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model: support Step3V (#8583)

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Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>
Co-authored-by: nnnobody-code <nnnobody@foxmail.com>
Co-authored-by: ispobock <ispobaoke@gmail.com>
Co-authored-by: Qiaolin-Yu <qy254@cornell.edu>
Co-authored-by: Qiaolin-Yu <liin1211@outlook.com>
Co-authored-by: Xinyuan Tong <justinning0323@outlook.com>
Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
This commit is contained in:
Chang Su
2025-07-31 02:41:00 -07:00
committed by GitHub
parent 09f1a247ce
commit 51c38163c1
16 changed files with 2340 additions and 23 deletions
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8
python/sglang/srt/configs/__init__.py
View File

@@ -5,6 +5,11 @@ from sglang.srt.configs.exaone import ExaoneConfig
from sglang.srt.configs.janus_pro import MultiModalityConfig
from sglang.srt.configs.kimi_vl import KimiVLConfig
from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
from sglang.srt.configs.step3_vl import (
Step3TextConfig,
Step3VisionEncoderConfig,
Step3VLConfig,
)
__all__ = [
"ExaoneConfig",
@@ -14,4 +19,7 @@ __all__ = [
"MultiModalityConfig",
"KimiVLConfig",
"MoonViTConfig",
"Step3VLConfig",
"Step3TextConfig",
"Step3VisionEncoderConfig",
]

3
python/sglang/srt/configs/model_config.py
View File

@@ -335,6 +335,8 @@ class ModelConfig:
"num_key_value_heads",
# For ChatGLM:
"multi_query_group_num",
# For Step3
"num_attention_groups",
]
for attr in attributes:
num_kv_heads = getattr(self.hf_text_config, attr, None)
@@ -644,6 +646,7 @@ multimodal_model_archs = [
"InternS1ForConditionalGeneration",
"Phi4MMForCausalLM",
"VILAForConditionalGeneration",
"Step3VLForConditionalGeneration",
]

172
python/sglang/srt/configs/step3_vl.py Normal file
View File

@@ -0,0 +1,172 @@
from typing import Any, Optional, Union
from transformers.configuration_utils import PretrainedConfig
class Step3VisionEncoderConfig(PretrainedConfig):
model_type = "step3_vision_encoder"
def __init__(
self,
hidden_size=1792,
intermediate_size=3072,
output_hidden_size=4096,
num_hidden_layers=63,
num_attention_heads=16,
num_channels=3,
image_size=728,
patch_size=14,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
**kwargs,
):
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.output_hidden_size = output_hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
super().__init__(**kwargs)
class Step3TextConfig(PretrainedConfig):
model_type = "step3_text"
architectures = ["Step3TextForCausalLM"]
def __init__(
self,
hidden_size: int = 7168,
intermediate_size: int = 18432,
num_attention_heads: int = 64,
num_attention_groups: int = 1,
num_hidden_layers: int = 61,
max_seq_len: int = 65536,
vocab_size: int = 128815,
rms_norm_eps: float = 1e-5,
moe_intermediate_size: int = 5120,
moe_num_experts: int = 48,
moe_top_k: int = 3,
rope_theta: float = 500000,
rope_scaling: Optional[dict[str, Any]] = None,
max_position_embedding: int = 65536,
share_expert_dim: int = 5120,
share_q_dim: int = 2048,
head_dim: int = 256,
norm_expert_weight: bool = False,
moe_layers_enum: tuple[int] = (
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
),
**kwargs,
) -> None:
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_attention_groups = num_attention_groups
self.num_hidden_layers = num_hidden_layers
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.rms_norm_eps = rms_norm_eps
self.moe_intermediate_size = moe_intermediate_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.max_position_embedding = max_position_embedding
self.share_expert_dim = share_expert_dim
self.share_q_dim = share_q_dim
self.head_dim = head_dim
self.norm_expert_weight = norm_expert_weight
self.moe_layers_enum = moe_layers_enum
super().__init__(**kwargs)
class Step3VLConfig(PretrainedConfig):
model_type = "step3_vl"
def __init__(
self,
vision_config: Optional[Union[dict, Step3VisionEncoderConfig]] = None,
text_config: Optional[Union[dict, Step3TextConfig]] = None,
understand_projector_stride: int = 1,
projector_bias: bool = True,
image_token_id: int = 128001,
**kwargs,
) -> None:
if vision_config is None:
vision_config = Step3VisionEncoderConfig()
elif isinstance(vision_config, dict):
vision_config = Step3VisionEncoderConfig(**vision_config)
self.vision_config = vision_config
if text_config is None:
text_config = Step3TextConfig()
elif isinstance(text_config, dict):
text_config = Step3TextConfig(**text_config)
self.text_config = text_config
self.understand_projector_stride = understand_projector_stride
self.projector_bias = projector_bias
self.hidden_size = text_config.hidden_size
self.image_token_id = image_token_id
super().__init__(**kwargs)