|
{
|
|
'type': 'text',
|
|
'text': 'The content of the first image is:'
|
|
},
|
|
img_part,
|
|
{
|
|
'type': 'text',
|
|
'text': 'The content of the second image is:'
|
|
},
|
|
img_part,
|
|
{
|
|
'type': 'text',
|
|
'text': 'What animal is in the first image?'
|
|
},
|
|
]
|
|
}]
|
|
|
|
# Build a config for the model
|
|
model_config = ModelConfig(model,
|
|
runner="generate",
|
|
limit_mm_per_prompt={
|
|
"image": 2,
|
|
})
|
|
|
|
# Build the tokenizer group and grab the underlying tokenizer
|
|
tokenizer_group = TokenizerGroup(
|
|
model,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
)
|
|
tokenizer = tokenizer_group.tokenizer
|
|
|
|
# Build and parse a conversation with {"type": "image"} using the tokenizer
|
|
hf_conversation = get_conversation(is_hf=True)
|
|
hf_result = tokenizer.apply_chat_template(
|
|
hf_conversation,
|
|
tokenize=False,
|
|
add_generation_prompt=True,
|
|
)
|
|
|
|
# Now parse with vLLMs chat utils & apply the template
|
|
vllm_conversation = get_conversation(is_hf=False)
|
|
conversation, _ = parse_chat_messages(
|
|
vllm_conversation,
|
|
model_config,
|
|
tokenizer_group,
|
|
content_format="openai",
|
|
)
|
|
|
|
vllm_result = apply_hf_chat_template(
|
|
tokenizer=tokenizer,
|
|
conversation=conversation,
|
|
chat_template=None,
|
|
model_config=model_config,
|
|
tools=None,
|
|
add_generation_prompt=True,
|
|
)
|
|
|
|
assert hf_result == vllm_result
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model",
|
|
[
|
|
QWEN2VL_MODEL_ID, # tokenizer.chat_template is of type str
|
|
HERMES_MODEL_ID, # tokenizer.chat_template is of type dict
|
|
])
|
|
@pytest.mark.parametrize("use_tools", [True, False])
|
|
def test_resolve_hf_chat_template(sample_json_schema, model, use_tools):
|
|
"""checks that chat_template is a dict type for HF models."""
|
|
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
|
|
model_info.check_available_online(on_fail="skip")
|
|
|
|
model_config = ModelConfig(
|
|
model,
|
|
tokenizer=model_info.tokenizer or model,
|
|
tokenizer_mode=model_info.tokenizer_mode,
|
|
revision=model_info.revision,
|
|
trust_remote_code=model_info.trust_remote_code,
|
|
hf_overrides=model_info.hf_overrides,
|
|
)
|
|
|
|
# Build the tokenizer group and grab the underlying tokenizer
|
|
tokenizer_group = TokenizerGroup(
|
|
model,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
)
|
|
tokenizer = tokenizer_group.tokenizer
|
|
|
|
tools = [{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "dummy_function_name",
|
|
"description": "This is a dummy function",
|
|
"parameters": sample_json_schema
|
|
}
|
|
}] if use_tools else None
|
|
|
|
# Test detecting the tokenizer's chat_template
|
|
chat_template = resolve_hf_chat_template(
|
|
tokenizer,
|
|
chat_template=None,
|
|
tools=tools,
|
|
model_config=model_config,
|
|
)
|
|
assert isinstance(chat_template, str)
|
|
|
|
|
|
# NOTE: Qwen2-Audio default chat template is specially defined inside
|
|
# processor class instead of using `tokenizer_config.json`
|
|
# yapf: disable
|
|
@pytest.mark.parametrize(
|
|
("model", "expected_format"),
|
|
[(PHI3V_MODEL_ID, "string"),
|
|
(QWEN2VL_MODEL_ID, "openai"),
|
|
(QWEN25VL_MODEL_ID, "openai"),
|
|
(ULTRAVOX_MODEL_ID, "string"),
|
|
(QWEN2AUDIO_MODEL_ID, "openai"),
|
|
(MLLAMA_MODEL_ID, "openai"),
|
|
(LLAMA_GUARD_MODEL_ID, "openai")],
|
|
)
|
|
# yapf: enable
|
|
def test_resolve_content_format_hf_defined(model, expected_format):
|
|
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
|
|
model_info.check_available_online(on_fail="skip")
|
|
|
|
model_config = ModelConfig(
|
|
model,
|
|
tokenizer=model_info.tokenizer or model,
|
|
tokenizer_mode=model_info.tokenizer_mode,
|
|
revision=model_info.revision,
|
|
trust_remote_code=model_info.trust_remote_code,
|
|
hf_overrides=model_info.hf_overrides,
|
|
)
|
|
|
|
tokenizer_group = TokenizerGroup(
|
|
model,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
)
|
|
tokenizer = tokenizer_group.tokenizer
|
|
|
|
# Test detecting the tokenizer's chat_template
|
|
chat_template = resolve_hf_chat_template(
|
|
tokenizer,
|
|
chat_template=None,
|
|
tools=None,
|
|
model_config=model_config,
|
|
)
|
|
assert isinstance(chat_template, str)
