From f7a0bcda1e089d4631f302665b4c3872d2789fbd Mon Sep 17 00:00:00 2001
From: Yuhao Yang <47235274+yhyang201@users.noreply.github.com>
Date: Thu, 22 Jan 2026 23:15:08 +0800
Subject: [PATCH] model: step3-vl-10b (#17513)
---
python/sglang/srt/configs/model_config.py | 1 +
.../srt/function_call/function_call_parser.py | 2 +
.../srt/function_call/hermes_detector.py | 120 ++++
python/sglang/srt/models/step3_vl_10b.py | 582 ++++++++++++++++++
.../srt/multimodal/processors/step3_vl.py | 10 +-
python/sglang/srt/utils/common.py | 1 +
6 files changed, 712 insertions(+), 4 deletions(-)
create mode 100644 python/sglang/srt/function_call/hermes_detector.py
create mode 100644 python/sglang/srt/models/step3_vl_10b.py
diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
index 0a7a86cab..9bfd16210 100644
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -1160,6 +1160,7 @@ multimodal_model_archs = [
"JetVLMForConditionalGeneration",
"PaddleOCRVLForConditionalGeneration",
"MiDashengLMModel",
+ "StepVLForConditionalGeneration",
]
if external_mm_model_arch := envs.SGLANG_EXTERNAL_MM_MODEL_ARCH.get():
diff --git a/python/sglang/srt/function_call/function_call_parser.py b/python/sglang/srt/function_call/function_call_parser.py
index b01ddb19f..10d14cc43 100644
--- a/python/sglang/srt/function_call/function_call_parser.py
+++ b/python/sglang/srt/function_call/function_call_parser.py
@@ -17,6 +17,7 @@ from sglang.srt.function_call.deepseekv32_detector import DeepSeekV32Detector
from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
from sglang.srt.function_call.glm47_moe_detector import Glm47MoeDetector
from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
+from sglang.srt.function_call.hermes_detector import HermesDetector
from sglang.srt.function_call.internlm_detector import InternlmDetector
from sglang.srt.function_call.kimik2_detector import KimiK2Detector
from sglang.srt.function_call.lfm2_detector import Lfm2Detector
@@ -64,6 +65,7 @@ class FunctionCallParser:
"minimax-m2": MinimaxM2Detector,
"trinity": TrinityDetector,
"interns1": InternlmDetector,
+ "hermes": HermesDetector,
}
def __init__(self, tools: List[Tool], tool_call_parser: str):
diff --git a/python/sglang/srt/function_call/hermes_detector.py b/python/sglang/srt/function_call/hermes_detector.py
new file mode 100644
index 000000000..ff55461d8
--- /dev/null
+++ b/python/sglang/srt/function_call/hermes_detector.py
@@ -0,0 +1,120 @@
+import json
+import logging
+import re
+from typing import List
+
+from sglang.srt.entrypoints.openai.protocol import Tool
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+ StreamingParseResult,
+ StructureInfo,
+ _GetInfoFunc,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class HermesDetector(BaseFormatDetector):
+ """
+ Detector for Hermes tool call format.
+
+ Format:
+ {"name": "...", "arguments": {...}}
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.bot_token = ""
+ self.eot_token = ""
+ self.tool_call_regex = re.compile(
+ r"(.*?)|(.*)", re.DOTALL
+ )
+ self._normal_text_buffer = ""
+
+ def has_tool_call(self, text: str) -> bool:
+ return self.bot_token in text
+
+ def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+ """
+ One-time parsing: Detects and parses tool calls in the provided text.
