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Support for Qwen2.5-VL Model in bitsandbytes Format (#5003)

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This commit is contained in:
yhyang201
2025-04-14 17:03:40 +08:00
committed by GitHub
parent defede5073
commit 072df75354
6 changed files with 375 additions and 45 deletions
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1
.github/workflows/vllm-dependency-test.yml vendored
View File

@@ -33,6 +33,7 @@ jobs:
run: |
bash scripts/ci_install_dependency.sh
pip install "vllm>=0.6.4.post1,<=0.7.2"
pip install "bitsandbytes>=0.44.0"
- name: Run VLLM dependency tests
timeout-minutes: 60

16
python/sglang/srt/model_loader/loader.py
View File

@@ -1071,6 +1071,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
param_dict = dict(model.named_parameters())
stacked_quant_state_dict: Dict[str, Dict[int, Any]] = {}
model_type = model_config.hf_config.model_type
for quant_param_name in quant_state_dict:
non_stacked_param_name = quant_param_name
@@ -1079,11 +1080,24 @@ class BitsAndBytesModelLoader(BaseModelLoader):
weight_name,
index,
) in model.bitsandbytes_stacked_params_mapping.items():
if (
model_type in ["qwen2_vl", "qwen2_5_vl"]
and "visual" in quant_param_name
):
break
if shard_name in quant_param_name:
shard_index = index
quant_param_name = quant_param_name.replace(shard_name, weight_name)
break
if (
model_type in ["qwen2_vl", "qwen2_5_vl"]
and "visual" in quant_param_name
):
quant_param_name = quant_param_name.replace(
r"attn.qkv.", r"attn.qkv_proj."
)
if quant_param_name not in param_dict:
raise ValueError(
f"Parameter {quant_param_name} not found in the model."
@@ -1111,6 +1125,8 @@ class BitsAndBytesModelLoader(BaseModelLoader):
num_elements[seq] = math.prod(quant_state.shape) // pack_ratio
offsets = np.concatenate(([0], np.cumsum(num_elements)))
# Make torch infer_schema happy(Compatible with vLLM)
offsets = torch.tensor(offsets).cpu()
set_weight_attrs(param, {"bnb_shard_offsets": offsets})
if load_8bit:

46
python/sglang/srt/models/qwen2_5_vl.py
View File

@@ -141,7 +141,7 @@ class Qwen2_5_VisionBlock(nn.Module):
embed_dim=dim,
num_heads=num_heads,
projection_size=dim,
use_qkv_parallel=False,
use_qkv_parallel=True,
use_context_forward=use_context_forward,
softmax_in_single_precision=softmax_in_single_precision,
flatten_batch=flatten_batch,
@@ -325,7 +325,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
@property
def dtype(self) -> torch.dtype:
return self.blocks[0].mlp.gate_proj.weight.dtype
return self.patch_embed.proj.weight.dtype
@property
def device(self) -> torch.device:
@@ -429,6 +429,25 @@ cached_get_processor = lru_cache(get_processor)
class Qwen2_5_VLForConditionalGeneration(nn.Module):
# BitandBytes specific attributes
default_bitsandbytes_target_modules = [
".gate_proj.",
".down_proj.",
".up_proj.",
".q_proj.",
".k_proj.",
".v_proj.",
".o_proj.",
]
bitsandbytes_stacked_params_mapping = {
# shard_name, weight_name, index
"q_proj": ("qkv_proj", 0),
"k_proj": ("qkv_proj", 1),
"v_proj": ("qkv_proj", 2),
"gate_proj": ("gate_up_proj", 0),
"up_proj": ("gate_up_proj", 1),
}
def __init__(
self,
config: Qwen2_5_VLConfig,
@@ -441,9 +460,9 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
self.visual = Qwen2_5_VisionTransformer(
config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
# NOTE: Qwen2-VL vision encoder does not support any
# quantization method now.
quant_config=None,
# NOTE: Qwen2_5-VL vision encoder currently supports BitsAndBytes 4-bit quantization.
# Other quantization methods (e.g., GPTQ, AWQ) are untested and may not be supported.
quant_config=quant_config,
prefix=add_prefix("visual", prefix),
)
@@ -573,23 +592,6 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
weight_loader(param, loaded_weight, shard_id)
break
else:
if "visual" in name and "qkv.weight" in name:
visual_num_heads = self.config.vision_config.num_heads
visual_embed_dim = self.config.vision_config.hidden_size
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(
3, visual_num_heads, head_size, visual_embed_dim
)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
elif "visual" in name and "qkv.bias" in name:
visual_num_heads = self.config.vision_config.num_heads
visual_embed_dim = self.config.vision_config.hidden_size
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1)
if "visual" in name:
# adapt to VisionAttention
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")

