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[feat] Support session control for vision language models (#2210)

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This commit is contained in:
Ying Sheng
2024-11-27 00:03:29 -08:00
committed by GitHub
parent c754652fcd
commit 37c8a5761f
7 changed files with 265 additions and 21 deletions
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15
python/sglang/srt/managers/image_processor.py
View File

@@ -131,6 +131,7 @@ class LlavaImageProcessor(BaseImageProcessor):
if not image_data:
return None
modalities = request_obj.modalities or ["image"]
aspect_ratio = getattr(self.hf_config, "image_aspect_ratio", None)
grid_pinpoints = (
self.hf_config.image_grid_pinpoints
@@ -139,9 +140,12 @@ class LlavaImageProcessor(BaseImageProcessor):
else None
)
if isinstance(image_data, str):
image_data = [image_data]
if isinstance(image_data, list) and len(image_data) > 0:
# Multiple images
if len(image_data) > 1:
if "multi-images" in modalities or "video" in modalities:
# Multiple images
aspect_ratio = "pad" # LLaVA OneVision Handling: more than one image --> interleaved image mode or video mode. We do not use anyres
pixel_values, image_hashes, image_sizes = [], [], []
res = []
@@ -166,13 +170,6 @@ class LlavaImageProcessor(BaseImageProcessor):
)
image_hashes = [image_hash]
image_sizes = [image_size]
elif isinstance(image_data, str):
# A single image
pixel_values, image_hash, image_size = await self._process_single_image(
image_data, aspect_ratio, grid_pinpoints
)
image_hashes = [image_hash]
image_sizes = [image_size]
else:
raise ValueError(f"Invalid image data: {image_data}")

38
python/sglang/srt/managers/schedule_batch.py
View File

@@ -31,6 +31,7 @@ import dataclasses
import logging
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import triton
import triton.language as tl
@@ -167,6 +168,30 @@ class ImageInputs:
return ret
def merge(self, other, vocab_size):
assert self.pixel_values.shape[1:] == other.pixel_values.shape[1:]
self.pixel_values = np.concatenate([self.pixel_values, other.pixel_values])
self.image_hashes += other.image_hashes
self.pad_values = [
(self.image_hashes) % vocab_size,
(self.image_hashes >> 16) % vocab_size,
(self.image_hashes >> 32) % vocab_size,
(self.image_hashes >> 64) % vocab_size,
]
optional_args = [
"image_sizes",
"image_offsets",
# "modalities", # modalities should be ["multi-images"] (one entry) even for multiple images
"aspect_ratio_ids",
"aspect_ratio_mask",
"image_grid_thws",
]
for arg in optional_args:
if getattr(self, arg, None) is not None:
setattr(self, arg, getattr(self, arg) + getattr(other, arg))
class Req:
"""The input and output status of a request."""
@@ -177,6 +202,7 @@ class Req:
origin_input_text: str,
origin_input_ids: Tuple[int],
sampling_params: SamplingParams,
origin_input_ids_unpadded: Optional[Tuple[int]] = None,
lora_path: Optional[str] = None,
input_embeds: Optional[List[List[float]]] = None,
session_id: Optional[str] = None,
@@ -184,7 +210,11 @@ class Req:
# Input and output info
self.rid = rid
self.origin_input_text = origin_input_text
self.origin_input_ids_unpadded = origin_input_ids # Before image padding
self.origin_input_ids_unpadded = (
origin_input_ids_unpadded
if origin_input_ids_unpadded
else origin_input_ids # Before image padding
)
self.origin_input_ids = origin_input_ids
self.output_ids = [] # Each decode stage's output ids
self.fill_ids = None # fill_ids = origin_input_ids + output_ids
@@ -260,6 +290,12 @@ class Req:
# The number of cached tokens, that were already cached in the KV cache
self.cached_tokens = 0
def extend_image_inputs(self, image_inputs, vocab_size):
if self.image_inputs is None:
self.image_inputs = image_inputs
else:
self.image_inputs.merge(image_inputs, vocab_size)
# whether request reached finished condition
def finished(self) -> bool:
return self.finished_reason is not None