+ """
+ idx = text.find(self.bot_token)
+ normal_text = text[:idx].strip() if idx != -1 else text
+ if self.bot_token not in text:
+ return StreamingParseResult(normal_text=normal_text, calls=[])
+
+ calls = []
+ try:
+ for match in self.tool_call_regex.findall(text):
+ raw = match[0] or match[1]
+ if not raw:
+ continue
+ parsed = json.loads(raw.strip())
+ if isinstance(parsed, list):
+ calls.extend(self.parse_base_json(parsed, tools))
+ else:
+ calls.extend(self.parse_base_json(parsed, tools))
+ return StreamingParseResult(normal_text=normal_text, calls=calls)
+ except Exception as e:
+ logger.error(f"Error in detect_and_parse: {e}")
+ return StreamingParseResult(normal_text=text)
+
+ def _clean_normal_text(self, text: str) -> str:
+ if not text:
+ return text
+
+ self._normal_text_buffer += text
+
+ if self.eot_token in self._normal_text_buffer:
+ cleaned = self._normal_text_buffer.replace(self.eot_token, "")
+ self._normal_text_buffer = ""
+ return cleaned
+
+ partial_len = self._ends_with_partial_token(
+ self._normal_text_buffer, self.eot_token
+ )
+ if partial_len:
+ safe_text = self._normal_text_buffer[:-partial_len]
+ self._normal_text_buffer = self._normal_text_buffer[-partial_len:]
+ return safe_text
+
+ cleaned = self._normal_text_buffer
+ self._normal_text_buffer = ""
+ return cleaned
+
+ def parse_streaming_increment(
+ self, new_text: str, tools: List[Tool]
+ ) -> StreamingParseResult:
+ """
+ Streaming parsing: handle normal text, partial tags, and tool calls.
+ """
+ self._buffer += new_text
+ current_text = self._buffer
+
+ if self.bot_token not in current_text:
+ partial_len = self._ends_with_partial_token(current_text, self.bot_token)
+ if partial_len:
+ safe_text = current_text[:-partial_len]
+ self._buffer = current_text[-partial_len:]
+ else:
+ safe_text = current_text
+ self._buffer = ""
+ return StreamingParseResult(normal_text=self._clean_normal_text(safe_text))
+
+ bot_pos = current_text.find(self.bot_token)
+ if bot_pos > 0:
+ normal_text = current_text[:bot_pos]
+ self._buffer = current_text[bot_pos:]
+ return StreamingParseResult(normal_text=normal_text)
+
+ result = super().parse_streaming_increment(new_text="", tools=tools)
+ if result.normal_text:
+ result.normal_text = self._clean_normal_text(result.normal_text)
+ return result
+
+ def structure_info(self) -> _GetInfoFunc:
+ return lambda name: StructureInfo(
+ begin='{"name":"' + name + '", "arguments":',
+ end="}",
+ trigger="",
+ )
diff --git a/python/sglang/srt/models/step3_vl_10b.py b/python/sglang/srt/models/step3_vl_10b.py
new file mode 100644
index 000000000..c043191ce
--- /dev/null
+++ b/python/sglang/srt/models/step3_vl_10b.py
@@ -0,0 +1,582 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""This is basically a copy from perception_models/core/vision_encoder/pe.py"""
+
+from functools import partial
+from typing import Callable, Iterable, List, Optional, Tuple
+
+import torch
+from einops import rearrange, repeat
+from torch import nn
+from torch.nn import functional as F
+from transformers.activations import ACT2FN
+
+from sglang.srt.configs.step3_vl import Step3VLConfig
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+ MultiModalityDataPaddingPatternMultimodalTokens,
+ general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import (
+ Modality,
+ MultimodalDataItem,
