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Remove V0 Encoder-Decoder Support (#24907)

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Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai>
This commit is contained in:
Woosuk Kwon
2025-09-15 21:17:14 -07:00
committed by GitHub
parent 5206ab20ba
commit 759ef49b15
47 changed files with 13 additions and 9661 deletions
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199
tests/entrypoints/test_chat_utils.py
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@@ -20,7 +20,6 @@ from vllm.entrypoints.chat_utils import (_try_extract_ast, load_chat_template,
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, MultiModalUUIDDict
from vllm.multimodal.utils import (encode_audio_base64, encode_image_base64,
encode_video_base64)
@@ -38,7 +37,6 @@ 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"
@@ -125,27 +123,6 @@ def qwen25omni_tokenizer():
)
@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(
@@ -2249,180 +2226,6 @@ def test_parse_chat_messages_multiple_images_interleave_with_placeholders(
)
### 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, mm_uuids = 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_mm_uuids(mm_uuids, 1, expected_uuids=[None])
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, mm_uuids = 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_mm_uuids(mm_uuids, 2, expected_uuids=[None, None])
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",
[
@@ -2486,7 +2289,6 @@ def test_resolve_hf_chat_template(sample_json_schema, model, use_tools):
(QWEN25VL_MODEL_ID, "openai"),
(ULTRAVOX_MODEL_ID, "string"),
(QWEN2AUDIO_MODEL_ID, "openai"),
(MLLAMA_MODEL_ID, "openai"),
(LLAMA_GUARD_MODEL_ID, "openai")],
)
# yapf: enable
@@ -2545,7 +2347,6 @@ def test_resolve_content_format_hf_defined(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"),