47
python/sglang/srt/models/qwen2_vl.py
View File

@@ -152,7 +152,7 @@ class Qwen2VisionBlock(nn.Module):
embed_dim=dim,
num_heads=num_heads,
projection_size=dim,
use_qkv_parallel=False,
use_qkv_parallel=True,
use_context_forward=use_context_forward,
softmax_in_single_precision=softmax_in_single_precision,
flatten_batch=True,
@@ -351,7 +351,7 @@ class Qwen2VisionTransformer(nn.Module):
@property
def dtype(self) -> torch.dtype:
return next(self.parameters()).dtype
return self.patch_embed.proj.weight.dtype
@property
def device(self) -> torch.device:
@@ -423,6 +423,25 @@ cached_get_processor = lru_cache(get_processor)
class Qwen2VLForConditionalGeneration(nn.Module):
# BitandBytes specific attributes
default_bitsandbytes_target_modules = [
".gate_proj.",
".down_proj.",
".up_proj.",
".q_proj.",
".k_proj.",
".v_proj.",
".o_proj.",
]
bitsandbytes_stacked_params_mapping = {
# shard_name, weight_name, index
"q_proj": ("qkv_proj", 0),
"k_proj": ("qkv_proj", 1),
"v_proj": ("qkv_proj", 2),
"gate_proj": ("gate_up_proj", 0),
"up_proj": ("gate_up_proj", 1),
}
def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
processor = cached_get_processor(self.config._name_or_path)
grid_t, grid_h, grid_w = image_grid_thw
@@ -447,9 +466,9 @@ class Qwen2VLForConditionalGeneration(nn.Module):
self.visual = Qwen2VisionTransformer(
config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
# NOTE: Qwen2-VL vision encoder does not support any
# quantization method now.
quant_config=None,
# NOTE: Qwen2-VL vision encoder currently supports BitsAndBytes 4-bit quantization.
# Other quantization methods (e.g., GPTQ, AWQ) are untested and may not be supported.
quant_config=quant_config,
prefix=add_prefix("visual", prefix),
)
@@ -578,24 +597,6 @@ class Qwen2VLForConditionalGeneration(nn.Module):
weight_loader(param, loaded_weight, shard_id)
break
else:
if "visual" in name and "qkv.weight" in name:
visual_num_heads = self.config.vision_config.num_heads
visual_embed_dim = self.config.vision_config.embed_dim
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(
3, visual_num_heads, head_size, visual_embed_dim
)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
elif "visual" in name and "qkv.bias" in name:
visual_num_heads = self.config.vision_config.num_heads
visual_embed_dim = self.config.vision_config.embed_dim
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1)
if "visual" in name:
# adapt to VisionAttention
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")

1
test/srt/run_suite.py
View File

@@ -97,6 +97,7 @@ suites = {
TestFile("test_awq.py"),
TestFile("test_gguf.py", 78),
TestFile("test_gptqmodel_dynamic.py", 72),
TestFile("test_bnb.py"),
],
}