+ MultimodalInputs,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen3 import Qwen3ForCausalLM
+from sglang.srt.utils import add_prefix
+
+_DEFAULT_NORM_LAYER = partial(nn.LayerNorm, eps=1e-5)
+
+
+def rotate_half(x):
+ x = rearrange(x, "... (d r) -> ... d r", r=2)
+ x1, x2 = x.unbind(dim=-1)
+ x = torch.stack((-x2, x1), dim=-1)
+ return rearrange(x, "... d r -> ... (d r)")
+
+
+def apply_rotary_emb(freqs, t, start_index=0, scale=1.0, seq_dim=-2):
+ dtype = t.dtype
+
+ if t.ndim == 3:
+ seq_len = t.shape[seq_dim]
+ freqs = freqs[-seq_len:]
+
+ rot_dim = freqs.shape[-1]
+ end_index = start_index + rot_dim
+
+ assert rot_dim <= t.shape[-1], (
+ "feature dimension {} is not of sufficient size to rotate in all the "
+ "positions {}".format(t.shape[-1], rot_dim)
+ )
+
+ t_left, t, t_right = (
+ t[..., :start_index],
+ t[..., start_index:end_index],
+ t[..., end_index:],
+ )
+ t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+ out = torch.cat((t_left, t, t_right), dim=-1)
+
+ return out.type(dtype)
+
+
+class PerceptionEncoderRope2D(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ max_grid_height: int,
+ max_grid_width: int,
+ use_cls_token: bool = False,
+ theta=10000,
+ max_freq=10,
+ num_freqs=1,
+ theta_rescale_factor=1.0,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.max_grid_height = max_grid_height
+ self.max_grid_width = max_grid_width
+ self.use_cls_token = use_cls_token
+ self.theta = theta * theta_rescale_factor ** (dim / (dim - 2))
+ self.max_freq = max_freq
+ self.num_freqs = num_freqs
+ cache = self._compute_2d_freqs()
+ self.register_buffer("freqs_cache", cache, persistent=False)
+
+ def _compute_inv_freq(self, base: int | float, dim: int) -> torch.Tensor:
+ freqs = 1.0 / (base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+ return freqs
+
+ def _compute_freqs(self, t: torch.Tensor, inv_freq: torch.Tensor):
+ freqs = torch.einsum("..., f -> ... f", t.type(inv_freq.dtype), inv_freq)
+ freqs = repeat(freqs, "... n -> ... (n r)", r=2)
+ return freqs
+
+ def _compute_2d_freqs(self) -> torch.Tensor:
+ grid_h_range = torch.arange(self.max_grid_height, dtype=torch.float)
+ grid_w_range = torch.arange(self.max_grid_width, dtype=torch.float)
+ if self.use_cls_token:
+ grid_h_range += 1
+ grid_w_range += 1
+ inv_freq = self._compute_inv_freq(self.theta, self.dim // 2)
+ freqs_h = self._compute_freqs(grid_h_range, inv_freq)[:, None].expand(
+ self.max_grid_height, self.max_grid_width, -1
+ )
+ freqs_w = self._compute_freqs(grid_w_range, inv_freq)[None, :].expand(
+ self.max_grid_height, self.max_grid_width, -1
+ )
+ freqs = torch.cat([freqs_w, freqs_h], dim=-1).reshape(
+ self.max_grid_height * self.max_grid_width, -1
+ )
+ if self.use_cls_token:
+ freqs = torch.cat([torch.zeros(1, freqs.shape[-1]), freqs], dim=0)
+ freqs = freqs[None, None, ...]
+ return freqs
+
+ def forward(
+ self, q: torch.Tensor, k: torch.Tensor, grid_hw: tuple[int, int], x_shape
+ ):
+ if grid_hw[0] != self.max_grid_height or grid_hw[1] != self.max_grid_width:
+ rows = torch.arange(grid_hw[0], device=q.device).view(-1, 1)
+ cols = torch.arange(grid_hw[1], device=q.device).view(1, -1)
+ positions = (rows * self.max_grid_width + cols).reshape(-1).to(torch.long)
+ if self.use_cls_token:
+ positions = torch.cat(
+ [torch.zeros(1, device=q.device), positions + 1], dim=0
+ )
+ positions = positions.to(torch.long)
+ freqs = self.freqs_cache.index_select(2, positions)