|
|
|
|
print("[TEXT]")
|
|
print(chat_template)
|
|
print("[AST]")
|
|
print(_try_extract_ast(chat_template))
|
|
|
|
resolved_format = resolve_chat_template_content_format(
|
|
None, # Test detecting the tokenizer's chat_template
|
|
None,
|
|
"auto",
|
|
tokenizer,
|
|
model_config=model_config,
|
|
)
|
|
|
|
assert resolved_format == expected_format
|
|
|
|
|
|
# yapf: disable
|
|
@pytest.mark.parametrize(
|
|
("model", "expected_format"),
|
|
[("Salesforce/blip2-opt-2.7b", "string"),
|
|
("facebook/chameleon-7b", "string"),
|
|
("deepseek-ai/deepseek-vl2-tiny", "string"),
|
|
("microsoft/Florence-2-base", "string"),
|
|
("adept/fuyu-8b", "string"),
|
|
("google/paligemma-3b-mix-224", "string"),
|
|
("Qwen/Qwen-VL", "string"),
|
|
("Qwen/Qwen-VL-Chat", "string")],
|
|
)
|
|
# yapf: enable
|
|
def test_resolve_content_format_fallbacks(model, expected_format):
|
|
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
|
|
model_info.check_available_online(on_fail="skip")
|
|
|
|
model_config = ModelConfig(
|
|
model,
|
|
tokenizer=model_info.tokenizer or model,
|
|
tokenizer_mode=model_info.tokenizer_mode,
|
|
revision=model_info.revision,
|
|
trust_remote_code=model_info.trust_remote_code,
|
|
hf_overrides=model_info.hf_overrides,
|
|
)
|
|
|
|
tokenizer_group = TokenizerGroup(
|
|
model_config.tokenizer,
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
)
|
|
tokenizer = tokenizer_group.tokenizer
|
|
|
|
# Test detecting the tokenizer's chat_template
|
|
chat_template = resolve_hf_chat_template(
|
|
tokenizer,
|
|
chat_template=None,
|
|
tools=None,
|
|
model_config=model_config,
|
|
)
|
|
assert isinstance(chat_template, str)
|
|
|
|
print("[TEXT]")
|
|
print(chat_template)
|
|
print("[AST]")
|
|
print(_try_extract_ast(chat_template))
|
|
|
|
resolved_format = resolve_chat_template_content_format(
|
|
None, # Test detecting the tokenizer's chat_template
|
|
None,
|
|
"auto",
|
|
tokenizer,
|
|
model_config=model_config,
|
|
)
|
|
|
|
assert resolved_format == expected_format
|
|
|
|
|
|
# yapf: disable
|
|
@pytest.mark.parametrize(
|
|
("template_path", "expected_format"),
|
|
[("template_alpaca.jinja", "string"),
|
|
("template_baichuan.jinja", "string"),
|
|
("template_chatglm.jinja", "string"),
|
|
("template_chatglm2.jinja", "string"),
|
|
("template_chatml.jinja", "string"),
|
|
("template_dse_qwen2_vl.jinja", "openai"),
|
|
("template_falcon_180b.jinja", "string"),
|
|
("template_falcon.jinja", "string"),
|
|
("template_inkbot.jinja", "string"),
|
|
("template_teleflm.jinja", "string"),
|
|
("template_vlm2vec.jinja", "openai"),
|
|
("tool_chat_template_granite_20b_fc.jinja", "string"),
|
|
("tool_chat_template_hermes.jinja", "string"),
|
|
("tool_chat_template_internlm2_tool.jinja", "string"),
|
|
("tool_chat_template_llama3.1_json.jinja", "openai"),
|
|
("tool_chat_template_llama3.2_json.jinja", "openai"),
|
|
("tool_chat_template_mistral_parallel.jinja", "string"),
|
|
("tool_chat_template_mistral.jinja", "string")],
|
|
)
|
|
# yapf: enable
|
|
def test_resolve_content_format_examples(template_path, expected_format):
|
|
model_config = ModelConfig(
|
|
PHI3V_MODEL_ID, # Dummy
|
|
tokenizer=PHI3V_MODEL_ID, # Dummy
|
|
trust_remote_code=True,
|
|
)
|
|
|
|
tokenizer_group = TokenizerGroup(
|
|
PHI3V_MODEL_ID, # Dummy
|
|
enable_lora=False,
|
|
max_num_seqs=5,
|
|
max_input_length=None,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
)
|
|
dummy_tokenizer = tokenizer_group.tokenizer
|
|
dummy_tokenizer.chat_template = None
|
|
|
|
chat_template = load_chat_template(EXAMPLES_DIR / template_path)
|
|
assert isinstance(chat_template, str)
|
|
|
|
print("[TEXT]")
|
|
print(chat_template)
|
|
print("[AST]")
|
|
print(_try_extract_ast(chat_template))
|
|
|
|
resolved_format = resolve_chat_template_content_format(
|
|
chat_template,
|
|
None,
|
|
"auto",
|
|
dummy_tokenizer,
|
|
model_config=model_config,
|
|
)
|
|
|
|
assert resolved_format == expected_format
|
|
|
|
|
|
def test_parse_chat_messages_include_thinking_chunk(mistral_model_config,
|
|
mistral_tokenizer):
|
|
messages = [{
|
|
"role":
|
|
"system",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "You are a helpful assistant."