5
python/sglang/srt/managers/scheduler.py
View File

@@ -559,12 +559,13 @@ class Scheduler:
# Image inputs
if recv_req.image_inputs is not None:
req.image_inputs = ImageInputs.from_dict(
image_inputs = ImageInputs.from_dict(
recv_req.image_inputs, self.model_config.vocab_size
)
req.origin_input_ids = self.pad_input_ids_func(
req.origin_input_ids_unpadded, req.image_inputs
req.origin_input_ids, image_inputs
)
req.extend_image_inputs(image_inputs, self.model_config.vocab_size)
if len(req.origin_input_ids) > self.max_req_input_len:
req.finished_reason = FINISH_ABORT(

19
python/sglang/srt/managers/session_controller.py
View File

@@ -41,16 +41,27 @@ class Session:
]
+ req.input_ids
)
input_ids_unpadded = (
self.reqs[-1].origin_input_ids_unpadded
+ self.reqs[-1].output_ids[
: self.reqs[-1].sampling_params.max_new_tokens
]
+ req.input_ids
)
else:
input_ids = req.input_ids
input_ids_unpadded = req.input_ids
new_req = Req(
req.rid,
None,
input_ids,
req.sampling_params,
rid=req.rid,
origin_input_text=None,
origin_input_ids=input_ids,
origin_input_ids_unpadded=input_ids_unpadded,
sampling_params=req.sampling_params,
lora_path=req.lora_path,
session_id=self.session_id,
)
if len(self.reqs) > 0:
new_req.image_inputs = self.reqs[-1].image_inputs
new_req.tokenizer = tokenizer
if req.session_rid is not None and len(self.reqs) == 0:
new_req.finished_reason = FINISH_ABORT(

8
python/sglang/srt/models/llava.py
View File

@@ -49,7 +49,13 @@ class LlavaBaseForCausalLM(nn.Module):
image_sizes, pad_values = image_inputs.image_sizes, image_inputs.pad_values
# hardcode for spatial_unpad + anyres
image_aspect_ratio = "anyres" if len(image_sizes) == 1 else "pad"
if image_inputs.modalities is not None and (
"multi-images" in image_inputs.modalities
or "video" in image_inputs.modalities
):
image_aspect_ratio = "pad"
else:
image_aspect_ratio = "anyres"
offset_list = []
for image_s in image_sizes:
if len(image_sizes) > 16:

1
test/srt/run_suite.py
View File

@@ -36,6 +36,7 @@ suites = {
"test_triton_attention_backend.py",
"test_update_weights.py",
"test_vision_openai_server.py",
"test_session_control.py",
],
"sampling/penaltylib": glob.glob(
"sampling/penaltylib/**/test_*.py", recursive=True