+ else:
+ freqs = self.freqs_cache
+ ori_shape = q.shape
+ bs, seq_len, _ = x_shape
+ q = q.view(bs, seq_len, -1, self.dim).permute(0, 2, 1, 3)
+ k = k.view(bs, seq_len, -1, self.dim).permute(0, 2, 1, 3)
+ q = apply_rotary_emb(freqs, q)
+ k = apply_rotary_emb(freqs, k)
+ q = q.permute(0, 2, 1, 3).reshape(ori_shape)
+ k = k.permute(0, 2, 1, 3).reshape(ori_shape)
+ return q, k
+
+
+class PerceptionEncoderLayerScale(nn.Module):
+ def __init__(self, dim, init_values=1e-5, inplace=False):
+ super().__init__()
+ self.inplace = inplace
+ self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+ def forward(self, x):
+ return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class PerceptionEncoderMLP(nn.Module):
+ def __init__(
+ self,
+ input_dim: int,
+ hidden_dim: int,
+ act_layer: Callable[[], nn.Module],
+ quant_config: QuantizationConfig | None = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+ self.fc1 = ColumnParallelLinear(
+ input_dim,
+ hidden_dim,
+ bias=True,
+ quant_config=quant_config,
+ prefix=f"{prefix}.fc1",
+ )
+ self.activation = act_layer
+ self.fc2 = RowParallelLinear(
+ hidden_dim,
+ input_dim,
+ bias=True,
+ quant_config=quant_config,
+ prefix=f"{prefix}.fc2",
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x, _ = self.fc1(x)
+ x = self.activation(x)
+ x, _ = self.fc2(x)
+ return x
+
+
+class PerceptionEncoderVisionBlock(nn.Module):
+ def __init__(
+ self,
+ d_model: int,
+ n_head: int,
+ max_grid_height: int,
+ max_grid_width: int,
+ mlp_ratio: float = 4.0,
+ ls_init_value: float = None,
+ act_layer: Callable = nn.GELU,
+ norm_layer: Callable = nn.LayerNorm,
+ use_cls_token: bool = False,
+ quant_config: QuantizationConfig | None = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+ self.head_dim = d_model // n_head
+ self.rope = PerceptionEncoderRope2D(
+ dim=self.head_dim,
+ max_grid_height=max_grid_height,
+ max_grid_width=max_grid_width,
+ use_cls_token=use_cls_token,
+ )
+ self.attn = VisionAttention(
+ embed_dim=d_model,
+ num_heads=n_head,
+ projection_size=d_model,
+ use_qkv_parallel=True,
+ proj_bias=True,
+ # flatten_batch=True,
+ quant_config=quant_config,
+ prefix=add_prefix("attn", prefix),
+ customized_position_embedding_applier=self.rope,
+ )
+ self.ls_1 = (
+ PerceptionEncoderLayerScale(d_model, ls_init_value)
+ if ls_init_value is not None
+ else nn.Identity()
+ )
+ self.ls_2 = (
+ PerceptionEncoderLayerScale(d_model, ls_init_value)
+ if ls_init_value is not None
+ else nn.Identity()
+ )
+ self.ln_1 = norm_layer(d_model)
+ self.ln_2 = norm_layer(d_model)
+ hidden_dim = int(d_model * mlp_ratio)
+ self.mlp = PerceptionEncoderMLP(
+ d_model,
+ hidden_dim,
+ act_layer,
+ quant_config=quant_config,
+ prefix=f"{prefix}.mlp",
+ )
+
+ def forward(self, x: torch.Tensor, grid_hw: tuple[int, int]):
+ x = x + self.ls_1(self.attn(self.ln_1(x), position_embeddings=grid_hw)) # hacky
+ x = x + self.ls_2(self.mlp(self.ln_2(x)))
+ return x
+
+
+class PerceptionEncoderVisionTransformer(nn.Module):
+ def __init__(
+ self,
+ width: int,
+ layers: int,
+ heads: int,
+ max_grid_height: int,
+ max_grid_width: int,
+ mlp_ratio: float = 4.0,
+ ls_init_value: float = None,
+ act_layer: Callable = nn.GELU,
+ norm_layer: Callable = nn.LayerNorm,
+ use_cls_token: bool = False,
+ quant_config: QuantizationConfig | None = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+ self.width = width
