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Refactor vlm embedding routine to use precomputed feature (#6543)

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Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>
This commit is contained in:
Xinyuan Tong
2025-05-24 18:39:21 -07:00
committed by GitHub
parent 0d47788025
commit 681fdc264b
8 changed files with 285 additions and 203 deletions
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2
test/srt/run_suite.py
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@@ -81,7 +81,7 @@ suites = {
TestFile("test_update_weights_from_tensor.py", 48),
TestFile("test_vertex_endpoint.py", 31),
TestFile("test_vision_chunked_prefill.py", 175),
TestFile("test_vlm_accuracy.py", 60),
TestFile("test_vlm_input_format.py", 300),
TestFile("test_vision_openai_server_a.py", 700),
TestFile("test_vision_openai_server_b.py", 700),
TestFile("test_w8a8_quantization.py", 46),

140
test/srt/test_vlm_accuracy.py
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@@ -10,15 +10,8 @@ import requests
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import (
AutoModel,
AutoProcessor,
AutoTokenizer,
Gemma3ForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
)
from transformers import AutoModel, AutoProcessor, AutoTokenizer
from sglang import Engine
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.conversation import generate_chat_conv
from sglang.srt.managers.mm_utils import embed_mm_inputs, init_embedding_cache
@@ -41,9 +34,6 @@ class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
def setUpClass(cls):
cls.image_url = "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cls.model_path = ""
cls.chat_template = ""
cls.processor = ""
response = requests.get(cls.image_url)
cls.main_image = Image.open(BytesIO(response.content))
@@ -274,131 +264,3 @@ class TestMiniCPMVLogits(VisionLLMLogitsBase):
)
self.compare_outputs(sglang_output, hf_output)
class TestQwenVLUnderstandsImage(VisionLLMLogitsBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
cls.chat_template = "qwen2-vl"
cls.processor = AutoProcessor.from_pretrained(
cls.model_path, trust_remote_code=True, use_fast=True
)
cls.visual = (
Qwen2_5_VLForConditionalGeneration.from_pretrained(
cls.model_path, torch_dtype=torch.bfloat16
)
.eval()
.visual.to(cls.device)
)
def setUp(self):
self.engine = Engine(
model_path=self.model_path,
chat_template=self.chat_template,
device=self.device.type,
mem_fraction_static=0.8,
)
def tearDown(self):
self.engine.shutdown()
async def test_qwen_vl_understands_image(self):
req = self.get_completion_request()
conv = generate_chat_conv(req, template_name=self.chat_template)
text = conv.get_prompt()
output = await self.engine.async_generate(
prompt=text,
image_data=[self.main_image],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
async def test_qwen_vl_understands_precomputed_features(self):
req = self.get_completion_request()
processor_output = self.get_processor_output(req=req)
with torch.inference_mode():
precomputed_features = self.visual(
processor_output["pixel_values"], processor_output["image_grid_thw"]
)
output = await self.engine.async_generate(
input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
image_data=[
dict(
modality="IMAGE",
image_grid_thws=processor_output["image_grid_thw"],
precomputed_features=precomputed_features,
)
],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
class TestGemmaUnderstandsImage(VisionLLMLogitsBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.model_path = "google/gemma-3-4b-it"
cls.chat_template = "gemma-it"
cls.processor = AutoProcessor.from_pretrained(
cls.model_path, trust_remote_code=True, use_fast=True
)
model = Gemma3ForConditionalGeneration.from_pretrained(
cls.model_path, torch_dtype=torch.bfloat16
)
cls.vision_tower = model.vision_tower.eval().to(cls.device)
cls.mm_projector = model.multi_modal_projector.eval().to(cls.device)
@classmethod
def visual(cls, pixel_values):
vision_outputs = cls.vision_tower(pixel_values=pixel_values).last_hidden_state
image_features = cls.mm_projector(vision_outputs)
return image_features
def setUp(self):
self.engine = Engine(
model_path=self.model_path,
chat_template=self.chat_template,
device=self.device.type,
mem_fraction_static=0.5,
enable_multimodal=True,
)
def tearDown(self):
self.engine.shutdown()
async def test_gemma_understands_image(self):
req = self.get_completion_request()
conv = generate_chat_conv(req, template_name=self.chat_template)
text = conv.get_prompt()
output = await self.engine.async_generate(
prompt=text,
image_data=[self.main_image],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
async def test_gemma_understands_precomputed_features(self):
req = self.get_completion_request()
processor_output = self.get_processor_output(req=req)
with torch.inference_mode():
precomputed_features = self.visual(processor_output["pixel_values"])
output = await self.engine.async_generate(
input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
image_data=[
dict(
modality="IMAGE",
precomputed_features=precomputed_features,
)
],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
if __name__ == "__main__":
unittest.main()