|
|
}, {
|
|
"type":
|
|
"thinking",
|
|
"closed":
|
|
True,
|
|
"thinking":
|
|
"Only return the answer when you are confident."
|
|
}]
|
|
}, {
|
|
"role": "user",
|
|
"content": "What is 2+2?"
|
|
}, {
|
|
"role":
|
|
"assistant",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Let me think about it."
|
|
}, {
|
|
"type": "thinking",
|
|
"closed": True,
|
|
"thinking": "2+2 = 4"
|
|
}, {
|
|
"type": "text",
|
|
"text": "The answer is 4.",
|
|
}],
|
|
}]
|
|
|
|
conversation_with_thinking, _ = parse_chat_messages(
|
|
messages,
|
|
mistral_model_config,
|
|
mistral_tokenizer,
|
|
content_format="openai",
|
|
)
|
|
|
|
expected_conversation = [{
|
|
"role":
|
|
"system",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "You are a helpful assistant."
|
|
}, {
|
|
"type": "text",
|
|
"text": "Only return the answer when you are confident."
|
|
}],
|
|
}, {
|
|
"role":
|
|
"user",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "What is 2+2?"
|
|
}],
|
|
}, {
|
|
"role":
|
|
"assistant",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "Let me think about it."
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "2+2 = 4"
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "The answer is 4."
|
|
},
|
|
]
|
|
}]
|
|
|
|
assert conversation_with_thinking == expected_conversation
|
|
|
|
|
|
def test_apply_mistral_chat_template_thinking_chunk():
|
|
# Moved import here to avoid yapf and isort conflicts
|
|
from vllm.entrypoints.chat_utils import apply_mistral_chat_template
|
|
messages = [{
|
|
"role":
|
|
"system",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "You are a helpful assistant."
|
|
}, {
|
|
"type":
|
|
"thinking",
|
|
"closed":
|
|
True,
|
|
"thinking":
|
|
"Only return the answer when you are confident."
|
|
}]
|
|
}, {
|
|
"role": "user",
|
|
"content": "What is 2+2?"
|
|
}, {
|
|
"role":
|
|
"assistant",
|
|
"content": [{
|
|
"type": "text",
|
|
"text": "Let me think about it."
|
|
}, {
|
|
"type": "thinking",
|
|
"closed": True,
|
|
"thinking": "2+2 = 4"
|
|
}, {
|
|
"type": "text",
|
|
"text": "The answer is 4.",
|
|
}],
|
|
}, {
|
|
"role": "user",
|
|
"content": "Thanks, what is 3+3?"
|
|
}]
|
|
|
|
# TODO(Julien): upon model release change to a tokenizer already configured.
|
|
# =================================================================
|
|
mistral_tokenizer = MistralTokenizer.from_pretrained(
|
|
"mistralai/Devstral-Small-2507")
|
|
assert isinstance(mistral_tokenizer.tokenizer, Tekkenizer)
|
|
# Add think special tokens to the tokenizer
|
|
mistral_tokenizer.tokenizer._all_special_tokens[35] = SpecialTokenInfo(
|
|
rank=35, is_control=True, token_str=SpecialTokens.begin_think.value)
|
|
mistral_tokenizer.tokenizer._all_special_tokens[36] = SpecialTokenInfo(
|
|
rank=36, is_control=True, token_str=SpecialTokens.end_think.value)
|
|
mistral_tokenizer.tokenizer._special_tokens_reverse_vocab = {
|
|
k: v
|
|
for k, v in
|
|
mistral_tokenizer.tokenizer._special_tokens_reverse_vocab.items()
|
|
if v not in {35, 36}
|
|
}
|
|
mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
|
|
SpecialTokens.begin_think.value] = 35
|
|
mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
|
|
SpecialTokens.end_think.value] = 36
|
|
mistral_tokenizer.instruct.BEGIN_THINK = 35
|
|
mistral_tokenizer.instruct.END_THINK = 36
|
|
# =================================================================
|
|
|
|
tokens_ids = apply_mistral_chat_template(mistral_tokenizer,
|
|
messages,
|
|
chat_template=None,
|
|
tools=None)
|
|
|
|
string_tokens = mistral_tokenizer.mistral.decode(
|
|
tokens_ids, special_token_policy=SpecialTokenPolicy.KEEP)
|
|
|
|
expected_tokens = (
|
|
r"<s>[SYSTEM_PROMPT]You are a helpful assistant.[THINK]Only return the"
|
|
r" answer when you are confident.[/THINK][/SYSTEM_PROMPT]"
|
|
r"[INST]What is 2+2?[/INST]"
|
|
r"Let me think about it.[THINK]2+2 = 4[/THINK]The answer is 4.</s>"
|
|
r"[INST]Thanks, what is 3+3?[/INST]")
|
|
|
|
assert string_tokens == expected_tokens
|