+ self.layers = layers
+ self.resblocks = nn.ModuleList(
+ [
+ PerceptionEncoderVisionBlock(
+ d_model=width,
+ n_head=heads,
+ max_grid_height=max_grid_height,
+ max_grid_width=max_grid_width,
+ mlp_ratio=mlp_ratio,
+ ls_init_value=ls_init_value,
+ act_layer=act_layer,
+ norm_layer=norm_layer,
+ use_cls_token=use_cls_token,
+ quant_config=quant_config,
+ prefix=f"{prefix}.resblocks.{i}",
+ )
+ for i in range(layers)
+ ]
+ )
+
+ def forward(self, x: torch.Tensor, grid_hw: tuple[int, int]):
+ for block in self.resblocks:
+ x = block(x, grid_hw=grid_hw)
+ return x
+
+
+class PerceptionEncoder(nn.Module):
+ def __init__(
+ self,
+ config,
+ act_layer: Callable,
+ norm_layer: Callable = _DEFAULT_NORM_LAYER,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+ self.patch_size = config.patch_size
+
+ self.output_dim = config.output_dim or config.width
+ self.heads = config.heads
+ self.width = config.width
+ self.layers = config.layers
+
+ self.use_abs_posemb = config.use_abs_posemb
+ self.use_cls_token = config.use_cls_token
+ self.use_rope2d = config.use_rope2d
+ if not self.use_rope2d:
+ raise ValueError("use_rope2d must be True")
+ self.image_size = config.image_size
+
+ self.conv1 = nn.Conv2d(
+ in_channels=3,
+ out_channels=config.width,
+ kernel_size=config.patch_size,
+ stride=config.patch_size,
+ bias=False,
+ )
+
+ self.ln_pre = norm_layer(config.width) if config.use_ln_pre else nn.Identity()
+ self.ln_post = norm_layer(self.width) if config.use_ln_post else nn.Identity()
+
+ self.transformer = PerceptionEncoderVisionTransformer(
+ config.width,
+ config.layers,
+ config.heads,
+ max_grid_height=self.image_size // self.patch_size,
+ max_grid_width=self.image_size // self.patch_size,
+ mlp_ratio=config.mlp_ratio,
+ ls_init_value=config.ls_init_value,
+ act_layer=act_layer,
+ norm_layer=norm_layer,
+ use_cls_token=self.use_cls_token,
+ quant_config=quant_config,
+ prefix=f"{prefix}.transformer",
+ )
+
+ self.vit_downsampler1 = nn.Conv2d(
+ config.width, config.width * 2, kernel_size=3, stride=2, padding=1
+ )
+ self.vit_downsampler2 = nn.Conv2d(
+ config.width * 2, config.width * 4, kernel_size=3, stride=2, padding=1
+ )
+
+ if self.use_cls_token:
+ self.class_embedding = nn.Parameter(
+ (self.width**-0.5) * torch.randn(self.width)
+ )
+
+ if self.use_abs_posemb:
+ self.posemb_grid_size = self.image_size // self.patch_size
+ self.positional_embedding = nn.Parameter(
+ (self.width**-0.5)
+ * torch.randn(
+ int(self.use_cls_token) + self.posemb_grid_size**2,
+ self.width,
+ )
+ )
+
+ @property
+ def dtype(self) -> torch.dtype:
+ return self.conv1.weight.dtype
+
+ def sample_abs_posemb(self, grid_h: int, grid_w: int):
+ if self.posemb_grid_size == grid_h and self.posemb_grid_size == grid_w:
+ return self.positional_embedding[None, ...]
+
+ pos_embed = self.positional_embedding
+ if self.use_cls_token:
+ cls_token_embed, pos_embed = pos_embed[:1], pos_embed[1:]
+
+ pos_embed = (
+ pos_embed.reshape(1, self.posemb_grid_size, self.posemb_grid_size, -1)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+ pos_embed = F.interpolate(
+ pos_embed, size=(grid_h, grid_w), mode="bilinear", align_corners=False
+ )
+ pos_embed = pos_embed.permute(0, 2, 3, 1).reshape(-1, self.width)
+
+ if self.use_cls_token:
+ pos_embed = torch.cat([cls_token_embed, pos_embed], dim=0)
+
+ return pos_embed[None, ...]