200
test/srt/test_session_control.py
View File

@@ -1,7 +1,7 @@
"""
Usage:
python3 -m unittest test_session_control.TestSessionControl.test_session_control
python3 -m unittest test_session_control.TestSessionControl.test_session_control_vlm
python3 -m unittest test_session_control.TestSessionControlVision.test_session_control
"""
import unittest
@@ -61,6 +61,8 @@ class TestSessionControl(unittest.TestCase):
"max_new_tokens": (
16 if i > 0 else 0
), # prefill only for the first chunk
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
@@ -79,6 +81,8 @@ class TestSessionControl(unittest.TestCase):
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
@@ -93,6 +97,8 @@ class TestSessionControl(unittest.TestCase):
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
@@ -113,6 +119,8 @@ class TestSessionControl(unittest.TestCase):
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
@@ -133,13 +141,16 @@ class TestSessionControl(unittest.TestCase):
"max_new_tokens": (
16 if i > 0 else 0
), # prefill only for the first chunk
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
if i > 0:
input_ids += tokenizer.encode(response["text"])[
1:
] # drop the bos token
output_ids = tokenizer.encode(response["text"])
if output_ids[0] == tokenizer.bos_token_id:
output_ids = output_ids[1:]
input_ids += output_ids
outputs_normal.append(response["text"])
if i == 0:
input_ids_first_req = input_ids.copy()
@@ -152,6 +163,187 @@ class TestSessionControl(unittest.TestCase):
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
outputs_normal.append(response["text"])
print("outputs from chunked queries with session control:")
print(outputs_from_session)
print("outputs from normal queries:")
print(outputs_normal)
assert outputs_from_session == outputs_normal
class TestSessionControlVision(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model = "lmms-lab/llava-onevision-qwen2-7b-ov"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
# other_args={"--disable-radix"},
)
@classmethod
def tearDownClass(cls):
kill_child_process(cls.process.pid, include_self=True)
def test_session_control(self):
text_chunks = [
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n",
"<|im_start|>user\n<image>\nDescribe this image in a very short sentence.<|im_end|>\n<|im_start|>assistant\n",
"<|im_start|>user\n<image>\nIs this image same with the previous image? Answer yes or no.<|im_end|>\n<|im_start|>assistant\n",
"<|im_start|>user\n<image>\nIs this image same with the previous image? Answer yes or no.<|im_end|>\n<|im_start|>assistant\n",
]
image_chunks = [
"https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png",
"https://raw.githubusercontent.com/sgl-project/sglang/main/test/lang/example_image.png",
"https://raw.githubusercontent.com/sgl-project/sglang/main/test/lang/example_image.png",
]
assert len(text_chunks) == len(image_chunks) + 1
tokenizer = get_tokenizer(self.model)
text_input_ids = [tokenizer.encode(x) for x in text_chunks]
# 1. using session control
session_id = requests.post(
self.base_url + "/open_session",
json={"capacity_of_str_len": 1000},
).json()
rid = None
first_rid = None
outputs_from_session = []
for i in range(len(text_input_ids)):
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": text_input_ids[i],
"image_data": image_chunks[i - 1] if i > 0 else None,
"modalities": ["multi-images"],
"session": [session_id, rid],
"sampling_params": {
"temperature": 0,
"max_new_tokens": (
16 if i > 0 else 0
), # prefill only for the first chunk
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
rid = response["meta_info"]["id"]
if i == 0:
first_rid = rid
if i > 0:
outputs_from_session.append(response["text"])
# backtrack to the first request and regenerate
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": text_input_ids[-1],
"image_data": image_chunks[-1:],
"modalities": ["multi-images"],
"session": [session_id, first_rid],
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
outputs_from_session.append(response["text"])
# query with a non-existing rid (the last one should be disappeared becuase of backtrack), should see abort
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": text_input_ids[-1],
"image_data": image_chunks[-1:],
"modalities": ["multi-images"],
"session": [session_id, rid],
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
assert response["meta_info"]["finish_reason"]["type"] == "abort"
ret = requests.post(
self.base_url + "/close_session",
json={"session_id": session_id},
)
assert ret.status_code == 200
# send a request to a closed session, should see abort
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": text_input_ids[-1],
"session": [session_id, first_rid],
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
assert response["meta_info"]["finish_reason"]["type"] == "abort"
# 2. not use session control
input_ids_first_req = None
input_ids = []
outputs_normal = []
for i in range(len(text_input_ids)):
input_ids += text_input_ids[i]
image_data = image_chunks[:i] if i > 0 else None
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": input_ids,
"image_data": image_data,
"modalities": ["multi-images"],
"sampling_params": {
"temperature": 0,
"max_new_tokens": (
16 if i > 0 else 0
), # prefill only for the first chunk
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()
if i > 0:
output_ids = tokenizer.encode(response["text"])
if output_ids[0] == tokenizer.bos_token_id:
output_ids = output_ids[1:]
input_ids += output_ids
outputs_normal.append(response["text"])
if i == 0:
input_ids_first_req = input_ids.copy()
input_ids_first_req += text_input_ids[-1]
response = requests.post(
self.base_url + "/generate",
json={
"input_ids": input_ids_first_req,
"image_data": image_chunks[-1:],
"modalities": ["multi-images"],
"sampling_params": {
"temperature": 0,
"max_new_tokens": 16,
"no_stop_trim": True,
"skip_special_tokens": False,
},
},
).json()