309
test/srt/test_bnb.py Normal file
View File

@@ -0,0 +1,309 @@
"""
Usage:
python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_mixed_batch
python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_multi_images_chat_completion
"""
import base64
import io
import json
import multiprocessing as mp
import os
import unittest
from concurrent.futures import ThreadPoolExecutor
from types import SimpleNamespace
import numpy as np
import openai
import requests
from PIL import Image
from sglang.srt.utils import kill_process_tree
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_ci,
popen_launch_server,
)
VISION_MODELS = [
("unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit", "qwen2-vl"),
("unsloth/Qwen2-VL-7B-Instruct-bnb-4bit", "qwen2-vl"),
]
LANGUAGE_MODELS = [
"unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
"unsloth/Qwen2-7B-Instruct-bnb-4bit",
]
# image
IMAGE_MAN_IRONING_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/man_ironing_on_back_of_suv.png"
IMAGE_SGL_LOGO_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/sgl_logo.png"
# video
VIDEO_JOBS_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/videos/jobs_presenting_ipod.mp4"
# audio
AUDIO_TRUMP_SPEECH_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/audios/Trump_WEF_2018_10s.mp3"
AUDIO_BIRD_SONG_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/audios/bird_song.mp3"
def popen_launch_server_wrapper(base_url, model, other_args):
process = popen_launch_server(
model,
base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=other_args,
)
return process
class TestVisionModel(CustomTestCase):
@classmethod
def setUpClass(cls):
mp.set_start_method("spawn", force=True)
cls.base_url = DEFAULT_URL_FOR_TEST
cls.base_url += "/v1"
cls.api_key = "sk-123456"
def _run_single_image_chat_completion(self):
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
response = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": IMAGE_MAN_IRONING_URL},
},
{
"type": "text",
"text": "Describe this image in a very short sentence.",
},
],
},
],
temperature=0,
)
assert response.choices[0].message.role == "assistant"
text = response.choices[0].message.content
assert isinstance(text, str)
# `driver` is for gemma-3-it
assert "man" in text or "person" or "driver" in text, text
assert "cab" in text or "taxi" in text or "SUV" in text, text
# MiniCPMO fails to recognize `iron`, but `hanging`
assert "iron" in text or "hang" in text, text
assert response.id
assert response.created
assert response.usage.prompt_tokens > 0
assert response.usage.completion_tokens > 0
assert response.usage.total_tokens > 0
def _run_multi_turn_chat_completion(self):
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
response = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": IMAGE_MAN_IRONING_URL},
},
{
"type": "text",
"text": "Describe this image in a very short sentence.",
},
],
},
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "There is a man at the back of a yellow cab ironing his clothes.",
}
],
},
{
"role": "user",
"content": [
{"type": "text", "text": "Repeat your previous answer."}
],
},
],
temperature=0,
)
assert response.choices[0].message.role == "assistant"
text = response.choices[0].message.content
assert isinstance(text, str)
assert "man" in text or "cab" in text, text
assert response.id
assert response.created
assert response.usage.prompt_tokens > 0
assert response.usage.completion_tokens > 0
assert response.usage.total_tokens > 0
def _run_multi_images_chat_completion(self):
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
response = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": IMAGE_MAN_IRONING_URL},
"modalities": "multi-images",
},
{
"type": "image_url",
"image_url": {"url": IMAGE_SGL_LOGO_URL},
"modalities": "multi-images",
},
{
"type": "text",
"text": "I have two very different images. They are not related at all. "
"Please describe the first image in one sentence, and then describe the second image in another sentence.",
},
],
},
],
temperature=0,
)
assert response.choices[0].message.role == "assistant"
text = response.choices[0].message.content
assert isinstance(text, str)
print("-" * 30)
print(f"Multi images response:\n{text}")
print("-" * 30)
assert "man" in text or "cab" in text or "SUV" in text or "taxi" in text, text
assert "logo" in text or '"S"' in text or "SG" in text, text
assert response.id
assert response.created
assert response.usage.prompt_tokens > 0
assert response.usage.completion_tokens > 0
assert response.usage.total_tokens > 0
def run_decode_with_image(self, image_id):
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
content = []
if image_id == 0:
content.append(
{
"type": "image_url",
"image_url": {"url": IMAGE_MAN_IRONING_URL},
}
)
elif image_id == 1:
content.append(
{
"type": "image_url",
"image_url": {"url": IMAGE_SGL_LOGO_URL},
}
)
else:
pass
content.append(
{
"type": "text",
"text": "Describe this image in a very short sentence.",
}
)
response = client.chat.completions.create(
model="default",
messages=[
{"role": "user", "content": content},
],
temperature=0,
)
assert response.choices[0].message.role == "assistant"
text = response.choices[0].message.content
assert isinstance(text, str)
def _run_test_mixed_batch(self):
image_ids = [0, 1, 2] * 4
with ThreadPoolExecutor(4) as executor:
list(executor.map(self.run_decode_with_image, image_ids))
def test_vlm(self):
models_to_test = VISION_MODELS
if is_in_ci():
models_to_test = [random.choice(VISION_MODELS)]
for model, template in models_to_test:
with self.subTest(model=model):
other_args = [
"--chat-template",
template,
"--mem-fraction-static",
"0.6",
"--load-format",
"bitsandbytes",
]
try:
process = popen_launch_server_wrapper(
DEFAULT_URL_FOR_TEST, model, other_args
)
self._run_test_mixed_batch()
self._run_multi_images_chat_completion()
self._run_multi_turn_chat_completion()
self._run_single_image_chat_completion()
finally:
kill_process_tree(process.pid)
class TestLanguageModel(CustomTestCase):
@classmethod
def setUpClass(cls):
mp.set_start_method("spawn", force=True)
cls.base_url = DEFAULT_URL_FOR_TEST
# cls.base_url += "/v1"
cls.api_key = "sk-123456"
def test_mmlu(self):
models_to_test = LANGUAGE_MODELS
if is_in_ci():
models_to_test = [random.choice(LANGUAGE_MODELS)]
for model in models_to_test:
with self.subTest(model=model):
other_args = [
"--mem-fraction-static",
"0.6",
"--load-format",
"bitsandbytes",
]
try:
process = popen_launch_server_wrapper(
DEFAULT_URL_FOR_TEST, model, other_args
)
args = SimpleNamespace(
base_url=self.base_url,
model=model,
eval_name="mmlu",
num_examples=32,
num_threads=16,
)
metrics = run_eval(args)
print(f"{metrics=}")
self.assertGreater(metrics["score"], 0.3)
finally:
kill_process_tree(process.pid)