+
+ def forward_features(self, x: torch.Tensor):
+ batch, _, h, w = x.shape
+ grid_h, grid_w = h // self.patch_size, w // self.patch_size
+
+ x = self.conv1(x)
+ x = x.permute(0, 2, 3, 1).reshape(batch, -1, self.width)
+
+ if self.use_cls_token:
+ x = torch.cat(
+ [self.class_embedding.view(1, 1, -1).expand(batch, -1, -1), x], dim=1
+ )
+
+ if self.use_abs_posemb:
+ x = x + self.sample_abs_posemb(grid_h, grid_w)
+
+ x = self.ln_pre(x)
+ x = self.transformer(x, grid_hw=(grid_h, grid_w))
+ x = self.ln_post(x)
+
+ if self.use_cls_token:
+ x = x[:, 1:, :]
+
+ return x
+
+ def forward(self, x: torch.Tensor):
+ x = self.forward_features(x)
+ B, P, C = x.shape
+ T = int(P**0.5)
+ x = x.transpose(2, 1).contiguous()
+ x = x.view(B, C, T, T)
+
+ x = self.vit_downsampler1(x)
+ x = self.vit_downsampler2(x)
+
+ B, C, T, T = x.shape
+ return x.view(B, -1, T * T).transpose(1, 2)
+
+
+class StepVLForConditionalGeneration(nn.Module):
+ def __init__(
+ self,
+ config: Step3VLConfig,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+
+ self.config = config
+ self.vision_model = PerceptionEncoder(
+ config.vision_config,
+ ACT2FN[config.vision_config.hidden_act],
+ quant_config=quant_config,
+ prefix=add_prefix(prefix, "vision_model"),
+ )
+ self.vit_large_projector = ColumnParallelLinear(
+ config.vision_config.width * 4,
+ config.text_config.hidden_size,
+ bias=config.projector_bias,
+ gather_output=True,
+ quant_config=quant_config,
+ prefix=add_prefix(prefix, "vit_large_projector"),
+ )
+
+ self.language_model = Qwen3ForCausalLM(
+ config=config.text_config,
+ quant_config=quant_config,
+ prefix=add_prefix(prefix, "language_model"),
+ )
+
+ def _get_vision_model_output(self, input_tensor: torch.Tensor) -> torch.Tensor:
+ return self.vision_model(input_tensor)
+
+ @property
+ def device(self) -> torch.device:
+ return self.vit_large_projector.weight.device
+
+ def _flatten_embeddings(self, embeddings) -> torch.Tensor:
+
+ if isinstance(embeddings, torch.Tensor):
+ # Flatten all but the last dimension.
+ return embeddings.flatten(0, -2)
+
+ return torch.cat(tuple(self._flatten_embeddings(t) for t in embeddings))
+
+ def _process_image_features(self, image_features: torch.Tensor) -> torch.Tensor:
+ image_features, _ = self.vit_large_projector(image_features)
+ return image_features
+
+ def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+ assert len(items) == 1 # We only have images.
+
+ item = items[0]
+ pixel_values = item.feature.type(self.vision_model.dtype).to(self.device)
+ num_patches = item.model_specific_data.get("num_patches")
+ patch_pixel_values = item.model_specific_data.get("patch_pixel_values", None)
+ if patch_pixel_values is not None:
+ patch_pixel_values = patch_pixel_values.type(self.vision_model.dtype).to(
+ self.device
+ )
+
+ image_features = self._get_vision_model_output(pixel_values)
+ patch_image_features = (
+ self._get_vision_model_output(patch_pixel_values)
+ if patch_pixel_values is not None
+ else None
+ )
+ image_features = self._process_image_features(image_features)
+ patch_image_features = (
+ self._process_image_features(patch_image_features)
+ if patch_image_features is not None
+ else None
+ )
+ merged_image_features = []
+ cur_patch_idx = 0
+ for i, num_patch in enumerate(num_patches):
+ cur_feature = []
+ if num_patch > 0:
+ patch_slice = patch_image_features[
+ cur_patch_idx : cur_patch_idx + num_patch
+ ]
+ cur_feature.append(patch_slice.view(-1, patch_slice.shape[-1]))
+ cur_feature.append(image_features[i].view(-1, image_features.shape[-1]))
+ cur_patch_idx += num_patch
+ merged_image_features.append(
+ torch.cat(cur_feature) if len(cur_feature) > 1 else cur_feature[0]
+ )
+ return self._flatten_embeddings(merged_image_features)
+
+ def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+ pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+ return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ forward_batch: ForwardBatch,