187
test/srt/test_vlm_input_format.py Normal file
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@@ -0,0 +1,187 @@
import json
import unittest
from io import BytesIO
from typing import Optional
import requests
import torch
from PIL import Image
from transformers import (
AutoProcessor,
Gemma3ForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
)
from sglang import Engine
from sglang.srt.conversation import generate_chat_conv
from sglang.srt.openai_api.protocol import ChatCompletionRequest
TEST_IMAGE_URL = "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
class VLMInputTestBase:
model_path = None
chat_template = None
processor = None
visual = None # Should be a callable for precomputed features
@classmethod
def setUpClass(cls):
assert cls.model_path is not None, "Set model_path in subclass"
assert cls.chat_template is not None, "Set chat_template in subclass"
cls.image_url = TEST_IMAGE_URL
cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
response = requests.get(cls.image_url)
cls.main_image = Image.open(BytesIO(response.content))
cls.processor = AutoProcessor.from_pretrained(
cls.model_path, trust_remote_code=True, use_fast=True
)
cls._init_visual()
@classmethod
def _init_visual(cls):
"""Override in subclass to set up cls.visual as a callable for precomputed features."""
raise NotImplementedError
def setUp(self):
self.engine = Engine(
model_path=self.model_path,
chat_template=self.chat_template,
device=self.device.type,
mem_fraction_static=0.8,
enable_multimodal=True,
disable_cuda_graph=True,
)
def tearDown(self):
self.engine.shutdown()
def get_completion_request(self) -> ChatCompletionRequest:
json_structure = {
"model": self.model_path,
"messages": [
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": self.image_url}},
{"type": "text", "text": "What's in this picture?"},
],
}
],
}
json_str = json.dumps(json_structure)
return ChatCompletionRequest.model_validate_json(json_str)
def get_processor_output(self, req: Optional[ChatCompletionRequest] = None):
if req is None:
req = self.get_completion_request()
conv = generate_chat_conv(req, template_name=self.chat_template)
text = conv.get_prompt()
# Process inputs using processor
inputs = self.processor(
text=[text],
images=[self.main_image],
return_tensors="pt",
).to(self.device)
return inputs
async def test_understands_image(self):
req = self.get_completion_request()
conv = generate_chat_conv(req, template_name=self.chat_template)
text = conv.get_prompt()
output = await self.engine.async_generate(
prompt=text,
image_data=[self.main_image],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
async def test_understands_precomputed_features(self):
req = self.get_completion_request()
processor_output = self.get_processor_output(req=req)
with torch.inference_mode():
precomputed_features = self.__class__.visual(processor_output)
output = await self.engine.async_generate(
input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
image_data=[
self._precomputed_image_data(processor_output, precomputed_features)
],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
async def test_understands_pixel_values(self):
req = self.get_completion_request()
processor_output = self.get_processor_output(req=req)
output = await self.engine.async_generate(
input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
image_data=[self._pixel_values_image_data(processor_output)],
sampling_params=dict(temperature=0.0),
)
self.assertIn("taxi", output["text"].lower())
def _precomputed_image_data(self, processor_output, precomputed_features):
"""This should not be overridden."""
return dict(
modality="IMAGE",
precomputed_features=precomputed_features,
)
def _pixel_values_image_data(self, processor_output):
"""Override in subclass to pass the correct set of arguments."""
raise NotImplementedError
class TestQwenVLUnderstandsImage(VLMInputTestBase, unittest.IsolatedAsyncioTestCase):
model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
chat_template = "qwen2-vl"
@classmethod
def _init_visual(cls):
cls.visual_model = (
Qwen2_5_VLForConditionalGeneration.from_pretrained(
cls.model_path, torch_dtype=torch.bfloat16
)
.eval()
.visual.to(cls.device)
)
cls.visual = lambda processor_output: cls.visual_model(
processor_output["pixel_values"], processor_output["image_grid_thw"]
)
def _pixel_values_image_data(self, processor_output):
return dict(
modality="IMAGE",
image_grid_thws=processor_output["image_grid_thw"],
pixel_values=processor_output["pixel_values"],
)
class TestGemmaUnderstandsImage(VLMInputTestBase, unittest.IsolatedAsyncioTestCase):
model_path = "google/gemma-3-4b-it"
chat_template = "gemma-it"
@classmethod
def _init_visual(cls):
model = Gemma3ForConditionalGeneration.from_pretrained(
cls.model_path, torch_dtype=torch.bfloat16
)
cls.vision_tower = model.vision_tower.eval().to(cls.device)
cls.mm_projector = model.multi_modal_projector.eval().to(cls.device)
cls.visual = lambda processor_output: cls.mm_projector(
cls.vision_tower(
pixel_values=processor_output["pixel_values"]
).last_hidden_state
)
def _pixel_values_image_data(self, processor_output):
return dict(
modality="IMAGE",
pixel_values=processor_output["pixel_values"][0],
)
if __name__ == "__main__":
unittest.main()