+ get_embedding: bool = False,
+ ):
+ hidden_states = general_mm_embed_routine(
+ input_ids=input_ids,
+ forward_batch=forward_batch,
+ language_model=self.language_model,
+ data_embedding_funcs={
+ Modality.IMAGE: self.get_image_feature,
+ },
+ positions=positions,
+ )
+
+ return hidden_states
+
+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+ """Load weights for the model, separating vision and language weights"""
+ weights = list(weights)
+
+ # Separate vision tower weights and language model weights
+ vision_weights = []
+ language_weights = []
+
+ for name, loaded_weight in weights:
+ if "vision_model" in name or "vit_large_projector" in name:
+ name = name.replace(r".attn.in_proj_weight", r".attn.qkv_proj.weight")
+ name = name.replace(r".attn.in_proj_bias", r".attn.qkv_proj.bias")
+ name = name.replace(r".attn.out_proj.bias", r".attn.proj.bias")
+ name = name.replace(r".attn.out_proj.weight", r".attn.proj.weight")
+ name = name.replace(".mlp.c_fc", ".mlp.fc1")
+ name = name.replace(".mlp.c_proj", ".mlp.fc2")
+ vision_weights.append((name, loaded_weight))
+ else:
+ # All other weights go to language model
+ language_weights.append((name, loaded_weight))
+
+ # Load vision tower weights
+ vision_state_dict = dict(vision_weights)
+ params_dict = dict(self.named_parameters(remove_duplicate=False))
+ for name, loaded_weight in vision_state_dict.items():
+ if name not in params_dict:
+ raise ValueError(f"Weight {name} not found in params_dict")
+ param = params_dict[name]
+ weight_loader = getattr(param, "weight_loader", default_weight_loader)
+ # loaded_weight = self._pad_vit_attn_dummy_heads(name, loaded_weight)
+ weight_loader(param, loaded_weight)
+
+ # Load language model weights
+ if language_weights:
+ self.language_model.load_weights(language_weights)
+
+
+EntryClass = StepVLForConditionalGeneration
diff --git a/python/sglang/srt/multimodal/processors/step3_vl.py b/python/sglang/srt/multimodal/processors/step3_vl.py
index 6bd691ecf..b6720fc5c 100644
--- a/python/sglang/srt/multimodal/processors/step3_vl.py
+++ b/python/sglang/srt/multimodal/processors/step3_vl.py
@@ -11,6 +11,7 @@ from torchvision.transforms import InterpolationMode
from transformers import BatchFeature, ProcessorMixin, TensorType
from sglang.srt.models.step3_vl import Step3VLForConditionalGeneration
+from sglang.srt.models.step3_vl_10b import StepVLForConditionalGeneration
from sglang.srt.multimodal.processors.base_processor import (
BaseMultimodalProcessor as SGLangBaseProcessor,
)
@@ -469,16 +470,17 @@ class Step3VLProcessor:
class Step3VLImageProcessor(SGLangBaseProcessor):
- models = [Step3VLForConditionalGeneration]
+ models = [Step3VLForConditionalGeneration, StepVLForConditionalGeneration]
def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
# TODO, check _processor is tokenizer or processor.
processor = Step3VLProcessor(hf_config, _processor)
super().__init__(hf_config, server_args, processor, *args, **kwargs)
- self.IM_TOKEN_ID = 128001
+ self.IM_TOKEN = ""
+ self.IM_TOKEN_ID = self._processor.tokenizer.get_vocab()[self.IM_TOKEN]
self.mm_tokens = MultimodalSpecialTokens(
- image_token="",
- image_token_id=128001,
+ image_token=self.IM_TOKEN,
+ image_token_id=self.IM_TOKEN_ID,
image_token_regex=re.compile(r"(?:)"),
).build(_processor)
diff --git a/python/sglang/srt/utils/common.py b/python/sglang/srt/utils/common.py
index df1b5f066..894d54f89 100644
--- a/python/sglang/srt/utils/common.py
+++ b/python/sglang/srt/utils/common.py
@@ -2898,6 +2898,7 @@ def is_fa3_default_architecture(hf_config):
"Glm4vForConditionalGeneration",
"Glm4vMoeForConditionalGeneration",
"Step3VLForConditionalGeneration",
+ "StepVLForConditionalGeneration",
"MiMoV2FlashForCausalLM",
}
return architectures[0] in default_archs